Wireless on-road charging is an emerging charging method in addition to plug-in charging. And it is a promising application for the future smart grid. Hence, in this paper, a reputation-based wireless on-road vehicle-to-vehicle (V2V) energy trading strategy is formulated in vehicular energy networks. First, a three-stage wireless V2V energy trading algorithm is established to capture the interaction between charging electric vehicles (EVs) and discharging EVs and obtain the optimal energy trading matching results. Second, the trustworthiness of the discharging EV is evaluated using the proposed reputation index. Both explicit reputation and implicit reputation indices are incorporated to rigorously derive the real-time reputation index based on the consortium blockchain system. Third, two irrational behaviors of EV users, namely, the weighting effect and range anxiety, are mathematically modeled based on Prospect theory. Numerical results indicate that efficient wireless energy matching can be achieved. Moreover, the proposed wireless V2V energy trading strategy is effective in increasing the utility of both charging EVs and discharging EVs.

The deregulation of the electricity market has led to the proliferation of diverse electricity retail plans, posing substantial information filtering challenges for residential users. In the meantime, the digitalization of electricity systems has driven the development of electricity plan recommendation services with advanced metering infrastructure (AMI) data to offer personalized decision support for all residential customers in selecting suitable electricity plans. This paper proposes a robust deep feature learning-based Electricity Plan Recommender System (RDFL-EPRS), designed to address the complexity and reliability of the multiplex features significantly influencing the recommendation performance for residential electricity plan selection. The system leverages both user-input and electricity plan data, employs multiple imputation on stacked denoising autoencoders to address and rectify missing or unreliable user-input features, and incorporates various deep learning techniques in the recommendation model to accommodate the diverse types of input features. This model learns intricate feature interactions and maps non-linear relationships with electricity plan ratings, ultimately generating a recommendation list for the Top- N most appropriate electricity plans. Extensive simulations validate RDFL-EPRS for significant improvements in missing/abnormal value imputation and electricity plan recommendations. Compared to state-of-the-art techniques, the proposed system provides more precise and more robust recommendations, supporting target users in making reliably informed decisions.

Dynamic Security Assessment (DSA) is critical for maintaining stability in large-scale smart grids, especially with the growing integration of renewable energy sources and the inherent uncertainties. Traditional model-based analytical methods are increasingly inadequate under these complex conditions. To address these challenges, we propose a pioneering Quantum Federated Learning-based DSA (QFLDSA) method by combining hybrid quantum-classical machine learning and federated learning. QFLDSA offers an effective way to deal with high-dimensional data and uncertainties inherent in the grid. Moreover, QFLDSA leverages the unique capabilities of quantum computing to enhance the processing of differential-algebraic equations that underpin grid stability. This paper demonstrates through extensive simulations that QFLDSA significantly outperforms traditional methods, achieving the highest average F1-score performance at 97.94%, while maintaining 97.67$\pm$0.17% prediction accuracy on both classical and quantum computing devices only with fewer transmitted model parameters (reducing up to$\sim$1000X). These enhancements enable more reliable and rapid deployment of preventive stability control measures across smart grids. Our results underscore QFLDSA’s potential as a robust solution for the dynamic security challenges of modern smart grids, paving the way for future innovations in grid management technology.Note to Practitioners—In the rapidly evolving world of smart cyber-physical grids, ensuring the stability of electric power systems is paramount. Failures in these systems can lead to catastrophic blackouts, affecting countless homes and businesses. Traditional DSA methods to assess and ensure this stability, while effective, are becoming increasingly complex and vulnerable to single points of failure or cyberattacks. Enter the QFLDSA method, a novel approach we introduce in this paper. In simple terms, this method combines the strengths of quantum machine learning and federated learning to analyze data efficiently across a distributed system. Here’s why these matters: 1) Localized Analysis: Instead of relying on a central hub to analyze all data, QFLDSA allows for localized data analysis. This means that if one part of the system fails, it does not bring down the entire grid’s analysis capabilities. It is akin to having multiple control rooms instead of one, ensuring that a problem in one room does not halt the entire operation. 2) Future-Ready: As we move towards a future where quantum computing becomes more prevalent, QFLDSA is designed to work seamlessly with both today’s classical devices and tomorrow’s quantum devices. This ensures that as technology evolves, our method remains relevant and efficient. 3) Proven Performance: We have not just introduced a new method; we have rigorously tested it. Our theoretical proofs and practical tests confirm that QFLDSA offers accurate and efficient data analysis for smart grids. For industry professionals, the takeaway is clear: if looking for a resilient, future-ready, and proven method to ensure the stability of smart grid, QFLDSA offers a compelling solution.

Protein-protein interactions (PPIs) are fundamental to many biological processes, including cell signaling, gene expression regulation, immune responses, and protein complex formation. Small molecule inhibitors targeting specific PPIs are expected to treat diseases such as cancer and viral infections by modulating pathophysiological processes. Despite their clinical importance, the development of PPI inhibitors is challenging due to limited experimental validation data, which complicates the accurate prediction of novel inhibitors. Therefore, there is an urgent need for advanced computational methods that can effectively integrate multiple data types and improve prediction accuracy. In this study, we proposed a new framework, PTPPI, to efficiently predict protein-protein interaction inhibitors (PPIIs). PTPPI integrates multiple molecular features, including extended connectivity fingerprints (ECFPs) for structural representation and deep semantic embeddings of SMILES sequences generated by the ChemBERTa pre-trained model. These features are processed by independent encoders and fused using an interactive attention mechanism, which enhances the molecular representation. In addition, PTPPI adopts a multi-task learning approach, enabling the model to both reconstruct input features and accurately predict inhibition scores. Experimental results on eight PPI target families, focusing on inhibitor identification and potency prediction, demonstrate that PTPPI outperforms existing methods. It not only integrates multiple molecular features effectively but also achieves superior prediction performance. This makes PTPPI a valuable and reliable tool for discovering new PPI inhibitors, thus opening up new possibilities for drug discovery and disease treatment.

Climate change is progressively reshaping the spatiotemporal dynamics of renewable energy sources such as wind and solar, intensifying the complexity and uncertainty of long-term power system planning. Existing planning frameworks are largely focused on climate mitigation strategies but often overlook the critical dimension of climate adaptation, limiting their efficacy in managing evolving climatic risks. In response, this article proposes a multistage stochastic low-carbon planning framework that incorporates climate-related uncertainties into system planning decision-making. By embedding climate evolution trajectories into the planning horizon, the proposed approach determines optimal stage-wise planning pathways that jointly accommodate mitigation goals and adaptation imperatives under long-term climate uncertainties. First, a systematic climate uncertainty modeling approach is developed to capture both scenario uncertainty and climate response uncertainty through the construction of a representative scenario tree. Second, to reconcile the temporal mismatch between coarse-resolution climate projections and the finegrained requirements of power system planning, a climate-consistent temporal downscaling method is proposed to transform long-term climate projections into high-resolution, hourly level data. Third, to address the computational complexity inherent in the multistage planning problem, a tailored decomposition-based stochastic dual dynamic programming algorithm is developed, which operates on a stage-wise clustered scenario tree to leverage the tree’s structural compactness for accelerated convergence and scalable optimization under climate-related uncertainties. Numerical studies demonstrate that the proposed climate-adaptive planning framework enhances the power system’s ability to manage climate-induced risks while maintaining cost-effectiveness across a wide range of plausible climate futures.

Wind power data often suffers from missing values due to sensor faults and unstable transmission at edge sites. While federated learning enables privacy-preserving collaboration without sharing raw data, it remains vulnerable to anomalous updates and privacy leakage during parameter exchange. These challenges are amplified in open industrial environments, necessitating zero-trust (ZT) mechanisms, where no participant is inherently trusted. To address these challenges, this work proposes ZTFed-MAS2S, a ZT federated learning framework that integrates a multihead attention-based sequence-to-sequence imputation model. ZTFed integrates verifiable differential privacy with noninteractive zero-knowledge proofs and a confidentiality and integrity verification mechanism to ensure verifiable privacy preservation and secure model parameters transmission. A dynamic trust-aware aggregation mechanism is employed, where trust is propagated over similarity graphs to enhance robustness, and communication overhead is reduced via sparsity- and quantization-based compression. MAS2S captures long-term dependencies in wind power data for accurate imputation. Extensive experiments on real-world wind farm datasets validate the superiority of ZTFed-MAS2S in both federated learning performance and missing data imputation, demonstrating its effectiveness as a secure and efficient solution for practical applications in the energy sector.

The increasing penetration of renewable energy introduces significant uncertainties in power systems, necessitating advanced strategies to maintain economic efficiency and frequency stability. This article addresses the multireserve constrained unit commitment (MRCUC) problem by simultaneously allocating spinning reserve and frequency regulation reserve in systems with high renewable energy penetration. First, spinning reserve requirements are accurately determined by an optimization model addressing imbalances caused by renewable energy volatility and load variations. Second, primary and secondary frequency regulation requirements are quantified through detailed frequency-domain simulations utilizing each unit's frequency-to-active-power-output transfer function, thereby overcoming limitations of traditional first-order frequency approximations applicable mostly to synchronous generators. The proposed comprehensive MRCUC model, characterized by partial observability of load uncertainties and high-ordernonconvexity stemming from precise frequency simulations, cannot be efficiently solved using conventional optimization methods. Therefore, a hybrid optimization framework combining RL with convex optimization techniques is developed to address these complexities, ensuring feasible and effective decision-making. Extensive case studies conducted on IEEE 39-bus and 118-bus test systems confirm the efficacy of the proposed method, highlighting enhanced economic performance, accurate reserve allocation, and robust frequency stability.

Flexible charging and discharging of battery energy storage systems (BESSs) and reactive power compensation of photovoltaic (PV) inverters can be coordinated to support distribution network operation. Given the feature of their rapid response, local droop control is employed to address random fluctuations of renewable outputs and loads. Therefore, this paper proposes a three-stage hierarchical coordination method of BESS-PV scheduling and droop control, aiming to minimize network power loss, voltage deviation, and operating costs. Considering capacity limitation and energy temporal coupling, an optimal state of charge (SoC) interval for one day is determined in a day-ahead stage. Then, theP–Vdroop control functions of BESSs and theQ–Vdroop control functions of PV inverters are optimized hourly, while satisfying the day-ahead SoC interval, to enable efficient real-time local control. Thus, the central day-ahead SoC interval management, the central hourly droop control function scheduling, and the local real-time droop control are hierarchically coordinated. A general model of five-segment droop control functions with reduced binary variables is introduced, and accordingly, a partial segment reduction technique and a penalized linear approximation solution method are developed. The proposed method is tested and compared with other methods. Simulation results verify its high performance in improving the network operational efficiency.

High renewable penetration and reduced system inertia introduce significant challenges for transient stability assessment and control. This article proposes a causality-aware, large language model–enhanced distribution-preserving graph representation learning framework (LLM-DP-GRL) for fast and accurate stability prediction and decision-making. The DP-GRL model captures both structural and distributional properties of network states, whereas large language models provide physics-informed priors that improve data efficiency and generalization under multicontingency and out-of-distribution scenarios. A causal intervention module further quantifies bus-level influence on stability margins, offering interpretable insights consistent with system dynamics. The learned surrogate model is integrated into a cooperative preventive–emergency control strategy, enabling real-time stability margin evaluation and optimization. Tests on the IEEE 39-bus and 118-bus systems show that LLM-DP-GRL achieves higher accuracy, faster convergence, and improved robustness compared with conventional machine learning, LSTM, and GNN-based methods. The proposed approach reduces online control computation from over 35 min (TDS-based) to 39 s while maintaining inference latency below 30 ms. These results demonstrate that combining graph learning, LLM-guided priors, and causal analysis provides an effective and scalable solution for stability assessment and emergency control in low-inertia, high-renewable power systems.

While Electric Vehicles (EVs) have the potential to contribute to low-carbon transportation, the burgeoning adoption of EVs and uncoordinated charging can place a significant burden on the power grid. This paper addresses the challenges of the integration of massive EVs into smart grids by introducing a comprehensive framework with heterogeneous flexibility aggregation. First, a Spatio-Temporal Heterogeneous Graph Neural Network (STH-GNN) is proposed to accurately predict EV charging demands by analyzing complex spatio-temporal relationships. Furthermore, a bi-level coordinated charging scheduling framework optimizes grid operations and individual EV charging schedules, relieving grid burden and enhancing efficient energy use based on the STH-GNN prediction. It solves the gaps between prediction errors and real-time actual charging demand. Additionally, the EV flexibility set is modelled to effectively aggregate heterogeneous EV resources, allowing for optimized deployment of large-scale EV assets by constructing the inner approximation of the Minkowski sum of the individual flexibility sets. The proposed method is verified in simulation, utilizing data from a realistic urban setting with diverse EV charging behaviours and grid conditions. Simulations demonstrate the effectiveness of the STH-GNN in accurately forecasting EV charging demands across different times and locations. The bi-level charging scheduling framework successfully manages grid burden while showcasing significant improvements in operational efficiency and cost reduction. Results validate the proposed model’s robustness and scalability, proving its potential applicability in real-world smart grid environments.

Battery Energy Storage Systems (BESS) play a vital role in enhancing grid flexibility, enabling renewable integration, and supporting peak shaving and frequency regulation. To fully realize their economic potential, BESS operators often participate in electricity market arbitrage—charging when prices are low and discharging during peak price periods. However, effective arbitrage strategies critically depend on accurate forecasting of electricity prices, particularly under extreme market conditions, which are often driven by sudden load surges, grid failures, or severe weather events. In this paper, we propose a novel framework that leverages a Large Language Model (LLM) for data augmentation to address the scarcity of extreme price scenarios in historical data. The LLM agent, guided by structured prompts that embed domain knowledge and physical constraints, generates realistic synthetic samples of rare peak-price events. These augmented datasets improve the robustness of a Bayesian electricity price forecasting model based on Monte Carlo dropout, which provides not only point estimates but also predictive confidence intervals. Finally, a scenario-based stochastic optimization model is developed to guide BESS arbitrage decisions using the probabilistic price forecasts. Simulation results show that the proposed framework significantly enhances the predictive accuracy and economic efficiency of storage arbitrage under uncertainty.

False data injection attacks (FDIAs) refer to attackers exploiting vulnerabilities in the detection of bad data in smart grid energy management systems to maliciously manipulate state estimation results in the cyber-physical system, resulting in unstable operation of the power system. Existing deep learning-based detection schemes often fail to capture the spatial topology features and long-term dependencies in power grid data well. Meanwhile, the complexity of deep detection models makes them a "black box", reducing the credibility of detection results. To address the aforementioned challenges, this paper presents a dependency-aware deep interpretable FDIA detection model. The proposed model firstly introduces Graph Sample and Aggregate (GraphSAGE) network to extract spatial topological features, which are used to represent the deep spatial topological dependencies of adjacent data nodes. Subsequently, we build a Bidirectional Long Short-Term Memory (BiLSTM) network with a Squeeze-and-Excitation (SE) attention module, which can efficiently aggregate attack characteristics and long-term dependency information by dynamically capturing the potential correlations between FDIAs detection and measurement data. Furthermore, the SHapley Additive exPlanations (SHAP) method is used to demonstrate the interpretability of the model in the spatial-temporal dimensions and then provide the basis for high-precision detection results. A series of extensive experiments are carried out over the IEEE 14-bus and 118-bus test systems. The experimental results demonstrate that the proposed model presents a superior overall performance comparing with several state-of-the-art FDIA detection models, and provides reasonable interpretability from the spatial-temporal dimensions.

Distribution system state estimation (DSSE) plays a crucial role in the real-time monitoring, control, and operation of distribution networks. Besides intensive computational requirements, conventional DSSE methods need high-quality measurements to obtain accurate states, whereas missing values often occur due to sensor failures or communication delays. To address these challenging issues, a forecast-then-estimate framework of edge learning is proposed for DSSE, leveraging large language models (LLMs) to forecast missing measurements and provide pseudo-measurements. First, natural language-based prompts and measurement sequences are integrated by the proposed LLM to learn patterns from historical data and provide accurate forecasting results. Second, a convolutional layer-based neural network model is introduced to improve the robustness of state estimation under missing measurement. Third, to alleviate the overfitting of the deep-learning-based DSSE, it is reformulated as a multitask learning framework containing shared and task-specific layers. The uncertainty weighting algorithm is applied to find the optimal weights to balance different tasks. The numerical simulation on the Simbench case is used to demonstrate the effectiveness of the proposed forecast-then-estimate framework.

In the era of smart cyber-physical grid, dynamic insecurity risk has become a significant concern due to the increasing integration of renewable energy sources and the inherent uncertainties in smart grid. Dynamic security assessment (DSA) has been adopted to hedge against such risks by estimating the stability of large-scale smart grids. Existing DSA approaches often involve complex high dimensional models which incur high communication and computational costs, hindering their practical adoption. In this paper, we address these limitations with the Quantum Federated Ensembled Variational Adaptive Learning (QFEVAL) approach for smart grid DSA. QFEVAL is designed to combine quantum machine learning and federated learning to handle the differential-algebraic equations that describe smart grid stability, providing an efficient way to deal with high-dimensional data and uncertainties. QFEVAL enables the training of the hybrid quantum-classical neural networks on distributed DSA datasets located at different nodes in smart grids, without requiring large numbers of parameters to be transmitted. QFEVAL accurately predicts the stability of the smart grid under various conditions, enabling the implementation of preventive stability control measures. Through extensive experiments, we demonstrate that QFEVAL achieves comparable performance to 9 state-of-the-art DSA approaches with more than 2 orders of magnitude fewer model parameter transmissions. QFEVAL paves the way for reliable, secure, and continuous electricity supply, offering a robust solution to the challenges of DSA in smart grids.

For DC microgrids (MGs), real-time adjustment of current sharing ratios and secure voltage restoration are paramount for optimizing load allocation and enhancing dynamic performance. In this paper, a dual-objective distributed model-free adaptive control (MFAC) scheme is designed for the first time to guarantee voltage transient performance and adjustable current sharing. First, an output-constrained nonlinear MG model with ZIP (constant impedance, constant current and constant power) load is established, and subsequently it is converted into an equivalent unconstrained data model using system transformation and dynamic linearization techniques. Second, a new prescribed performance control algorithm with asymmetrical preset boundaries is proposed to restrict voltage transient responses. This algorithm is updated with real-time input and output data at discrete instants, making it independent of line resistance and ZIP load measurements. To enhance the robustness of the control method, an internal observer is designed to actively compensate for the unknown nonlinear dynamics generated by time-varying system parameters. The stability conditions of the transformed systems in the presence of ZIP loads and time-varying line resistance are derived, which can indirectly ensure the prescribed voltage performance of the original system. Finally, the effectiveness of the proposed control method is validated through some simulations and hardware experiments.

This paper explores the observer-based event-triggered security control problem for linear systems. Initially, a distributed dynamic-memory event-triggered scheme (DMETS) is proposed based on observed system states. This scheme is designed using information from past transmitted data packages, with the flexibility to dynamically adjust the quantity of transmitted data packages. Subsequently, a comprehensive mathematical model of hybrid cyber-attacks is established, and an observer-based distributed feedback control approach is proposed by utilizing the memory states. Furthermore, the asymptotic stability criterion of system is established by using Lyapunov direct method, and the design approaches of controller and observer are provided. Finally, contrast illustrative examples are presented to validate the advantage of DMETS and the effectiveness of the distributed control method. Note to Practitioners—This paper is prompted by the challenges of excessive consumption of network resources and the need for enhanced security control in linear systems. The prevailing approach to addressing this issue involves designing appropriate data transmission schemes, specifically ETS. However, a common limitation among these existing ETSs is the inability to efficiently utilize past transmitted data or dynamically adjust the quantity of data utilized in response to changes in system states. To overcome this limitation, we propose a DMETS, which aims to integrate memory characteristics and facilitate dynamic adjustments in memory data quantities.

The federated learning (FL) technique can provide a promising solution for the timely training of a deep learning model with the critical requirement of privacy protection. However, the existing FL frameworks still confront challenging issues including heterogeneous data sources, edge device heterogeneity, sensitive information leakage, nonconvex loss, and communication resource constraints which place obstacles in terms of practicality. In this article, first, a federated multitask learning (FedMTL) approach is introduced to reformulate the FL model as a multiobjective optimization problem which results in federated multigradient descent algorithm (FedMGDA) with a better model personalization against data heterogeneity and Byzantine attack. Second, a new semi-asynchronous model aggregation method is developed to asynchronously aggregate small partial clients for compensating impacts of the straggler and staleness. Third, a distributed differential privacy technique is applied to enhance the privacy protection of asynchronous FedMGDA with the convergence guarantee where the convergence analysis of differentially private asynchronous federated multiple gradient descent algorithm (DP-AsynFedMGDA) is studied for both the convex and the nonconvex loss functions. Empirical examples and comparative studies are presented to illustrate the effectiveness of the proposed DP-AsynFedMGDA.

A physics-data-driven economic model predictive control (EMPC) is proposed in this article for effective energy harvesting in wave energy converters (WECs). By combining artificial intelligence techniques, this article develops a new method that applies a data-driven model upon physical WEC models to address the challenges associated with the nonlinearity and uncertainty in WEC physical models. By collecting the error data between the actual system and the physical model, a deep Koopman operator is applied to transform the nonlinear and uncertain parts of the actual system into a linear model, which is then embedded into the physical model to establish a physical-data-driven model. By iteratively optimizing the physical data-driven model, EMPC generates the optimal control sequence for the WEC. Theoretical analysis is conducted to prove that the physical-data-driven EMPC algorithm ensures that the Lyapunov function converges to the neighborhood of the optimal steady state. Simulation results show that the proposed physical-data-driven model achieves faster convergence and higher accuracy during training compared to data-driven models. This improves the system’s control and optimization performance under EMPC, demonstrating the effectiveness of the proposed algorithm.

The lack of meteorological forecast data has increased the inaccuracy of output power forecasting in distributed photovoltaic systems. Especially, for newly built distributed sites across regions, modeling based on data-driven methods is limited by insufficient historical data. Therefore, a domain adversarial temporal network (DATN) based transfer learning (TL) framework is proposed, which contains two main modules, power temporal forecaster and domain classifier. Among them, the domain classifier considering the hidden layer weights of long short-term memory network is designed to reduce the distribution mismatch between source and target domains. The DATN employs a TL strategy of cross-domain adversarial pretraining with target-specific prediction tuning. In four cross-regional transfer experiments, the effects of domain adaptation methods and transfer strategies are compared. The breakthrough is that the transfer effect on different target data volumes is analyzed for the first time. The results prove that the proposed transferable framework DATN consistently performs best.

In recent years, the rapid growth of distributed energy resources (DERs) and the emergence of local energy markets (LEMs) have dramatically transformed the energy trading landscape, emphasizing the crucial role of DER aggregators in optimizing bidding strategies. Traditional model-based methods for optimizing DER aggregator bidding in LEMs face significant challenges, including information asymmetry, an inability to adapt to changing market dynamics, and issues with computational scalability in real-time decision-making. Recognized as a promising alternative, deep reinforcement learning (DRL) forms the basis of our proposed solution. This article introduces a human–machine (HM) framework that utilizes a multiagent adversarial inverse reinforcement learning (MA-AIRL) approach to address these challenges. The HM framework enables the DER agent to imitate human demonstrations and leverages a HM hybrid experiment to augment insufficient data, effectively tackling the problem of data inadequacy in new market environments. Concurrently, the MA-AIRL algorithm employs inverse reinforcement learning to capture underlying reward functions, risk preferences, and expert knowledge, significantly enhancing the model’s adaptability to dynamic market conditions. Additionally, the adversarial learning component allows the DER agent to robustly respond to uncertainties and the strategic maneuvers of rival agents, thereby mitigating information asymmetry. Moreover, this DRL-based approach is designed to ensure rapid responsiveness without compromising scalability in real-time contexts. Through extensive case studies, we have verified that the proposed HM framework and MA-AIRL algorithm offer a more robust, data-efficient, and adaptive approach for optimizing DER aggregator bidding in LEMs.

The increasing penetration of distributed energy resources has prompted distribution system operators (DSOs) at the retail electricity market level to coordinate with the independent system operator (ISO) at the wholesale market level, for greater benefits. However, interaction mechanisms between the ISO and DSOs, and impacts of prices and power injections, have not been adequately investigated in literature. This article proposes a distributed coordination framework for the ISO and DSOs across wholesale-retail (bi-level) electricity markets, considering their interactions more fairly. Moreover, to mitigate the challenges arising from the interdependence between the ISO and heterogeneous DSOs, a coupled training mechanism based on the response model is devised. This mechanism iteratively trains the ISO and DSOs by solely exchanging prices and power injections, ensuring the demand–supply balance at both retail and wholesale levels. In addition, a deep reinforcement learning algorithm is introduced for the three-stage iterative training process of heterogeneous agents. Results demonstrate the effectiveness of the proposed method and its advantages in terms of lowering energy prices, clearing of cheaper clean resources and thus, improving overall market efficiency.

The development of mobile charging facilities for electric vehicles (EVs) has provided significant help in alleviating the pressure on active distribution networks (ADN) and traffic flow. This article proposes using the interaction between mobile charging facilities for EVs and the ADN to improve the power quality while ensuring the utility of mobile charging facility operators. First, the utility function of mobile charging facility operators is established with normal operation and emergency operation modes. The normal operation is to dispatch the mobile charging facilities for EVs requesting to be charged, while maximizing the charging benefits. To ensure the power quality for the ADN, the emergency operation is proposed to realize the power interaction between the mobile charging facilities and power grid. Furthermore, we propose an electricity price incentive mechanism to encourage optimal charging and discharging for mobile charging facilities. During the emergency operation, coordination between the mobile charging facilities and ADN is formulated as a Stackelberg game. We propose a sensitivity-based electricity price regulation algorithm and theoretically prove its equilibrium. Simulation results confirm the effectiveness and superiority of this approach, showing that the mobile charging facility and ADN can achieve a mutually beneficial outcome.

On-road wireless charging is an emerging charging method, in addition to plug-in charging, that is a promising application in the future smart grid. Hence, in this paper, a competitive pricing strategy for the wireless charging lane (WCL) is proposed to maximize the economic benefits of the WCL operator. First, the competitive pricing strategy is formulated based on a non-cooperative game between the WCL operator and the charging station (CS). An iterative optimal pricing searching algorithm is developed to find the Nash equilibrium of the game. Second, a tri-level framework is established to derive the optimal competitive price considering the interaction among the WCL operator, the power distribution network (PDN) operator, and EV users. Third, the range anxiety of EV users is mathematically modeled based on Prospect theory. Numerical results indicate that the pricing strategy is effective in enhancing the attractiveness and profitability of the WCL operator. In addition, the utility of EV users is increased as well. Moreover, the PDN loss cost can be reduced, and downward voltage violation can be avoided.

Quantum federated learning (QFL) is an emerging interdisciplinary field that merges the principles of quantum computing (QC) and federated learning (FL), with the goal of leveraging quantum technologies to enhance privacy, security, and efficiency in the learning process. Currently, there is no comprehensive survey for this interdisciplinary field. This review offers a thorough, holistic examination of QFL. We aim to provide a comprehensive understanding of the principles, techniques, and emerging applications of QFL. We discuss the current state of research in this rapidly evolving field, identify challenges and opportunities associated with integrating these technologies, and outline future directions and open research questions. We propose a unique taxonomy of QFL techniques, categorized according to their characteristics and the quantum techniques employed. As the field of QFL continues to progress, we can anticipate further breakthroughs and applications across various industries, driving innovation and addressing challenges related to data privacy, security, and resource optimization. This review serves as a first-of-its-kind comprehensive guide for researchers and practitioners interested in understanding and advancing the field of QFL.

This study addresses the frequency control problem in multiarea power systems through the design of a decentralized periodic event-triggered scheme (DPETS). Initially, a unified discrete-time multiarea model is developed for power systems by adopting the general discretization method. Subsequently, a discrete-type DPETS is designed, relying solely on the locally available area control error to determine the time instants for transmitting the control signal to the equivalent generating unit. Furthermore, the ultimate boundedness of power systems under time-varying load demand disturbance is demonstrated by constructing novel discrete-time Lyapunov-Krasovskii functionals, leading to a rigorous bound of ultimate states. Finally, a case study involving three-area interconnected power systems is presented to validate the feasibility of the designed control strategy.

Enhanced by machine learning (ML) techniques, data-driven dynamic security assessment (DSA) in smart cyber-physical grids has attracted great research interests in recent years. However, as existing DSA methods generally rely on centralized ML architectures, the scalability, privacy, and cost effectiveness of existing methods are limited. To address these issues, we propose a novel quantum-secured distributed intelligent system for smart cyber-physical DSA based on Federated learning (FL) and quantum key distribution (QKD), namely, quantum-secured federated DSA (QFDSA). QFDSA aggregates the knowledge learned from various local data owners (also known as clients) to predict and evaluate the system stability status in a decentralized fashion. In addition, in order to preserve the privacy of the distributed DSA data, QFDSA adopts the measurement-device-independent QKD, which can further improve the security of local DSA model transmission. Moreover, to accommodate the typical fast system environment and requirement changes, QFDSA alleviates the issues of limited key generation rates by utilizing secret-key pool that guarantee the availability of adequate secret-key materials. Extensive experiments based on the New England 10-machine 39-bus testing system and the synthetic Illinois 49-machine 200-bus testing system demonstrate that the proposed QFDSA method can achieve more advantageous DSA performance while protecting the privacy of local data for real-time DSA applications compared to the benchmarks. Besides, the secret-key generation rate can be improved to adjust its parameters dynamically in real time.

Enhanced by machine learning (ML) techniques, data-driven stability assessment (SA) in smart cyber–physical grids has attracted significant research interest in recent years. However, the current centralized ML architectures have limited scalability, are vulnerable to privacy exposure, and are costly to manage. To resolve these limitations, we propose a novel secure distributed SA method based on federated learning (FL) and differential privacy (DP), namely, Secure Federated SA (SecFedSA). It leverages local system operating data to predict and estimate the system stability status and optimize the power systems in a decentralized fashion. In order to preserve the privacy of the distributed SA operating data, SecFedSA incorporates Gaussian mechanism into DP. Theoretical analysis on the Gaussian mechanism of SecFedSA provides formal DP guarantees. Extensive experiments conducted on the New England 10-machine 39-bus testing system and the synthetic Illinois 49-machine 200-bus testing system demonstrate that the proposed SecFedSA method can achieve advantageous SA performance, while protecting the privacy of the local model information compared to the state of the art.

The current state-of-the-art approach for battery state-of-health (SOH) estimation typically employs a centralized computing framework, wherein data from local battery management systems (BMSs) is aggregated and trained on a cloud server, due to limited computing resources at the BMS. However, this framework presents various challenges, including frequent data communication, latency, data security, and degraded prediction accuracy. To address these issues, this study proposes a novel adaptive multipersonalized federated learning (FL) algorithm for evaluating the SOH of multiple batteries, aggregating multiple local SOH estimation models into a global model while locally preserving battery data. The algorithm utilizes the difference of importance weights between global and local models to regulate the local loss, incorporates adaptive personalization layers with loss variation, and employs clustering techniques to form multiple global models from distinct local models, leading to a more accurate and tailored prediction. Additionally, an adaptively SOH-related differential privacy protection mechanism is integrated to enhance the protection of local battery data while ensuring robust model performance. An extensive case study has demonstrated that the adaptive multipersonalized FL algorithm outperforms other methods in terms of estimation accuracy and operational risk. Specifically, it achieves a reduction in mean absolute error by 0.14% and 6.01% compared to traditional FL and local training methods, respectively, and exhibits nearly fivefold lower operational risk compared to centralized training.

Deploying energy-efficient appliances is one of the most effective ways to save energy bills for residents. However, the existing recommender systems for energy-efficient appliances passively rely on energy consumption patterns without the knowledge of users’ true needs. This paper proposes a user-centric energy-efficient appliance personalized recommender system (EEA-PRS) based on information collected from load monitoring platforms and e-commerce websites. The proposed system is built in a novel multi-task learning approach to collaboratively infer user's preference on: (1) common types of appliances that appear in historical data; (2) energy-efficient models of common appliances; and (3) types of appliances that are novel to the users. The proposed system provides supervisory recommendation services with user feedback preferences on appliances as data labelling, which enables closed-loop evaluation to adhere to users’ needs and interests. Simulation studies with comparative analysis have been conducted to validate its leading recommendation performance in terms of conforming to user preferences.

Energy storage systems (ESS) are pivotal component in the energy market, serving as both energy suppliers and consumers. ESS operators can reap benefits from energy arbitrage by optimizing operations of storage equipment. To further enhance ESS flexibility within the energy market and improve renewable energy utilization, a heterogeneous photovoltaic-ESS (PV-ESS) is proposed, which leverages the unique characteristics of battery energy storage (BES) and hydrogen energy storage (HES). For scheduling tasks of the heterogeneous PV-ESS, a practical cost function plays a crucial role in guiding operator’s strategies to maximize benefits. We develop a comprehensive cost function that takes into account degradation, capital, and operation/maintenance costs to reflect real-world scenarios. Moreover, while numerous methods excel in optimizing ESS energy arbitrage, they often rely on black-box models with opaque decision-making processes, limiting practical applicability. To overcome this limitation and enable explainable scheduling strategies, a prototype-based policy network with inherent interpretability is introduced. This network employs human-designed prototypes to guide decision-making by comparing similarities between prototypical situations and encountered situations, which allows for naturally explained scheduling strategies. Comparative results across four distinct cases demonstrate the effectiveness and practicality of our proposed pre-hoc interpretable optimization method when contrasted with black-box models.

Nonintrusive load monitoring (NILM) aims to analyze the aggregate information of power consumption and recognize the separate operation states of each individual electrical appliance, in which methods of machine learning are frequently used for efficient and effective computation. This article employs multilabel learning models as the main technique to measure the NILM problems with multiple electrical appliances. To precisely extract the information among the aggregate dataset and obtain better effects of data modeling, feature extraction based on interpretation analysis is carried out along of model training. Meanwhile, swapping the order of input labels is also implemented to further characterize the interrelationships and influences among the labels themselves during the modeling process. The results of simulation show that the feature extraction could improve the model performance as it may mitigate the mutual interference among input features and find crucial information to the target electrical appliances. Also, different sort of labels about the operation states of appliances would have impact on the model performance, on both individual and global predictions.

The hydrogen-based multimicrogrid (HMMG) has emerged as a game changer for energy transition. However, it encounters new challenges in tackling uncertainty data streams stemming from intermittent renewable energy and load. This article presents a multiple-time-scale HMMG energy management framework. In the day-ahead stage, the optimal scheduling is determined. The deviations of day-ahead predictions are redressed by intraday rescheduling. To accommodate the uncertainty data streams, a novel data-stream-driven distributionally robust model predictive control is proposed for the HMMG real-time operation. Specifically, a Dirichlet process mixture model is leveraged to construct an online-updated ambiguity set, which adequately characterizes the multimodality and local moment information of uncertainties. Based upon this ambiguity set, the data-stream-driven distributionally robust model predictive control enhances the real-time tracking performance of energy storage references. Its salient feature is the capability of greatly reducing conservatism while ensuring probabilistic operational constraints even under time-varying uncertainty distributions. Since this real-time operation is an intractable infinite-dimensional optimization problem, a novel constraint-tightening technique is proposed to address the computational challenge. Case studies demonstrate that the proposed approach offers advantages over state-of-the-art methods in out-of-sample performance.

Developing the deep learning (DL) technique is a promising way to improve renewable energy forecasting accuracy and offset the negative impacts of renewable energy on the power system. However, the application of the DL technique brings novel cyberthreats to the renewable energy forecast, and its cybersecurity has not received enough attention in previous literatures. To fill the gap, the vulnerability of renewable energy forecasting is, among the first, studied in-depth in this article. First, a novel cyberattack named adversarial learning attack (ALA) is proposed. The ALA is achieved by tampering with the meteorological data obtained by online weather forecasts from external application programming interfaces to undermine the renewable energy forecasting performance, which jeopardizes the power system operation. Then, an iterative algorithm is proposed to solve the ALA-based optimization problem. As the DL model is involved as optimization constraints, the optimization problem is nonconvex and NP-hard, which is unable to be solved by traditional approaches. The proposed algorithm utilizes the proximal gradient descent principle and is effective in iteratively exploring the near-optimal solution. At last, the impact of the ALA strategy on the power system operation is assessed, which considers the economic loss incurred and the potential hazards. The feasibility and efficacy of the ALA strategy are validated by conducting comprehensive and extensive experiments on the IEEE 30-bus benchmarks. The simulation results reveal that the ALA is able to impose severe economic losses on the operation and even induces disastrous hazards, such as power system collapse.

Many machine learning models are naturally multitask, which may involve regression and classification tasks, in which they can be trained by the multitask network to yield a more generalized model with the aid of correlated features. When these learning models are deployed on Internet-of-Things devices, the computation efficiency and the privacy of the data can pose a significant challenge to developing a federated learning (FL) algorithm for both higher learning performance and better privacy protection. In this article, a new FL framework is proposed for a class of multitask learning problems with hard parameter-sharing model through which the learning tasks are reformulated as a multiobjective optimization problem for better performance. Specifically, the stochastic multiple gradient descent approach and differential privacy are integrated into this FL algorithm for achieving a Pareto optimality that obtains a good tradeoff among different learning tasks while providing data protection. The outstanding performance of this algorithm is demonstrated by the empirical experiments on multiMINIST, the Chinese city parking dataset, and Cityscapes dataset.

In this article, a multiple time-scale voltage regulation is proposed based on the three-level coordinated control approach. This approach aims at solving the voltage issues in different scopes of space-time for active distribution networks. First, to address the global voltage issue of the entire network on a relatively slow-time scale, a multimode switching control is designed based on the Petri-net, which can effectively switch the on-load tap changer to extend its service life. Second, a multiobjective optimization considering the voltage differences with the security boundary and the transmission loss of the entire network is proposed. As such the global voltage issue can be handled by cooperating with the first-level switching control. Third, to solve the local voltage issue on a fast-time scale, a fully distributed optimal control integrating the active/reactive power sharing of all the distributed units is proposed. This control facilitates the control efficiency and active power consumption for renewable energy sources. Finally, the efficiency and effectiveness of the proposed method are validated under different scenarios in case studies.

The rapid development of electric vehicles (EVs) brings great challenges to the charging infrastructure construction and the smooth operation of power systems, which facilitates emerging of a new charging scheme based on mobile charging robots (MCRs) cluster. In this article, a novel framework with three-level optimization and control on multiple time scales is proposed, and the interactions among the MCR operator, power systems and EVs are realized by means of the cloud-edge-terminal coordination-based architecture. In the first level, the operation dispatch scheduling of the MCRs is formulated as a multiobjective optimization problem considering the voltage security of power grids, which improves the charging service efficiency of MCRs on a slow time scale. In the second level, a new solution algorithm based on the proposed resource competition and occupation model is used to handle the charging decision for the MCR operator, which greatly reduces the calculation time and ensures the solution accuracy simultaneously. In the third level, a local coordinated control is proposed to enable cooperation among the MCRs on a fast time scale, which provides charging services for EVs within the optimization interval of the second level and achieves the fairness of the residual state of charge of the MCRs. Finally, simulation results validate the effectiveness and superiority of the proposed three-level coordination with comparisons of existing methods.

Federated learning techniques provide a promising framework for collaboratively training a machine learning model without sharing users’ data, and delivering a security solution to guarantee privacy during the model training of IoT devices. Nonetheless, challenges posed by data heterogeneity and communication resource constraints make it difficult to develop an efficient federated learning algorithm in terms of the low order of convergence rate. It could significantly deteriorate the quality of service for critical machine learning tasks, e.g., facial recognition, which requires an edge-ready, low-power, low-latency training algorithm. To address these challenges, a communication-efficient federated learning approach is proposed in this paper where the momentum technique is leveraged to accelerate the convergence rate while largely reducing the communication requirements. First, a federated multi-task learning framework by which the learning tasks are reformulated by the multi-objective optimization problem is introduced to address the data heterogeneity. The multiple gradient descent algorithm is harnessed to find the common gradient descending direction for all participants so that the common features can be learned and no sacrifice on each clients’ performance. Second, to reduce communication costs, a local momentum technique with global information is developed to speed up the convergence rate, where the convergence analysis of the proposed method under non-convex case is studied. It is proved that the proposed local momentum can actually achieve the same acceleration as the global momentum, whereas it is more robust than algorithms that solely rely on the acceleration by the global momentum. Third, the generalization of the proposed acceleration approach is investigated which is demonstrated by the accelerated variation of FedAvg. Finally, the performance of the proposed method on the learning model accuracy, convergence rate, and robustness to data heterogeneity, is investigated by empirical experiments on four public datasets, while a real-world IoT platform is constructed to demonstrate the communication efficiency of the proposed method.

Real-time License Plate Recognition plays a significant role in traffic congestion control and road safety monitoring. Practically, a network camera may capture multiple license plates in one frame while the data collected by different network cameras cannot be shared due to privacy concern. In this paper, a federated learning framework is introduced to simultaneously detect multiple license plates over different network cameras through semantic communication. Specifically, to achieve a high efficiency of multiple license plates recognition in real time, the semantic segmentation model is applied to locally extract the important features of an image with multiple license plates. And then, an autoencoder is developed to carry out the semantic encoding which translates the meaningful information. Moreover, a multi-task learning approach for multiple license plates recognition is proposed through a multi-objective optimization technique which can train the license plate recognition model with stronger generalization. To improve the reliability, an asynchronous federated learning algorithm is also considered to ensure the training process can be tolerant to the transmission delay. Empirical experiments on the Chinese City Parking Dataset (CCPD) show that the proposed approach can effectively improve the recognition performance while providing robust service.

The end-edge-cloud orchestration of the virtual power plant (VPP) enables the edge server to timely serve community users. By deploying the community energy storage system (CESS) and the community peer-to-peer (P2P) market, prosumers can form energy communities to achieve self-sufficiency of energy and independence from fuel-based power generators. This article proposed a two-stage community energy trading model under end-edge-cloud orchestration. The community P2P trading is the first stage where the edge server can execute the automatic bidding process for multiple buyers and sellers based on the real-time users’ energy profiles and the Bayesian-game-based pricing mechanism. The trading between the retailer and energy communities is the second stage where the edge server can dynamically update the optimal operation of the CESS based on the dynamic pricing mechanism. An original centralized optimization problem is decomposed into subproblems for each stakeholder and solved through the alternating direction method of multipliers (ADMM). Considering ADMM needs multiple information exchanges, a general form of the communication-censored ADMM for sharing problems is proposed to decrease the communication cost. Numerical simulations prove that the proposed mechanism can effectively increase transaction efficiency, avoid the new demand peak brought by the utilization of the CESS, and decrease the communication cost.

Considering the popularity of electric vehicles and the flexibility of household appliances, it is feasible to dispatch energy in home energy systems under dynamic electricity prices to optimize electricity cost and comfort residents. In this paper, a novel home energy management (HEM) approach is proposed based on a data-driven deep reinforcement learning method. First, to reveal the multiple uncertain factors affecting the charging behavior of electric vehicles (EVs), an improved mathematical model integrating driver’s experience, unexpected events, and traffic conditions is introduced to describe the dynamic energy demand of EVs in home energy systems. Second, a decoupled advantage actor-critic (DA2C) algorithm is presented to enhance the energy optimization performance by alleviating the overfitting problem caused by the shared policy and value networks. Furthermore, separate networks for the policy and value functions ensure the generalization of the proposed method in unseen scenarios. Finally, comprehensive experiments are carried out to compare the proposed approach with existing methods, and the results show that the proposed method can optimize electricity cost and consider the residential comfort level in different scenarios.

Constrained optimization (CO) has made a profound impact in solving many real-world problems. Due to the high computation burden in exact solvers, data-driven CO based on machine learning techniques is recently receiving extensive research interests for its capability to solve CO problems in real time. The existing data-driven CO approaches only serve for optimization problems with rather simple constraints that can be directly incorporated into model training. However, constraints that are computationally infeasible or burdensome to evaluate are commonly experienced in realistic optimization applications, especially in the engineering sector. This paper proposes an athlete–referee dual learning system (ARDLS) for end-to-end CO with large-scale complex constraints, where an athlete model is trained as the main optimizer while a referee model is trained as a probabilistic constraint classifier to guide the athlete training. A risk-based constrained loss function is designed to fine-tune the athlete model for constraint satisfaction. A case study on electric power system emergency control application is conducted to validate the proposed ARDLS, where the testing results demonstrate the excellent capability of ARDLS to improve the likelihood of satisfying large-scale complex constraints in CO.

In Home Energy Management System (HEMS), the scheduling of energy storage equipment and shiftable loads has been widely studied to reduce home energy costs. However, existing data-driven methods can hardly ensure the transferability amongst different tasks, such as customers with diverse preferences, appliances, and fluctuations of renewable energy in different seasons. This paper designs a transferable scheduling strategy for HEMS with different tasks utilizing a Meta-Reinforcement Learning (Meta-RL) framework, which can alleviate data dependence and massive training time for other data-driven methods. Specifically, a more practical and complete demand response scenario of HEMS is considered in the proposed Meta-RL framework, where customers with distinct electricity preferences, as well as fluctuating renewable energy in different seasons are taken into consideration. An inner level and an outer level are integrated in the proposed Meta-RL-based transferable scheduling strategy, where the inner and the outer level ensure the learning speed and appropriate initial model parameters, respectively. Moreover, Long Short-Term Memory (LSTM) is presented to extract the features from historical actions and rewards, which can overcome the challenges brought by the uncertainties of renewable energy and the customers’ loads, and enhance the robustness of scheduling strategies. A set of experiments conducted on practical data of Australia’s electricity network verify the performance of the transferable scheduling strategy.

The spread of electric vehicles (EVs) could reduce greenhouse gas emissions and achieve sustainable travel patterns. However, the rapidly increasing charging demand will bring challenges to the operation of charging stations and power systems. Therefore, a two-stage EV management scheme is introduced in this article to overcome these challenges and promote sustainable transport. A charging dispatch model based on fuzzy multicriteria decision making is proposed in the first stage, where users' preferences are in the form of intuitionistic fuzzy sets to address the fuzziness and uncertainty of subjective factors and human judgment. A$\sigma$-cut similarity matrix is proposed to increase the users' satisfaction by excluding options with lower similarity. In the second stage, a noncooperative game model is proposed to incentive EVs to participate in supplementary frequency regulation (SFR). A fuzzy set is employed to reflect users' willingness to adjust charging power. The existence and uniqueness of the Nash equilibrium are investigated. Moreover, a distributed proximal best response algorithm with linear convergence is employed to find Nash equilibrium. Numerical simulations indicate that the proposed method can reduce charging costs while meeting users' preferences and facilitate EVs to participate in SFR.

Intensive use of photovoltaic (PV) inverter in the volt/var control (VVC) methods in active distribution networks can impair inverter reliability. This article proposes a PV inverter reliability-constrained (PiReCon-) VVC method with a power smoothing scheme under uncertainties. First, considering impacts on inverter reliability, new reliability constraints are developed with a power smoothing factor to constrict inverter apparent power variation. Second, a new VVC optimization model is proposed with the reliability constraints, which minimizes power losses by optimizing both inverter var output and PV curtailment. Uncertainties of PV generation and loads are fully considered and addressed via a stochastic optimization method. Third, this article develops a penalty convex-concave programming method to effectively tackle nonconvexity of the proposed optimization model. The proposed PiReCon-VVC method is tested on a 33-bus distribution network, and simulation results verify its high efficiency in both minimizing the power losses and enhancing the PV inverter reliability.

Automatic generation control (AGC) is paramount in maintaining the stability and operation of power grids. Its dependence on communication systems makes it vulnerable to various cyberphysical attacks. False data injection attacks (FDIA) are particularly difficult to detect and represent a major threat to AGC systems. This article proposes a novel spatio-temporal learning algorithm that can learn the normal dynamics of the power grid with AGC system to deal with this problem. The algorithm first uses a long short-term memory autoencoder to learn the normal dynamics. It then utilizes this unsupervised learned model in detecting the various possibilities of FDIA affecting the AGC system by evaluating the reconstruction residual of each measurements sample. The proposed algorithm is data-driven which makes it resilient against AGC's parameters uncertainties and modeling nonlinearities. The effectiveness of the developed algorithm is evaluated through test cases with various basic and stealth FDIAs.

Wind energy utilization is essential for realizing global energy transformation. To capture steady and powerful wind resources, wind turbines tend to be more remotely located with increasingly larger scales. This requires more sophisticated wind farm (WF) planning techniques to increase power production, reduce investment cost, and enhance power supply reliability, or pursue a balance among them. This article establishes a research framework for large-scale WF planning, with a technical review of the optimization methodologies involved, aiming to provide an updated, broader, and forward-looking vision for WF planners. Beyond the state-of-the-art summary of WF planning in the realm of wind turbines’ micro-siting and collector system design techniques, the related considerations on various practical factors are also well assessed including reliability, equipment rating, environment, and landscape. Further, viewing WF as an integrated system with scale expansion, the new research areas including WF joint-planning and redevelopment are explored and discussed to provide strong technical references for the foreseeable future.

Memristive LSTM networks have been proven as a powerful Neuromorphic Computing Architecture (NCA) for various time series forecasting tasks and are recognized as the next generation of AI. However, a lack of model explainability makes it hard to properly interpret forecasting results for existing memristive LSTM networks, which makes this NCA unreliable, unaccountable and untrustworthy. In this paper, an interpretable memristive (IM) LSTM network design is proposed for time series forecasting, where the mixture attention technique is embedded into IM-LSTM cells for characterizing the variable-wise feature and the temporal importance. The updating rules and training approach are also presented for this interpretable memristive LSTM network. We evaluate this approach on a probabilistic residential load forecasting task incorporating PV. By improving model interpretability, the most influential predictive factors can be verified by Built Environment domain experts, demonstrating the effectiveness of our design.

Setting retail electricity prices is one of the significant strategies for energy management of multi-microgrid (MMG) systems integrated with renewable energy. Nevertheless, the need of privacy preservation, the uncertainties of renewable energy and loads, as well as the time-varying scenarios, bring challenges for pricing problems. In this paper, a two-level pricing framework is proposed based on interval predictions and model-free reinforcement learning to address these challenges. In particular, at the higher level, the distribution system operator (DSO) is viewed as an agent, which sets retail electricity prices without detailed user information for privacy protection to maximize the total revenue from selling energy with reinforcement learning. For time-varying scenarios with intermittent photovoltaic power generation and diverse loads, a differentiable trust region layer is considered in reinforcement learning to improve the robustness of the policy updating process. While at the lower level, operators in microgrids solve three-phase unbalanced optimal power flow (OPF) problems to minimize generation cost and network power loss. Additionally, to deal with the challenges from the uncertainties of renewable power generation and user loads, interval predictions are chosen to quantify prediction errors and improve the flexibility of pricing policies. Finally, a set of experiments are conducted to validate the effectiveness of the proposed method for pricing problems in MMG systems.

Due to vulnerabilities exposed to cyberattacks in the cyber physical power system, increasing concerns have been paid to its cybersecurity, especially on the so-called false data injection attack. Timely recovering true values of measurements and states after encountering cyber-attacks is of paramount importance for ensuring the subsequent controls and operations of the cyber physical power system. This article, for the first time, discovers a measurement data inertia effect, and uses this effect to deduce coarse values of preattack measurements as a preliminary work for data recovery. Then, based on the deduced coarse values and suggested state bounds, an optimization model is proposed to recover the measurements and states contaminated by attacks in-time. Moreover, an error criterion named interval error is proposed to assess the entire performance of the proposed recovery scheme. Extensive and comprehensive experiments are implemented on the IEEE 30-bus test benchmark to verify the feasibility and effectiveness of the proposed recovery scheme. The numerical studies reveal that the proposed method can achieve high accuracy and efficient timeliness for data recovery.

To ensure a successful bid while maximizing profits, generation companies (GENCOs) need a self-scheduling strategy that can cope with a variety of scenarios. Therefore, distributionally robust optimization (DRO) is a good choice because it can provide an adjustable self-scheduling strategy for GENCOs in an uncertain environment, which can balance robustness and economics well compared to strategies derived from robust optimization and stochastic optimization. In this article, a novel moment-based DRO model with conditional value-at-risk is proposed to solve the self-scheduling problem under electricity price uncertainty. The size of the model mainly depends on the system size, and the computational burden increases sharply as the system size increases. For this drawback, two effective approximate models are proposed: one approximate model based on vector splitting (DRA-VS) and another based on the alternate direction multiplier method (DRA-ADMM). Both can greatly reduce calculation time and resources, while ensuring the quality of the solution, and DRA-ADMM only needs the information of the current area in each step of the solution, thus, private information is guaranteed. Simulations of three IEEE test systems are conducted to demonstrate the correctness and effectiveness of the proposed DRO model and two approximate models.

In this article, a rapid sensor fault diagnosis (SFD) method is presented for a class of nonlinear systems. First, by exploiting the linear adaptive observer technology and the deterministic learning method (DLM), an adaptive neural network (NN) observer is constructed to capture the information of the unknown sensor fault function. Second, when the NN input orbit is a period or recurrent one, the partial persistent excitation (PE) condition of the NNs can be guaranteed through the DLM. Based on the partial PE condition and the uniformly completely observable property of a linear time-varying system, the accurate state estimation and the sensor fault identification can be achieved by properly choosing the observer gain. Third, a bank of dynamical observers utilizing the experiential knowledge is constructed to achieve rapid SFD and data recovery. The attractions of the proposed approach are that accurate approximations of sensor faults can be achieved through the DLM, and the data that are destroyed by the sensor faults can be recovered by using the learning results. Simulation studies of a robot system are utilized to show the effectiveness of the proposed method.

In actual microgrids (MGs) networks, the information exchange between distributed energy resources (DERs) agents may be subject to various types of measurement noises and effected by communication time delays. This article proposes a resilient distributed multiagent control scheme for ac MG networks subject to additive noise and time-delay disturbances. The proposed multiagent control scheme is composed of three distributed consensus protocols, which is able to synchronize the output voltages and frequencies of inverter-based DERs to their reference values and achieve the optimal active power-sharing property by a low bandwidth communication network with noise and time-delay disturbances in almost sure convergence. By means of the stochastic analysis tools and algebraic graph theory, distributed consensus control protocols are designed to be employed for the secondary control level of MGs. On this basis, we deduce the stability criteria of the closed-loop MG system under noise and time-delay disturbances. As a result, the proposed consensus protocols can well restore the voltage and frequency’s derivation produced at the primary control level, meanwhile, can well achieve the optimal power sharing even though there exist communication disturbances. Several simulation scenarios on an islanded MG network are provided to verify the proposed control protocols’ performance.

Recent findings show that smart vehicles can be exposed to relay attacks resulting from weaknesses in cryptographic operations, such as authentication and key derivation, or poor implementation of these operations. Relay attacks refer to attacks in which authentication is evaded without needing to attack a smart vehicle itself. They are a recurrent problem in practice. In this paper, we formulate the necessary relay resilience settings for strengthening authentication and key derivation and achieving the secure design and efficient implementation of cryptographic protocols based on universal composability, which allows the modular design and analysis of cryptographic protocols. We introduce Crypto-Chain, a relay resilience framework that extends Kusters’s universal composition theorem on a fixed number of protocol systems to prevent bypass of cryptographic operations and avoid implementation errors. Our framework provides an ideal crypto-chain functionality that supports several cryptographic primitives. Furthermore, we provide an ideal functionality for mutual authentication and key derivation in Crypto-Chain by which cryptographic protocols can use cryptographic operations, knowledge about the computation time of the operations, and cryptographic timestamps to ensure relay resilience. As a proof of concept, we first propose and implement a mutual authentication and key derivation protocol (MKD) that confirms the efficiency and relay resilience capabilities of Crypto-Chain and then apply Crypto-Chain to fix two protocols used in smart vehicles, namely Megamos Crypto and Hitag-AES/Pro.

At present, in the face of the huge and complex data in cloud computing, the parallel computing ability of quantum computing is particularly important. Quantum principal component analysis algorithm is used as a method of quantum state tomography. We perform feature extraction on the eigenvalue matrix of the density matrix after feature decomposition to achieve dimensionality reduction, proposed quantum principal component extraction algorithm (QPCE). Compared with the classic algorithm, this algorithm achieves an exponential speedup under certain conditions. The specific realization of the quantum circuit is given. And considering the limited computing power of the client, we propose a quantum homomorphic ciphertext dimension reduction scheme (QHEDR), the client can encrypt the quantum data and upload it to the cloud for computing. And through the quantum homomorphic encryption scheme to ensure security. After the calculation is completed, the client updates the key locally and decrypts the ciphertext result. We have implemented a quantum ciphertext dimensionality reduction scheme implemented in the quantum cloud, which does not require interaction and ensures safety. In addition, we have carried out experimental verification on the QPCE algorithm on IBM's real computing platform. Experimental results show that the algorithm can perform ciphertext dimension reduction safely and effectively.

The papers in this special section focus on applications of artificial intelligence in industrial power electronics and systems. The grid infrastructures and modernization, as well as the integration of renewable energies and using smart meters, can generate a large amount of data. These can lead to high complexity in the power system/electronics operation and control. Moreover, grid contingencies due to the natural disasters and cyber/physical attacks are highly unpredictable and costly preventable, which require fast and reliable data processing to preserve grid reliability and resiliency. Furthermore, the reliability of the power electronics devices and interfaces are very important, and can be improved by using the large data of measurements during long term operation. To this end, artificial intelligence (AI) techniques can potentially make it possible to provide new solutions to power electronics and power system operations and analysis. This special issue aims to investigate applications of AI in power system operation, analysis, planning, cybersecurity, as well as power electronics control, modulation techniques, reliability of the power electronics switches, and efficiency improvement in power electronics applications.

Residential demand response (DR) is recognized as a promising approach to improve grid energy efficiency and relieve the network stress. Many studies have been conducted to design home energy management systems that directly schedule and control the household appliances. Distinguished from existing works, this article proposes a personalized recommendation system (PRS) to learn energy-efficient household appliance usage experiences from a large scale of residential users, and recommends suitable appliance usage plans to users while taking their lifestyles into account. The proposed system is based on a collaborative filtering recommendation technique. The PRS first classifies a collection of users as “highly responsive users” and “less responsive users” based on their DR degree analysis. Then, for each less responsive user, the PRS infers the user's lifestyle from usage profiles of nonshiftable appliances and finds out users who have similar habits with the target user from the set of highly responsive users. Based on this, the PRS evaluates the lifestyle similarity between the target user and each smart user, aggregates the appliance usage experiences of highly responsive users, and makes appliance-use recommendations to the target user. Experiments based on a residential data simulator “SimHouse” are designed to validate the proposed system.

The economic dispatch problem (EDP) plays a fundamental and significant role in smart grids. Its purpose is to decide the output power of every generator in smart grids for achieving the minimal generation cost. With advantages in flexibility, robustness, and scalability, it is desirable to apply distributed optimization methods to solve EDPs. In most existing distributed optimization approaches, all generators explicitly exchange their states with neighbors to obtain the optimal solution, which may result in disclosing the privacy information of generators. This problem becomes worse if there are some adversaries aimed at inferring privacy information from the communication network for nefarious purposes. For privacy preservation, a privacy preserving distributed optimization algorithm over time-varying directed communication networks is proposed in this article by adding conditional noises to the exchanged states. It is proved that this proposed algorithm is able to solve the EDP. Moreover, the convergence rate and privacy analysis of the proposed algorithm are also shown in this article. An example is provided to confirm the effectiveness of this proposed algorithm.

The increasing adoption of multiway communications in the advanced metering infrastructure (AMI) of the energy Internet, which is known as the Internet-based smart grid, raises a new question about the security of customers' sensitive data and how the data can be protected from growing cyber attacks such as side-channel and false data injection attacks. The dynamic nature of remote connect/disconnect of components in the AMI also brings new types of security threats. To achieve secure multiway communications and remote connect/disconnect of components, the AMI requires a key exchange protocol (KEP) that meets a number of its security requirements such as confidentiality, integrity, availability, identification, authentication, and access control. In this context, in this article we present a KEP that uses an ideal crypto functionality and an ideal AMI key exchange functionality based on universal composability, which allows modular design and analysis of cryptographic protocols. The former functionality enables AMI components or users to perform authenticated cryptographic operations, while the later functionality enables the users to meet the AMI security requirements before generating a shared secret session key, which can be used in an ideal manner. We carry out experiments to validate the performance of our protocol, and the results show that our protocol offers better performance benefits compared to the existing related protocols and is suitable for the Energy Internet. We further demonstrate the usefulness of our ideal functionalities as a security reinforcement for a widely used KEP, namely the Elliptic Curve Diffie-Hellman.

Situation awareness (SA) has been recognized as a critical guarantee for the stable and secure operation of electric power systems, especially under complex uncertainties after renewable energy integration. In this article, an artificial-intelligence-powered solution is presented to reach a full realization of SA covering perception, comprehension, and prediction, the last of which is more advanced but challenging and hence has not been discussed in any literature before. A novel SA model is proposed by aggregating two powerful deep learning structures: convolutional neural network (CNN) and long short-term memory (LSTM) recurrent neural network. The proposed CNN-LSTM model has superiority to achieve collaborative data mining on spatiotemporal measurement data, i.e., to learn both spatial and temporal features simultaneously from phasor measurement units data. Two functional branches are designed within the SA model: a contingency locator to detect the exact fault location at present and a stability predictor to predict stability status of the system in the future. Test results have shown high performance (accuracy) of the model even on a low level of data adequacy. The proposed SA model can promisingly facilitate very fast postfault actions by the system operators to prevent the power system from any unstable operational status.

Constructing large-scale offshore wind farms (OWFs) has become the main direction of utilizing wind power to help realize the energy transformation. Traditionally, the planning of the OWF collector system would rely on either heuristic or deterministic optimization algorithms, which, respectively, suffer from unstable outputs and a lack of freedom in searching for a globally optimal solution. This article innovatively designs a hybrid optimization approach combining algorithms in these two categories to achieve a balance between improved economic efficiency and stable outputs. The whole design consists of two layers of hybrid optimizations. The outer layer is to partition wind turbines (WTs) into groups, where each group is allocated with an offshore substation with the optimized location for power collection and transmission. This partitioning and locating optimization is solved through a combination of the deterministic fuzzy C-means clustering method and the genetic algorithm (GA). The inner layer is to arrange optimal connections using proper cable ratings among WTs within each group, and GA is properly integrated into the deterministic two-phase Clark and Wright's saving algorithm to solve the problem. The collector system planning, in this article, concerns both the investment cost and the long-term power-loss cost. The former consists of the networks of internal medium voltage and the external high voltage, which collect the power from WTs and transmit it to the onshore grid. The proposed design is tested on a benchmark OWF collector system, and the test result verifies its achievements in higher economic efficiency with stable outputs.

This article proposes an economic model predictive algorithm for optimal load frequency control, in which both the frequency regulation and economic load dispatch (ELD) are considered, in interconnected power systems. Two-layer hierarchical control can be achieved through one level by EMPC. An economic stage cost function, including ELD and frequency regulation, which can be written in general convex form, is optimized by the controller. The distributed way is utilized to realize the control of large-scale power systems. Each subsystem-based controller works cooperatively with neighboring subsystems to achieve system-wide control performance. Asymptotic stability of the system is guaranteed by the proper terminal cost function. The efficiency and advantages of the proposed method are manifested by the simulation.

In this article, the problem of the distributed convex optimization is investigated, where the target is to collectively minimize a sum of local convex functions over an unbalanced directed multiagent network. Each agent in the network possesses only its private local objective function, and the sum of all local objective functions constitutes the global objective function. We particularly consider the scenario, where the underlying interaction network is strongly connected and the relevant weight matrix is row stochastic. To collectively figure out the optimization problem, a distributed accelerated convergence algorithm where agents utilize uncoordinated step-sizes is presented by incorporating consensus of multiagent networks into distributed inexact gradient tracking technique. Most of the existing methods require all agents to possess the out-degree information of their in-neighbors, which is impractical and hardly inevitable as interpreted in this article. By utilizing the small-gain theorem, we prove that if the maximum step-size is positive and sufficiently small (constrained by a specific upper bound), the proposed algorithm, termed as SGT-FROST, converges geometrically to the optimal solution given that the objective functions are smooth and strongly convex. A certain convergence rate is also shown. Simulations confirm the findings in this article.

The economic dispatch problem (EDP) is one of the fundamental and important problems in power systems. The objective of EDP is to determine the output generation of generators to minimize the total generation cost under various constraints. In this article, a finite-time consensus-based distributed optimization algorithm is proposed to solve EDP. It is only required that each device in the communication network has access to its own local generation cost function, designed virtual local demand and its neighbors' local optimization variables. The proposed finite-time algorithm can solve EDP, if the gain parameters in the algorithm satisfy some conditions under undirected and connected time-varying graphs. Moreover, the bounded or linear increasing assumption on the gradient and subgradient of objecive functions is relaxed in this algorithm. Examples under several cases are provided to verify the effectiveness of the proposed distributed optimization algorithm.

This paper aims to investigate the optimal scheduling of stochastic reconfigurable hybrid ac-dc microgrid (MG) in the presence of renewable energies and also considering dynamic line rating (DLR) constraint. DLR is a practical limitation that can potentially affect the ampacity of lines, particularly in the islanded mode when the lines reach their maximum capacity in lack of main generation source at the point of interconnection with the utility. In order to prevent overloading of the lines, the reconfiguration technique is developed to change the topology of the network by some prelocated switches. A linearization technique is adapted to address the nonlinearity of both nodal ac power flow and the DLR constraints. The unscented transform technique is utilized to model uncertainties including renewable energy generations, hourly load demands, and hourly market prices along with the DLR uncertainties such as solar radiation, wind speed, and ambient temperature. Finally, a sensitivity analysis is performed to see the effect of wind speed and solar radiation on the energy management of hybrid ac-dc MG. The performance of the proposed methodology is examined on a modified IEEE-33 bus test system, which demonstrates the high efficiency and importance of the proposed techniques in minimizing the hybrid ac-dc MG operation cost while all of the constraints of the network are satisfied.

The increased complexity of modern energy network raises the necessity of flexible and reliable methods for smart grid operation. To this end, this article is centered on the economic dispatch problem (EDP) in smart grids, which aims at scheduling generators to meet the total demand at the minimized cost. This article proposes a fully distributed algorithm to address the EDP over directed networks and takes into account communication delays and noisy gradient observations. In particular, the rescaling gradient technique is introduced in the algorithm design and the implementation of the distributed algorithm only resorts to row-stochastic weight matrices, which allows each generator to locally allocate the weights on the messages received from its in-neighbors. It is proved that the optimal dispatch can be achieved under the assumptions that the nonidentical constant communication delays inflicting on each link are uniformly bounded and the noises embroiled in gradient observation of every generator are bounded variance zero mean. Simulations are provided to validate and testify the effectiveness of the presented algorithm.

In this article, the impact of prediction errors on the performance of a domestic power demand management is thoroughly investigated. Initially, real-time peak power demand management system using battery energy storage systems (BESSs), electric vehicles (EVs), and photovoltaics (PV) systems is designed and modeled. The model uses real-time load demand of consumers and their roof-top PV power generation capability, and the charging-discharging constraints of BESSs and EVs to provide a coordinated response for peak power demand management. Afterward, this real-time power demand management system is modeled using autoregressive moving average and artificial neural networks-based prediction techniques. The predicted values are used to provide a day-ahead peak power demand management decision. However, any significant error in the prediction process results in an incorrect energy sharing by the energy management system. In this research, two different customers connected to a real-power distribution network with realistic load pattern and uncertainty are used to investigate the impact of this prediction error on the efficacy of an energy management system. The study shows that in some cases the prediction error can be more than 300%. The average capacity of energy support due to this prediction error can go up to 0.9 kWh, which increases battery charging-discharging cycles, hence reducing battery life and increasing energy cost. It also investigates a possible relationship between environmental conditions (solar insolation, temperature, and humidity) and consumers' power demand. Considering the weather conditions, a day-ahead uncertainty detection technique is proposed for providing an improved power demand management.

This article presents a distributed alternating direction method of multipliers (ADMMs) approach for solving the direct current dynamic optimal power flow with carbon emission trading (dc-DOPF-CET) problem. Generally, the ADMM-based distributed approaches disclose boundary buses and branches information among adjacent subsystems. As opposed to these methods, the proposed method (dc-ADMM-P) adopts a novel strategy which uses consensus ADMM to solve the dual of dc-DOPF-CET while only discloses boundary branches information among adjacent subsystems. Moreover, the convergence performance of dc-ADMM-P is improved by reducing the number of dual multipliers and employing an improved update step of the multiplier. DC-ADMM-P is tested on cases ranging from 6 to 1062 buses, with comparison with other distributed/decentralized methods. The simulation results verify the high efficiency of dc-ADMM-P in solving the dc-DOPF problem with complex (nonlinear) factors which can be formulated as convex separable functions. Meanwhile, it also shows the improvement of convergence performance by reducing the number of dual multipliers and employing a new update strategy for the multiplier.

Data-driven condition monitoring is an essential function for power plant because of its potential to enhance asset longevity and reduce the operation and maintenance costs. This article explains the complicated relationship in multiplex power plant data as a mixture of temporal dependency and cross-variable association and proposes a composite anomaly detection system that incorporates the two data relationships on a probabilistic basis for more reliable power plant condition monitoring. It is able to dynamically capture the most significant relationship to develop more reliable normal condition interval, based on which the potential faults can be timely detected and the abnormal variable can be accurately identified. The proposed system was tested on a realistic thermal power plant. The testing results demonstrate its reliable condition monitoring and accurate anomaly detection performance, which necessitates the composite modeling of temporal dependency and cross-variable association in data-driven power plant condition monitoring.

Manufactory load allocation can be used as an effective industrial demand response scheme to reduce operating costs for industrial multienergy microgrids (iMEMGs). In addition, combined cooling, heat, and power (CCHP) plants with auxiliary devices can provide low-cost multiple energies for industrial plants. However, uncertain power generation from renewable energy sources impairs the iMEMG's operation, leading to challenges such as increased operating costs and energy supply deficiency. To conquer these challenges, this paper proposes a multitimescale coordinated adaptive robust operation approach where manufactory load allocation and iMEMG operation are optimally coordinated on different timescales. In the weekly scheduling stage, industrial loads and CCHP units are scheduled for the following week and the hourly iMEMG operation is optimized within the week. Besides, this paper applies an adaptive robust optimization method where the uncertain renewable power generation is fully addressed. The proposed approach is tested on an iMEMG with various industrial manufactories, and it is compared with conventional methods. The simulation results indicate that compared to the conventional ones, the proposed approach can guarantee a robustly optimal operation solution for the iMEMG against any uncertainty realization.

Cooperative wind farm control is a complex problem due to wake effect, and it is hard to find the proper model. Reinforcement learning can find the optimal policy in a dynamic environment using “trial and error,” but may damage the machine and cause high cost during the learning process. In order to address this challenge, this article proposes the knowledge-assisted reinforcement learning framework by combining the low-fidelity analytical model with a reinforcement learning framework. Moreover, the knowledge-assisted deep deterministic policy gradient (KA-DDPG) algorithm and three kinds of knowledge-assisted learning methods are proposed based on the framework. The proposed methods are tested in nine different scenarios of WFSim. The simulation results show that the KA-DDPG algorithm can reach the maximum power output and ensure safety during learning. In addition, the learning cost is reduced by accelerating the learning process.

A prevailing problem in many machine learning tasks is that the training (i.e., source domain) and test data (i.e., target domain) have different distribution [i.e., non-independent identical distribution (i.i.d.)]. Unsupervised domain adaptation (UDA) was proposed to learn the unlabeled target data by leveraging the labeled source data. In this article, we propose a guide subspace learning (GSL) method for UDA, in which an invariant, discriminative, and domain-agnostic subspace is learned by three guidance terms through a two-stage progressive training strategy. First, the subspace-guided term reduces the discrepancy between the domains by moving the source closer to the target subspace. Second, the data-guided term uses the coupled projections to map both domains to a unified subspace, where each target sample can be represented by the source samples with a low-rank coefficient matrix that can preserve the global structure of data. In this way, the data from both domains can be well interlaced and the domain-invariant features can be obtained. Third, for improving the discrimination of the subspaces, the label-guided term is constructed for prediction based on source labels and pseudo-target labels. To further improve the model tolerance to label noise, a label relaxation matrix is introduced. For the solver, a two-stage learning strategy with teacher teaches and student feedbacks mode is proposed to obtain the discriminative domain-agnostic subspace. In addition, for handling nonlinear domain shift, a nonlinear GSL (NGSL) framework is formulated with kernel embedding, such that the unified subspace is imposed with nonlinearity. Experiments on various cross-domain visual benchmark databases show that our methods outperform many state-of-the-art UDA methods. The source code is available at https://github.com/Fjr9516/GSL.

As cyber attacks to Industrial Internet of Things (IIoT) remain a major challenge, blockchain has emerged as a promising technology for IIoT security due to its decentralization and immutability characteristics. Existing blockchain designs, however, introduce high computational complexity and latency challenges which are unsuitable for IIoT. This paper proposes Xyreum, a new high-performance and scalable blockchain for enhanced IIoT security and privacy. Xyreum uses a Time-based Zero-Knowledge Proof of Knowledge (T-ZKPK) with authenticated encryption to perform Mutual Multi-Factor Authentication (MMFA). T-ZKPK properties are also used to support Key Establishment (KE) for securing transactions. Our approach for reaching consensus, which is a blockchain group decision-making process, is based on lightweight cryptographic algorithms. We evaluate our scheme with respect to security, privacy, and performance, and the results show that, compared with existing relevant blockchain solutions, our scheme is secure, privacy-preserving, and achieves a significant decrease in computation complexity and latency performance with high scalability. Furthermore, we explain how to use our scheme to strengthen the security of the REMME protocol, a blockchain-based security protocol deployed in several application domains.

In this paper, based on the deterministic learning (DL) theory, an approach for detection for small faults in a class of nonlinear closed-loop systems is proposed. First, the DL-based neural control approach and identification approach are employed to extract the knowledge of the control effort that compensates the fault dynamics (change of the control effort) and the fault dynamics (the change of system dynamics due to fault). Second, two types of residuals are constructed. One is to measure the change of system dynamics, another one is to measure change of the control effort. By combining these residuals, an enhanced residual is generated, in which the fault dynamics and the control effort are combined to diagnose the fault. It is shown that the major fault information is compensated by the control, and the major fault information is double in the enhanced residual. Therefore, the fault information in the diagnosis residual is enhanced. Finally, an analysis of the fault detectability condition of the diagnosis scheme is given. Simulation studies are included to demonstrate the effectiveness of the approach.

The Energy Internet is an advanced smart grid solution to increase energy efficiency by jointly operating multiple energy resources via the Internet. However, such an increasing integration of energy resources requires secure and efficient communication in the Energy Internet. To address such a requirement, we propose a new secure key bootstrapping protocol to support the integration and operation of energy resources. By using a universal composability model that provides a strong security notion for designing and analyzing cryptographic protocols, we define an ideal functionality that supports several cryptographic primitives used in this paper. Furthermore, we provide an ideal functionality for key bootstrapping and secure communication, which allows exchanged session keys to be used for secure communication in an ideal manner. We propose the first secure key bootstrapping protocol that enables a user to verify the identities of other users before key bootstrapping. We also present a secure communication protocol for unicast and multicast communications. The ideal functionalities help in the design and analysis of the proposed protocols. We perform some experiments to validate the performance of our protocols, and the results show that our protocols are superior to the existing related protocols and are suitable for the Energy Internet. As a proof of concept, we apply our functionalities to a practical key bootstrapping protocol, namely generic bootstrapping architecture.

The eleven papers in this special section focus on new trends in residential and home energy management. As an important branch of power demand side management, residential energy management plays an important role in reducing the emission and enhancing the energy efficiency in the energy delivery side. Recent technical advances bring significant transformations to energy end-users. First, increasing penetrations of residential renewable energy source, electric vehicle, and residential energy storage system have been transforming residential energy consumers to be “Energy Prosumers (Producer and Consumer. Second, the two-way communication infrastructure enables residential energy entities interact and exchange information flows with the external environment. Third, recent advances in ubiquitous sensing and metering technologies, such as Internet of Things, nonintrusive load monitoring, and advanced metering infrastructure, enable the deep understanding on behaviors of energy end-users and related environments. These technical advances consequently drive residential energy entities to become complex cyber-physical-social systems, which require newsolutions for coordinating, managing, and optimizing residential energy resources with the active participations of end users.

Advances in bilateral communication technology foster the improvement and development of home energy management system (HEMS). This paper proposes a new HEMS to optimally schedule home energy resources (HERs) in a high rooftop photovoltaic penetrated environment. The proposed HEMS includes three stages: forecasting, day-ahead scheduling, and actual operation. In the forecasting stage, short-term forecasting is performed to generate day-ahead forecasted photovoltaic solar power and home load profiles; in the day-ahead scheduling stage, a peak-to-average ratio constrained coordinated HER scheduling model is proposed to minimize the one-day home operation cost; in the actual operation stage, a model predictive control based operational strategy is proposed to correct HER operations with the update of real-time information, so as to minimize the deviation of actual and day-ahead scheduled net-power consumption of the house. An adaptive thermal comfort model is applied in the proposed HEMS to provide decision support on the scheduling of the heating, ventilating, and air conditioning system of the house. The proposed approach is then validated based on Australian real datasets.

Owning to electricity market deregulation, residential customers now enjoy the freedom to choose their preferred electricity retailers. This paper investigates the application of recommender system, a fast-developing technique in machine learning, into the task of recommending electricity plans for the individual residential customer. Based on a collaborative filtering strategy, an electricity plan recommender system (EPRS) is developed. By providing easily obtainable data of some household appliances, residential customers of the EPRS are recommended with predicted ratings of different plans, which can provide effective guidance to customers in the selection of suitable plans and proper tariffs. Different numerical tests are carried out to evaluate the performance of the EPRS. The EPRS outperforms other strategies in the accuracy of recommendation result and is verified to be a promising solution to electricity plan recommendation task.

The deregulation of the electricity market enables residential customers to select suitable electricity retailing plans. This paper proposes a Bayesian hybrid collaborative filtering-based electricity plan recommender system (BHCF-EPRS), which is constructed in a two-stage model integrated with model-based and memory-based collaborative filtering methods. Bayesian inference is developed for missing feature estimation and user classification. Free from the requirements on total electricity use data and historical plan transaction data, the BHCF-EPRS can recommend suitable retailers and plans based on some easily obtainable features quantifying home appliance usage patterns. The BHCF-EPRS is verified to be a reliable recommender system with low error in full-ranking recommendation and high precision in top-N recommendation, which can improve the competitive operation of the electricity market.

As one of the most complex and largest dynamic industrial systems, a modern power grid envisages the wide-area measurement protection and control (WAMPAC) system as the grid sensing backbone to enhance security, reliability, and resiliency. However, based on the massive wide-area measurement data, how to realize real-time short-term voltage stability (STVS) assessment is an essential yet challenging problem. This paper proposes a hierarchical and self-adaptive data-analytics method for real-time STVS assessment covering both the voltage instability and the fault-induced delayed voltage recovery phenomenon. Based on a strategically designed ensemble-based randomized learning model, the STVS assessment is achieved sequentially and self-adaptively. Besides, the assessment accuracy and the earliness are simultaneously optimized through the multiobjective programming. The proposed method has been tested on a benchmark power system, and its exceptional assessment accuracy, speed, and comprehensiveness are demonstrated by comparing with existing methods.

The growth in residential air-conditioning is a primary contributor to electric utility critical peak load causing millions of dollars spent on extra network infrastructure to cater for these peak times. This paper aims to provide an attempt to coordinate multiple groups of aggregated air-conditioners for distribution network loading management. Through limited communication to exchange information among neighboring aggregators, the proposed dispatch strategy shares the required active power curtailment among aggregators, maintaining room temperatures to keep occupants comfort in the meanwhile. Three case studies and sensitivity analysis are conducted to show the performance of the proposed scheme. The results show that it can provide technical and economic benefits to both participating residents and network operators.

This paper deals with the optimal energy management approach by using multi-objective optimization algorithm for two modes of micro grid operation: non-autonomous and autonomous which includes the optimally allocated and sized Distributed Generation (DG) units working at optimal power factor. For micro grid operation, objectives are optimization of DGs sizes with loss minimization and voltage profile improvement. A multi objective optimization algorithm is proposed to achieve the targets. Voltage stability indices based priority list has developed to optimally allocate the DGs. After siting DGs, optimal sizing has been executed in terms of loss minimization for both modes of micro grid. Numerical experimentations have been carried out on DGs penetration levels increasing gradually from 0% to 100%. The proposed methodology is programmed under MATLAB software and it is tested for standard IEEE-15 bus and IEEE-33 bus radial distribution systems. 33-bus radial system with 100% of penetration level of DGs has been converted into autonomous micro grid for verification of the suggested algorithm. Reduction in losses and improvement in voltage profile proves the effectiveness of the proposed method.

An optimal placement of protective devices could increase the reliability and quality level of a distribution network. An innovative mixed-integer nonlinear programming model is proposed in this paper to find the type, optimal siting, and number of protective devices to be accurately installed in distribution networks. The customer outage and protective devices costs are considered to derive a value-based reliability equation. To ensure the effectiveness of the proposed formulation economic and technical constraints is considered. Further, this paper aims at aiding decision-makers in providing appropriate protective device allocation by minimizing the expected interruption cost index. Case studies are employed to demonstrate the reliability optimization of a test network and a typical real-size network in which the several cost constraints and protection schemes are assumed to extract the results. Accuracy and effectiveness of the proposed method are assessed and sensitivities analysis is carried out.

Increasing penetration of plug-in electric vehicles (PEVs) has a substantial impact on the operation of power distribution networks. Given the fast-growing load demands from PEVs and unmatched infrastructure investment in transformer and feeder capacity, the PEV charging is subjected to both spatially and temporally security constraints beyond which the network failure may occur. This paper proposes a game-theory-based distributed charging control method to coordinate large-scale PEVs without compromising the security of the distribution network. Under a noncooperative game framework, a price-driven charging model is designed to minimize the cost of each individual PEV customer while satisfying the network loading constraints. Then, a Newton-type method is developed to find a better Nash equilibrium of the game model at a superlinear convergence rate. Furthermore, an accelerated gradient method is proposed to tackle the subproblem for each user's best response. The update of the user's best response is implemented in a distributed way in order to protect user's privacy. The convergence rate of the proposed algorithms is rigorously proved. The effectiveness and efficiency of the proposed methods are tested on the IEEE 13-bus system.

The increasing prevalence of electric vehicles (EVs) calls for the effective planning of the charging infrastructure. In this study, a multi-objective, multistage collaborative planning model is proposed for the coupled EV charging station infrastructure and power distribution network. The planning model aims to minimize the investment and operation costs of the distribution system while maximize the annually captured traffic flow. The uncertainties of EV charging loads are modeled for three different types of charging stations. The FISK's stochastic traffic assignment model is utilized to model realistic traffic flows. And a new class of volume-delay functions, conical congestion functions, is employed to overcome the shortcomings of the conventional Bureau of Public Roads function. The multi-objective evolutionary algorithm based on decomposition (MOEA/D) algorithm is applied to find the nondominated solutions of the proposed collaborative planning model. Finally, simulations based on a 54-node distribution system are conducted to validate the effectiveness of the proposed method.

The growth in installed solar photovoltaic (PV) capacity and the ever-increasing power demand due to the use of energy-hungry appliances have caused voltage issues. In this paper, a hierarchical dispatch strategy is proposed for coordinating multiple groups of virtual energy storage systems (VESSs), i.e., residential houses with air conditioners, to regulate voltage in low-voltage (LV) grids with high solar PV penetration. Specifically, the two levels of the proposed model are: 1) in the lower level, VESSs within each intelligent residential district are controlled locally by individual aggregator; 2) in the upper level, multiple aggregators are coordinated to achieve voltage regulation through a consensus control strategy. By exchanging information through sparse communication links, each aggregator shares the required active power adjustment among all participating groups, without compromising users' thermal comfort. Simulation result demonstrates that the proposed control scheme can effectively regulate voltage in LV grids with greater robustness and scalability.

The transition towards a cyber-physical energy system (CPES) entails an increased dependency on valid data.Simultaneously, an increasing implementation of renewable generation leads to possible control actions at individual distributed energy resources (DERs).A state estimation covering the whole system, including individual DER, is time consuming and numerically challenging.This paper presents the approach and results of implementing a simplified state estimator onto an embedded system for improving DER monitoring.The implemented state estimator is based on numerically robust orthogonal factorization and used on a set of state equations of a generic wind turbine generator (WTG).The simplified state estimator is tested by simulating a generic WTG model and evaluated based on its execution time and estimation accuracy.Results show its fast execution time, its accuracy in handling normal measurement error and its ability to provide reliable data in the case of gross errors in the set of measurements.

It is expected that there are a variety of energy resources to be jointly operated by the power system in the future. Through jointly operating all types of energy resources via the Internet, Energy Internet is a promising solution to increase energy efficiency. However, the increasing integration of energy resources inevitably imposes challenges in secure communication in the Energy Internet. Resourceful and reliable communication with high Quality of Security Service (QoSS) are crucial to the success of information exchange in Energy Internet. In this light, we advocate a new identity-based key bootstrapping protocol to support energy resources integration and operation, and satisfy the increasing requirements in QoSS. The proposed scheme enables a component to verify the identities of other components and ensure the authenticity and integrity of messages for key bootstrapping, which supports an identity-based communication paradigm for unicast and multicast communication. A system model is defined, and the identity-based communication paradigm is applied to address communication security concerns in real-time. The security and performance analyses show that the proposed scheme is superior to existing schemes.

This paper proposes an operational planning framework for large-scale thermostatically controlled load (TCL) dispatch. The proposed framework consists of a day-ahead scheduling stage and a real-time operation stage. A thermal comfort model is employed to estimate the occupants' thermal comfort degree. A self-adaptive TCL grouping method is proposed to group the TCLs based on the similarity of the TCL model parameters. Then, a hierarchical day-ahead scheduling model is proposed to make the optimal dispatch plan for the TCL aggregators based on the day-ahead forecasted information. In the real-time operation stage, a predictive control model is proposed for the TCL aggregators to make the real-time TCL dispatch decision based on the updated real-time information. The simulation results prove the efficiency of the proposed framework.

This paper has proposed an electric vehicle (EV) route selection and charging navigation optimization model, aiming to reduce EV users' travel costs and improve the load level of the distribution system concerned. Moreover, with the aid of crowd sensing, a road velocity matrix acquisition and restoration algorithm is proposed. In addition, the waiting time at charging stations is addressed based on the queue theory. The formulated objective of the presented model is to minimize the EV users' travel time, charging cost or the overall cost based on the time of use price mechanism, subject to a variety of technical constraints such as path selections, travel time, battery capacities, and charging or discharging constraints, etc. Case studies are carried out within a real-scale zone in a city where there are four charging stations and the IEEE 33-bus distribution system. The effects of real-time traffic information acquisition and different decision targets on EV users' travel route and effects of charging or discharging of EVs on the load level of the distribution system are also analyzed. The simulation results have demonstrated the feasibility and effectiveness of the proposed approach.

In the smart grid and future energy internet environment, a regional energy service provider (RESP) may be able to integrate multiple energy resources such as generator units, demand response, electrical vehicle charging/swapping stations, and carbon emission trading to participate in the market. By imploring a well-known portfolio optimization theory conditional value-at-risk to tackle electricity price uncertainty, this paper formulates the risk-averse day-ahead operational planning for such a RESP as a mixed-integer quadratically constrained programming (MIQCP), named as RA-RESP. A global optimization method, named as multiple perspective-cuts outer approximation method (MPC-OAM) is proposed to solve this model efficiently. A remarkable stronger and tighter mixed integer linear programing master problem is designed to accelerate the convergence of the proposed method. Comprehensive simulation results show that, compared with existing day-ahead planning models, the RA-RESP is a good compromise between profit-based models and cost-based ones. The proposed MPC-OAM can solve complicated RA-RESP problem efficiently, and compared with state-of-the-art solution techniques, the MPC-OAM outperforms in both computing speed and solution quality, especially for scenario which includes more nonlinear factors.

An electric vehicle aggregator (EVA) that manages geographically dispersed electric vehicles offers an opportunity for the demand side to participate in electricity markets. This paper proposes an optimization model to determine the day-ahead inflexible bidding and real-time flexible bidding under market uncertainties. Based on the relationship between market price and bid price, the proposed optimal bidding model of EVA aims to minimize the conditional expectation of electricity purchase cost in two markets considering price volatility. Moreover, the penalty cost of the deviation between the bidding quantities is included to avoid large power variation and arbitrage. The conditional expectation optimization model is formulated as an expectation minimization problem with the conditional value-at-risk constraints. Based on the price data in the PJM market, simulation results verify that our model is a decision-making tool in electricity markets, which can help market players comprehend the variants of bid price, expected cost and probability of successful bidding.

Renewable share in the global total energy mix is predicted to grow, and this leads to an increase in the required capacity for frequency regulation. While an electric vehicle (EV) is gaining more popularity, a collection of retired EV battery packs provides an economic option for meeting the additional frequency regulation needs. In this paper, a battery market operation model is proposed to maximize financial return, and a battery operation cost estimator is built to evaluate the potential impacts of market operations on the battery lifespan. Specifically, the model is designed for retired EV lithium batteries under the Australian national electricity market framework. It predicts the automatic-generation-control energy due to the frequency regulation service offers. Battery cycle life cost and battery capacity degradation are considered in the model. It can be used to determine multimarket offers based on the expected profit. Nonetheless, the model can be generalized for other electricity market frameworks and battery types.

This paper proposes a new robust security-constrained optimal power flow (SCOPF) method to balance the economy. and security requirements under uncertainties associated with renewable generation and load demand. Given the significant growth in microgrid (MG) deployments over the world, this paper explores the potential of using multiple MGs in supporting main grid's security control. Corrective control is employed to relieve postcontingency overflows by effectively coordinating system generators and multiple MGs. An incentive-based mechanism is designed to encourage the MGs to actively cooperate with the main grid for postcontingency recovery, which makes the proposed method to distinguish from the previous models using a traditional centralized control method, such as direct load control. A scenario-decomposition-based approach is then developed to solve the proposed robust SCOPF problem. Numerical simulations on IEEE 14- and IEEE 118-bus systems demonstrate the effectiveness and efficiency of the proposed method.

Dynamic insecurity risk of a power system has been increasingly concerned due to the integration of stochastic renewable power sources (such as wind and solar power) and complicated demand response. In this paper, an intelligent early-warning system to achieve reliable online detection of risky operating conditions is proposed. The proposed intelligent system (IS) consists of an ensemble learning model based on extreme learning machine (ELM) and a decision-making process under a multiobjective programming framework. Taking an ensemble form, the randomness existing in individual ELM training is generalized and reliable classification results can be obtained. The decision making is designed for ELM ensemble whose parameters are optimized to search for the optimal tradeoff between the warning accuracy and the warning earliness of the proposed IS. The compromise solution turns out to significantly speed up the overall computation with an acceptable sacrifice in the accuracy (e.g., from 100% to 99.9%). More importantly, the proposed IS can provide multiple and switchable performances to the operators in order to satisfy different local dynamic security assessment requirements.

In terms of machine learning-based power system dynamic stability assessment, it is feasible to collect learning data from massive synchrophasor measurements in practice. However, the fact that instability events rarely occur would lead to a challenging class imbalance problem. Besides, short-term feature extraction from scarce instability seems extremely difficult for conventional learning machines. Faced with such a dilemma, this paper develops a systematic imbalance learning machine for online short-term voltage stability assessment. A powerful time series shapelet (discriminative subsequence) classification method is embedded into the machine for sequential transient feature mining. A forecasting-based nonlinear synthetic minority oversampling technique is proposed to mitigate the distortion of class distribution. Cost-sensitive learning is employed to intensify bias toward those scarce yet valuable unstable cases. Furthermore, an incremental learning strategy is put forward for online monitoring, contributing to adaptability and reliability enhancement along with time. Simulation results on the Nordic test system illustrate the high performance of the proposed learning machine and of the assessment scheme.

Communication data rates and energy constraints are two important factors that have to be considered in the coordination control of multiagent networks. Although some encoder-decoder-based consensus protocols are available, there still exists a fundamental theoretical problem: how can we further reduce the update rate of control input for each agent without the changing consensus performance? In this paper, we consider the problem of average consensus over directed and time-varying digital networks of discrete-time first-order multiagent systems with limited communication data transmission rates. Each agent has a real-valued state but can only exchange binary symbolic sequence with its neighbors due to bandwidth constraints. A class of novel event-triggered dynamic encoding and decoding algorithms is proposed, based on which a kind of consensus protocol is presented. Moreover, we develop a scheme to select the numbers of time-varying quantization levels for each connected communication channel in the time-varying directed topologies at each time step. The analytical relation among system and network parameters is characterized explicitly. It is shown that the asymptotic convergence rate is related to the scale of the network, the number of quantization levels, the system parameter, and the network structure. It is also found that under the designed event-triggered protocol, for a directed and time-varying digital network, which uniformly contains a spanning tree over a time interval, the average consensus can be achieved with an exponential convergence rate based on merely 1-b information exchange between each pair of adjacent agents at each time step.

Quality of service (QoS) has been playing an increasingly important role in today's Web service environment. Many techniques have been proposed to recommend personalized Web services to customers. However, existing methods only utilize the QoS information at the client-side and neglect the contextual characteristics of the service. Based on the fact that the quality of Web service is affected by its context feature, this paper proposes a new QoS-aware Web service recommendation system, which considers the contextual feature similarities of different services. The proposed system first extracts the contextual properties from WSDL files to cluster Web services based on their feature similarities, and then utilizes an improved matrix factorization method to recommend services to users. The proposed framework is validated on a real-world dataset consisting of over 1.5 million Web service invocation records from 5825 Web services and 339 users. The experimental results prove the efficiency and accuracy of the proposed method.

This paper proposes a new evolutionary algorithm (EA), which is called the natural aggregation algorithm (NAA). NAA is inspired by the collective decision making intelligence of the group-living animals. Distinguished from other EAs, NAA distributes individuals to several sub-populations (called `shelters'), and uses a stochastic migration model to dynamically mitigate the individuals among the shelters. The inter-individual attraction effect and crowding effect are considered in the migration model to balance the exploration and exploitation. In each generation, both of the located search and generalized search are performed simultaneously, and the distributions of the individuals are self-adaptively updated. 7 benchmark functions with different dimensionality settings are used to validate the efficiency of NAA, and the results clearly show that NAA has strong performance for solving the real-parameter optimization problems.

Driven by the energy crisis and global warming problem, smart grid was proposed in the early 21th century as a solution for the sustainable development of human society. With the two-way communication infrastructure available in smart grids, a current challenge is to interpret and gain knowledge from the collected grid big data to optimize grid operations. Service recommendation techniques provide promising tools to discover knowledge from the grid data, and recommend energy-aware products/services/suggestions to the smart grid participators. This paper is among the first to investigate the prospective of introducing service recommendation techniques into the smart grid demand side management (DSM). In the first part of the paper, the backgrounds of smart grid DSM and service recommendation techniques are reviewed, followed by the presentation and discussion of key technologies that can facilitate the development of smart grid recommender systems. An outline on potential application scenarios of smart grid recommender systems as well as future challenges are also provided.

In this paper, we consider the event-triggered distributed average-consensus of discrete-time first-order multiagent systems with limited communication data rate and general directed network topology. In the framework of digital communication network, each agent has a real-valued state but can only exchange finite-bit binary symbolic data sequence with its neighborhood agents at each time step due to the digital communication channels with energy constraints. Novel event-triggered dynamic encoder and decoder for each agent are designed, based on which a distributed control algorithm is proposed. A scheme that selects the number of channel quantization level (number of bits) at each time step is developed, under which all the quantizers in the network are never saturated. The convergence rate of consensus is explicitly characterized, which is related to the scale of network, the maximum degree of nodes, the network structure, the scaling function, the quantization interval, the initial states of agents, the control gain and the event gain. It is also found that under the designed event-triggered protocol, by selecting suitable parameters, for any directed digital network containing a spanning tree, the distributed average consensus can be always achieved with an exponential convergence rate based on merely one bit information exchange between each pair of adjacent agents at each time step. Two simulation examples are provided to illustrate the feasibility of presented protocol and the correctness of the theoretical results.

Nonintrusive load monitoring (NILM) aims to disaggregate the total power consumption profile measured at the household power inlet into device-level insights. While many studies focus on the modeling methodologies, few of them address the challenge of the computation efficiency which is critical for practical applications. The NILM problem is essentially a nondeterministic polynomial-time hard problem, meaning that obtaining the exact optimal solution is technically intractable. This paper proposes a fast method to address the approximation to the solutions of such problems from an optimization point of view. It is shown that by taking advantage of the constraint programing framework, the computational efficiency of the proposed NILM scheme can be significantly improved while comparable solution accuracy can also be preserved. Simulations conducted on the popular public datasets validate the effectiveness and efficiency of our proposed method.

Recently, low-rank representation (LRR) has shown promising performance in many real-world applications such as face clustering. However, LRR may not achieve satisfactory results when dealing with the data from nonlinear subspaces, since it is originally designed to handle the data from linear subspaces in the input space. Meanwhile, the kernel-based methods deal with the nonlinear data by mapping it from the original input space to a new feature space through a kernel-induced mapping. To effectively cope with the nonlinear data, we first propose the kernelized version of LRR in the clean data case. We also present a closed-form solution for the resultant optimization problem. Moreover, to handle corrupted data, we propose the robust kernel LRR (RKLRR) approach, and develop an efficient optimization algorithm to solve it based on the alternating direction method. In particular, we show that both the subproblems in our optimization algorithm can be efficiently and exactly solved, and it is guaranteed to obtain a globally optimal solution. Besides, our proposed algorithm can also solve the original LRR problem, which is a special case of our RKLRR when using the linear kernel. In addition, based on our new optimization technique, the kernelization of some variants of LRR can be similarly achieved. Comprehensive experiments on synthetic data sets and real-world data sets clearly demonstrate the efficiency of our algorithm, as well as the effectiveness of RKLRR and the kernelization of two variants of LRR.

In the smart grid paradigm, growing integration of large-scale intermittent renewable energies has introduced significant uncertainties to the operations of an electric power system. This makes real-time dynamic security assessment (DSA) a necessity to enable enhanced situational-awareness against the risk of blackouts. Conventional DSA methods are mainly based on the time-domain simulation, which are insufficiently fast and knowledge-poor. In recent years, the intelligent system (IS) strategy has been identified as a promising approach to facilitate real-time DSA. While previous works mainly concentrate on the rotor angle stability, this paper focuses on another yet increasingly important dynamic insecurity phenomenon-the short-term voltage instability, which involves fast and complex load dynamics. The problem is modeled as a classification subproblem for transient voltage collapse and a prediction subproblem for unacceptable dynamic voltage deviation. A hierarchical IS is developed to address the two subproblems sequentially. The IS is based on ensemble learning of random-weights neural networks and is implemented in an offline training, a real-time application, and an online updating pattern. The simulation results on the New England 39-bus system verify its superiority in both learning speed and accuracy over some state-of-the-art learning algorithms.

The architecture of an Advanced Metering Infrastructure with Device Level Load Monitoring (AMI-DLLM) is proposed in this paper. The AMI-DLLM architecture is an upgrade from currently available Advanced Metering Infrastructure (AMI) that is enabled by large-scale smart meter rollouts under National Smart Metering Program (NSMP) across Australia. Potentials of such massive volume of demand side data generated from AMI have not yet been fully explored. The proposed new architecture aims to leverage such affluent demand data from smart meters and enhance both interactivity between utility companies and customers and demand side management. General structure and information flows extended from smart meters in the proposed framework are elaborated in this paper. Discussions of preliminary results and potential applications enhanced by the proposed architecture are also given in detail.

Dynamic security assessment (DSA) is an important issue in modern power system security analysis. This paper proposes a novel pattern discovery (PD)-based fuzzy classification scheme for the DSA. First, the PD algorithm is improved by integrating the proposed centroid deviation analysis technique and the prior knowledge of the training data set. This improvement can enhance the performance when it is applied to extract the patterns of data from a training data set. Secondly, based on the results of the improved PD algorithm, a fuzzy logic-based classification method is developed to predict the security index of a given power system operating point. In addition, the proposed scheme is tested on the IEEE 50-machine system and is compared with other state-of-the-art classification techniques. The comparison demonstrates that the proposed model is more effective in the DSA of a power system.

Summary form only given. The trend of integrating power systems with advanced computer and communication technologies has introduced serious cyber security concerns, especially in a smart grid environment where the cyber system is no longer regarded as 100% reliable to support power system communications and control as before. Power system security therefore extends to potential cyber security domain in the smart grid era. Risks from the cyber system as well as non-conventional physical power system contingencies start to contributing to the overall grid security. This will be particularity important considering the potential risks from targeted attacks on vulnerable system components which may bring done the overall system. The presentation gives an overview of the work done by the research team on power system security, including conventional stability as well as cyber security assessment. A framework for smart grid cyber security and vulnerability assessment will be illustrated as well. The framework includes two main components, which are respectively cyber system security assessment and fast power system security assessment. Complex networks theory and data mining based approaches are also employed to identify the vulnerable components of the physical power system. The proposed cyber system models can be integrated with existing power system models to study the complex interactions between the cyber and physical parts of the smart grid. Advanced modeling tools are proposed to model cybThe trend of integrating power systems with advanced computer and communication technologies has introduced serious cyber security concerns, especially in a smart grid environment where the cyber system is no longer regarded as 100% reliable to support power system communications and control as before. Power system security therefore extends to potential cyber security domain in the smart grid era. Risks from the cyber system as well as non-conventional physical power system contingencies start to contributing to the overall grid security. This will be particularity important considering the potential risks from targeted attacks on vulnerable system components which may bring done the overall system. The presentation gives an overview of the work done by the research team on power system security, including conventional stability as well as cyber security assessment. A framework for smart grid cyber security and vulnerability assessment will be illustrated as well. The framework includes two main components, which are respectively cyber system security assessment and fast power system security assessment. Complex networks theory and data mining based approaches are also employed to identify the vulnerable components of the physical power system. The proposed cyber system models can be integrated with existing power system models to study the complex interactions between the cyber and physical parts of the smart grid. Advanced modeling tools are proposed to model cyber attacks and evaluate their impacts on smart grid security have been developed as well.er attacks and evaluate their impacts on smart grid security have been developed as well.

Increasing penetration of photovoltaic (PV), as well as increasing peak load demand, has resulted in poor voltage profile for some residential distribution networks. This paper proposes coordinated use of PV and battery energy storage (BES) to address voltage rise and/or dip problems. The reactive capability of PV inverter combined with droop-based BES system is evaluated for rural and urban scenarios (having different \mbi R/X ratios). Results show that reactive compensation from PV inverters alone is sufficient to maintain acceptable voltage profile in an urban scenario (low-resistance feeder), whereas coordinated PV and BES support is required for the rural scenario (high-resistance feeder). Constant, as well as variable, droop-based BES schemes are analyzed. The required BES sizing and associated cost to maintain the acceptable voltage profile under both schemes are presented. Uncertainties in PV generation and load are considered, with probabilistic estimation of PV generation and randomness in load modeled to characterize the effective utilization of BES. Actual PV generation data and distribution system network data are used to verify the efficacy of the proposed method.

In this paper, we argue that Asia's unique geography, abundant low-emission energy resources, rapid economic growth, and rising energy demands merit consideration of a Pan-Asian Energy Infrastructure. In our study, we focus on development of wind and solar resources in Australia, China, Mongolia, and Vietnam as the potential foundation for an electricity grid stretching from China to Australia. Hourly climate data for a full year are used to estimate renewable energy generation, electricity demand, generation capacity are projected forward to the year 2025, and economic dispatch in an international market is simulated to demonstrate cost benefits. Intermittency, connectivity, future dispatch orders, storage, line losses, and engineering and financial issues are all addressed.

The increasing penetration of wind power can alter the dynamic security characteristic of a power system. To accommodate rapid and volatile wind power variations, dynamic security assessment (DSA) against foreseeable disturbances is required to be carried out online and provide security monitoring results within sufficiently small time frame. Based on soft computing (SC) technologies, this paper develops an intelligent framework for real-time DSA of power systems with large penetration of wind power. It consists of a DSA engine whose role is to perform real-time DSA of the power system, a wind power and load demand (W&LF) forecasting engine for offline and online predicting wind power generation and electricity load demand, a database generation (DBG) engine for generating instances to train the DSA engine, and a model updating (MU) engine for online updating the DSA engine. Case studies are conducted on two benchmark systems where high DSA efficiency and accuracy are obtained. This framework can be an ideal candidate for advanced security monitoring in the future SmartGrid control centres.

In this paper, a computational framework for integrating wind power uncertainty and carbon tax in economic dispatch (ED) model is developed. The probability of stochastic wind power based on nonlinear wind power curve and Weibull distribution is included in the model. In order to solve the revised dispatch strategy, quantum-inspired particle swarm optimization (QPSO) is also adopted, which shows stronger search ability and quicker convergence speed. The dispatch model is tested on a modified IEEE benchmark system involving six thermal units and two wind farms using the real wind speed data obtained from two meteorological stations in Australia.

In this paper, an economic dispatch model, which can take into account the uncertainties of plug-in electric vehicles (PEVs) and wind generators, is developed. A simulation based approach is first employed to study the probability distributions of the charge/discharge behaviors of PEVs. The probability distribution of wind power is also derived based on the assumption that the wind speed follows the Rayleigh distribution. The mathematical expectations of the generation costs of wind power and V2G (vehicle to grid) power are then derived analytically. An optimization algorithm is developed based on the well-established particle swarm optimization (PSO) and interior point method to solve the economic dispatch model. The proposed approach is demonstrated by the IEEE 118-bus test system.

Power system simulator for engineering (PSS_E) has gained great success in power energy industry for its powerful simulation and analysis functions. Along with market deregulation, power system planning and stability analysis warrants more effective and fast techniques due to the ever expanding large-scale interconnection of power networks. Running large-scale system multiple case studies on PSS_E will cost intensive time and efforts. In this paper, we accelerate PSS_E dynamic simulations with EnFuzion based distributed computing technique. This approach is proved to be effective by testing with 39-bus New England power system ¿n-1¿ and ¿n-1-1¿ contingency analysis. The results show that the simulation process can be speeded dramatically and the total elapsed time can be reduced proportionally with the increase of computer nodes.

Chaotic simulated annealing (CSA) is a relatively new heuristic optimization technique and has been widely applied to optimization problems because of its simplicity and capability of finding fairly good solutions rapidly. However, currently only experimental results are used for verifying its superiority. In this paper, a new of chaotic simulated annealing method (CSA) is introduced and then a mathematic proof is given. It shows that the Markov Chain associated with the algorithm is weakly ergodic, which guarantees that the asymptotic behavior of the algorithm is independent of initial states. Furthermore, the theoretical analysis of the proposed CSA is very important to understand the essential features which make the algorithm work well.

In recent years, evolutionary computation (EC) techniques have proven to be an useful alternative approach for solving many highly nonlinear power system planning and operation problems. The objective of this paper is to investigate mathematically-complex voltage collapse problems using EC techniques, in particular the particle swarm optimization (PSO) and differential evolution (DE) algorithms. It demonstrates the exceptional searching capabilities of both the PSO and DE algorithms to locate voltage collapse point solutions (also widely known as nose points or critical points), which are at least comparable to those obtained using the well-known continuation power flow (CPF) technique. The feasibility and practicality of this approach has been tested on a 3-machine 9-bus, the IEEE 118-bus and the IEEE 300-bus power systems.

Load modeling is very important to power system operation and control. Measurement-based load modeling has been widely practiced in recent years. Mathematically, measurement-based load modeling problem are closely related to the parameter identification area. Consequently, an efficient optimization method is needed to derive the load model parameters based on the feedback of estimation errors between the measurements and model outputs. This paper reports our work on applying genetic algorithms on measurement-based load modeling research. Due to its robustness to the initial guesses on the load model parameter identification. Two cases including both the real measurement in a power station and the digital simulation are studied in the paper. For comparison purpose, the classical nonlinear least square estimation method is also applied to find the load model parameters. The simulated outputs from the load model confirm the efficiency of genetic algorithms in measurement-based load modeling analysis. Future work will focus on fastening the converging speed of the genetic algorithms, and/or utilizing more efficient evolutionary computation methods.

The employment of performance based regulation (PBR) in distribution systems could provide some incentive for improving operating efficiency and reducing electricity prices. However, if the PBR mechanism is not properly designed, the enforcement of the PBR may have a negative effect on the supply reliability. In this paper, a mathematical model for optimally setting the parameters of the PBR with a reward/penalty structure is presented, with the minimization of the costs associated with the enforcement of the PBR as the objective and the required reliability level for the distribution system operation as the constraint. Finally, the well-known genetic algorithm is employed for solving the optimization problem. The effectiveness of the approach is demonstrated on a sample example.

Following the recent devastating blackouts in North America, UK and Italy, blackout prevention has attracted significant attention, though it is known as a notoriously difficult task. To prevent the blackout, it is essential to accurately predict the instable status of power network components. In the large-scale power network however, existing analysis tools fail to perform accurate and in-time prediction of component instability, because of the sophisticated structure of real-world power networks and the huge amount of system variables to be analyzed. To prevent the blackout, we need an accurate and efficient method that (a) can discover interesting features and patterns relevant to the blackout, from the highly complex structure and ten thousands of system variables of a power network, and (b) can give accurate and fast prediction of system instability whenever required, so that the network operator can take necessary actions in time. In this paper, we report our tool developed for power network instability prediction. The proposed method consists of two major stages. In the first stage,a novel type of patterns namely Local Correlation Network Pattern (LCNP) is mined from the structure and system variables of the power network. Correlation rules, which are useful for the network operator to locate potentially instable components, can be further generated from the LCNP. In the second stage, a kernel based network classification method is developed to predict the system instability. By testing on a real world power network (the New England system), we demonstrate that the proposed tool is effective in predicting system instability and thus highly useful for blackout prevention.

Power system planning is a complex multi-objective optimization problem. It aims at locating the minimum cost of additional transmission lines that must be installed to satisfy the forecasted load in a power system. A number of different methods for power system planning have been investigated over the past decades. In this paper, a differential evolution (DE) based approach is proposed as an optimization tool to solve the power system planning problem. A comparison between genetic algorithms, evolutionary strategy (ES), and five different DE schemes are carried out on two benchmark power systems. The results shown that, as a relatively new heuristic optimization method, DE is able to provide robust and efficient solution to power system planning problems.

A video fingerprint is a kind of digital watermark used in digital video for tracking pirate copies in a multi-user environment. Different users receive the same video with different watermarks designed for uniquely identifying designated users. As a value-added business service, fingerprinting is independent from video compression. In current fingerprinting schemes, a compressed video has to be decoded once for every user in order to add on individual fingerprints. Then the video is encoded again before the dispatch. However, the multiple decode/re-encode operations can result in poor system performance. In this paper, we propose a new integrated fingerprint algorithm, which can be applied directly to the compressed video without decoding/reencoding. Our experiments show that the performance of fingerprinting improved with no compromise to the robustness.

User requirements of multimedia authentication are various. In some cases, the user requires an authentication system to monitor a set of specific areas with respective sensitivity while neglecting other modification. Most current existing fragile watermarking schemes are mixed systems, which can not satisfy accurate user requirements. Therefore, in this paper we designed a sensor-based multimedia authentication architecture. This system consists of sensor combinations and a fuzzy response logic system. A sensor is designed to strictly respond to given area tampering of a certain type. With this scheme, any complicated authentication requirement can be satisfied, and many problems such as error tolerant tamper method detection will be easily resolved. We also provided experiments to demonstrate the implementation of the sensor-based system

A potential security problem in frequency domain video encryption is that some trivial information such as the distribution of DCT coefficients may leak out secret. To illuminate this problem, we performed a successful attack on video using the distribution information of DCT coefficients. Then, according to the weak points discovered, a novel video encryption algorithm, working on run-length coded data, is proposed. It has amended identified security problems, while preserving high efficiency and the adaptability to cooperate with compression schemes.

We derive the performance union bound of space-time trellis codes in orthogonal frequency division multiplexing system (STTC-OFDM) over quasistatic frequency selective fading channels based on the distance spectrum technique. The distance spectrum is the enumeration of the codeword difference measures and their multiplicities by exhausted searching through all the possible error event paths. Exhaustive search approach can be used for low memory order STTC with small frame size. However with moderate memory order STTC and moderate frame size the computational cost of exhaustive search increases exponentially, and may become impractical for high memory order STTCs. This requires advanced computational techniques such as genetic algorithms (GAs). A GA with sharing function method is used to locate the multiple solutions of the distance spectrum for high memory order STTCs. Simulation evaluates the performance union bound and the complexity comparison of nonGA aided and GA aided distance spectrum techniques. It shows that the union bound give a close performance measure at high signal-to-noise ratio (SNR). It also shows that GA sharing function method based distance spectrum technique requires much less computational time as compared with exhaustive search approach but with satisfactory accuracy.

DNA microarray is a powerful tool to measure the level of a mixed population of nucleic acids at one time, which has great impact in many aspects of life sciences research. In order to distinguish nucleic acids with very similar composition by hybridization, it is necessary to design probes with high specificities, i.e. uniqueness, and also sensitivities, i.e., suitable melting temperature and no secondary structure. We make use of available biology tools to gain necessary sequence information of human chromosome 12, and combined with evolutionary strategy (ES) to find unique subsequences representing all predicted exons. The results are presented and discussed.

We present an approach for optimal design of a fully regenerative dynamic dynamometer using genetic algorithms. The proposed dynamometer system includes an energy storage mechanism to adaptively absorb the energy variations following the dynamometer transients. This allows the minimum power electronics requirement at the mains power supply grid to compensate for the losses. The overall dynamometer system is a dynamic complex system and design of the system is a multiobjective problem, which requires advanced optimisation techniques such as genetic algorithms. The case study of designing and simulation of the dynamometer system indicates that the genetic algorithm based approach is able to locate a best available solution in view of system performance and computational costs.

Ancillary service plays a key role in maintaining operation security of the power system in a competitive electricity market. The spinning reserve is one of the most important ancillary services that should be provided effectively. This paper presents the design of an integrated market for energy and spinning reserve service with particular emphasis on coordinated dispatch of bulk power and spinning reserve services. A new market dispatching mechanism has been developed to minimize the cost of service while maintaining system security. Genetic algorithms (GA) are used for finding the global optimal solutions for this dispatch problem. Case studies and corresponding analyses have been carried out to demonstrate and discuss the efficiency and usefulness of the proposed method.

In this paper, a novel approach is developed to evaluate the overall performance of a local area network as well as to monitor some possible intrusion detections. The data is obtained via system utility 'ping' and huge data is analyzed via statistical methods. Finally, an overall performance index is defined and simulation experiments in three months proved the effectiveness of the proposed performance index. A software package is developed based on these ideas.