Probabilistic energy flow (PEF) is a fundamental task for the operation of conventional model-based integrated energy systems (IES), electricity–gas–heating coupled systems, while challenged by heavy computational burdens and uncertainties introduced by renewable energy sources (RES). To overcome this bottleneck, a novel data-driven hybrid quantum-transformer network (HQTN) is proposed for fast PEF calculation. Specifically, the classical Transformer network captures the dynamic spatial uncertainty features via a novel graph attention mechanism to model the uncertain fluctuating RES features. Additionally, a quantum neural network is proposed to extract the high-dimensional, complex relationships of IES operational data, thereby improving calculational accuracy. Case studies are conducted on both IEEE 33-node electric/20-node gas/17-node heating and 118-node electric/48-node gas/14-node heating systems. Experimental evaluation results verify that the proposed approach achieves high accuracy and maintains strong computational performance in PEF calculation.

Accurate and real-time topology identification is essential for the reliable operation of power distribution networks (PDNs). The increasing complexity and dynamism of modern PDNs pose significant challenges for real-time topology identification, especially with limited synchronized measurement devices. While deep learning methods show promise for topology inference, the lack of physical awareness would lead to unreliable predictions. In this article, we propose an adaptive physics-informed learning model for topology identification in PDNs, where a physics-informed loss function derived from linear coupled power flow equations is introduced to improve identification accuracy and ensure physical consistency. An uncertainty-based weighting strategy is used to dynamically balance the contributions of supervised learning and physical constraints. To support this learning objective, the model employs a multiscale convolutional neural network enhanced with a channel attention mechanism to extract diverse features under limited deployment of synchronized measurement devices. The proposed method jointly considers both branch-level and topology-level inference accuracy, supports real-time application, and does not rely on radial topology assumptions. To validate the proposed method, experiments are conducted on the 33-node, 69-node, and 118-node distribution systems, and the simulation results demonstrate its effectiveness at both the branch and topology levels.

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.

Grid interconnection is a key strategy for strengthening power system resilience to extreme weather events by facilitating intersystem mutual assistance. Despite the overall reduction in risk exposure, significant residual risks remain that could still lead to catastrophic consequences. While insurance offers a means to transfer these risks, conventional standalone models struggle to balance insurer solvency with premium affordability and fail to incentivize participation from lower risk areas. Inspired by spatial risk diversification, this article proposes a novel coalitional insurance framework for weather-related risk management of interconnected transmission systems (ITS). The framework is built on a joint resilience assessment model that quantifies power outage risks in ITS, accounting for intersystem mutual assistance. To solve this model with guaranteed convergence and well-preserved privacy, a distributed optimization approach based on the Bregman alternating direction method of multipliers and iterative optimization is developed. Furthermore, specially designed exante premium and expost indemnity policies ensure equitable allocation and promote coalition participation. Numerical experiments on the IEEE RTS-96 system validate the effectiveness and superiority of the proposed coalitional insurance scheme.

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.

The escalating electrical demands of large-scale computational models in Internet data centers (IDCs) coupled with their significant carbon footprint underscore the potential synergy with demand response (DR) for promoting sustainable power system operations. Despite its potential, this intersection has been insufficiently investigated in existing studies. To fill the gap, an electricity-carbon collaborative demand response (ECCDR) framework is developed and a privacy-preserving bi-level optimization model is proposed to fulfill this goal. First, the ECCDR framework is designed by combining dynamic carbon emissions from power systems with traditional DR, aiming to concurrently maximize the economic and emission reduction benefits. Second, a privacy-preserving bi-level optimization model is proposed to orchestrate computational task distribution within IDCs, facilitating load shifting in power systems. It is done by exchanging non-sensitive information between power systems and IDCs, ensuring privacy yet paving the way for ECCDR’s pragmatic deployment. Third, distributed photovoltaic (PV) and battery energy storage systems (BESS) are integrated into IDC operations, further amplifying ECCDR’s potential. Simulation results reveal that the bi-level optimization model results in cost-efficient operations for both the power system and IDCs without invading privacy, while the ECCDR paradigm demonstrates superior advantages compared to the conventional DR.

Short-term forecasting of multienergy loads is of paramount significance for integrated energy systems operation. The central forecasting framework is confronted with the privacy disclosure issue. Besides, the intricate interdependencies among diverse energy loads present an opportunity to improve prediction accuracy. To this end, a privacy-preserving spatial-temporal adaptive personalized federated learning model is proposed in this article. Specifically, the proposed federated learning-based decentralized framework enables the sharing of local model weights while ensuring the confidentiality of raw measurement data. Besides, the spatial-temporal transformer leverages the self-attention mechanism to synchronously capture the complex dynamic dependencies among different types of energy load demand. Furthermore, the adaptive local aggregation mechanism is proposed to personalize the local model to address the data heterogeneity and subsequently improve forecasting accuracy. The proposed model is applied to a publicly available dataset. The results show that the proposed model can achieve highly efficient and effective forecasting accuracy.

Regenerative braking energy (RBE) utilization plays a vital role in improving the energy efficiency of electrified railways. To date, various power flow control-based solutions have been developed to recycle the RBE for utilization within railway power systems (RPSs). In this paper, an overview of the state-of-the-art power flow control-based solutions for RBE utilization in AC electrified railways is presented. It provides a technical analysis of four primary power flow control-based solutions for RBE utilization, including power sharing-based, energy feedback-based, energy storage-based, and composite solutions. The critical architectures of power flow conditioners for each solution are analyzed in depth. Meanwhile, the power flow control strategies for these solutions are reviewed from the perspectives of power flow management and converter control. From the industrial point of view, the critical challenges associated with fault protection, economy, and environmental impact are discussed. In addition, future trends are comprehensively elaborated from internal and extended improvements. This comprehensive review provides an insightful understanding of the technology readiness, constraints, and perspectives regarding the power flow control-based RBE utilization in electrified railways, contributing to bridging the gaps between academic research and industry implementation.

Due to the limited presence of monitoring and measurement devices, timely identification of distribution grid topology has been challenging. Therefore, this article proposes a power grid topological generative adversarial network (Gridtopo-GAN) model to identify the distribution grid topology of either meshed or radial structure with limited measurements. By leveraging the topology preserved node embedding architecture, this model can efficiently handle large-scale systems with different topological configurations. Because of the generative capability of GAN, the model is robust enough when fed with bad measurement data, including missing data, commonly encountered in practical applications. Numerical simulations are carried out on the IEEE 33-node system, 118-node, 415-node, and real 76-node distribution systems to demonstrate the effectiveness and efficiency of the proposed topology identification model.

In this article, a novel machine learning based data-driven pricing method is proposed for sharing rooftop photovoltaic (PV) generation and energy storage in an electrically interconnected residential building cluster (RBC). In the studied problem, the energy sharing process is modeled by the leader-follower Stackelberg game where the owner of the rooftop PV system is responsible for pricing self-generated PV energy and operating ES devices. Meanwhile, local electricity consumers in the RBC choose their energy consumption with the given internal electricity prices. To track the stochastic rooftop PV panel outputs, the long short-term memory network based rolling-horizon prediction function is developed to dynamically predict future trends of PV generation. With system information, the predicted information is fed into a Q-learning based decision-making process to find near-optimal pricing strategies. The simulation results verify the effectiveness of the proposed approach in solving energy sharing problems with partial or uncertain information.

Accurate power system state estimation is essential for power system control, optimization, and security analysis. In this work, a model-free approach was proposed for power system static state estimation based on conditional Generative Adversarial Networks (GANs). Comparing with conventional state estimation approach, i.e., Weighted Least Square (WLS), any appropriate knowledge of system model is not required in the proposed method. Without knowing the specific model, the GANs can learn the inherent physics of underlying state variables purely relying on historic samples. Once the model has been well trained, it can generate the corresponding estimated system state given the system raw measurements. Particularly, the raw measurements are sometimes characterized by incompletion and corruption, which gives rise to significant challenges for conventional analytic methods..The case study on IEEE 9-bus system validates the effectiveness of the proposed approach.

In this paper, we propose a distributed online voltage control algorithm for distribution networks with multiple photovoltaic (PV) systems based on dual-ascent method. Conventional distributed algorithms implement voltage control only when the algorithms converge. However, our proposed algorithm is able to carry out voltage control immediately. In particular, we derive a closed-form solution for PV controllers to locally update the active and reactive power set points aiming at minimizing the total loss and maintaining bus voltages within the acceptable ranges. The optimality is guaranteed and the convergence is established analytically. Moreover, our proposed algorithm only requires the information exchange between neighboring PV systems, thus reducing communication complexity. Finally, numerical tests on IEEE 37-bus distribution system verify the effectiveness and robustness of our proposed algorithm.

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.

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.

The planning of charging facilities (CFs) for electric vehicles (EVs) plays an important role for the extensive applications of EVs. Uncertainties existing in the development of future EV technology should be properly modeled to ensure the robustness of the planning scheme. The uncertainties concerned include EV development types, growth rate of load and traffic flow, and distributions of load and traffic flow in the smart grid. The existing single-stage planning model cannot fully evaluate the risks brought by all kinds of uncertainties. Given this background, the multistage CF planning problem considering uncertainties is studied in this work. First, several typical uncertainties in future smart grid with a high penetration of EVs are considered to generate development scenarios for multistage planning. Then, the well-established data envelopment analysis is utilized to evaluate the planning schemes while the novel EV expected energy not supplied cost is defined to measure the service ability of CFs. The final planning result obtained by the proposed framework will not only have good performance in the current stage but also exhibit robustness for all the considered scenarios in the future stage with respect to uncertainties. The application potential of the designed multistage planning framework is proved by an example with both the distribution network and traffic network included.

Due to various weather perturbation effects, the stochastic nature of real-life solar irradiance has been a major issue for solar photovoltaic (PV) system planning and performance evaluation. This paper aims to discover clearness index (CI) patterns and to construct centroids for the daily CI profiles. This will be useful in being able to provide a standardized methodology for PV system design and analysis. Four years of solar irradiance data collected from Johannesburg (26.21 S, 28.05 E), South Africa are used for the case study. The variation in CI could be significant in different seasons. In this paper, cluster analysis with Gaussian mixture models (GMM), K-Means with Euclidean distance (ED), K-Means with Manhattan distance, Fuzzy C-Means (FCM) with ED, and FCM with dynamic time warping (FCM DTW) are performed for the four seasons. A case study based on sizing a stand-alone solar PV and storage system with anaerobic digestion biogas power plants is used to examine the usefulness of the clustering results. It concludes that FCM DTW and GMM can determine the correct PV farm rated capacity with an acceptable energy storage capacity, with 36 and 46 rather than 1457 solar irradiance profiles, respectively.

This paper proposes a cooperative control framework for the coordination of multiple microgrids. The framework is based on the multiagent system. The control framework aims to encourage the resource sharing among different autonomous microgrids and solve the energy imbalance problems by forming the microgrid coalition self-adaptively. First, the conceptual model of the integrated microgrids and the layered cooperative control framework is presented. Then, an advanced dynamic coalition formation scheme and corresponding negotiation algorithm are introduced to model the coordination behaviors of the microgrids. The proposed control framework is implemented by the Java Agent Development Framework. A loop distribution system with multiple interconnected microgrids is simulated, and the case studies are conducted to prove the efficiency of the proposed framework.

The aim of this Special Section is to attract and report the latest advances toward the trend of applying advanced informatics techniques resolving complex problems facing power system operation and planning in the new era of smart grids. Special interests are given to the new methods that can handle various tasks of risk hedging and decision making appeared in eleven system operation and planning, though the scope has been slightly expanded to other topical issues in smart grids as well. The accepted eleven high-quality papers represent how the newadvances and solutions toward resolving complex problems facing power system operation and planning can be brought forward by continuously leveraging emerging techniques in the field of data analytics and informatics. It should be highlighted that with the increased penetration of various emerging technologies such as renewables and electric vehicles (EVs), secure and economic system operation and planning deserve continuous research efforts in producing the most up-to-date methods and solutions dealing with issues of diversified natures and complexities in future power grids. Specifically, the covered topics in this Special Section are topical and broad, concerning mainly power system security analysis and electricity market planning and operation under risks and uncertainties, which are briefly summarized.

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.

The network reconfiguration during power system restoration after blackouts usually takes a long and complex procedure. To address the uncertainties in the restoration steps and time involved, a CCP (Chance Constrained Programming) based method for network reconfiguration scheme optimization is proposed in this paper. The proposed method can generate the best restoration sequence to maximize the benefit of the reconfiguration scheme by taking into account the number of restarted generator-nodes and the cost of power outage saved by the load restoration accordingly. The Differential Evolution (DE) is employed to solve for the optimal solution subject to special requirements of the network reconfiguration operation. A numerical example over the New England 39-bus power system is conducted to demonstrate the effectiveness of the proposed method.

Successful development of smart grid demands strengthened system security and reliability, which requires effective security analysis in conducting system operation and expansion planning. Classical N - 1 criterion has been widely used to examine every creditable contingency through detailed computations in the past. The adequacy of such approach becomes doubtful in many recent blackouts where cascading outages are usually involved. This may be attributed to the increased complexities and nonlinearities involved in operating conditions and network structures in context of smart grid development. To address security threats, particularly from cascading outages, a new and efficient security analysis approach is proposed, which comprises cascading failure simulation module (CFSM) for post-contingency analysis and risk evaluation module (REM) based on a decorrelated neural network ensembles (DNNE) algorithm. This approach overcomes the drawbacks of high computational cost in classical N-k-induced cascading contingency analysis. Case studies on two different IEEE test systems and a practical transmission system-Polish 2383-bus system have been conducted to demonstrate the effectiveness of the proposed approach for risk evaluation of cascading contingency.

As plug-in hybrid electric vehicles (PHEVs) are expected to be widely used in the near future, a mathematical model is developed based on the traditional security constrained unit commitment (SCUC) formulation to address the power system dispatching problem with PHEVs taken into account. With the premise of power system secure operation, both the economic benefit for PHEV users and the carbon-emission costs are taken into account. Then, the features of PHEVs as mobile energy storage units are exploited to decouple the developed model into two sub-models, involving the unit commitment model and the charging and discharging scheduling model that includes AC power flow constraints. The optimal plug-in capacities for PHEVs and the schemes, including when and where charging and discharging occur, are obtained through a mixed integer programming algorithm and the Newton-Raphson load flow algorithm in addition to the optimal day-ahead unit commitment scheme. Finally, the feasibility and efficiency of the proposed model are verified with a 6-bus test system.

Smart grid development highlights the "self-healing" capability as it enables a power system to efficiently and automatically react to disturbances and guide the system to the best possible state. Effectively partitioning the power network (PN) into suitable areas or zones to accommodate subsequent control actions is useful. In this paper, a novel partitioning approach that combines Laplacian spectrum of a PN and self organizing map (SOM) algorithm is proposed. This approach aims to optimize the partitioning solution so as to minimize the real power imbalance and simultaneously maintain a satisfactory voltage profile. Case study is carried out on New England 39-bus system, which demonstrates the effectiveness of the proposed approach.

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.

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.