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.

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.

Power systems are becoming increasingly vulnerable to cyberattacks due to extensive information exchange. This article proposes a novel fuzzy inference system (FIS)-enhanced data integrity attack (DIA)-recovery framework and analyzes the combined effects of attacks simultaneously launched by groups with different motivations. These range from market participants seeking a competitive edge for personal economic benefits to outsider groups aiming to drive the system into an uneconomic operation state. A two-stage optimization problem and a fuzzy-Bayesian approach are formulated to counteract the potential attacks on the Electric Vehicle Charging Management Center (EVCMC), where successful manipulation can result in substantial financial gains or disruptions to the power grid. According to the simulation results: 1) the proposed FIS approach provides a dynamic assessment of the attacker’s capability and the vulnerability of EVCMC to potential DIAs; 2) the combined effects of attack launched by market participants and outsider groups can cause more severe economic impacts compared to attack conducted separately; and 3) the proposed attack-recovery scheme can identify the most vulnerable EVCMC to attackers and recover the optimal power dispatch under DIAs.

With the increasing penetration of distributed energy resources (DERs), traditional grid-dependent consumers are evolving into active prosumers, who can control their generation and demand while interacting with peer neighbors to earn profits. This paper presents a novel encryption-based coordinated peer-to-peer (P2P) trading framework for kilowatt (kW) and negawatt (nW) transactions, incorporating the utilization of second-life batteries (SLBs) from retired EVs. In this framework, two different types of P2P transactions are coordinated, allowing prosumers to switch their market roles freely between kW and nW markets based on optimization results and market clearing information. The optimal market bidding strategy is determined by the household energy management system (HEMS) optimization, and a double-sided auction method is employed for market clearing. To protect prosumer privacy and prevent potential data integrity attacks (DIAs) in the P2P markets, Rivest-Shamir-Adleman (RSA) digital signatures and Goldreich-Goldwasser-Halevi (GGH) encryption algorithms are implemented. Unlike the traditional battery energy storage systems (BESS) that assume constant charge/discharge power and efficiency, the non-ideal battery models are imbedded to capture the impacts of BESS operation on the real-life P2P trading markets. The battery cycling degradation issue is also incorporated. The effectiveness of the proposed framework is demonstrated on the modified distribution system.

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.

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.

This paper introduces a novel approach that leverages Large Language Models (LLMs) and Generative Agents to enhance time series forecasting by reasoning across both text and time series data. With language as a medium, our method adaptively integrates social events into forecasting models, aligning news content with time series fluctuations to provide richer insights. Specifically, we utilize LLM-based agents to iteratively filter out irrelevant news and employ human-like reasoning to evaluate predictions. This enables the model to analyze complex events, such as unexpected incidents and shifts in social behavior, and continuously refine the selection logic of news and the robustness of the agent's output. By integrating selected news events with time series data, we fine-tune a pre-trained LLM to predict sequences of digits in time series. The results demonstrate significant improvements in forecasting accuracy, suggesting a potential paradigm shift in time series forecasting through the effective utilization of unstructured news data.

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.

Electric vehicles (EVs) are believed to be effective in reducing the use of fossil fuels. However, the increasing penetration of EVs can lead to challenges in managing EV charging/refueling. Typically, when EVs simultaneously gather at the stations, long queueing lengths and waiting times might occur, which might result in EV disutility. Thus, in this paper, novel decision-making strategies considering the irrational behaviors of EVs are formulated to ensure the utility of EVs. Three types of EVs are investigated, i.e., plug-in electric vehicles (PEVs), fuel cell electric vehicles (FCEVs), and plug-in hybrid hydrogen and electric vehicles (PH2EVs). First, three typical types of irrationalities are modeled based on prospect theory and irrational herding theory, including risk attitude irrationality, probability distortion, and irrational herding behaviors. Second, decision-making strategies, including station selection, navigation, and energy purchasing, are modeled based on EV irrationalities. Additionally, when making the station selection decision, the selection ranking index is proposed to balance energy price and waiting time. Third, the energy substitution of PH2EV is investigated to determine the optimal energy-purchasing mixture of electricity and hydrogen. Simulation results show that the average waiting time and charging/refueling time of EV users of the proposed model are reduced compared with the other two cases where EV users are assumed to be rational. In addition, the overall utility of EV users is enhanced.

Achieving carbon neutrality is widely recognized as the key measure to mitigate climate change. As the basis for achieving carbon neutrality, corporate carbon footprint (CCF) estimation is mainly based on the disclosed information of corporates to roughly estimate the direct carbon emission, but the estimation may not be comprehensive, timely, and accurate. In this article, the CCF estimation problem is formulated and a novel estimation methodology is proposed for the first time to estimate the direct and indirect carbon emissions of factories in real time. An appliance identification method based on the multihead self-attention mechanism and gated recurrent unit is proposed to identify the device states, and then, calculate the corresponding direct carbon emission. The indirect carbon emission is derived from the electricity consumption of the factory and the marginal carbon emission factor of the connected bus. A dataset containing load and device state data from six different industries is released and used to verify the effectiveness of the proposed method. Experiments show that the proposed appliance identification method is significantly superior to the benchmarks in the literature, and the proposed method can achieve a comprehensive and accurate estimation of the minute-level CCF.

Maritime transportation takes a major responsibility in public travel between islands while producing a large quantity of greenhouse gas (GHG) emissions. All-electric ships (AESs) can be applied to mitigate GHG emissions through energy storage systems (ESS), renewable integration, and cold ironing. In this paper, we propose a flexible voyage scheduling strategy for AESs based on the temporal-spatial dynamics (TSD) to satisfy the transportation demand while mitigating the burden of the AESs on the power grids during charging. The interaction between the AESs and island-based microgrids is modeled. Furthermore, the AESs are utilized to enhance the resilience of the power grids. The AESs can be dispatched to realize the load restoration under contingencies. The proposed methodology is verified on a three-island system. It can be concluded that under normal operation, the proposed voyage scheduling can reduce the total energy consumption cost of the AESs and the islands. Besides, the voltage violation can be improved. Under the emergency, the proposed method can help the grids restore more critical and normal loads.

With the burgeoning of plug-in electric vehicles (PEVs), as well as the emergence of fuel cell vehicles (FCVs) and plug-in hybrid electricity/hydrogen vehicles (PH2EVs), the synergistic effect of electricity, hydrogen, and transportation networks should be investigated. To enhance the security of the coupled network and ensure the reliability of the charging/refueling services of EVs, a vulnerability assessment is required to assist in system contingency planning as well as post-contingency measures. In this paper, a vulnerability assessment strategy is formulated for coupled transportation and multi-energy networks. First, a novel graph representation for the coupled transportation and multi-energy networks is proposed. Then, a critical asset identification tool is applied to find the vulnerability point of the coupled networks, and the transfer margin ratio (TMR) is put forward to assess the dynamic vulnerability level under cascading contingencies. Finally, a vulnerability envelope (the lower bound and upper bound of vulnerability) is found based on a bi-level optimization problem. To demonstrate the effectiveness of the proposed methodology, case studies are performed on the IEEE 39-bus electricity network coupled with a 25-node transportation network and a 50-node hydrogen/gas network. It is verified that the vulnerable point of the coupled network can be found. Besides, it is concluded that the penetration of FCVs and PH2EVs can enhance energy flexibility through the energy substitution effect and thus mitigate the system vulnerability.

Promotion of charging facilities (CFs) can ameliorate range anxiety and facilitate long-distance travel by electric vehicles (EVs). In this context, a multistage low-carbon EV CFs planning model is proposed in this paper for the coupled transportation and power systems. This model not only takes into account the construction costs of newly-built CFs and their adverse impacts on the distribution system, but also considers the carbon emissions of CFs and the penalty due to the inconvenience for EVs to be recharged. In addition, a rational travel route choice model is significantly important to accurately evaluate the service capability of CFs to be constructed and thereby to obtain an optimal CF planning result. Therefore, a novel travel route choice model developed in this work allows EV drivers to take detours according to the CF locations to charge their EVs multiple times and then finish their trips. Carbon emission flow (CEF) model is innovatively employed to precisely calculate CFs’ carbon emission amount from the perspective of consumption side. Subsequently, uncertainties involved in CF planning, i.e., distribution and growth rate of traffic flow and electric load, popularity of different types of EVs, as well as location of the connected node of clean electricity, are fully considered to obtain a robust CF planning scheme that can achieve a good performance in the current stage and exhibit robustness for the uncertainties in the future stage. Finally, numerical experiments are conducted to verify the effectiveness of the proposed model. The impacts of future carbon price and EV cruising range on the planning results are also comprehensively evaluated.

The cloud-based application is a major developing feature of smart grids. Apart from centralized Internet data centers (IDCs), distributed cloud resources (CRs) can also provide cloud computing services with low latency and high reliability. For the distributed cloud computing, CRs aggregators (CRAs) will integrate the distributed idle computing resources, which are dispersed energy consumers in the system, to form virtual IDCs. This article presents a market-based computing resource allocation method for distributed cloud computing services. The computing resource allocation refers to the approach to allocating the computing workloads to different CRs. First, the batch workload scheduling (BWS)-based virtual energy storage system (VESS) model and thermal inertia (TI)-based VESS model are proposed to help CRAs better aggregate the distributed CRs and characterize the energy consumption flexibility of the virtual IDCs. Then, the energy trading behavior of the CRAs in the transactive energy market is modeled in the resource allocation process. Case studies are conducted on a 55-bus electricity system. It can be found that energy consumption costs can be reduced by applying the proposed methodology, and revenues from providing cloud computing services can be increased.

A number of electric devices in buildings can be considered as important demand response (DR) resources, for instance, the battery energy storage system (BESS) and the heat, ventilation, and air conditioning (HVAC) systems. The conventional model-based DR methods rely on efficient on-demand computing resources. However, the current buildings suffer from the high cost of computing resources and lack a cost-effective automation system, which becomes the main obstacle to the popularization and implementation of the DR program. Therefore, in this paper, we present a hybrid cloud and edge control strategy for BESS and HVAC based on deep reinforcement learning (DRL). On the cloud infrastructure, the agent learns the control strategy online based on the proposed continuous dueling deep Q-learning (C-DDQN) algorithm, and the learned strategy is distributed to the edge devices for execution. Under this framework, the data-intensive application of cloud computing in real-time DR shows advantages in high processing speed, unlimited data aggregation, fault-tolerant, cost-saving, security, and confidentiality. However, if every controller is trained from the beginning, the cloud resources are wasted to a large extent. Therefore, we propose a transfer deep reinforcement learning methodology to transfer the control strategies between BESS and HVAC units. The transfer learning is realized based on fine-tuning and the proposed Evolving Domain Adaptation Network (EDAN). In case studies, it is verified that the proposed transfer deep reinforcement learning algorithm shows better convergence and learning capability compared with not applying transfer learning technologies. Compared with the conventional model-based method, the proposed methodology speeds up the decision-making time by 105times.

With the increasing use of distributed renewable energy to generate electricity, energy storage sharing has become more promising because it is capable of smoothing renewable power generation and reducing energy purchasing costs. In this article, we present a two-stage pricing mechanism between the coordinator who operates the shared energy storage and the prosumers who are borrowing the shared capacity from the coordinator. Individualized pricing is derived via the two-stage pricing process. It is a pricing strategy that can facilitate the coordinator to capture the most considerable possible net profits through price discrimination. First, prosumers are clustered into different groups using the data-driven approach. Then, novel concepts of bulk capacity borrowing and discount sensitivity are introduced to model the individualized pricing for the first time. As a result, the price structures and the price levels can be jointly optimized. From the simulation results, it can be found that the proposed individualized pricing can increase the net profits of the coordinator, enhance the utilization efficiency of the energy storage system, and reduce the energy consumption costs of the prosumers.

Nonintrusive load monitoring (NILM) is a technique to disaggregate an appliance's load consumption from the aggregate load in a house. Monitoring the energy behavior has become increasingly important for home energy management. For many machine learning-based models, model training needs enough, and diverse appliance-level labeled data from different houses, which is very time-consuming, expensive, and unacceptable for users. In this article, we propose an algorithm based on the adversarial network and the joint adaptation network for energy disaggregation to decrease the distribution gaps of both the feature space and the label space between the source and target domains. With only very limited labeled data in the source domain and enough unlabeled data in the target domain, our proposed algorithm can obtain satisfactory accuracy results for NILM. Extensive experiments for intradomain and interdomain demonstrate that the proposed algorithm can significantly improve the domain adaptation. Comparing with the baseline method that without any domain adaptation, the improvement on mean absolute error with the proposed algorithm can reach 67.72%, 67.53%, and 66.56% for the washing machine (W.M), the dishwasher (D.W), and the microwave (M.V), respectively.

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.

With the increasing penetration of distributed energy resources (DERs) in smart grids, customers can be aggregated to participate in the local energy market (LEM). In the LEM, on the one hand, the aggregated customers can purchase electricity from local DERs at a price that may be lower than the electricity price from the utility. On the other hand, when there is abundant energy, the aggregated customers can sell them in the LEM at a higher price, supplementing the grid power supply with clean renewable energy. Therefore, the customers' dependency on the utility is reduced. In this context, this article presents a bidding strategy for electric vehicle aggregators (EVAs) based on data analytics and deep reinforcement learning (DRL). To achieve this goal, an asynchronous learning framework is put forward to help EVAs formulate bids, including bidding price and bidding volume. Compared with the conventional model-based strategy, the learning-based strategy shows advantages in rapid decision-making and reduced reliance on stochastic models. Besides, the EVAs can cope with the information asymmetry in the LEM by using the DRL method. A modified deep deterministic policy gradient methodology is utilized to speed up the online training to avoid high losses at the training stage. According to the simulation results, it can be concluded that the profit of the learning-based strategy is 63.3% higher than that of the random strategy. The coefficient of variation of the learning-based strategy is 76.4% lower than that of the random strategy. Therefore, the proposed learning-based method is effective.

The increasing penetration of distributed energy resources and a large volume of unprecedented data from smart metering infrastructure can help consumers transit to an active role in the smart grid. In this article, we propose a human-machine reinforcement learning (RL) framework in the smart grid context to formulate an energy management strategy for electric vehicles and thermostatically controlled loads aggregators. The proposed model-free method accelerates the decision-making speed by substituting the conventional optimization process, and it is more capable of coping with the diverse system environment via online learning. The human intervention is coordinated with machine learning to: 1) prevent the huge loss during the learning process; 2) realize emergency control; and 3) find preferable control policy. The performance of the proposed human-machine RL framework is verified in case studies. It can be concluded that our proposed method performs better than the conventional deep Q-learning and deep deterministic policy gradient in terms of convergence capability and preferable result exploration. Besides, the proposed method can better deal with emergent events, such as a sudden drop of photovoltaic (PV) output. Compared with the conventional model-based method, there are slight deviations between our method and the optimal solution, but the decision-making time is significantly reduced.

This work proposes a novel uncertainty quantification framework for long-term traffic flow prediction (TFP) based on a sequential deep learning model. Quantifying the uncertainty of TFP is crucial for intelligent transportation system (ITS) to make robust traffic congestion analysis and efficient traffic management due to the inherent uncertain and fluctuating nature of traffic flow. However, the performance (e.g., reliability and sharpness) of uncertainty quantification is hard to guarantee, particularly for long-term traffic flow (e.g., one week or two weeks in advance). To this end, this work develops a nonparametric performance-oriented prediction interval (PI) construction approach based on an enhanced sequential convolutional long short-term memory units (ConvLSTM) model, which is named as PI-ConvLSTM. This model can well learn the temporal correlations involved in the multivariate explanatory samples. Specifically, a periodic pattern learning strategy and a performance-oriented loss function are developed to ensure the quality of the derived PIs. Through validating on the real-life England freeway traffic flow dataset, the proposed PI-ConvLSTM proves to be capable of producing the skillful PIs for long-term TFP. For instance, the performance of derived PIs for two-week ahead is 0.175%, 0.198 and 1957.127 in average in terms of reliability, average width and sharpness, respectively. As compared to the benchmark models the proposed model shows at least 68.1% improvement on reliability, 3.4% on average width and 1.7% on sharpness.

With the increasing penetration of intermittent renewable energy resources, electricity distribution networks may face many challenges in terms of system security and reliability. In this context, mobile power sources can provide various distribution network services, including load leveling, peak shaving, voltage regulation, and emergency backup. Different from the stationary energy storage system (SESS), mobile power sources show advantages in mobility and flexibility. In the current literature, the dispatch of the mobile power sources relies on day-ahead scheduling based on optimization, which lacks the capability of dealing with emerging power problems. In order to realize that the mobile power sources can provide on-demand local service efficiently, an uber energy storage system (UESS) is presented based on a learning and planning integrated approach. First, each bus generates the UESS service order. Then, a centralized platform dispatches the orders to the UESSs through a planning problem. To solve the dispatch problem efficiently, we convert the optimization to a bipartite graph problem with low complexity. With the assistance of deep reinforcement learning, the value of each order dispatch action is learned, and the weight of each edge in the bipartite graph equals the corresponding action value. The proposed method is verified in case studies. Simulation results reveal that the daily cost savings and the finish rate of the proposed method are around$\$ $1562 and 15% higher than that of the nearest rule, respectively. Compared with the case without UESS, the voltage violation and the power loss issues are alleviated, and the profit of the system operator can be increased by 16.7%. Compared with SESS, the load curtailment cost with UESS can be reduced by 21.9k under contingency, which indicates that the resilience of the system is enhanced. Compared with MESS, UESS can better realize load restoration under emergencies.

With the emerging electric vehicle (EV) and fast charging technologies, EV load forecasting has become a concern for planners and operators of EV charging stations (CSs). Due to the nonstationary feature of the traffic flow (TF) and the erratic nature of the charging procedures, EV charging load is difficult to accurately forecast. In this article, TF is first predicted using a deep-learning-based convolutional neural network (CNN), and different forecast uncertainties are evaluated to formulate the TF prediction intervals (PIs). Then, the EV arrival rates are calculated according to the historical data and the proposed mixture model. Based on TF forecasting and arrival rate results, the EV charging process is studied to convert the TF to the charging load using a novel probabilistic queuing model that takes into consideration charging service limitations and driver behaviors. The proposed models are assessed using the actual TF data, and the results show that the uncertainties of the EV charging load can be learned comprehensively, indicating significant potential for practical applications.

The increasing electric vehicles (EVs) at charging stations will impose great challenges on the conventional voltage control in distribution networks. In this article, a two-stage voltage control strategy based on deep reinforcement learning is proposed to mitigate voltage violations caused by the uncertainty of EVs and load. In the first stage, the charging demand of EVs is predicted based on trip chain theory and simulated by Monte Carlo simulation. The optimal power flow is then performed to determine the day-ahead dispatch of on-load tap changer and capacitor banks. In the second stage, the real-time voltage control problem is formulated as a Markov Game considering both reactive power control and vehicle to grid modes of EVs. The problem is solved by the deep deterministic policy gradient algorithm to develop a well-trained control strategy that can be implemented online. Moreover, a novel customized charging criterion is proposed to conduct the charging behavior of EVs and guarantee full charging at the departure time. The proposed approach is tested on the IEEE 33-bus and 123-bus distribution systems and comparative simulation results show the effectiveness in addressing voltage problems.

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 key feature of an electricity distribution market is that it may be dominated by renewable generation with zero marginal cost. Existing market mechanisms are likely to fail in this context since it cannot generate a reasonable price signal to compensate for the investment cost of renewable generators. Given this background, a double-sided auction market mechanism is presented for pricing the zero marginal cost renewable generation in the distribution system. Honesty is proved to be a dominant strategy for participants, which would enable the proposed mechanism to develop into a set-and-forget bidding market. The proposed market mechanism is also shown to be compatible with the nodal pricing system. Finally, case studies are carried out, and the results show that under the proposed market mechanism, the problem of always bidding a zero price by renewable generators in some existing markets can be avoided. Even when only renewable generation units with zero marginal costs participate in the bidding, the proposed mechanism can still produce a reasonable market clearing price. When adopting the average pricing market mechanism, merits of nodal pricing can still be retained and contribute to the enhancement of the operating efficiency of the distribution network.

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.

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.

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.

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.