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.

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.

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.

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.

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.

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.

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.