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.

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

The 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.

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.

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.