The growing integration of distributed generators introduces increased vulnerability to cyber threats in modern power distribution systems, especially false data injection attacks (FDIAs) targeting communication network. This paper presents a novel real-time voltage regulation framework that enhances both system security and operational efficiency under FDIA scenarios. A time-frequency visual (TFV) model is first developed to identify FDIA types and accurately detect their duration in real-time. Based on the detection outcomes, a redirected control mechanism is employed to correct compromised control signals. These components are embedded into a Markov decision process (MDP) that guides voltage regulation strategies. To solve the MDP, an attention-based multi-agent soft actor-critic (AMS) algorithm is introduced, leveraging attention mechanisms to enhance decision-making under complex and uncertain environments. Integrating the TFV and AMS modules, a visual-based reinforcement learning (VRL) approach is formulated for robust and adaptive voltage control. Extensive simulations conducted on a modified IEEE 33-bus system using real-world synchrophasor data demonstrate that the proposed method effectively contains voltage deviations within 0.02085 p.u., significantly improving the resilience of the distribution network against cyberattacks.

Alternating Current Optimal Power Flow (AC-OPF) is essential for efficient power system planning and realtime operation, but remains an NP-hard and non-convex optimization problem with significant computational challenges. This paper proposes a novel hybrid quantum-classical deep learning (QCNN) framework for AC-OPF problem, integrating parameterized quantum circuits (PQCs) for feature extraction with classical deep learning for data encoding and decoding. Specifically, the proposed framework integrates two types of residual connec tion structures to mitigate the "barren plateau" problem in quantum circuits, enhancing training stability and convergence. Furthermore, a physics-informed neural network (PINN) module is incorporated to guarantee toler able constraint violations, improving the physical consistency and reliability of AC-OPF solutions. Experimental evaluations on multiple IEEE test systems demonstrate that the proposed approach achieves superior accuracy, generalization, and robustness to quantum noise while requiring minimal quantum resources.

This paper proposes a flexible section post (FSP) that integrates unified power flow controllers, photovoltaics (PVs), and energy storage systems (ESSs) into existing split-phase electric railways (ERs). This FSP provides more spatial and temporal power flexibility for ERs than existing FSP schemes. To reduce the complexity of the operational constraints of the proposed FSP, an active and reactive power flexibility model is derived. Then, a two-stage power flow management strategy is proposed, which fully utilizes the power flexibility of the FSP to achieve multi-objective operation. The first stage optimizes the output power of ESSs and the active exchange power between the FSP and tied TSSs. It aims to utilize PV and regenerative braking energy and reduce the maximum power demand of tied TSSs. The second stage optimizes the reactive exchange power between the FSP and TSSs to stabilize traction network voltages. Furthermore, an exchange power-based NSC constraint is employed to prevent the exchange power from exacerbating the negative sequence current (NSC) of tied TSSs. A series voltage magnitude-adaptive exchange power variation dynamic constraint is designed to enhance system dynamics while ensuring the operational constraints of the FSP. Finally, the feasibility of the proposed scheme is verified through hardware-in-the-loop tests.

Various impedance modeling methods have been developed in different reference frames for the impedance-based stability assessment (IBSA) of inverter-based resources (IBRs). In practice, these impedance models are extracted by applying electromagnetic transient (EMT)-type impedance scanning techniques to manufacturer specific (black-box) models of IBRs. However, the relationships between these models require a clearer and more comprehensible explanation. This work derives and systematically explains model equivalence through linear and coordinate transformations, as well as model simplification in the sequence multi-input multi-output (MIMO) to sequence singleinput single-output (SISO) impedance transformation. To validate the theoretical analysis, EMT-type impedance scanning is performed on a full-size converter (FSC)-based wind park (WP) in a benchmark test system exhibiting weak grid issues.

This letter proposes an efficient progressive polyhedral approximation (PA) method to tackle the high nonlinearity and nonconvexity of optimal electricity-water nexus (EWN) dispatch caused by hyperbolic nonlinear partial differential equations (HNPDEs). In this method, the HNPDE-constrained EWN dispatch model can be reformulated into a tractable mixed-integer linear programming (MILP) problem by tailored adaptive discretization and piecewise PA. Furthermore, a progressive approximation refinement technique is developed to dynamically strengthen the MILP for efficient convergence to a near-optimal solution. Comparative studies have validated the effectiveness of the proposed method in reducing decision-making time for the EWN dispatch.

The global transition to renewable energy is crucial for mitigating climate change, but the increasing penetration of renewable sources introduces challenges such as uncertainty and intermittency. The electricity market plays a vital role in encouraging renewable generation while ensuring operational security and grid stability. This Review examines the optimization of market design for power systems with high renewable penetration. We explore recent innovations in renewable-dominated electricity market designs, summarizing key research questions and strategies. Special focus is given to multi-agent reinforcement learning (MARL) for market simulations, its performance and real-world applicability. We also review performance evaluation metrics and present a case study from the Horizon 2020 TradeRES project, exploring European electricity market design under 100

The rising frequency of extreme weather events calls for urgent measures to improve the resilience and reliability of power systems. This paper, therefore, presents a robust preventive-corrective security-constrained optimal power flow (PCSCOPF) model designed to strengthen power system reliability during N-k outages. The model integrates fast-response virtual power plants (VPPs), dynamically adjusting their injections to mitigate post-contingency overloads and maintain branch flows within emergency limits. Additionally, a novel approach combining deep reinforcement learning (DRL) with Lagrangian relaxation is introduced to efficiently solve the PCSCOPF decision-making problem. By framing the problem as a constrained Markov decision process (CMDP), the proposed Lagrangian-based soft actor-critic (L-SAC) algorithm optimizes control actions while ensuring constraint satisfaction during the exploration process. Extensive investigations have been conducted on the IEEE 30-bus and 118-bus systems to evaluate their computational efficiency and reliability.

Active distribution network (ADN) is faced with significant challenges, including frequent and fast voltage violations, due to the increased integration of intermittent renewable energy resources. This paper proposes a two-stage multi-mode voltage control strategy based on a deep reinforcement learning (DRL) algorithm, designed to alleviate voltage violations in ADN and minimize network power loss. In the first stage, a DRL algorithm, the soft actor-critic (SAC), is introduced to determine the hourly dispatch of on-load tap changers and capacitor banks, ensuring voltage security during the day-ahead stage. A multi-mode voltage regulation strategy is then proposed to obtain real-time dispatch of PV inverters, aiming to save energy and enforce voltage constraints under various conditions. The real-time voltage regulation problem is formulated as a Markov decision process and solved using a multi-agent SAC integrated with an attention mechanism. All agents undergo centralized offline training to learn the optimal coordinated voltage control strategy, then make decentralized online decisions based on locally available information only. The effectiveness of the proposed approach is confirmed through extensive testing on the IEEE 33-bus distribution system, with simulation results conclusively demonstrating its ability to address voltage violation challenges.

Real-time pricing and demand response (RTP-DR) is a key problem for profit-maximizing and policy-making in the deregulated retail electricity market (REM). However, previous studies overlooked the non-convexity and multi-equilibria caused by the network constraints and the temporally-related non-linear power consumption characteristics of end-users (EUs) in a privacy-protected environment. This paper employs mixed strategy Nash equilibrium (MSNE) to analyze the multiple equilibria in the non-convex game of the RTP-DR problem, providing a comprehensive view of the potential transaction results. A novel multi-agent Q-learning algorithm is developed to estimate subgame perfect equilibrium (SPE) in the proposed game. As a multi-agent reinforcement learning (MARL) algorithm, it enables players in the game to be rational "agents" that learn from "trial and error" to make optimal decisions across time periods. Moreover, the proposed algorithm has a bi-level structure and adopts probability distributions to denote Q-values, representing the belief in environmental response. Through validation on a Northern Illinois utility dataset, our proposed approach demonstrates notable advantages over benchmark algorithms. Specifically, it provides more profitable pricing decisions for monopoly retailers in REM, leading to strategic outcomes for EUs. The numerical results also find that multiple optimal pricing decisions over a day exist simultaneously by providing almost identical profits to the retailer, while leading to different energy consumption patterns and also significant differences in total energy usage on the demand side.

Building integrated energy systems (BIESs) are pivotal for enhancing energy efficiency by accounting for a significant proportion of global energy consumption. Two key barriers that reduce the BIES operational efficiency mainly lie in the renewable generation uncertainty and operational non-convexity of combined heat and power (CHP) units. To this end, this paper proposes a soft actor-critic (SAC) algorithm to solve the scheduling problem of BIES, which overcomes the model nonconvexity and shows advantages in robustness and generalization. This paper also adopts a temporal fusion transformer (TFT) to enhance the optimal solution for the SAC algorithm by forecasting the renewable generation and energy demand. The TFT can effectively capture the complex temporal patterns and dependencies that span multiple steps. Furthermore, its forecasting results are interpretable due to the employment of a self-attention layer so as to assist in more trustworthy decision-making in the SAC algorithm. The proposed hybrid data-driven approach integrating TFT and SAC algorithm, i.e., TFT-SAC approach, is trained and tested on a real-world dataset to validate its superior performance in reducing the energy cost and computational time compared with the benchmark approaches. The generalization performance for the scheduling policy, as well as the sensitivity analysis, are examined in the case studies.

The urgent need to address global warming has led to a focus on decarbonizing the power sector, particularly by shifting to 100% renewable energy sources. However, the variability and unpredictability of renewable energy create significant challenges for the stability and efficiency of power systems. Addressing these issues requires not just technological solutions like Battery Energy Storage Systems (BESS) but also strategic approaches in electricity market operations. Crucially, the success of electricity markets, especially those fully penetrated by renewable energy, hinges on effective market design. Market design must optimize resource allocation, align incentives of market participants with system operator goals, and prevent market manipulation to maximize social welfare and ensure market integrity. However, existing market designs have limitations including insufficient methods for simulation and validation. This paper proposes a comprehensive market design for a 100% renewable-penetrated electricity market with BESS integration, focusing on coordinated operation aspects like bidding regulations, market clearing, pricing, and real-time imbalance penalties. A detailed modeling and simulation methodology is developed to provide reliable guidance to the market design. Specifically, a Markov Game model and a novel Recurrent multi-agent policy proximal optimization (MAPPO) algorithm are proposed for simulating bidding games, with efficient computational performance and consistence with real-world market operation. Finally, a case study is conducted to demonstrate how to use the simulation results as market operation performance indicators, to guide the market design optimization.

Unit commitment (UC) optimizes the start-up and shutdown schedules of generating units to meet load demand while minimizing costs. However, the increasing integration of renewable energy introduces uncertainties for real-time scheduling. Existing solutions face limitations both in modeling and algorithmic design. At the modeling level, they fail to incorporate widely adopted virtual power plants (VPPs) as flexibility resources, missing the opportunity to proactively mitigate potential real-time imbalances or ramping constraints through foresight-seeing decision-making. At the algorithmic level, existing probabilistic optimization, multi-stage approaches, and machine learning, face challenges in computational complexity and adaptability. To address these challenges, this study proposes a novel two-stage UC framework that incorporates foresight-seeing sequential decision-making in both day-ahead and real-time scheduling, leveraging VPPs as flexibility resources to proactively reserve capacity and ramping flexibility for upcoming renewable energy uncertainties over several hours. In particular, we develop quantum reinforcement learning (QRL) algorithms that integrate the foresight-seeing sequential decision-making and scalable computation advantages of deep reinforcement learning (DRL) with the parallel and high-efficiency search capabilities of quantum computing. Experimental results demonstrate that the proposed QRL-based approach outperforms in computational efficiency, real-time responsiveness, and solution quality.

This work proposes an event-triggered real-time voltage regulation strategy to defend against abnormal events in the distribution system. Firstly, a wavelet spectrum network (WSN) technology is designed to increase the awareness of abnormal events, where the wavelet spectrum block is developed to extract the global and local time and frequency information from events. Then, a recovery strategy is designed to categorize the detected events from WSN into three different control strategies. These detected events, along with the recovery strategy, are subsequently integrated into a Markov decision process (MDP) designed for real-time optimization of voltage regulation. Furthermore, a multi-agent soft actor-critic (MASAC) algorithm is employed to refine the MDP's performance by adjusting the output of the PV inverter and curtailing power demand to ensure the economical and safe operation of the distribution system. Finally, experiments under abnormal events are executed to verify the proposed WSN-MASAC strategy based on a modified IEEE 33-bus distribution system.

The integration of individual microgrids (MGs) into Microgrid Alliances (MGAs) significantly improves the reliability and flexibility of energy supply. The dispatch of MGAs is the key challenge to ensure the secure and economic operation of the distribution network. Currently, there is a lack of coordination mechanism that aligns the individual MGs' objectives with the overall welfare of the alliance. In addition, current optimization method cannot simultaneously achieve requirements of MGAs' dispatch, including fast computation speed, scalability, foresight-seeing capability, and risk mitigation against uncertainty due to high penetration of renewable distributed energy resources. In this paper, a cooperation mechanism for MGs in the MGA is proposed to harmonize MGs' own profit and the global profit of the MGA, with the guarantee of fairness. Aligned with this mechanism, a novel Risk-Sensitive Trust Region Policy Optimization (RS-TRPO), as a risk-averse multi-agent reinforcement learning algorithm, is proposed to help MGs to optimize their own dispatch strategy. This algorithm tackles the deficiencies of conventional methods, enabling the distributed, fast-speed, and foresight-seeing dispatch of MGs in a scalable manner, while considering the uncertain risks. In particular, the optimality of this algorithm is theoretically guaranteed. The outstanding computational performance is demonstrated in comparison with conventional algorithms in a modified IEEE 30-Bus Test System with 4 MGs.

The Nash Equilibrium (NE) estimation in bidding games of electricity markets is the key concern of both generation companies (GENCOs) for bidding strategy optimization and the Independent System Operator (ISO) for market surveillance. However, existing methods for NE estimation in emerging modern electricity markets (FEM) are inaccurate and inefficient because the priori knowledge of bidding strategies before any environment changes, such as load demand variations, network congestion, and modifications of market design, is not fully utilized. In this paper, a Bayes-adaptive Markov Decision Process in FEM (BAMDP-FEM) is therefore developed to model the GENCOs' bidding strategy optimization considering the priori knowledge. A novel Multi-Agent Generative Adversarial Imitation Learning algorithm (MAGAIL-FEM) is then proposed to enable GENCOs to learn simultaneously from priori knowledge and interactions with changing environments. The obtained NE is a Bayesian Nash Equilibrium (BNE) with priori knowledge transferred from the previous environment. In the case study, the superiority of this proposed algorithm in terms of convergence speed compared with conventional methods is verified. It is concluded that the optimal bidding strategies in the obtained BNE can always lead to more profits than NE due to the effective learning from the priori knowledge. Also, BNE is more accurate and consistent with situations in real-world markets.

The optimal dispatch of virtual inertia and damping (DID) of virtual synchronous generator (VSG) inverters plays a key role in improving system frequency stability. However, DID is mostly modeled based on linearized models due to the limited ability of mathematical programming to solve non-linear problems, which cannot reflect the system dynamics after contingency. To this end, this paper aims to employ deep reinforcement learning (DRL) to solve the DID problem based on a non-linear detailed system model. The formulated system model contains detailed models for grid-forming (GFM) and grid-following (GFL) inverters and is simulated in the time domain. Moreover, the soft actor-critic (SAC) algorithm is employed to solve the DID and trained by the system metrics from time domain simulations. Finally, a case study based on a three-area system is presented to demonstrate the effectiveness of the proposed DRL approach and the performance of DID to enhance power system frequency stability.

The integrated community energy system (ICES) has emerged as a promising solution for enhancing the efficiency of the distribution system by effectively coordinating multiple energy sources. However, the concept and modeling of ICES still remain unclear, and operational optimization of ICES is hindered by the physical constraints of heterogeneous integrated energy networks. This paper, therefore, provides an overview of the state-of-the-art concepts for techno–economic modeling of ICES by establishing a Multi-Network Constrained ICES (MNC-ICES) model. The proposed model underscores the diverse energy devices at community and consumer levels and multiple networks for power, gas, and heat in a privacy-protection manner, providing a basis for practical network-constrained community operation tools. The corresponding operational optimization in the proposed model is formulated into a constrained Markov decision process (C-MDP) and solved by a Safe Reinforcement Learning (RL) approach. A novel Safe RL algorithm, Primal-Dual Twin Delayed Deep Deterministic Policy Gradient (PD-TD3), is developed to solve the C-MDP. By optimizing operations and maintaining network safety simultaneously, the proposed PD-TD3 method provides a solid backup for the ICESO and has great potential in real-world implementation. The non-convex modeling of MNC-ICES and the optimization performance of PD-TD3 is demonstrated in various scenarios. Compared with benchmark approaches, the proposed algorithm merits training speed, higher operational profits, and lower violations of multi-network constraints. Potential beneficiaries of this work include ICES operators and residents who could be benefited from improved ICES operation efficiency, as well as reinforcement learning researchers and practitioners who could be inspired for safe RL applications in real-world industry.

The new frontier maritime magnetohydrodynamic (MHD) engine drive generates the fields of magnetic and electric to accelerate seawater inside the engine duct. The dc system is overwhelmed by the ac for no explosive and poison gases emission. The proposed power circuit applies a transformer to provide a new drive design for the ac system, by reallocating the field generation currents for propulsion. The excitation current for magnetic field generation now is higher in the primary side of transformer and thus to decrease the current flowing the liquid that drew plenty of the conduction loss in previous ac MHD drive in series-resonant design. To conduct the high-frequency ac to mitigate the gas emission and the phase difference between the fields, three resonant tanks are deployed, constructing a resonant circuit for high circuitry efficiency operation. This article starts from introducing the concept of maritime MHD engine and the research fundamental. The comparison to the conventional ac MHD engine drive stresses its advantage. A steady-state analysis elaborates that the proposed topology and design consideration highlights the optimal design to the turn ratio and engine duct design. The electrical-fluid experiment and the concept verified converter will be demonstrated with the 82% converter efficiency at 470 W, maximum 92% efficiency to back the validity.

The integration of individual microgrids (MGs) into Microgrid Clusters (MGCs) significantly improves the reliability and flexibility of energy supply, through resource sharing and ensuring backup during outages. The dispatch of MGCs is the key challenge to be tackled to ensure their secure and economic operation. Currently, there is a lack of optimization method that can achieve a trade-off among top-priority requirements of MGCs' dispatch, including fast computation speed, optimality, multiple objectives, and risk mitigation against uncertainty. In this paper, a novel Multi-Objective, Risk-Sensitive, and Online Trust Region Policy Optimization (RS-TRPO) Algorithm is proposed to tackle this problem. First, a dispatch paradigm for autonomous MGs in the MGC is proposed, enabling them sequentially implement their self-dispatch to mitigate potential conflicts. This dispatch paradigm is then formulated as a Markov Game model, which is finally solved by the RS-TRPO algorithm. This online algorithm enables MGs to spontaneously search for the Pareto Frontier considering multiple objectives and risk mitigation. The outstanding computational performance of this algorithm is demonstrated in comparison with mathematical programming methods and heuristic algorithms in a modified IEEE 30-Bus Test System integrated with four autonomous MGs.

Due to the increasing pressure from environmental concerns and the energy crisis, transportation electrification constitutes one of the key initiatives for global decarbonization. The zero on-road global greenhouse gas emissions feature of electric vehicles (EVs) and hydrogen fuel cell vehicles (FCVs) are encouraged to facilitate the electrification of the transportation sector to reduce carbon emissions. However, the benefits of these vehicles in terms of carbon emission reduction would be hindered if the fast-charging stations (FCSs) and hydrogen production stations (HPSs) were powered by coal-fired power plants. To achieve overall emission reduction, a low-carbon expansion planning strategy is proposed in this paper to determine the eco-friendly configuration of IES consisting of electricity-gas-hydrogen networks associated with FCSs and HPSs to supply electricity and hydrogen to EVs and FCVs, respectively. Then a novel carbon emission allocation strategy based on the carbon emission flow (CEF) model is developed to specify the locational carbon emission in the IES and facilitate the low-carbon expansion planning strategy. Given locational-differentiated carbon intensities, the expansion planning scheme installs low-carbon generation devices in a rational place to satisfy the carbon emission constraint. Furthermore, the Wasserstein distance (WD) method and an adaptation cost technique are innovatively applied to cope with the uncertainties in the proposed planning model, namely the traffic flow levels, renewable-based power generation levels, and conventional load levels. Finally, numerical experiments validated the effectiveness of the proposed expansion planning strategy in effectually achieving the lowest carbon emission of the proposed IES under a representative scenario set.