ABSTRACT This paper presents a novel optimal sizing model for a grid‐tied microgrid operating under real‐time pricing (RTP) for electricity trading with the main grid. The model determines the optimal capacities of solar photovoltaic (PV), wind turbine (WT), battery energy storage (BES) and inverter using a deep reinforcement learning approach. A double deep Q‐network (DDQN) is proposed to solve the complex sizing problem efficiently. The sizing model incorporates a rule‐based energy management strategy, where the average of day‐ahead electricity price forecasts is used to guide the battery's state of charge (SoC) decisions. Although the model is designed to be generic, a residential building in Australia is used as a case study to validate its practical applicability. Numerical results demonstrate that the proposed method under RTP conditions achieves a lower net present cost (NPC) of electricity compared to existing sizing models from previous studies. The effectiveness and robustness of the proposed deep learning‐based approach are further confirmed through comparative analysis with other machine learning techniques and metaheuristic algorithms.
ABSTRACT Accurate short‐term forecasting of electric vehicle (EV) charging demand is crucial for demand‐side management and grid stability in modern power systems, especially within the context of virtual power plant (VPP) operations. However, EV load profiles exhibit strong stochasticity and multi‐scale variability, making traditional single‐model predictors prone to overfitting, mode mixing, or degraded performance under shifting operating conditions. This study proposes a hybrid decomposition–clustering–adaptive forecasting framework that integrates variational mode decomposition (VMD), Louvain community detection and a lightweight adaptive model pool. First, VMD decomposes the raw load signal into mode components with reduced frequency overlap. Second, a correlation‐based similarity graph is constructed and processed by the Louvain algorithm to automatically group modes with coherent temporal characteristics. Finally, an adaptive prediction mechanism selects or refines models for each community based on a normalised MSE threshold. Experiments on three real‐world charging‐station datasets show that the proposed method significantly improves forecasting performance, achieving a 60.78%–75.18% reduction in MAPE compared with conventional single‐model baselines, and a 40.55%–53.70% improvement compared with nonadaptive VMD–LSTM schemes, while maintaining manageable computational cost. These results demonstrate the framework's robustness and its potential applicability to real‐time EV charging management and other nonstationary energy forecasting tasks.
ABSTRACT State‐of‐charge (SOC) estimation for lithium iron phosphate (LFP) batteries is a challenging task, owing to the wide plateau region in the open‐circuit voltage (OCV) characteristic, over which the low sensitivity of voltage to SOC renders voltage‐based correction methods unreliable. This paper proposes an ampere‐hour integration and adaptive extended Kalman filter (AH‐AEKF) fusion strategy within a region‐dependent estimation framework: in the plateau region, AH integration serves as the primary estimator to avoid errors induced by low‐voltage sensitivity. In the steep region, the AEKF is activated to perform closed‐loop correction via voltage residuals. Under operating profiles with sufficient current excitation, the dynamic parameters of the two resistor‐capacitor (2RC) equivalent circuit model (ECM) are identified online using an adaptive forgetting‐factor recursive least‐squares (FFRLS) algorithm, enabling real‐time tracking of ECM parameter variations under dynamic operating conditions. For low‐excitation constant‐current (CC) validation, offline HPPC‐identified parameters are used to avoid unreliable online updates. Furthermore, the identified parameters are incorporated into the AEKF to predict the terminal voltage, whose residual with respect to the measured voltage is used to perform closed‐loop correction of the SOC estimate. Finally, validation is conducted on a 280‐Ah LFP battery under CC tests and dynamic stress test (DST). Under correct initialisation, the proposed method maintains the maximum absolute SOC estimation error below under the HPPC, CC and DST profiles. Additional tests demonstrate improved convergence and robustness under initial SOC uncertainty, sensor disturbances and parameter perturbations.
ABSTRACT This paper proposes an MTL‐TCN‐Transformer model based on multi‐task learning in response to the challenges including climate change faced by multi‐load forecasting in integrated energy systems. To tackle the issues in multi‐load forecasting, such as the increased volatility due to climate change and the complex coupling relationships among loads, the proposed model is designed with an integrated architecture that effectively extracts long‐term temporal dependencies, captures correlations between loads and establishes global dependencies. By using the maximum information coefficient method for feature screening, nine features representing the correlation between loads, such as the ratio and difference between loads, were constructed to capture the complex correlations among multiple loads. The application of an adaptive weight update strategy suppresses fluctuations during the training process, enhancing the robustness and convergence speed of the model. The experimental results show that the model proposed in this paper exhibits better performance than the traditional methods in the four‐season multi‐load forecasting, with an average reduction of 67.952% in the MAPE of the electrical load. This paper provides a solution for high‐precision multi‐element load forecasting and points out future expansion directions such as enhancing cross‐regional generalisation capabilities.
ABSTRACT With the rapid expansion of electric vehicles (EVs), effective charging‐service‐fee (CSF) pricing is increasingly important for coordinating charging‐station operation and user‐side charging decisions. This paper proposes an operator‐side dynamic CSF pricing model within a simulation‐driven sequential leader–follower framework. The charging operator acts as the leader and jointly adjusts the CSFs of its self‐operated electric vehicle charging stations (EVCSs), whereas EV users respond through route‐ and queue‐informed station selection. The operator objective coordinates revenue, charging‐pile utilisation and a station‐level power distribution network (PDN) imbalance proxy. Standard soft actor‐critic (SAC) is retained as the pricing‐policy backbone and is equipped with three task‐specific stabilisation modules: the demand‐ and congestion‐aware stratified experience pool (DCA‐SEP), the critic‐disagreement‐regularised joint Bellman loss (CDR‐JBL) and the constraint‐triggered adaptive delayed update (CT‐ADU). In a Wuxi case study based on five independent experiment‐level runs, the proposed model increases the mean revenue of self‐operated EVCSs by 45.70%, reduces the PDN‐imbalance proxy by 18.40% and lowers total charging time under the reference demand setting.
ABSTRACT In recent years, peer‐to‐peer (P2P) energy trading has attracted significant attention with the goals of making optimal use of local generation, reducing costs, increasing the share of renewables in energy consumption and enhancing the resilience of the local grid. One aspect that has received less attention in designed settlement mechanisms for P2P energy trading is the prioritisation of generation and consumption by energy sellers and buyers. Energy sellers/buyers typically own different devices for generation/consumption and incur different rates/costs depending on their production/consumption level; therefore, prioritisation of generation and consumption in P2P markets is always a fundamental element. In this paper, this issue is modelled in detail: sellers and buyers prioritise their generation and consumption devices in each trading time interval and enter the P2P market according to those priorities. The prioritisation is based on price and the amount of energy produced/consumed, effectively giving prosumers full control over managing their resources. On the other hand, the determination of settlement and transaction prices is entirely dependent on supply and demand, which results in trades being settled fairly. In fact, the settlement price for transactions leads to satisfaction for both buyers and sellers, because it fully reflects their preferences. Additionally, a negotiation‐based approach between buyers and sellers is proposed, relying on direct negotiation between them. In this approach, buyers and sellers interact with the aim of increasing social welfare and thereby can achieve their energy trading objectives. The designed market‐settlement mechanism offers technical advantages such as fast response and low latency, computational scalability, ease of implementation, compatibility with variable resources and storage and reduced need to exchange sensitive information. Simulation results show that, besides improving metrics such as simulation time and social welfare, the proposed mechanism is also highly effective in aspects like fairness in energy distribution.
ABSTRACT As inputs to wind power forecasting models, meteorological factors are vulnerable to adversarial attacks, leading to deviations in forecasted wind power. Existing research on adversarial attacks against renewable energy forecasting models mostly focuses on a single destructive objective, neglecting attack stealthiness. This makes it challenging to achieve actual destructive effects in scenarios where example detection mechanisms are deployed. Therefore, this paper proposes a Dual‐Objective Adversarial Attack (DOAA) algorithm that balances attack destructiveness and stealthiness to achieve the joint optimisation of both. First, a wind power forecasting model based on deep neural network is constructed, which accurately captures the complex coupling relationships between meteorological factors and wind power. Second, a graph autoencoder (GAE) model suitable for time‐series example detection is designed, which fuses the temporal correlations of data from adjacent time periods using graph structures and realises the detection of adversarial examples through reconstruction loss. Finally, the above models are integrated to construct the DOAA algorithm based on Projected Gradient Descent. By adjusting the coefficient of the reconstruction loss term, the preference for attack destructiveness or stealthiness can be achieved. Experimental results verify that the proposed DOAA algorithm achieves a favourable balance between attack destructiveness and stealthiness.
ABSTRACT The studies pertaining to scheduling of plug‐in electric vehicles (PEVs) and distributed energy resources (DERs) in unbalanced AC microgrids (MGs) usually consider time varying lumped load models. This may lead to unrealistic values and inappropriate system support as unbalanced MGs have different loading levels for each phase. Therefore, in this paper optimal scheduling of DERs and PEVs is performed in 3‐phase unbalanced AC MG whilst considering different per‐phase loading levels of time‐varying voltage dependent (TVVD) load models. At first, the impact of lumped loading and per phase loading approach on DERs scheduling in unbalanced AC MG is investigated for TVVD load models. Then, PEVs are connected in presence of DERs whilst considering per phase loading in case of all TVVD loads to minimise the generation cost and system losses. Enhanced grasshopper optimization algorithm (EGOA) is utilised to evaluate the optimum performance of unbalanced AC MG. The results demonstrate that the values obtained through a per‐phase loading approach are more realistic and PEVs integration has a significant impact on cost reduction.
ABSTRACT This paper proposes a Learning‐Augmented Distributionally Robust OPF (LA‐DROPF) framework for coordinated transmission‐distribution dispatch under deep uncertainty. The framework embeds GNN‐derived power flow sensitivities within Wasserstein‐metric DRO, where surrogate errors are absorbed by an inflated ambiguity radius. A hybrid Benders‐ADMM decomposition maintains inter‐operator privacy with convergence guarantees, and a CVaR layer controls tail voltage‐security risk. Finite‐sample guarantees ensure exponential decay of out‐of‐sample constraint violations, and an online mirror‐descent mechanism adapts the ambiguity set in real time. Experiments on 217‐bus and 630‐bus coupled systems—including tests with heterogeneous distribution feeder topologies—show that LA‐DROPF reduces DS‐level worst‐case cost by 33% relative to stochastic OPF and eliminates voltage violations that afflict deterministic and GNN‐only methods. A controlled ablation isolating the CVaR and radius‐inflation effects—including a fair comparison against exact‐sensitivity DRO augmented with the same CVaR layer and equivalent total radius—confirms that the combined mechanism yields up to 7% worst‐case cost reduction under heavy‐tailed uncertainty beyond what the Wasserstein radius alone provides. Benders‐ADMM convergence to within 1% optimality gap and AC power flow feasibility are verified explicitly across all test systems.
ABSTRACT A rapid integration of distributed energy resources and electrification has increased the need for intelligent energy management in microgrids under grid‐connected and islanded operation. The variability of renewables, energy storage system cycle limits and market interactions are primary drivers for learning‐based coordination in microgrids. Therefore, this papers presents a comprehensive review of reinforcement learning and deep reinforcement learning methods for microgrid energy management. The comparison of RL and DRL methods is provided for MG EMS applications. Multiagent DRL frameworks and constrained safe RL architectures are also discussed. The states, actions, rewards, evaluation metric and grid‐connectivity are also presented for reinforcement learning based microgrid energy management systems. Finally, limitations of existing methods are identified, including challenges related to reward function design, partial observability, uncertainty management, cybersecurity and real‐world deployment along with future research directions.
ABSTRACT Cross‐provincial electricity trading in China expands the spatial scope of resource allocation and allows the adequacy benefits provided by provincial capacity mechanisms to extend beyond local jurisdictions. However, the costs of such mechanisms are still largely recovered within the provinces where they are implemented, giving rise to cross‐provincial cost leakage and a mismatch between the beneficiaries of capacity adequacy and the entities bearing the associated costs. This mismatch distorts market incentives and weakens the effectiveness of long‐term supply adequacy signals in cross‐provincial trading. To address this issue, this paper reveals the formation mechanism of the rights–responsibilities mismatch in capacity mechanisms under coupled cross‐provincial electricity trading and analyzes its critical impact on market incentive signals and power supply security. Based on this analysis, an improvement scheme for fairer capacity cost allocation is proposed, which is compatible with the current market operation environment. The proposed scheme corrects cross‐provincial market incentive signals by allowing capacity costs to be transmitted to external beneficiaries that actually receive adequacy support through cross‐provincial transactions, thereby achieving a cross‐provincial match between costs and benefits. Simulation results demonstrate that the proposed method improves the fairness of capacity cost sharing and mitigates distorted trading incentives. This study provides a useful reference for the design and operation of cross‐provincial electricity markets in the presence of non‐localised capacity adequacy benefits.
ABSTRACT Hyper‐scale data centers, with rapid load modulation capabilities, introduce a novel cyber‐physical attack vector for power systems. This paper investigates coordinated resonance attacks wherein adversaries exploit data center load flexibility to excite inter‐area oscillation modes. We formulate the attack as a Markov Decision Process and develop a Deep Reinforcement Learning framework using Proximal Policy Optimization that enables model‐free adversaries to discover mode‐matched forcing patterns through local frequency observations alone. We extend the analysis to multi‐agent scenarios using Centralised‐Training‐Decentralised‐Execution, demonstrating that geographically distributed data centers can learn anti‐phase coordination without real‐time communication. Simulations on a reduced‐order dynamic equivalent (tuned to the IEEE 39‐bus 0.64 Hz mode) show that: (1) a single agent learns mode‐targeted forcing using 200 MW of controllable load; and (2) two coordinated agents achieve comparable amplification with only 100 MW each. Sensitivity analysis reveals attack severity scales linearly with load magnitude and inversely with system inertia, and that the learned forcing pattern degrades gracefully under mode frequency mismatch (> 50% effectiveness at 6% mismatch, still ∼3 × random at 25%). High‐fidelity validation using ANDES on IEEE 39‐bus and WECC 179‐bus benchmarks confirms that narrowband forcing produces consistently larger frequency deviations than random modulation—specifically, 1.20 × and 2.20 × the random baseline on the respective benchmarks—while remaining bounded (tens of mHz) under standard control loops. The active‐power focus of the attack is justified by the predominantly real‐power nature of data center loads and the electromechanical coupling governing inter‐area modes; secondary reactive power effects are analysed and shown to be an order of magnitude smaller. These findings highlight an emerging security concern as data center penetration increases and grid inertia declines.
Impedance-based approaches can be employed to address harmonic stability issues in inverter grid-connected systems. The system stability can be analysed and improved by constructing an impedance model for the inverter and then performing impedance reshaping. The Lyapunov-Function-based Control (LFC) strategy ensures global stability of the inverter grid-connected system under large-signal transient conditions. However, research on the impedance modelling and stability analysis for inverters using this method remains insufficient. To address the harmonic stability issue of the inverter based on LFC under weak grid conditions, this article first establishes a mathematical model for the inverter's output impedance. Then the influence of various parameters on the stability of the inverter grid-connected system is analysed based on the impedance model. Furthermore, a method for impedance reshaping is proposed to improve system stability by tuning the active damping coefficient. Finally, simulations and experiments are conducted to validate the theoretical output impedance model and the proposed stability enhancement approach.
ABSTRACT The rapid expansion of renewable energy and the growing role of electricity market trading have created an urgent demand for ultra‐short‐term probabilistic forecasting of wind and solar generation. Existing studies often emphasise deterministic accuracy, yet lightweight and deployment‐friendly approaches for calibrated uncertainty modelling remain scarce. This paper proposes a practical framework for ultra‐short‐term probabilistic forecasting based on a Swin Transformer backbone combined with a learnable perturbation strategy. Unlike conventional ensemble methods that require training multiple models, the proposed approach introduces adaptive sample‐level disturbances into a single deterministic predictor, enabling efficient uncertainty quantification without additional training overhead. The framework is validated on 15‐min resolution wind and solar power data from a region with diverse meteorological and spatial conditions. Results show that the disturbance‐based ensemble achieves reliable predictive intervals while preserving high deterministic accuracy, supporting risk‐aware scheduling and energy management in renewable‐rich power systems. The proposed method offers a scalable and computationally efficient alternative for uncertainty‐aware forecasting in large‐scale zero‐carbon energy applications.
ABSTRACT The increasing penetration of renewable energy (RE) and hydrogen technologies is reshaping the operational requirements of modern decarbonised energy systems, necessitating advanced control strategies to coordinate renewable generation, hydrogen storage, and heterogeneous end‐use demands. This paper presents a mixed‐integer Model Predictive Control (MPC) framework for a grid‐connected hybrid microgrid integrating solar photovoltaics, wind turbines, and a proton exchange membrane electrolyser. The system simultaneously satisfies two distinct hydrogen demand profiles: continuous residential heating and discrete mobility demand via tube‐trailer refuelling, capturing temporal dynamics rarely addressed in prior studies. To manage discrete mobility demand beyond the prediction horizon, a critical‐time mechanism was introduced to ensures timely tube‐trailer dispatch while preventing premature grid electricity imports. In contrast to existing works that primarily adopt fixed horizons, this study systematically compares fixed and shrinking prediction horizons with 12‐h and 24‐h lengths, evaluating their impact on operational performance and mixed‐integer linear programming computational complexity. Simulation results show RE utilisation above 96% for heating and above 90% for mobility. Hydrogen production and grid electricity consumption vary by less than 0.1% across horizon configurations. Shrinking horizons cut computation time by up to 70%, demonstrating the MPC approach as a scalable, computationally efficient solution for integrating dual hydrogen demands.
ABSTRACT This paper presents a prototyping framework that supports decisions on the design and deployment of prosumer‐oriented control strategies in low‐voltage (LV) grids. Unlike existing approaches that focus either on operational detail or on scalable planning studies, the framework combines conceptualisation, planning‐oriented and operational modelling within a single data‐driven process. It structures strategy development and establishes a KPI basis that captures grid‐related, customer‐related and practical aspects, thereby supporting data‐informed assessments of control strategies, such as smart electric vehicle (EV) charging, and their implications for grid planning and operation. A data‐driven case study on private EV charging illustrates the capabilities of the framework. Several practically relevant charging strategies are developed and assessed across a large set of LV grids using complementary simulation methods. In the case study, grid‐oriented strategies yield favourable outcomes, reducing the occurrence of critical grid situations significantly, for example, undervoltages by up to 50%. Price‐based concepts with grid‐dependent behaviour can also mitigate load peaks when parameterised appropriately. These strategies also contribute to delaying grid reinforcement and extension. A simplified bidirectional variant, focusing on local voltage‐based active power adjustments, provides initial insights into the grid‐supportive potential of vehicle‐to‐grid approaches. The case study demonstrates how the framework supports multiperspective KPI‐based evaluation and thereby strengthens decision‐making for the integration of prosumers into LV grids and for data‐driven smart charging concepts.
ABSTRACT The growing adoption of electric vehicles (EVs) and renewable energy sources has accelerated the need for intelligent bidirectional energy management systems. Vehicle‐to‐Grid (V2G) and Grid‐to‐Vehicle (G2V) technologies offer promising solutions to enhance grid stability, optimise energy use and leverage EVs as mobile storage units. However, real‐time control of these systems remains challenging due to the dynamic nature of power flows, user behaviour and energy demand uncertainties. This review explores how Artificial Intelligence (AI) and Machine Learning (ML) methods–such as supervised learning, reinforcement learning, deep learning and hybrid models–address these challenges. This paper highlight key applications, recent case studies, and experimental advances in AI‐driven V2G and G2V control, emphasising improvements in system efficiency, battery health and grid reliability. Critical challenges including data quality, real‐time computation, and cybersecurity are discussed, along with future directions such as Explainable AI, blockchain integration, and lightweight decentralised models. This review aims to present a critical overview of AI‐enabled control strategies for V2G and G2V systems and to guide future innovations in sustainable and intelligent energy management.
ABSTRACT One mainstream approach to alleviate insufficient voltage support in renewable‐energy power delivery systems is to introduce a portion of grid‐forming (GFM) inverters. However, the distinct characteristics of grid‐following (GFL) and grid‐forming (GFM) controls complicate the quantitative assessment of system stability capability. To address this issue, this paper proposes an amplitude mapping‐based method for quantitatively evaluating stability capability, yielding a simple and practical analytical expression. Specifically, we introduce the idea of assessing system stability through waveform‐amplitude stability. Based on this concept, the mathematical model of the renewable‐energy delivery system is established, and the expression of the stability capability index (SA) is derived. Next, the impacts of key parameters on SA are analysed, and the critical GFL–GFM power ratio is quantified. Finally, the theoretical findings are validated through case studies.
ABSTRACT This paper proposes a two‐phase risk‐averse optimisation strategy for pre‐scheduling and rolling dispatch to account for the grid integration of cluster wind power during cold waves. This method aims to address the imbalance between supply and demand in the power system that may be caused by the low output power of cluster wind power and load surges. Initially, a Wasserstein distance‐based generative adversarial network (WGAN) is employed to generate a set of cluster wind power output scenarios. Subsequently, a two‐stage risk‐averse optimisation strategy is proposed for pre‐dispatch–rolling dispatch. At the rolling‐dispatch stage, the conditional value at risk (CVaR) is used to quantify the risk cost of the power system caused by the cold wave, and the stochastic rolling optimisation strategy is formulated to solve the unit redispatch and power shortage in a refined way. Finally, the model is applied to the improved IEEE‐24 node system, and the results are analysed. The results demonstrate that the proposed method can reduce the cost of system risk and power shortage compared to deterministic optimisation, rolling optimisation and deterministic risk‐averse optimisation.
ABSTRACT Demand response programmes (DRPs) increasingly rely on accurate load forecasting, realistic modelling of consumer participation, and strong resilience against data manipulation. This paper presents an integrated cyber‐secure DRP design framework that jointly addresses these requirements. A hybrid convolutional–bidirectional long short‐term memory (CB‐LSTM) model is first employed to predict consumption behaviour with improved accuracy, supporting consumer categorisation into low‐, medium‐, and high‐usage groups. Building on these predictions, a comprehensive DRP portfolio incorporating TOU and DLC‐based schemes is dynamically assigned to each category. To capture behavioural uncertainty, consumer participation is modelled using a Z‐number possibilistic–probabilistic formulation, enabling more reliable estimation of engagement levels and enhancing the fairness of DRP allocation. A key contribution of this study is the introduction of a CB‐LSTM‐assisted deviation bound‐based detection/correction (CBLADBDC) mechanism to mitigate false data injection (FDI) attacks. The proposed method exhibits strong detection capability, achieving a 98.0% recall and maintaining a low false positive rate of 1.1%, thereby preserving the integrity of load profiles and preventing unnecessary incentive payments. Simulation results demonstrate that the integrated framework reduces peak demand by 24%–27% while maintaining cost‐efficient DRP performance.
