Search Results (1,025)

Non-OCPP electric vehicle (EV) chargers lack site-integrated monitoring and supply-control, limiting their use in real-time energy management systems. This paper presents a Smart Distribution Panel that closes this gap via panel-side observability and an operator-commanded single-phase/three-phase (1$\Phi$/3$\Phi$) supply reconfiguration. In a single-site feasibility study, the panel is characterised with one consumer Tesla Wall Connector Gen 3 (Non-OCPP) and one Tesla Model Y at a Taiwan 3$\Phi$3W 220 V site, using a CPM-80 meter (IEC 62053-22 class 0.2S), interlocked contactors, an e-stop relay, and a Raspberry Pi 4 data path. Phase-mode transitions interrupt charging for ∼5 s, resolved by the IEC 61851-1 handshake. A 74.5-min stepwise session—driven by the OCPP DC fast charger with the Non-OCPP Wall Connector idle—yields a site-specific ensemble PCC-bus sensitivity $V=224.85-0.147P$ ($R^2=0.991$), characterising the site + DC-charger + base-load ensemble observed through the panel’s metering rather than the panel’s Non-OCPP path; an OpenDSS bounded sanity check gives an upper-bound slope of 0.26–0.28 V/kW. The one-second data stream supports a non-autoregressive (Non-AR) LSTM residual-detection layer (8-seed RMSE $0.310\pm 0.084$ V). A synthetic voltage-drop sensitivity sweep (ROC-AUC 0.81–0.97) characterises sensitivity to injected perturbations, not real-world fault detection. Feasibility is demonstrated only for the tested single-site configuration; multi-site, multi-EVSE, multi-EV generalisation, autonomous demand response, and OCPP session-level features are not demonstrated and are stated as future work. Full article

The increasing penetration of distributed energy resources, variable renewable generation, battery energy storage systems, electric vehicles, and power-electronic interfaces is changing the way modern smart grids and microgrids are operated, protected, and controlled. This systematic literature review follows the PRISMA 2020 framework and examines 87 original research papers, complemented by a supplementary synthesis of 18 contextual studies that provide bibliometric, historical, and conceptual perspectives on the evolution of AI in smart grids. The primary studies are organized into six thematic clusters: energy management and forecasting; cybersecurity and intrusion detection; renewable energy integration and microgrid management; fault detection, diagnosis, and grid stability; explainable and trustworthy artificial intelligence; and emerging technologies, including digital twins, blockchain, the Internet of Things, edge computing, and federated learning. The review shows that deep learning, reinforcement learning, and ensemble machine learning are increasingly used for load forecasting, demand response, converter-interfaced renewable integration, intrusion detection, and operational optimization. However, the literature remains uneven. Fault detection, converter-aware protection, and real-time stability assessment receive considerably less attention than energy management and cybersecurity, despite their importance for inverter-based resources, grid-forming converters, electric-vehicle charging systems, and battery interfacing. Four critical gaps are identified: limited cross-grid generalizability, weak validation under realistic converter and protection constraints, insufficient adversarial robustness of AI-enabled defense systems, and limited explainability in real-time safety-critical applications. The paper provides a structured taxonomy, identifies deployment barriers, and proposes research directions for trustworthy AI in power-electronic-rich smart grids and microgrids. Full article

The goal of the study presented in this article is to investigate all current issues related to the proper deployment of load forecasting (LF) techniques in smart grids (SGs). The latter concept has recently emerged as a potential solution to the global energy problem as well as to the ever-increasing and diverse consumer demands. To this end, more flexible dispersed production units are involved, mainly based on renewable energy sources (RESs). Another key novelty of SGs is their ability to gather information directly from consumers and production units in real time, thus facilitating optimum network planning and recovery as well as minimization of outage probability. Hence, it is important to use appropriate advanced infrastructure, which, in combination with modern telecommunication networks, will enable the full exploitation of SGs. In this context, to make the electricity system more efficient, avoid voltage and frequency imbalance issues and implement optimal production and consumption planning, LF is a vital process and plays a key role in the management of future electricity systems. Therefore, recent state-of-the art approaches in LF methods are also presented and discussed. In the same context, current limitations and proposals for future work are identified as well. Full article

This study proposes dynamic resource allocation and coordinated dispatch based on a source–load association graph for an electric–gas–heat system with wind, photovoltaics, fixed and mobile storage, power-to-gas (P2G), combined heat and power (CHP), and soft open points (SOPs). At each operating update, storage states, mobile-storage location and availability, and forecast profiles determine five typed relations and the subgraph classifications. Capacity-weighted centering then maps the state scores to time-varying device bounds without changing installed capacities or locations. The coordinated dispatch is formulated as a mixed-integer second-order cone program with SOC DistFlow constraints and an SOS2 gas-flow approximation. For the normal operating day, graph-guided dispatch yields an operating cost of 51,996.84 CNY, compared with 52,294.33 CNY for static equal-budget allocation and 52,829.05 CNY for topology-only allocation. The corresponding reductions are 0.5689% and 1.5753%, respectively, while all three policies serve 100% of demand and use 100% of available renewable energy within numerical tolerance. The graph-guided solution reaches a 0.0340% optimality gap, supporting its operating-cost advantage for the tested day. Full article

Renewable hydrogen storage can absorb surplus wind and solar generation, reduce curtailment, and provide a flexible energy carrier. Its value depends not only on the electrolyzer, storage tank, and fuel cell, but also on the operational strategy used to coordinate these components. This study develops a sensor-informed model predictive control (MPC) framework for renewable hydrogen storage and evaluates its performance against a no-hydrogen configuration and a reactive hydrogen storage controller under identical operating conditions, component parameters, and evaluation metrics. The MPC uses receding horizon optimization with measured hydrogen storage and safety states, together with short-term forecasts of renewable generation, load, electricity price, and hydrogen service demand. Using synthetic but physically motivated time-series profiles, the results show that hydrogen hardware under reactive rule-based operation eliminates renewable curtailment and reduces operating cost; however, it frequently depletes the tank to the reserve boundary and fails to reliably meet subsequent hydrogen demand. In contrast, the sensor-informed MPC achieves full hydrogen accessibility, eliminates curtailment, reduces total operating cost to 71.2% of the no-hydrogen baseline, and lowers cost by 46.3% relative to the rule-based controller. The MPC imports more grid electricity than the rule-based case, showing a clear trade-off between hydrogen service reliability and grid dependence. These results indicate that reliable renewable-hydrogen operation depends not only on storage hardware, but also on predictive supervisory control that coordinates renewable use, hydrogen service, reserve security, and grid interaction. Full article

With the rapid expansion of renewable energy and the formal operation of provincial electricity spot markets in China, smart aggregators that coordinate flexible loads, storage resources, demand-response portfolios, and distributed energy resources increasingly face imbalance-settlement risk across the day-ahead and real-time segments of the electricity spot market. Existing studies often focus on average prices or point forecasts, which may overlook regime persistence, negative-price clustering, and tail exposure in high-frequency price spreads. This paper develops a regime-switching and tail-risk signal framework to characterize and forecast day-ahead–real-time price spreads in the Shandong electricity spot market and to translate these forecasts into risk-aware trading signals for representative smart aggregators. Using 35,136 non-public observations at 15 min resolution provided by State Grid Shandong Electric Power Company for 2024, the spread is analyzed using descriptive statistics, Markov regime-switching models, quantile regression, out-of-sample forecasting, trading-signal backtesting, component ablation, and robustness checks. The spread, defined as real-time price minus day-ahead price, has a mean of −7.50 Chinese yuan per megawatt-hour (CNY/MWh), a median of −0.005 CNY/MWh, 5% and 95% quantiles of −196.84 and 137.65 CNY/MWh, and 1% and 99% quantiles of −372.68 and 338.04 CNY/MWh, respectively. A three-state Markov model identifies negative-deviation high-volatility, near-zero low-volatility, and positive-deviation regimes with multi-hour persistence. In the December out-of-sample test, the upper- and lower-tail quantile signals achieve recall rates of 0.872 and 0.841, respectively, and removing lagged spreads increases mean absolute error (MAE) from 24.015 to 54.278 CNY/MWh. The framework provides risk-warning signals rather than causal identification or realized-profit evaluation. Full article

The rapid growth of Electric Vehicles (EVs) has accelerated the deployment of Electric Vehicle Charging Stations (EVCSs), making their integration into modern power systems increasingly important. While EVCSs support transportation electrification and global decarbonization goals, large-scale integration introduces technical challenges that affect power system operation, reliability, and planning. This review provides a comprehensive assessment of the impact of EVCS integration on power system performance by examining charging technologies, charging stations, charging modes, and the principal components of EVCSs. The review discusses the effects of EV charging on load demand, peak load, voltage profile, voltage stability, active and reactive power losses, transformer loading, and overall grid performance. It further evaluates mitigation strategies, including smart charging, coordinated charging, Demand Response (DR), Renewable Energy Sources (RESs), Battery Energy Storage Systems (BESSs), Vehicle-to-Grid (V2G) technology, and Artificial Intelligence (AI)-based energy management. The application of Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), and advanced optimization algorithms for charging coordination and demand forecasting is also reviewed. Finally, the paper identifies current research challenges and future directions related to charging uncertainty, renewable energy integration, cybersecurity, interoperability, and infrastructure development. The findings demonstrate that intelligent charging strategies combined with renewable energy integration, energy storage, V2G, and AI significantly improve the reliability, efficiency, resilience, and sustainability of future EV-integrated power systems. Full article

Accurate short-term electrical load forecasting is of paramount importance for economic dispatch, reliable grid operation, and efficient demand-side management. However, hybrid forecasting frameworks constructed with deep learning models exhibit strong sensitivity to hyperparameter settings. Moreover, swarm-intelligence optimization algorithms are prone to premature convergence when tuning the hyperparameters of forecasting models, thereby degrading prediction performance. In addition, complex load sequences contain local fluctuations and key temporal segments that are difficult to capture using a single recurrent architecture. To address these challenges, this paper proposes a short-term electrical load forecasting method based on a BiGRU-Attention network optimized by an improved multi-strategy black-winged kite algorithm (IMBKA). The BiGRU extracts bidirectional temporal dependencies from historical load windows, while the attention module assigns adaptive weights to informative time steps and suppresses redundant historical information. To improve hyperparameter optimization, IMBKA introduces Sobol sequence initialization and adaptive elite differential mutation. Sobol sequence initialization enhances population coverage, and adaptive elite differential mutation strengthens information exchange among high-quality individuals. Experimental results on electrical load datasets from Singapore, Australia, and Belgium show that IMBKA-BiGRU-Attention achieves favorable forecasting performance among the compared models. The proposed model obtains RMSE values of 70.07 MW, 159.49 MW, 231.82 MW, and 163.43 MW in the Singapore, Australian, Belgian weekday, and Belgian weekend experiments, respectively. Compared with the best-performing model among the evaluated baselines in each experiment, the RMSE is reduced by 4.65%, 16.48%, 3.34%, and 11.39%, respectively. Full article

To address the coordinated economic, low-carbon, and robust operation problem caused by source–load forecast uncertainty in hydrogen-based integrated energy systems, this paper proposes a multi-time-scale distributionally robust scheduling framework based on the Wasserstein distance. First, a hydrogen-based polygeneration model is established by coordinating an electrolyzer, a hydrogen storage tank, a methanation reactor, and a hydrogen fuel cell. A dual-heat-source organic Rankine cycle is further introduced to recover waste heat from the microturbine and hydrogen fuel cell, thereby strengthening the coupling among electricity, heat, gas, and hydrogen. Second, a source–load coordination mechanism is developed by integrating pre-contracted stepped demand response with output-based carbon allocation and stepped carbon trading. On this basis, a two-stage day-ahead Wasserstein distributionally robust optimization model is formulated to account for joint wind-power and multi-energy-load forecast errors, while 15 min intraday and 5 min real-time rolling optimization are used to correct scheduling deviations. Finally, mechanism-ablation, physical-feasibility, uncertainty-handling, and parameter-sensitivity studies are conducted. The results show that M-5 reduces the total operating cost and net carbon emissions by 13.66% and 23.87%, respectively, relative to M-1, while reducing the wind-curtailment rate from 16.43% to zero. Among the tested uncertainty-handling methods, W-DRO achieves the lowest held-out total cost with a 3% shortfall rate, and the lowest-cost range is obtained for Wasserstein radii of 0.02–0.05. These results demonstrate that the proposed framework provides a favorable case-specific trade-off among economic performance, carbon reduction, renewable-energy accommodation, and operational robustness. Full article

Modern power transmission grids are increasingly operated under volatile load, variable generation, and near-limit line-flow conditions. In such environments, deterministic line-flow forecasting is insufficient for operational decision support because operators require calibrated risk probabilities, uncertainty intervals, and reliable early warning signals. This article proposes B-TGPRF, a Bayesian Temporal Graph Probabilistic Risk Forecaster for calibrated overload-risk forecasting in power transmission grids. The proposed framework is positioned as a hybrid probabilistic graph-temporal forecasting system rather than as a new end-to-end graph neural network; its novelty lies in the leakage-controlled sequential combination of temporal forecasting, electrical graph descriptors, calibrated Bayesian risk estimation, residual correction, and compact interval uncertainty assessment. The model integrates temporal line-flow, load, and generation features with graph-topological descriptors, operating-regime indicators, residual correction, conformal interval estimation, probability calibration, and a Bayesian risk layer. A leakage-controlled data preparation pipeline was built using an open large-scale benchmark for machine learning applications in transmission grids. The final modeling dataset contains more than 3.48 million observations, 100 selected critical lines, train-only risk thresholds, and chronological train, validation, test, and external-like scenario splits. B-TGPRF was compared with persistence baselines, linear models, Bayesian baselines, tree ensembles, boosting models, neural temporal models, and compact state-of-the-art-style temporal and graph-temporal architectures. On the strict external-like test, B-TGPRF achieved MAE = 0.3650, RMSE = 0.5478, R² = 0.9962, Brier Score = 0.0147, and ECE = 0.0064. The results show that the proposed model provides a strong overall balance between line-flow forecasting accuracy, calibrated risk estimation, compact interval prediction, and low false-positive risk-signaling, while boosting models remain highly competitive for pure risk-class detection. Full article

To address the coordinated operation of renewable generation, load, and storage in zero-carbon parks, this paper proposes a source–load–storage (SLS) coordinated multi-resource optimization method. First, a parallel forecasting model combining partial least squares regression (PLSR) and ModernTCN, denoted PLSR-Modern TCN, is developed. PLSR extracts an eight-dimensional latent representation from each lagged-load window, while ModernTCN independently captures nonlinear temporal dependencies from the original one-channel sequence. The two representations are aligned by sample index, concatenated, and mapped to the one-day-ahead forecasting horizon. The model achieves an R² of 0.987, outperforming traditional TCN, convolutional neural network (CNN), random forest, and linear regression models. Based on the forecasts, a multi-objective SLS optimization model is established by considering time-of-use electricity prices, supply–demand balance, renewable curtailment, operation cost, carbon emissions, energy storage operation, PCC voltage, and equivalent harmonic power. The entropy weight method determines the objective weights, and an improved genetic algorithm (IGA) solves the optimization model. Simulation results show that IGA achieves the lowest mean comprehensive fitness and the smallest repeated-run variation among the compared algorithms. In the illustrative scheduling result, IGA provides a modest energy-related operating-cost reduction of approximately 0.11–0.13% and a carbon-emission reduction of approximately 2.2–4.5%. Full article

Accurate short-term load forecasting for EV public charging stations is essential for grid and station operations. However, predictions are challenging because charging loads are non-stationary, spatially heterogeneous, and closely coupled with external factors such as weather and pricing. In this study, we forecast hourly regional charging energy using the open UrbanEV benchmark dataset, which includes hourly charging records from 1362 public charging stations across 275 traffic-analysis zones in Shenzhen from September 2022 to February 2023. We propose ST-Attention, a lightweight and modular forecasting model. It integrates temporal self-attention, spatial self-attention, an adjacency-matrix bias, and a residual prediction head. We compare ST-Attention with five baselines using a leakage-free rolling time-series evaluation protocol. For the 3 h horizon, ST-Attention achieves an MAE of 62.44 kWh, an RMSE of 291.8 kWh, and an MAPE of 5.91%, reducing the MAE by approximately 41% compared with the last-observation baseline. The model also maintains superior MAE performance at the 6 h and 9 h horizons. A modular ablation study shows that temporal attention and the residual head are the most stable sources of improvement, whereas dense spatial attention does not automatically provide benefits at hourly granularity with limited samples; removing it further reduces the 3 h MAE to 59.58 kWh. We present this as a cautionary finding: local temporal inertia dominates dense spatial coupling in hourly forecasting, and spatial model complexity must align with data granularity. Full article

Electricity price uncertainty directly affects the economy and reliability of scheduling, especially when flexible loads are scheduled only according to point forecasts. To improve the coupling between price forecasting uncertainty and load scheduling, this paper proposes a two-stage affine adjustable robust optimization method for flexible loads based on the confidence level of electricity price prediction via an improved hybrid convolutional neural network temporal convolutional network (CNN-TCN) model. An attention-enhanced CNN-TCN model is used to obtain day-ahead electricity price forecasts, and conformalized quantile regression (CQR) is introduced to construct calibrated asymmetric prediction intervals under different confidence levels. The interval bounds are then converted into a budgeted price uncertainty set and embedded in a two-stage affine adjustable robust optimization model for industrial, commercial, and residential loads. The model considers power limits, ramping constraints, total energy requirements, baseline deviation limits, and smoothing penalties, enabling load transfer from high-price periods to low-price periods while preserving operational feasibility. Case studies based on Spanish electricity market data show that the proposed method reduces operating costs under forecast, worst-case, and abnormal disturbance scenarios compared with the original load plan. The results also show that the 90% confidence level provides a suitable balance among cost reduction, risk coverage, and scheduling conservatism in the studied case. Full article

Electricity load forecasting is essential for balancing energy supply and demand, reducing energy waste, and maintaining power grid stability. Accurate forecasts enable power utilities to optimize energy dispatch and mitigate the risk of supply shortages or outages. However, conventional forecasting methods often struggle to capture highly nonlinear local fluctuations in electricity consumption and long-term temporal dependencies. To address these challenges, this study proposes MSCNN-ResLSTM, a hybrid model for multi-step electricity load forecasting. The proposed model integrates Multi-Scale Convolutional Neural Networks (MSCNNs) to extract local time-series features at multiple temporal scales, residual blocks (ResBlocks) to enhance feature representation through residual connections, and Long Short-Term Memory (LSTM) networks to model long-range temporal dependencies. To comprehensively evaluate its effectiveness, a cross-paradigm experimental framework is established in which MSCNN-ResLSTM is compared with seven representative benchmark models from three methodological categories: traditional machine learning (Extreme Gradient Boosting-XGBoost), classical recurrent and convolutional neural networks (LSTM, Temporal Convolutional Network-TCN, CNN-LSTM, MSCNN-LSTM, and Direct LSTM (Seq2Seq)), and self-attention-based architectures (Transformer). Experimental results show that MSCNN-ResLSTM achieves higher forecasting accuracy and greater stability across the full 24-step prediction horizon, consistently outperforming all competing baselines while effectively suppressing recursive error propagation. Full article

Data centers are facing rapidly increasing electricity demand and carbon emissions, while the intermittency of renewable energy creates a significant temporal mismatch between renewable generation and data center load demand. To bridge this temporal mismatch, we propose a coordinated optimization strategy that integrates shared hydrogen energy storage facilities with load scheduling mechanisms. A multi-objective MILP model is formulated to minimize annualized cost, renewable energy curtailment, and carbon emissions. Simulation results show that, compared with the no-shared-station case, the proposed electricity–hydrogen coordination strategy with load shifting yields significant benefits: the annualized total cost decreases from 13.65 to 4.74 million yuan; annual carbon emissions are reduced from 6297 to 1432 tons; and peak-period electricity purchases are reduced from 5373 to 906 MWh. Under the representative daily forecast condition, Scenario S4 achieves zero renewable curtailment when grid export is permitted; therefore, the renewable-electricity utilization rate reaches 100.00% within the model boundary. When grid export is prohibited, the utilization rate decreases to 98.59%, with 179,100 kWh of annualized renewable curtailment. The research findings indicate that integrating shared hydrogen energy storage with the load flexibility of data centers can effectively reduce the system’s overall operating costs, promote the integration of renewable energy, and achieve low-carbon operation. Full article