Search Results (837)

False data injection attacks (FDIAs) pose a growing threat to distributed energy resource (DER) aggregators because a compromised aggregator can expose and affect a number of enrolled DERs. However, DER aggregators only have access to limited measurements from DERs and the systems. This makes existing FDIA detection methods ineffective in this setting due to two main limitations: (1) they are grounded in full observability of a microgrid or distribution system and therefore are incompatible with the limited observability of a DER aggregator; (2) they can only either detect anomalies or explain why a deviation occurs, but not both simultaneously. To address these limitations, we propose a physics-guided fusion-based cyberattack detection method specifically designed for DER aggregators. This approach integrates two complementary modules: a forecasting-assisted residual method for rapidly anomaly detection, and a PV-aware sensitivity-based method to diagnose and explain their underlying physical causes. A gradient boosting machine (GBM) is then leveraged to fuse these outputs, optimizing the precision–recall tradeoff. The proposed method is tested on one microgrid test system with different bus observability levels across static and gradual attack scenarios with multiple levels of attack sophistication. Across the scenarios, the proposed method achieves a 0.91–0.93 precision–recall area under the curve score (PR-AUC), demonstrating the method’s effectiveness in securing DER aggregators with partial system visibility. Full article

A transition towards sustainable transportation requires efficient integration of electric vehicles (EVs) with renewable energy sources. This work proposes a two-stage Parking Lot Energy Management Scheme (PLEMS) to minimize charging costs while maximizing solar photovoltaic utilization in commercial parking facilities. In the first stage, the optimization phase is formulated using a mixed-integer linear programming (MILP) that minimizes the overall cost of EV charging while ensuring maximum utilization of locally available PV energy. In the second stage, a gated recurrent unit (GRU)-based deep learning model performs state of charge (SOC) forecasting for EVs parked in the parking lot. Using the predicted SOC for the next time step, the system decides whether each EV will be charged or discharged, ensuring consistency with the cost-optimal MILP strategy from the first stage. The proposed PLEMS achieves up to 62% daily cost savings in charging compared to uncoordinated direct grid charging. However, this cost saving is the outcome of proposed optimization as well as the integration of PV panels in power grid. When compared with nine similar vehicles to grid (V2G)-enabled approaches from the literature, which report cost savings ranging from 9.73% to 52%, the proposed framework shows an improvement of 10% to 52% over these methods. This hybrid MILP-GRU framework offers practical V2G operation and high scalability for large EV fleets in solar-powered smart parking lots. Full article

Photovoltaic (PV) power interval forecasting models are commonly trained on data dominated by non-ramp samples, which may weaken uncertainty characterization during rapid power changes. This study proposes a ramp-aware quantile regression Temporal Fusion Transformer (RQR-TFT) that jointly estimates PV power quantiles and the probability of a future ramp event. Ramp labels are constructed from the normalized power change between adjacent sampling instants. A shared Temporal Fusion Transformer (TFT) encoder extracts temporal representations from historical PV power and meteorological variables, and two output branches perform quantile forecasting and ramp-event identification. Ramp-sample-weighted quantile loss and positive-class-weighted classification loss are jointly optimized to increase the influence of minority ramp samples. The proposed method is evaluated for 4 h ahead forecasting using measurements collected from a 50 MW PV power station during 2019–2020. For the nominal 90% prediction interval, RQR-TFT achieves a ramp-sample prediction interval coverage probability (${\mathrm{PICP}}_R$) of 0.864, an overall prediction interval normalized average width (PINAW) of 0.209, and an overall normalized interval score (NIS) of 0.365. The area under the precision–recall curve for ramp-event identification is 0.906. The results demonstrate improved ramp-sample coverage and overall interval quality, although ramp-sample coverage remains below the nominal level. Full article

The flexible and precise design of photovoltaic (PV) skin morphologies for building facades constitutes a critical element for optimizing building energy efficiency and enhancing indoor spatial performance. However, the complex interrelationships among climatic parameters and their spatiotemporal variations pose significant challenges to the prior validity and accuracy of climate-adaptive parametric skin morphology adjustments. To address these limitations, this study proposes a morphological generative framework for climate-adaptive Building-Integrated Photovoltaics (BIPV) facades integrating Artificial Intelligence Algorithms and Bayesian prior-parameterized building envelopes. This framework is specifically designed to facilitate morphological decision-making regarding the overall climate-adaptive opening states of parametric PV skins under spatiotemporal dynamics. The proposed method integrates AI-based pattern recognition in spatiotemporal climate data with Bayesian Network-based prior probability techniques to derive optimal facade morphology schemes with the highest overall climate adaptability scores derived from weather forecasts, thereby achieving optimal transformations of the building envelope. Specifically, the model first employs an Artificial Intelligence Algorithm to generate the Bayesian Network structure required for overall climate adaptability scoring. Secondly, utilizing the Chinese Standard Weather Data (CSWD), the GRASSHOPPER algorithm is applied to implement variable parametric design on the facade skin, generating dynamic parametric skins and visual climatic data analysis cloud maps for energy benefit assessment. Finally, facade updates are executed based on the overall climate adaptability scores. The results demonstrate that the proposed framework effectively enables the real-time selection of optimal morphologies and opening states for dynamic skins based on comprehensive climatic adaptability criteria. Following model training and validation using 2025 Panjin meteorological data in the EnergyPlus Weather (EPW) format, the generated facade morphologies yielded solar radiation gains of 166.9 kWh/m²·month (peak month) for one of the optimal summer configurations and 90.5 kWh/m²·month (December) for one of the optimal winter configurations. Furthermore, by providing definitive evaluations of PV energy yields and indoor comfort levels across diverse weather scenarios, this framework offers explicit guidance for skin design, thereby reconciling the multi-objective optimization relationship between building energy conservation and occupant comfort. Full article

This paper proposes a stochastic dispatch framework for multi-terminal high-voltage direct current (MT-HVDC) systems that explicitly accounts for uncertainty in photovoltaic (PV) generation and electrical demand while preserving computational tractability. The economic–environmental dispatch problem is formulated as a mixed-integer second-order cone programming (MI-SOCP) model, where the SOCP relaxation provides a convex representation of the network constraints, and the mixed-integer component captures the discrete charging/discharging states of battery energy storage systems (BESS). This formulation ensures that, for any fixed set of binary decisions, the remaining problem reduces to a standard convex SOCP, enabling efficient solution via branch-and-bound methods with tight continuous relaxations. Uncertainty is incorporated through a chance-constrained optimization (CCP) approach, where forecast errors are modeled using bounded truncated distributions and reformulated into deterministic convex constraints via quantile-based approximations, yielding a risk-aware dispatch strategy that avoids optimistic bias. Numerical studies on an 11-bus MT-HVDC test system demonstrate that accounting for uncertainty increases total operating costs by up to 31.87% and CO₂ emissions by up to 37.41% when both PV and demand uncertainties are considered simultaneously at a confidence level of 0.9, compared to the deterministic solution. Power demand uncertainty has a substantially greater impact than PV generation uncertainty, leading to cost increases of 28.09% versus 3.85% at the highest confidence level. Validation on a modified IEEE 24-bus MT-HVDC system confirms the scalability and computational efficiency of the proposed approach, achieving global optimality in a pure solver time of 1.34 s with a maximum relative relaxation gap of $5.81\times {10}^{-9}$, demonstrating numerical exactness and suitability for day-ahead scheduling. The results also highlight the critical role of BESSs in providing operational flexibility, with storage strategies differing significantly under uncertainty during early hours while converging to deterministic behavior later. The findings reveal a clear trade-off between economic performance and operational reliability as the confidence level increases, confirming the effectiveness of the proposed approach for integrating renewables and storage in modern HVDC grids, while also identifying important limitations regarding independence assumptions and scalability to larger systems. Full article

The energy transition requires efficient management of decentralized resources, in which Renewable Energy Communities (RECs) play an increasingly important role. However, the variability of solar generation and the unpredictability of consumption create complex balancing challenges. To address the limitations of existing planning tools—which often rely on synthetic profiles or small-scale validations—this study presents a data-driven Digital Twin-inspired simulation framework The unique contribution of this work lies in the combination of three elements: the use of high-resolution sub-hourly smart-meter data, the application of a novel demographic filtering methodology to accurately isolate permanent community load profiles, and the integration of an AI-driven N-HiTS (Neural Hierarchical Interpolation for Time Series) forecasting model. The framework was implemented using the PyECOM simulation engine and applied to the Culatra Island Energy Community, Portugal, processing empirical data from 338 dwellings. Multiple scenarios were evaluated, including demand flexibility, photovoltaic (PV) expansion, and battery energy storage (BESS) deployment. The baseline scenario revealed a substantial dependence on the external grid, with a Self-Sufficiency (SS) rate of 12.51%. Expanding PV capacity by 200 kWp increased SS to 32.1% but generated significant energy surpluses. The optimal configuration, integrating a 600 kWh BESS, increased SS to 37.3% while restoring the Self-Consumption (SC) rate to 99.8%. Furthermore, the integrated N-HiTS predictive model achieved a coefficient of determination of 0.64 under highly variable weather conditions. Ultimately, the results demonstrate the critical value of combining empirical simulation, optimized storage sizing, and advanced forecasting techniques to support robust REC planning. Full article

The decarbonization of industrial steam production, representing up to 57% of energy use in the food industry, is critical for achieving EU climate neutrality goals. This study developed an integrated digital framework for the research project Hy4GreenSteam to optimize green-hydrogen integration through advanced predictive modeling. The employed LightGBM gradient-boosting algorithms were trained on 68,697 PV power measurements and 57,000 meteorological observations from 2020 to 2022. A “Production-Split” methodology was introduced for 24 h ahead forecasting, segmenting training into high (>2 kW) and low (≤2 kW) production regimes to manage solar heteroscedasticity. Results show the 15 min model achieved an R² of 0.868 and the 1 h model an R² of 0.832, while the day-ahead model—trained exclusively on information available at forecast issue time—achieved an R² of 0.701, a 70% relative improvement over same-time-yesterday persistence. A complementary regime analysis shows that the production regime is predictable with 90.7% accuracy and quantifies the accuracy headroom of regime-specialized models (oracle R² 0.794). These methods were integrated into a real-time React-based platform that calculates optimal H₂/CH₄ blending; for the reference pilot configuration, driven by measured on-site PV generation, the computed CO₂ emission reduction reaches 34% relative to natural-gas-only operation during high-solar operating intervals. Predictive modeling combined with a Digital Twin interface provides a TRL 6 decision-support solution, demonstrated in a relevant industrial environment, for managing renewable sources in industrial hydrogen applications. Full article

The building sector is one of the largest contributors to global energy demand and carbon emissions, making the transition to net-zero buildings (NZBs) a critical component of climate change mitigation strategies. However, the persistent building energy performance gap (BEPG), defined as the discrepancy between predicted and actual energy consumption, continues to hinder the achievement of net-zero operational performance. Accordingly, this review examines the role of artificial intelligence (AI) in enabling NZBs through the integration of energy-efficient building systems, renewable energy technologies, and intelligent operational control. A comprehensive review of the literature published between 2018 and 2025 was conducted, focusing on three complementary domains: heating, ventilation, and air conditioning (HVAC) system efficiency as the demand-side pillar, renewable energy integration as the supply-side pillar, and AI as the enabling layer connecting both domains. Synthesis of the reviewed literature reveals that demand-side HVAC technologies achieve energy savings ranging from 20% to 67%, while supply-side renewable energy integration increases photovoltaic (PV) self-consumption by 11–13%. Furthermore, AI-driven optimization, particularly through reinforcement learning (22.3% ± 8.4% energy savings) and digital twins (up to 70% renewable energy utilization), substantially enhances building performance within integrated energy management frameworks. The reviewed studies further demonstrate that AI techniques, including machine learning, deep learning, reinforcement learning, and digital twins, enable accurate energy forecasting (R² > 0.90), intelligent operational control, and effective coordination of integrated PV–battery energy storage system–electric vehicle systems, improving building energy flexibility and reducing grid fluctuations by up to 12.78%. Despite these advances, challenges related to data quality, interoperability, model explainability, cybersecurity, and limited large-scale real-world validation remain significant barriers to widespread adoption. Overall, the evidence indicates that AI serves as a key enabler for reducing the BEPG and improving the reliability, resilience, and operational efficiency of NZBs, thereby supporting the transition toward intelligent, low-carbon built environments. Full article

Water flooding in low-permeability, highly heterogeneous reservoirs often causes a rapid increase in water cut and inefficient pressure maintenance because injected water preferentially flows through high-permeability channels. In this study, a field-scale compositional simulation model was established to evaluate CO₂ water-alternating-gas (WAG) enhanced oil recovery in a mature waterflooded reservoir in the Daqing Oilfield. The model was constrained by geological data, experimentally tuned pressure–volume–temperature (PVT) behavior, relative-permeability measurements, and slim-tube tests. The minimum miscibility pressure (MMP) of the CO₂-oil system was estimated to be 19.8 MPa. An 187-month production history was matched using field oil rate, water production, water cut, and reservoir-pressure data. At the current development stage, the reservoir has an oil recovery of 23.6%, an average water cut of 61.34%, and an average reservoir pressure of approximately 6.9 MPa. A 30-year prediction was then performed to compare continued water flooding with several CO₂-WAG development strategies. Sensitivity analyses were conducted for the pressure-restoration level, pre-injection fluid, well-pattern conversion, slug size, and gas/water slug-size ratio. Continued water flooding increased the final oil recovery to only 28.4% and resulted in a water cut of 92.8%. Sequential scenario screening identified a best-performing case among the tested scenarios, consisting of CO₂ pre-injection to restore the average reservoir pressure to 11 MPa, conversion to a staggered line-drive well pattern, a slug size of 0.025 PV, and a gas/water slug-size ratio of 1:1. Under this sequentially selected case, the end-of-forecast oil recovery reached approximately 57.24%, which was the highest value among the cases evaluated in this study and was 28.84 percentage points higher than continued water flooding. The predicted recovery is conditional on the adopted geological, relative-permeability, EOS, and history-matching assumptions. Because the designed average reservoir pressure is below the measured MMP and local pressure above the MMP was not demonstrated, the modeled process is consistently interpreted as immiscible CO₂-WAG. The predicted recovery improvement is interpreted as being associated with pressure support, gas-mobility control, improved sweep efficiency, and compositional CO₂–oil interactions represented by the model, including CO₂ dissolution, oil swelling, and viscosity reduction. The contribution of this work is a field-scale, experimentally constrained workflow for selecting CO₂-WAG operating parameters in mature waterflooded low-permeability reservoirs; CO₂ storage performance should be quantified separately in future work. This study provides an experimentally constrained and history-validated field-scale workflow for identifying a best-performing CO₂-WAG operating case among the tested scenarios in mature waterflooded low-permeability reservoirs. Full article

With the advancement of China’s “dual carbon” goals and the green transformation of the railway sector, railway stations, as key energy-consuming nodes, require integrated energy systems that support low-carbon and renewable energy utilization. This review focuses on zero-carbon railway station integrated energy systems incorporating photovoltaic (PV) generation, energy storage, hydrogen systems, and charging facilities. Based on existing studies, the paper systematically reviews system configuration methods, operational strategies, and capacity optimization approaches. It first summarizes the roles of photovoltaic, energy storage, and hydrogen systems in railway station energy supply and outlines representative integration frameworks. It then compares standalone operation and coordinated multi-energy complementary operation, with particular attention to technical challenges in renewable energy accommodation, energy storage coordination, and hydrogen utilization. Mainstream capacity optimization approaches are further reviewed according to different energy configurations, including photovoltaic systems, energy storage systems (ESSs), hydrogen systems, and multi-energy complementary systems, with emphasis on optimization objectives, constraint formulations, and solution methodologies. The review shows that existing studies have gradually shifted from single-energy configurations toward coordinated multi-energy planning, but limitations remain in load forecasting accuracy, dynamic operational optimization, and large-scale engineering validation. Existing uncertainty management methods mainly include stochastic programming, robust optimization, chance-constrained optimization, and scenario-based approaches, which are used to address renewable energy fluctuations and load uncertainties. Future research should strengthen uncertainty modeling, real-time scheduling, and case study platforms considering diverse meteorological and load scenarios. This review provides a theoretical reference for planning and optimizing zero-carbon railway station integrated energy systems. Full article

Photovoltaic (PV) power exhibits high variability and non-stationarity due to irradiance fluctuations, cloud shading, and seasonal changes, which complicate ultra-short-term multi-step forecasting. This study proposes a Solar-Historical Prior Network (SHPNet) for forecasting at 5 min resolution. SHPNet integrates a solar-geometry clear-sky prior-guided temporal convolutional network (SGCP-TCN), a similar historical window (SHW) branch, and horizon-wise adaptive fusion (HA). SGCP-TCN estimates clear-sky power potential from site coordinates and timestamps and reformulates direct power prediction as clear-sky power ratio forecasting. SHW retrieves training windows from the same intra-day time slot that exhibit similar power–irradiance evolution, thereby constructing a non-parametric historical prior, while HA determines horizon-specific fusion weights based on validation errors. Unlike purely data-driven predictors and conventional similar-day methods, SHPNet combines a physically interpretable power scale with input-window-level historical evolution patterns and adaptively balances the two priors across forecasting horizons. Across the two sites, SHPNet reduced the mean MAE and RMSE by 14.07% and 10.26%, respectively, compared with the original TCN, while increasing the mean R² from 0.8279 to 0.8613. Evaluations under different weather conditions and across seasons demonstrate consistent forecasting performance, while convergence analysis confirms stable training behavior. Full article

Photovoltaic (PV) technology is now one of the main options for expanding the power of low-carbon electricity generation. However, in practical operation, PV systems still face several persistent difficulties, including the variability of solar irradiance, gradual performance degradation, fault occurrence, suboptimal control, and the growing complexity of grid-connected operation. These issues explain why artificial intelligence (AI) has become increasingly relevant in PV research, not only as a prediction tool, but also to improve monitoring, control, diagnosis, and decision-making. This review investigates the applications of AI in the major stages of the PV system lifecycle: solar resource assessment, power forecasting, fault detection, condition monitoring, system sizing, maximum power point tracking (MPPT), and grid integration. Rather than treating these applications as separate research topics, the review attempts to connect them through the common factors that determine their practical value: data quality, sensing configuration, model complexity, physical operating conditions, and deployment constraints. The reviewed studies indicate that AI-based MPPT methods can achieve tracking efficiencies close to 99%, while recent forecasting models, particularly LSTM, CNN–LSTM, and transformer-based architectures, can reduce prediction errors under changing weather conditions. At the same time, PV fault detection is moving beyond electroluminescence image classification toward more practical multimodal strategies that combine infrared thermography, RGB and drone imagery, electrical measurements, and SCADA/IoT data. Nevertheless, the progress reported in the literature should be interpreted with caution. Many proposed models are still evaluated on limited or non-standardized datasets, and their performance may decrease when they are transferred to different PV technologies, climates, fault severities, or operating conditions. Other recurring limitations include class imbalance, high computational cost, weak generalization, and the limited interpretability of deep-learning models. For this reason, hybrid neural networks, explainable AI, physics-informed learning, edge-AI, federated learning, and quantum machine learning are discussed as possible directions for making AI-based PV solutions more reliable and deployable. This review aims to critically synthesize recent advances and remaining gaps in order to support the practical integration of AI into efficient, reliable, and sustainable PV systems. Full article

With the rise in small-scale distributed photovoltaic (PV) power generation technology, the behind-the-meter PV problem has greatly increased the difficulty of power system regulation and management and accurate net-load forecasting is of great significance to the economic and stable operation of the power system. The timing features of the net-load sequence are complex due to a variety of factors. In order to improve the extraction effect of the timing model on the timing features of the net-load sequence and to increase the accuracy of the net-load prediction, a net-load prediction method considering multidimensional timing information is proposed. A Mamba module is introduced into the model to filter the input data, retaining some of the effective contextual information while improving the operational efficiency of the model. The structure of Bi-Mamba is used to construct a bidirectional time-series feature extraction model, which fuses the date attributes and the positive and negative time-series features of the net load to improve the stability and accuracy of the model prediction. The results of the validation algorithms show that the proposed method can reduce the normalized Mean Absolute Error (nMAE) by 17.72% and the normalized Root Mean Squared Error (nRMSE) by 21.51% compared with the temporal convolutional network (TCN) baseline. Furthermore, the model exhibits robust stability across different seasons and day types, providing a reliable reference for scheduling decisions in power systems with high PV penetration. Full article

Accurate photovoltaic (PV) energy forecasting is vital for grid stability and the global low-carbon transition. However, existing data-driven and channel-independent PV energy forecasting models struggle to capture nonlinear meteorological couplings, heterogeneous physical scales across stations, and high-frequency non-stationary fluctuations. To address these limitations, this study proposes a Physics-Guided Cross-Variable Temporal Transformer architecture. Building upon a channel-independent foundation, we introduce a Cross-Variable Attention mechanism to explicitly reconstruct nonlinear photothermal couplings via dynamic attention weights. To resolve multi-station physical scale discrepancies, a Physical Feature-wise Linear Modulation network utilizes installed capacity as a static prior for adaptive cross-station scale alignment. During optimization, a Time Dynamics-Aware Perceiving Loss jointly penalizes absolute errors and first-order time differences, constraining the network’s tracking ability for transient ramping. Experiments demonstrate that the proposed architecture overcomes traditional channel-isolation limitations. The model achieves a 21.6% reduction in MSE compared to PatchTST, a 44.0% reduction compared to Autoformer, and a 2.7% improvement in R² over Informer. This provides an accurate, generalizable, and physically interpretable solution for collaborative multi-station distributed PV energy forecasting. Full article

Optimal sizing and design of renewable microgrids and hybrid renewable energy systems require balancing renewable resource variability, demand uncertainty, storage operation, reliability, and techno-economic constraints. Artificial intelligence and metaheuristic optimization strategies have been increasingly used to address these challenges; however, the evidence remains methodologically heterogeneous. This scoping review maps the literature on artificial intelligence, learning-based, metaheuristic, heuristic, and optimization-based strategies for renewable microgrid sizing and design. The review followed PRISMA-ScR guidelines. Searches were conducted in Scopus and the Web of Science Core Collection for research articles published between 2009 and March 2026. A total of 69 studies were included. Metaheuristics dominated the field, appearing in 63 studies, with particle swarm optimization and genetic algorithm-based strategies as the most frequent methodological families. Artificial intelligence and learning-based strategies were mainly used to support forecasting, surrogate modeling, uncertainty handling, and energy management. The most recurrent configurations involved photovoltaic, wind, and battery storage systems, often with diesel backup in stand-alone or off-grid contexts. The literature is strongly oriented toward metaheuristic sizing of PV–wind–battery microgrids, with emerging integration of AI-assisted prediction and decision-support strategies. Future studies should address reproducibility, uncertainty modeling, real-world validation, degradation assessment, explainability, and scalability. Full article