Search Results (534)

Amid global sustainability transitions, campus energy systems confront growing pressure to balance operational efficiency, resilience to extreme weather events, and sustainable development education. This study proposes an artificial intelligence-driven framework for smart campus microgrids that synergistically advances environmental sustainability and disaster resilience, while deepening students’ understanding of sustainable development. The framework integrates an enhanced multi-scale gated temporal attention network (MS-GTAN+) to realize end-to-end meteorological hazard-state recognition for adaptive dispatch mode selection. Compared with Transformer and Informer baselines, MS-GTAN+ reduces prediction RMSE by approximately 48.5% for wind speed and 46.0% for precipitation while maintaining a single-sample inference time of only 1.82 ms. For daily operations, a multi-intelligence co-optimization algorithm dynamically balances economic efficiency with carbon reduction objectives. During disaster scenarios, an improved PageRank algorithm incorporating functional necessity and temporal sensitivity enables precise identification of critical loads and adaptive power redistribution, achieving an average critical-load assurance rate of approximately 75%, nearly doubling the performance of the traditional topology-based method. Furthermore, the framework bridges the divide between theoretical knowledge and educational practice via an educational digital twin platform. Simulation results demonstrate that the framework substantially improves carbon footprint reduction, resilience to power disruptions, and student sustainability competency development. By unifying technical innovation with pedagogical advancement, this study offers a holistic model for educational institutions seeking to advance sustainability transitions while preparing the next generation of sustainability leaders. Full article

Wind power generation forecasting is a key technology for wind power projects. It directly determines the stability of grid integration and the accuracy of power dispatching. The interval belief rule base (IBRB) is an uncertainty modeling method; it can be applied to wind power generation forecasting. On the one hand, IBRB uses fixed interval matching. This method tends to cause boundary jumps when predicting continuously variable parameters, which threatens the stability of the grid integration. On the other hand, IBRB underutilizes the correlation information of adjacent intervals in modeling, and its rule activation mechanism limits expressions of complex generation mechanisms. To address these issues, a method based on belief rule base with fuzzy reference value (BRB-f) for wind power generation forecasting is proposed. Firstly, the method replaces fixed interval matching with fuzzy membership functions to reduce the impact of wind power output fluctuations on the grid. Then, through a multi-rule-weighted fusion mechanism and optimization algorithms, it improves the accuracy of scheduling under complex generation mechanisms. Finally, the effectiveness and accuracy of the model are validated using a wind turbine power generation forecasting dataset. It provides a better method choice to ensure grid integration safety and enhance the scientific basis of power dispatch decisions. Full article

Under the dual targets of carbon peaking and carbon neutrality, virtual power plants (VPPs) are expected to coordinate distributed energy resources in distribution networks to ensure low-carbon operation. This paper introduces a distribution-level dynamic carbon emission factor (DCEF), derived from nodal carbon potentials on an IEEE 33-bus distribution network, and uses it as a time-varying carbon signal to guide VPP scheduling. A bi-objective ε-constraint mixed-integer linear programming model is formulated to minimise daily operating costs and CO₂ emissions, with a demand response and battery storage being dispatched under network constraints. Four seasonal typical working days are constructed from measured load data and wind/PV profiles, and three strategies are compared: pure economic dispatch, dispatch with a static average carbon factor, and dispatch with the proposed spatiotemporal DCEF. Our results show that the DCEF-based strategy reduces daily CO₂ emissions by up to about 8–9% in the typical summer day compared with economic dispatch, while in spring, autumn, and winter, it achieves smaller but measurable reductions in the order of 0.1–0.3% of daily emissions. Across all seasons, the average and peak carbon potential are noticeably lowered, and renewable energy utilisation is improved, with limited impacts on costs. These findings indicate that feeder-level DCEFs provide a practical extension of existing carbon-aware demand response frameworks for low-carbon VPP dispatch in distribution networks. Full article

This paper proposes a privacy-preserving framework that couples probabilistic wind power forecasting with decentralized anomaly detection in complex power networks. We first design an adaptive federated learning (FL) scheme to produce probabilistic forecasts for multiple geographically distributed wind farms while keeping their raw data local. In this scheme, an artificial neural network with quantile regression is trained collaboratively across sites to provide calibrated prediction intervals for wind power outputs. These forecasts are then embedded into an alternating direction method of multipliers (ADMM)-based load-side dispatch and anomaly detection model for decentralized power systems with plug-and-play industrial users. Each monitoring node uses local measurements and neighbor communication to solve a distributed economic dispatch problem, detect abnormal load behaviors, and maintain network consistency without a central coordinator. Experiments on the GEFCom 2014 wind power dataset show that the proposed FL-based probabilistic forecasting method outperforms persistence, local training, and standard FL in RMSE and MAE across multiple horizons. Simulations on IEEE 14-bus and 30-bus systems further verify fast convergence, accurate anomaly localization, and robust operation, indicating the effectiveness of the integrated forecasting–dispatch framework for smart industrial grids with high wind penetration. Full article

Traditional single-prediction models often exhibit limitations in meeting wind power prediction requirements in complex operational scenarios. Furthermore, the inherent “black-box” nature of deep learning models leads to limited interpretability of predictions, hindering effective support for grid dispatch planning. To address these issues, this study proposes a novel day-ahead wind power prediction method, referred to as SHapley Additive exPlanations (SHAP)–Mixture of Experts (MoE), which integrates SHAP into an MoE framework. Here, SHAP is employed for interpretability purposes. This study innovatively transforms SHAP analysis into prior knowledge to guide the decision-making of the MoE gating network and proposes a two-layer dynamic interpretation mechanism based on the collaborative analysis of gating weights and SHAP values. This approach clarifies key meteorological factors and the model’s advantageous scenarios, while quantifying the uncertainty among multiple expert decisions. Firstly, each expert model was pre-trained, and its parameters were frozen to construct a candidate expert pool. Secondly, the SHAP vectors for each pre-trained expert were computed over all sample features to characterize their decision-making logic under varying scenarios. Thirdly, an augmented feature set was constructed by fusing the original meteorological features with SHAP attribution matrices from all experts; this set was used to train the gating network within the MoE framework. Finally, for new input samples, each frozen expert model generates a prediction along with its corresponding SHAP vector, and the gating network aggregates these predictions to produce the final forecast. The proposed method was validated using operational data from an offshore wind farm located in southeastern China. Compared with the best individual expert model and traditional ensemble forecasting models, the proposed method reduces the Root Mean Square Error (RMSE) by 0.23% to 4.92%. Furthermore, the method elucidates the influence of key features on each expert’s decisions, offering insights into how the gating network adaptively selects experts based on the input features and expert-specific characteristics across different scenarios. Full article

The rapid development of renewable energy has highlighted the issue of its accommodation, which has become a critical challenge for power grids with high renewable energy penetration. Accurately assessing a grid’s renewable energy accommodation capability is essential for ensuring power grid operational security, as well as for the rational planning and efficient operation of renewable energy sources and adjustable power resources. This paper adopts a long-term chronological power balance simulation approach, integrating the dynamic balance among multiple types of power sources, loads, and outbound transmission. Dispatch schemes suitable for different types of power sources, including hydropower, thermal power, wind power, solar power, and nuclear power, were designed based on their operational characteristics. Key operational constraints, such as output limits, staged water levels, pumping/generation modes of pumped storage, and nuclear power regulation duration, were considered. A refined analysis model for renewable energy accommodation in regional power grids was constructed, aiming to maximize the total accommodated renewable energy electricity. Using actual data from the Northeast China Power Grid in 2024, the model was validated, showing results largely consistent with actual accommodation conditions. Analysis based on next-year forecast data indicated that the renewable energy utilization rate is expected to decline to 90.6%, with the proportion of curtailment due to insufficient peaking capacity and grid constraints expanding to 8:2. Sensitivity analysis revealed a clear correlation between the renewable energy utilization rate and the scale of newly installed renewable capacity and energy storage. It is recommended to control the expansion of new renewable energy installations while increasing the construction of flexible power sources such as pumped storage and other energy storage technologies. Full article

Accurate short-term wind speed forecasting is essential for mitigating wind power variability and supporting stable grid operation. This study proposes a model selection forecasting system (MSFS) that dynamically integrates six deep learning models to enhance predictive accuracy and robustness. Using multi-turbine data from a wind farm in northwest China, the framework identifies the optimal model at each time step through iterative evaluation and retrains the selected models to further improve performance. The Kruskal–Wallis test shows that all forecasting models, including MSFS, maintain statistical consistency with the real wind speed distribution at the 95% confidence level. Uncertainty analysis demonstrates that MSFS more reliable forecasting interval. By coupling MSFS-derived wind speed forecasts with turbine-specific power curves, the system enables reliable theoretical power estimation, offering critical reference information for dispatch planning, reserve allocation, and distinguishing resource-driven variability from turbine performance deviations. The slightly conservative yet highly stable forecasting behavior of MSFS reduces overestimation risks and enhances decision reliability. Overall, the proposed MSFS framework provides a robust, interpretable, and operationally valuable solution for short-term wind energy forecasting, with strong potential for wind farm operation and power system management. Full article

The increasing deployment of hybrid renewable energy systems has introduced significant challenges in optimal energy dispatch and load balancing due to the intrinsic stochasticity and temporal variability of renewable sources, along with the multi-dimensional optimization requirements of simultaneously achieving economic efficiency, grid stability, and environmental sustainability. This paper presents GreenMind, a scalable Deep Reinforcement Learning framework designed to address these challenges through a hierarchical multi-agent architecture coupled with Long Short-Term Memory (LSTM) networks for predictive energy management. The framework employs specialized agents responsible for generation dispatch, storage management, load balancing, and grid interaction, achieving an average decision accuracy of 94.7% through coordinated decision-making enabled by hierarchical communication mechanisms. The integrated LSTM-based forecasting module delivers high predictive accuracy, achieving a 2.7% Mean Absolute Percentage Error for one-hour-ahead forecasting of solar generation, wind power, and load demand, enabling proactive rather than reactive control. A multi-objective reward formulation effectively balances economic, technical, and environmental objectives, resulting in 18.3% operational cost reduction, 23.7% improvement in energy efficiency, and 31.2% enhancement in load balancing accuracy compared to state-of-the-art baseline methods. Extensive validation using synthetic datasets representing diverse hybrid renewable energy configurations over long operational horizons confirms the practical viability of the framework, with 19.6% average cost reduction, 97.7% system availability, and 28.6% carbon emission reduction. The scalability analysis demonstrates near-linear computational growth, with performance degradation remaining below 9% for systems ranging from residential microgrids to utility-scale installations with 2000 controllable units. Overall, the results demonstrate that GreenMind provides a scalable, robust, and practically deployable solution for predictive energy dispatch and load balancing in hybrid renewable energy systems. Full article

With the large-scale integration of new energy and power electronic devices into power systems, frequency stability has become an increasingly critical concern. To maintain frequency stability while mitigating the high capital expenditure of energy storage systems (ESSs), this paper develops a control framework centered on edge energy management terminals (EEMTs). The design is based on a demonstration project in which distributed energy resources (DERs) and flexible loads collaboratively provide frequency regulation. A monitoring station is implemented to make fast frequency response (FFR) resources dispatchable, detectable, measurable, and tradable. Furthermore, a control strategy tailored for building- and factory-level applications is proposed. This strategy enables real-time optimal scheduling of DERs and flexible loads through coordinated communication between EEMTs and net load units (NLUs). Two field tests further demonstrate the effectiveness and advantages of the proposed approach. In addition, this paper proposes a coordinated scheme in which wind farms and NLUs jointly participate in frequency regulation, aiming to mitigate the response delay of NLUs and the secondary frequency drop observed in wind farms. The feasibility and benefits of this scheme are validated through experimental tests. Full article

This paper focuses on power system scheduling problems, aiming to enhance energy utilization efficiency through multi-energy complementarity. To support the “dual-carbon” strategic goals, this paper proposes a coordinated dispatch model for hydro–thermal–wind–solar–pumped storage integrated energy systems, aiming to enhance energy utilization efficiency and system flexibility while reducing carbon emissions. To address issues such as premature convergence and low computational efficiency in traditional optimization algorithms for multi-energy complementary dispatch, an improved Artificial Bee Colony algorithm named Super-quality Variation Burst Artificial Bee Colony (SVBABC) is developed, which incorporates elite solution guidance and an explosion variation mechanism. Simulation results based on a regional practical power system demonstrate that compared to classical methods (e.g., Artificial Bee Colony, Fireworks Algorithm, and Ant Lion Optimizer), SVBABC exhibits significant advantages in global optimization capability and convergence stability. This study provides an innovative solution for efficient dispatch of multi-energy complementary systems. Through synergistic regulation of pumped storage and thermal power, the accommodation capability of renewable energy is effectively enhanced, thereby providing critical technical support for the development of new power systems. Full article

This paper develops an optimal dispatch model for an integrated wind–solar hydrogen-to-ammonia system to address the mismatch between renewable-energy fluctuations and chemical production loads. The model incorporates renewable variability, electrolyzer dynamics, hydrogen-storage regulation, and ammonia-synthesis load constraints, and is solved using a multi-time-scale MILP framework. An efficiency-priority power allocation strategy is further introduced to account for performance differences among electrolyzers. Using real wind–solar output data, a 72-h case study compares three operational schemes: the Balanced Scheme, the Steady-State Scheme, and the Following Scheme. The proposed Balanced Scheme reduces renewable curtailment to 2.4%, lowers ammonia load fluctuations relative to the Following Scheme, and decreases electricity consumption per ton of ammonia by 19.4% compared with the Steady-State Scheme. These results demonstrate that the integrated dispatch model and electrolyzer-cluster control strategy enhance system flexibility, energy efficiency, and overall economic performance in renewable-powered ammonia production. Full article

The large-scale integration of wind power into the grid has led to a reduction in system inertia, threatening frequency stability. There is an urgent need to accurately assess the inertia support capability of wind turbines, providing a theoretical basis for grid inertia dispatch and supporting grid frequency stability. However, due to factors such as wake effects, time-delay effects, and wind shear effects, the rotational speeds of different wind turbines within a wind farm under certain wind speed conditions exhibit probabilistic distribution characteristics. Existing research on wind turbine inertia assessment rarely accounts for the rotational speed randomness. To address this, this paper proposes a probabilistic assessment method for the available inertia of wind turbines that considers rotational speed randomness, establishes a joint probability model for wind speed and rotational speed, deriving the conditional probability density function of rotational speed. By substituting this into the frequency-domain inertia model, we achieve probabilistic inertia assessment. Using operational data from a wind farm in China, a practical case study is constructed, verifying the accuracy of the proposed probabilistic assessment method. At a wind speed of 6 m/s, the proposed method accurately captures the actual system inertia within its 90% confidence interval, in contrast to a conventional approach which yielded a significant 6.5% error. Full article

The rapid integration of wind power plants (WPPs) into modern electrical power systems (MEPSs) is crucial to global decarbonization, but it introduces significant technical challenges. Variability, intermittency, and forecasting uncertainty compromise frequency stability, voltage regulation, and grid reliability, particularly at high levels of renewable energy integration. To address these issues, adaptive control strategies have been proposed at the turbine, plant, and system levels, including reinforcement learning-based optimization, cooperative plant-level dispatch, and hybrid energy schemes with battery energy storage systems (BESS). At the same time, interoperability frameworks based on international standards, notably IEC 61850 and IEC 61400-25, provide the communication backbone for vendor-independent coordination; however, their application remains largely limited to monitoring and protection, rather than holistic adaptive operation. Real-Time Automation Controllers (RTACs) emerge as promising platforms for unifying monitoring, operation, and protection functions, but their deployment in large-scale WPPs remains underexplored. Validation of these frameworks is still dominated by simulation-only studies, while real-time digital simulation (RTDS) and hardware-in-the-loop (HIL) environments have only recently begun to bridge the gap between theory and practice. This review consolidates advances in adaptive control, interoperability, and validation, identifies critical gaps, including limited PCC-level integration, underutilization of IEC standards, and insufficient cyber–physical resilience, and outlines future research directions. Emphasis is placed on holistic adaptive frameworks, IEC–RTAC integration, digital twin–HIL environments, and AI-enabled adaptive methods with embedded cybersecurity. By synthesizing these perspectives, the review highlights pathways toward resilient, secure, and standards-compliant renewable power systems that can support the transition to a low-carbon future. Full article

In the context of the rapid development of renewable energy and frequent extreme weather, accurate evaluation of the backup operation flexibility of multiple distributed resources is a prerequisite for improving the resilience of power systems. However, it is difficult to consider the detailed model of each distributed resource and evaluate its regulation ability in the operation of power systems because of the small number of distributed resources. Therefore, this paper first quantifies the capacity boundaries of distributed reserve resources on the power generation, load, and energy storage sides under different meteorological conditions through economic self-dispatching optimization and Minkowski aggregation methods. Subsequently, the maximum correlation–minimum redundancy (mRMR) principle and Granger causality test are combined to reduce the dimensionality of high-dimensional meteorological features. Finally, the stacking ensemble learning method is introduced to build an end-to-end modelling framework from multi-source weather input to reserve capability prediction. The results show that (1) the reserve capacity of multivariate distributed resources has significant intra-day and intra-day periodicity and seasonal differences; (2) the mRMR algorithm considering the Granger causality test can capture the correlation and causality between high-dimensional meteorological features and reserve capabilities, and the obtained features are more explanatory; (3) the average R² of the stacking model in both upper-reserve and lower-reserve predictions reaches 0.994. In terms of computational efficiency, the training time of the proposed model is 130.85 s for upper-reserve prediction and 133.71 s for lower-reserve prediction, which is significantly lower than that of conventional hybrid models while maintaining stable performance under extreme meteorological conditions such as high temperatures and strong winds; (4) compared with integration methods such as simple averaging and error weighting, the stacking integration strategy proposed in this paper remains stable in the mean and variance of prediction results, verifying its comprehensive advantages in structural design and performance integration. Full article

With the large-scale integration of wind power into the grid, the accuracy of wind farm cluster power prediction has become a key factor for the sustainability of modern power systems. Reliable ultra-short-term forecasts support the secure dispatch of high-penetration renewable energy, reduce wind curtailment, and improve the low-carbon and economical operation of power systems. Aiming at the problem of significant differences in wind turbine characteristics, this paper proposes a prediction method based on an improved density-based spatial clustering of applications with noise (DBSCAN) and a hybrid deep learning model. First, the wind speed signal is decomposed at multiple scales using successive variational modal decomposition (SVMD) to reduce non-stationarity. Subsequently, the DBSCAN parameters are optimized by the fruit fly optimization algorithm (FOA), and dimensionality reduction is performed by principal component analysis (PCA) to achieve efficient clustering of wind turbines. Next, the representative turbines with the highest correlation are selected in each cluster to reduce computational complexity. Finally, the SVMD-TCN-BiGRU-MSA-GJO hybrid model is constructed, and long-term dependence is extracted using a temporal convolutional network (TCN); the temporal features are captured by bidirectional gated recurrent units (BiGRUs); the feature weights are optimized by a multi-head self-attention mechanism (MSA), and the hyper-parameters are, in turn, optimized by golden jackal optimization (GJO). The experimental results show that this method reduces the MAE, RMSE, and MAPE by 14.02%, 12.9%, and 13.84%, respectively, and improves R² by 3.9% on average compared with the traditional model, which significantly improves prediction accuracy and stability. These improvements enable more accurate scheduling of wind power, lower reserve requirements, and enhanced stability and sustainability of power system operation under high renewable penetration. Full article