International Journal of Electrical Power & Energy Systems最新文献

{"title":"Optimal power flow with synchronous generator capability curves using the branch flow model","authors":"Dakota Hamilton,&nbsp;Dionysios Aliprantis","doi":"10.1016/j.ijepes.2025.110695","DOIUrl":"10.1016/j.ijepes.2025.110695","url":null,"abstract":"<div><div>A method for incorporating synchronous generator capability curves (GCC) into the constraints of the optimal power flow (OPF) problem is proposed. The steady-state equivalent circuit of the synchronous machine in <em>qd</em> variables is added as a branch in the branch flow model (BFM) of the power system equations. Second-order cone programming is used to formulate a computationally efficient, convex relaxation of the BFM-based OPF with the GCC constraints. This enables the inclusion of nonlinear GCC equations, which vary with terminal voltage magnitude and include the impact of rotor saliency and stator resistance, in the OPF. Numerical verification and case studies are provided.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"169 ","pages":"Article 110695"},"PeriodicalIF":5.0,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Critical inertia thresholds for frequency stability in renewable Energy-Integrated power systems","authors":"Abolfazl Hadavi,&nbsp;Mehrdad Tarafdar Hagh,&nbsp;Saeid Ghassem Zadeh","doi":"10.1016/j.ijepes.2025.110733","DOIUrl":"10.1016/j.ijepes.2025.110733","url":null,"abstract":"<div><div>Recent developments in the electricity sector, coupled with the increasing adoption of Renewable Energy Sources (RESs), have led to a decline in the use of synchronous generators. Power grid systems are progressively optimizing the integration of RESs. As the proportion of renewable resource generators rises, the overall inertia of the power system diminishes, thereby affecting the stability of system frequency. This inertial reduction is characterized as either structural and permanent or as fluctuating on a daily and hourly basis, depending on the characteristics of the renewable resources involved. The present study addresses a significant research gap concerning the intricate relationship between the integration of renewable energy and system inertia, particularly in relation to frequency stability. While prior research has touched upon these themes, this study aims to provide a more comprehensive analysis. It examines the effects of varying levels of renewable energy penetration on the frequency stability of a simplified power system. By investigating the correlation between inertia and frequency stability, the study identifies the critical inertia threshold required to sustain system stability during significant disturbances. The results underscore the potential hazards associated with substantial reductions in inertia, which may heighten the risk of frequency instability and potential grid failure.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"169 ","pages":"Article 110733"},"PeriodicalIF":5.0,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Title: A bidding concession Enabled two-stage Peer-to-Peer market design for distributed energy storage service provision","authors":"Ziyao Wang,&nbsp;Huaqiang Li,&nbsp;Zhenyu Huang,&nbsp;Yikui Liu,&nbsp;Yang Liu,&nbsp;Yifan Pan","doi":"10.1016/j.ijepes.2025.110746","DOIUrl":"10.1016/j.ijepes.2025.110746","url":null,"abstract":"<div><div>The increasing deployment of distributed energy resources has driven significant interest in peer-to-peer (P2P) energy trading frameworks, particularly for optimizing distributed energy storage service provision (DESSP). Traditional bidding concession mechanisms primarily employ linearized models that oversimplify real-world market interactions and fail to capture the nonlinear relationship between participants’ concession strategies and market-clearing dynamics. To address this gap, this study proposes a novel P2P market design integrating a two-stage framework and a bidding concession model. Initially, we construct an iterative bidding model to quantify the nonlinear relationship between concession behavior and clearing prices, minimizing market mismatches and reducing dependence on the distribution network. We then introduce a two-stage P2P trading framework, incorporating day-ahead and intraday markets to mitigate deviations in renewable generation and load through DESSP. Finally, we construct a cross-framework credit mechanism, integrating credit into the trading rank to enhance transaction completion and market integrity and regulate pricing practices. Experimental results demonstrate that the proposed framework decreases reliance on the distribution network by 26.29%, improves local energy matching, and reduces total operational costs by 12.12%. The credit mechanism further stabilizes market dynamics, reducing operational costs by an additional 3.36%. These findings demonstrate the effectiveness of our proposed approach in enhancing the efficiency, stability, and fairness of P2P energy markets, providing valuable insights for future distributed energy trading systems.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"169 ","pages":"Article 110746"},"PeriodicalIF":5.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Frequency stabilization of a sophisticated multi-area interconnected hybrid power system considering non-inertia sources","authors":"Diaa M. Gawad ,&nbsp;Gaber Magdy ,&nbsp;M.A. Ebrahim ,&nbsp;Eduard Petlenkov","doi":"10.1016/j.ijepes.2025.110730","DOIUrl":"10.1016/j.ijepes.2025.110730","url":null,"abstract":"<div><div>This study introduces a novel interconnected power system model, which combines four real areas, integrating conventional power plants with renewable energy sources (RESs). This comprehensive system model aims to address the load frequency control (LFC) challenges that such systems face. Where the integration of RESs into power systems poses significant challenges, particularly in maintaining frequency stability. The LFC approach utilizes a proportional-integral-derivative (PID) controller, meticulously optimized through the Runge-Kutta (RUN) optimizer based on its mathematical principles. Dealing with the uncertainties of RESs and system nonlinearities poses a significant challenge for this controller. The efficacy of the proposed PID controller based on the RUN algorithm is examined by analyzing the frequency stability of the Egyptian power system (EPS), which stays interconnected with neighboring grids in Jordan, Libya, and Sudan. This study accounts for various loading scenarios and system nonlinearities to validate the algorithm’s effectiveness. Furthermore, the superior performance of the RUN optimizer is verified by contrasting its results with other widely recognized optimization techniques, such as the Ant Lion optimizer (ALO) and Chernobyl disaster optimizer (CDO). Additionally, the PID controller based on the RUN optimizer is also evaluated against literature that employed a moth swarm algorithm (MSA) to design the PID controller-based LFC in the EPS. Simulation results in the MATLAB environment demonstrate the effectiveness and robustness of the proposed PID controller based on the RUN algorithm. The RUN-based PID controller outperforms other PID controllers optimized using algorithms from the literature (e.g., ALO, CDO, and MSA), achieving superior performance across various contingencies, including load variations, RES uncertainties, and system nonlinearities. The results highlight the effectiveness of the proposed controller compared to those in the literature, improving the maximum overshoot by approximately 20% and reducing the minimum undershoot by about 30%. As a result, the system reaches steady-state conditions roughly 15 s faster. Finally, to leverage the precision of physical simulation and the flexibility of numerical simulation, the proposed PID controller, based on the RUN algorithm, is validated and implemented in a real-time environment using the OPA-RT platform.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"169 ","pages":"Article 110730"},"PeriodicalIF":5.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intra-day dispatch method via deep reinforcement learning based on pre-training and expert knowledge","authors":"Yanbo Chen ,&nbsp;Qintao Du ,&nbsp;Huayu Dong ,&nbsp;Tao Huang ,&nbsp;Jiahao Ma ,&nbsp;Zitao Xu ,&nbsp;Zhihao Wang","doi":"10.1016/j.ijepes.2025.110719","DOIUrl":"10.1016/j.ijepes.2025.110719","url":null,"abstract":"<div><div>Traditional economic dispatch algorithms rely on the accuracy of all parameters and also lack the adaptability to the high uncertainties brought by the dynamic changes happening in the current power systems. Its computing efficiency also needs to be improved with the increased operational complexities. In recent years, due to high self-learning and self-optimization ability, reinforcement learning has emerged in the field of economic dispatch, which can solve model-free dynamic programming problems that cannot be effectively solved by traditional optimization methods. In this paper, we construct a reinforcement agent for intra-day dispatch to optimize generator output, using a twin delayed deep deterministic policy gradient algorithm based on pre-training and expert knowledge (PEK-TD3). Aiming at solving the problems of long exploration time and poor convergence of conventional deep reinforcement learning, we propose an initial policy network training method based on pre-training with supervised learning, which significantly speeds up the training process of deep reinforcement learning and greatly reduces the model development cycle. At the same time, expert knowledge is embedded in the deep reinforcement learning to guide the training of the agent. With the guidance of expert knowledge, on the one hand, the agent quickly learns to limit the search direction to the feasible region of the power system operation so as to improve the convergence. On the other hand, in order to obtain higher rewards, agent learns to prioritize the renewable energy utilization which significantly reduces the curtailment rate of renewable energy. Finally, the modify IEEE 118-node system is used to verify the performance of the proposed method.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"169 ","pages":"Article 110719"},"PeriodicalIF":5.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A composite photovoltaic power prediction optimization model based on nonlinear meteorological factors analysis and hybrid deep learning framework","authors":"Mengji Yang ,&nbsp;Haiqing Zhang ,&nbsp;Xi Yu ,&nbsp;Aicha Sekhari Seklouli ,&nbsp;Abdelaziz Bouras ,&nbsp;Yacine Ouzrout","doi":"10.1016/j.ijepes.2025.110660","DOIUrl":"10.1016/j.ijepes.2025.110660","url":null,"abstract":"<div><div>Key factors influencing photovoltaic (PV) power generation predictions encompass solar radiation, aerosols, sunshine duration, temperature, humidity, wind direction, wind speed, cloud cover, and so on. The various influencing factors exhibit nonlinear correlation correlations, causing high volatility and discreteness in PV power time series. Firstly, to reduce the redundancy of the input for the prediction model and the computational time complexity, while enhancing the robustness and stability of the prediction model, nonlinear correlation search algorithm based on time window extending and time window shrinking strategies have been proposed. Key sequences from nonlinear correlation analysis are used in the next time series prediction model. Afterward, a novel <strong><u>d</u></strong>ual-<strong><u>b</u></strong>ranch architecture that has synthesized the <strong><u>S</u></strong>tructured <strong><u>G</u></strong>lobal <strong><u>C</u></strong>onvolution (SGC) and iTrans<strong><u>former</u></strong> branches has been proposed which is called <strong>DBSGCformer</strong>. This framework enhances the ability to capture long-term dependencies through the combined effects of efficient convolution parameter optimization and variable-oriented multivariate modeling. We perform comprehensive experiments to investigate DBSGCformer’s potential in tackling complex multivariate time series forecasting challenges. Experiments conducted on two PV power datasets and five additional real-world datasets demonstrate that DBSGCformer significantly improves the accuracy of PV power forecasting and exhibits strong generalizability.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"169 ","pages":"Article 110660"},"PeriodicalIF":5.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated energy microgrid bi-level game scheduling optimization taking into account electricity-heat-hydrogen enriched compressed natural gas coupling and shared energy storage","authors":"Changgang Wang ,&nbsp;Xiaoning Sun","doi":"10.1016/j.ijepes.2025.110735","DOIUrl":"10.1016/j.ijepes.2025.110735","url":null,"abstract":"<div><div>Hydrogen-enriched compressed natural gas (HCNG) is a promising technology capable of significantly reducing carbon emissions in conventional gas units. Its integration into microgrids enhances the coupling and complementarity of diverse energy sources. Shared energy storage systems, which play a crucial role in improving energy utilization, have garnered increasing attention in recent research. However, the deployment of HCNG technology involves multiple stakeholders, posing challenges in balancing their respective benefits. To address this, we propose a novel scheduling method for Integrated Energy Management that accounts for electricity-heat-HCNG coupling and shared energy storage services. First, we model the proton exchange membrane electrolyzer and HCNG energy coupling device, incorporating HCNG loads on the demand side to enhance hydrogen production and utilization efficiency. Additionally, tiered carbon trading mechanisms, integrated demand response strategies, and shared energy storage are introduced to optimize carbon reduction on both the demand and storage sides while effectively constraining energy emissions. The strategic interactions between integrated energy microgrid operators and load aggregators are modeled using a Stackelberg game approach, and the existence and uniqueness of equilibrium solutions are rigorously proven. Finally, the proposed model is solved using the Black-winged Kite algorithm in conjunction with the CPLEX solver.Case studies demonstrate the effectiveness of the proposed method, showing revenue increases of 9.15% for integrated energy microgrid operators and 8.29% for load aggregators, alongside a reduction in carbon emissions by 8.76%.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"169 ","pages":"Article 110735"},"PeriodicalIF":5.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An improved decomposition-aggregation strategy for analyzing power systems stability","authors":"Minquan Chen,&nbsp;Wenqing Hao,&nbsp;Deqiang Gan","doi":"10.1016/j.ijepes.2025.110728","DOIUrl":"10.1016/j.ijepes.2025.110728","url":null,"abstract":"<div><div>This paper begins by establishing the presence of a feedforward-feedback control structure within power system dynamics. Subsequently, specific nonlinear analysis tools are employed to investigate the inherent properties of subsystems: Kuramoto Coupled Oscillator Theory for the feedforward rotor subsystem and Monotone System Theory for the feedback voltage subsystem. Finally, the stability of the entire closed-loop system and the impact of control parameters are examined using Small Gain Theorem. Test results are presented to further complement the theoretical findings. The introduced decomposition-aggregation strategy helps reduce the complexity of stability analysis and also provides insights into the dynamics study of other interconnected nonlinear systems.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"168 ","pages":"Article 110728"},"PeriodicalIF":5.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143934867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust deep neural network-based internet of things for power transformer fault diagnosis under imbalanced data and uncertainties","authors":"Elahe Moradi ,&nbsp;Mahmoud Elsisi ,&nbsp;Karar Mahmoud ,&nbsp;Matti Lehtonen ,&nbsp;Mohamed M.F. Darwish","doi":"10.1016/j.ijepes.2025.110731","DOIUrl":"10.1016/j.ijepes.2025.110731","url":null,"abstract":"<div><div>One of the most vital components of power systems is power transformers, which provide an essential link in the chain of other devices used to supply electricity to consumers. According to the literature, the Duval pentagon method (DPM) is one of the most accurate and reliable dissolved gas analysis (DGA) interpretation methodologies. However, implementing large amounts of data in DPM is still challenging and has several limitations. To overcome these limitations, this paper introduces a robust deep neural network (DNN) method for precise DGA monitoring. Another merit is the proposal of synthetic minority over-sampling technique-edited nearest neighbor (SMOTE-ENN) preprocessing to eliminate noise from the imbalanced dataset, resulting in cleaner merged DGA samples. Furthermore, a unique RobustScaler technique is employed to maintain high performance against uncertain data noise. To visualize transformer faults remotely and enhance the acceleration of decision-making regarding the transformer status, this paper utilizes an industrial Internet of Things (IoT) platform. Specifically, the designed deep learning model is hybridized with an IoT platform to analyze the transferred DPM dataset of the gases concentration and send the classification results using the IoT gateway to the cloud for visualizing the detected fault on the IoT dashboard. The empirical results display that the proposed method outperforms several state-of-the-art approaches. The proposed method achieves satisfaction in diagnosing faults for the assessment dataset, with an accuracy of 98.19 %. Besides, the obtained results illustrate the effectiveness of the proposed model against uncertainty noise up to 20 % with a superior prediction diagnosis of the transformer faults.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"168 ","pages":"Article 110731"},"PeriodicalIF":5.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143934866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wind speed forecasting approach using conformal prediction and feature importance selection","authors":"Cesar Vinicius Zuege ,&nbsp;Stefano Frizzo Stefenon ,&nbsp;Cristina Keiko Yamaguchi ,&nbsp;Viviana Cocco Mariani ,&nbsp;Gabriel Villarrubia Gonzalez ,&nbsp;Leandro dos Santos Coelho","doi":"10.1016/j.ijepes.2025.110700","DOIUrl":"10.1016/j.ijepes.2025.110700","url":null,"abstract":"<div><div>Wind energy is a rising renewable energy source that plays an important role in the transition to a more sustainable energy system. Variation in wind power generation is one of the main challenges facing this energy source. Wind forecasting approaches are essential for planning and operating wind farms, but are complex due to the dynamic nature of the wind and the influence of local factors. This paper evaluates short-term forecasting of time series with a measure of uncertainty associated with wind speeds. The proposed method considers the conformal prediction approach and, based on Shapley values, uses optimal selection of features given their importance. Furthermore, a Bayesian Optimization with Tree-structured Parzen Estimators (BO-TPE) will be used to tune the hyperparameters of the models. The results showed that using Variational Mode Decomposition (VMD) allied with Singular Spectrum Analysis (SSA) to feed into a conformal prediction model improved the performance of the model. Taking into account a Beutenberg data set, Germany, the best model was Light Gradient Boosting Machine (LGBM)-VMD-SSA without partial fit, resulting in a root mean squared error (RMSE) criterion of 0.25031, coverage measure of 94.4%, and width measure of 1.008. When considering a dataset from Limoeiro, Brazil, the best model was also LGBM-VMD-SSA without partial fit, resulting in an RMSE of 0.21597, a coverage of 90.3%, and a width of 0.678. SHapley Additive exPlanations (SHAP) bring explainability to the model results. The models proposed in this study, hypertuned by BO-TPE with interpretable results based on SHAP, can be useful in predicting wind speed and power generation.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"168 ","pages":"Article 110700"},"PeriodicalIF":5.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143934862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}