International Journal of Electrical Power & Energy Systems最新文献

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Coordinated optimization of IES in electrolytic aluminum industrial park considering hybrid CSP-CCHP system, demand response, and CCER-carbon trading 考虑CSP-CCHP混合系统、需求响应和ccer -碳交易的电解铝工业园区IES协同优化
IF 5 2区 工程技术
International Journal of Electrical Power & Energy Systems Pub Date : 2025-08-05 DOI: 10.1016/j.ijepes.2025.110943
Yutao Zeng , Zuguo Chen , Yi Huang , Chaoyang Chen
{"title":"Coordinated optimization of IES in electrolytic aluminum industrial park considering hybrid CSP-CCHP system, demand response, and CCER-carbon trading","authors":"Yutao Zeng ,&nbsp;Zuguo Chen ,&nbsp;Yi Huang ,&nbsp;Chaoyang Chen","doi":"10.1016/j.ijepes.2025.110943","DOIUrl":"10.1016/j.ijepes.2025.110943","url":null,"abstract":"<div><div>The optimization of integrated energy systems (IES) in electrolytic aluminum industrial parks (EAIPs) is critical for advancing low-carbon transitions in this industry. In this paper, with the objective of minimizing the total operation cost of the park, an optimal scheduling model of IES in the EAIP is proposed, which considers the participation of the electrolytic aluminum load (EAL) in demand response (DR) and Chinese certified emission reduction (CCER)-carbon trading. Firstly, the IES takes hybrid concentrated solar power (CSP) and combined cooling, heating and power (CCHP) systems as the main energy sources, which can utilize the steam generated by CSP to drive the turbine of CCHP and allow the boiler of CCHP to assist in heating the molten salt in CSP. Then, EAL is integrated into the DR adjustment scope based on its operational characteristics. Finally, the EAIP is included in the carbon trading market that integrates CCER transactions, allowing it to gain economic benefits through the CCER-carbon trading. Gurobi calculation results show that, compared with the baseline scenario, the proposed strategy reduces total IES operating costs by 4.92% and increases renewable energy consumption by 1.91%, effectively lowering operating costs while promoting clean energy consumption.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"171 ","pages":"Article 110943"},"PeriodicalIF":5.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771711","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}
引用次数: 0
Adaptive hydrogen fuel cell vehicle scheduling strategy based on traffic state assessment in power-transportation coupled networks 电运耦合网络中基于交通状态评估的自适应氢燃料电池车辆调度策略
IF 5 2区 工程技术
International Journal of Electrical Power & Energy Systems Pub Date : 2025-08-05 DOI: 10.1016/j.ijepes.2025.110970
Yizhen Huang , Haiteng Han , Zhinong Wei , Bei Yang , Chuanshen Wu , Yizhou Zhou , Guoqiang Sun
{"title":"Adaptive hydrogen fuel cell vehicle scheduling strategy based on traffic state assessment in power-transportation coupled networks","authors":"Yizhen Huang ,&nbsp;Haiteng Han ,&nbsp;Zhinong Wei ,&nbsp;Bei Yang ,&nbsp;Chuanshen Wu ,&nbsp;Yizhou Zhou ,&nbsp;Guoqiang Sun","doi":"10.1016/j.ijepes.2025.110970","DOIUrl":"10.1016/j.ijepes.2025.110970","url":null,"abstract":"<div><div>As the global demand for energy increases and the transition to renewable and clean sources accelerates, microgrid (MG) has emerged as a promising solution. Hydrogen fuel cell vehicles (HFCVs) offer significant advantages over gasoline vehicles in terms of reducing carbon dioxide emissions. However, the development of HFCVs is hindered by the substantial up-front costs of hydrogen refueling stations (HRSs), coupled with the high cost of hydrogen transportation and the limitations of the hydrogen supply chain. This research proposes a multi-microgrid (MMG) system that integrates hydrogen energy and utilizes it as the HRS for fuel vehicle refueling. An adaptive hydrogen energy management method is employed for fuel cell vehicles to optimize the coupling between the transportation network and the power system. An integrated transportation state assessment model is developed, and a smart MMG system is deployed to receive information from the transportation network. Building on this foundation, an adaptive hydrogen scheduling model is developed. HFCVs are influenced by the hydrogen price adjustments, leading them to travel to different MGs for refueling, which in turn regulates the unit output of the MMG system. The MMG system is then integrated with the IEEE 33 bus distribution system to analyze the daily load balance. This integrated approach results in reduced traffic congestion, lower MG costs, and optimized power distribution network load balance.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"171 ","pages":"Article 110970"},"PeriodicalIF":5.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771860","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}
引用次数: 0
Psychological insights for electric vehicles 电动汽车的心理学洞察
IF 5 2区 工程技术
International Journal of Electrical Power & Energy Systems Pub Date : 2025-08-05 DOI: 10.1016/j.ijepes.2025.110931
Alexis Pengfei Zhao , Shuangqi Li , Mohannad Alhazmi , Zhaoyao Bao , Xi Cheng
{"title":"Psychological insights for electric vehicles","authors":"Alexis Pengfei Zhao ,&nbsp;Shuangqi Li ,&nbsp;Mohannad Alhazmi ,&nbsp;Zhaoyao Bao ,&nbsp;Xi Cheng","doi":"10.1016/j.ijepes.2025.110931","DOIUrl":"10.1016/j.ijepes.2025.110931","url":null,"abstract":"<div><div>The integration of electric vehicles (EVs) into modern power systems has introduced unprecedented opportunities for enhancing grid flexibility, integrating renewable energy, and reducing operational costs. However, managing the uncertainties associated with user behavior, renewable energy generation, and dynamic grid demand poses significant challenges to achieving optimal vehicle-to-grid (V2G) system performance. This paper presents a novel interdisciplinary framework that combines Self-Determination Theory (SDT) with Differentiable Distributionally Robust Optimization (DRO) to address these challenges. By embedding user-centric psychological insights into a robust optimization model, the proposed framework prioritizes user satisfaction and engagement while ensuring technical efficiency and system resilience. The mathematical modeling employs a multi-objective optimization approach to minimize total operational costs, maximize user satisfaction, and enhance system robustness. Constraints reflect real-world operational limits, including energy balance, grid dependency, and renewable curtailment. The methodology incorporates advanced neural network-based energy forecasting, gamification-driven user participation strategies, and dynamic clustering to foster community-based V2G collaboration. The differentiable nature of the DRO model enables real-time adaptability, making it scalable for large-scale V2G networks. Case studies on a simulated urban V2G network of 10,000 EVs demonstrate the framework’s efficacy. Results indicate that integrating user engagement metrics into energy dispatch decisions can increase participation rates by up to 20% while reducing peak grid dependency by 25%. Furthermore, the system effectively mitigates renewable energy intermittency, achieving a 15% reduction in curtailment and ensuring robust performance under worst-case uncertainty scenarios. These findings underscore the transformative potential of combining psychological theories with advanced optimization techniques in energy management. This study makes four key contributions: (1) a user-centric V2G optimization framework leveraging SDT principles to enhance engagement and satisfaction; (2) a differentiable DRO approach for real-time robust energy management under uncertainty; (3) the integration of gamification and community-based clustering to promote sustained participation; and (4) a scalable methodology applicable to large-scale V2G networks. This interdisciplinary approach sets a new benchmark for addressing the technical and behavioral complexities of V2G systems, paving the way for more sustainable and resilient energy solutions.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"171 ","pages":"Article 110931"},"PeriodicalIF":5.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771760","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}
引用次数: 0
A risk-averse optimal dispatch scheme for data centers considering carbon emission and water constraints based on enhanced ADMM 一种考虑碳排放和水资源约束的数据中心风险规避优化调度方案
IF 5 2区 工程技术
International Journal of Electrical Power & Energy Systems Pub Date : 2025-08-05 DOI: 10.1016/j.ijepes.2025.110976
Haotian Ma, Bo Zeng, Jiayu Wang, Shengyi Wang, Chen Liang, Jiayi Zhang
{"title":"A risk-averse optimal dispatch scheme for data centers considering carbon emission and water constraints based on enhanced ADMM","authors":"Haotian Ma,&nbsp;Bo Zeng,&nbsp;Jiayu Wang,&nbsp;Shengyi Wang,&nbsp;Chen Liang,&nbsp;Jiayi Zhang","doi":"10.1016/j.ijepes.2025.110976","DOIUrl":"10.1016/j.ijepes.2025.110976","url":null,"abstract":"<div><div>With the background of global climate change, the significance of energy utilization considering environmentally friendly is widely recognized. As a heavy load user in the power system, the role of data center (DC) in environment protection needs to be considered in the operation of the power system. This paper proposes a distributed risk-averse optimal dispatch scheme for DCs considering carbon emission and water restriction. Firstly, a model for the consumption of cooling water was established based on the equation of heat conduction, and the utilization of water resources in data centers was restricted by imposing an excess penalty price; Furthermore, the conditional value at risk (CVaR) method is introduced to manage the risks arising from diverse uncertainties, including workload, power of photovoltaic and temperature in the proposed scheme. To improve computational performance and protect the privacy between the DCs, the improved multiplier based alternating direction method of multipliers (M−ADMM) distributed solution method is developed to solve the dispatch model for DCs, and consider the time difference between photovoltaics and temperature caused by geographical longitude location. Numerical results illustrate the benefits for environment protection and economic, risk management ability, and computational performance of the proposed scheme.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"171 ","pages":"Article 110976"},"PeriodicalIF":5.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771863","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}
引用次数: 0
A hybrid framework for assessing regional inertia estimation in bulk power systems using COI-driven spectral clustering 基于coi驱动谱聚类的大容量电力系统区域惯性估计混合评估框架
IF 5 2区 工程技术
International Journal of Electrical Power & Energy Systems Pub Date : 2025-07-25 DOI: 10.1016/j.ijepes.2025.110929
Alexander Sanchez-Ocampo , Mario R. Arrieta Paternina , Juan M. Ramirez , Lucas L. Fernandes , Alejandro Zamora-Mendez , Petr Korba , Miguel Ramirez-Gonzalez
{"title":"A hybrid framework for assessing regional inertia estimation in bulk power systems using COI-driven spectral clustering","authors":"Alexander Sanchez-Ocampo ,&nbsp;Mario R. Arrieta Paternina ,&nbsp;Juan M. Ramirez ,&nbsp;Lucas L. Fernandes ,&nbsp;Alejandro Zamora-Mendez ,&nbsp;Petr Korba ,&nbsp;Miguel Ramirez-Gonzalez","doi":"10.1016/j.ijepes.2025.110929","DOIUrl":"10.1016/j.ijepes.2025.110929","url":null,"abstract":"<div><div>Modern power systems are facing growing challenges in frequency stability due to the increasing integration of renewable energy sources. Accurate regional inertia estimation is essential to address this issue. This paper proposes a novel grid partitioning method that enhances spectral clustering to identify regional centres of inertia (RCOIs) using complex network analysis. These RCOIs enable precise inertia estimation through an auto-regressive moving average (ARMAX) model, which combines data-driven and model-based approaches to achieve this. Validated on the NETS-NYPS test system under conventional and renewable generation scenarios and on the NPCC test system under traditional generation, the method achieves estimation errors below 3%, with renewable-specific accuracy nearing 10%. The framework offers a robust solution for monitoring regional inertia, thereby enhancing grid stability, operational efficiency, and resilience in dynamic power systems.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"170 ","pages":"Article 110929"},"PeriodicalIF":5.0,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703467","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}
引用次数: 0
Power system corrective control considering topology adjustment: An evolution-enhanced reinforcement learning method 考虑拓扑调整的电力系统校正控制:一种进化增强强化学习方法
IF 5 2区 工程技术
International Journal of Electrical Power & Energy Systems Pub Date : 2025-07-25 DOI: 10.1016/j.ijepes.2025.110879
Haoran Zhang , Peidong Xu , Ji Qiao , Yuxin Dai , Yuyang Bai , Tianlu Gao , Fan Yang , Jun Zhang , Jun Hao , Wenzhong Gao
{"title":"Power system corrective control considering topology adjustment: An evolution-enhanced reinforcement learning method","authors":"Haoran Zhang ,&nbsp;Peidong Xu ,&nbsp;Ji Qiao ,&nbsp;Yuxin Dai ,&nbsp;Yuyang Bai ,&nbsp;Tianlu Gao ,&nbsp;Fan Yang ,&nbsp;Jun Zhang ,&nbsp;Jun Hao ,&nbsp;Wenzhong Gao","doi":"10.1016/j.ijepes.2025.110879","DOIUrl":"10.1016/j.ijepes.2025.110879","url":null,"abstract":"<div><div>Effective corrective control is critical for maintaining secure power system operations. Conventional corrective control methods often struggle to balance computational speed with control optimality. In contrast, deep reinforcement learning facilitates faster control. However, significant uncertainties introduced by the high penetration can lead to repeated and irregular line overloads, impeding the accurate estimation of action values. To address these problems, this paper proposes an evolution-enhanced reinforcement learning method. The proposed method integrates the double dueling deep Q-network with the evolutionary algorithm, utilizing cumulative rewards to evaluate agents and reduce value estimation errors for corrective actions. Furthermore, we propose the top-K strategy to ensure that generated actions comply with complex operational constraints and the Monte Carlo evaluation method to enhance training stability. Case studies conducted on 36-node and 118-node power systems demonstrate that the proposed method can ensure stable system operations via topology adjustment, re-dispatching generators, and dispatching energy storage systems. It outperforms traditional corrective control methods and mainstream deep reinforcement learning algorithms. Results indicate that the proposed method extends the power system’s operating time by 14.94% to 22.81% and reduces computational costs.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"170 ","pages":"Article 110879"},"PeriodicalIF":5.0,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703466","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}
引用次数: 0
Interactive optimization scheduling method for distribution network and charging stations based on fuzzy logic and multi-strategy serial implementation mechanism 基于模糊逻辑和多策略串行实现机制的配电网与充电站交互优化调度方法
IF 5 2区 工程技术
International Journal of Electrical Power & Energy Systems Pub Date : 2025-07-25 DOI: 10.1016/j.ijepes.2025.110939
Ping Dong, Kunming Sui, Mingbo Liu, Run He, Shiqi Liu, Wu Xie, Sai Zhang
{"title":"Interactive optimization scheduling method for distribution network and charging stations based on fuzzy logic and multi-strategy serial implementation mechanism","authors":"Ping Dong,&nbsp;Kunming Sui,&nbsp;Mingbo Liu,&nbsp;Run He,&nbsp;Shiqi Liu,&nbsp;Wu Xie,&nbsp;Sai Zhang","doi":"10.1016/j.ijepes.2025.110939","DOIUrl":"10.1016/j.ijepes.2025.110939","url":null,"abstract":"<div><div>In the vehicle-to-grid (V2G) interaction scheduling process, the incentive price is a crucial factor affecting the user-side response. Therefore, how to reasonably set the incentive price is a key issue in V2G technology. Based on this, this paper first establishes an incentive-based user response model to determine the mathematical relationship between the incentive price and the response rate. Then, considering that changes in user states lead to the time-varying nature of user-side model parameters, fuzzy inputs such as charging price, remaining dwell time, and current state of charge (SOC) are used to update the parameters of different user response models in real-time through fuzzy logic, achieving user-specific modeling. Secondly, to avoid the shortage of response volume and ensure effective response volume, three scheduling strategies are designed, and a multi-strategy serial implementation mechanism is established, enabling the electric vehicle aggregator to dispatch electric vehicles according to the scheduling tasks of each period and adjust the scheduling strategy in real-time based on the response completion. Next, the PSO (Particle Swarm Optimization) algorithm is used to minimize the difference between the optimized total load and peak load control targets and the aggregator’s incentive cost, resulting in the optimized charging power of each electric vehicle and the corresponding incentive price. Finally, simulation results verify the effectiveness of the proposed multi-strategy serial implementation mechanism and the fuzzy logic method for determining response model parameters.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"170 ","pages":"Article 110939"},"PeriodicalIF":5.0,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703465","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}
引用次数: 0
Utility harmonic impedance estimation based on time series clustering and improved Pettitt method 基于时间序列聚类和改进Pettitt方法的电力谐波阻抗估计
IF 5 2区 工程技术
International Journal of Electrical Power & Energy Systems Pub Date : 2025-07-24 DOI: 10.1016/j.ijepes.2025.110935
Wentao Li, Shunliang Wang, Hao Tu, Hong Miao, Ning Jiao, Tianqi Liu
{"title":"Utility harmonic impedance estimation based on time series clustering and improved Pettitt method","authors":"Wentao Li,&nbsp;Shunliang Wang,&nbsp;Hao Tu,&nbsp;Hong Miao,&nbsp;Ning Jiao,&nbsp;Tianqi Liu","doi":"10.1016/j.ijepes.2025.110935","DOIUrl":"10.1016/j.ijepes.2025.110935","url":null,"abstract":"<div><div>Utility harmonic impedance estimation is critical for power quality assessment and improvement. Noninvasive methods without injecting harmonics are widely adopted to estimate utility harmonic impedance using natural load variations. However, background harmonic voltage fluctuations and abrupt harmonic impedance changes can lead to significant errors in utility harmonic impedance estimation. In this paper, a new noninvasive method is proposed to solve the above problem. To overcome the errors in harmonic impedance estimation caused by background harmonic voltage fluctuations, a time series clustering (TSC) method based on the cross-correlation principle is proposed to filter harmonic data. Moreover, an improved Pettitt method is proposed to identify the change points of harmonic impedance. Finally, the self-born weighted least squares (SBWLS) method is used to calculate harmonic impedance by iteratively weighting the anomalous data to weaken its influence, thereby improving the accuracy of the utility harmonic impedance. Simulation and field results validate the proposed method.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"170 ","pages":"Article 110935"},"PeriodicalIF":5.0,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144695029","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}
引用次数: 0
A hybrid ultra-short-term photovoltaic power prediction framework integrating ant colony optimization for clustering with Bi-GRU 集成蚁群聚类优化与Bi-GRU的混合超短期光伏功率预测框架
IF 5 2区 工程技术
International Journal of Electrical Power & Energy Systems Pub Date : 2025-07-24 DOI: 10.1016/j.ijepes.2025.110905
Xinyi Liu , Zitao Wang , Yang Wang , Shanke Liu , Lijun Yu
{"title":"A hybrid ultra-short-term photovoltaic power prediction framework integrating ant colony optimization for clustering with Bi-GRU","authors":"Xinyi Liu ,&nbsp;Zitao Wang ,&nbsp;Yang Wang ,&nbsp;Shanke Liu ,&nbsp;Lijun Yu","doi":"10.1016/j.ijepes.2025.110905","DOIUrl":"10.1016/j.ijepes.2025.110905","url":null,"abstract":"<div><div>To address the challenges posed to stable power grid operation by the intermittency and volatility of photovoltaic (PV) power generation, this paper proposes an ultra-short-term hybrid PV power prediction model based on a logical framework integrating a scenario-based prediction strategy. This hybrid framework employs an improved Ant Colony Optimization algorithm fused with K-Means pre-clustering (K-MACO) to perform unsupervised learning on samples within the physical feature space of radiation patterns, humidity, and temperature dynamics, classifying weather scenarios into sunny, cloudy, and rainy types. Compared to derivative models, this approach improves the Dunn Index by 24 % and the Silhouette Coefficient by 34 %. Kendall’s Rank Correlation Coefficient is utilized as the core metric for feature selection to identify the optimal predictive feature subset, resulting in a 9-feature set that achieves the best predictive performance (R<sup>2</sup> = 0.8153). Bidirectional Gated Recurrent Unit (Bi-GRU) models are independently constructed based on these scenario labels to enable scenario-adaptive predictions. Evaluated on 15-minute resolution data from a power station, the proposed framework shows an average performance improvement of 3 %–10 % on R<sup>2</sup>, RMSE, and MAE metrics over baseline models such as BP, TCN, and Transformer (R<sup>2</sup> = 0.8187, MAE = 8.01), with the scenario-based strategy contributing an additional 4.5 % reduction in MAE (7.65 vs. 8.01). The research indicates that this synergistic design, which integrates scenario division, feature selection, and scene-adaptive prediction, significantly enhances the accuracy of PV power prediction and can provide a reliable basis for decision-making in grid dispatching with high renewable energy penetration.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"170 ","pages":"Article 110905"},"PeriodicalIF":5.0,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694601","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}
引用次数: 0
An improvement in the design process of sustainable peak power rating transformer for solar utility 太阳能可持续峰值额定功率变压器设计过程的改进
IF 5 2区 工程技术
International Journal of Electrical Power & Energy Systems Pub Date : 2025-07-24 DOI: 10.1016/j.ijepes.2025.110928
Emir Yükselen , Ebrahim Rahimpour
{"title":"An improvement in the design process of sustainable peak power rating transformer for solar utility","authors":"Emir Yükselen ,&nbsp;Ebrahim Rahimpour","doi":"10.1016/j.ijepes.2025.110928","DOIUrl":"10.1016/j.ijepes.2025.110928","url":null,"abstract":"<div><div>The transformer industry faces critical challenges, such as maintaining reliable production while meeting rising sustainability requirements. This paper introduces a significant advancement in the design and optimization of a transformer dedicated to a photovoltaic power plant, which exhibits a unique loading cycle distinct from standard power and distribution transformers. The main aim is to minimize the carbon footprint by enhancing the design process to improve operational performance, efficiency, and functional reliability. Such upgrades are essential for transitioning to a zero-emission electricity system and developing green energy projects.</div><div>In this paper, a transformer has been studied using a combination of electrical design and 3D finite element method simulation to evaluate various design parameters. An optimization study has been conducted using an innovative multi-objective genetic algorithm utilizing a cost function that factors in size and material costs to identify the most efficient and cost-effective design solutions.</div><div>The proposed design method was then validated through thermal model simulations and experimental tests based on the photovoltaic load cycle. A comparison of critical thermal parameters directly affecting the transformer’s lifetime and reliability was also drawn. The results were consistent with the expected outcomes, confirming the effectiveness and reliability of the proposed design methodology.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"170 ","pages":"Article 110928"},"PeriodicalIF":5.0,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694602","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}
引用次数: 0
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