Electric Power Systems Research最新文献

Enhancing peak electricity demand forecasting for commercial buildings using novel LSTM loss functions

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-04-19 DOI: 10.1016/j.epsr.2025.111722

Heidi S. Nygård , Sigurd Grøtan , Kjersti Rustad Kvisberg , Leonardo Rydin Gorjão , Thomas Martinsen

{"title":"Enhancing peak electricity demand forecasting for commercial buildings using novel LSTM loss functions","authors":"Heidi S. Nygård , Sigurd Grøtan , Kjersti Rustad Kvisberg , Leonardo Rydin Gorjão , Thomas Martinsen","doi":"10.1016/j.epsr.2025.111722","DOIUrl":"10.1016/j.epsr.2025.111722","url":null,"abstract":"<div><div>The increasing integration of variable renewable energy necessitates new measures for grid balancing and cost optimization. Consumers can contribute through demand response, provided accurate predictions, particularly during peak periods. This study aims to enhance electricity demand forecasting using a Long Short-Term Memory (LSTM) neural network with two novel loss functions: Weighted Mean Squared Error (WMSE) and Negative Log Likelihood (NLL). WMSE emphasizes high-demand periods, while NLL captures prediction uncertainty. The model, trained on two years of hourly data for Oslo Airport Gardermoen (Norway), incorporates temporal features, historical demand, passenger numbers, and outdoor air temperature. Optimal model architecture is determined through grid search and cross-validation. Results reveal that top-performing model configurations have minimal architecture, suggesting that a simple model is sufficient for capturing temporal and seasonal demand variations. Models trained with WMSE and NLL demonstrate reliable peak predictions and valuable uncertainty estimations, with the top performing model achieving a Mean Absolute Percentage Error (MAPE) of 5.54 ± 1.00 %. Visual inspections confirm reproducible daily demand patterns, including characteristic dual peaks and weekday-weekend distinctions. This research demonstrates that LSTM models are effective and easy to use for electricity demand forecasts, empowering consumers in making informed decisions about flexibility management and demand response strategies.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"246 ","pages":"Article 111722"},"PeriodicalIF":3.3,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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-based scheduling optimization strategy with explainability enhanced multi-scenario photovoltaic forecasting

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-04-19 DOI: 10.1016/j.epsr.2025.111729

Haiteng Han, Xiangchen Jiang, Simin Zhang, Chen Wu, Shuyu Cao, Haixiang Zang, Guoqiang Sun, Zhinong Wei

{"title":"A risk-based scheduling optimization strategy with explainability enhanced multi-scenario photovoltaic forecasting","authors":"Haiteng Han, Xiangchen Jiang, Simin Zhang, Chen Wu, Shuyu Cao, Haixiang Zang, Guoqiang Sun, Zhinong Wei","doi":"10.1016/j.epsr.2025.111729","DOIUrl":"10.1016/j.epsr.2025.111729","url":null,"abstract":"<div><div>Photovoltaic (PV) power generation, as a crucial technology for addressing energy transition and climate change, is increasingly becoming essential for power systems. However, its inherent randomness and uncertainty challenge stable operation and economic efficiency. To address these, this paper proposes a risk-based scheduling optimization strategy with explainable multi-scenario photovoltaic forecasting to mitigate these uncertainties. First, by employing an enhanced Copula function and ISODATA clustering, multiple joint output scenarios are generated to capture PV uncertainty. Building on this, a Stacking regression model is utilized to improve forecasting accuracy, while Shapley Additive Explanations (SHAP) explainability analysis is incorporated to enhance the transparency and decision-making of the model. Furthermore, to optimize the dispatch strategy for PV generation, this paper introduces the GlueVaR risk measurement method, which combines the benefits of Value at Risk (VaR) and Conditional Value at Risk (CVaR), thereby refining risk management and increasing the reliability of decision-making. Case studies demonstrate that the proposed strategy enhances PV forecasting reliability, with the R² reaching 0.86, and improves model explainability through SHAP-based analysis. In addition, the GlueVaR-based risk scheduling reduces potential risk by approximately 6 %, while maintaining a balance between power system economy and stability.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"246 ","pages":"Article 111729"},"PeriodicalIF":3.3,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 improved dynamic model for overcurrent relays in protection of electrical networks: Addressing two-level fault currents

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-04-18 DOI: 10.1016/j.epsr.2025.111713

Salar Rashaei, Amin Yazdaninejadi

{"title":"An improved dynamic model for overcurrent relays in protection of electrical networks: Addressing two-level fault currents","authors":"Salar Rashaei, Amin Yazdaninejadi","doi":"10.1016/j.epsr.2025.111713","DOIUrl":"10.1016/j.epsr.2025.111713","url":null,"abstract":"<div><div>The paper aims to enhance the dynamic model of directional overcurrent relays (DOCRs), ensuring that it satisfies selectivity constraints not only for two-level faults but also for faults with resistance. To this end, a new scaling factor is introduced and integrated with the coefficient of fault current in the relay model which virtually increases the fault currents seen by the corresponding DOCR. In the proposed approach, this scaling factor is considered as an optimization variable and individual setting for the DOCR to create a proper distinction between fault currents and load currents. By doing so, this new variable facilitates the optimal relay coordination within a new framework that results in meeting selectivity constraints and reducing the relays operation times. However, the integration of this scaling factor alongside the coefficient of fault current in the model of relays may leads to unintentional tripping during normal conditions. Therefore, through an innovative modification in the logic of the DOCR, this challenge is addressed. Since the developed logic necessitates the use of numerical relays, the associated costs of replacing traditional relays with numerical ones should be taken into account. Therefore, the proposed coordination scheme is subsequently extended through a techno-economic model and the gray relational analysis (GRA) algorithm is employed to navigate the complex decision-making process from the Pareto front. The results demonstrate that replacing 25 % of the relays with numeric ones can satisfy selectivity requirements even in the presence of fault resistance, while the overall operation times of primary and backup relays can be reduced dramatically by up to 65 %. The proposed is tested not only on the IEEE 14-bus distribution network but also on the 30-bus network, validating its applicability across larger systems.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"246 ","pages":"Article 111713"},"PeriodicalIF":3.3,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 fault identification for multi-level relays using fault tree and user-defined inverse-time characteristics equation

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-04-18 DOI: 10.1016/j.epsr.2025.111672

Nana Chang , Guobing Song , Jiaheng Jiang

{"title":"Adaptive fault identification for multi-level relays using fault tree and user-defined inverse-time characteristics equation","authors":"Nana Chang , Guobing Song , Jiaheng Jiang","doi":"10.1016/j.epsr.2025.111672","DOIUrl":"10.1016/j.epsr.2025.111672","url":null,"abstract":"<div><div>The traditional inverse-time overcurrent (ITOC) protection setting method relies on a fixed topology, and the setting workload in large-scale networks is very large and difficult to adapt to topology changes. An adaptive fault identification (AFI) scheme is proposed, which is applicable to ring or large networks, and does not require prior knowledge of the primary/backup coordination relationships between relays or the stepwise setting of inverse-time characteristic parameters. First, the positive sequence fault component voltages (PSFCVs) with natural distribution characteristics is analyzed. By utilizing the communication channels in existing wide-area protection systems (WAPS), only the protection action signal (PAS) of primary protection is transmitted, generating a fault tree that automatically reflects the relationship between each relay and the fault point. Secondly, a user-defined inverse-time characteristic equation (ITCE) and an online adaptive method for setting parameters are provided. The calculated tripping time depends solely on the parameters of the individual lines. Finally, an IEEE 30-bus test system is built in PSCAD/EMTDC to verify the effectiveness of the proposed scheme under different network topologies and when distributed generation (DG) is connected.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"246 ","pages":"Article 111672"},"PeriodicalIF":3.3,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 load forecasting method based on heterogeneous network for integrated electricity and heat systems

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-04-18 DOI: 10.1016/j.epsr.2025.111725

Bohan Li, Xueping Li, Yao Cai, Qi Han

{"title":"A load forecasting method based on heterogeneous network for integrated electricity and heat systems","authors":"Bohan Li, Xueping Li, Yao Cai, Qi Han","doi":"10.1016/j.epsr.2025.111725","DOIUrl":"10.1016/j.epsr.2025.111725","url":null,"abstract":"<div><div>Accurate load forecasting in Integrated Electricity and Heat Systems (IEHS) is crucial for optimizing energy utilization and ensuring system stability. However, existing models struggle to capture the complex interdependencies between electricity and heat networks, leading to suboptimal predictions, particularly at coupling nodes. This study proposes a Heterogeneous Graph Attention Network-Gated Recurrent Unit (HAN-GRU) model within a Heterogeneous Network (HetNet) framework to enhance forecasting accuracy. By leveraging node-level and semantic-level attention mechanisms, HAN-GRU effectively integrates heterogeneous spatial dependencies and temporal correlations, prioritizing critical coupling nodes and optimizing information aggregation. The proposed model is validated on the Bali integrated energy system, as well as a 23-node radial heating network coupled with IEEE 33-bus and IEEE 118-bus power distribution networks. Simulation results demonstrate that HAN-GRU significantly outperforms baseline models in both accuracy and efficiency, highlighting its potential as a scalable and interpretable solution for multi-energy system forecasting.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"246 ","pages":"Article 111725"},"PeriodicalIF":3.3,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Non-steady-state modelling method for multi-material temperature field in dry-type transformers based on Proper Orthogonal Decomposition

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-04-18 DOI: 10.1016/j.epsr.2025.111671

Zeyi Zhang, Haijuan Lan, Jiahao Gong, Xiongwen Xu, Wenhu Tang

{"title":"Non-steady-state modelling method for multi-material temperature field in dry-type transformers based on Proper Orthogonal Decomposition","authors":"Zeyi Zhang, Haijuan Lan, Jiahao Gong, Xiongwen Xu, Wenhu Tang","doi":"10.1016/j.epsr.2025.111671","DOIUrl":"10.1016/j.epsr.2025.111671","url":null,"abstract":"<div><div>Power transformers play a crucial role in power systems, and the accurate analysis of transformer temperature fields, particularly hot-spot temperatures, is a key factor in ensuring the stable operation of transformers. A method for rapid prediction of temperature fields in fluid–structure interaction problems based on POD is presented. An SCB11-800/10kV dry-type transformer serves as the research subject, with both a full-order model and a reduced-order model constructed to solve for the temperature field and its unsteady temperature rise process at a given inlet wind speed. The discontinuity in heat flux density at the junctions of different materials is addressed by creating virtual vertices at these points, enabling the handling of heat transfer in multi-material reduced-order models. Steady-state temperature fields and the unsteady temperature rise process of hot-spots are used as indicators to validate the accuracy and efficiency of the proposed reduced-order method by computing the spatio-temporal distribution of transformer temperature fields at different load rates. Results demonstrate that reduced-order computations based on POD not only ensure the computational accuracy but also enhance the computational speed, suggesting a new effective method for the rapid prediction of the temperature field, especially the hot-spot temperature under different loading rates of the transformer.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"246 ","pages":"Article 111671"},"PeriodicalIF":3.3,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Analysis of space charge and electric field behavior in LDPE and XLPE insulation for multi-layer HVDC extruded cables

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-04-17 DOI: 10.1016/j.epsr.2025.111714

Reza Sobhkhiz Juybari, Amir Abbas Shayegani Akmal, Kourosh Khalaj Monfared

{"title":"Analysis of space charge and electric field behavior in LDPE and XLPE insulation for multi-layer HVDC extruded cables","authors":"Reza Sobhkhiz Juybari, Amir Abbas Shayegani Akmal, Kourosh Khalaj Monfared","doi":"10.1016/j.epsr.2025.111714","DOIUrl":"10.1016/j.epsr.2025.111714","url":null,"abstract":"<div><div>As power networks expand and the demand for long-distance and submarine power transmission grows, HVDC cables are becoming more common. Polymers are widely used for insulation, but the main challenge is the accumulation of space charge within the insulation, which hinders development. This study presents a two-dimensional model of an HVDC cable with multi-layer insulation. A refined bipolar charge transfer (BCT) model was employed in Comsol Multiphysics software to independently simulate space charge dynamics and electric field distribution in the Fresh LDPE and XLPE layers of the HVDC cable. The BCT model was modified, with the ion dissociation coefficient defined as a function of both electric field and temperature to optimize the simulation of cross-linking by-product dissociation in XLPE cables. The results reveal that the load current, the temperature gradient and the electric field affect space charge behavior. Additionally, the presence of heterocharge and homocharge alters the electric field distribution near the electrodes and within the midsection of the insulation. The research innovates by applying a modified bipolar charge transport model to study space charge behavior and electric field variations in multi-layer HVDC cable insulation under realistic conditions.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"246 ","pages":"Article 111714"},"PeriodicalIF":3.3,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Short-term photovoltaic power prediction based on CEEMDAN-PE and BiLSTM neural network

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-04-17 DOI: 10.1016/j.epsr.2025.111706

Jianwei Liang , Liying Yin , Yanli Xin , Sichao Li , Yuqian Zhao , Tian Song

{"title":"Short-term photovoltaic power prediction based on CEEMDAN-PE and BiLSTM neural network","authors":"Jianwei Liang , Liying Yin , Yanli Xin , Sichao Li , Yuqian Zhao , Tian Song","doi":"10.1016/j.epsr.2025.111706","DOIUrl":"10.1016/j.epsr.2025.111706","url":null,"abstract":"<div><div>The volatility and uncertainty associated with photovoltaic (PV) energy production impose considerable challenges to the reliable operation of power grid systems. In order to address this challenge, it is necessary to obtain accurate forecasts of the output. In this paper, a hybrid model is proposed, which incorporates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), permutation entropy (PE) and bidirectional long short-term memory (BiLSTM) networks. Firstly, CEEMDAN is utilized to decompose PV power series into multiple intrinsic mode functions (IMFs) to reduce non-stationary and volatility impacts on prediction. Then PE is used to reconstruct the decomposed IMFs into new simplified sequences. This approach reduces computation complexity while effectively retaining fluctuation characteristics of original signals. Secondly, the minimum meteorological factors that have a great impact on PV power are identified through Pearson correlation analysis. Subsequently, a BiLSTM model is built to predict each reconstructed new sequence, final results are obtained by superimposing the reconstructed sequences, which exploits their bidirectional spatiotemporal correlations. Finally, model performance is evaluated with four evaluation metrics, outlier tests and Friedman tests. Results demonstrate that under different weather conditions, the CEEMDAN-PE-BiLSTM hybrid model exhibits higher accuracy, better generality, and stronger robustness compared to other similar models.</div><div>© 2017 Elsevier Inc. All rights reserved.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"246 ","pages":"Article 111706"},"PeriodicalIF":3.3,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 power quality disturbance classification method based on improved Shapelet method

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-04-17 DOI: 10.1016/j.epsr.2025.111673

Jiabin Luo, Anqi Jiang, Shuqing Zhang, Hao Hu

{"title":"A power quality disturbance classification method based on improved Shapelet method","authors":"Jiabin Luo, Anqi Jiang, Shuqing Zhang, Hao Hu","doi":"10.1016/j.epsr.2025.111673","DOIUrl":"10.1016/j.epsr.2025.111673","url":null,"abstract":"<div><div>The extensive grid connection of new energy and nonlinear power electronic devices has made power quality disturbance (PQD) problems more frequent, seriously affecting the stable operation of the power grid system. In response to the real-time response requirements of the research model of this problem, this study proposed an improved Shapelet method and applied it to the classification of PQDs. First, the concept of subsequence blocks was proposed, and the diversity of Shapelet was enhanced by multiple subsequences of multiple length ranges. In order to solve the problem of high time complexity of searching subsequence blocks, the length range of subsequence blocks was determined by the multi-scale extreme point peak distance method. This method uses the Black Kite Algorithm (BKA) to optimize the parameters of the Variable Mode Decomposition (VMD), decomposes the PQD signal into multiple modal components, and then screens out the disturbance components through permutation and combination entropy and calculates the average peak distance of the extreme points; secondly, a multiple loss function is used to optimize the quality of the selected subsequence blocks through the similarity loss and distance loss between subsequence blocks; finally, the K-means weight initialization method is used to accelerate the convergence of the model. Experimental results show that this method has an accuracy rate of 98.63 % in identifying PQDs in 16 simulated environments, with an average time consumption of 0.141 ms for per data sample. On the measured real data, the recognition accuracy rate is 98.20 % with a time consumption of 0.08 ms for per data sample. This method can provide a good choice for real-time PQD analysis of power grid systems.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"246 ","pages":"Article 111673"},"PeriodicalIF":3.3,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 new multi-objective human learning algorithm for environmental-economic dispatch of power systems

IF 3.3 3区工程技术

Electric Power Systems Research Pub Date : 2025-04-16 DOI: 10.1016/j.epsr.2025.111687

Chuanliang Cheng , Yuanjie Fang , Jing Wang , Chen Peng

{"title":"A new multi-objective human learning algorithm for environmental-economic dispatch of power systems","authors":"Chuanliang Cheng , Yuanjie Fang , Jing Wang , Chen Peng","doi":"10.1016/j.epsr.2025.111687","DOIUrl":"10.1016/j.epsr.2025.111687","url":null,"abstract":"<div><div>The aim of environmental-economic dispatch (EED) is to balance power supply and demand, optimizing both economic and environmental factors. It involves a complex multi-objective optimization with conflicting goals and numerous variables, where traditional methods face issues with local optima and solution diversity. To overcome these challenges, this paper introduces a new multi-objective human learning optimization (MOHLO) algorithm. The diversity of the pareto-optimal front is enhanced through a crowding distance metric, thereby reducing the risk of convergence to local optima. In addition, mechanisms for handling dominance resistant solutions and eliminating sub-optimal solutions based on the pareto approximate midpoint are introduced to identify and discard weak solutions, thus improving the overall quality of the solution set. Finally, the algorithm is tested on a EED model in power systems. By compared with the comparative algorithms, the proposed algorithm achieved a maximum improvement of 31.01% in pure diversity and 6.27% improvement in hypervolume. These enhancements significantly optimized the uniformity of the solution set and overall performance, providing superior decision support for power system dispatch.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"246 ","pages":"Article 111687"},"PeriodicalIF":3.3,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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