Electric Power Systems Research最新文献_第4页

Probabilistic analysis of electricity load forecasting errors 电力负荷预测误差的概率分析

IF 4.2 3区工程技术

Electric Power Systems Research Pub Date : 2026-06-01 Epub Date: 2026-02-05 DOI: 10.1016/j.epsr.2026.112807

Eduardo Caro, Jesús Juan

{"title":"Probabilistic analysis of electricity load forecasting errors","authors":"Eduardo Caro, Jesús Juan","doi":"10.1016/j.epsr.2026.112807","DOIUrl":"10.1016/j.epsr.2026.112807","url":null,"abstract":"<div><div>Accuracy metrics such as MAE, RMSE and WAPE are widely used to evaluate forecasting models in energy systems, yet they are commonly interpreted as fixed values despite being computed from stochastic, temporally correlated errors. This practice leads to an underestimation of the statistical variability and sampling dispersion of these accuracy metrics.</div><div>This paper introduces a probabilistic framework for analyzing accuracy metrics under temporal dependence. Using Taylor-based approximations and the covariance structure of dependent errors, we derive closed-form expressions for the mean and variance of MAE and WAPE. Two practical methods for constructing confidence intervals are proposed: (i) a theoretical approach based on the autocorrelation function of absolute errors, and (ii) an aggregation-based method that reduces short-term dependence through weekly averaging.</div><div>Monte Carlo simulations validate the proposed intervals and quantify the impact of different dependence patterns. A ten-year case study of hourly electricity demand in Spain shows that standard methods underestimate error variability, while the proposed ones correctly detect significant changes in forecast performance.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"255 ","pages":"Article 112807"},"PeriodicalIF":4.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189655","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

Two-stage robust optimal capacity configuration method for wind solar hydrogen storage system considering source-side uncertainty 考虑源侧不确定性的风能太阳能储氢系统两阶段鲁棒优化容量配置方法

IF 4.2 3区工程技术

Electric Power Systems Research Pub Date : 2026-06-01 Epub Date: 2026-02-12 DOI: 10.1016/j.epsr.2026.112822

Miaomiao Ma , Rui Zhang , Xingyao Guo , Guobin Fu , Yujie Ding

{"title":"Two-stage robust optimal capacity configuration method for wind solar hydrogen storage system considering source-side uncertainty","authors":"Miaomiao Ma , Rui Zhang , Xingyao Guo , Guobin Fu , Yujie Ding","doi":"10.1016/j.epsr.2026.112822","DOIUrl":"10.1016/j.epsr.2026.112822","url":null,"abstract":"<div><div>The planning of wind solar hydrogen storage systems (WS-HSS) is challenging due to source-side uncertainties and inadequate load-side regulation, often leading to over-investment and unreliable supply. Therefore, this paper proposes a two-stage robust optimization (RO) method for capacity configuration of WS-HSS to balance the economic efficiency and robustness of the system. First, source-side uncertainty is fully captured by modeling its fluctuation range with a polyhedral uncertainty set. Then, demand response (DR) is introduced to reduce capacity requirements for critical equipment and the total costs. Subsequently, a two-stage RO model is constructed to minimize the total cost while ensuring robustness of the system. To solve the model efficiently, a column-and-constraint generation (C<span><math><mo>&</mo></math></span>CG) algorithm is employed to dynamically search key scenarios representing uncertainty, thereby improving computational efficiency. Case study demonstrates that the proposed approach achieves a balance between cost-effectiveness and robustness, reducing the capacity of the energy storage system by approximately 20% and lowering the total cost by 7.08% of WS-HSS.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"255 ","pages":"Article 112822"},"PeriodicalIF":4.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189660","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

MILP model for reliability assessment of active distribution networks with microgrids 微电网有源配电网可靠性评估的MILP模型

IF 4.2 3区工程技术

Electric Power Systems Research Pub Date : 2026-06-01 Epub Date: 2026-02-04 DOI: 10.1016/j.epsr.2026.112811

Gederson Alvaro da Cruz , Sérgio Haffner , Mariana Resener

引用次数: 0

A day-ahead building load forecasting method based on IBKA-CNN-BiLSTM-Attention model 基于IBKA-CNN-BiLSTM-Attention模型的日前建筑物负荷预测方法

IF 4.2 3区工程技术

Electric Power Systems Research Pub Date : 2026-06-01 Epub Date: 2026-02-12 DOI: 10.1016/j.epsr.2026.112837

Zengxi Feng , Zhenghao Zhu , Hang Ge , Lang Han , Anjun Zhao , Wei Quan , Xiao Xue

{"title":"A day-ahead building load forecasting method based on IBKA-CNN-BiLSTM-Attention model","authors":"Zengxi Feng , Zhenghao Zhu , Hang Ge , Lang Han , Anjun Zhao , Wei Quan , Xiao Xue","doi":"10.1016/j.epsr.2026.112837","DOIUrl":"10.1016/j.epsr.2026.112837","url":null,"abstract":"<div><div>Forecasting building energy consumption is essential for improving energy efficiency and enabling energy scheduling. However, existing energy consumption prediction models face two main issues: (1) Although some building energy consumption prediction models have demonstrated good predictive performance, they may have limitations when confronted with the complex nonlinear and time-dependent characteristics of building load data. (2) Existing studies mainly focus on comparing the performance of different prediction models, with limited attention given to the impact of optimizing combinations of hyperparameters within the models. Therefore, this study proposes a CNN-BiLSTM-Attention hybrid prediction model optimized by the improved Black Kite Algorithm (IBKA). The improved IBKA is then applied to optimize the hyperparameters under different combinations. Simulation results indicate that the building energy consumption prediction model optimized by IBKA algorithm outperforms models optimized by other algorithms, such as PSO or BWO. The study further demonstrates that optimizing six hyperparameters, including learning rate, the number of bidirectional long short-term (BiLSTM) hidden layers, and the maximum number of iterations, leads to optimal prediction accuracy. On Dataset 1, compared with models optimized by other algorithms, the proposed model achieves lower root mean square error (RMSE) and mean absolute error (MAE) values, with an R² of 98.74% under the same six-hyperparameter configuration. In addition, on Dataset 2, the proposed model also exhibits good prediction performance for the test days from summer, transitional, and winter seasons, indicating reliable prediction capability.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"255 ","pages":"Article 112837"},"PeriodicalIF":4.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189602","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

Voltage out-of-limit traceability simulation model of distribution network based on graph convolutional network and causal reasoning 基于图卷积网络和因果推理的配电网电压超限溯源仿真模型

IF 4.2 3区工程技术

Electric Power Systems Research Pub Date : 2026-06-01 Epub Date: 2026-02-09 DOI: 10.1016/j.epsr.2026.112766

Wen He , Rui Ma , Jiangbo Sha , Dongge Zhu , Shuang Zhang

{"title":"Voltage out-of-limit traceability simulation model of distribution network based on graph convolutional network and causal reasoning","authors":"Wen He , Rui Ma , Jiangbo Sha , Dongge Zhu , Shuang Zhang","doi":"10.1016/j.epsr.2026.112766","DOIUrl":"10.1016/j.epsr.2026.112766","url":null,"abstract":"<div><div>Due to the high penetration of renewable energy sources in distribution networks, the problem of voltage limit violations has become increasingly prominent. Conventional methods relying on physical models and comprehensive measurement data struggle to address the challenges posed by the stochastic fluctuations of photovoltaics (PV) and frequent topology changes. In response, a hybrid provenance model integrating Graph Convolutional Networks with Causal reasoning (GCN+Causal) is proposed. The model characterizes time-varying coupling relationships among nodes using a dynamic adjacency matrix, incorporates counterfactual reasoning to quantify causal effects along fault propagation paths via double machine learning, and utilizes a spatio-temporal GCN-based feature encoder to handle non-Euclidean characteristics of electrical measurement data for feature extraction, thereby overcoming the limitations of traditional correlation-based analysis. In simulation tests on an IEEE node system, the proposed method improves the comprehensive performance of voltage anomaly localization to 93.2 % (a 21.5 % increase over conventional state estimation methods) and reduces the false alarm rate to 4.3 %. The experimental results demonstrate that the model accuracy remains at 87.6 % even with 30 % missing measurements and controls traceability time within 500 ms. The research outcome provides a novel analytical tool for enhancing the security of active distribution networks, and its causal interpretability facilitates the development of precise control strategies.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"255 ","pages":"Article 112766"},"PeriodicalIF":4.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189627","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 mathematical analysis combined with machine-learning optimal control method for bidirectional converter devices of urban rail transit system 城市轨道交通系统双向变换器装置数学分析与机器学习相结合的最优控制方法

IF 4.2 3区工程技术

Electric Power Systems Research Pub Date : 2026-06-01 Epub Date: 2026-01-16 DOI: 10.1016/j.epsr.2026.112715

Jiajun Duan , Leipeng Hao , Yongtao Wang , Xiuming Han , Xiong Wei , Tingting Wang

{"title":"A mathematical analysis combined with machine-learning optimal control method for bidirectional converter devices of urban rail transit system","authors":"Jiajun Duan , Leipeng Hao , Yongtao Wang , Xiuming Han , Xiong Wei , Tingting Wang","doi":"10.1016/j.epsr.2026.112715","DOIUrl":"10.1016/j.epsr.2026.112715","url":null,"abstract":"<div><div>This study proposes a Mathematical Analysis combined with Machine Learning (MAcML) optimal control method for bidirectional converters (BCs) in urban rail transit (URT) systems. To avoid the potential additional costs of a communication system, the MAcML method is designed for localized control of BCs. First, a mathematical analysis is performed to establish the underlying control mechanism—specifically, clustering URT daily operations into typical periods (e.g., workday peak, weekend flat) and reducing the BC control action space via a decoupled fast-search method. Subsequently, a machine learning approach is employed to predict unmeasured total energy consumption using local BC measurements. Finally, a dynamic model is constructed in a Controller-Hardware-in-the-Loop test bed to generate an optimal control-action table. Field tests on a real Chinese URT segment demonstrate that the proposed method achieves a 17.8 % reduction in net energy consumption compared to the natural droop control of BCs.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"255 ","pages":"Article 112715"},"PeriodicalIF":4.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980428","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 PV hosting capacity in distribution grids considering users’ voltage-regulation responsibility under dynamic Time-of-Use pricing 动态分时电价下考虑用户调压责任的配电网光伏装机容量分析

IF 4.2 3区工程技术

Electric Power Systems Research Pub Date : 2026-06-01 Epub Date: 2026-01-10 DOI: 10.1016/j.epsr.2026.112713

Jun Qi, Yi Xu, Da Jiang, Dan Zhou

{"title":"Analysis of PV hosting capacity in distribution grids considering users’ voltage-regulation responsibility under dynamic Time-of-Use pricing","authors":"Jun Qi, Yi Xu, Da Jiang, Dan Zhou","doi":"10.1016/j.epsr.2026.112713","DOIUrl":"10.1016/j.epsr.2026.112713","url":null,"abstract":"<div><div>Due to the inherent intermittency and variability of solar energy, the increasing penetration of photovoltaic (PV) generation brings significant challenges to distribution grid operation. To enhance PV hosting capacity, it is crucial to fully leverage user-side flexible resources (FRs) within Internet of Things (IoT) environments. This paper proposes a dynamic time‑of‑use (TOU) pricing mechanism designed to guide demand response (DR) and mitigate PV power fluctuations by emphasizing user-side responsibility and advantages in voltage regulation. A Stackelberg game framework between the distribution system operator (DSO) and users is established, and a power fluctuation penalty (PFP) term reflecting voltage-regulation responsibility (VRR) is incorporated into user-side objective functions. When user-side FRs adjust their electricity consumption according to day-ahead dynamic TOU tariffs, voltage violations that limit PV hosting capacity can be effectively alleviated. Simulation results demonstrate that power fluctuations can be smoothed by considering user-side VRR, and the PV hosting capacity will be significantly raised by dynamic TOU, compared with constant or peak-valley pricing schemes.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"255 ","pages":"Article 112713"},"PeriodicalIF":4.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980429","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

Optimal capacity allocation for wind farm grid-forming units based on Markov transition field and soft actor-critic algorithm 基于马尔可夫过渡场和软行为者评判算法的风电场并网机组最优容量分配

IF 4.2 3区工程技术

Electric Power Systems Research Pub Date : 2026-06-01 Epub Date: 2026-01-08 DOI: 10.1016/j.epsr.2025.112689

Xinglong Fang , Yang Hu , Ziqiu Song , Jizhen Liu

{"title":"Optimal capacity allocation for wind farm grid-forming units based on Markov transition field and soft actor-critic algorithm","authors":"Xinglong Fang , Yang Hu , Ziqiu Song , Jizhen Liu","doi":"10.1016/j.epsr.2025.112689","DOIUrl":"10.1016/j.epsr.2025.112689","url":null,"abstract":"<div><div>With the increasing penetration of renewable energy in power system,grid-forming (GFM) control has become an important method to improve system stability. Nevertheless, the optimal allocation of GFM units that harmonizes dynamic performance with system stability has not yet been adequately addressed. To this end, this paper focuses on the optimization of the proportion of GFM units in wind farms. First, a modeling method based on the Markov transition field-based (MTF) is proposed to represent the dynamic evolution of wind farms under multi-stationary operating conditions. Second, a composite capacity evaluation indicator that jointly accounts for system stability and economic cost is introduced, providing a quantitative basis for assessing the grid-support capability of GFM units and guiding optimal capacity allocation. Finally, the Soft Actor-Critic (SAC) algorithm in reinforcement learning (RL) for capacity allocation is introduced and verified under typical disturbance conditions such as voltage dips and sudden changes in grid strength. Experimental results demonstrate that the proposed method effectively reduces voltage and frequency fluctuations by more than 7% and accelerates system response time, enabling faster frequency recovery and superior voltage regulation.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"255 ","pages":"Article 112689"},"PeriodicalIF":4.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145915238","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

Coordinated power smoothing control for wind storage integrated system with physics-informed deep reinforcement learning 基于物理信息深度强化学习的风力存储集成系统功率平滑协调控制

IF 4.2 3区工程技术

Electric Power Systems Research Pub Date : 2026-06-01 Epub Date: 2026-01-09 DOI: 10.1016/j.epsr.2025.112707

Shuyi Wang , Huan Zhao , Yuji Cao , Zibin Pan , Guolong Liu , Gaoqi Liang , Junhua Zhao

{"title":"Coordinated power smoothing control for wind storage integrated system with physics-informed deep reinforcement learning","authors":"Shuyi Wang , Huan Zhao , Yuji Cao , Zibin Pan , Guolong Liu , Gaoqi Liang , Junhua Zhao","doi":"10.1016/j.epsr.2025.112707","DOIUrl":"10.1016/j.epsr.2025.112707","url":null,"abstract":"<div><div>The Wind Storage Integrated System with Power Smoothing Control (PSC) has emerged as a promising solution for efficient and reliable wind energy generation. However, existing PSC strategies exhibit several limitations. Many fail to capture the cooperative interactions and distinct control frequencies between wind turbines and battery energy storage systems (BESSs). In addition, the impacts of wake effects and battery degradation costs are often overlooked. This paper proposes a novel multi-agent coordinated control framework to address these challenges, which explicitly integrates a wake model and a battery degradation model to construct a more realistic operating environment. The problem is formulated as a multi-agent Markov decision process (MMDP), where the wind farm and the BESS agents pursue complementary objectives to achieve optimal control. Furthermore, a Physics-informed Neural Network-assisted Multi-agent Deep Deterministic Policy Gradient (PAMA-DDPG) algorithm is introduced, embedding a partial differential equation of power fluctuation as a physics-guided loss term to accelerate learning and enhance physical consistency. Simulations using WindFarmSimulator (WFSim) in four scenarios demonstrate that the proposed method outperforms benchmark approaches, achieving an 11% increase in total profit and a 19% reduction in power fluctuation. These results effectively address the dual objectives of economic efficiency and grid reliability.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"255 ","pages":"Article 112707"},"PeriodicalIF":4.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145915241","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

Parameter optimization design of bidirectional CLLC resonant converter under variable frequency control 变频控制下双向CLLC谐振变换器参数优化设计

IF 4.2 3区工程技术

Electric Power Systems Research Pub Date : 2026-06-01 Epub Date: 2026-02-10 DOI: 10.1016/j.epsr.2026.112834

Dangshu Wang , Zelong Hou , Licong Zhao , Guangzhen Wu , Nongke Li , Pengpeng Xie , Xinxia Wang

{"title":"Parameter optimization design of bidirectional CLLC resonant converter under variable frequency control","authors":"Dangshu Wang , Zelong Hou , Licong Zhao , Guangzhen Wu , Nongke Li , Pengpeng Xie , Xinxia Wang","doi":"10.1016/j.epsr.2026.112834","DOIUrl":"10.1016/j.epsr.2026.112834","url":null,"abstract":"<div><div>The bidirectional CLLC resonant converter exhibits the capability for bidirectional energy flow, along with significant advantages of high efficiency and high power density. However, traditional parameter design methods suffer from complex procedures and high computational difficulty. To address this issue, this paper employs the Fundamental Harmonic Analysis (FHA) method to model the CLLC resonant converter, deriving its voltage gain characteristics and the conditions required for achieving soft-switching. Based on the gain characteristics and soft-switching constraints, a simplified resonant network parameter design methodology integrating mathematical analytical expressions and graphical analysis is proposed. This method optimizes the design process and significantly reduces the complexity of parameter design. A 500 W laboratory prototype was built using the optimized parameters. Experimental results demonstrate that the prototype meets the voltage gain requirements while achieving soft-switching operation, with a forward power transmission efficiency of up to 97.6%, thereby validating the correctness of the theoretical analysis and the practical feasibility of the parameter optimization design.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"255 ","pages":"Article 112834"},"PeriodicalIF":4.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189604","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