International Journal of Electrical Power & Energy Systems最新文献

{"title":"Risk and resilience based residential electric vehicle integration framework for restoration of modern power distribution networks","authors":"Abdullah Ali M. Alghamdi ,&nbsp;Dilan Jayaweera","doi":"10.1016/j.ijepes.2025.110690","DOIUrl":"10.1016/j.ijepes.2025.110690","url":null,"abstract":"<div><div>The restoration of power distribution networks following extreme events is a critical challenge, particularly when traditional utility-owned mobile power sources (MPSs) are limited in number, capacity, and deployment flexibility. Residential electric vehicles (EVs) present a scalable and decentralized alternative due to their widespread availability and bidirectional charging capabilities. However, the large-scale integration of non-predetermined residential EVs into power restoration remains under-explored. This paper proposes the Individually-Owned EV Integration Innovative Framework (IIIF) to optimize residential EV participation in grid restoration. The IIIF introduces a novel EV user behaviour model, suggesting a 24% likely improvement in participation prediction accuracy used in the restoration process and leading to more effective EV mobilization. Additionally, a dynamic clustering framework is integrated to enhance EV allocation to public EV charging points (EVCPs), increasing EVCP utilization and optimizing EV distribution to reduce congestion and waiting times. The IIIF also incorporates an advanced EV dispatch model, which optimizes routing decisions to minimize power consumption and enhance discharging efficiency, leading to improved power delivery compared to conventional strategies. To ensure resilience, the IIIF integrates a risk assessment and backup strategy, activating MPSs when EVs alone are insufficient to meet restoration demand. Performance evaluation on the IEEE 123-bus system demonstrates that the IIIF reduces power restoration time by 27% and decreases total operational costs by 19% compared to conventional EV-based and MPS-only restoration approaches. These findings establish the IIIF as a benchmark framework for resilient, cost-effective, and large-scale EV integration in power distribution network restoration.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"168 ","pages":"Article 110690"},"PeriodicalIF":5.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924725","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 hybrid model for short-term offshore wind power prediction combining Kepler optimization algorithm with variational mode decomposition and stochastic configuration networks","authors":"Bingbing Yu ,&nbsp;Yonggang Wang ,&nbsp;Jun Wang ,&nbsp;Yuanchu Ma ,&nbsp;Wenpeng Li ,&nbsp;Weigang Zheng","doi":"10.1016/j.ijepes.2025.110703","DOIUrl":"10.1016/j.ijepes.2025.110703","url":null,"abstract":"<div><div>With the burgeoning development of the wind power industry, the significance of wind power forecasting in enhancing electricity generation efficiency, minimizing energy waste, and improving electrical grid management is increasingly highlighted. To enhance the stability and accuracy of wind power forecasting, a hybrid model integrating Kepler optimization algorithm (KOA), variational mode decomposition (VMD), and stochastic configuration network (SCN) is proposed. Firstly, the series of wind power data is decomposed using the VMD method optimized by the KOA, aiming to smooth the wind power series while preserving its inherent characteristics. Subsequently, permutation entropy (PE) is employed to order and reconstruct the decomposed wind power subsequences, with the selection of input features by the maximal information coefficient (MIC) and autocorrelation function (ACF). Following this, KOA is utilized to optimize the parameters of the SCN model, further enhancing the predictive performance of the SCN. Finally, a multi-seasonal and multi-scenario wind power forecasting analysis is conducted by using an actual data set from an offshore wind farm in China. Compared with the basic VMD model, the data decomposition efficiency of the optimized VMD model has been improved by 28.86%. Meanwhile, the prediction average error of the proposed model has decreased by 0.1385 compared with the basic prediction<!--> <!-->model. The results demonstrate that the proposed hybrid model exhibits superior stability and accuracy in short-term wind power prediction.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"168 ","pages":"Article 110703"},"PeriodicalIF":5.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924726","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":"Enhancing DC microgrid security: A comprehensive review of protection challenges and solutions","authors":"Rachita R. Sarangi ,&nbsp;Prakash K. Ray ,&nbsp;Asit Mohanty ,&nbsp;Shafi Khadem ,&nbsp;Sandipan Patra","doi":"10.1016/j.ijepes.2025.110687","DOIUrl":"10.1016/j.ijepes.2025.110687","url":null,"abstract":"<div><div>DC microgrids (DCMG) hold great potential for effectively managing energy in residential, commercial, and industrial settings due to their ease of integration, distribution, economic advantages, energy efficiency, flexibility, and reliability. However, the adoption of DCMG is significantly hindered by the intermittent nature of Renewable Energy Sources (RES) and the lack of efficient and accurate commercial protection strategies which could enhance reliability and stability. To encourage new researchers and technology developers to create DCMG protection schemes, standards and technologies similar to those in AC microgrids (ACMG), a thorough evaluation of the existing DCMG protection methods is essential. It is crucial to propose appropriate solutions and future directions for challenges encountered in DCMG protection schemes, such as bidirectional power flow, grounding, and high-impedance fault protection. This study also emphasises the importance of intelligent and self-healing power systems, highlighting the growing significance of incorporating Artificial Intelligence (AI) into a cyber secured system. A comprehensive literature review and analysis of existing approaches were conducted to identify research gaps and future opportunities. The goal is to motivate academics, regulators, and policymakers to evaluate and establish standards for DCMG protection that are analogous to those in ACMG, ensuring the stability and durability of DCMG systems.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"168 ","pages":"Article 110687"},"PeriodicalIF":5.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906851","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":"CNN-based state prediction for a varying number of storage in economic dispatch","authors":"Xiang Pan ,&nbsp;Wei Lin ,&nbsp;Linze Yang ,&nbsp;Yanfang Mo","doi":"10.1016/j.ijepes.2025.110590","DOIUrl":"10.1016/j.ijepes.2025.110590","url":null,"abstract":"<div><div>Economic dispatch (ED) is essential for power system operations. However, the large-scale energy storage (ES) integration introduces numerous binary state variables into ED formulations. Although relaxation-based methods and machine learning techniques have been developed to alleviate the computational burden from ES binary variables, the former is restricted due to critical application conditions that may not hold in practice, and the latter cannot deal with a varying number of ES in the real-world deregulation of electricity markets. To this end, this paper proposes a data-driven state prediction method for a varying number of ES in an ED problem. A symmetrical convolutional neural network (CNN) structure is leveraged to learn the states from the concatenation of the input load and operating limits. Such an architecture effectively extracts multi-scale features through multiple convolutional layers, pooling, and upsampling operations. It excels at handling the coupled relationships between input loads and states with information from other time intervals. A projection layer is further designed to adjust the CNN outputs to handle a varying number of ES. The effectiveness of the proposed method is demonstrated in the IEEE 118-bus test system and a real-world 661-bus utility system.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"168 ","pages":"Article 110590"},"PeriodicalIF":5.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906852","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":"Short-term scheduling optimization of battery electric buses in the context of sustainable energy resources under uncertainty","authors":"Muhammad Ahmad Iqbal ,&nbsp;Ismail I. Almaraj","doi":"10.1016/j.ijepes.2025.110715","DOIUrl":"10.1016/j.ijepes.2025.110715","url":null,"abstract":"<div><div>With growing emphasis on sustainability goals, particularly in adopting electric vehicles (EVs) as a public transportation mode, battery electric buses (BEBs) have attained significant market attention. However, a critical obstacle lies in efficiently assigning BEBs to suitable charging stations (CSs) during daily transit operations, which still need enough space to be filled. The primary goal is to allocate BEB to the best CSs while focusing on increasing overall profit by serving the grid and passenger needs effectively. To solve this issue, several factors are considered, including transit hours, sustainable energy resources, state-of-charge (SOC), vehicle-to-grid (V2G) and grid-to-vehicle (G2V) service trading, bus, CS capacity, data-sharing, and route dynamics. A mixed-integer linear programming (MILP) model framework is constructed, utilizing energy network flows and operational-level information to optimize short-term scheduling. Due to the large-scale nature of the problem, metaheuristic algorithms are used to solve the proposed model. The objective seeks to maximize the profit of the transportation company (TC), which owns both CSs and BEBs, by optimally scheduling the CS selection for its in-transit buses. In addition, the model simultaneously considers the peak hours of energy capacities and its transactions. The model is enhanced through a robust counterpart formulation, incorporating a realistic case study that addresses uncertainties in critical parameters such as electricity selling prices and purchasing costs. By dynamically optimizing charging station selection and energy trading strategies, the robust model successfully maintains 90 % of the deterministic profit under independent price fluctuations (box uncertainty) and 78 % under correlated market risks (polyhedral uncertainty). Consequently, the proposed framework effectively balances operational efficiency with resilience against price volatility, supporting reliable scheduling operations while optimizing renewable energy integration and enhancing grid flexibility.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"168 ","pages":"Article 110715"},"PeriodicalIF":5.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906850","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":"Evaluating the substitution of coal-fired power plants with renewable energy and multi-temporal energy storage for decarbonization","authors":"Jingyu Liu ,&nbsp;Lanyi Wei ,&nbsp;Ming Zhou ,&nbsp;Zhi Zhang ,&nbsp;Bo Yuan ,&nbsp;Zhaoyuan Wu","doi":"10.1016/j.ijepes.2025.110716","DOIUrl":"10.1016/j.ijepes.2025.110716","url":null,"abstract":"<div><div>Replacing coal-fired power plants (CFPPs) with variable renewable energy (VRE) and energy storage is a critical pathway to achieving carbon neutrality. However, a key challenge lies in how to balance multi-timescale adequacy and security to achieve the optimal portfolio of CFPP and energy storage resources. Therefore, this paper proposes a comprehensive assessment approach for the decision-making of VRE and energy storage integration, as well CFPP phase-out, taking power supply adequacy and frequency security into account. In addition, a comprehensive evaluation index system is constructed from technical, economic, and environmental perspectives to provide a reference for striving for the optimal generation and energy storage portfolio under the premise of ensuring the safe and stable operation during decarbonization. Considering frequent and large volatility of VRE generation leads to the risks of seasonal electric energy shortage and short-term power imbalance, even frequency insecurity, the assessment model decouples multi-timescale power and energy balance constraints into short-term power and long-term energy balances to achieve whole timescale power supply adequacy, fully leveraging the synergistic coordination of diverse resources across different timescales, meanwhile the rate of change of frequency (RoCoF) as an indicator of frequency security is embedded to decide CFPP phase-out. Case studies based on real-world datasets demonstrate that a strategic substitution of VRE and multi-temporal energy storage for CFPPs can significantly reduce the cost of low-carbon transition by 17% and promote a 52% reduction in carbon emissions compared to scenarios without CFPP substitution, while achieving a load shedding rate below 0.1% and a 98% VRE accommodation rate.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"168 ","pages":"Article 110716"},"PeriodicalIF":5.0,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899447","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":"Unit commitment with bidirectional ramping constraints of flexibility retrofit thermal power unit","authors":"Jingxiao Jiang ,&nbsp;Yanran Zhu ,&nbsp;Ying Wang ,&nbsp;Xiaorui Guo ,&nbsp;Jianhu Lv ,&nbsp;Jianxiang Qin ,&nbsp;Xingyun Yan ,&nbsp;Kaifeng Zhang","doi":"10.1016/j.ijepes.2025.110709","DOIUrl":"10.1016/j.ijepes.2025.110709","url":null,"abstract":"<div><div>Flexibility retrofits of thermal power units have been widely implemented to improve power system balancing. These retrofits enable operation below the regular minimum output, known as deep peak regulation, to accommodate increasing renewable energy integration. Due to the inherent characteristics of thermal power units, ramp-up rates and ramp-down rates generally differ. Existing studies often overlook this difference by assuming equal ramping rates. To bridge this gap, this paper systematically models and analyses bidirectional ramping constraints in unit commitment (UC) models, providing a more accurate and realistic representation of the ramping process. Specifically, this paper proposes three innovative bidirectional ramping models. The first is the all-period fixed bidirectional ramping (AFBR) model, which assumes a constant ramp rate across all periods. The second model is the interperiod dynamic bidirectional ramping (InterDBR) model, which captures variations in ramping rates across different peak regulation states, including deep peak regulation with oil (DPRO), deep peak regulation (DPR), and regular peak regulation (RPR). These ramping rate adjustments are restricted to interperiod scheduling periods. The third is the intraperiod dynamic bidirectional ramping (IntraDBR) model, which considers transitions between DPRO, DPR, and RPR within an intraperiod scheduling period, thereby reflecting actual operational conditions of the unit. We validate the effectiveness of the proposed bidirectional ramping models on the IEEE 118-bus system and a real-world Ningxia Power Grid system. Results demonstrate that bidirectional ramping models more accurately and effectively support dispatch scheduling decisions, improving system security and economic efficiency.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"168 ","pages":"Article 110709"},"PeriodicalIF":5.0,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899446","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 load margin calculation method considering optimized reactive power support","authors":"Yuejian Wu,&nbsp;Xiaoming Dong,&nbsp;Tianguang Lu,&nbsp;Shunxiang Yu,&nbsp;Chengfu Wang,&nbsp;Zhengshuo Li","doi":"10.1016/j.ijepes.2025.110706","DOIUrl":"10.1016/j.ijepes.2025.110706","url":null,"abstract":"<div><div>With the development of modern low-carbon power systems, emerging ubiquitous reactive power supply gets more attention in improving power transmission capability. However, reactive power optimization for traditional and emerging forms of reactive power sources is ignored in conventional methods to calculate load margin (LM), which could cause conservative results or even decision-making misplay. Accordingly, this study proposes an improved LM calculation model incorporating optimal reactive power flow (ORPF) and continuation power flow (CPF) models, abbreviated as ORCPF. An approximate linear mixed-integer ORPF is established and integrated into Newton iterations and predictor–corrector steps. The ORPF model involves the Jacobian Matrix as the linearization of power balance relations and generates an approximate optimal solution in which the errors are corrected by following Newton iterations. A constraint interval adjustment scheme is presented to guarantee the errors under control. Meanwhile, the proposed step size control and rollback framework ensure robust iteration and accurate calculation for CPF. Employment of the LU decomposition method allows the CPF predictor coupling with the sensitivity calculation of voltage to reactive power changes (VQ Sensitivity), decreasing the computational burden in identifying the limit-induced bifurcation (LIB). The included case studies based on four test systems demonstrate the effectiveness of the proposed approaches.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"168 ","pages":"Article 110706"},"PeriodicalIF":5.0,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899448","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":"Green-fitting scheduling equilibrium model of virtual power plant based on cooperative game with improved shapley value under new-type power system","authors":"Shuo Zhang ,&nbsp;Luming Pang ,&nbsp;Yingzi Li ,&nbsp;Yuanli Chen ,&nbsp;Kangxiang Li ,&nbsp;Meixia Zheng","doi":"10.1016/j.ijepes.2025.110704","DOIUrl":"10.1016/j.ijepes.2025.110704","url":null,"abstract":"<div><div>The new-type power system serves as a basic support for achieving the dual carbon goals of China. Simultaneously, the virtual power plant (VPP) emerges as a significant mode for aggregating decentralized resources on the demand side, which has a guaranteed role in the safe and green operation of the new-type power system. This paper firstly analyzes the green operation framework of VPP, which includes photovoltaic, distributed wind power, gas turbine, energy storage and load. Furthermore, considering the economic characteristics and green consumption characteristics of VPP, a dual-objective optimization scheduling model is constructed, aiming to maximize the VPP’s revenue and achieve the highest green-fitting degree between the VPP’s external power purchase curve and the green energy output curve in the power grid. To solve the model effectively, the improved particle swarm optimization algorithm has been employed. Then, the improved Shapley value method is applied to realize the fair and reasonable distribution of VPP internal income. Finally, the validity of the model is verified by comparative scenarios and sensitivity analysis on the fluctuations of wind and photovoltaic power and Time-of-Use electricity price is conducted. The results show that considering both profits and green-fitting degree objectives can enhance the activity of VPP’s internal members and the overall revenue of the VPP.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"168 ","pages":"Article 110704"},"PeriodicalIF":5.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895617","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":"Long-term planning of Low-Voltage networks using reference network Models: Slovenian use case","authors":"Klemen Knez,&nbsp;Leopold Herman,&nbsp;Marjan Ilkovski,&nbsp;Boštjan Blažič","doi":"10.1016/j.ijepes.2025.110707","DOIUrl":"10.1016/j.ijepes.2025.110707","url":null,"abstract":"<div><div>The increasing penetration of distributed energy resources (DERs), electric vehicles (EVs), and heat pumps (HPs) presents significant challenges for low-voltage (LV) distribution networks, requiring advanced planning methodologies to ensure grid reliability and cost-effectiveness. However, existing studies primarily focus on individual network simulations, which are computationally intensive and lack scalability. Moreover, most research relies on synthetic network models rather than real-world distribution system operator (DSO) data, limiting practical applicability. This study addresses these gaps by developing a Reference Network Model (RNM) tailored to the Slovenian LV distribution system. The first objective is to establish reference radial network models based on real DSO data, enabling simulation generalization across the entire distribution network. Using k-medoids clustering, LV networks are categorized into representative groups, facilitating efficient analysis without exhaustive individual network simulations. The second objective is to develop a generalization methodology that extrapolates simulation results from reference networks to the entire LV distribution system. Unlike conventional RNM applications, this approach integrates real-world Slovenian DSO data and incorporates scenario-based reinforcement planning to address the evolving impact of DERs, EVs, and HPs. A key result is cost-benefit analysis, which evaluates investment requirements and operational savings, offering insights for policymakers and DSOs to optimize network planning. Simulation results indicate that most required reinforcements will focus on LV line upgrades, particularly in regions with long feeders and high demand growth. The findings demonstrate that the proposed methodology significantly reduces computational burdens while maintaining high accuracy in predicting network reinforcement needs, making it a scalable and practical tool for long-term distribution system planning.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"168 ","pages":"Article 110707"},"PeriodicalIF":5.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899445","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}