International Transactions on Electrical Energy Systems最新文献

{"title":"A Dynamic Adaptive Multilevel Optimization Algorithm for Comprehensive Evaluation of Vehicle–Grid Coordination Schemes","authors":"Tianlu Luo,&nbsp;Xiaowei Li,&nbsp;Yaxiong Wu,&nbsp;Houke Zhou,&nbsp;Lixiao Wang","doi":"10.1155/etep/4140372","DOIUrl":"https://doi.org/10.1155/etep/4140372","url":null,"abstract":"<p>With the deepening integration of electric vehicles and power grids, the scientific and precise evaluation of complex vehicle–grid coordination planning schemes has become a critical challenge. Traditional evaluation algorithms, constrained by fixed subjective weights and static assessment frameworks, struggle to meet the multilevel, multistakeholder decision-making requirements of modern energy systems. To address this gap, this study proposes a Dynamic Adaptive Multilevel Optimization Evaluation (DYNAMO) algorithm. The DYNAMO algorithm introduces an innovative dynamic adaptive weighting mechanism that synergistically integrates expert knowledge with data-driven dispersion metrics. It dynamically adjusts the balance between subjective and objective weights using adaptive factors derived from indicator variability. Furthermore, the algorithm enhances robustness through the adoption of an adaptive Minkowski distance metric and a nonlinear score mapping strategy. Experimental results demonstrate that the proposed algorithm effectively captures the comprehensive performance profiles of alternative schemes, accurately identifying their core strengths and weaknesses. Notably, under 5% data perturbation, the score variation of the DYNAMO algorithm (7.40%) is significantly lower than that of the AHP-TOPSIS algorithm (21.84%), demonstrating its superior reliability and resilience in real-world applications.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/4140372","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of the Friendliness of Live Working on Distribution Network Based on AHP-EWM Method","authors":"Teng Yang,&nbsp;Wen-Juan Zhang,&nbsp;Xu-Qiang Gong,&nbsp;Yu-Hang Ou,&nbsp;Li Huang","doi":"10.1155/etep/2113945","DOIUrl":"https://doi.org/10.1155/etep/2113945","url":null,"abstract":"<p>The traditional evaluation of the friendliness of live working on distribution networks relies on construction experience. This approach is not only susceptible to individual subjective factors, with the inherent risk of judgment errors, but also presents a significant challenge in establishing a unified quantitative standard. Therefore, the study analyzes the method and process of live working on distribution networks and identifies six types of evaluation indicators, including the structure of the distribution network frame, pole paths, same-pole erection, pole head and lead layout, equipment installation, and metal fittings. Then, the subjective and objective weights of each indicator are calculated based on the analytic hierarchy process–entropy weight method (AHP–EWM) method, and finally, the friendliness of the live working plan is calculated through the decision matrix, which realizes the reliable evaluation of the friendliness of the live working on the distribution network. Taking the live working construction of a distribution network line as an example, the assessment results using the AHP–EWM method are consistent with the actual operation situation, which verifies the reliability of the method. The assessment method of friendly degree of live working on distribution network based on the AHP–EWM method not only helps to optimize the spatial structure of distribution network but also improves the safety of live working on distribution network.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/2113945","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148462729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal Relay Coordination in Renewable Microgrids Using Multiobjective Optimization","authors":"Shahimol Basheer,&nbsp;K. C. Sindhu Thampatty","doi":"10.1155/etep/5719432","DOIUrl":"https://doi.org/10.1155/etep/5719432","url":null,"abstract":"<p>The penetration of distributed generation (DG) into distribution networks has transformed traditional protection philosophies, often resulting in protection miscoordination due to bidirectional fault currents and varying system topologies. This work presents an enhanced optimal coordination strategy for directional overcurrent relays (DOCRs) in a renewable-assisted microgrid. The proposed framework evaluates four multiobjective optimization algorithms—Nondominated Sorting Genetic Algorithm-II (NSGA-II), Multiobjective Particle Swarm Optimization (MOPSO), hybrid Genetic Algorithm–Sequential Quadratic Programming (GA-SQP) method, and Jaya algorithm. The objectives are as follows: (i) minimizing overall relay operating time, (ii) maximizing selectivity, and (iii) reducing coordination time interval (CTI). A modified IEEE 14-bus system with photovoltaic (PV) generation is used as the test environment. The comparative assessment highlights the strengths and trade-offs of the four algorithms for different fault conditions. Results show that Jaya provides the most balanced performance, while NSGA-II GA-SQP and MOPSO excel in protection reliability and computational efficiency, respectively. The findings demonstrate that advanced multiobjective optimization techniques can significantly enhance DOCR performance in dynamic microgrids.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/5719432","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148462728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scenario-Based Optimization Framework for Virtual Power Plant Operation Integrating Renewable Energy, Electric Vehicles, and Contingency Management Using the Harris Hawks Optimization Algorithm","authors":"Sripriya Ranganathan,&nbsp;M. Thirumalai,&nbsp;Ibraheem Al-Tarawneh,&nbsp;Mebratu Sintie Geremew","doi":"10.1155/etep/2300239","DOIUrl":"https://doi.org/10.1155/etep/2300239","url":null,"abstract":"<p>The rapid penetration of renewable energy resources, electric vehicles (EVs), and distributed energy resources (DERs) is significantly transforming the operation of modern distribution networks. Conventional radial distribution systems were primarily designed for unidirectional power transfer from centralized generating stations. However, large-scale integration of intermittent renewable generation and decentralized energy systems introduces operational challenges such as voltage deviation, feeder congestion, uncertainty in power generation, and increased network losses. To address these issues, this paper proposes a virtual power plant (VPP)–based energy management framework implemented on the IEEE 33-bus radial distribution system. The proposed system model uses grid power sources, PV and wind power plants, and EV battery charging and discharging capabilities for energy storage as its inputs. Performance evaluation is conducted based on the following eight scenarios: generation uncertainties, EVs’ penetration level, wind power uncertainty, peak load demand time, contingency, dual stress situations, and disruptions in communication or trading markets. The optimization problem of scheduling DERs and the EV’s activities using the HHO algorithm is solved by considering voltage stability. A simulation-based analysis shows that the developed VPP approach greatly improves the performance of distribution networks in various scenarios. The active power losses get decreased from 210.99 to 105.89 kW, while the reactive power losses become lower from 143.13 to 69.80 kVAr. Besides, the use of renewable energy sources and energy storage by EVs leads to the reduction of reliance on the existing grid. It is clear that the combined usage of VPPs, DERs, and EV energy storage systems may improve the functionality of future distribution systems.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/2300239","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148462814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Newton–Raphson-Based Optimizer for the Optimal Generation Allocation Problem Considering Operation and Maintenance Costs of Renewable Energy Sources","authors":"Ly Huu Pham,&nbsp;Nguyen Vu Quynh,&nbsp;Nguyen Van Minh,&nbsp;Thang Trung Nguyen,&nbsp;Quoc Trung Nguyen","doi":"10.1155/etep/1698833","DOIUrl":"https://doi.org/10.1155/etep/1698833","url":null,"abstract":"<p>A large share of the world’s electricity is produced from coal and similar energy sources. Although these sources are commonly used, they carry significant costs and environmental issues, including the emission of harmful gases that contribute to global warming. To foster a more sustainable future, it is crucial to focus on generating electricity cost-effectively while prioritizing low-emission strategies. These strategies are implemented in the combined economic emission load dispatch (EELD) problem and economic load dispatch (ELD) problem considering renewable energy sources (RESs) by allocating the optimal power output of thermal power and renewable energy plants. This study presents a new method, called the Newton–Raphson-based optimizer (NRBO), which draws inspiration from the Newton–Raphson approach. The NRBO thoroughly explores the optimal solutions search process using two main components: the Newton–Raphson Search Rule and the trap avoidance operator. Furthermore, the method utilizes several groups of matrices to enhance the results when addressing such problem variants. In the study, the proposed NRBO method has demonstrated its effectiveness through application in three distinct test systems, considering transmission line losses, generator limits, and overall system constraints. The proposed NRBO method was compared with other techniques in the literature via various criteria. The comparison results show that NRBO effectively explores and exploits options to find the optimal global solution, outperforming other methods while respecting all constraints and limits.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/1698833","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148381182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimisation of Power Flow in Renewable-Rich Distribution Networks Using Fibonacci Search and Heuristic Scheduling Algorithms","authors":"Tlotlollo Sidwell Hlalele","doi":"10.1155/etep/8516574","DOIUrl":"https://doi.org/10.1155/etep/8516574","url":null,"abstract":"<p>The integration of high penetration of renewable energy sources (RES) in distribution networks introduces challenges, such as voltage instability, intermittency and high power losses. A hybrid optimisation method is proposed, which uses a combination of the Fibonacci search algorithm (FSA) and heuristic scheduling for the optimal reactive power flow (ORPF) problem in a distribution network where a large amount of renewable generation exists. The FSA manages continuous variables, while the heuristic scheduling manages variables, such as load shifting, dispatch of energy storage and dispatch of demand response. The scenario is simulated on the IEEE 33-bus test network, which is enhanced by 1.8 MW of solar PV, 1.2 MW of wind generation and 500 kWh of battery storage. It shows that the active power loss was reduced from 202.67 to 121.85 kW (39.9%), and the active power loss in the bus voltage was reduced from 0.9131 to 0.9687 pu (the utilisation of the renewable energy was increased from 78.3% to 94.2%). The method is also the most computationally efficient and fastest when it comes to computing time, converging in just 2.35 s, whereas particle swarm optimisation (PSO) takes 5.87 s, and genetic algorithm (GA) requires 7.21 s with considerably lesser variance in the solutions. The Fibonacci search is chosen because it is a golden ratio–based search method that can consistently converge without the need for stochastic parameter tuning, and this can be an advantage in real-time energy management systems (EMS).</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/8516574","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148324426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing EV Load Uncertainty in Rental Car Fleets: Statistical Insights for Effective Charging Infrastructure","authors":"Ahmed Alanazi,&nbsp;Abdulaziz Almutairi","doi":"10.1155/etep/4350284","DOIUrl":"https://doi.org/10.1155/etep/4350284","url":null,"abstract":"<p>Many countries worldwide are actively working towards achieving the United Nations Sustainable Development Goals (UNSDGs) 2030, focusing on implementing sustainable solutions to environmental, economic, and social issues. One promising and environmentally friendly solution that has gained significant international attention is the use of electric vehicles (EVs), which help reduce emissions and reliance on fossil fuels. This paper presents a qualitative study aimed at assessing the uncertainty surrounding EV load in rental car fleets. As rental agencies increasingly adopt EVs, they face challenges in forecasting charging demand, especially within limited space and infrastructure. By providing valuable data and reliable statistical models, the study estimates EV consumption for car rental companies, which might face difficulties in charging multiple vehicles efficiently. Accurate EV load estimation is essential for designing effective charging infrastructure and developing optimal load management models that reduce technical issues and economic costs. Over twelve statistical functions were tested to capture variables such as fleet size, usage patterns, and charging behavior that influence EV load formation. Additionally, this paper includes case studies to support planners and developers in improving EV charging infrastructure, offering practical insights for scaling EV integration in the rental sector and contributing to broader sustainability goals.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/4350284","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148282472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multilevel Energy Scheduling in Smart Multiple Energy Grid Considering Optimal Participation of the Consumers in Energy Consumption","authors":"Y. Romani,&nbsp;Samariddin Makhmudov,&nbsp;Rustem Shichiyakh,&nbsp;Elvir Akhmetshin,&nbsp;Mirzobek Avezov Komiljonovich,&nbsp;Mehmet Ali. Yuzbasioglu,&nbsp;Daniyor Kurbanov","doi":"10.1155/etep/5602680","DOIUrl":"https://doi.org/10.1155/etep/5602680","url":null,"abstract":"<p>Efficient energy use is a key aspect of sustainable development in numerous nations, aimed at enhancing economic, technical, and environmental metrics within the energy sector. This study presents an energy scheduling approach within a smart multienergy grid with promoting optimal energy usage by consumers. The energy scheduling is represented through a multiobjective optimization strategy with three levels, taking into account demand side management (DSM) approaches. The DSM approaches encompass shifting power demand and generating local energy from gas and power storage devices. The shifting power demand is modeled to decrease consumers’ bills at the first level. Also, generating local energy by storage devices is done to decrease consumers’ bills at the second level. The maximizing flexibility index, minimizing power losses, and operational costs are modeled as multiobjective functions in optimization of the third level. In the optimization process, the third level utilizes DSM approaches to optimize energy demand for multiobjective functions. The grasshopper optimization algorithm (GOA) along with fuzzy-based weight sum methods is employed to tackle the optimization approach. The fuzzy-based weight sum methods are introduced to identify the optimal solution in the Pareto front solutions for multiobjective functions. The proposed energy scheduling is executed on a 69-node test grid. Finally, the results demonstrate the optimum rate of the objectives and emphasize consumer participation via a comparative assessment of different case studies. With the implementation of the DSM approaches, the flexibility index is improved by 4.3%, and power losses and operational cost are minimized by 19.21% and 13.51%, respectively.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/5602680","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148282357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Interval Optimization and Generalized Nash Bargaining Approach for Power Trading Among Multiple Virtual Power Plants","authors":"Yunda Li,&nbsp;Lixia Zhang,&nbsp;Xiaoying Song,&nbsp;Zhongjian Kang,&nbsp;Qingwei Meng","doi":"10.1155/etep/1792474","DOIUrl":"https://doi.org/10.1155/etep/1792474","url":null,"abstract":"<p>To address the impact of distributed source–load uncertainty on the optimization and power trading of virtual power plants (VPPs), this paper integrates interval optimization (IO) and generalized Nash bargaining (GNB) to develop a power trading model for multiple virtual power plants (MVPPs). The source–load uncertainty is represented by day-ahead prediction intervals of photovoltaic (PV) generation and flexible loads. The bargaining power of each VPP is assessed to allocate cooperative profits. A case study demonstrates the scheduling, trading, and cost intervals of each VPP, showing that IO effectively captures power flow uncertainty between VPPs and the grid under source–load variability, while GNB ensures fair allocation of cooperative profits. By applying the Hurwicz criterion to compare IO and robust optimization (RO), it is observed that when the decision-maker’s optimism level exceeds a critical threshold, IO yields less conservative scheduling solutions than RO. In addition, a comparison with stochastic optimization (SO) shows that while SO can achieve lower expected costs under accurate probabilistic information, IO provides a distribution-free and computationally efficient alternative when such information is unavailable. Furthermore, a comparison of GNB, Nash bargaining (NB), and the Shapley value method for profit allocation among MVPP shows that GNB, unlike NB, distributes cooperative profits in proportion to each VPP’s contribution to the coalition. In addition, compared with the Shapley value method, GNB achieves significantly lower computational costs when a large number of VPPs participate in cooperative operation, thereby enhancing its engineering feasibility. The proposed model also exhibits superadditivity, indicating that larger alliances generate higher profits. These findings demonstrate that the integration of IO and GNB offers a computationally tractable and engineering-feasible approach for MVPP coordination under source–load uncertainty.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/1792474","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148238047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Operation Mode Identification for High Renewable Energy Penetration Power Systems Based on Planning–Operation Coupling and SHAP Interpretability","authors":"Haotian Yang,&nbsp;Shiyu Zhu,&nbsp;Mengyu Fu,&nbsp;Peng Wang,&nbsp;Yijin Xiong,&nbsp;Han Wang","doi":"10.1155/etep/5130077","DOIUrl":"https://doi.org/10.1155/etep/5130077","url":null,"abstract":"<p>Under high renewable energy penetration, power system operation modes exhibit significant diversity and structural complexity, posing challenges to traditional scenario-based analysis methods in capturing their formation mechanisms. This paper proposes an interpretable analytical framework for operation mode identification and driving mechanism analysis. Based on year-round operational data, clustering is employed to label similar operation states, while the core analysis focuses on a decision tree model combined with the SHapley Additive Explanations (SHAP) method to automatically identify key driving factors and quantify their contributions. A robustness verification strategy is further developed through clustering parameter perturbation and model simplification comparison, ensuring the stability and explanatory adequacy of the identified factors. Case studies on both the HRP-38 benchmark test system and a real-world 154 node power system demonstrate that, as renewable energy penetration increases, operation modes become more numerous and structurally complex, with their formation mechanism shifting from load- and thermal-dominated patterns to renewable–storage coordinated structures. The proposed framework provides accurate mode identification while enabling clear and physically interpretable insights into their formation mechanisms, offering practical support for operation analysis and planning-oriented decision-making in high-renewable power systems.</p>","PeriodicalId":51293,"journal":{"name":"International Transactions on Electrical Energy Systems","volume":"2026 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/etep/5130077","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}