International Journal of Electrical Power & Energy Systems最新文献

{"title":"Distributionally robust optimal scheduling of integrated energy system considering adaptive Copula function and dynamic reserve","authors":"P.H. Jiao ,&nbsp;J.J. Chen ,&nbsp;L.L. Wang ,&nbsp;Z.H. Zhao","doi":"10.1016/j.ijepes.2025.111073","DOIUrl":"10.1016/j.ijepes.2025.111073","url":null,"abstract":"<div><div>Deploying an integrated energy system represents a critical pathway to alleviate energy supply pressure and improve energy efficiency. However, in existing works on the integrated energy system, the uncertainties and multi-type reserves over different scheduling stages have not been fully considered to warrant the stable operation performance of integrated energy systems under multiple scenarios. Based on these considerations, a distributed robust optimal scheduling of an integrated energy system considering adaptive Copula function and dynamic reserve is proposed. First, an adaptive Copula function is developed to accurately describe the dynamic correlation of wind/solar power output and the characteristics of joint output. At the same time, the quasi-Monte Carlo method is used to form a typical scenario set aiming at the uncertainty of power generation for renewable energy sources. Furthermore, the reserve provision model is proposed, and the ineffective upward reserve, ineffective downward reserve, loss load, and power curtailment are respectively developed to address the effect caused by the uncertainty of renewable energy sources. Then, based on scenario information of renewable energy sources, the operating cost in the day-ahead stage and the adjustment cost of the system under the worst scenario in the real-time stage are taken as the optimization objectives, and a two-stage distribution robust optimization scheduling model is constructed. The two-stage model is solved using a column-and-constraint generation algorithm. Finally, case studies are carried out to verify by Gurobi that the operation cost of distributionally robust optimization is <span><math><mrow><mn>2</mn><mo>.</mo><mn>0704</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>4</mn></mrow></msup><mi>$</mi></mrow></math></span>, the lowest ineffective reserve cost of 208$ is the lowest, the proposed method has a good economy and robustness and is suitable for dealing with the uncertainty of renewable energy sources.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111073"},"PeriodicalIF":5.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049225","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 multi-objective distributionally robust chance-constrained model for power grid resilience enhancement with limited offensive information","authors":"Ze Zhang,&nbsp;Shengjun Huang,&nbsp;Xueyang Zhang,&nbsp;Tao Zhang,&nbsp;Rui Wang","doi":"10.1016/j.ijepes.2025.111083","DOIUrl":"10.1016/j.ijepes.2025.111083","url":null,"abstract":"<div><div>Frequent deliberate attacks have greatly affected the security of power grids. Defense resources, such as mobile emergency generators (MEGs), have been deployed to enhance the resilience of the grid. However, the uncertainty of offensive resource information poses a serious challenge to developing defense strategies. This paper introduces a multi-objective distributionally robust chance-constrained (MODRCC) method for planning the pre-storage capacity of MEGs, aiming to address the uncertainty of the number of attack resources and enhance grid resilience. A combinatorial optimization framework for planning MEG storage and generation capacity is developed, and the operational state of the attacked grid and the MEG scheduling scheme are modeled. Subsequently, the Distributed Robust Chance Constraints (DRCC) approach is developed to address the uncertainty of offensive resources. A deterministic reformulation over Wasserstein balls is used to convert DRCC method into mixed-integer conic programs that can be solved directly by commercial solvers. Furthermore, a non-dominated sorting genetic algorithm-II (NSGA-II) solution is designed to continuously update the MEG storage scheme based on the expected load shedding obtained by the DRCC. Finally, case studies are conducted on the IEEE 24-bus and 118-bus systems to verify the effectiveness of the proposed method.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111083"},"PeriodicalIF":5.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049227","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":"Leveraging Multi-Task Learning for multi-label power system security assessment","authors":"M.E. Za’ter ,&nbsp;A. Sajadi ,&nbsp;B.M. Hodge","doi":"10.1016/j.ijepes.2025.111067","DOIUrl":"10.1016/j.ijepes.2025.111067","url":null,"abstract":"<div><div>This paper introduces a novel approach to the power system security assessment using Multi-Task Learning, and reformulating the problem as a multi-label classification task. The proposed Multi-Task learning framework simultaneously assesses static, voltage, transient, and small-signal stability, improving both accuracy and interpretability with respect to the most state of the art machine learning methods. It consists of a shared encoder and multiple decoders, enabling knowledge transfer between stability tasks. Experiments on the IEEE 68-bus system demonstrate a measurable superior performance of the proposed method compared to the extant state-of-the-art approaches.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111067"},"PeriodicalIF":5.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049221","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":"Multi-module series suppressor for the protection of wind farm transformers against resonance overvoltages","authors":"Amir Heidary,&nbsp;Mohamad Ghaffarian Niasar,&nbsp;Marjan Popov","doi":"10.1016/j.ijepes.2025.111090","DOIUrl":"10.1016/j.ijepes.2025.111090","url":null,"abstract":"<div><div>Large-scale integration of renewable energy sources (RESs) presents significant challenges for modern power grids, particularly with wind farms playing a crucial role in energy generation. However, frequent switching operations during the (dis)connection of wind farms and lightning strikes on wind turbines can induce severe transient overvoltages. These fast transients (FTs) pose a serious risk to wind farm substations, potentially compromising the reliability of renewable energy generation. In particular, protecting wind farm transformers from FTs, resonances, and resulting overvoltages is essential for ensuring stable operation. This paper proposes a modular series transient suppressor (MSTS) designed to enhance the protection of wind farm transformers against FTs, thereby improving system reliability. The MSTS consists of multiple resonant circuit modules, including a core, a low-voltage capacitor, and a resistor connected in series with the transformer. Its operational behavior is analyzed using analytical methods and validated through simulation studies. Furthermore, experimental testing performed on a developed MSTS prototype confirms its effectiveness in mitigating transient overvoltages for a wind farm transformer model circuit at a 60 kV transient voltage level.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111090"},"PeriodicalIF":5.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049223","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 bi-level operational reliability evaluation model for power systems considering economic dispatch and demand response","authors":"Congcong Pan ,&nbsp;Xueying Yu ,&nbsp;Bo Hu ,&nbsp;Changzheng Shao ,&nbsp;Hui Lu ,&nbsp;Kaigui Xie ,&nbsp;Amjad Anvari-Moghaddam","doi":"10.1016/j.ijepes.2025.111087","DOIUrl":"10.1016/j.ijepes.2025.111087","url":null,"abstract":"<div><div>The increasing deregulation and integration of renewable energy are contributing to growing uncertainties in the power systems. The main uncertainties include the equilibrium game process among different stakeholders, the uncertainty of renewable generation, and random failures of components. It is critical to quantify their impact on the reliability of the power system accurately. This paper develops a bi-level operational reliability evaluation model of the deregulated power system with renewables (DPSR), considering the interaction between the economic dispatch scheme of generation and transmission operators (GTOs) and the benefits of load aggregators (LAs). In the upper level, the electricity consumption of end-users is incentivized by LA according to the dynamic locational marginal prices (LMP) and demand response (DR) capacity. In the lower level, an economic dispatch model is proposed to determine the risk-averse LMP. The developed model is solved using Karush-Kuhn-Tucker (KKT) conditions, strong duality theorem, and linear relaxation techniques. The results show that a win–win situation is achieved for both the GTO and LA, with a reduction in operational costs and an improvement in load profile.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111087"},"PeriodicalIF":5.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049224","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":"Estimating area inertia of power systems with a high share of RES using deep learning","authors":"Bingzhang Liu,&nbsp;Ming Zhou,&nbsp;Zhi Zhang,&nbsp;Zhaoyuan Wu,&nbsp;Guangyin Li,&nbsp;Gengyin Li","doi":"10.1016/j.ijepes.2025.111105","DOIUrl":"10.1016/j.ijepes.2025.111105","url":null,"abstract":"<div><div>Inertia, the ability to maintain frequency stability, is crucial for power system secure operation. With the increasing integration of inverter-interfaced renewable energy sources (RESs), represented by wind and solar power, the inertia of the<!--> <!-->power system rapidly declines and exhibits spatial–temporal variation. Estimating area inertia becomes more complex, yet more vital for power systems with high share of RESs. Most model-based inertia estimation approaches rely on a linearized and simplified representation to system frequency dynamics, limiting their accuracy in presence of large amount of virtual inertia provided by RESs due to its time-varying feature. Here, we propose time-series-based residual neural network (TS-ResNet), a deep learning model integrating one-dimensional convolution operation and residual blocks to estimate area inertia with a mix of synchronous inertia and virtual inertia. TS-ResNet extracts frequency dynamic features from nodal frequencies and tie-line powers utilizing probing signals without affecting system stable operation. Additionally, to enable model’s robustness to complex scenarios, a loss function with elastic net regularization is introduced for the training process. Numerical results on a 3-region AC/DC hybrid system demonstrate its high accuracy and low computation efficiency. It also generalizes to unseen time delays of virtual synchronous generators (VSGs), DC power transmission variations, and different RES shares, and demonstrates strong robustness under various noise levels. Our findings suggest that TS-ResNet offers a fresh perspective on incorporating data-driven approaches to inertia estimation.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111105"},"PeriodicalIF":5.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049226","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 two-layer optimal dispatch model for thermal unit deep peaking shaving and dynamic line rating","authors":"Jingwen Huang ,&nbsp;Zhiye Du ,&nbsp;Yue Yu ,&nbsp;Huashi Zhao ,&nbsp;Yiping Chen","doi":"10.1016/j.ijepes.2025.111098","DOIUrl":"10.1016/j.ijepes.2025.111098","url":null,"abstract":"<div><div>To address the challenges of limited system regulation and renewable energy accommodation capacity in new power system, this paper proposes a two-layer optimal dispatch model incorporating thermal unit deep peak shaving and dynamic line rating. The upper-layer model aims to minimize net load fluctuation and enhance energy storage utilization by optimizing the peak regulation schedule of thermal units through deep regulation capability, while analyzing load flow distribution. The lower-layer model calculates real-time dynamic capacity limits for overloaded transmission lines using conductor thermal balance equations and meteorological parameters, enhancing line transmission capacity to improve renewable energy utilization. The model aims to minimize system operating costs and reduce wind and PV curtailment. Taking IEEE39 as an example to verify the effectiveness of the proposed model, combined with the actual power system, it is calculated that the line capacity can be increased by up to 21.27% and 64.83% during the peak load period in different seasons, while wind and PV curtailment rates can be decreased by 4.44% and 9.43%, respectively. This model effectively reduces the consumption pressure of the system on renewable energy, promotes system stability, and lowers operating costs.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111098"},"PeriodicalIF":5.0,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049219","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":"Early wind turbine alarm prediction based on machine learning—Alarm Forecasting","authors":"Syed Shazaib Shah,&nbsp;Daoliang Tan","doi":"10.1016/j.ijepes.2025.110980","DOIUrl":"10.1016/j.ijepes.2025.110980","url":null,"abstract":"<div><div>Alarm data is pivotal in curbing fault behavior in Wind Turbines (WTs) and forms the backbone for advanced predictive monitoring systems. Traditionally, research cohorts have been confined to utilizing alarm data solely as a diagnostic tool—merely indicative of unhealthy status. However, this study aims to offer a transformative leap towards preempting alarms, preventing alarms from triggering altogether, and consequently averting impending failures. Our proposed Alarm Forecasting and Classification (AFC) framework is designed on two successive modules: first, the regression module based on long short-term memory (LSTM) for time-series alarm forecasting, and thereafter, the classification module to implement alarm tagging on the forecasted alarm. This way, the entire alarm taxonomy can be forecasted reliably rather than a few specific alarms. 14 Senvion MM82 turbines with an operational period of 5 years are used as a case study; the results demonstrated 82%, 52%, and 41% accurate forecasts for 10, 20, and 30 min alarm forecasts, respectively. The results substantiate anticipating and averting alarms, which is significant in curbing alarm frequency and enhancing operational efficiency through proactive intervention.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 110980"},"PeriodicalIF":5.0,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049215","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":"Co-optimizing network investment, reconfiguration, and third-party flexibility for congestion management in active distribution networks","authors":"Orlando Valarezo ,&nbsp;Tomás Gómez ,&nbsp;José Pablo Chaves-Ávila ,&nbsp;Cristian Alcarruz ,&nbsp;Pierluigi Mancarella","doi":"10.1016/j.ijepes.2025.111094","DOIUrl":"10.1016/j.ijepes.2025.111094","url":null,"abstract":"<div><div>This paper introduces a unified optimization model to evaluate three coordinated strategies for congestion management in distribution networks: investment in new assets, distribution network reconfiguration, and the procurement of third-party flexibility. Unlike existing models that consider these strategies in isolation, the proposed approach jointly optimizes capital and operational expenditures, explicitly capturing their trade-offs. This integrated perspective aligns with evolving regulatory frameworks that advocate for the coordination of conventional planning with operational and market-based flexibility. The model is applied across a range of scenarios, including load growth, varying flexibility capacities and costs, and different weightings of representative days.</div><div>Results indicate that reconfiguration alone may suffice under moderate demand growth, while greater availability of third-party flexibility can defer or substitute costly grid reinforcements. Under higher congestion conditions, the coordinated deployment of all three strategies delivers the most cost-effective and operationally robust outcomes. The analysis further reveals that grid reconfiguration dynamically alters the eligibility of service providers to deliver flexibility, underscoring the importance of incorporating reconfiguration into the design of third-party flexibility mechanisms and distribution network planning. The paper concludes by identifying key implementation challenges and future research directions, including the selection and weighting of representative days, assessment of switching costs, reliability of reconfigured topologies, uncertainty modeling, and the implications of coordinating multiple flexibility mechanisms for congestion management.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111094"},"PeriodicalIF":5.0,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049214","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":"Online verification of adjustable robust optimization for electric hydrogen integrated energy systems with comprehensive demand response","authors":"Peng Ren ,&nbsp;Yinchao Dong ,&nbsp;Hongli Zhang ,&nbsp;Jing Wang ,&nbsp;Xiaochao Fan","doi":"10.1016/j.ijepes.2025.111092","DOIUrl":"10.1016/j.ijepes.2025.111092","url":null,"abstract":"<div><div>Traditional energy systems are gradually transitioning to new energy systems dominated by clean sources such as wind, solar, and hydrogen. As the penetration of renewable energy increases, the high uncertainty in their output presents significant challenges to the security and flexibility of energy system planning. This study develops a unified planning framework for the electrical hydrogen integrated energy system (EHIES) that considers demand response from industrial areas. To enhance the interaction between supply and demand, a comprehensive load electricity, heat, cooling, and hydrogen demand response mechanism based on day-ahead pricing in industrial areas is established. Additionally, a multi-scale hydrogen energy control system is designed to enable seasonal energy migration. Furthermore, to ensure the safe and stable operation of various devices within the EHIES, a novel online verification adjustable robust optimization method is proposed to address the uncertainties arising from fluctuations in renewable energy sources. The simulation results of the case study demonstrate that the proposed method can obtain the planning solution corresponding to the minimum uncertain budget under a limited robustness level, assisting decision-makers in making appropriate choices between risk and conservatism. Furthermore, with the introduction of demand response and the multi-scale hydrogen energy control mechanism, the EHIES planning and operational costs were reduced by 3.25%, carbon emission costs decreased by 6.36%, and the total cost was reduced by 4.23%. The proposed model and method can enhance the economic efficiency and security of energy systems, supporting the low-carbon transition of traditional energy systems.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"172 ","pages":"Article 111092"},"PeriodicalIF":5.0,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049217","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}