Sustainable Energy Grids & Networks最新文献

{"title":"Renewable energy communities virtual islanding: A decentralized service to improve distribution grid security","authors":"Angelo Velini ,&nbsp;Manuela Minetti ,&nbsp;Sergio Bruno ,&nbsp;Andrea Bonfiglio ,&nbsp;Renato Procopio ,&nbsp;Massimo La Scala","doi":"10.1016/j.segan.2025.101700","DOIUrl":"10.1016/j.segan.2025.101700","url":null,"abstract":"<div><div>This article presents a novel service provided by Renewable Energy Communities (RECs) for the distribution network. This service, called Virtual Islanding (VI) and ideally requested to the RECs by the Distribution System Operator (DSO), basically consists in requiring that the net active power exchange between the involved REC and the main grid is zero for a specified time frame. The effectiveness of RECs VI operation is assessed using an Optimal Power Flow (OPF) that considers distribution network operational limits. Simulations, performed in a test case distribution system with massive penetration of RES, highlight the VI benefits both from a technical and sustainability viewpoint.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101700"},"PeriodicalIF":4.8,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 novel MILP formulation for optimal allocation and coordination of protective and switching devices in active distribution networks","authors":"Wandry Rodrigues Faria ,&nbsp;Gregorio Muñoz-Delgado ,&nbsp;Javier Contreras ,&nbsp;Benvindo Rodrigues Pereira Jr.","doi":"10.1016/j.segan.2025.101692","DOIUrl":"10.1016/j.segan.2025.101692","url":null,"abstract":"<div><div>In the last decade, the increasing penetration of distributed generation has prompted the proposal of new formulations for distribution protection system planning, as the typical indications of coordination may not be reliable for active networks. In this context, a few papers that explicitly enforce coordination constraints have been published. However, these papers are mostly based on heuristics and metaheuristics; therefore, although the solutions are feasible, there is no guarantee of optimality. This paper presents a mixed-integer linear formulation for the allocation and coordination of control and protective devices in distribution systems with distributed generators. Thus, the proposed approach guarantees both the optimal investment plan and feasibility of the protection system operation. The proposed formulation is tested for a 69-node system considering load restoration possibilities via island operation, using protective devices, and load transfer to neighboring feeders and fault permanent isolation, using switching devices. The results attest to the cost-effectiveness of the protection system and its operational feasibility, as well as the superiority of the proposed model over simpler existing ones.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101692"},"PeriodicalIF":4.8,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748617","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":"Optimal strategy of the charging station operator participating in day-ahead electricity market: A hierarchical game framework","authors":"Jinkai Shi ,&nbsp;Weige Zhang ,&nbsp;Yan Bao ,&nbsp;David Wenzhong Gao ,&nbsp;Senyong Fan ,&nbsp;Zhihao Wang","doi":"10.1016/j.segan.2025.101686","DOIUrl":"10.1016/j.segan.2025.101686","url":null,"abstract":"<div><div>The charging station operator (CSO) is responsible for the charging scheduling and electricity procurement of many charging stations, participating in the day-ahead electricity market to achieve economic benefits. Due to the oligopoly structure of the market, different bidding curves affect market clearing results. This paper proposes a hierarchical game framework for CSOs participating in the day-ahead market. Specifically, we introduce a power-feasible and energy-feasible region model to characterize the flexible charging load of an electric bus (EB). Then, we establish an aggregation model to aggregate charging operational regions of a large amount of EB energy consumption. Taking into account the uncertainty of arrival time and energy consumption, we adopt the distributionally robust optimization to describe the upper and lower boundaries, which is expressed by the data-driven ambiguity set based on Wasserstein distance. Due to the Stackelberg game between the CSO and the independent system operator (ISO), we formulate its bidding strategy based on schedulable charging load potential as a bilevel optimization model. The collective optimization of bidding curves and scheduling power is formulated as mathematical programming with equilibrium constraints (MPEC), and Karush–Kuhn–Tucker (KKT) conditions are used to accelerate the solution. Finally, a modified 6-bus test system is used to validate effectiveness. In addition, we compare the market clearing results of individual bidding, cooperation bidding, central dispatching mode, and fixed electricity prices. The results show that the cooperation bidding of the charging operator alliance has strong market power, and the average electricity price has been reduced to 0.27 CNY/kWh.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101686"},"PeriodicalIF":4.8,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859032","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 stochastic-robust optimal strategy of integrated electricity and gas grids in the presence of multi-energy hubs and responsible loads","authors":"W. Abdulrazzaq Oraibi ,&nbsp;Ali Salam Al-Khayyat ,&nbsp;Ahmed K. Abbas","doi":"10.1016/j.segan.2025.101697","DOIUrl":"10.1016/j.segan.2025.101697","url":null,"abstract":"<div><div>The optimal performance of multi-carrier energy systems is becoming increasingly important with the rise of highly efficient and cost-effective utilities in gas and electricity distribution networks. However, the collaborative management of interconnected electricity and gas networks, particularly in the context of renewable energy resources and flexible demand, poses a significant challenge for system operators. To address this, a stochastic-robust optimization approach is recommended to concurrently model the operating costs for both networks integrated with the multi-energy hub system (MEH), incorporating various energy conversion technologies, renewable energy resources (RERs), and gas storage (GS) units to meet heat, cooling, and gas demands. The consequences of power pricing uncertainty are modelled to deal with market power prices using a robust optimization strategy. Other significant uncertainties, such as solar (PV), wind power, electrical demands, gas loads, cooling loads, heating loads, and gas prices, all are handled by stochastic optimization. Additionally, the impact of installed gas and electricity loads during peak hours is examined through adjustable flexible demand response (FDR). The proposed model is formulated as a mixed-integer linear programming (MILP) model and is solved using a CPLEX solver with the GAMS optimization software. The results demonstrate that interconnected MEHs can significantly impact operating costs for both the Gas Distribution Network (GDN) and Electric Distribution Network (EDN) when considering flexible demands and energy sources. Specifically, compared to the risk-neutral case, the total operating cost increased by 2.74 %, rising from $30311.739 to $31141.653 under the worst conditions. In contrast, in the case of risk-averse optimal scheduling of the proposed problem, in the presence of FDR and GS units, the total operating cost decreased to $27283.08, representing a reduction of 12.39 %. This indicates that integrating MEHs and GS units, along with responsive demands, can effectively improve the flexibility of EDN and GDN while significantly reducing overall operating costs.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101697"},"PeriodicalIF":4.8,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739843","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":"Probabilistic generator contingency assessment for power grids with high renewable penetration","authors":"Oliver Stover,&nbsp;Pranav Karve,&nbsp;Sankaran Mahadevan","doi":"10.1016/j.segan.2025.101681","DOIUrl":"10.1016/j.segan.2025.101681","url":null,"abstract":"<div><div>In modern-day power grids, increasing participation of inverter-based generation (i.e., wind/solar generation) increases supply uncertainty, reduces grid inertia, and exacerbates security-related problems. This article develops a stochastic framework to assess the grid’s ability to withstand generator failure, while explicitly considering the supply and demand uncertainty. The framework enables proactive risk quantification and management to support secure operation of the modern-day power grid. It also allows consideration of adverse event probability after a generator failure to assess the relative importance of generator failure events. We demonstrate the proposed framework using a 200-bus synthetic grid. We find that probabilistic assessment is able to identify important contingencies, which would have been missed by deterministic analyses performed using mean values. We also develop a method for identifying important generator contingencies based on the probabilistic security and reliability analyses. We find that the resulting importance ranking is not identical to the generator capacity-based ranking and depends on the uncertainty in the generator’s active power output as well as its contribution to grid inertia.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101681"},"PeriodicalIF":4.8,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715262","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":"Deep learning-driven robust model predictive control based active cell equalisation for electric vehicle battery management system","authors":"Sairaj Arandhakar,&nbsp;Jayaram Nakka","doi":"10.1016/j.segan.2025.101694","DOIUrl":"10.1016/j.segan.2025.101694","url":null,"abstract":"<div><div>Stabilizing the cells in Electric Vehicle (EV) batteries allows for optimal efficiency, longer battery life, and greater performance. This research presents a deep learning tuning based on Robust Model Predictive Control (RMPC) to address the issue of EV cell imbalance. Deep learning is used to recognize the patterns of battery operation and hence the equalization of active cells is maintained. The equilibrium is maintained through the observation of the state of charge (SoC) of the cell. Parameters, such as Mean Absolute Error (MAE) and Mean Square Error (MSE) are employed to assess the efficiency of active cell balancing through the use of RMPC. The validity of proposed technique was shown by the use of MATLAB/Simulink in modelling, training, and testing the models as well as enhancing the battery performance. To perform the assessment, Multi-Layer Neural Network (MLNN), Long Short-Term Memory (LSTM) network and Recurrent Neural Network (RNN) are used. The proposed RMPC-based balancing demonstrated better accuracy with lower MSE and MAE values for RNN (0.712, 0.34), LSTM (0.724, 0.59), and MLNN (0.73, 0.65) as compared to Adaptive Model Predictive Control (AMPC) mechanism. The simulation results prove that proposed method efficiently provides the maximum voltage during the active cell balancing process.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101694"},"PeriodicalIF":4.8,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726206","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":"The role of thermostatically controlled loads in power system frequency management: A comprehensive review","authors":"Sumit Nema ,&nbsp;Vivek Prakash ,&nbsp;Hrvoje Pandžić","doi":"10.1016/j.segan.2025.101680","DOIUrl":"10.1016/j.segan.2025.101680","url":null,"abstract":"<div><div>Thermostatically Controlled Loads (TCLs) operate as decentralized Energy Storage Systems (ESS) that have the ability to adapt their power usage in accordance with fluctuations of the grid frequency. They offer a viable solution for addressing the decline in inertia and can be utilized to provide Fast Frequency Response (FFR). This review paper extensively examines control strategies developed in the recent literature on grid frequency control from TCLs in power systems. There is a need to create Fast Frequency Control (FFC) mechanisms that optimize the utilization of TCLs to address the specific challenges and opportunities in such systems. This review paper provides a thorough audit on the current state of research in the FFR, specifically focusing on control schemes, strategies and methodologies proposed for integrating TCLs. Additionally, it investigates the diverse controllers and techniques discussed in academic works. By identifying the strengths and limitations of these strategies, mechanisms, and controllers, this review provides insights into the design and optimization of the frequency control mechanisms for power systems that incorporate TCLs.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101680"},"PeriodicalIF":4.8,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705186","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 decision-making framework for multi-microgrids scheduling considering joint P2P energy and reserve trading floor","authors":"Fatemeh Nouri ,&nbsp;Mostafa Vahedipour-Dahraie ,&nbsp;Reza Shariatinasab ,&nbsp;Pierluigi Siano","doi":"10.1016/j.segan.2025.101685","DOIUrl":"10.1016/j.segan.2025.101685","url":null,"abstract":"<div><div>This paper proposes a two-stage stochastic bi-level framework for joint energy and reserve scheduling of grid-connected Multi-Microgrids (MMGs) to achieve a win-win outcome in the presence of renewable resources and demand response programs (DRPs). In this framework, interconnected Microgrids (MGs) collaborate to facilitate bilateral energy exchange, leveraging economic advantages through peer-to-peer (P2P) energy and reserve trading platforms. Also, an MMG operator (MMGO) facilitates the interaction between MGs and plays a pivotal role in supplying loads, ensuring safety, and providing reserve as well as trading energy with the main grid, covering both day-ahead and real-time markets. To this end, a bi-level problem is formulated in which, at the upper level of the problem, the MMGO reschedules the MGs based on the P2P energy trading by considering the targets of each MG, while, at the lower level, each MG tries to optimize the local energy and reserve scheduling. In this model, flexible resources of MGs can provide upward/downward reserves to the grid through reserve trading, where the MMGO is responsible for reserve procurement. Numerical results show that the simultaneous energy trading and reserve services between MGs can help them achieve economic benefits. Moreover, DRPs can assist MGs in sharing more energy and reserve when the P2P trading floor is considered.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101685"},"PeriodicalIF":4.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143734813","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 behavioural model for negawatt sharing management based on the prospect theory in a citizen energy community","authors":"Seyedeh Soudabeh Zadsar ,&nbsp;Masoud Rashidinejad ,&nbsp;Seyed Farshad Fatemi Ardestani ,&nbsp;Amir Abdollahi ,&nbsp;Sobhan Dorahaki","doi":"10.1016/j.segan.2025.101693","DOIUrl":"10.1016/j.segan.2025.101693","url":null,"abstract":"<div><div>Citizen Energy Communities (CECs) have emerged as an attractive solution to reduce carbon emissions and enhance sustainability. One of the principal features of CECs is Peer-to-Peer (P2P) resource sharing, which is divided into two types: kilowatt P2P sharing and Negawatt P2P sharing. While kilowatt P2P sharing is extensively addressed in the literature, the role of Negawatt P2P sharing in prosumer-centric structures like CECs is not adequately explored. To address such a crucial challenge, this paper presents an optimal prosumer-centric energy management framework for CECs, taking into account human behaviours. Prospect theory is utilized to model prosumers' emotions and preferences in energy-related decisions within the CEC. The proposed model is evaluated using two case studies with different time frames: a one-day scheme and a one-year scheme. The results indicate that the Negawatt P2P sharing can enable prosumers to achieve significant electricity cost savings. By incorporating the prosumer's behavioural attitudes associated with Negawatt P2P sharing within a CEC for one-day scheduling, it is shown that electricity costs can be reduced by approximately 5.4 %. It is worth emphasizing that the overall value is enhanced by about 14 %. Moreover, in a one-year scheme, the integration of Negawatt P2P sharing led to a reduction in total operational costs by approximately 3.6 %, demonstrating the effectiveness of optimizing energy management while enhancing the economic performance of CECs throughout different horizons.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101693"},"PeriodicalIF":4.8,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748614","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":"Heterogeneous-agent coordinated secure and contribution-aware equilibrium strategy for integrated electricity-gas system: A fully decentralized approach","authors":"Chaoxian Lv ,&nbsp;Kang Peng ,&nbsp;Qin He ,&nbsp;Yuanyuan Chai ,&nbsp;Kaiping Qu ,&nbsp;Rui Liang","doi":"10.1016/j.segan.2025.101691","DOIUrl":"10.1016/j.segan.2025.101691","url":null,"abstract":"<div><div>The individual voltage level and pressure stability for independent power distribution systems (PDSs) and natural gas systems (NGSs) are crucial for the secure and cost-effective operation of integrated electricity-gas systems (IEGSs). However, the interest coordination of different stakeholders with privacy preservation is challenging. Therefore, the heterogeneous-agent coordinated secure and contribution-aware equilibrium strategy for IEGS with a fully decentralized approach is proposed. The flexible operation of PDS, NGS, and community-integrated energy stations (CIESs) enables bidirectional power interaction between PDS and NGS, enhancing the security of IEGS. This interaction is optimized by integrating diverse demand responses, including interruptible, transferrable, and replaceable loads, to ensure more effective power exchanges. A contribution-aware asymmetric Nash bargaining method is proposed for revenue allocation, determined by the stakeholder contribution degrees calculated through a nonlinear energy sharing mapping method. Additionally, the study employs the adaptive alternating direction method of multipliers (ADMM) to resolve the coordinated secure operation challenges and contribution-aware equilibrium issues among heterogeneous agents, thereby achieving full decentralization of stakeholder collaboration with ensured privacy and operational independence. A numerical study on a modified IEEE 33-bus PDS and 9-node NGS, connecting 2 CIESs, proves the strategy’s efficacy. The proposed strategy boosts voltage by 60.96 % and pressure by 93.33 %. The profit rates of cost for PDS, NGS, CIES1, and CIES2 surge to 198.69 %, 112.55 %, 26.44 %, and 25.78 %.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101691"},"PeriodicalIF":4.8,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715261","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}