Sustainable Energy Grids & Networks最新文献

{"title":"Social welfare maximization in unbalanced distribution networks under dynamic pricing and power exchange limits","authors":"Afshin Najafi-Ghalelou ,&nbsp;Mohsen Khorasany ,&nbsp;M.Imran Azim ,&nbsp;Reza Razzaghi","doi":"10.1016/j.segan.2025.101923","DOIUrl":"10.1016/j.segan.2025.101923","url":null,"abstract":"<div><div>This paper presents a two-stage framework to manage bidirectional power exchange between distributed energy resources (DER) and the distribution network. In the first stage, the framework optimizes and schedules DER operations and sets dynamic power exchange prices. The second stage focuses on network constraint management. By shifting from fixed tariffs to dynamic, time-varying pricing, the framework encourages active participation in power exchange among players and the distribution network at the lowest prices. The proposed framework aims to maximize social welfare by coordinating power exchanges, dynamic consumption prices and feed-in tariffs. It seeks to optimize the participation of all involved parties, ensuring effective system management through dynamic pricing and flexible limits on power exchange levels between players and the distribution network. The new pricing scheme is tested with and without profit-seeking community batteries and public charging stations, resulting in a 15.53 %–21.51 % improvement in overall social welfare. The results show that increased player participation enhances social welfare in distribution-level markets without violating any technical constraints of the distribution network.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101923"},"PeriodicalIF":5.6,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933102","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":"Optimizing market driven usage modulation in regional integrated energy systems for sustainable energy efficiency and peak load balancing","authors":"Xinghua Qi ,&nbsp;Bahadar Nawab Khattak ,&nbsp;Arif Alam ,&nbsp;Wenfu Liu","doi":"10.1016/j.segan.2025.101938","DOIUrl":"10.1016/j.segan.2025.101938","url":null,"abstract":"<div><div>With ongoing reforms reshaping the energy market, the operation and optimization of energy systems are increasingly influenced by market-driven dynamics. This study examines the complexities of energy flows within regional integrated energy systems, extending the concept of consumption modulation to encompass the electrical energy, heating, and cooling demands. A comprehensive framework is developed that incorporates market-oriented modulation strategies aimed at balancing peak and off-peak loads, minimizing carbon emissions, and improving overall system efficiency. These strategies are designed to enhance economic outcomes for both energy retailers and consumers. The proposed framework utilizes a balanced formula augmentation method to evaluate equipment characteristics, hourly energy outputs, and operational policies. Notable innovations include dynamic pricing mechanisms, flexible modulation of dispatchable and transferable loads, and optimal scheduling based on real-time load redistribution. These approaches contribute to system stability, operational flexibility, and sustainability. A case study focused on a manufacturing park reveals peak electricity demand of 46.10 MW at 17:00, with a peak-to-valley ratio of 12.20. Cooling and heating demand peak at 28.59 MW and 23.34 MW at 15:00 and 12:00, respectively. Implementation of demand response strategies effectively reduces these peaks, demonstrating significant load shifting and improved demand curve flattening across all energy forms.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101938"},"PeriodicalIF":5.6,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903236","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 novel user-centric decentralized multi-objective energy management system for energy communities","authors":"Bahman Ahmadi,&nbsp;Aditya Pappu,&nbsp;Gerwin Hoogsteen,&nbsp;Marco E.T. Gerards,&nbsp;Johann L. Hurink","doi":"10.1016/j.segan.2025.101936","DOIUrl":"10.1016/j.segan.2025.101936","url":null,"abstract":"<div><div>This paper presents a decentralized energy management approach based on a Multi-Objective Energy Management System called DMOEMS, designed for Energy Communities (ECs), aiming to create resilient and sustainable energy systems. DMOEMS integrates a multi-objective optimization framework that aggregates conflicting goals–minimizing electricity cost and CO₂, reducing Photovoltaic (PV) curtailment, and maximizing self-consumption–by converting them into a single objective using user-defined weight factors. Each local controller optimizes the operation of distributed assets based on localized constraints and user preferences, while an EC controller coordinates aggregated power profiles through an iterative feedback mechanism. This coordination dynamically adjusts weight factors and curtailment strategies to resolve grid congestion without compromising individual privacy. Simulation studies on the realistic Aardehuizen EC demonstrate that DMOEMS effectively mitigates overloading scenarios across diverse operating conditions (high EV charging, normal demand, and excess PV generation), enhances user satisfaction, reduces operational costs, and lowers CO₂ emissions. The proposed framework highlights the potential of a democratic, decentralized approach to energy management in modern ECs. The numerical results for asset management using DMOEMS indicate improvements in different aspects such as reduction of 20 % in CO₂ emissions, improvement of 4 % in electricity cost savings, and a 30 % reduction in PV curtailment relative to baseline scenarios. Furthermore, the proposed mechanism in the DMOEMS shows improvement in computational cost by converging faster to resolve grid congestion compared to conventional approaches.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101936"},"PeriodicalIF":5.6,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903235","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":"Enhanced site selection for solar power plants utilizing the geographic information system and pythagorean fuzzy analytical hierarchy process method","authors":"Seda Hatice Gökler","doi":"10.1016/j.segan.2025.101929","DOIUrl":"10.1016/j.segan.2025.101929","url":null,"abstract":"<div><div>The rising global energy demand and environmental concerns have made the transition to renewable and sustainable energy sources essential. Solar energy is a promising option due to its availability, cost-efficiency, and environmental compatibility. However, the efficiency of solar power plants (SPPs) strongly depends on optimal site selection involving multiple spatial and non-spatial criteria. This study introduces a hybrid approach integrating the Pythagorean Fuzzy Analytic Hierarchy Process (PF-AHP) with Geographic Information Systems (GIS) to address uncertainty and subjectivity in multi-criteria decision-making (MCDM). Additionally, a comparative analysis between PF-AHP and Intuitionistic Fuzzy AHP (IF-AHP) was conducted, focusing on criterion weights and GIS-based suitability maps. Results show that IF-AHP produces nearly uniform weight distributions, making it less effective at prioritizing key factors such as solar radiation and access to transportation—both essential for efficient and cost-effective SPP planning. Conversely, PF-AHP offers a more differentiated that aligns with expert judgment and literature. The PF-AHP-based suitability map correctly identifies operational SPP regions in Eskişehir (Sivrihisar, Tepebaşı, Günyüzü, and Odunpazarı) as highly suitable. In contrast, the IF-AHP map misclassifies these same regions as poorly suitable, highlighting its limitations in spatial accuracy. The methodology was applied in Eskişehir, Turkey, using criteria such as solar radiation, slope, and proximity to restricted areas. Through GIS-based weighted overlay analysis, approximately 154 distinct areas—covering around 46.47 % of the study area—were identified as suitable for SPP installation. This approach provides a reliable and spatially transparent decision-support tool for sustainable energy planning.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101929"},"PeriodicalIF":5.6,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144895561","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-level decomposition algorithm for robust unit commitment in large-scale power systems with variable wind generation","authors":"Lizhong Huang ,&nbsp;Mingbo Liu ,&nbsp;Min Xie","doi":"10.1016/j.segan.2025.101939","DOIUrl":"10.1016/j.segan.2025.101939","url":null,"abstract":"<div><div>As wind generation integration in power systems has become more common, the two-stage robust unit commitment (TSR-UC) problem, which accounts for wind power uncertainty, has emerged as a critical research area. For large-scale power systems, the TSR-UC has a large computational burden and difficulty obtaining an optimal solution in an acceptable amount of time. To address this problem, we propose a two-level decomposition algorithm for TSR-UC to reduce the complexity of the model from the time dimension. In the first level, the column and constraint generation (C&amp;CG) algorithm is used to split the original problem into a master problem and a subproblem. In the second level, the entire scheduling horizon is partitioned into several consecutive intervals, and the C&amp;CG master problem is further decomposed into smaller-scale problems by replicating the variables in the last period of each interval and introducing on/off time counters. Similarly, the C&amp;CG subproblem is decomposed into smaller-scale subproblems by introducing an uncertainty subset budget. Moreover, the analytical-target-cascading algorithm, coupled with an effective initialization strategy, is proposed to solve the decomposed C&amp;CG master problems/C&amp;CG subproblems in parallel. Finally, numerical experiments across three different scales of power systems demonstrated that the proposed algorithm significantly enhanced solution speed while maintaining solution quality compared with the C&amp;CG algorithm.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101939"},"PeriodicalIF":5.6,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907284","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":"Tight and compact MILP formulation for a high-resolution of start-up costs in the medium-term unit commitment","authors":"Luis Montero ,&nbsp;Germán Morales-España ,&nbsp;Antonio Bello ,&nbsp;Javier Reneses","doi":"10.1016/j.segan.2025.101935","DOIUrl":"10.1016/j.segan.2025.101935","url":null,"abstract":"<div><div>Nowadays, most modern power systems are evolving towards a considerable capacity expansion in their energy storage and interconnection facilities. However, these great developments are not being accomplished fast enough to accommodate the high penetration of variable renewable energy sources. This situation raises demand variability, requiring more flexibility from thermal generators, especially due to their more frequent start-up and shut-down processes. Consequently, the unit commitment requires more accurate and detailed modeling while maintaining computational efficiency. This paper analyzes some of the best models to manage long-duration start-up costs according to the real fuel-consumption curves of a gas-fired generation portfolio. Moreover, we propose a tight and compact MILP piecewise formulation that enhances the resolution of start-up representations and achieves outstanding results compared to the literature benchmarks. The successful performance of this methodology is proven in several large-size case studies focusing on the medium term. Furthermore, conventional day-ahead problems are also run to demonstrate the overall competitiveness of the formulation.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101935"},"PeriodicalIF":5.6,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907285","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":"Research on superposition-level dual power allocation control strategy for energy storage integration in electrified oil rigs: Load shock mitigation and energy utilization efficiency","authors":"Chuan Wang ,&nbsp;Guangtao Chen ,&nbsp;Xianfeng Shi ,&nbsp;Guorong Wang ,&nbsp;Bo Wang ,&nbsp;Ling Zhong ,&nbsp;Maoyuan Luo","doi":"10.1016/j.segan.2025.101928","DOIUrl":"10.1016/j.segan.2025.101928","url":null,"abstract":"<div><div>Changes in geological and working conditions during drilling result in the intermittent and cyclical operation of high-power equipment on oil drilling rigs, which leads to substantial alternating load impacts on the grid. To address this problem, this paper collects full-cycle and full-operating power data and operating parameters from \"electrified\" oil rigs. The power fluctuation characteristics have been derived under multiple operating conditions and multi-dimensional variables. A physically-coupled, hierarchical control strategy utilizing a storage system is proposed to address grid stability and power supply challenges. This framework resolves the intrinsic conflict between grid stability and drilling safety. In the first transient phase, condition-dependent peak shaving is achieved using multi-dimensional power signatures extracted from field data. During the safety-critical period, dynamic coordination of state-of-charge constraints with downhole physical boundaries is formulated as a mixed-integer nonlinear programming problem. This paper validates the proposed control strategy on oil rig field platforms, demonstrating a reduction in the daily peak-to-valley power difference by 42 %. This results in annual savings of approximately 538,000 RMB per rig, with a payback period of 4.5 years, while reducing CO₂ emissions by 18.7 tonnes per well. The control strategy represents a pioneering integration of short-term load balancing and medium-term constraint adaptation, bridging the gap between transient power fluctuations and mechanical safety limits. Furthermore, it marks the industry's first field validation of storage-assisted electrification in complex drilling cycles. This methodology establishes a replicable framework for high-transient industrial microgrids, advancing the transition to sustainable energy systems in fossil fuel-based industries.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101928"},"PeriodicalIF":5.6,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144889719","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":"Self-healing power systems using reinforcement learning over graphs for controlled grid islanding","authors":"Sobhan Badakhshan ,&nbsp;Roshni Anna Jacob ,&nbsp;Binghui Li ,&nbsp;Pingfeng Wang ,&nbsp;Jie Zhang","doi":"10.1016/j.segan.2025.101937","DOIUrl":"10.1016/j.segan.2025.101937","url":null,"abstract":"<div><div>Natural disasters and sudden faults create rapidly changing conditions that significantly challenge the power system’s resilience. Intelligent algorithms can enable operators to take informed actions for rapid restoration by suggesting efficient switching strategies to enhance the resilience of power grids against extreme events. The intentional islanding restoration strategy isolates vulnerable areas and identifies self-sustaining subsystems to prevent system collapse and minimize risk exposure to extreme events. In this paper, we develop a graph-based reinforcement learning (GRL) model to design AI-assisted switching in transmission networks that mitigate risks by strategically isolating affected areas for self-healing during power outages. To train the AI agent with an awareness of the transmission network’s topology for decision-making, the adjacency graph of the transmission network is mapped to the convolutional network of the reinforcement learning model. The intentional controlled islanding problem is modeled as a Markov decision process, where the optimal switching policy is learned using the GRL approach. The dynamic model of the transmission network used in training the agent incorporates generator inertia and load behavior for stability and frequency control, while also reducing power flow mismatches within the formed islands. The effectiveness of this framework is demonstrated using the modified IEEE 118-bus network and validated using dynamic simulations.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101937"},"PeriodicalIF":5.6,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144895565","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 community resilience and energy efficiency through centralized peer-to-peer energy trading with a case study on photovoltaic systems and dynamic pricing","authors":"Fushuai Wang ,&nbsp;Yizhi Qin ,&nbsp;Mengxia Wang ,&nbsp;Junda He ,&nbsp;Qiang Sheng","doi":"10.1016/j.segan.2025.101933","DOIUrl":"10.1016/j.segan.2025.101933","url":null,"abstract":"<div><div>The current paper offers a unified management framework for peer-to-peer (P2P) community energy sharing. The framework is coordinated via an Energy Pawn agent, which facilitates dynamic energy exchange among prosumers equipped with photovoltaic generation and flexible demand. Also, a centralized storage and time-varying electricity pricing are incorporated. The trading, arbitrage, and dispatching procedures are optimized by the Energy Pawn to assess a balance between local generation, consumption, and grid interaction. The proposed framework’s key contribution is its Multi-Resolution Forecast Assimilation and Market-Adaptive Dispatch (MFAMAD) strategy. This strategy decomposes the load profiles into multiple temporal resolutions using wavelet transforms, which leads to enhanced forecast precision. The prediction errors are corrected via real-time measurements to support the adaptive dispatch under uncertainty and improve system resilience. The framework transmits the dynamic price signals to prosumers and encourages the load shifting according the individual utility maximization. In this regard, the peak load was reduced and the grid stability enhanced. The optimization procedure aimed at minimizing social costs and reliance on the main grid and maximizing the renewable energy utilization. Subsequently, it offers a scalable and adaptive solution for future energy communities aiming to balance autonomy, efficiency, and sustainability.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101933"},"PeriodicalIF":5.6,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926181","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":"Study on market mechanisms to promote local consumption of distributed renewable energy resources","authors":"Zuyu Li,&nbsp;Lang Gao","doi":"10.1016/j.segan.2025.101930","DOIUrl":"10.1016/j.segan.2025.101930","url":null,"abstract":"<div><div>China's distributed renewable energy systems in rural area are facing challenges like reverse overload, power quality deviations, and limited grid access due to inadequate transformer capacity planning and high PV concentration in solar-rich areas. While transformer upgrades are fundamental, cost and timelines make market-driven flexibility solutions more viable. This study proposes three market mechanisms—Price-Driven-Response (PDR), Explicit-Response (ER), and Mixed-Response (MR)—to ordinate local power generation and consumption in rural areas in China. Simulations on a transformer court in China show that all three models effectively reduce reverse overload, with PDR model takes lower direct costs by avoiding third-party operators. However, insufficient user-side flexibility or \"load migration\" effects require complementary solutions like energy storage or expanded flexibility resources, since it is found that physical resource limitations cannot be fully offset by market mechanisms alone, necessitating integrated solutions. Key implementation recommendations include: targeting regions with high flexibility potential and intelligent electrification; prioritizing simpler PDR/ER models, advancing MR if needed; ensuring adjustable pricing/cost-sharing frameworks to balance equity and scarcity principles; scaling successful cases across grids while monitoring aggregated load migration impacts.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"44 ","pages":"Article 101930"},"PeriodicalIF":5.6,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144895564","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}