Sustainable Energy Grids & Networks最新文献

{"title":"Soft open points scheduling in unbalanced active distribution networks based on multi-agent graph reinforcement learning","authors":"Liu Hong,&nbsp;Li Qizhe,&nbsp;Zhang Qiang,&nbsp;Xu Zhengyang,&nbsp;He Xingtang","doi":"10.1016/j.segan.2025.101689","DOIUrl":"10.1016/j.segan.2025.101689","url":null,"abstract":"<div><div>This paper proposes an innovative unbalanced ADN operation strategy utilizing multi-agent graph reinforcement learning (MAGRL), where SOPs are scheduled to mitigate the three-phase unbalance and minimize system loss. The SOP scheduling problem in unbalanced ADN is modeled as a multi-agent partially observable Markov decision process (POMDP). Then, a direct approach based Backward/Forward Sweep (BFS) power flow model is proposed in our framework to provide precise power flow results within a few iterations to the training environment. The graph convolution networks (GCNs) are embedded in the policy network to further improve the agent capability of learning and capturing spatial correlations and topological linkages among nodes in complex unbalanced ADN, hence promoting the effectiveness of action strategy for the agents. This model has been tested on modified three-phase unbalanced IEEE 123-node system and IEEE 8500-node system. The results illustrate the notable regulation capability of the proposed method for unbalanced ADN.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101689"},"PeriodicalIF":4.8,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705185","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":"Resilience-oriented distributed generation planning of distribution network under multiple extreme weather conditions","authors":"Junji Zhou,&nbsp;Xiong Wu,&nbsp;Xuhan Zhang,&nbsp;Fengshuo Xiao,&nbsp;Yifan Zhang,&nbsp;Xiuli Wang","doi":"10.1016/j.segan.2025.101675","DOIUrl":"10.1016/j.segan.2025.101675","url":null,"abstract":"<div><div>Planning resilient and elastic distribution networks has become an effective strategy for withstanding extreme weather events. However, conventional research often overlooks the impact of multiple extreme weather conditions and only considers planning for one specific type of extreme weather. This paper proposes a resilience-oriented distributed generation planning approach for distribution networks, which takes into account multiple extreme weather conditions. Firstly, a line fault probability model is established to capture the impact of typhoons, rainstorms, ice and snow weather. Secondly, a fault scenario generation and simplification method based on modified monte carlo simulation and <em>k</em>-means clustering is proposed to ensure the representativeness and computational efficiency of the selected scenarios. Additionally, a two-stage stochastic mixed integer programming model is introduced to enhance system resilience through distributed generation configuration and network topology reconstruction. The first stage focuses on determining the number, location, and size of distributed generation with economic objectives, while the second stage addresses the recovery method after an uncertain extreme event based on the distributed generation configuration obtained in the first stage. The proposed model is applied to modified IEEE 33-bus system and IEEE 123-bus system. Compared with the conventional method, the DG configuration results are consistent and the solution time is reduced by 70–80 %, achieving a balance between accuracy and efficiency. Furthermore, it effectively reduces load shedding by nearly 90 % by optimizing DG utilisation.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101675"},"PeriodicalIF":4.8,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679631","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":"Large-scale generation of geo-referenced power distribution grids from open data with load clustering","authors":"Alfredo Oneto,&nbsp;Blazhe Gjorgiev,&nbsp;Filippo Tettamanti,&nbsp;Giovanni Sansavini","doi":"10.1016/j.segan.2025.101678","DOIUrl":"10.1016/j.segan.2025.101678","url":null,"abstract":"<div><div>The availability of real power distribution grid data is often restricted due to privacy concerns and the lack of digitized representations, limiting spatially-resolved assessments of these systems. This inaccessibility has motivated the development of methods for generating synthetic grids. However, existing methods face challenges such as computational intractability for large-scale zones, restrictive topological assumptions, insufficient representation of electrical components, and inadequate consideration of geographical constraints. This work addresses the challenges by developing a model for the large-scale generation of synthetic geo-referenced low- and medium-voltage grids using publicly accessible data. It comprises a geographic load clustering algorithm, a procedure for generating graphical grid layouts, and a method for selecting operational topologies and line types. The model’s effectiveness and computational performance are demonstrated by generating synthetic low- and medium-voltage grids for Switzerland, with all generated grids made openly available.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101678"},"PeriodicalIF":4.8,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679628","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":"Behavioral analytics for optimized self-scheduling in sustainable local multi-carrier energy systems: A prospect theory approach","authors":"Sobhan Dorahaki ,&nbsp;S.M. Muyeen ,&nbsp;Nima Amjady ,&nbsp;Syed Shuibul Qarnain ,&nbsp;Mohamed Benbouzid","doi":"10.1016/j.segan.2025.101679","DOIUrl":"10.1016/j.segan.2025.101679","url":null,"abstract":"<div><div>The transition towards sustainable energy systems demands innovative solutions to overcome the challenges of integrating diverse energy carriers, fluctuating market dynamics, and operator decision-making complexities. The active involvement of local multi-carrier energy systems (LMCES) as virtual power plants in upstream energy markets is particularly hindered by the limitations of conventional optimization methods, which fail to capture the nuanced behavioral aspects of decision-making. This paper presents a novel prescriptive behavioral analytics framework for LMCES self-scheduling, integrating insights from prospect theory to address the operator’s behavioral tendencies, including loss aversion, subjective risk attitudes, and mental reference points. By embedding these behavioral considerations into a mixed integer linear programming (MILP) model, the proposed approach accounts for real-world decision-making complexities often overlooked in conventional economic theories based on rationality. Comparative analyses demonstrate that the proposed framework not only enhances the modeling of LMCES operators’ decision-making processes but also improves energy scheduling efficiency and supports sustainable energy transitions. The findings provide actionable insights for optimizing LMCES operations, advancing their role in achieving energy sustainability goals.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101679"},"PeriodicalIF":4.8,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643797","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 scalable energy internet approach for hop regulated peer-to-peer power trading with connectivity and preference constraints","authors":"Neethu Maya ,&nbsp;Bala Kameshwar Poolla ,&nbsp;Seshadhri Srinivasan ,&nbsp;Alessandra Parisio ,&nbsp;Narasimman Sundararajan ,&nbsp;Suresh Sundaram","doi":"10.1016/j.segan.2025.101668","DOIUrl":"10.1016/j.segan.2025.101668","url":null,"abstract":"<div><div>Incentives to maximize Peer-to-Peer (P2P) power trading and the establishment of consumer-friendly distributed power markets are essential contributions to the decarbonization of the power sector. This paper presents a Connectivity and Preference Constrained Hop-Regulated Approach for Peer-to-Peer Trading (CPHPT) in sparsely connected communities with reduced infrastructure requirements. The CPHPT approach leverages graph theory to optimize P2P subscriber matching by regulating the maximum hops between the nodes in each routed path of P2P exchange. Simulations using real-world datasets in a 10-home community demonstrate that the CPHPT increases community participation by 29.49%, with P2P power exchanges comparable to full connectivity at reduced infrastructure requirements. When scaled to a 100-home community, the CPHPT approach achieves a marginal performance difference of 2.71% compared to full connectivity while lowering the connectivity infrastructure by 93.4%. The CPHPT approach has a mean runtime of 8.9 s for a 3-h window with 30-min intervals in a 100-home community, indicating its scalability and feasibility for real-time implementation.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101668"},"PeriodicalIF":4.8,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704196","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":"Extreme value statistics of peak residential electricity demand: Effect of aggregation and moving-average smoothing","authors":"M.W. Jack ,&nbsp;M.M. Bandi","doi":"10.1016/j.segan.2025.101674","DOIUrl":"10.1016/j.segan.2025.101674","url":null,"abstract":"<div><div>Understanding the fluctuations in power demand is critical to the integration of variable renewable resources and the design of future electricity grids. We present an approach to determining the full statistical distribution of peak values of power demand based on extreme value statistics. We apply this method to characterizing the tails of the consumer demand distribution and exploring how peak electricity demand scales with aggregation over increasing numbers of consumers and moving-average smoothing at increasing timescales for two very different consumer groups. The results show evidence of fat tail distributions for some consumers. For both consumer groups, extreme values scale as an inverse power law with aggregation over increasing numbers of consumers and as a decaying exponential with the timescale of moving-average smoothing. Peak reduction by moving-average smoothing is much more sensitive to different sets of consumers than aggregation. As smoothing about a moving average is the primary effect of battery storage, this means that, in general, battery storage cannot play the same role as aggregation in reducing peak demand.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101674"},"PeriodicalIF":4.8,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679630","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":"On the participation of energy storage systems in reserve markets using Decision Focused Learning","authors":"Ángel Paredes ,&nbsp;Jean-François Toubeau ,&nbsp;José A. Aguado ,&nbsp;François Vallée","doi":"10.1016/j.segan.2025.101677","DOIUrl":"10.1016/j.segan.2025.101677","url":null,"abstract":"<div><div>Battery Energy Storage Systems (BESSs) are particularly well-suited to deepen the decarbonisation of reserve markets, traditionally dominated by non-renewable generators. BESSs operators often rely on Predict-Then-Optimise (PTO) methods to participate in these markets, which focus on forecasting market conditions without directly considering the impact of subsequent decisions during training. Recently, learning models have evolved to incorporate decision outcomes during training, known as Decision Focused Learning (DFL) methodologies, which have the potential to increase market benefits. This paper introduces a DFL approach that integrates the decision-making process of BESSs when participating in reserve markets into the training of their predictive models. By expressing the optimisation problem as a primal–dual mapping using the Karush–Kuhn–Tucker (KKT) conditions, the proposed DFL method enables the regressor to learn from the BESS’s decisions, refining its predictions based on observed outcomes, improving decision accuracy and market performance. Results show that the proposed DFL approach outperforms traditional PTO methods, with up to a 9.5% increase in profits for a case study based on the Belgian secondary reserve market, highlighting its effectiveness in managing the complexities of dynamic market conditions.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101677"},"PeriodicalIF":4.8,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631992","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":"Machine learning-driven multi-agent-based AC optimal power flow with effective dataset creation for data privacy and interoperability","authors":"Burak Dindar ,&nbsp;Can Berk Saner ,&nbsp;Hüseyin K. Çakmak ,&nbsp;Veit Hagenmeyer","doi":"10.1016/j.segan.2025.101672","DOIUrl":"10.1016/j.segan.2025.101672","url":null,"abstract":"<div><div>As power systems continue to evolve, the demand for effective collaboration among institutions has grown, driven by the challenges of balancing production and consumption, as well as by the increasing need for redispatch. Despite this, achieving interoperability in such a complex landscape is often hindered by concerns regarding data privacy. In response to these challenges, our paper presents a novel approach: a multi-agent system (MAS)-based AC optimal power flow (AC-OPF), empowered by machine learning (ML), designed for safeguarding data privacy and promoting interoperability. In the proposed method, the technical operator agent creates an effective dataset using n-ball, multivariate Gaussian distribution (MGD), and perturbation techniques. It also formulates valid inequalities to reduce the search space. Then, neural network (NN) models are developed to map the feasible space of the AC-OPF by utilizing only active power. Notably, these models conceal both the grid topology and sensitive data before transmission to another agent. Subsequently, the market operator agent integrates these NN models and valid inequalities into a mixed-integer linear programming (MILP) problem. This resulting MILP can be solved with various market based objective functions and constraints considering the power system limits. Thus, if there are private market-based data, they are kept confidential without being shared with the other agent. In addition, mapping is performed using the effective dataset generation technique that ensures a balanced representation of feasible and infeasible data points around the boundary. Furthermore, this effective dataset contributes to achieving remarkable accuracy in NN models, even with a relatively small volume of data points. The results on 30-, 57-, and 162-bus benchmark models of PGLib-OPF demonstrate that the proposed method can be effectively conducted while concurrently enhancing data privacy, and thus interoperability among institutions.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101672"},"PeriodicalIF":4.8,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642603","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":"Optimal electric vehicle navigation through smart grid synergy and innovative routing strategies","authors":"Sima Maleki,&nbsp;Mahdiyeh Eslami,&nbsp;Mahdi Jafari Shahbazzadeh,&nbsp;Alimorad Khajehzadeh","doi":"10.1016/j.segan.2025.101669","DOIUrl":"10.1016/j.segan.2025.101669","url":null,"abstract":"<div><div>The synergistic integration of the smart grid and smart transportation network presents a wealth of data pertaining to the main grid and transportation infrastructure, offering valuable insights for electric vehicle (EV) owners to navigate their vehicles efficiently. However, the unpredictable nature of traffic conditions, charging prices, and waiting times at charging stations poses a significant challenge to achieving optimal EV navigation. In response to this challenge, a novel navigation system is proposed that strives to minimize both total travel time and charging costs at charging stations. The approach of this paper involves leveraging a unique methodology to determine the shortest path to the optimal charging station, which will be one of the renewable charging station, non-renewable charging station and mobile EV chargers, employing Dijkstra's algorithm for efficient route planning. The system takes into account real-time data on traffic dynamics, charging station availability, and pricing fluctuations to dynamically adjust navigation routes, ensuring that EV owners can make informed decisions on the go. To validate the effectiveness of the proposed approach, a series of experiments are conducted. The results demonstrate the system's ability to optimize both travel time and charging costs, providing a practical solution for EV navigation in the face of unpredictable variables. These findings validate the effectiveness of the proposed system in optimizing EV navigation under dynamic and uncertain conditions, offering practical solutions for diverse EV mobility configurations.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101669"},"PeriodicalIF":4.8,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631991","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":"Incentive-based demand response program with phase unbalance mitigation: A bilevel approach","authors":"Abhishek Tiwari ,&nbsp;Bablesh K. Jha ,&nbsp;Naran M. Pindoriya","doi":"10.1016/j.segan.2025.101671","DOIUrl":"10.1016/j.segan.2025.101671","url":null,"abstract":"<div><div>This article proposes an adaptable incentive framework for an incentive-based demand response (IBDR) program. The framework is based on changes in demand from end-consumers using the bilevel approach to optimize the scheduling of flexible loads. The distribution system operator (DSO) acts as a leader with a multi-objective optimization problem. The objective is to maximize profit while minimizing network energy loss and peak load at the point of common coupling. The DSO’s strategy involves changing demand-based adaptive incentive offers to enhance end-consumers participation in the DR program. Furthermore, the DSO aimed to mitigate phase unbalancing as an objective to address power quality issues caused by imbalances in phase voltage and power. Aggregators are regarded as followers in the bilevel approach, aiming to maximize incentives for mitigating the discomfort caused by scheduling flexible energy resources in the IBDR program. By utilizing Karush-Kuhn–Tucker conditions, the previously mentioned bilevel problem transformed into a single-level optimization problem. This work examined two case studies to determine the effectiveness of the proposed adaptable IBDR model. The efficacy of the proposed framework was assessed on a modified IEEE 25 bus unbalanced distribution system. The evaluation reveals that adaptive IBDR confers advantages to all participants, including DSO and end-consumers.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101671"},"PeriodicalIF":4.8,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143593192","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}