Sustainable Energy Grids & Networks最新文献

{"title":"A coordinated charging/discharging strategy for EVs with flexible mobility in multi-temporary microgrids","authors":"Zhixian Wang ,&nbsp;Davide Falabretti ,&nbsp;Ying Wang ,&nbsp;Kaifeng Zhang","doi":"10.1016/j.segan.2025.101720","DOIUrl":"10.1016/j.segan.2025.101720","url":null,"abstract":"<div><div>The increasing occurrence of extreme weather events can lead to more major power blackouts, which attracts attention to the utilization of temporary microgrids (TMGs) in the restoration stage. However, single TMGs originating after a blackout usually have weak resilience, because of their limited available resources. Therefore, cooperation between microgrids needs urgently to be studied: to this end, this paper proposes a novel charging/discharging strategy for electric vehicles (EVs) to achieve optimal power coordination between TMGs, by taking advantage of EV’s temporal-spatial flexible mobility. First, a new user response willingness model for EVs is established considering move distance and charging compensation, and this model is combined with the EV eligibility assessment based on traveling time to evaluate the EV mobile possibility across TMGs. Then, a suitable resilience factor is proposed to measure the resilience of TMGs considering both the current and future operation conditions. Finally, a novel coordinated EV charging/discharging strategy exploiting the EVs’ mobility is developed based on a mixed integer optimization model, to enhance system resilience and reduce total regulation costs. A case study based on the realistic topology of Milan’s urban area is analyzed, comparing the results obtained with the strategy without EV spatial cooperation. As a result, the proposed strategy can enhance the resilience of TMGs by 20.93 % and reduce the total regulation cost which is paid to regulation resources for ensuring the power balance of each TMG by 8.43 %.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101720"},"PeriodicalIF":4.8,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143885979","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":"Sustainable local energy communities: The role of peer-to-peer trading, EVs, and RECs on social welfare and emissions","authors":"Elham Mokaramian ,&nbsp;Vito Calderaro ,&nbsp;Vincenzo Galdi ,&nbsp;Giuseppe Graber ,&nbsp;Lucio Ippolito ,&nbsp;Pierluigi Siano","doi":"10.1016/j.segan.2025.101715","DOIUrl":"10.1016/j.segan.2025.101715","url":null,"abstract":"<div><div>Peer-to-Peer (P2P) energy trading is known as a decentralized method of energy management in local energy communities (LECs), which allows consumers and prosumers to directly exchange energy. This model improves resource distribution, balances local demand and supply, and integrates renewable energy sources (RES) and distributed generation (DG), inducing energy independence and sustainability. This study introduces a novel P2P energy trading model for LECs that addresses three main objectives: maximizing welfare, minimizing environmental emissions, and minimizing grid consumption due to high costs and emissions. The proposed model includes RES, electric vehicles (EVs), charging stations, DG, and flexible storage, combined with a multi-objective approach for optimal energy management. In our study, the LEC has been clustered into three zones (residential, commercial, and industrial) each with specific energy needs and resources. These LECs allow for customized energy plans while fostering collaboration across sectors. The model also integrates EV charging stations, hydrogen-based systems (fuel cells and electrolyzers), and distributed electric storage to ensure efficient energy use. Moreover, centralized and decentralized storage and DG systems, enabling seamless energy exchanges both within and across zones, are considered. This cross-zone interaction, facilitated by the P2P trading energy enhances flexibility, optimizes resource use, and promotes energy autonomy. Additionally, the model integrates real-time energy management, allowing prosumers to dynamically manage energy consumption, storage, and trading. The flexibility of P2P exchanges between batteries, DGs, and EVs further improves efficiency, adaptability, and sustainability, making the system more resilient and environmentally friendly. To prove the superiority of the proposed method, three scenarios are considered as an independent operation of LECs, LECs equipped with batteries, and LECs utilizing P2P energy trading with batteries. P2P trading significantly reduces grid consumption by 2.7 % from Scenario 1–2 and 13.77 % from Scenario 2–3. Emissions are also reduced by 2.73 % between Scenario 1 and 2, and a further 13.77 % between Scenario 2 and 3.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101715"},"PeriodicalIF":4.8,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881840","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":"Distribution system state estimation for system identification and network model validation: An experience on a real low voltage network","authors":"Marta Vanin ,&nbsp;Reinhilde D’hulst ,&nbsp;Dirk Van Hertem","doi":"10.1016/j.segan.2025.101710","DOIUrl":"10.1016/j.segan.2025.101710","url":null,"abstract":"<div><div>Distribution network data in utility databases are known to present multiple issues that may lead to problematic results when used in physics-based engines, e.g., leading to constraint violations in (optimal) power flow. This paper discusses the application of state and parameter estimation methods to a real low voltage network, where power and voltage time series from digital meters are used to improve the utility’s network data. Good input data are crucial for the advanced decision support tools that are needed to manage networks with increased shares of low carbon technology.</div><div>Conventional state and parameter estimation methods leverage measurements from a single (or few) time stamp(s) to detect sparse, local data errors or sudden changes in the system (e.g., a line being de-energized). The methods in this paper differ in that their goal is to estimate “historical” states and reconstruct system parameters from scratch for <em>all</em> users and branches. This is possible through the augmentation of conventional state vectors (i.e., voltage phasors) to include asset properties (e.g., phase connectivity), and binding the asset states as time-independent throughout the time series.</div><div>Discussions of real-life experiences are uncommon, but valuable to highlight the differences between working with synthetic or field data. For example, the main contribution of this work rests in exploring the use of state estimation for the statistical validation of data-driven models for real networks, for which the ground-truth is not available (contrary to the case of synthetic data).</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101710"},"PeriodicalIF":4.8,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869827","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":"Expansion planning via decomposition to achieve fully renewable power and freshwater systems","authors":"Mubarak J. Al-Mubarak ,&nbsp;Antonio J. Conejo","doi":"10.1016/j.segan.2025.101713","DOIUrl":"10.1016/j.segan.2025.101713","url":null,"abstract":"<div><div>As the reliance on electricity for producing freshwater continues to grow, the development of an expansion planning model that captures the interdependency between power and freshwater systems becomes increasingly important. This paper proposes a two-stage stochastic expansion planning model that represents the interdependence of these systems and accounts for the uncertainties involved. The first stage represents investments for achieving fully renewable power and freshwater systems, while the subsequent stage represents the operation of both systems. The model accounts for both long-term uncertainties, which pertain to growth in power and freshwater demands, and short-term uncertainties, which pertain to the daily fluctuations in freshwater and power demands as well as in renewable production. Due to the complexity of representing the operation of both systems under numerous operating conditions, expansion planning models often become computationally burdensome. To reduce the computational burden, we propose an effective partitioning technique that relies on Benders’ decomposition, dividing the expansion planning problem into a sufficiently small master problem and numerous subproblems. To enhance convergence, we incorporate the operation constraints pertaining to the worst operating condition into the master problem. Numerical experiments underscore the efficacy of utilizing the proposed technique to solve the expansion planning of large-scale systems.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101713"},"PeriodicalIF":4.8,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859031","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":"Day-ahead joint market operation strategy of grid-connected wind farms with flexible allowable generation deviation rates","authors":"Tianhui Meng,&nbsp;Jilai Yu,&nbsp;Yufeng Guo","doi":"10.1016/j.segan.2025.101714","DOIUrl":"10.1016/j.segan.2025.101714","url":null,"abstract":"<div><div>The uncertainty of wind power output affects the efficient operation of the electricity spot market and has become a key factor restricting the participation of wind farms in the market. To this end, this paper proposes a day-ahead joint market operation strategy that considers allowable deviation rates of wind power output. Unlike traditional electricity markets which impose uniform deviation requirements on all wind farms, the main grid side provides a more diverse range of selectable deviation rates. The bidding strategy for wind farms in the joint day-ahead and balancing markets is explored, allowing them to independently select deviation rates and submit schedule curves and offer prices. A joint clearing model for the day-ahead energy-reserve and balancing market is established, incorporating the carbon emission trading costs of thermal power units, with the aim of minimizing the system operating cost. Numerical results indicate that compared with the traditional market participation method, the proposed strategy not only encourages wind farms to improve output accuracy, but also reflects the market economic principle of high quality and high price. Meanwhile, integrating carbon emission trading costs into the model helps to reduce carbon emissions while ensuring the economic operation of the system.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101714"},"PeriodicalIF":4.8,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143854935","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 Load Forecasting of distribution power systems based on empirical copulas","authors":"Pål Forr Austnes ,&nbsp;Celia García-Pareja ,&nbsp;Fabio Nobile ,&nbsp;Mario Paolone","doi":"10.1016/j.segan.2025.101708","DOIUrl":"10.1016/j.segan.2025.101708","url":null,"abstract":"<div><div>Accurate and reliable electricity load forecasts are becoming increasingly important as the share of intermittent resources in the system increases. <em>Distribution System Operators</em> (DSOs) are called to accurately forecast their production and consumption to place optimal bids in the day-ahead market. Violations of their dispatch-plan requires activation of reserve-power which has a direct cost for the DSO, and also necessitates available reserve-capacity. Forecasts must account for the volatility of weather-parameters that impacts both the production and consumption of electricity. If DSO-loads are small or lower-granularity forecasts are needed, parametric statistical methods may fail to provide reliable performance since they rely on a priori statistical distributions of the variables to forecast. In this paper, we introduce a <em>Probabilistic Load Forecast</em> (PLF) method based on Empirical Copulas (ECs). The model is data-driven, does not need a priori assumption on parametric distribution for variables, nor the dependence structure (copula). It employs a kernel density estimate of the underlying distribution using beta kernels that have bounded support on the unit hypercube. The method naturally supports variables with widely different distributions, such as weather data (including forecasted ones) and historic electricity consumption, and produces a conditional probability distribution for every time step in the forecast, which allows inferring the quantiles of interest. The proposed non-parametric approach differs significantly from previous forecasting methods based on copulas, which typically uses copulas to model hierarchical dependence. Our approach is highly flexible and can produce meaningful forecasts even at very low aggregated levels (e.g. neighborhoods). The bandwidth of the beta kernel density estimators is optimized using <em>Integrated Square Error</em> (ISE) and such optimization can be performed online (i.e. without knowing the realization). We also investigate rule-of-thumb and <em>Quantile Loss</em> (QL) as objectives for the bandwidth-optimization. We present results from an open dataset and showcase the strength of the model with respect to <em>Quantile Regression</em> (QR) using standard probabilistic evaluation metrics.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101708"},"PeriodicalIF":4.8,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873808","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":"Ensuring secure connectivity in smart vehicular to grid technology: An elliptic curve-based authentication key agreement framework","authors":"Samiulla Itoo ,&nbsp;Faheem Syeed Masoodi ,&nbsp;Musheer Ahmad","doi":"10.1016/j.segan.2025.101696","DOIUrl":"10.1016/j.segan.2025.101696","url":null,"abstract":"<div><div>The seamless and secure operation of Vehicle-to-Grid (V2G) networks is paramount for the future of smart grid technology, where Electric Vehicles (EVs) not only draw power but also supply it back to the grid. However, the integration of EVs into the grid introduces significant security and privacy challenges, particularly in the exchange of sensitive information between EV owners and charging station aggregators. This paper presents a novel authentication key agreement protocol specifically designed to address these challenges within V2G networks. The proposed protocol leverages Elliptic Curve Cryptography (ECC) and a robust hash function to establish a secure communication channel, ensuring that personal data remains protected from a wide array of security threats, including eavesdropping, impersonation, and replay attacks. The protocol’s effectiveness and security are rigorously validated through simulation analysis using the Scyther tool, which confirms its resilience against potential vulnerabilities. Moreover, the protocol is designed for compatibility with various encryption techniques, ensuring its adaptability across different V2G network configurations. Our analysis also highlights the protocol’s efficiency, demonstrating minimal processing and communication overhead, making it suitable for real-time applications in resource-constrained environments. The findings of this study suggest that the proposed protocol not only enhances the security and privacy of V2G networks but also contributes to the broader goal of creating a more secure, reliable, and user-friendly smart grid ecosystem. By safeguarding the exchange of sensitive information, this protocol ensures that V2G networks can operate safely, fostering greater confidence among EV owners and facilitating the wider adoption of this innovative technology.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101696"},"PeriodicalIF":4.8,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828255","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":"Regression based anomaly detection in electric vehicle state of charge fluctuations through analysis of electric vehicle charging infrastructure Data","authors":"Sagar Babu Mitikiri ,&nbsp;Yash Tiwari ,&nbsp;Vedantham Lakshmi Srinivas ,&nbsp;Mayukha Pal","doi":"10.1016/j.segan.2025.101704","DOIUrl":"10.1016/j.segan.2025.101704","url":null,"abstract":"<div><div>With the increase in the number of electric vehicles (EV), there is a need for the development of the EV charging infrastructure (EVCI) to facilitate fast charging, thereby mitigating the EV congestion at charging stations. The role of the public charging station depot is to charge the vehicle, prioritizing the achievement of the desired state of charge (SoC) value for the EV battery or charging till the departure of the EV, whichever occurs first. The integration of cyber and physical components within EVCI defines it as a cyber physical power system (CPPS), increasing its vulnerability to diverse cyber attacks. When an EV interfaces with the EVCI, mutual exchange of data takes place via various communication protocols like the Open Charge Point Protocol (OCPP), and IEC 61850. Unauthorized access to this data by intruders leads to cyber attacks, potentially resulting in consequences like energy theft, and revenue loss. These scenarios may cause the EVCI to incur higher charges than the actual energy consumed or the EV owners to remit payments that do not correspond adequately to the amount of energy they have consumed. This article proposes an EVCI architecture connected to the utility grid and uses the EVCI data to identify the anomalies or outliers present in the EV transmitted data, particularly focusing on SoC irregularities. The proposed methodology involves utilizing a ridge regression based machine learning (ML) model for predicting changes in the SoC. The adversaries have the capability of spoofing these change in SoC values, consequently making the EVCI incapable of achieving the desired task. Three distinct spoofing techniques namely, decimal shifting, incremental array spoofing, and random spoofing are implemented on the data and subsequently tested with the proposed methodology. The results show that the proposed methodology detects the anomaly accurately and also classifies the type of spoofing that causes the anomaly.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101704"},"PeriodicalIF":4.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834106","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":"DNkS: A distance-based neighborhood k-search algorithm for determining meter–transformer connectivity in low-voltage grids","authors":"Iker Garcia ,&nbsp;Roberto Santana ,&nbsp;Jennifer Gonzalez","doi":"10.1016/j.segan.2025.101707","DOIUrl":"10.1016/j.segan.2025.101707","url":null,"abstract":"<div><div>The Distance-based Neighborhood k-Search (DN<span><math><mi>k</mi></math></span>S) algorithm, introduced in this article, offers a novel approach to enhancing meter–transformer connectivity modeling in low-voltage grids. By employing a local <span><math><mi>k</mi></math></span>-neighborhood search strategy, DN<span><math><mi>k</mi></math></span>S effectively subdivides the grid into manageable sections, ensuring robust connectivity assessments. Utilizing metrics such as Adjusted Mutual Information and Accuracy, DN<span><math><mi>k</mi></math></span>S demonstrated superior performance in trials, achieving up to 100% accuracy in certain cases, significantly outperforming existing state-of-the-art methods such as deep convolutional time-series clustering and spectral embedding-based meter–transformer mapping. Although DN<span><math><mi>k</mi></math></span>S is effective, its performance critically depends on the accuracy of meter and transformer coordinates. In comparative analyses across various network configurations, DN<span><math><mi>k</mi></math></span>S consistently outperformed other methods, affirming its utility and effectiveness. The versatile nature of the algorithm would allow its integration into existing systems in various ways, for example, through an API or a web interface. Implementing DN<span><math><mi>k</mi></math></span>S promises substantial improvements in the reliability and accuracy of utility network models, directly contributing to enhanced grid management practices.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101707"},"PeriodicalIF":4.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834108","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":"Analytical introduction of uncertainty into long term distribution systems decision-making","authors":"J.-P. Dib ,&nbsp;M.-C. Alvarez-Herault ,&nbsp;O. Ionescu Riffaud ,&nbsp;B. Raison","doi":"10.1016/j.segan.2025.101711","DOIUrl":"10.1016/j.segan.2025.101711","url":null,"abstract":"<div><div>Distribution system planning consists in imagining the evolution of the design and operation of distribution systems over a horizon going from several years to decades. There is a lack of standard methodologies that integrate the growing number of uncertainties. In this article, our aim is to provide a framework for integrating uncertainty, from the diagnosis of network constraints and the setup of solutions to their economic evaluations. To do so, we start by modeling the network under load uncertainty. This allows us to use probabilistic power flow calculations for constraint estimations. We use these to determine the best strategy, between line reinforcement and demand response. Finally, we use a compound option model to assess the economic validity of undertaking a unique action, or series of actions, when uncertainty is taken into account. This framework was successfully applied to the IEEE 70 bus network with a discussion on DSO’s options: ”waiting for more information”, ”investing” or ”activating demand response”. Results show that demand response is not optimal on the lower part of the network but should be used on the upper part since only some nodes would see under-voltage constraints during 0.2% of the year. Also, a sensitivity analysis on the cost of demand response enables drawing the DSO’s willingness to pay considering two scenarios (expected load evolution and worst case).</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"42 ","pages":"Article 101711"},"PeriodicalIF":4.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859027","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}