Sustainable Energy Grids & Networks最新文献

{"title":"Analysis on improvement of photovoltaic hosting capacity through the flexible connection policy","authors":"Jae Hyeon Shin ,&nbsp;Jin Hyeok Kim ,&nbsp;Seung Wan Kim ,&nbsp;Dam Kim","doi":"10.1016/j.segan.2025.102071","DOIUrl":"10.1016/j.segan.2025.102071","url":null,"abstract":"<div><div>The rapid integration of renewable energy sources, including photovoltaics (PV), presents operational challenges for distribution networks, such as reverse power flow, voltage fluctuations, and network congestion. In industrial parks, growing demand for on-site and shared renewables has spurred interest in deploying microgrids, where the concentration of variable generation creates hosting capacity constraints at feeder and substation. Conventional firm connection policies impose strict capacity limits based on worst-case scenarios, delaying interconnection and underutilization of the grid. To address these limitations, this study introduces a time-series bi-level optimization framework for evaluating flexible connection policies that allow controlled PV curtailment. A linearized power flow-based hosting capacity optimization model is developed and applied to evaluate maximum hosting capacity and optimize the siting of PV systems under firm and flexible connection cases. A case study on an IEEE 40-bus networked microgrid system demonstrates that allowing modest annual PV curtailment (1–11 %) can significantly enhance the hosting capacity of the network—up to 45 % greater than that achieved under firm connection approaches—while maintaining or even increasing the total annual renewable generation. Furthermore, an economic analysis reveals that although curtailment may slightly reduce developer profitability, significant savings from deferred grid upgrades provide substantial benefits to both microgrid and distribution system operators. Therefore, we establish a cost-effective pathway for large-scale renewable energy integration by proposing practical incentive mechanisms, such as net present value and benefit-cost ratio-based compensation. These findings emphasize the importance of strategically flexible connection policies in enabling efficient, economical, and high-capacity renewable energy integration into future power grids.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102071"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685697","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":"Applying two-stage risk-based market structures for energy hub-based plug-in electric vehicles using information decision gap theory and a hybrid recurrent convolutional network","authors":"A. Heidari ,&nbsp;R.C. Bansal ,&nbsp;R. Bo","doi":"10.1016/j.segan.2025.102085","DOIUrl":"10.1016/j.segan.2025.102085","url":null,"abstract":"<div><div>This paper investigates the optimal operation of an energy hub engaged in both day-ahead and real-time trading. A two-stage optimization framework Information Gap Decision Theory (IGDT) for day-ahead bidding and stochastic programming with Monte Carlo scenarios for real-time recourse is applied. Risk-neutral, risk-averse, and risk-taking strategies are considered to capture different risk preferences. The hub integrates combined heat and power, renewable energy, plug-in electric vehicles, and vehicle-to-grid and grid-to-vehicle technologies. Price and load forecasts are generated using a hybrid recurrent convolutional network (HRCN). Results highlight the trade-off between risk management and economic performance: costs are 16.5 % higher in the risk-averse mode than in the risk-neutral mode, and 55.6 % higher than in the risk-taking mode. Natural gas accounts for the most in the risk-taking case, at ∼33 % of the total cost. Under the tested conditions, the proposed IGDT–stochastic–HRCN framework improves expected costs relative to baselines, though outcomes may vary under different market rules, fuel prices, or volatility regimes.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102085"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738365","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":"Nonlinear integrated energy market optimization based on smoothing approaches","authors":"Jian Jia,&nbsp;Weifeng Chen","doi":"10.1016/j.segan.2025.102089","DOIUrl":"10.1016/j.segan.2025.102089","url":null,"abstract":"<div><div>To address the computational complexity of the mixed-integer programming (MIP) model in integrated energy system (IES) optimization, a smooth nonlinear programming (NLP) method based on a bi-level optimization model is proposed. In this approach, the upper-level model maximizes the profit of the energy hub (EH) by coordinating supply and demand decisions with the lower-level system. Integer variables are replaced with continuous variables through a smoothing method, which reduces computational complexity while preserving operational equivalence. Relaxed complementarity constraints are incorporated into the KKT conditions to ensure that the smoothed nonlinear model can be effectively solved. Furthermore, incorporating the full nonlinear power flow (NLPF) model in the optimization allows a more accurate representation of the system’s intrinsic characteristics. This approach also helps prevent potential safety risks associated with constraint violations in linear power flow (LPF) models. The case study results demonstrate that the smooth NLP model produces results comparable to the mixed-integer linear programming (MILP) model, and demonstrate its good applicability in handling nonlinear problems.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102089"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738305","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":"Uncertainty quantification in load profiles with rising EV and PV adoption: The case of residential, industrial, and office buildings","authors":"Aiko Fias,&nbsp;Md Umar Hashmi ,&nbsp;Geert Deconinck","doi":"10.1016/j.segan.2025.102078","DOIUrl":"10.1016/j.segan.2025.102078","url":null,"abstract":"<div><div>The integration of photovoltaic (PV) generation and electric vehicle (EV) charging introduces significant uncertainty in electricity consumption patterns, particularly at the distribution level. This paper presents a comparative study for selecting metrics for uncertainty quantification (UQ) for net load profiles of residential, industrial, and office buildings under increased DER penetration. A variety of statistical metrics is evaluated for their usefulness in quantifying uncertainty, including, but not limited to, standard deviation, entropy, ramps, and distance metrics. The proposed metrics are classified into baseline-free, with baseline and error-based. These UQ metrics are evaluated for increased penetration of EV and PV. The results highlight suitable metrics to quantify uncertainty per consumer type and demonstrate how net load uncertainty is affected by EV and PV adoption. Additionally, it is observed that joint consideration of EV and PV can reduce overall uncertainty due to compensatory effects of EV charging and PV generation resulting from temporal alignment during the day. Uncertainty reduction is observed across all datasets and is most pronounced for the office building dataset.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102078"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685701","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":"Uncertainty interval assessment method for joint output of renewable power stations considering time-varying volatility correlations","authors":"Xilong Li ,&nbsp;Feng Zheng ,&nbsp;Jianjian Zhao ,&nbsp;Cheng Tang ,&nbsp;Lingfeng Zhou ,&nbsp;Ziwen Liu ,&nbsp;Zhenghua Chen","doi":"10.1016/j.segan.2026.102157","DOIUrl":"10.1016/j.segan.2026.102157","url":null,"abstract":"<div><div>The construction of wind-solar renewable power stations exhibits regional clustering characteristics. Compared with individual stations, the time-varying volatility correlations among wind and solar resources amplify the uncertainties in their joint output, leading to reduced accuracy in forecasting results. To address this, this paper proposes an uncertainty assessment method for the joint output of wind-solar stations based on the Long Short Term Memory-Generalized Auto Regressive Conditional Heteroskedasticity-Difference (LSTM-GARCH-D) model and R-vine Copula. First, a probability distribution function for wind-solar unit output uncertainty is constructed by fitting residuals using LSTM-GARCH-D. Specifically, the difference series of wind-solar point forecasts is introduced as a correction term to the traditional GARCH model to enhance residual fitting accuracy. Second, R-vine Copula functions are employed to establish interdependencies among multiple residual variables, effectively capturing time-varying volatility correlations in multi-dimensional wind-solar output. This approach resolves the challenge of jointly characterizing multi-dimensional uncertain variables and enables uncertainty assessment for coordinated wind-solar output. Finally, case studies using operational data from a regional wind-solar cluster validate the effectiveness and superiority of the proposed method. Assessing wind–solar joint output uncertainty helps select more reliable forecasts and improves dispatch decision credibility under high renewable penetration. By revealing worst-case scenarios within a reasonable prediction error range, the proposed uncertainty assessment quantitatively supports reserve capacity configuration—avoiding excessive conservatism from blind reserve expansion. It also enables dispatch optimization, reducing wind and solar curtailment and improving power system economic performance.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102157"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395625","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 transformer and CNN-based hybrid model for localization detection of false data injection attacks in smart grids","authors":"Huan Pan ,&nbsp;Hang Yang ,&nbsp;Chunning Na ,&nbsp;Jiayi Jin","doi":"10.1016/j.segan.2026.102150","DOIUrl":"10.1016/j.segan.2026.102150","url":null,"abstract":"<div><div>False data injection attacks (FDIAs) pose a serious threat to the secure and economic operation of smart grids, particularly in medium- and large-scale networks where attacks may occur at multiple locations. Failure to detect and localize FDIAs in a timely manner prevents grid operators from isolating compromised buses, thereby hindering effective loss mitigation. To address this challenge, this paper proposes a deep learning-based FDIA localization detection model. The proposed model consists of three main components: coordinate attention (CA), a convolutional neural network (CNN), and a Transformer. The CA mechanism enhances the feature representation capability of the network, while the CNN and Transformer extract local and global characteristics of the input tensor, respectively. Using the IEEE-14 and IEEE-39 bus systems as test cases, attacked measurement data are generated with PYPOWER, and the proposed Transformer+CNN-based model is evaluated against several benchmark methods. Experimental results demonstrate that the proposed hybrid model achieves superior FDIA localization performance.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102150"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395627","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":"Hierarchical multi-spatial-temporal scale coordinated optimization of power-transportation interconnected system based on dynamic user equilibrium with point queue","authors":"Min Hou ,&nbsp;Xinrui Liu ,&nbsp;Ruohan Fu ,&nbsp;Rui Wang ,&nbsp;Yushuai Li ,&nbsp;Zhengmao Li ,&nbsp;Qiuye Sun","doi":"10.1016/j.segan.2026.102146","DOIUrl":"10.1016/j.segan.2026.102146","url":null,"abstract":"<div><div>The rapid development of electric vehicles (EVs) has promoted the deep coupling of power distribution network (PDN) and traffic network (TN), which has brought great uncertainty and time coupling problems to the power-transportation interconnected system (PTIS). A hierarchical dynamic interaction optimization of PTIS considering both economic factors and uncertainty is constructed. First, in view of the time scale difference between PDN and TN, a hierarchical multi-spatial-temporal scale coordinated optimal framework of PTIS is constructed to coordinate the path selection of EVs and the dispatching strategy of PDN considering the uncertainty of renewable energy. Then, dynamic differential equations are adopted to describe the dynamic transmission process of traffic flow, and based on the queue problem within fast charging stations, a model considering the spatial-temporal dynamic traffic flow distribution based on dynamic user equilibrium with point queue is established. Furthermore, to address the issue of power distribution for charging of EVs under the same charging station, a model predictive control strategy is adopted to redistribute the power to meet the charging and discharging requirements of EVs with different demands. Finally, a simulation analysis is conducted to verify the effectiveness of the method proposed in this paper. Compared with SUE, the proposed dynamic queue update of FCS and DUE reduced the congestion degree of TN by 3.65% and the TN cost by 570,000 CNY.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102146"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395628","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":"Practical sensitivity-based optimization technique to solve the hosting capacity problem in unbalanced low voltage networks","authors":"Rubén Carmona-Pardo ,&nbsp;Rafael Morán-Corbacho ,&nbsp;Álvaro Rodríguez del Nozal ,&nbsp;Esther Romero-Ramos","doi":"10.1016/j.segan.2026.102140","DOIUrl":"10.1016/j.segan.2026.102140","url":null,"abstract":"<div><div>This work addresses the problem of computing the hosting capacity of distributed energy resources, both generation and demand, in a three-phase four-wire low voltage network. A new methodology, based on the use of voltage and current sensitivity coefficients to model the system, allows defining a second-order cone programming formulation that guarantees the convexity of the problem. This approach results in a very practical and accurate tool capable of solving the hosting capacity problem, for generation and demand hosting capacity, in large unbalanced distribution networks, regardless of the initial operating conditions or its radial or meshed topology, and considering the limiting constraints on voltages, currents, reverse power flows and voltage unbalances. Tests on numerous different real low-voltage networks demonstrate the practical usefulness of the tool, highlighting the accuracy of the results obtained. For the largest tested distribution network, a comparison has been included between the results obtained with the proposed methodology and those derived from using a Monte Carlo-based probabilistic approach, demonstrating the computational advantage of the new method and the good accuracy of the optimum obtained. With the new hosting capacity computation tool, it has been possible to identify technically safe scenarios that allow for the accurate quantification and localization of the nodes and phases to which the new generation/demand must be connected, reaching penetration levels of up to 32%/21% with respect to the transformer’s rated power.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102140"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147395632","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 new convex Stackelberg game theory oriented optimization model for resilient day-ahead planning of distribution network by optimal distributed generation pricing and incentive-based demand response program","authors":"Saeed Behzadi ,&nbsp;Mehdi Naserian","doi":"10.1016/j.segan.2026.102134","DOIUrl":"10.1016/j.segan.2026.102134","url":null,"abstract":"<div><div>Following severe disasters in distribution systems, distribution network operators (DNOs) should employ various methods to minimize load-shedding. A highly effective strategy is offering incentive-oriented rewards to consumers to reduce load in critical conditions. In this paper, two case studies have been compared under low-probability and high-impact (LPHI) outage conditions to indicate the impact of incentive-based demand response program (IBDRP) on load restoration. In the best case, the offered optimal incentive reward price to the consumers is determined based on the optimal pricing of distributed generation (DG) in critical conditions. These proposed prices have been obtained by taking into account the optimal benefit view of both consumers and DNOs. To reach an optimal solution for day-ahead pricing in resilient distribution system planning according to this point of view, the Stackelberg game theory (SGT) is utilized. On the other side, accurate day-ahead network load forecasting is obtained by using machine learning and classical methods. In addition, all the formulations have been convexified and implemented in GAMS software and tested in the IEEE 33-bus system. Finally, the Pareto optimization scenarios have been considered, and the optimal solution is reached by the fuzzy satisfying method.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102134"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077421","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":"Prosumers' peer-to-peer multi-energy transactions considering distributed energy resources and demand response","authors":"Mohammad Bagher Moradi ,&nbsp;Mohammad Hassan Nazari ,&nbsp;Hamed Nafisi ,&nbsp;Hossein Askarian Abyaneh ,&nbsp;Seyed Hossein Hosseinian ,&nbsp;Marco Merlo","doi":"10.1016/j.segan.2025.102113","DOIUrl":"10.1016/j.segan.2025.102113","url":null,"abstract":"<div><div>Smart grids support the integration of renewable resources and enable demand response, transforming consumers into prosumers who both generate and use energy. Prosumers can improve their economic outcomes by selling surplus energy through peer‑to‑peer (P2P) transactions instead of relying solely on the upstream grid. In residential microgrids, this can reduce energy costs while increasing revenues from surplus energy sales. This study investigates P2P energy sharing as a mechanism for energy exchange among prosumers and examines how different optimization objectives affect individual benefits in smart grid environments. Two primary objectives are considered: maximizing revenues from energy sales and minimizing energy procurement costs. The approach determines transactive energy through prosumer self‑scheduling and formulates a P2P model that maximizes social welfare. The objective functions are assessed under three scenarios: P2P energy sharing within a resource‑constrained smart grid, an expanded‑resource setting that evaluates the influence of additional prosumer capacity, and a multi‑energy hub in which participants can trade both electrical and thermal energy. Mixed‑integer linear programming simulations are carried out under two pricing schemes, with and without differentiation between renewable and conventional energy prices, and are complemented by a demand sensitivity analysis. The results indicate that prosumers prioritizing cost minimization achieve substantially lower energy expenses, with reductions between 2.2 % and 67.8 % compared with prosumers focused on revenue maximization. Furthermore, increased prosumer resources and energy price variations significantly affect profitability under each objective. Appropriate adjustments to resources and prices can enhance profits when energy sales are prioritized over cost reduction, as confirmed by sensitivity analysis and comparison with prior work.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"45 ","pages":"Article 102113"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925938","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}