Sustainable Energy Grids & Networks最新文献_第2页

Optimising green hydrogen injection into gas networks: Decarbonisation potential and influence on quality-of-service indexes 优化向天然气网络注入绿色氢气：脱碳潜力及对服务质量指数的影响

IF 4.8 2区工程技术

Sustainable Energy Grids & Networks Pub Date : 2024-10-16 DOI: 10.1016/j.segan.2024.101543

引用次数: 0

Optimal operation of multi-energy carriers considering energy hubs in unbalanced distribution networks under uncertainty 不确定性条件下不平衡配电网络中考虑能源枢纽的多能源载体优化运行

IF 4.8 2区工程技术

Sustainable Energy Grids & Networks Pub Date : 2024-10-15 DOI: 10.1016/j.segan.2024.101538

{"title":"Optimal operation of multi-energy carriers considering energy hubs in unbalanced distribution networks under uncertainty","authors":"","doi":"10.1016/j.segan.2024.101538","DOIUrl":"10.1016/j.segan.2024.101538","url":null,"abstract":"<div><div>This article presents a two-stage stochastic programming model to address the dispatching scheduling problem in an energy hub, considering an unbalanced active low-voltage (LV) network. A three-phase version of the second-order cone relaxation of DistFlow AC optimal power flow (AC-OPF) is employed to incorporate unbalanced network constraints, while the objective minimizes the Local Energy Community (LEC) operational cost. The model results have been validated using OpenDSS, encompassing energy losses, voltage levels, and active/reactive power. Likewise, a comparative analysis between the three-phase model and the traditional single-phase model, using a modified version of the IEEE European LV Test Feeder as a case study, reveals interesting differences, such that the single-phase model underestimates voltage limits during photovoltaic (PV) system operation and overestimates energy purchased from the main grid, compared with the three-phase model. Similarly, the comparison results reveal that discrepancies between the single and three-phase models intensify as the power injected from PV systems rises. This notably impacts the total energy purchased from the grid, battery operation, and the satisfaction of thermal consumption through electricity. Finally, while the three-phase model offers valuable insights into security levels for voltage and grid energy purchase, its longer computational time makes it more suitable for strategic use rather than daily operational frameworks.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532467","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}

引用次数: 0

Optimal design model for a public-private Renewable Energy Community in a small Italian municipality 意大利小城市公私可再生能源社区的优化设计模型

IF 4.8 2区工程技术

Sustainable Energy Grids & Networks Pub Date : 2024-10-15 DOI: 10.1016/j.segan.2024.101545

{"title":"Optimal design model for a public-private Renewable Energy Community in a small Italian municipality","authors":"","doi":"10.1016/j.segan.2024.101545","DOIUrl":"10.1016/j.segan.2024.101545","url":null,"abstract":"<div><div>Energy communities (ECs) are currently seen as an important pathway to increase the participation of citizens in the energy transition. The present work proposes a mixed integer linear programming (MILP) optimization model that provides the optimal design of a renewable energy community (REC) in terms of best technologies and chosen members. Different objective functions are investigated so that the REC’s design can be studied from different perspectives. The first objective is related to the minimization of total annualized costs (TAC) while the second one regards the maximization of the shared energy. The model considers one year as time horizon with a timestep of one hour. A case study is defined by considering the municipality of Plodio, located in the northwest of Italy, as the host of a potential REC. A total of 11 possible users are introduced, including municipality and residential users. In cost-optimized scenarios, the REC design is characterized by fewer users but has the maximum installation of PV modules. However, most of the revenues are obtained due to the selling of electricity and not due to its sharing. When the shared energy is maximized, all the candidate members are chosen and technologies such as wind turbines and batteries are exploited to increase the number of periods characterized by the injection of electricity into the grid. It is also noted that higher electricity prices increase the profitability of the investment. Finally, it is shown that the inclusion of an industrial user positively influences energy-sharing indicators.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572246","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}

引用次数: 0

Acquiring better load estimates by combining anomaly and change point detection in power grid time-series measurements 结合电网时间序列测量中的异常点和变化点检测，获取更好的负荷估计值

IF 4.8 2区工程技术

Sustainable Energy Grids & Networks Pub Date : 2024-10-15 DOI: 10.1016/j.segan.2024.101540

引用次数: 0

Distributed photovoltaic power forecasting based on personalized federated adversarial learning 基于个性化联合对抗学习的分布式光伏发电功率预测

IF 4.8 2区工程技术

Sustainable Energy Grids & Networks Pub Date : 2024-10-14 DOI: 10.1016/j.segan.2024.101537

{"title":"Distributed photovoltaic power forecasting based on personalized federated adversarial learning","authors":"","doi":"10.1016/j.segan.2024.101537","DOIUrl":"10.1016/j.segan.2024.101537","url":null,"abstract":"<div><div>Existing distributed photovoltaic (PV) power forecasting methods fail to address the impact of sample scarcity and heterogeneity in PV power data. Moreover, training a single prediction model proves challenging to meet the personalized forecasting needs of different PV stations in distributed environments. This paper proposes a personalized federated generative adversarial network (PFedGAN)-based DPV power forecasting method. Leveraging the federated learning (FL) framework, it achieves collaborative training of prediction models among DPV stations while preserving data privacy. y introducing generative adversarial networks (GAN) and personalized strategy optimization into the FL training process, it alleviates data scarcity issues and reduces the impact of data heterogeneity. A TimesNet-DeepAR (TNE-DeepAR) power prediction model is designed, where the TimesNet module extracts correlations between PV power data from different time periods, and the DeepAR module facilitates PV power prediction, mitigating the effects of meteorological factors' multi-periodic variations on PV power. Experimental results show that the proposed hybrid prediction model reduces the average mean absolute percentage error (MAPE) by 30–40 % compared to single models. The proposed approach reduces the MAPE by 9.79 % compared to traditional methods and by 49.62 % for PV stations with scarce data.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532468","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}

引用次数: 0

Operational reliability and non-deterministic resilience estimation of active distribution network incorporating effect of real-time dynamic hosting capacity 包含实时动态托管能力影响的主动配电网运行可靠性和非确定弹性估计

IF 4.8 2区工程技术

Sustainable Energy Grids & Networks Pub Date : 2024-10-11 DOI: 10.1016/j.segan.2024.101541

{"title":"Operational reliability and non-deterministic resilience estimation of active distribution network incorporating effect of real-time dynamic hosting capacity","authors":"","doi":"10.1016/j.segan.2024.101541","DOIUrl":"10.1016/j.segan.2024.101541","url":null,"abstract":"<div><div>Active distribution networks are increasingly recognized essential for achieving sustainable development goals. Traditionally, hosting capacity was considered as a static measure for planning distributed energy resources integration. This work introduces the concept of dynamic hosting capacity, which recurrently re-evaluates hosting capacity in response to erratic modern grid conditions. The introduction of dynamic hosting capacity facilitated testing variations of power injection from minimum to 100 %, sustaining power system governing parameter limits. This embarked the need of operational reliability assessment and enhancing situational awareness for optimum power injection and balance. To achieve operational reliability analysis based on dynamic hosting capacity, hybrid probability distribution function-based Monte Carlo simulation is proposed. This resulted in 85–90 %. improvisation of solar photovoltaic generation and load alignment, as this methodology provides comprehensive and accurate assessment of system performance under diverse uncertainties. The framework's validation includes projection of time-varying operational reliability indices, over time independent reliability indices i.e., dynamic loss of load probability, dynamic loss of load expectation, dynamic loss of load duration, dynamic loss of load frequency, dynamic grid margin, and dynamic grid dependency. This resulted in 30 % improvement in assessment of grid margin, facilitating reliable uncertainty handling competence. Additionally, expectation maximization algorithm is proposed to evaluate non-deterministic resilience due to ambiguities associated with solar photovoltaic distributed energy resources. The non-deterministic resilience assessment testified 80 % bounce-back rate, demonstrating better adaptability and robustness. The entire analysis is conducted in MATLAB, validated using Typhoon Hardware-in-Loop real-time platform, and compared with existing literatures to demonstrate its effectiveness.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532465","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}

引用次数: 0

A reinforcement learning-based energy management strategy for fuel cell electric vehicle considering coupled-energy sources degradations 基于强化学习的燃料电池电动汽车能源管理策略，考虑耦合能源退化问题

IF 4.8 2区工程技术

Sustainable Energy Grids & Networks Pub Date : 2024-10-11 DOI: 10.1016/j.segan.2024.101548

{"title":"A reinforcement learning-based energy management strategy for fuel cell electric vehicle considering coupled-energy sources degradations","authors":"","doi":"10.1016/j.segan.2024.101548","DOIUrl":"10.1016/j.segan.2024.101548","url":null,"abstract":"<div><div>An effective energy management strategy (EMS) is crucial for fuel cell electric vehicles (FCEVs) to optimize fuel consumption and mitigate fuel cell (FC) aging by efficiently distributing power from multiple energy sources during vehicle operation. The Proton Exchange Membrane Fuel Cell (PEMFC) is a preferred main power source for fuel cell vehicles due to its high power density, near-zero emissions, and low corrosivity. However, it is expensive, and its lifespan is significantly affected by rapid power fluctuations. To address this issue, the proposed method of minimizing instantaneous cost (MIC) reduces the frequency of abrupt changes in the FC load. Additionally, by analyzing driving condition characteristics, the Ensemble Bagging Tree (EBT) facilitates real-time recognition (WCI) of composite conditions, thereby enhancing the EMS's adaptability to various operating conditions. This paper introduces an advanced EMS based on double-delay deep deterministic policy gradient (TD3) deep reinforcement learning, which considers energy degradation, economic efficiency, and driving conditions. Training results indicate that the TD3-based policy, when integrated with WCI and MIC, not only achieves a 32.6 % reduction in FC system degradation but also lowers overall operational costs and significantly accelerates algorithm convergence.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432145","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}

引用次数: 0

Optimization of the electric vehicle charging strategy problem for sustainable intercity travels with multiple refueling stops 多加油站可持续城际旅行的电动汽车充电策略优化问题

IF 4.8 2区工程技术

Sustainable Energy Grids & Networks Pub Date : 2024-10-11 DOI: 10.1016/j.segan.2024.101546

{"title":"Optimization of the electric vehicle charging strategy problem for sustainable intercity travels with multiple refueling stops","authors":"","doi":"10.1016/j.segan.2024.101546","DOIUrl":"10.1016/j.segan.2024.101546","url":null,"abstract":"<div><div>Electric vehicle (EV) drivers considering long-distance trips still face range anxiety due to the limited range of EVs and the scarcity of charging stations. Thus, it becomes important to ensure the feasibility of the selected route and determine an optimal charging strategy. As a crucial aspect of decision support for EV drivers, this study proposes a mixed integer linear programming (MILP) approach for the EV charging strategy problem (EVCSP), incorporating a piecewise linear approximation technique to address the challenges posed by nonlinear charging times. The proposed optimization model, namely CSPM determines where, when, and how much to charge an EV for a specified route to minimize travel time and cost. The solution time of large-scale test problems and a case study on Türkiye reveal the robustness and reliability of the CSPM. Furthermore, two multi-objective optimization methods (the weighted sum and the lexicographic method) are applied to the case study, and the results are analyzed. The results indicate that the travel cost is more sensitive to the selected charging strategy, with a range of 46.09 % across the applied charging strategies, whereas travel time remains more resilient, with a maximum fluctuation of 19.77 %. A comparative analysis with a full charging strategy reveals that the CSPM reduces the travel time by 60.1 % and improves the cost efficiency by 105.72 %.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532471","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}

引用次数: 0

Reliability evaluation of direct current gathering system in onshore wind farm based on reliability block diagram-sequential monte carlo 基于可靠性框图-序列蒙特卡罗的陆上风电场直流集电系统可靠性评估

IF 4.8 2区工程技术

Sustainable Energy Grids & Networks Pub Date : 2024-10-11 DOI: 10.1016/j.segan.2024.101549

{"title":"Reliability evaluation of direct current gathering system in onshore wind farm based on reliability block diagram-sequential monte carlo","authors":"","doi":"10.1016/j.segan.2024.101549","DOIUrl":"10.1016/j.segan.2024.101549","url":null,"abstract":"<div><div>The full DC wind power generation system has effectively overcome the harmonic resonance, reactive power transmission, and other problems of the traditional AC wind power system, which has broad prospects for development. As a key component of the mentioned system, the reliability of the collection system is critical to the safe and stable operation of the entire onshore wind farm. Firstly, this paper investigates the key equipment and topology of the onshore wind farm DC collection system. Secondly, considering both the internal components and external environment of the wind farm, a component outage probability model based on weather factors is constructed to provide accurate data for the reliability evaluation of the DC collection system of the wind farm. The Reliability Block Diagram is used to analyze the internal logical connection of different topologies of onshore wind farm DC collection systems in detail. Then, a reliability evaluation method of an onshore full DC wind farm collection system based on Reliability Block Diagram-Sequential Monte Carlo is proposed. Finally, a 50 MW onshore wind farm is studied as a sample to compare and analyze the assessment results of the reliability of different collection system topologies. The results show that the reliability of the DC collection system of onshore wind farms has significant advantages.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142532464","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}

引用次数: 0

Optimization of offshore wind farm cluster transmission system topology based on Stackelberg game 基于 Stackelberg 博弈的海上风电场集群输电系统拓扑优化

IF 4.8 2区工程技术

Sustainable Energy Grids & Networks Pub Date : 2024-10-11 DOI: 10.1016/j.segan.2024.101542

引用次数: 0