Smart Energy最新文献_第2页

A mixed-integer linear programming model for BESS sizing optimization considering aging effects and emission costs 考虑老化和排放成本的BESS尺寸优化混合整数线性规划模型

IF 5 Q2 ENERGY & FUELS

Smart Energy

Pub Date : 2026-02-01 Epub Date: 2025-12-26 DOI: 10.1016/j.segy.2025.100225

Nguyen Quoc Minh, Tran Van Dai, Pham Minh Hoang, Pham Hong Hai, Le Thi Minh Chau

With the rapid expansion of renewable energy sources (RES), battery energy storage systems (BESS) have become essential for ensuring grid stability, reliability, and operational efficiency. However, optimizing BESS sizing is a complex challenge that requires balancing economic, technical, and environmental factors while considering the long-term impact of battery degradation and replacement costs. This study presents a comprehensive optimization model for determining the optimal capacity of BESS within a microgrid, explicitly incorporating battery aging effects and associated lifecycle costs. The proposed model employs a mixed-integer linear programming (MILP) approach to minimize the total system cost, which includes investment, operation, and replacement expenses, while ensuring that load demand is met, RES integration is maximized, and system reliability is maintained. By considering the degradation of BESS performance over time, the model provides a more accurate estimation of long-term economic and technical feasibility. Simulation results validate the effectiveness of the model in optimizing BESS sizing and installation costs. Additionally, the study evaluates different BESS technologies and examines the impact of various factors, such as RES penetration and emission costs on overall system performance. The findings offer valuable insights into developing cost-effective BESS operation schedules and management strategies, contributing to improved energy efficiency and sustainability in microgrid applications.

随着可再生能源（RES）的快速发展，电池储能系统（BESS）已成为确保电网稳定性、可靠性和运行效率的关键。然而，优化BESS尺寸是一项复杂的挑战，需要平衡经济、技术和环境因素，同时考虑电池退化和更换成本的长期影响。本研究提出了一个综合优化模型，用于确定微电网中BESS的最佳容量，明确地考虑了电池老化效应和相关的生命周期成本。该模型采用混合整数线性规划（MILP）方法，在保证满足负荷需求、RES集成最大化和保持系统可靠性的前提下，使包括投资、运行和更换费用在内的系统总成本最小化。通过考虑BESS性能随时间的退化，该模型提供了对长期经济和技术可行性的更准确的估计。仿真结果验证了该模型在优化BESS尺寸和安装成本方面的有效性。此外，该研究还评估了不同的BESS技术，并检查了各种因素的影响，例如RES渗透和排放成本对整体系统性能的影响。研究结果为制定具有成本效益的BESS运行计划和管理策略提供了宝贵的见解，有助于提高微电网应用的能源效率和可持续性。

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引用次数: 0

Integrating system dynamics modeling and EnergyPLAN for national energy planning: The case of Georgia 在国家能源规划中整合系统动力学建模和EnergyPLAN：以格鲁吉亚为例

IF 5 Q2 ENERGY & FUELS

Smart Energy

Pub Date : 2026-02-01 Epub Date: 2026-02-24 DOI: 10.1016/j.segy.2026.100232

Alaize Dall-Orsoletta , Géremi Gilson Dranka , Gaioz Partskhaladze , Paula Ferreira

Transitioning to sustainable and resilient energy systems is a key challenge for countries with rising demand, climate vulnerabilities, and geopolitical constraints. In Georgia, substantial renewable energy (RE) potential, growing emissions, and seasonal reliance on energy imports highlight the need for diversified, low-carbon planning. This study models Georgia's long-term generation capacity expansion to 2050 under alternative scenarios using a soft-linked hybrid modeling framework that couples system dynamics (SD) with EnergyPLAN. The SD model captures long-term investment dynamics, learning effects, and policy-driven transitions, while EnergyPLAN evaluates the operational feasibility of resulting system configurations under hourly constraints. Results indicate that variable renewable energy (VRE) can satisfy domestic demand and reduce annual net electricity imports if supported by timely transmission and grid investments. The installed capacity of wind and utility-scale solar energy can reach up to 2.5 GW by 2050, while net imports can decrease from 3.3 TWh to below 1.5 TWh, and power sector emissions can be halved relative to 2023 levels. Hydropower remains central, expanding to 5.3 GW in high-renewable scenarios, though seasonal deficits persist. Methodologically, the study contributes by demonstrating the value of integrating long-term SD-based scenario exploration with high-resolution operational analysis to assess the consistency of energy transition pathways. Future research should focus on strengthening the coupling framework through iterative feedbacks, expanding sectoral coverage, and leveraging improved data availability to support more robust and policy-relevant energy system analysis.

对于需求不断增长、气候脆弱性和地缘政治制约的国家来说，向可持续和有弹性的能源系统过渡是一项关键挑战。在格鲁吉亚，巨大的可再生能源潜力、不断增长的排放量以及对能源进口的季节性依赖凸显了对多元化低碳规划的需求。本研究使用将系统动力学（SD）与EnergyPLAN相结合的软链接混合建模框架，对格鲁吉亚到2050年的长期发电能力扩张进行了建模。SD模型捕获了长期投资动态、学习效果和政策驱动的转换，而EnergyPLAN则在小时约束下评估最终系统配置的操作可行性。结果表明，在及时的输电和电网投资支持下，可变可再生能源（VRE）可以满足国内需求并减少年净电力进口。到2050年，风能和公用事业规模的太阳能装机容量可达到2.5吉瓦，净进口量可从3.3太瓦时降至1.5太瓦时以下，电力部门的排放量可相对于2023年的水平减少一半。水电仍然是核心，在高可再生能源情景下扩大到5.3吉瓦，尽管季节性赤字仍然存在。在方法上，该研究通过展示将基于sd的长期情景探索与高分辨率操作分析相结合的价值，来评估能源转换路径的一致性。未来的研究应侧重于通过迭代反馈来加强耦合框架，扩大部门覆盖范围，并利用改进的数据可用性来支持更稳健和与政策相关的能源系统分析。

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引用次数: 0

Analysis of bidirectional EV charging infrastructures within industrial DC grids 工业直流电网内电动汽车双向充电基础设施分析

IF 5 Q2 ENERGY & FUELS

Smart Energy

Pub Date : 2026-02-01 Epub Date: 2026-01-30 DOI: 10.1016/j.segy.2026.100227

Henning Rahlf , Lukas Knorr , Simon Althoff , Henning Meschede

Industrial electrification is increasing to reduce fossil fuel dependence, alongside a growing share of volatile renewables. A secure and reliable energy supply is crucial for industry, leading to a shift from centralised to decentralised grid structures. DC microgrids becoming increasingly popular in industry, since they enable energy recuperation from braking, reduce components and cables, and integrate storage and local generation to manage supply interruptions or peak loads. EVs add further synergies by serving as mobile storage units, helping to store and redistribute locally generated renewable energy. This paper analyses how EV integration in droop-controlled DC grids can contribute to a more stable, low-emission and peak-reduced load profile to the supply grid through load shifting and bridge interruptions. A droop-controlled DC grid model has been developed, incorporating an EV charging park based on probability functions. Scalable scenarios allow for diverse condition analysis using an energy management system that utilises fuzzy logic and sequential MILP optimisation. It has been shown that a 7% improvement of coefficient represented grid-serving behaviour is possible by load shifting. It has also been demonstrated that an optimised EMS can reduce the demand-based CO₂ emissions by 41 kg for a representative day compared to a fuzzy logic EMS. At the same time peak load is decreased yielding a more constant residual load. These results highlight the potential of a controlled bidirectional charging infrastructure in DC grids and underscore the need to explicitly consider charging processes to ensure a residual load as constant as possible.

工业电气化正在增加，以减少对化石燃料的依赖，同时不稳定的可再生能源的份额也在增加。安全可靠的能源供应对工业至关重要，这将导致电网结构从集中式向分散式的转变。直流微电网在工业中越来越受欢迎，因为它们可以从制动中回收能量，减少组件和电缆，并集成存储和本地发电以管理供应中断或峰值负荷。电动汽车作为移动存储单元，有助于储存和重新分配当地生产的可再生能源，从而进一步增强了协同效应。本文分析了电动汽车在下垂控制的直流电网中的集成如何通过负荷转移和桥式中断为电网提供更稳定、低排放和降峰的负荷。建立了一种基于概率函数的包含电动汽车充电园的下垂控制直流电网模型。可扩展的场景允许使用利用模糊逻辑和顺序MILP优化的能源管理系统进行多种状态分析。它已经表明，7%的改进系数表示电网服务行为是可能的负荷转移。研究还表明，与模糊逻辑EMS相比，优化后的EMS可以在一个代表日减少41公斤的基于需求的二氧化碳排放量。同时，峰值负荷降低，产生更恒定的剩余负荷。这些结果强调了在直流电网中控制双向充电基础设施的潜力，并强调了明确考虑充电过程以确保剩余负载尽可能恒定的必要性。

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引用次数: 0

Sustainable energy transition towards self-sufficiency for densely populated regions: the case of Belgium 人口稠密地区向自给自足的可持续能源转型：比利时的案例

IF 5 Q2 ENERGY & FUELS

Smart Energy

Pub Date : 2026-02-01 Epub Date: 2026-02-16 DOI: 10.1016/j.segy.2026.100231

Rasul Satymov, Mahdi Dashti, Tansu Galimova, Christian Breyer

The energy transition is driven by climate change, emission reduction targets, and the declining cost of renewable energy technologies. This study examines Belgium's transition to a 100% renewable energy system, considering the country's high population density, limited land availability, and high energy use. Specifically, the potential of ocean energy, such as offshore wind power, floating offshore solar photovoltaics, and wave power, is investigated to achieve energy self-sufficiency. Using the EnergyPLAN model for hourly simulations across the power, heat, transport, and industry sectors, the research compares the current system against two 2050 scenarios: full self-sufficiency and e-fuel imports. The results suggest that while self-sufficiency is attainable through significant investment in hydrogen storage and chemical synthesis, importing e-fuels remains the more cost-effective strategy. Both scenarios, however, show a significant decrease in annual energy system costs, with 16.9-22.4 b€ in 2050 compared to 40.4 b€ in 2019. Additionally, the integration of agrivoltaics, hybrid photovoltaics-wind plants, and wave power can optimise land use. Achieving self-sufficiency requires larger initial investments but offers long-term economic and environmental advantages. This study offers a path for Belgium's defossilisation, highlighting the significance of local renewable energy sources and international cooperation in trading e-fuels.

能源转型受到气候变化、减排目标和可再生能源技术成本下降的推动。本研究考察了比利时向100%可再生能源系统的过渡，考虑到该国的高人口密度、有限的土地可用性和高能源消耗。具体来说，研究了海洋能源的潜力，如海上风电、浮动海上太阳能光伏发电和波浪能，以实现能源自给自足。该研究利用EnergyPLAN模型对电力、供热、运输和工业部门进行了每小时一次的模拟，将当前系统与2050年的两种情景进行了比较：完全自给自足和电子燃料进口。结果表明，虽然通过对氢储存和化学合成的大量投资可以实现自给自足，但进口电子燃料仍然是更具成本效益的策略。然而，这两种情景都显示出年度能源系统成本的显著下降，2050年为16.9亿至224亿欧元，而2019年为404亿欧元。此外，农业发电、混合光伏-风力发电厂和波浪能的整合可以优化土地利用。实现自给自足需要更大的初始投资，但具有长期的经济和环境优势。这项研究为比利时的去化石化提供了一条道路，突出了当地可再生能源和国际合作在电子燃料贸易中的重要性。

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引用次数: 0

Requirements analysis for Model Predictive Control in a decentralized district heating network 分布式集中供热网络模型预测控制的需求分析

IF 5 Q2 ENERGY & FUELS

Smart Energy

Pub Date : 2025-11-01 Epub Date: 2025-09-20 DOI: 10.1016/j.segy.2025.100188

Theda Zoschke , Christian Wolff , Armin Nurkanović , Gregor Rohbogner , Daniel Weiß , Lilli Frison , Moritz Diehl , Axel Oliva

This study introduces a method to derive requirements for non-linear formulations in optimization problems for Model Predictive Control (MPC) of district heating networks. Those formulations become particularly relevant in decentralized networks where thermohydraulic effects stemming from pressure and temperature distribution impact the optimal dispatch schedule of producers. This is illustrated through a case study of the network in Weil am Rhein, Germany. Initially, a linear MPC formulation that neglects thermohydraulic dynamics was evaluated using one year of measurement data, revealing potential cost reductions of 14.3%. These savings primarily result from reduced operation of fossil fuel boilers and increased utilization of Combined Heat and Power plants. Subsequently, hydraulic simulations and monitoring data were analyzed, revealing that at least one of the production sites is unable to supply its installed capacity into the network during high-load scenarios due to hydraulic limitations. Furthermore, the analysis of thermal losses suggested that supply temperature optimization has an additional cost-saving potential of approximately 1.8%. The study concludes that future versions of the optimization framework require the consideration of pressure losses and pumping limitations to enhance operational reliability, while also recognizing additional improvement potential offered by supply temperature optimization.

本文介绍了一种推导区域供热网络模型预测控制（MPC）优化问题非线性公式要求的方法。这些配方在分散网络中尤为重要，因为压力和温度分布产生的热工效应会影响到生产者的最佳调度计划。通过对德国莱茵河畔韦尔（Weil am Rhein）的网络进行案例研究，可以说明这一点。最初，利用一年的测量数据对忽略热水力动力学的线性MPC配方进行了评估，结果显示，该配方的潜在成本降低了14.3%。这些节省主要是由于减少了化石燃料锅炉的运行和增加了热电联产电厂的利用。随后，对水力模拟和监测数据进行了分析，发现由于水力限制，至少有一个生产基地无法在高负荷情况下向网络提供其装机容量。此外，热损失分析表明，优化供电温度可以额外节省约1.8%的成本。该研究得出结论，未来版本的优化框架需要考虑压力损失和泵送限制，以提高运行可靠性，同时也要认识到供应温度优化提供的额外改进潜力。

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引用次数: 0

Coordinated energy systems in decentralized districts: Evaluating the cellular approach for improved grid stability and renewable integration 分散地区的协调能源系统：评估改善电网稳定性和可再生能源整合的蜂窝方法

IF 5 Q2 ENERGY & FUELS

Smart Energy

Pub Date : 2025-11-01 DOI: 10.1016/j.segy.2025.100215

Lukas Richter , Volker Lenz , Martin Dotzauer , Joachim Seifert

引用次数: 0

A comprehensive evaluation of prediction techniques and their influence on model predictive control in smart energy storage systems 智能储能系统预测技术及其对模型预测控制的影响

IF 5 Q2 ENERGY & FUELS

Smart Energy

Pub Date : 2025-11-01 Epub Date: 2025-09-04 DOI: 10.1016/j.segy.2025.100202

Ulrich Ludolfinger , Thomas Hamacher , Maren Martens

The increasing share of intermittent renewable energy calls for intelligent building energy management systems to maintain grid stability. A widely used method for operating on-site storage is model predictive control (MPC), whose effectiveness heavily depends on forecast accuracy. This paper systematically evaluates the impact of prediction models on MPC performance in smart energy storage systems (SESS). Using a three-year, multi-building dataset with 15 min resolution, we compare five forecasting methods, linear model, XGBoost, RNN, TimeMixer, and TimesNet, for load, PV generation, and electricity price prediction. While XGBoost achieves the lowest mean squared error (MSE) and yields the highest revenue gain of 104% over a no-storage baseline during a four-month winter–spring test period, other models reveal a mismatch between forecast accuracy and control performance. Notably, the linear model, ranking mostly lowest in MSE, delivers the third-highest revenue (73%), nearly on par with the second best (79%). This illustrates that prediction accuracy alone is not a reliable proxy for control quality. Even the best realistic setup remains far from the ideal benchmark using perfect forecasts (235% gain). Daily retraining improves some models substantially (linear model to 105%) but has limited effect on others (XGBoost to 107%). These findings emphasize three key insights: (1) standard metrics like MSE may misrepresent the utility of forecasts for control, (2) errors across multiple inputs compound degradation in MPC, and (3) frequent retraining can mitigate losses. Overall, the results underscore the importance of robust forecasting and carefully chosen loss functions in the smart energy systems concept.

间歇性可再生能源的份额不断增加，需要智能建筑能源管理系统来维持电网的稳定。模型预测控制（MPC）是一种应用广泛的现场存储操作方法，其有效性在很大程度上取决于预测精度。本文系统地评估了预测模型对智能储能系统（SESS）中MPC性能的影响。使用15分钟分辨率的三年多建筑数据集，我们比较了五种预测方法，线性模型，XGBoost， RNN， TimeMixer和TimesNet，用于负荷，光伏发电和电价预测。在为期四个月的冬春测试期间，XGBoost实现了最低的均方误差（MSE），并在无存储基线的情况下获得了104%的最高收益，但其他模型显示，预测精度与控制性能之间存在不匹配。值得注意的是，线性模型虽然在MSE中排名最低，但却提供了第三高的收入（73%），几乎与第二高的收入（79%）持平。这说明预测精度本身并不是控制质量的可靠代表。即使是最现实的设定，也与使用完美预测（235%的涨幅）的理想基准相差甚远。每天的再训练大大提高了一些模型（线性模型提高到105%），但对其他模型的影响有限（XGBoost提高到107%）。这些发现强调了三个关键的见解：(1)像MSE这样的标准指标可能会歪曲预测对控制的效用；(2)MPC中多个输入的复合退化误差；(3)频繁的再训练可以减轻损失。总的来说，结果强调了在智能能源系统概念中稳健预测和精心选择损失函数的重要性。

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引用次数: 0

Feasibility study of a renewable energy community using stochastic methods: a case-study in Genoa city 基于随机方法的可再生能源社区可行性研究——以热那亚市为例

IF 5 Q2 ENERGY & FUELS

Smart Energy

Pub Date : 2025-11-01 Epub Date: 2025-10-24 DOI: 10.1016/j.segy.2025.100212

Johan Augusto Bocanegra , Vincenzo Bianco , Mattia De Rosa , Federico Scarpa , Corrado Schenone , Luca Antonio Tagliafico

Renewable energy communities (RECs) provide a novel approach to organizing the production-consumption of renewable energy, involving multiple stakeholders who generate and utilize electricity from renewable sources (commonly wind turbines or solar panels). The REC's economic feasibility depends on sociotechnical factors that are location-dependent and determine costs and benefits. A significant advantage is the shared energy, which balances the energy production and consumption. Approximate estimations of shared energy can be derived from monthly-based models; a more comprehensive analysis requires an hourly-based model. This study develops a stochastic methodology to assess the feasibility of RECs under uncertainty. The approach combines Monte Carlo simulations with hourly energy balance and economic evaluation. The methodology is applied to a condominium-scale case in Genoa, Italy, as a representative example, but can be generalized to other urban contexts. The proposed case study involves a cluster of private buildings with a PV infrastructure and some apartments (consumers) that participate in the REC. This analysis aims to assess the feasibility of the REC under various scenarios, considering factors such as installed power capacity and the number of apartments comprising the community. The results of this study provide valuable insights into the viability of forming a REC in private buildings, offering a methodology for stakeholders involved in sustainable energy planning. The proposed approach can be extrapolated to other locations by selecting the proper parameters.

可再生能源社区（RECs）提供了一种组织可再生能源生产和消费的新方法，涉及从可再生能源（通常是风力涡轮机或太阳能电池板）产生和利用电力的多个利益相关者。REC的经济可行性取决于地理位置相关的社会技术因素，这些因素决定了成本和收益。一个显著的优势是共享能源，它平衡了能源的生产和消费。共享能量的近似估计可以从基于月的模型中得到；更全面的分析需要一个基于小时的模型。本研究发展了一种随机方法来评估不确定条件下RECs的可行性。该方法将蒙特卡罗模拟与小时能量平衡和经济评估相结合。该方法应用于意大利热那亚的一个公寓规模的案例，作为一个代表性的例子，但可以推广到其他城市环境。建议的案例研究涉及一组拥有光伏基础设施的私人建筑物和一些参与可再生能源中心的公寓（消费者）。该分析旨在评估可再生能源中心在各种情况下的可行性，考虑到装机容量和组成社区的公寓数量等因素。本研究的结果为在私人建筑中建立可再生能源中心的可行性提供了宝贵的见解，为参与可持续能源规划的利益相关者提供了一种方法。通过选择适当的参数，可以将所提出的方法外推到其他位置。

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引用次数: 0

Shifting toward dynamic pricing of electricity: What did we learn from the 2021–2024 energy crises? 向动态电价转变：我们从2021-2024年能源危机中学到了什么？

IF 5 Q2 ENERGY & FUELS

Smart Energy

Pub Date : 2025-11-01 Epub Date: 2025-10-17 DOI: 10.1016/j.segy.2025.100210

Sini Numminen , Mikko Jalas , Salvatore Ruggiero , Arina Värä

As the share of variable renewable energy sources grows, balancing electricity supply and demand increasingly relies on demand response (DR). One important way to enable DR is through dynamic pricing (DP), which encourages consumers to adjust the timing of their electricity use based on price signals. Yet, despite regulatory support and technological progress, the adoption of DP among domestic energy consumers remains limited. This study examines the role of Finnish electricity retailers in promoting DR through DP, using the lens of institutional complexity. We draw on 66 interviews and data on the markets and product offerings from 2021 to 2024, a time when companies needed to respond to energy crises affecting electricity retail. Our findings reveal that despite the energy crises pushed most electricity retailers in Finland to actively offer dynamic pricing as their risk management strategy, the industry did not converge uniformly towards this option. Instead, stable, flat electricity prices continue to hold a dominant position in the Finnish retail market. The resurgence of flat electricity tariffs can be attributed to retailer scepticism regarding consumer interest, societal concerns about pricing vulnerability, loyalty towards regional customers, and the conflicting principles of market efficiency versus responsibilities of regulated network servicing and management.

随着可变可再生能源份额的增长，平衡电力供需越来越依赖于需求响应（DR）。实现DR的一个重要方法是通过动态定价（DP），它鼓励消费者根据价格信号调整用电时间。然而，尽管有监管支持和技术进步，国内能源消费者对发展规划的采用仍然有限。本研究从制度复杂性的角度考察了芬兰电力零售商在通过DP促进DR中的作用。我们利用了从2021年到2024年的66次采访和市场和产品供应的数据，这段时间公司需要应对影响电力零售的能源危机。我们的研究结果表明，尽管能源危机促使芬兰大多数电力零售商积极提供动态定价作为其风险管理策略，但该行业并未统一向此选项靠拢。相反，稳定、平坦的电价继续在芬兰零售市场占据主导地位。统一电价的复苏可归因于零售商对消费者利益的怀疑，社会对价格脆弱性的担忧，对区域客户的忠诚度，以及市场效率与受监管的网络服务和管理责任的冲突原则。

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引用次数: 0

Risk-aware sellers and comfort-driven buyers: A game-theoretic P2P energy trading framework 有风险意识的卖家和舒适驱动的买家：一个博弈论的P2P能源交易框架

IF 5 Q2 ENERGY & FUELS

Smart Energy

Pub Date : 2025-11-01 Epub Date: 2025-10-28 DOI: 10.1016/j.segy.2025.100211

Waqas Amin , Qi Huang , Abdullah Aman Khan , Jian Li , Muhammad Afzal

A transitional shift from the centralized energy system to the distributed energy system has promised to address several concerns of today’s energy system such as rising pollution, variation in energy price, energy availability, and sustainability. However, the increased penetration of renewable energy sources and their stochastic nature also create challenges for the grid, such as economic threats, and meeting energy demand for consumers with limited generation capacity to cope with the buyers’ comfort level. This paper presents a novel method based on a game-theoretic framework for energy trading in the peer-to-peer energy market to meet these challenges. For this purpose, firstly, a model is proposed which invites energy buyers and sellers to form a trading place. Then, a model has been proposed to determine how the energy demand of the buyers and their comfortable index varies. In the case of uncertainty in supply from the grid and when the sellers have no prior information about it determining a fair energy trading price becomes a challenging task. For this purpose, a game-theoretic framework is proposed among energy sellers and the grid to determine the optimal energy price. Thirdly, a game-theoretic framework is used for energy allocation policy, ensuring the buyers’ comfortable index. Fourth, Vogel’s approximation-based optimization problem is proposed to minimize energy losses. The proposed model is evaluated on an IEEE-14 interconnected bus system having 22 players i.e., (11 buyers and 11 sellers) for the dataset of one year. Simulation results show that the proposed model helps to satisfy the energy demand of buyers with an increase in profitability to the sellers and grid. The proposed framework also helps to reduce stress on the grid

从集中式能源系统到分布式能源系统的过渡转变有望解决当今能源系统的几个问题，如日益严重的污染、能源价格的变化、能源的可用性和可持续性。然而，可再生能源的日益普及及其随机性也给电网带来了挑战，如经济威胁，以及在有限的发电能力下满足消费者的能源需求，以满足买家的舒适水平。本文提出了一种基于博弈论框架的点对点能源交易方法来解决这些问题。为此，首先提出了一个邀请能源买卖双方组成交易场所的模型。然后，提出了一个模型来确定买家的能源需求和他们的舒适指数是如何变化的。在电网供应不确定的情况下，当卖方没有事先信息时，确定一个公平的能源交易价格成为一项具有挑战性的任务。为此，提出了一个能源卖家与电网之间的博弈框架来确定最优能源价格。第三，运用博弈论框架制定能源分配政策，保证购买者的舒适指数。第四，提出基于Vogel近似的优化问题，使能量损失最小化。所提出的模型在一个IEEE-14互连总线系统上进行评估，该系统有22个参与者，即（11个买家和11个卖家），用于一年的数据集。仿真结果表明，该模型在满足买方能源需求的同时，提高了卖方和电网的盈利能力。提出的框架还有助于减少电网的压力

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引用次数: 0