Optimizing virtual energy sharing in renewable energy communities of residential users for incentives maximization

IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Sustainable Energy Grids & Networks Pub Date : 2024-07-28 DOI:10.1016/j.segan.2024.101492
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Abstract

Renewable energy communities (RECs) are considered a promising tool for putting the citizens at the center of the energy transition, while also promoting self-sufficiency coming from local resources and decarbonization through high penetration of renewables. A key challenge when operating RECs is represented by the number of decision variables to consider depending on the number and type of community participants and distributed technologies, while also considering the associated uncertainties. Moreover, the monetarization of energy shared in the community for benefitting residential users is crucial. The contribution of this paper is to present an innovative stochastic linear programming model for optimizing the energy sharing in RECs to maximize revenues associated with the incentives for the energy shared as established by the Italian regulation. The REC under study consists of a condominium with a PV plant installed on the rooftop, and air conditioning and battery storage systems installed in each apartment. The problem is to find the optimal control strategies for air conditioning systems and batteries with a 15-minute time-step, which maximize the expected revenue from energy sharing while meeting the users’ comfort requirements and preventing users’ bills from increasing. Numerical results demonstrate the effectiveness of the optimization model to maximize the energy shared and the related revenues through the optimal control of installed assets. The combined optimized strategies of both air conditioning and batteries allow for finding the best performance of the REC in terms of maximization of the energy shared. In this latter case, the expected total revenue for users for the energy sharing increases by 59.7 %, 38.7 % and 12.6 % as compared to the baseline case with no optimal control, the case with control of air conditioning only, and the case with control of batteries only, respectively.

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优化居民用户可再生能源社区中的虚拟能源共享,实现激励最大化
可再生能源社区(RECs)被认为是一种很有前途的工具,它可以将公民置于能源转型的中心,同时还可以通过可再生能源的高渗透率促进当地资源的自给自足和去碳化。运营可再生能源中心的一个主要挑战是,根据社区参与者和分布式技术的数量和类型,需要考虑大量决策变量,同时还要考虑相关的不确定性。此外,将社区共享的能源货币化,使居民用户受益也至关重要。本文的贡献在于提出了一个创新的随机线性编程模型,用于优化 REC 中的能源共享,以实现与意大利法规规定的能源共享激励措施相关的收益最大化。所研究的 REC 包括一个公寓,屋顶安装了光伏发电站,每个公寓都安装了空调和电池存储系统。问题是为时间步长为 15 分钟的空调系统和电池找到最佳控制策略,从而在满足用户舒适度要求和防止用户账单增加的同时,最大化能源共享的预期收益。数值结果证明了优化模型的有效性,即通过对已安装资产的优化控制,实现能源共享和相关收益的最大化。空调和电池的组合优化策略使 REC 在能源共享最大化方面达到最佳性能。在后一种情况下,与没有优化控制的基线情况、仅控制空调的情况和仅控制电池的情况相比,用户在能源共享方面的预期总收入分别增加了 59.7%、38.7% 和 12.6%。
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来源期刊
Sustainable Energy Grids & Networks
Sustainable Energy Grids & Networks Energy-Energy Engineering and Power Technology
CiteScore
7.90
自引率
13.00%
发文量
206
审稿时长
49 days
期刊介绍: Sustainable Energy, Grids and Networks (SEGAN)is an international peer-reviewed publication for theoretical and applied research dealing with energy, information grids and power networks, including smart grids from super to micro grid scales. SEGAN welcomes papers describing fundamental advances in mathematical, statistical or computational methods with application to power and energy systems, as well as papers on applications, computation and modeling in the areas of electrical and energy systems with coupled information and communication technologies.
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