A fully decentralized spatiotemporal decomposition method for real-time peer-to-peer trading in distribution network

IF 4.8 2区 工程技术 Q2 ENERGY & FUELS
Jianquan Zhu, Wenhao Liu, Langsen Fang, Ruibing Wu, Jiajun Chen
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引用次数: 0

Abstract

High penetration of distributed renewable energy (DRE) promotes the development of peer-to-peer (P2P) trading. In this study, P2P trading is extended from a deterministic single-period model to a stochastic multi-period model, which considers the interaction of both spatial and temporal dimensions. A stochastic dual dynamic programming (SDDP)-based decentralized method is proposed to coordinate this spatiotemporal effect. In the spatial dimension, the impact of bilateral transactions on power flow is considered by prosumers independently based on the cumulative effect of branch capacity and bus voltage shift, which protects the privacy of P2P trading while preventing power flow violation. In the temporal dimension, the influence between periods is coordinated by prosumers based on the state of charge (SOC), which gives them overview abilities to handle future uncertainties. Besides, the dynamic cut-set selecting strategy is presented to improve the solving efficiency of SDDP. Numerical simulations demonstrate the effectiveness of the proposed method, which can reduce the computational time by 52 %.
配电网实时点对点交易的全分散时空分解方法
分布式可再生能源(DRE)的高渗透率促进了P2P交易的发展。本文将P2P交易模型从确定性的单周期模型扩展到考虑时空维度相互作用的随机多周期模型。提出了一种基于随机对偶动态规划(SDDP)的分散方法来协调这种时空效应。在空间维度上,基于支路容量和母线电压漂移的累积效应,由产消方独立考虑双边交易对潮流的影响,在保护P2P交易隐私的同时防止潮流违规。在时间维度上,产消者根据充电状态(SOC)协调不同时期之间的影响,这使他们有能力处理未来的不确定性。此外,为了提高SDDP的求解效率,提出了动态割集选择策略。数值仿真结果表明了该方法的有效性,计算时间可减少52% %。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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