文章摘要
郁清云,彭 飞,孟凡奇,戴小妹,束云豪.基于Shapley值抽样估计法的电力用户参与互动效益分摊方法研究[J].电力需求侧管理,2023,25(6):15-20
基于Shapley值抽样估计法的电力用户参与互动效益分摊方法研究
Benefit sharing method of power consumer interaction based onShapley value sampling estimation
投稿时间:2023-06-09  修订日期:2023-09-22
DOI:10. 3969 / j. issn. 1009-1831. 2023. 06. 003
中文关键词: 电力用户  互动效益  分层随机抽样  样本分配  Shapley值  ε(t) 迭代估计最优分配方法
英文关键词: power consumer  interactive benefits  stratified random sampling  sample allocation  Shapley value  iterative ε(t)estimation optimal allocation method
基金项目:江苏省重点研发计划(BE2020688)
作者单位
郁清云 国网江苏省电力有限公司 常州供电分公司,江苏 常州 213004 
彭 飞 国网江苏省电力有限公司 常州供电分公司,江苏 常州 213004 
孟凡奇 国网江苏省电力有限公司 常州供电分公司,江苏 常州 213004 
戴小妹 国网江苏省电力有限公司 超高压分公司,南京 211100 
束云豪 国网江苏省电力有限公司 常州供电分公司,江苏 常州 213004 
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中文摘要:
      为促进用户与电网实施友好互动,降低能源消费和用电负荷,应研究如何将电网公司的互动效益按一定补贴比例公平地分摊给各参与用户。为解决传统Shapley值法的组合爆炸问题,提出一种基于Shapley值抽样估计法分摊用户互动效益的补偿方法,该方法在满足收支平衡的约束条件下,通过分层随机抽样方法减少样本量;为确定各层样本分配量,综合比较了随机分配法、平均分配法及Neyman最优分配法的优缺点;为解决Neyman最优分配法中参与者各层样本标准差未知的问题,提出一种基于强化学习算法的 ε(t) 迭代估计最优分配方法。算例表明所提出的方法具有Shapley值法的所有特性,能精确地估计Shapley值法的分摊结果,因而能实现公平合理的分配,同时能有效地减少计算时间。
英文摘要:
      In order to encourage users to implement friendly interaction with the power grid and reduce energy consumption and power load, the way to equitably share the interactive benefits of the power grid company to all participating users according to a certain subsidy proportion is studied. In order to solve the combination explosion problem of the traditional Shapley value method, a compensation method for sharing the interactive benefits of users based on the Shapley value sampling estimation is proposed. Under the constraint of meeting the balance of payments, the method can reduce sample size by stratified random sampling. In order to fix the sample allocation amount of each layer, the advantages and disadvantages of random allocation method, average allocation method and Neyman optimal allocation method are comprehensively compared. In order to solve the problem that the standard deviation of samples at each layer of participants is unknown in Neyman optimal allocation method, an iterative ε(t) estimation optimal allocation method based on reinforcement learning algorithm is proposed. Numerical examples show that the proposed method has all the characteristics of Shapley value method, can accurately estimate the allocation results of Shapley value method, and can achieve fair and reasonable allocation, while effectively reducing the calculation time.
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