摘要
如何解决电网购电成本最小化目标与发电商收益最大化目标相矛盾的问题,是新能源消纳研究的重要课题。针对寻求矛盾的均衡解问题,提出了一种在电力市场背景下基于主从博弈的新能源消纳模型。首先根据一主多从博弈理论,将电网作为博弈主体,并引入新能源消纳惩罚成本,以购电总成本最低、解决弃风弃光问题为目标;然后将各发电商作为博弈从体,以售电收益最高为目标,构建一主多从电力市场博弈模型;最后通过改进型自适应遗传算法与粒子群算法相结合的算法解出该模型的Stackelberg-Nash均衡解。仿真结果表明,所提出的模型能使博弈各方获得利益的最优分配,并能有效解决弃风弃光问题。
It is an important issue in the research on new energy consumption that the minimized electricity purchasing cost of power grid is contradictory to the maximized revenue of power producers.To search the equilibrium solution for this issue,a new energy consumption model based on Stackelberg game under the electricity market was put forward.Firstly,according to the theory of one-leader and multi-follower Stackelberg game,regarding the power grid as the game-agent and leading in the penalty cost of new energy composition,the solution of wind and solar energy curtailment was taken as the objective under the lowest total electricity purchasing cost,hereafter,regarding the power producers as the follower of the game and taking the revenue of electricity selling as the objective,a one-leader and multi-follower Stackelberg game model for electricity market was constructed.Finally,utilizing the algorithm combining the improved self-adaptive genetic algorithm with particle swarm optimization algorithm,the Stackelberg-Nash equilibrium solution of above-mentioned model was obtained by the combined algorithm.Simulation results show that using the proposed model the benefits can be optimally distributed among all participants of the game,and the wind and solar energy curtailment can be well solved.
作者
薛佳诚
唐忠
盛锐
赵铃光
谢琳宇
XUE Jiacheng;TANG Zhong;SHENG Rui;ZHAO Lingguang;XIE Linyu(College of Electrical Engineering,Shanghai University of Electric Power,Yangpu District,Shanghai 200090,China;State Grid Ningde Power Supply Company,Ningde 352100,Fujian Province,China)
出处
《现代电力》
北大核心
2020年第3期270-276,共7页
Modern Electric Power
关键词
均衡解
新能源消纳惩罚成本
一主多从博弈模型
改进型自适应遗传算法
粒子群算法
equilibrium solution
penalty cost of new energy consumption
one-leader and multi-follower Stackelberg game model
improved adaptive genetic algorithm
particle swarm optimization algorithm