Price-based Demand Response Mechanism of Prosumer Groups Considering Adjusting Elasticity Differentiation

被引:0
|
作者
Liu D. [1 ]
Sun Y. [1 ]
Li B. [1 ]
Huo M. [2 ,3 ]
Xi W. [2 ]
机构
[1] School of Electrical and Electronic Engineering, North China Electric Power University, Changing District, Beijing
[2] State Grid Energy Research Institute Co., Ltd., Changping District, Beijing
[3] State Grid (Suzhou) City & Energy Research Institute Co., Ltd., Suzhou, 215163, Jiangsu Province
来源
基金
中国国家自然科学基金;
关键词
Adjusting elasticity; Demand response; Edge cloud coordination; Prosumer groups balance;
D O I
10.13335/j.1000-3673.pst.2019.2047
中图分类号
O24 [计算数学];
学科分类号
070102 ;
摘要
In view of the prosumer adjustment elasticity differentiation, this paper proposes a price-based demand response mechanism considering the discount factor in the optimization process of the prosumer groups. Based on the load and distributed power output, the sellers set the purchase price and the discount factor for all the prosumers with the goal of maximizing profits. Under the premise of ensuring fairness, the prosumers who actively participate in the response can obtain a lower settlement price. Furthermore, an iteration strategy of edge cloud collaborative optimization considering the discount factor is proposed to guide the prosumers to increase or reduce load in different scenarios by means of price. The simulation results show that the proposed pricing strategy and the optimization method can effectively improve the benefits of both the sellers and the prosumers, and have good performance in different scenarios with strong robustness. © 2020, Power System Technology Press. All right reserved.
引用
下载
收藏
页码:2062 / 2070
页数:8
相关论文
共 28 条
  • [1] Shi Guirong, Sun Rongfu, Xu Haixiang, Et al., Active power stratification coordination control strategy for large-scale cluster of renewable energy, Power System Technology, 42, 7, pp. 2160-2167, (2018)
  • [2] Shi Lianjun, Zhou Lin, Pang Bo, Et al., Design ideas of electricity market mechanism to improve accommodation of clean energy in China, Automation of Electric Power Systems, 41, 24, pp. 83-89, (2017)
  • [3] Bai Yang, Xie Le, Xia Qing, Et al., Institutional design of Chinese retail electricity market reform and related suggestions, Automation of Electric Power Systems, 39, 14, pp. 1-7, (2015)
  • [4] Liu Dunnan, Xu Erfeng, Xu Xiaofeng, Source-Network-Load- Storage" integrated operation model for microgrid in park, Power System Technology, 42, 3, pp. 681-689, (2018)
  • [5] Cao Fang, Li Xinjia, Liu Sijia, Et al., Optimization of sales package for end-users based on user stickiness and reference pricing decision of consumers, Automation of Electric Power Systems, 42, 14, pp. 67-74, (2018)
  • [6] Wang Kedao, Chen Qixin, Guo Hongye, Et al., Effect of heating network characteristics on ultra-short-term scheduling of integrated energy system, Automation of Electric Power Systems, 42, 14, pp. 54-60, (2018)
  • [7] Dai Yeming, Gao Hongwei, Gao Yan, Et al., Real-time pricing mechanism in smart grid with forecasting update of power demand [J], Automation of Electric Power Systems, 42, 12, pp. 58-63, (2018)
  • [8] Nicholas G., Using behavioral economic theory in modeling of demand response[J], Applied Energy, 239, pp. 107-116, (2019)
  • [9] Li Bin, Chen Jingsheng, Li Dezhi, Et al., Analysis and prospect of key issues in China's demand response for further large scale implementation, Power System Technology, 43, 2, pp. 694-704, (2019)
  • [10] Zhang Qin, Wang Xifan, Wang Jianxue, Et al., Survey of demand response research in deregulated electricity markets[J], Automation of Electric Power Systems, 32, 3, pp. 97-106, (2008)