A new deregulated demand response scheme for load over-shifting city in regulated power market. (1st April 2022)

• Record Type: Journal Article
• Authors: Xu, Fangyuan; Zhu, Weidong; Wang, Yi Fei; Lai, Chun Sing; Yuan, Haoliang; Zhao, Yujia; Guo, Siming; Fu, Zhengxin
• Is Part Of: Applied energy. Volume 311(2022)
• Publication Date: 2022-04-01
• Subjects: Load over-shifting -- Deregulated demand response -- Independent tariff -- Nested optimization; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.118337
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A novel structure of Deregulated Demand Response (DDR) scheme is proposed for areas with over-shifting phenomenon. Facing the requirement of controlling variable affiliation, a special nested optimization model is designed for DDR. An approximate and efficient optimization algorithm is designed for nested optimization problem. A numerical study is implemented for model verification. Abstract: Time-based demand response (DR) is an effective way to improve the reliability of power grid and reduce energy costs. Time-Of-Use tariff (TOU) has been adopted by many countries and achieved good performance. However, in cities with a large proportion of industrial consumers, load over-shifting phenomenon leads to new peak electricity consumption and reduces the effect of TOU. This paper proposes a new deregulated demand response scheme (DDR) to solve the load over-shifting problem. The scheme selects industrial consumers with large shiftable load in the city as load adjustment component and provides independent tariff to each consumer. Different to other methods with the requirement of entire scheme replacement, the proposed DDR only influences a small group of consumers with much lower implementation risk. Also, the cost of consumers and profit of agent can be improved at the same time as shown in the numerical study. In the proposed scheme, the interests of consumers and the agent need to be considered in the formulation of independent tariff, which forms a nested optimization problem that is difficult to solve quickly. In this paper, a novel and efficient approximate algorithm is proposed to solve the optimization problem. The proposed algorithm can produce optimal solutions similar to Genetic Algorithm with higher computational efficiency.