期刊
APPLIED ENERGY
卷 311, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2021.118337
关键词
Load over-shifting; Deregulated demand response; Independent tariff; Nested optimization
资金
- Guangdong Basic and Applied Basic Research Foundation [2021A1515010742, 2020A1515011160, 2020A1515010801]
- National Natural Science Foundation of China [52007032]
- Basic Research Program of Jiangsu
This paper proposes a new deregulated demand response scheme (DDR) to solve the load over-shifting problem in cities with a large proportion of industrial consumers. The scheme selects industrial consumers with large shiftable load in the city as load adjustment component and provides independent tariff to each consumer. The proposed algorithm can produce optimal solutions similar to Genetic Algorithm with higher computational efficiency.
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.
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