Demand response model based on improved Pareto optimum considering seasonal electricity prices for Dongfushan Island

Xiaomin Wu, Weihua Cao, Dianhong Wang, Min Ding, Liangjun Yu, Yosuke Nakanishi

研究成果: Article査読

4 被引用数 (Scopus)

抄録

In this paper, an improved optimization model is proposed for demand response in a remote off-grid microgrid local on the Dongfushan Island, China to develop the energy dispatch and economic benefits considering different electricity price under different seasonal meteorological conditions. First, the seasonal electricity pricing model is built with the power generation of renewable sources in different seasonal meteorological conditions. Second, satisfaction is evaluated by the seasonal electricity price and the power consumption pattern. Improved Pareto optimum based on a distributed learning algorithm is proposed to maximize the satisfaction so that the electricity bills of consumers are reduced and the profits of the retailer is increased. The performance of the proposed optimization model is validated in the HOMER software and Matlab. Simulation results show that the electricity bills of consumers are lower by using the proposed method. For the retailer, the generation cost saves 1216$, and the utilization of renewable energy increased by 3.9% in January 2011.

本文言語English
ページ(範囲)926-936
ページ数11
ジャーナルRenewable Energy
164
DOI
出版ステータスPublished - 2021 2

ASJC Scopus subject areas

  • 再生可能エネルギー、持続可能性、環境

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