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

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)926-936
Number of pages11
JournalRenewable Energy
Volume164
DOIs
Publication statusPublished - 2021 Feb

Keywords

  • Demand response
  • Distributed learning algorithm
  • Off-grid microgrid
  • Pareto optimum
  • Seasonal electricity price

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment

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