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

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. (C) 2020 Published by Elsevier Ltd.

Automated summary

An improved optimization model for demand response in a remote off-grid microgrid in Dongfushan Island, China is proposed in this study. The model considers different electricity prices under different seasonal meteorological conditions, aiming to develop energy dispatch and economic benefits. By using an improved Pareto optimum and distributed learning algorithm, the model maximizes satisfaction, reduces electricity bills for consumers, and increases profits for retailers. Simulation results show that the proposed method can lower electricity bills for consumers and help retailers save on generation costs and increase renewable energy utilization.