Demand responsive charging strategy of electric vehicles to mitigate the volatility of renewable energy sources

Lili Gong, Wu Cao, Kangli Liu, Yue Yu and Jianfeng Zhao

Renewable Energy, 2020, vol. 156, issue C, 665-676

Abstract: The uncertainties caused by the high penetration of renewable energy sources (RESs) and electric vehicles (EVs) challenge the normal operation of the distribution system. In order to mitigate the negative impact of fluctuations of RES outputs, a smart charging strategy of EVs is presented in this paper. First, a novel uncertainty model using set pair analysis is proposed for the prediction of RES outputs, which provides a different choice of RES modeling method. Second, EVs are modeled as demand-responsive loads by introducing stochastic dynamic pricing. Then, two fluctuation indexes are defined to measure the volatility of RES outputs, and the charging cost is adopted as an economic index for protecting EV users from financial losses. Finally, an optimal charging model is established to minimize the volatility indexes and charging costs. The genetic algorithm is used to solve the model, the seasonality of RES outputs and the spatial-temporal characteristics of EV charging loads are discussed in the simulation under different conditions. Simulations are conducted in the modified IEEE 33 test system, results show that the proposed charging strategy is effective in alleviating the output fluctuations of RESs. The charging cost of EVs can be reduced by 7.6% and 10.3% respectively in winter and summer.

Keywords: Electric vehicles; Renewable energy sources; Uncertainty model; Set pair analysis; Demand response (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:156:y:2020:i:c:p:665-676

DOI: 10.1016/j.renene.2020.04.061

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