Two-Stage Physical Economic Adjustable Capacity Evaluation Model of Electric Vehicles for Peak Shaving and Valley Filling Auxiliary Services

Dunnan Liu, Tingting Zhang, Weiye Wang, Xiaofeng Peng, Mingguang Liu, Heping Jia and Shu Su
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Dunnan Liu: School of Economics and Management, North China Electricity Power University, Changping District, Beijing 102206, China
Tingting Zhang: School of Economics and Management, North China Electricity Power University, Changping District, Beijing 102206, China
Weiye Wang: School of Economics and Management, North China Electricity Power University, Changping District, Beijing 102206, China
Xiaofeng Peng: State Grid Electric Vehicle Service Company, Xicheng District, Beijing 100032, China
Mingguang Liu: School of Economics and Management, North China Electricity Power University, Changping District, Beijing 102206, China
Heping Jia: School of Economics and Management, North China Electricity Power University, Changping District, Beijing 102206, China
Shu Su: State Grid Electric Vehicle Service Company, Xicheng District, Beijing 100032, China

Sustainability, 2021, vol. 13, issue 15, 1-22

Abstract: A large number of renewable energy and EVs (electric vehicles) are connected to the grid, which brings huge peak shaving pressure to the power system. If we can make use of the flexible characteristics of EVs and effectively aggregate the adjustable resources of EVs to participate in power auxiliary services, this situation can be alleviated to a certain extent. In this paper, a two-stage physical and economic adjustable capacity evaluation model of EVs for peak shaving and valley filling ancillary services is constructed. The main steps are as follows: with the help of the deep learning ability of the AC (Actor-Critic) algorithm, the optimal physical charging scheme of EV fleet is determined to minimize the grid fluctuation under the travel constraints of private EVs, and the optimized charging power is transferred to the second stage. In the second stage, load aggregators encourage users to participate in ancillary services by setting subsidy prices. In this stage, the model constructs a user decision model based on a logistic function to describe the probability of users accepting dispatching instructions. With the goal of maximizing the revenue of load aggregators, the wolf colony algorithm is used to solve the optimal solution of the time-sharing subsidy level, and finally the economic adjustable capacity of the EV fleet considering the subjective decision of users is obtained.

Keywords: user decision-making; ancillary services; transfer load capacity assessment; AC algorithm; wolf colony algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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