Affiliation:
1. College of Electrical Engineering Sichuan University Chengdu People's Republic of China
Abstract
AbstractReducing carbon emissions is a crucial way to achieve the goal of green and sustainable development. To accomplish this goal, electric vehicles (EVs) are considered system‐schedulable energy storage devices, suppressing the negative impact of the randomness and fluctuation of renewable energy on the system's operation. In this paper, a coordination control strategy aimed at minimising the carbon emissions of a distribution network between EVs, energy storage devices, and static var compensators (SVCs) is proposed. A model‐free deep reinforcement learning (DRL)‐based approach is developed to learn the optimal control strategy with the constraint of avoiding system overload caused by random EV access. The twin‐delayed deep deterministic policy gradient (TD3) framework is applied to design the learning method. After the model learning is completed, the neural network can quickly generate a real‐time low‐carbon scheduling strategy according to the system operating situation. Finally, simulation on the IEEE 33‐bus system verifies the effectiveness and robustness of this method. On the premise of meeting the charging demand of electric vehicles, this method can optimise the system operation by controlling the charge‐discharge process of EVs, effectively absorbing the renewable energy in the system and reducing the carbon emissions of the system operation.
Funder
National Natural Science Foundation of China
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering,Energy Engineering and Power Technology,Control and Systems Engineering
Reference28 articles.
1. Global EV Outlook 2020.https://www.iea.org/reports/global‐ev‐outlook‐2020(2020). Accessed 10 December 2020
2. Renewables 2021 Global Status Report.https://www.ren21.net/reports/global‐status‐report/?gclid=Cj0KCQiAmaibBhCAARIsAKUlaKQPUHSWRAMyWUEpb5AQb5Ln888oCDyZWI34XovnPH_1ZvZbU_LMkLIaAkOlEALw_wcB. Accessed 26 February 2022
3. McKerracher C.:Electric Vehicle Outlook 2019.https://about.bnef.com/electricvehicle‐outlook/(2019). Accessed 16 March 2020
4. Allocation of Centralized Energy Storage System and Its Effect on Daily Grid Energy Generation Cost
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献