• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

ELECTRIC POWER CONSTRUCTION ›› 2022, Vol. 43 ›› Issue (9): 77-86.doi: 10.12204/j.issn.1000-7229.2022.09.008

• Key Technology and Application of Flexible Distributed Energy Resources Oriented to New Power System ·Hosted by Associate Professor JU Liwei and Professor TAN Zhongfu· • Previous Articles     Next Articles

Optimal Scheduling of Hierarchical Energy Systems with Electric Vehicles and Temperature-Controlled Load Demand Response

REN Xinfang1(), ZHANG Zhichao1(), XU Litianlun2(), WANG Shichao2(), LIU Zhanzhi2(), XU Fangyuan3()   

  1. 1. China Southern Power Grid EHV Power Transmission Company, Guangzhou 510670, China
    2. China Energy Engineering Group Guangdong Electric Power Design Institute Co., Ltd., Guangzhou 510663, China
    3. School of Automation, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2022-01-10 Online:2022-09-01 Published:2022-08-31
  • Contact: XU Litianlun E-mail:renxinfang@ehv.csg.cn;zhangzhichao@ehv.csg.cn;10024422467@qq.com;wangshichao@gedi.com.cn;liuzhanzhi@gedi.com.cn;datuan12345@hotmail.com
  • Supported by:
    Natural Science Foundation of Guangdong Province of China(2021A1515010742)


A hierarchical energy system management framework based on the demand response of electric vehicles (EVs) and temperature-controlled loads (TCLs) is developed to address the impact of large-scale EVs on the power system. Stimulated EV clusters and TCL clusters can quickly respond to the scheduling strategies of load aggregators to reduce the impact on the grid caused by the large number of flexible loads connected to the grid. Firstly, a hybrid model of convolutional neural network and long-and short-term memory network is used to predict each part of the load, and the load aggregator dispatches controllable flexible loads to maximize the fit of the predicted load profile. The load aggregator performs peer to peer (P2P) power trading with the power operator according to the current scheduling strategy and applies distributed optimization to solve the maximum benefit for both parties. For the remaining energy demand after local energy trading, a multi-objective optimization model for system operating cost, carbon emission, and wind energy spillover is considered. The Pareto frontier of this model is solved using NSGA-II with centralized optimization and verified by arithmetic cases in the IEEE 30-node system. The simulation results show that the proposed optimal energy dispatch strategy can not only meet the power requirements of EVs and TCLs, but also bring good economic and environmental benefits to the power system.

Key words: electric vehicle, temperature-controlled load, peer to peer (P2P) power transaction, distributed optimization, multi-objective optimization

CLC Number: