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

doi:10.12204/j.issn.1000-7229.2022.09.008

Abstract

Abstract:

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:

TM732

REN Xinfang, ZHANG Zhichao, XU Litianlun, WANG Shichao, LIU Zhanzhi, XU Fangyuan. Optimal Scheduling of Hierarchical Energy Systems with Electric Vehicles and Temperature-Controlled Load Demand Response[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(9): 77-86.

Figures/Tables 13

Fig.1 Hierarchical energy management framework based on load aggregators

Fig.2 EV charging and discharging structure

Fig.3 Structure of P2P trading system

Fig.4 Solving process of the hierarchical model

Table 1 Simulation parameter settings

Fig.5 Results of Case 1

Fig.6 Results of Case 2

Fig.7 Results of Case 3

Fig.8 Comparison of evaluation indicators of different cases

Fig.9 Transaction information in P2P transactions

Fig.10 Price evolution curve of electricity trading

Fig.11 Evolution curve of the number of electricity transactions

Fig.12 Pareto front for the multi-objective optimization model

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