Optimal Scheduling of Electric Vehicle Clusters in Integrated Energy System

doi:10.12204/j.issn.1000-7229.2021.04.004

Abstract

Abstract:

Large-scale electric vehicles (EV) accessing to the system brings opportunities and challenges to the scheduling of integrated energy system. With the power-heat-gas multi-energy flow and the scheduling flexibility of electric vehicles taken into consideration, this paper establishes a two-level scheduling model for integrated energy system with electric vehicles, performing grouped and hierarchical scheduling to make a rational charge-discharge strategy for each EV. Minimum scheduling plan cost, the smallest energy fluctuation and the best environmental protection are taken as objective functions in the scheduling plan level. Improved multi-objective particle swarm optimization (MOPSO) algorithm is adopted to solve the day-ahead scheduling plan. The EV scheduling level uses particle swarm optimization (PSO) algorithm to make the charge-discharge plan of each EV cluster with the goal of user satisfaction. According to the dynamic priority, the charge-discharge strategy of each EV in the cluster is solved. The analysis of case shows that the established scheduling model can effectively solve the scheduling problem of integrated energy system with electric vehicles, which has less calculation dimension, shorter simulation time and better practicality.

Key words: integrated energy system, electric vehicles (EV), optimal scheduling, cluster division, dynamic priority

CLC Number:

TM73

HUANG Wei, YE Bo. Optimal Scheduling of Electric Vehicle Clusters in Integrated Energy System[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(4): 27-39.

Figures/Tables 23

Fig.1 Charge/discharge strategy of EVs applying dynamic priority

Fig.2 Two-level scheduling model for IES

Fig.3 Flow chart of two-level scheduling model

Table 1 Cluster division and parameters of EVs

Table 2 Optimization results in three sceneries

Table 3 Optimization results in two modes

Fig.4 Energy fluctuation in two modes

Fig.5 Charge/discharge power and SOC in two modes

Fig.6 Optimization results of each EV cluster

Fig.7 SOC curve of each EV cluster

Table 4 SOC of each EV cluster before travel after scheduling

Table 5 SOC of EVs in the cluster before travel

Fig.B1 Topology of integrated energy system

Fig.B2 Predicted curve of heat load

Fig.B3 Predicted curve of renewable energy output in each period

Fig.B4 Day-ahead scheduling plan for power system

Fig.B5 Day-ahead scheduling plan in two modes

Fig.B6 Spin-up reserve in two modes

Fig.B7 Convergence curve of MOPSO algorithm

Fig.B8 Spin-down reserve in two modes

Table C1 Parameters of devices

Table C2 Electricity price, heat price, reserve price and compensation price for interruptible load 元/(MW·h)

Table C3 Charge-discharge strategy of EVs in the cluster

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