Strategy for large-scale electric vehicles cluster scheduling considering peak-shaving cost

doi:10.3969/j.issn.2097-0706.2022.11.002

Abstract

Abstract:

Large-scale grid-connection of new energy and electric vehicles（EVs）leads to volatile power fluctuation and excessive peak-shaving cost. In order to alleviate the pressure of peak shaving and realize the safe and economic operation of the power grid，an EV cluster scheduling strategy considering peak-shaving cost is proposed. Based on the interaction mode between EVs and the power grid and their charging and discharging characteristics，EV load models can be classified into three groups，uni-directional，bi-directional and battery swapping models. Then，an EV peak-shaving pricing model considering the impact of EV participation on peak-shaving cost is constructed. Aiming at the minimal load fluctuation， the pricing strategy for cluster scheduling of EVs in a regional power grid is proposed， and the dynamic electricity price considering EV participation in peak shaving is formulated according to the results obtained from the peak-shaving pricing strategy. EV loads are scheduled with the smallest sum of the expected values of various operating costs as the objective. The effectiveness of the peak load regulation strategy and the economy of the pricing strategy are verified by the simulation result of an improved regional power grid system

Key words: electric vehicle, cluster schedule, time-of-use tariff, two-stage optimization, auxiliary peak shaving, energy storage, new energy

CLC Number:

TK01

WANG Kangping, HUANG Qucan, ZHANG Xingke, WAN Fangru, WEI Chaoyang, LIU Caihua, TIAN Hao, WEI Cong, ZHOU Xia. Strategy for large-scale electric vehicles cluster scheduling considering peak-shaving cost[J]. Integrated Intelligent Energy, 2022, 44(11): 12-19.

Figures/Tables 7

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节点	最小出力/MW	最大出力/MW	燃料成本系数
节点	最小出力/MW	最大出力/MW	a_i/ （元·MW^-2）	b_i/ （元·MW^-1）	c_i/元
1	20	300	0.002 00	16.60	700
2	20	300	0.002 11	16.50	680
7	25	250	0.003 98	19.70	450
13	20	250	0.007 12	22.26	370
14	100	200	0.006 30	16.51	502