基于弹性优化机制的社区负荷EV分群优化策略

doi:10.16180/j.cnki.issn1007-7820.2022.12.009

摘要/Abstract

摘要：

针对EV无序充电对电网造成的负荷波动,文中提出了基于弹性优化机制的社区负荷EV分群优化策略。该策略以社区EV返程时刻为标准划分社区负荷序列和EV车辆序列,根据策略响应度将EV车辆序列划分策略响应车群和普通车群。电网侧与策略响应车群签订具有弹性限度的合约A与合约B,并制定各车群的充放电计划。合约A侧重考虑用户效益,通过控制策略响应车群各EV序列充放电,分别优化各车辆序列对应的社区负荷序列,尽力获取放电收益。合约B侧重考虑用户用车需求,通过调整EV的充放电计划,在平衡合约执行度和EV可用度的同时最大化降低社区序列负荷波动。文中以某社区居民家庭负荷为算例,以最小化负荷峰谷差和用户支出费用为目标函数,通过MATLAB、Yalmip平台和Gurobi求解器联合建模求解各合约场景。结果表明,策略实施后,各合约场景下的社区负荷峰谷差分别降低了3.74%、2.87%和5.04%,EV序列费用支出分别减少了10.80%、5.23%和10.55%。

关键词: EV, 弹性优化机制, 分群优化策略, 弹性限度合约, 策略响应度, EV可用度, EV序列和社区负荷序列, 社区负荷谷差

Abstract:

In view of the load fluctuation caused by EV disordered charging, this study proposes an EV clustering optimization community load strategy based on flexible optimization mechanism. The strategy divides the community load sequence and EV sequence according to the EV return time, and the EV vehicle sequence is divided into the strategic response vehicle group and the common vehicle group according to the policy responsivity. The grid side and the strategic response vehicle group sign contract A and contract B with flexible limit, and formulate the charging and discharging plan of each vehicle group. Contract A focuses on considering the user benefit, and optimizes the community load sequence of each vehicle sequence to obtain the discharge revenue by controlling the strategic response vehicle group. Contract B focuses on the demand of car usage, and adjusts the charge and discharge plan of EV to balance the contract execution and EV availability, while minimizing the fluctuation of community load sequence. In this study, the household load of a certain community is taken as an example, and the objective function is to minimize the load peak-to-valley difference and the user's expenditure cost, and the contract scenarios are solved by joint modeling through MATLAB, Yalmip platform and Gurobi solver. The results show that the peak valley difference of community load in each contract scenario is reduced by 3.74%, 2.87% and 5.04% respectively after the implementation of the strategy, and the EV sequence cost expense is reduced by 10.80%, 5.23% and 10.55%, respectively.

Key words: EV, flexible optimization mechanism, clustering optimization strategy, flexible limit contracts, strategy responsivity, EV availability, EV sequence and community load sequence, peak valley difference of community load

中图分类号:

TP273

段俊东,黄泓叶,王帅强. 基于弹性优化机制的社区负荷EV分群优化策略[J]. 电子科技, 2022, 35(12): 64-71.

DUAN Jundong,HUANG Hongye,WANG Shuaiqiang. EV Clustering Optimization Community Load Strategy Based on Flexible Optimization Mechanism[J]. Electronic Science and Technology, 2022, 35(12): 64-71.

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类别	数值
额定容量/kW·h	82
充电功率/kW	20
放电功率/kW	20
电池成本/元·(kW·h)^-1	3 000

时间	充电价格/元·(kW·h)^-1
2∶00~10∶00	0.865
11∶00~3∶00 21∶00~1∶00	1.371
14∶00~20∶00	2.132