Scheduling Strategies for Electric Vehicle Participation in Electricity Markets Under Multi-Network Collaboration

doi:10.12141/j.issn.1000-565X.220822

Abstract

Abstract:

In view of the time-space uncertainty of electric vehicle charging demand, how to participate in the electricity market and how to maximize the operating profit has become a problem that needs to be solved. Firstly, this study established an electric vehicle travel prediction model based on multi-layer deep learning algorithm. The multilayer perceptron and long short-term memory network were used to learn the travel data and road condition data of electric vehicles, and the travel behavior and road condition of the next day were predicted by the trained prediction model. Secondly, considering the influence of the variability of road conditions on the prediction accuracy, the future path rolling optimization method and the speed-energy consumption model were used to simulate the travel behavior of electric vehicles the next day, so as to obtain more accurate time-space state and charge state of electric vehicles. Finally, considering the coordinated scheduling of the energy market, the charging and discharging behavior of electric vehicles in different periods was planned through the charging and discharging scheduling model of the day-ahead market to maximize the interests of electric vehicle agents. In order to prove the accuracy of the proposed prediction method, it was compared with the commonly used Monte Carlo method and Latin hypercube method. The results show that the deep learning algorithm proposed in this study has higher accuracy. The model was applied to the IEEE33 node test system for verification. The experimental results show that the peak-valley difference of the power system can be effectively reduced under the scheduling of electric vehicle agents. In the case of system congestion, the problem of system line congestion can be alleviated by changing the scheduling strategy of electric vehicles. The agent’s revenue and the user’s travel cost were analyzed. The results show that under the agent’s scheduling, it can not only increase the income of agents, but also reduce the travel cost of users, and achieve a win-win situation.

Key words: electric vehicle, agent, demand response, machine learning

CLC Number:

TM73

DONG Ping, WEI Shuyang, LIU Mingbo. Scheduling Strategies for Electric Vehicle Participation in Electricity Markets Under Multi-Network Collaboration[J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(12): 83-94.

Figures/Tables 19

Fig.1

Fig.2

Fig.3

Table 1

Fig.4

Fig.5

Fig.6

Table 2

Description of the main parameters and variables of the charge/discharge scheduling model"

参量	描述	二进制参量	描述
$P m a x c h$	电动汽车最大充电功率	$I i, j p a r k (t)$	t时段车辆j在i节点停车
$P m a x d i s$	电动汽车最大放电功率	$I k, j t r (t)$	t时段车辆j在k路段行驶
$E m a x v e$	车辆容量的上限	$U i p i l e$	充电桩节点i
$E m i n v e$	车辆容量的下限	集合	描述
$η d$	放电效率系数	$I$	总的路网节点
$η c$	充电效率系数	$K$	总的路段
$Δ P k (t)$	t时刻路段k行驶消耗的能量	J	车辆的集合
$λ i d i s (t)$	t时段i节点预测放电价格	T	调度时间间隔
$λ i c h (t)$	t时段i节点预测充电价格	$S$	电池循环深度总的段数
$c s$	电池循环深度段的边际老化成本	变量	描述
$P i, m a x c h (t)$	t时段i节点的允许最大充电能量	$P i, j c h (t)$	t时段第j辆车在i节点的充电量
$P i, m a x d i s (t)$	t时段i节点的允许最大放电能量	$P i, j d i s (t)$	t时段第j辆车在i节点的放电量
$m k (t)$	t时刻在k路段上行驶所消耗的时长	$P i c h (t)$	t时段i节点充电量
		$P i d i s (t)$	t时段i节点放电量
		$P j, s c h (t)$	t时段j车在s段的充电量
		$P j, s d i s (t)$	t时段j车在s段的放电量
		$E j, s v e (t)$	t时刻车辆j在s段的电量

Table 2

Fig.7

Fig.8

Fig.9

Fig.10

Table 3

Fig.11

Fig.12

Fig.13

Fig.14

Table 4

Table 5

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字段名称	字段类型	字段说明
行程编号	Double	格式ddd，以数字编码
日期	Datatime	格式yyyy/mm/dd
时间	Datatime	格式hh：mm：ss P/AM
经度	Double	格式dd.dddd脱敏经度
纬度	Double	格式dd.dddd脱敏纬度
路段限速	Double	格式dd.dddd，单位km/h
瞬时速度	Double	格式dd.dddd，单位km/h
车辆状态	Double	格式dddd数据类型

调度方式	购电费用	卖电收益	放电电池折旧成本	净收益
充电调度	115.0	0.0	0.000	-115.0
充放电调度	431.5	481.9	1.748	48.6

充放电调度	购电费用	卖电收益	放电电池折旧成本	净收益
阻塞前	132.7	140.6	0.534	7.366
阻塞后	172.9	237.5	0.617	63.980

离家时间				停留时长
正偏差/min	正偏差对应的收益比/%	负偏差/min	负偏差对应的收益比/%	正偏差/min	正偏差对应的收益比/%	负偏差/min	负偏差对应的收益比/%
+20	4.4	-20	2.1	+20	4.4	-20	3.7
+40	5.7	-40	3.8	+40	4.5	-40	2.9
+60	7.8	-60	4.1	+60	5.2	-60	2.9

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