综合能源系统环境下电动汽车分群优化调度

doi:10.12204/j.issn.1000-7229.2021.04.004

摘要/Abstract

摘要：

电动汽车(electric vehicles, EV)的大规模接入,给综合能源系统调度带来了机遇和挑战。文章考虑电-热-气混合潮流和电动汽车的调度灵活性,建立了含电动汽车的综合能源系统两层嵌套调度模型,对电动汽车进行分群分层调度,合理制定每辆汽车的充放电策略。调度计划层以调度方案成本最小、能量波动最小和环保性最优为目标函数,采用改进的多目标粒子群优化(multi-objective particle swarm optimization, MOPSO)算法求解日前调度计划。EV调度层以用户满意度为目标,采用粒子群(particle swarm optimization, PSO)算法制定出各集群的充放电计划,集群内根据动态优先级制定每辆EV的充放电策略。算例分析表明,所建立的调度模型可有效求解含电动汽车的综合能源系统调度问题,且计算维度小、速度快,具有实用性。

关键词: 综合能源系统, 电动汽车(EV), 优化调度, 集群划分, 动态优先级

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

中图分类号:

TM73

黄伟, 叶波. 综合能源系统环境下电动汽车分群优化调度[J]. 电力建设, 2021, 42(4): 27-39.

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

图/表 23

图1 基于动态优先级的电动汽车充放电策略注:EV以额定充/放电功率充/放电,按松弛度大小安排充放电计划。

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

图2 综合能源系统两层嵌套调度模型

Fig.2 Two-level scheduling model for IES

图3 两层嵌套调度模型求解流程

Fig.3 Flow chart of two-level scheduling model

表1 电动汽车集群划分及参数

Table 1 Cluster division and parameters of EVs

表2 三种场景调度结果对比

Table 2 Optimization results in three sceneries

表3 两种方式调度结果对比

Table 3 Optimization results in two modes

图4 两种方式能量波动情况对比

Fig.4 Energy fluctuation in two modes

图5 两种方式充放电功率和荷电状态情况

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

图6 各EV集群优化调度结果

Fig.6 Optimization results of each EV cluster

图7 各EV集群SOC变化曲线

Fig.7 SOC curve of each EV cluster

表4 优化调度后各EV集群出行前SOC

Table 4 SOC of each EV cluster before travel after scheduling

表5 集群内电动汽车出行前SOC

Table 5 SOC of EVs in the cluster before travel

图B1 综合能源系统拓扑图

Fig.B1 Topology of integrated energy system

图B2 热负荷预测期望值曲线图

Fig.B2 Predicted curve of heat load

图B3 各时段可再生能源出力期望值曲线

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

图B4 仅电力系统的日前调度计划

Fig.B4 Day-ahead scheduling plan for power system

图B5 两种方式日前调度计划

Fig.B5 Day-ahead scheduling plan in two modes

图B6 两种方式上旋备用容量

Fig.B6 Spin-up reserve in two modes

图B7 MOPSO算法收敛曲线

Fig.B7 Convergence curve of MOPSO algorithm

图B8 两种方式下旋备用容量

Fig.B8 Spin-down reserve in two modes

表C1 设备参数

Table C1 Parameters of devices

表C2 主网电价、主网热价、备用价格及可中断负荷的补偿价格

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

表C3 集群内电动汽车各时刻充放电策略

Table C3 Charge-discharge strategy of EVs in the cluster

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