面向汽车充电预约的光储充电站能量调度策略

doi:10.19678/j.issn.1000-3428.0066052

摘要/Abstract

摘要：

光储充电站(PSCS)的规模化部署是电动汽车(EV)快速普及的关键因素。合理规划光储充电站的运行模式并有效调度多种能源，优化需求供给链，最大化运行效益，是光储充电站可持续运营发展的重点。针对目前光储充电站需求侧的不确定性和供给侧的协调性问题，面向汽车充电预约场景，在需求侧综合考虑汽车充电需求和剩余停车时长等因素，决策相应EV的充电方式。在供给调度侧设计一种基于带精英策略的遗传混合递推算法(EGAHR)进行能量优化调度，以最小化电网取电费用。以EV充电时间片为基本调控时间单元，通过协调需求侧和供给侧的调度信息，合理调度光伏、储能、电网等能源的能量，满足当前时间片内EV充电需求的同时优化系统电费。实验结果表明，基于EGAHR算法的策略相比基于遗传算法、灰狼算法、粒子群算法等经典算法的能量调度策略节约了2.1%~21.9%的充电成本。另外，EGAHR算法可以为多种不同的EV充电模型和差异化电价趋势模型提供参考，为PSCS合理配备储能系统和光伏提供科学经济的部署方案。

关键词: 电动汽车, 光储充电站, 充电规划, 能量调度, 需求供给链, 目标优化

Abstract:

The large-scale deployment of Photovoltaic Storage Charging Station(PSCS) is a crucial factor for the rapid popularization of Electric Vehicles(EV). Effectively planning the operation of PSCS, dispatching multiple energy sources, optimizing the demand-supply chain, and maximizing operational efficiency are keys to the sustainable growth and development of PSCS. To address the uncertainty of the demand side and coordination problem of the supply side of the current optical storage charging station, this paper focusses on EV charging reservation scenario. For demand, factors, such as the car charging demand and remaining parking time, are considered to decide the charging mode of the corresponding EV. On the supply scheduling side, a genetic hybrid recursive algorithm, Hybrid Recursive(EGAHR), is designed based on the band elite strategy for energy optimization scheduling to minimize the grid withdrawal cost. Using EV charging time slots as the primary regulatory unit, the strategy coordinates both demand and supply information, ensuring energy from Photo Voltaics(PV), storage, and the grid is dispatched efficiently. This satisfies the EV charging requirements within the current time slot and optimizes overall electricity expenses. Experimental results reveal that the EGAHR strategy offers a charging cost reduction in the range of 2.1-21.9% when compared to strategies based on traditional algorithms such as the genetic algorithm, gray wolf algorithm, and particle swarm algorithm. Additionally, the EGAHR strategy can be applied to various EV charging models and differential tariff trends, offering a scientifically and economically sound blueprint for PSCS to adequately equip Energy Storage System(ESS) and PV.

Key words: Electric Vehicle(EV), Photovoltaic Storage Charging Station(PSCS), charging planning, energy scheduling, demand supply chain, objective optimization

赵文薇, 林兵, 卢宇, 王明芬. 面向汽车充电预约的光储充电站能量调度策略[J]. 计算机工程, 2023, 49(12): 262-273, 281.

Wenwei ZHAO, Bing LIN, Yu LU, Mingfen WANG. Energy Scheduling Strategy for Photovoltaic Storage Charging Station for Vehicle Charging Reservation[J]. Computer Engineering, 2023, 49(12): 262-273, 281.

https://www.ecice06.com/CN/Y2023/V49/I12/262

图/表 19

图1 PSCS系统架构

Fig.1 Architecture of PSCS system

图2 PSCS系统的能量流调度

Fig.2 Energy flow scheduling for PSCS system

图3 EV充电时段关系示意图

Fig.3 Schematic diagram of EV charging time relationship

图4 EV停车时间和充电时间关系的示意图

Fig.4 Schematic diagram of relationship between parking time and charging time of EV

图5 EV充电状态示意图

Fig.5 Schematic diagram of EV charging state

图6 多种分布能源调度流程

Fig.6 Procedure of multi-distributed energy dispatch

图7 优化调度算法机制流程

Fig.7 Procedure of optimal scheduling algorithm mechanism

图8 变量变异示意图

Fig.8 Schematic diagram of variable variation

图9 EGA算法流程

Fig.9 Procedure of EGA algorithm

图10 EV数量的SOC分布图

Fig.10 SOC distribution map of EV number

图11 EV需求充电负荷分布

Fig.11 Distribution of EV demand charging load

图12 不同案例下电网供电及能源动态情况

Fig.12 Grid power supply and energy dynamics in different cases

图13 能量管理后的各能源变化示意图

Fig.13 Schematic diagram of the change of each energy source after energy management

图14 不同算法的性能对比

Fig.14 Performance comparison of different algorithms

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