摘要
针对当前社区负荷在夏季高峰时段偏高,峰谷差较大的问题,通过调用需求响应资源对社区负荷进行调控。主要研究社区能源平台中的可控电力负荷(居民、商业)调度策略,重点分析电动汽车和空调这两类负荷,在满足用户舒适度的前提下,构建用电成本最低与社区负荷峰谷差最小的多目标负荷优化调度模型。其中对居民变频空调和商业中央空调分别采取温控和轮控调控方式,电动汽车则采取有序充电模式,并采用改进粒子群算法对模型进行求解。仿真结果表明,通过对电动汽车、空调负荷以及储能设备的合理调控,可以明显地降低用户的用电成本,改善社区负荷曲线特性。
In view of the fact that the current community load is high and the load peak and off-peak difference is increasing in summer, community load is regulated by invoking demand response resources. This paper mainly studies the controllable power load(resident, commerce) scheduling strategy in the community energy platform. Focusing on the analysis of two types of loads, electric vehicles and air conditioning, this paper constructs a multi-objective load optimization dispatch model with the lowest electricity cost and the smallest difference between community load peaks and valleys under the satisfaction of user comfort level. For the resident inverter air conditioning and commercial central air conditioning, temperature control and periodic stopping control are adopted, respectively. Moreover, orderly charging mode is adopted for the electric vehicle. The improved particle swarm optimization algorithm is used to solve the model. Simulation results show that reasonable control of the electric vehicle, air conditioning load and energy storage can evidently reduce the user electricity costs and improve community load curve characteristics.
作者
张美霞
黄旭焱
杨秀
ZHANG Meixia;HUANG Xuyan;YANG Xiu(Electric Power Engineering,Shanghai University of Electric Power,Shanghai 200090,China)
出处
《电力科学与技术学报》
CAS
北大核心
2021年第3期76-83,共8页
Journal of Electric Power Science And Technology
基金
上海市科委项目(16020500900)。
关键词
需求响应
电动汽车
空调负荷
用户满意度
多目标优化
demand response
electric vehicle
air conditioning load
user satisfaction
multi-objective optimization
