协同调度电动汽车与储能装置的家庭能量管理策略  

Home Energy Management Strategy for Co-scheduling of Electric Vehicle and Energy Storage Device

作　　者：姚钢 茆中栋 周荔丹[2] 李东东 YAO Gang;MAO Zhongdong;ZHOU Lidan;LI Dongdong(College of Electrical Engineering,Shanghai University of Electric Power,Shanghai 200090,China;Department of Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]上海电力大学电气工程学院,上海200090 [2]上海交通大学电气工程系,上海200240

出　　处：《电力系统及其自动化学报》2020年第4期35-41,50,共8页Proceedings of the CSU-EPSA

基　　金：国家自然科学基金资助项目(61374155);上海市自然科学基金资助项目(18ZR1418400)。

摘　　要：针对未充分利用电动汽车可充放电特性而限制家庭能源经济性的问题,本文建立家庭负荷协同调度的能量控制策略。通过对电动汽车接入家庭时段内光伏出力总和与负载总功率进行对比,确定两种工作模式。在光伏出力富余的阶段中,充分利用电动汽车的可充电特性,提高系统的经济性与灵活性。在满足用户舒适度的前提下,以住户用能费用最少为目标,利用二进制粒子群算法进行求解。最后在光伏出力预测不确定的环境下,进行多场景算例仿真,验证了该能量管理策略的有效性。The charging and discharging characteristics of an electric vehicle(EV)are not fully utilized,thus restraining the household energy economy. To solve this problem,an energy control strategy for co-scheduling of household load is put forward. From the comparison between the total PV output and the sum of load power during the integration period of the EV,two working modes are determined. In the stage with surplus PV output,the charging and discharging characteristics of the EV are fully utilized to improve the economy and flexibility of the system. Under the premise of satisfying the user’s comfort,the binary particle swarm optimization algorithm is adopted with an objective of minimum energy cost of the user. Finally,simulations of an example are conducted under various scenarios in an environment with uncertain PV output predictions,which validate the effectiveness of the proposed energy management strategy.

关 键 词：家庭能量管理 智能家庭 电动汽车 二进制粒子群优化算法 

分 类 号：TM734[电气工程—电力系统及自动化]