| 引用本文: | 伍惠铖,王淳,左远龙,等.基于分时电价和蓄电池实时控制策略的家庭能量系统优化[J].电力系统保护与控制,2019,47(19):23-30.[点击复制] |
| WU Huicheng,WANG Chun,ZUO Yuanlong,et al.Home energy system optimization based on time-of-use price and real-time control strategy of battery[J].Power System Protection and Control,2019,47(19):23-30[点击复制] |
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| 摘要: |
| 为增强家庭负荷优化调度策略的灵活性以及保证蓄电池的安全运行，提出一种基于分时电价和蓄电池实时控制的家庭能量管理系统优化调度策略。首先，以可调度负荷和蓄电池工作状态为约束条件，以家庭用户用电成本最小和净负荷曲线平坦度最优为目标建立了家庭能量管理优化调度模型。然后，从蓄电池动态控制方法出发，提出一种基于分时电价和蓄电池实时控制的家庭能量管理系统优化调度策略。该调度策略根据分时电价和蓄电池实时荷电状态对蓄电池充放行为进行控制，有助于降低家庭用户用电成本，并保证蓄电池安全运行。最后，采用二进制粒子群算法对模型进行求解。算例结果验证了所提调度模型和调度策略的有效性和优越性。 |
| 关键词: 分时电价 储能系统 实时控制 家庭能量管理系统 优化调度 |
| DOI：10.19783/j.cnki.pspc.181396 |
| 投稿时间：2018-11-08修订日期：2019-03-25 |
| 基金项目:国家自然科学基金项目资助(51467012);国家国际科技合作专项(2014DFG72240) |
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| Home energy system optimization based on time-of-use price and real-time control strategy of battery |
| WU Huicheng,WANG Chun,ZUO Yuanlong,CHEN Yujie,LIU Kuan |
| (Department of Electrical and Automatic Engineering, Nanchang University, Nanchang 330031, China) |
| Abstract: |
| In order to enhance the flexibility of household load optimization and ensure the safe operation of battery, an optimal scheduling strategy for home energy management based on time-of-use price and real-time control of battery is proposed. Firstly, taking the working states of the dispatchable loads and battery as the constraints, the optimal scheduling model of home energy management is built, while considering the objectives of minimizing the electricity consumption cost of household users and optimizing the net flatness of load curve. Then, with the battery dynamic control method, an optimal scheduling strategy for home energy management based on time-of-use price and real-time control of battery is proposed. This scheduling strategy takes into account the regulation effect of the time-of-use price and the real-time state of charge for the battery on the charging and discharging behaviors of the battery, which is helpful to reduce the electricity consumption cost of the household users and ensure the safe operation of the battery. Finally, the binary particle swarm optimization algorithm is used to solve the model. The case results verify the effectiveness and superiority of the proposed scheduling model and strategy. This work is supported by National Natural Science Foundation of China (No. 51467012) and International Science and Technique Cooperation Program of China (No. 2014DFG72240). |
| Key words: time-of-use price energy storage system real-time control home energy management system optimal scheduling |
