文章摘要
靳小龙,穆云飞,贾宏杰,等.集成智能楼宇的微网系统多时间尺度模型预测调度方法[J].电力系统自动化,2019,43(16):25-33. DOI: 10.7500/AEPS20180629016.
JIN Xiaolong,MU Yunfei,JIA Hongjie, et al.Model Predictive Control Based Multiple-time-scheduling Method for Microgrid System with Smart Buildings Integrated[J].Automation of Electric Power Systems,2019,43(16):25-33. DOI: 10.7500/AEPS20180629016.
集成智能楼宇的微网系统多时间尺度模型预测调度方法
Model Predictive Control Based Multiple-time-scheduling Method for Microgrid System with Smart Buildings Integrated
DOI:10.7500/AEPS20180629016
关键词: 智能楼宇  微网(微电网)  虚拟储能  多时间尺度优化调度  模型预测控制
KeyWords: smart building  microgrid  virtual energy storage  multiple-time-scale optimal scheduling  model predictive control
上网日期:2019-07-05
基金项目:国家自然科学基金委员会-国家电网公司智能电网联合基金资助项目(U1766210);国家自然科学基金资助项目(51625702);国家电网公司科技项目(SGTJJY00GHJS1800123)
作者单位E-mail
靳小龙 智能电网教育部重点实验室, 天津大学, 天津市 300072  
穆云飞 智能电网教育部重点实验室, 天津大学, 天津市 300072 yunfeimu@tju.edu.cn 
贾宏杰 智能电网教育部重点实验室, 天津大学, 天津市 300072  
余晓丹 智能电网教育部重点实验室, 天津大学, 天津市 300072  
徐科 国网天津市电力公司, 天津市 300010  
徐晶 国网天津市电力公司, 天津市 300010  
摘要:
      针对含多智能楼宇的微网系统,提出一种基于模型预测的多时间尺度调度方法。首先,为有效利用建筑围护结构蓄热特性所带来的灵活性,构建了虚拟储能系统数学模型,并将其集成到智能楼宇微网多时间尺度调度方法中。随后,提出了基于模型预测的日内滚动修正方法,通过每个控制时域内的滚动优化,实现日内微网系统运行方案的精确修正。最后,以夏季制冷场景为例,利用含智能楼宇的微网系统验证了所提方法的有效性。结果表明,该方法可在保证楼宇室内温度舒适度的前提下,在日前经济优化调度阶段降低运行成本;在日内滚动修正阶段平抑由日前预测误差导致的微网联络线功率波动。
Abstract:
      A model predictive control based multiple time scale scheduling method is proposed for the microgrid system with smart buildings integrated. Firstly, the mathematical model of virtual energy storage system is developed to effectively use the flexibility of buildings due to their thermal dynamics of the envelope. The virtual energy storage system is optimized in the multiple time scale scheduling method for microgrid with smart buildings integrated. Then, a model predictive control based intraday rolling adjustment method is proposed. By using the rolling optimization in each control horizon, the operational schedules of the microgrid system can be adjusted accurately. Finally, taking refrigeration scenario in summer as an example, the effectiveness of the proposed method is verified by using the microgrid with smart buildings integrated. The results demonstrate that on the premise of guaranteeing comfort level of indoor temperature in the building, the proposed method can reduce the operating cost at the day-ahead economic optimization and dispatch stage and smooth the tie-line power fluctuations caused by day-ahead prediction error at the intra-day rolling adjustment stage.
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