摘要
近年来,空调负荷急剧增长,如何保证电网的安全、稳定运行是当前亟须解决的重要难题。针对含分散式空调的楼宇微网,提出一种基于模型预测控制(ModelPredictiveControl,MPC)两阶段优化调度策略。日前阶段,考虑楼宇微网设备间电能的互济互补能力,以系统运行经济性、环保性以及设备损耗作为综合成本目标,兼顾自身利益的同时最大限度地消纳可再生能源。在日内阶段,基于MPC方法建立融合分散式空调的楼宇微网预测模型,并反复滚动优化,以解决可再生能源出力、负荷预测精度随时间尺度增加而下降的问题。通过算例分析得出:分散式空调两阶段优化调度方法能够有效解决预测误差较大的问题,在平滑联络线的同时使系统鲁棒性得以提升。
In recent years,the air conditioning load has increased rapidly.Meanwhile,how to ensure the safe and stable operation of the power grid is an important problem to be solved.A two⁃stage optimal scheduling strategy based on model predictive control(MPC)was proposed for building microgrid with decentralized air conditioners.In day ahead stage,considering the mutual complementary ability of electric energy between the building’s microgrid equipment,the economy,environmental protection and equipment loss of the system operation were taken as the objective function to achieve the maximum consumption of renewable energy while taking into account their own interests.In the intra day stage,a building microgrid prediction model integrating decentralized air conditioning was established based on MPC method,and the problem that the precision of renewable energy output and load prediction decreases with the increase of time scale was solved through repeated rolling optimization.Through the example analysis,it is concluded that two⁃stage optimal scheduling method of decentralized air conditioning can effectively solve the problem of large prediction error,and the robust performance of the system can be improved while smoothing the contact line.
作者
林永君
张聪聪
孟耀兵
LIN Yongjun;ZHANG Congcong;MENG Yaobing(North China Electric Power University,Baoding 071003,China)
出处
《山东电力技术》
2023年第4期1-6,12,共7页
Shandong Electric Power
基金
中央高校基本科研业务费专项资金资助项目(2019MS100)。
关键词
模型预测控制
分散式空调
优化调度
负荷建模
model predictive control
decentralized air conditioning
optimized scheduling
load modeling
作者简介
林永君(1965),男,博士,教授,硕士生导师,主要研究方向为新能源发电技术;张聪聪(1998),女,硕士在读,主要从事微电网优化调度、新能源发电方向研究;孟耀兵(1996),男,硕士在读,主要从事微电网优化调度、新能源发电方向研究。
