电力系统自动化2019，Vol.43Issue(5)：30-38,9.DOI:10.7500/AEPS20180417007
含冰蓄冷空调的冷热电联供型微网多时间尺度优化调度
Multi-time-scale Optimal Scheduling of CCHP Microgrid with Ice-storage Air-conditioning
摘要
Abstract
Combined cooling, heating and power microgrid (CCHP-MG) has important application value for realizing sustainable energy development and building a low-carbon society. However, the complex energy structure and the coupling relationship between the equipment inside the systems, the renewable energy consumption and the smoothing of load fluctuations bring challenges to the optimal operation of CCHP-MG. This paper proposes a multi-time-scale optimal scheduling model of CCHP-MG with ice-storage air-conditioning, and studies the effects of different operation modes of the ice-storage air-conditioning system on the optimal scheduling. In the day-ahead scheduling, the uncertainty of renewable energy is represented by multi-scenarios, and the economic operation of the CCHP-MG during an optimization cycle is emphasized. Based on the day-ahead scheduling, a two-layer rolling optimization model is proposed in the intraday scheduling for smoothing the load fluctuation according to the coherency and complementarity of the cooling, heating and electricity load at different time scales. And the output power of each equipment can be identified. The simulation results show that the operation mode of ice-storage air-conditioning system is related to the improvement of comprehensive benefits of CCHP-MG, and the multi-time-scale optimal scheduling model can not only meet the requirements of users for cold, heat and electricity, but also can effectively smooth the random fluctuation on both supply and demand sides, and ensure economic and stable operation of CCHP-MG.关键词
微网(微电网)/冰蓄冷空调/滚动优化/冷热电联供/混合整数线性规划Key words
microgrid/ice-storage air-conditioning/rolling optimization/combined cooling, heating and power (CCHP)/mixed integer linear programming引用本文复制引用
程杉,黄天力,魏荣宗..含冰蓄冷空调的冷热电联供型微网多时间尺度优化调度[J].电力系统自动化,2019,43(5):30-38,9.基金项目
国家自然科学基金资助项目(51607105) （51607105）
This work is supported by National Natural Science Foundation of China (No. 51607105) （No. 51607105）
