Day-ahead interval optimization of combined cooling and power microgrid based on interval measurement

Abstract

The combined cooling and power (CCP) microgrid plays an important role in achieving the goal of dual carbon, while the uncertainty of renewable energy (RE) output and the equipment operation mode bring challenges to the operation optimization. To address this problem, a day-ahead interval optimization model based on interval measurement of CCP microgrid is proposed. First, a large number of scenarios of RE output are generated using Latin hypercube sampling. And the scenarios are reduced by the fast backward reduction method (FBRM). The reduced scenarios are combined with the extreme value theorem to obtain the RE output interval range. Then, the demand response (DR) models of electrical load and cooling load are established to achieve peak-cutting and valley-filling. And the optimization model of ice storage air conditioning (ISAC) with different connection modes is established. Furthermore, the day-ahead interval optimization model is constructed of the CCP microgrid. Finally, the results of the day-ahead optimization model for ISAC under different operation modes are analyzed. The simulation results show the accuracy and effectiveness of the proposed model. The interval radius of the proposed method is reduced by 29.408% and 57.409% compared to the traditional interval method and the original scenario interval method, respectively. And the upper limit of the interval is reduced by 519.9923¥, 1987.6423¥, respectively. In the optimization with the participation of DR and ISAC, the operation cost can be reduced. The economic and stable operation of the CCP microgrid can be realized.