Study on the capacity-operation collaborative optimization for multi-source complementary cogeneration system

Abstract Combining renewable energy with fossil fuel-based energy systems is a promising way to develop renewable energy power generation and decrease CO2 emissions with lower capital investment. In this paper, a multi-source complementary cogeneration (MCC) system is investigated in which the wind farm and solar aided combined heat and power (SA-CHP) system are integrated at the power grid level. A bi-level capacity-operation collaborative optimization model for this MCC system has been established to simultaneously optimize main components capacities and system annual load dispatch. In the proposed model, the upper level is a multi-objective optimization searching for the optimal trade-off between economy and CO2 emissions. At the same time, the lower level aims to obtain the optimal load dispatch of the MCC system for the whole year to maximize the annual operating income. The linear model of the SA-CHP system is built to simplify the lower-level problem–solution process. The bi-level optimization model is handled with a nested approach that combines non-dominated Sorting Genetic algorithm-II and linear programming. The proposed bi-level model is applied in a study case to simultaneously optimize thermal energy storage capacity, solar field size, wind farm capacity, as well as annual power and heat load dispatch of an MCC system located in Zhangbei, China. Besides, the influences of capacity parameters on the performances of the MCC system are analyzed.