Stochastic Multi-Objective Optimized Dispatch of Combined Cooling, Heating, and Power Microgrids Based on Hybrid Evolutionary Optimization Algorithm

• Main Authors: Bifei Tan, Haoyong Chen
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Microgrid; cooperative co-evolution theory; beta function; wind power forecast; CCHP; non-dominated sorting-based algorithm-II
• Online Access: https://ieeexplore.ieee.org/document/8911312/

The penetration of distributed power sources has been increasing with the continuous promotion of clean renewable energy sources. This paper seeks to improve the utilization rate of clean energy and reduce the cost of microgrid operation by first establishing a double-layer wind power prediction error model based on a comprehensive consideration of the time-of-use price and the operating characteristics of different types of clean energy sources, such as wind power, photovoltaic power, thermal power, and transmission tie lines. A combined cooling, heating, and power microgrid collaborative optimization model that considers wind power forecast uncertainty is established with the goal of minimizing economic cost, environmental cost, and degree of power-generation unit output asynchrony of the microgrid. The established multi-objective optimization model is solved using an improved intelligent optimization algorithm that combines the non-dominated sorting genetic algorithm (NSGA) with co-evolution theory and the beetle antennae search algorithm. This algorithm employs a variety of groups in the NSGA to help with correcting the approximations of group members through competition and cooperation. Therefore, the proposed algorithm can combine the excellent convergence of the NSGA and the powerful searching ability of co-evolutionary algorithms. Finally, a practical microgrid system in Northwest China is simulated as a case study, and the performance of the proposed algorithm is compared with that of the conventional NSGA. The simulation results demonstrate the superiority of the global search performance and the rapid convergence performance of the proposed hybrid algorithm.