A Multi Time-Scale and Multi Energy-Type Coordinated Microgrid Scheduling Solution—Part II: Optimization Algorithm and Case Studies

In part II of this two-part paper, the improved particle swarm optimization (IPSO) algorithm for solving the microgrid (MG) day-ahead cooling and electricity coordinated scheduling is proposed. Significant improvements are made in comparison with the conventional PSO algorithm from two aspects. First, the mandatory correction is implemented to ensure the complex coupled constraints among the components of a particle are met after the particle's position is updated, which could enhance the algorithm performance when solving the problem including complex constraints. Second, it is assumed that a solution denoted by a particle occupies a neighboring area, the size of which decreases from a certain value to nearly zero as the iteration step increases to its limitation, which helps to avoid the pre-maturity of algorithm. For an MG composed of the combined cooling, heating and power (CCHP) units, PV panels, wind turbines, and storage batteries, a range of case studies under different MG operating modes are carried out through simulations. The simulation results demonstrate the proposed multi time-scale, multi energy-type coordinated MG scheduling solution can achieve the co-optimization of multi energy-type supply to meet customer's cooling and electricity demands, and make the MG be controllable as seen from the connected main grid.