Multi-objective optimization scheduling of combined cold heat and power system with multi-renewable energy

To improve economic benefits and develop energy-saving and emission-reducing in operation of the combined cold heat and power (CCHP) system, an optimization model of multi-objective day-ahead scheduling is constructed based on operation cost, fossil energy consumption, and carbon dioxide emission. Moreover, the Pareto optimal solution set is obtained by heuristic scheduling rules and improved multi-objective cross entropy (MOCE) algorithm. The multi-objective optimization is defined as a small probability event based on the important sampling theory; the sample section generation strategy and parameter updating mechanism are introduced in order to ameliorate the diversity of samples and the distribution characteristics of its Pareto front. Simulation results demonstrate that the multi-objective model and algorithm which ensure the better economic and environmental benefits could be accomplished at the lower energy-consumption level in this system.