Stochastic multi-scenario optimization for a hybrid combined cooling, heating and power system considering multi-criteria

Combined cooling, heating and power (CCHP) system integrated with multiple renewable energies can improve the environmental performance while increasing the dependency on the national grid due to the multiple uncertainties. This paper proposes a multi-objective stochastic multi-scenario optimization method for the optimal capacity of a CCHP system integrated wind, solar and geothermal energy considering its energy supply independence, environmental impact, economic performance and energy efficiency. The spatiotemporal multiple uncertainties in wind velocity, solar irradiation and multiple loads are characterized by the stochastic multi scenarios generated by the stochastic hierarchy scenario-generation method. The hybrid system's flexibility is evaluated by both the grid integration level and net interaction level. The environmental performance is assessed by both the carbon emission reduction rate and renewable energy penetration. The economic and energy performances are characterized by the annual cost-saving rate and primary energy-saving rate, respectively. The multi-objective stochastic optimal design method is formulated and solved using non-dominated sorting genetic algorithm II. The case study results show that there are slight deviations of objectives between the stochastic multi-scenario and traditional optimizations, while the former can save 58.69% computation time. Moreover, the installed capacity increase of the electrical energy storage to 870kWh can reduce the system's dependency on the national grid and the net interaction level to 3.74% while deteriorating the economic performance and dropping the annual cost-saving rate to -217.37%. Inversely, the installed capacity increase of renewable energy generators can enhance economic and environmental performances while worsening the net interaction level.

Automated summary

This research introduces a multi-objective stochastic multi-scenario optimization method to determine the optimal capacity of wind, solar, and geothermal energy in a CCHP system, considering energy supply independence, environmental impact, economic, and energy efficiency. The optimization problem is solved using non-dominated sorting genetic algorithm II, demonstrating that stochastic multi-scenario optimization can save computation time compared to traditional optimization methods.