Multi-objective Capacity Configuration Optimization of a Nuclear-Renewable Hybrid System

Integrating the energy storage and the base-load energy can be an efficient solution to cover the fluctuation of renewable energy. A nuclear-renewable hybrid energy system consisting of a small modular thorium molten salt reactor, solar photovoltaics, wind turbines, thermal energy storage and battery storage with two operation modes is proposed to meet the community load demand. The methodology combining multi-objective evolutionary algorithms with multi-criteria decision-making method is used to identify the optimal operation mode and gain the optimal configuration of the proposed nuclear-renewable hybrid energy system. In the first stage, the multi-objective evolutionary algorithm with better performance is utilized to gain Pareto solutions to minimize the levelized cost of energy while ensuring an acceptable level of deficiency of power supply probability. In the second stage, the multi-criteria decision-making method is adopted to select the best solution from the obtained Pareto frontiers. The optimization results demonstrate that the nondominated sorting genetic algorithm has the best performance compared with other algorithms and the operation mode with battery discharged first is more cost competitive. It is concluded from the results analysis that the selection of optimal mode is influenced by seasonal variation that contributes to large energy storage capacity requirement.