Optimization of Temperature Difference Power Generation Energy System Based on Hybrid Multiple Swarm Evolutionary Algorithm

At present, there is still a gap between the development level of temperature difference power generation technology in China and developed countries, and the development is relatively slow. Especially in terms of implementation, China lacks relevant technology and market support. As an environmentally friendly and energy-saving technology, solar power generation plays a very important role in energy technology and also has potential applications in military, aerospace and other scientific research fields. The purpose of this paper is to study the optimization of temperature difference power generation energy system based on hybrid multiple swarm evolutionary algorithm. A temperature differential power generation energy system using solar heat absorbers is designed. A hybrid multi-group evolutionary genetic algorithm with simulated annealing has been introduced to optimize the location layout of the thermoelectric modules of the temperature differential power generation energy system, and the results show that the optimized temperature uniformity degree is higher and the system efficiency is better.