With the continuous increase of energy demand and the continuous escalation of traditional energy and environmental problems, energy based on carbon emissions can no longer meet the future energy demand, so renewable energy power generation has attracted global attention. The purpose of this paper is to study the multi-objective optimization of VM cycle heat pump by using NSGA-II genetic algorithm within a given range of heat source temperature, average cycle pressure, volume ratio of cold and hot chambers and rotational speed of a renewable energy system based on genetic algorithm. Finally, compared with the traditional genetic algorithm, neural network algorithm and traditional subcontracting system, the optimal solution set of the production system obtained in this work has the advantages of energy saving. The initial investment cost of Algorithms 1-3 is 160.1%, 170.2% and 178.4% higher than that of the production division system, respectively. In terms of annual operating costs, the optimization algorithm is obviously superior, saving 60.45%, 57.15% and 53.14%, respectively. Compared with the other two algorithms, the investment cost ratio of Algorithm 1 has the highest operating cost saving rate. Therefore, the multi-objective genetic algorithm based on NSGA-II is superior to the other two algorithms.

Xu Chen and Jinhua Ha. Renewable Energy System Based on Genetic Algorithm. Academic Journal of Energy (2021), Vol. 2, Issue 4: 9-17. https://doi.org/10.38007/RE.2021.020402.

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