In recent years, hybrid power systems (HPSs) with renewable energy sources, such as wind power and photo-voltaic (PV) power, have been used as one of the most effective methods to address resource shortages and environmental pollution. In this paper, a model based non-cooperative game theory is developed to optimize the capacity configuration problem with wind power, PV power, and electric vehicles (EVs). The model is integrated with economic factors, such as electricity sales income, investments, and power supply reliability costs. An iterative search algorithm is used to solve the proposed game model, and the particle swarm optimization al-gorithm was used to optimize the income of each player. Mixed integer multi-objective linear programming (MIMLP) is used to establish the representative days selection model of resources and load demands. To verify the rationality of the model, the grid-connected hybrid power generation system with different game participants and line transmission capacities was studied. Furthermore, the representative days results of the daily load rate and peak valley ratio generated by MIMLP are compared with those obtained using k-means clustering and a scenario reduction method. The results reveal that the proposed model and related strategies can realize a reasonable allocation of resources and effectively improve the operation economy of power systems.
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