Optimal Power Flow in Power Systems with Renewable Energy based on Modified NSGA-II Algorithm

In order to achieve zero carbon emissions, the decarbonization and cleanliness of the power system becomes more and more important. With the increase of renewable energy integration such as wind and photovoltaic (PV) power, the source-side uncertainty of the power systems has increased significantly, and traditional optimal power flow calculation methods encounter challenges to adapt to the demand of power system operation with large integration of new energy resources. In this paper, effects of wind and PV power output uncertainty are analyzed and represented by probabilistic model first. On this basis, the multi-objective optimization model for optimal power flow is constructed and solved with the modified non-dominated sorting genetic algorithm-II (NSGA-II). In the multi-objective optimization power flow model, the uncertainty of source-side wind and PV power generation, and the constraints of traditional generation and reactive power compensation are considered. Validations on IEEE 14 system show effectiveness of the proposed modified NSGA-II based optimal power flow method in reducing both economic generation cost and electric network power loss.