Multi-objective optimal power flow with stochastic wind and solar power

The classical optimal power flow problem is usually formulated with only thermal generators, in which the fuel used to generate power is limited and emissions from the network system are often ignored. Due to several promising features like renewability, richness, and cleanness, renewable energy sources have been drew growing attention. As a result, more and more renewable energy sources are penetrated into the electrical grid. In this paper, the standard IEEE-30 bus system is modified by integrating renewable energy sources as the case study, where the traditional thermal generators on buses 5 and 11 are replaced by wind generators, and bus 13 is replaced by solar generators. In addition, to address the intermittence and uncertainty of renewable sources, the Weibull probability density function is used to calculate the available wind power. Meanwhile, the lognormal probability density function is employed to calculate the available solar power. The optimal power flow with stochastic wind and solar energy is formulated as a multi-objective optimization problem. A multi objective evolutionary algorithm based on non-dominated sorting with constraint handling technique are presented to solve it. In addition, another larger test system i.e., IEEE-57 bus system is selected to further verify the performance of the proposed approach in handling large dimensional problem. Simulation results indicate that proposed approach can obtain competitive compromise solution on different optimization objectives. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study investigates the integration of renewable energy sources in power systems and addresses the uncertainty of wind and solar energy in the optimal power flow problem. A multi-objective optimization solution is proposed, and its performance is validated using the IEEE-30 bus system and the IEEE-57 bus system.