Multi-Objective Optimization of Solar/Wind Penetration in Power Generation Systems

Integrations of renewable energies, particularly solar and wind, are increasing worldwide due to carbon emission reduction efforts and maturing technologies that have driven down the cost of their energy productions. Due to the intermittency of these renewable sources, the battery energy storage system often coexists alongside solar/wind energy systems. Integrating these two aspects into power systems requires the consideration of reliability, social wellbeing and environmental factors, which collectively form a multi-objective optimization problem that this paper aims to solve with the non-dominated sorting genetic algorithm. The proposed method is able to find optimum solutions that are equally beneficial to all factors - Pareto front - without being heavily biased to any one of them. The proposed method is separated into two parts by first optimizing the penetration of solar/wind energy, followed by the optimization of the energy storage capacity in the second part. The fuzzy decision making method is utilized to select a preferred solution from the Pareto front based on the assignment of the membership function values to reflect operator's preferences. The proposed method was implemented on the IEEE Reliability Test System overlaid with the real sampled weather data. The proposed objectives in the optimization problem are also practical and useful for the expansion of generation systems.