Assessing Economic Complementarity in Wind–Solar Hybrid Power Plants Connected to the Brazilian Grid

Abstract

The share of electricity generation from Variable Renewable Energy Sources (VRES) has increased over the last 20 years. Despite promoting the decarbonization of the energy mix, these sources bring negative characteristics to the energy mix, such as power ramps, load mismatch, unpredictability, and fluctuation. One of the ways to mitigate these characteristics is the hybridization of power plants. This paper evaluates the benefits of hybridizing a plant using an AI-based methodology for optimizing the wind–solar ratio based on the Brazilian regulatory system. For this study, the hybrid plant was modeled using data collected over a period of 10 months. The measurements were obtained using two wind profilers (LIDAR and SODAR) and a sun tracker (Solys 2) as part of the EOSOLAR R&D project conducted in the state of Maranhão, Brazil. After the power plant modeling, a Genetic Algorithm (GA) was used to determine the optimal wind–solar ratio, considering costs with transmission systems. The algorithm achieved a monthly profit increase of more than 39% with an energy curtailment inferior to 1%, which indicates economic complementarity. Later, the same methodology was also applied to verify the wind–solar ratio’s sensitivity to solar energy pricing. The results show that a price increase of 15% would change the power plant’s optimal configuration.