Renewable energy sources are particularly important in clean energy transitions and need to be considered in Generation Expansion Planning (GEP) problems due to low cost, ease of installation, and ability to implement Demand Response (DR) programs. However, challenges such as the stochastic nature of renewable energy sources, consumer unawareness regarding participation in DR programs, and difficulties in integrating some resources have posed challenges to the use of these resources in the GEP problem. This paper addresses these challenges by using the Weibull distribution function to model wind power plants' uncertainty and rewards and penalties to motivate consumer participation in the GEP problem. To achieve these objectives, initially, the adequacy assessment of the generation system is performed analytically using the reliability index, which includes Expected Energy Not Supplied (EENS), considering the forced outage rate of generators in the DIgSILENT power factory through Python programming. Subsequently, an optimized GEP model is presented to enhance the generation system's adequacy against short-term demand for the next year. In this model, wind farms along with the DR program are integrated and optimized using the genetic algorithm, employing Python programming. The genetic algorithm works by selecting the number of existing turbines in the wind power plant and the level of consumer participation needed to reduce the EENS to the desired value at the minimum cost. Validation of the proposed model is conducted on a 9-bus network. The strength of the presented method lies in its applicability to real-world networks modeled in the DIgSILENT power factory.
