Cheetah Optimizer-based Framework for Least Cost Generation Expansion Planning Incorporating Renewable Energy Sources

The long-term Generation Expansion Planning (GEP) includes identification of location, types, and construction period of new power generating facilities which must be included to the existing power system to cater the increasing demand over a planning horizon. GEP is a highly constrained, dynamic, and mixed-integer nonlinear discrete problem in nature. When renewable energy sources are embedded in GEP problem it makes it more complicated and less reliable due to their alternating nature. Many researchers solve this problem with different techniques, but it is observed that metaheuristic techniques can solve this problem in a more efficient way. In this research article a combined technique of Cheetah Optimizer (CO) based Algorithm with Correction Matrix Method with Indicator (CO-CMMI) is utilized to solve the GEP problem along with consideration of Renewable Energy resources. The proposed (CO-CMMI) framework has given favorable results in terms of least cost GEP, generation forecasting, reduced emissions and runtime as compared to results given by recent research.