Application of optimization algorithms to generation expansion planning problem

摘要

Generation Expansion Planning (GEP) aims to define the least cost capacity expansion plan to meet forecasted demand inward a pre-defined reliability criterion and emission constraint over a planning horizon. This paper presents the application of Differential Evolution (DE), Opposition-based Differential Evolution (ODE) and Self-adaptive Differential Evolution (SaDE) algorithms to GEP problem, where the power generating system of an Indian state Tamil Nadu is taken as study region. GEP problem has been solved for short-term (6-years) and long-term (12-years) planning horizon by considering least-cost, reliable supply and lowest emission to the environment using DE, ODE and SaDE also validated by Dynamic Programming (DP). GEP problem is solved for seven diverse cases such as, Case 1: Base case, Case 2: GEP with Energy Conservation (EC), Case 3: GEP with high penetration of Renewable Energy Sources (RES), Case 4: GEP with penalty costs on emissions from high emission plants (HEP), Case 5: GEP with energy storage technologies (EST), Case 6: Combination of Cases 2, 3&4 and Case 7: Combination of Cases 2, 3, 4&5. The results simultaneously provide the type and capacity of each power plant need to be expanded in each year of the planning horizon at least cost.