Energy Source Expansion Planning for a Real-world Deregulated Electric Utility Considering Emission

Abstract

The Electricity Demand Prediction (EDP) and Generation Expansion Planning (GEP) make a real-world planning problem more optimal. In this study, the EDP problem has been solved using the optimization techniques such as Differential Evolution (DE), Genetic Algorithm (GA) and Artificial Immune System (AIS) for Tamil Nadu state, India. The electricity demand has been predicted and validated in terms of Mean Absolute Percentage Error (MAPE). The results reveal that DE outperformed other algorithms in solving the EDP problem. Continually, the GEP problem under partial deregulated environmental has been solved using the DE. In the GEP model, utilities and Independent Power Producer (IPP) have acted as the generating sources where IPP sells the power to utilities and utilities sell the power to the consumers. The utilities have been modeled to ensure the profit of all IPP, fuel mix ratio, system security, reliability, etc. The problem has been solved for the 6-year (till 2024) and 12-year (till 2030) planning horizons by considering three different scenarios, such as (i) without budget constraint of utilities, (ii) with budget constraint of utilities and (iii) penetration of huge Renewable Energy Source (RES) with budget constraint of utilities. The expansion capacities of each power plants were estimated over the planning horizon by maximizing the profit to the generating sources. The values of reliability indices and CO2 emission have been obtained.