Real-time electricity capacity expansion planning using chaotic ant lion optimization by minimizing carbon emission
J. Booma, P. Anitha, S. Amosedinakaran, A. Bhuvanesh
Abstract
The ultimate aim of demand forecasting and capacity expansion planning is to find an optimal solution toward creating a suitable objective function to find a minimum error, cost-effective, low-carbon, and sustainable alternative. To assist with the planning procedure, which takes into account environmental factors like greenhouse gases associated with generating electricity in addition to financial criteria, innovative approaches have been required. In this research, the Demand Forecasting Problem (DFP) and Capacity Expansion Problem (CEP) have been solved independently using the Chaotic Ant Lion Optimization technique (CALOT) for the Tamil Nadu power sector. Initially, DFP was solved using economic factors such as state gross domestic product (State GDP), individual revenue, and population. DFP has been solved until the year 2032, and the result of DFP has been fed as input CEP. In the CEP, Base Line Scenario (BLS), Carbon Mitigation Scenario 1 (CMS_1), and Carbon Mitigation Scenario 2 (CMS_2) have been made. Moreover, 5-year and 10-year planning periods have been solved until the year 2032. In this period, minimal finance, carbon emissions, and reliability parameters have been calculated. The Ant Lion Optimization Technique (ALOT) has been utilized for validating the outcomes of CALOT.