An optimal Renewable Portfolio Standard using Genetic Algorithm - Benders´ decomposition method in a Least Cost Approach

This paper presents the Least Cost Renewable Energy Portfolio Analysis (LCREPA) approach that utilizes the Genetic Algorithm - Benders´ decomposition (GA-BD) method, which could achieve an order of magnitude of improvement in terms of run times at larger instances, in determining the optimal Renewable Portfolio Standard (RPS) percentage. The detailed demonstration of the LCREPA approach is implemented through a case study that analyzes RPS scenarios - status quo, base case and sensitivity cases - in the Philippine Luzon grid. The timing of candidate generating units in the base case and sensitivity case models is calculated using the GA-BD computer program developed to evaluate the most economical power generation expansion planning for additional generating units subject to the integrated requirements of power demands, power capacities, energy availability and reliability. In sensitivity case models, the base case inputs - RPS cap, peak demand, capacity factor, fuel cost and investment cost - are adjusted. The outputs of the scenarios - total RPS percentage, levelized cost of electricity, total CO₂ emission reduction, capacity and energy mix - are examined and compared with the Philippines´ Planned RPS.