Optimal GENCO's bidding strategy in a power exchange facilitating combined power and emission trading using Intelligent Programmed Genetic Algorithm

Currently, for developing optimal bidding strategy considering carbon credit trading, Generating Company (GENCO) separately participates in electric power and emission market. Recently, researchers proposed a new scheme in which emission trading is facilitated within Power Exchange (PX). Developing optimal GENCO's bidding strategy considering carbon credit trading within PX is a complex single-objective optimization problem with generator's surplus as the main objective function. Obtaining its solution using a simple evolutionary optimization algorithm is difficult and time-consuming. Therefore, this paper presents an application of Intelligent Programmed Genetic Algorithm (IPGA) equipped with advanced deterministic diversity creating operator for solving optimal GENCO's bidding strategy problem with minimum number of objective function evaluations. The performance of IPGA is first tested on three different categories of standard test functions by comparing its simulation results with that obtained using binary-coded GA, simulated annealing, particle swarm optimization, biogeography-based optimization, teaching learning-based optimization, and differential evolution-based hybrid GA. A system comprising of six GENCOs and two buyers is used to validate the applicability of IPGA. IPGA along with other algorithms was used to obtain optimal bidding curve for 24 hours for GENCO GC-1. Simulation results clearly show much faster convergence for IPGA in terms of the number of objective function evaluations required to reach optima. Result also shows that GENCO GC-1 can obtain much higher GC-1 surplus, carbon credit limit, and social welfare by utilizing the obtained bidding strategy using IPGA as compared with the bidding strategy obtained by other algorithms.