AI based Break-even Spot Pricing and Optimal Participation of Generators in Deregulated Power Market

Optimal pricing of electricity in a power system was proposed during early eighties considering unit generation and consumer usages as decision variables. With restructuring followed by deregulation, a number of players have started participating in the competitive power market leading optimal pricing of electricity to a complicated level. Literature survey reveals that different models have been proposed and solved for evaluation of optimal prices using classical methods but many issues have not yet been exposed. This paper presents a novel algorithm for optimal allocation of generation schedule of generators to optimize generation cost under stressed condition of a system considering consumer welfare. Here, the optimal generation dispatch problem is formulated as a non-linear constrained optimization problem where real power generation and total generation cost are to be optimized simultaneously to find spot price. This proposed algorithm has been tested with standard IEEE 30 bus system using different artificial intelligence (AI) such as particle swarm optimization (PSO) and genetic algorithm (GA). The results demonstrate the capabilities of the proposed approach to generate true and well-distributed optimal solution of the dispatch problem in one run even in stressed condition of the system.