A Competitive Optimum Bidding in a Restructured Power Market with Elastic Demand using Crow Search Algorithm

The essential phase taken up by an Independent System Operator (ISO) in the auction is the optimal bidding of data for each Generating Company (GENCO) involved in the system at every trading phase. Strategic bidding data is quite a momentous task by an ISO for the profit maximization of the system with an appropriate bidding coefficient strategically. This paper recommends bidding data to maximize profit with a new proposed optimization technique named Crow Search Algorithm. It’s a meta-heuristic Algorithm performing a heuristic search to obtain a global optimal solution which had been applied to perform and obtain the maximized profit function. This proposed method is applied and tested on an Indian-75 Bus system consisting 15 suppliers to analyze new strategy application verifying the mathematical modeling and seen to have received a better value of profit with the minimum power generation for the system maintaining the demand of the market fulfilling all the inequality constraints. The calculations of the individual GENCO’s profit and power, shown under the result section, have been compared to the Grey Wolf Optimization (GWO), Shuffled Frog Leap Algorithm (SFLA) and Particle Swarm Optimization (PSO) for a wider explanation, and the bar graph has been included for better understanding.