An opposition theory enabled moth flame optimizer for strategic bidding in uniform spot energy market

Power generating companies have an opportunity to maximize their profit in electricity market by selling the energy at competitive prices under incomplete information of other rivals behavior. In a day-ahead energy market, Generating Company (GENCOs) sells the energy at optimal bid prices. To calculate the bid prices optimally and for maximizing the profit of generating company, this paper presents a solution for strategic bidding problem which is based on opposition theory enabled moth flame optimizer (OB-MFO). In this work, a new opposition theory enabled moth flame optimizer is proposed which have the additional concept of oppositional feature. First OB-MFO algorithm is tested on 22 benchmark and Congress on Evolutionary Computation (CEC-2017) functions, then it is applied to bidding problem of standard IEEE-14 bus (Test Case-1) and 7 generator power system (Test Case-2). The strategic bidding scheme for a generating company for single and multi hour trading duration in a day-ahead market is formulated. The major findings of the proposed approach are market clearing price (MCP), load dispatch and bid prices of five different blocks of different capacities. The meaningful comparison of the bidding results of other optimization techniques are presented. In addition to that price volatility analysis and exercise of market power analysis are also presented. The results confirm the effectiveness of proposed technique.