An intelligent energy bidding strategy based on opposition theory enabled grey Wolf optimizer

In an effort to increase competition, many countries around the world have changed their economies from monopoly to oligopoly. Restructuring of energy sector is considered as a key initiative to obtain consumer benefits and social welfare. A power generating company has opportunity to maximize their profit in electricity market through selling the energy in competitive prices under incomplete information of other competing generators. In a day-ahead energy market, generating company (GENCO) sell the energy at optimal bid prices. In this paper the problem of finding market clearing price (MCP), load dispatch (LD) and bid cost under three different capacity and price blocks is carried out by oppositional theory enabled grey wolf Optimizer (OGWO) algorithm. Normal probability distribution function is used to model the rival behaviors. The bidding strategy of a generator for each trading period in a day-ahead market is formulated as a stochastic optimization and the same is solved through Monte Carlo method. The OGWO encompasses opposition concept with the grey wolf optimizer (GWO) algorithm to accelerate the convergence rate. The approach is tested over a dynamically changing electricity market. The results are compared with other techniques namely PSO and GWO. The OGWO shows competitive results.