Profit maximization of a generation company based on Biogeography based optimization

In a deregulated electricity market, generating companies aim to maximize their profit, by bidding optimally in the day-ahead market, under incomplete information of the competing generators. This paper develops an optimal bidding strategy for a thermal generator, considering a nonlinear operating cost function. Each generating company offers block bid as price and quantity pairs and sealed auction with a pay-as-bid is employed. Rival bidding behavior is described using normal probability distribution function, and the optimal bidding strategy for a generation company is formulated as a stochastic optimization problem. This is solved using Monte Carlo Simulations with Biogeography based Optimization (BBO) approach. BBO is a new heuristic algorithm that retains the properties of all good solutions and improves the quality of poor solutions, in the entire population of feasible solutions. The effectiveness of the proposed method is tested on a sample system, and optimal bid quantities and prices are obtained.