Hybrid Differential Evolution with BBO for Genco's multi-hourly strategic bidding

Abstract

In Day-Ahead (DA) electricity markets, Generating Companies (Gencos) aim to maximize their profit by bidding optimally, under incomplete information of the competitors. This paper develops an optimal bidding strategy for 24 hourly markets over a day, for a multi-unit thermal Genco. Different fuel type units are considered and the problem has been developed for maximization of cumulative profit. Uncertain rivals' bidding behavior is modeled using normal distribution function, and the bidding strategy is formulated as a stochastic optimization problem. Monte Carlo method with a novel hybrid of Differential Evolution (DE) and Biogeography Based Optimization (BBO) (DE/BBO) is proposed as solution approach. The simulation results present the effect of operating constraints and fuel price on the bidding nature of different fuel units. The performance analysis of DE/BBO with GA and its constituents, DE and BBO, proves it to be an efficient tool for this complex problem.