Optimal risky bidding strategy for a generating company by self-organising hierarchical particle swarm optimisation

Abstract

In this paper, an optimal risky bidding strategy for a generating company (GenCo) by self-organising hierarchical particle swarm optimisation with time-varying acceleration coefficients (SPSO–TVAC) is proposed. A significant risk index based on mean–standard deviation ratio (MSR) is maximised to provide the optimal bid prices and quantities. The Monte Carlo (MC) method is employed to simulate rivals’ behaviour in competitive environment. Non-convex operating cost functions of thermal generating units and minimum up/down time constraints are taken into account. The proposed bidding strategy is implemented in a multi-hourly trading in a uniform price spot market and compared to other particle swarm optimisation (PSO). Test results indicate that the proposed SPSO–TVAC approach can provide a higher MSR than the other PSO methods. It is potentially applicable to risk management of profit variation of GenCo in spot market.