Day-Ahead Self-Scheduling of Thermal Generator in Competitive Electricity Market Using Hybrid PSO

• Published in: 2009 15th International Conference on Intelligent System Applications to Power Systems pp. 1 - 6
• Main Authors: Pindoriya, N.M., Singh, S.N., stergaard, J.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2009
• Subjects: Adaptive systems; Constraint optimization; Day-ahead self-scheduling; Economic forecasting; Electricity market; Electricity supply industry; Hybrid particle swarm optimization; Hybrid power systems; LMP forecast; Load forecasting; Particle swarm optimization; Power generation; Predictive models; Uncertainty
• ISBN: 9781424450978; 1424450977
• DOI: 10.1109/ISAP.2009.5352896

This paper presents a hybrid particle swarm optimization algorithm (HPSO) to solve the day-ahead self-scheduling for thermal power producer in competitive electricity market. The objective functions considered to model the self-scheduling problem are: 1) to maximize the profit from selling energy in day-ahead energy market subject to operational constraints and 2) at the same time, to minimize the risk due to uncertainty in price forecast. Therefore, it is a conflicting bi-objective optimization problem which has both binary and continuous optimization variables considered as constrained mixed integer nonlinear programming. To demonstrate the effectiveness of the proposed method for self-scheduling in a day-ahead energy market, the locational margin price (LMP) forecast uncertainty in PJM electricity market is considered. An adaptive wavelet neural network (AWNN) is used to forecast the day-ahead LMPs. The effect of risk is explicitly modeled by taking into account the estimated variance of the day-ahead LMPs.