A new comprehensive model to simulate the restructured power market for seasonal price signals by considering on the wind resources

• Main Authors: Askari, Mohammad Tolou, Ab Kadir, Mohd Zainal Abidin, Hizam, Hashim, Jasni, Jasronita
• Format: Article
• Published: AIP Publishing LLC 2014
• Online Access: http://psasir.upm.edu.my/id/eprint/36195/; http://scitation.aip.org/content/aip/journal/jrse/6/2/10.1063/1.4869141

Generation expansion planning requires simulating the medium term power market. This can be done based on electricity price signals in the power market. The market clearing price is one of the most important factors to determine the incremental rate of private investor's profit. When calculating this parameter, the planners encounter greater uncertainties in a restructured power market than in a centralized market. This can be critical when renewable energies participate in this type of electricity market. In this study, the scenario based method is used to model a wind power plant in the restructured power market. The hourly output of the wind turbine generators is simulated based on a hybrid Auto Regressive and Moving Average-Monte Carlo method. Each scenario of the wind power plant as well as its occurrence probability is determined based on a data mining technique. Then, a new comprehensive model for the restructured power market is proposed to maximize the profit of investors as well as to determine the market clearing price by considering stochastic and rational uncertainties. The stochastic uncertainties include the demand and fuel price that are modelled by using the Monte-Carlo method. The Nash equilibrium in the rational uncertainty as a strategic behaviour of players in the power market is determined by using the Cournot game. The effect of the CO2 tax rate and the bilateral contract are investigated in this study. Finally, the model is implemented in a test power market. According to the findings, this model can be used as a robust and comprehensive model to determine the market clearing price which can be applied for capacity expansion planning.