Study On The Electric Contract Decomposition Strategies Considering Uncertain Factors

With the development of electric power market in our country, more and more effective competition mechanism is introduced. Because electric energy has its own characteristics different from general commodity, most power markets in our country take long-term contracts as the main transaction mode. When long-term contracts are signed, grid company has to make a schedule to fulfill the contracts and it called contract electric power volume decomposing plan. The decomposing results directly affect the security and economic of power system. However, the decomposing schedule is made ahead of time; grid company's benefit is influenced by many uncertain factors, such as the fluctuation of electricity prices in future markets. The grid company will face risk of benefit loss. This thesis puts forward different models for the long-term contract decomposing strategy with uncertain factors in future power market taken into account. The main contents of this thesis are as follows:First, a model is developed for contract electric volume decomposing strategy for Grid Company with risk taken into account. Based on the electricity market clearing prices estimated, a new methodological framework is presented, in which variance is used as the measure of cost risk. This model can provide a decomposing strategy with a certain balance between total purchase power fees and the risk. Finally, sample examples are served for demonstration.Secondly, based on the estimated market prices and load, an effort is made to investigate the optimal strategies of contract power volume decomposing schedule for Grid Company with risk management, under the methodological framework of the well-developed chance constrained programming. This approach provides an explicit manner for risk management by setting a required confidence level of purchase power fees minimization. Genetic algorithm and Monte Carlo method are used to solve this mathematical model.Thirdly, in a power market in which installed capacity is not sufficient, the relationship between the market clearing price and load or contract volume is presented as a nonlinear function. Based on the loads forecasted and the nonlinear function, the mathematical model of the optimal contract volume decomposing strategy is developed in the framework of the chance-constrained programming.Lastly, conclusions are made based on the research outcomes, and directions for future research pointed out.