| dc.description.abstract | Energy is the foundation of country development, and it is also important to supply electric power constantly. The goal is to satisfy the growing power demand of each year under lowest constructing cost and operation cost. There are lots of research in power planning, such as Malcolm (1994)、Albornoz (2004)、Nagurney (2004), they discuss it form different viewpoint in uncertainty approach or network equilibrium approach. In addition, facing the raising price of fossil fuels, the uncertainty of power demand and supply in future, and international limitation of Greenhouse Gas emission by Kyoto Protocol; it prompt us to transfer the structure of power system from high-polluted form to low-polluted form. In order to deal with so many external uncertainty factors, this thesis introduce robust optimization which was proposed by Mulvey in 1995, to propose the power expansion model. By simplified it, which became a two-staged stochastic programming model, based on it we analyze in different scenario and field to obtain a policy. While we perform a plan of development electric power plant, there are some factors need to be considered, such as when, where, how many, and what kind of electric generator. We assume that thermal power, wind power, and unclear power will still be installed to increase electric capacity in the future. Decision maker have to deal with the uncertainty of producing cost, green power supply, and regional electric load in future. We using scenario analysis to present environment parameters and obtain best strategic decision, finding out appropriate capacity and location to be installed, reaching regional load balance which will decrease the losing of power. The resultant model is solved numerically by the application of the L-shaped method, whose implementation and development were executed using the software AMPL, with CPLEX as a solver. The small example will verify model’s validity, at last the stuy will discuss key issue by design some examples, through data collecting and sensitivity analyzing; we make the conclusion according to the result. | en_US |