Mathematical programming approaches for a generation expansion planning problem in a carbon-constrained environment

In this thesis, we study a Generation Expansion Planning (GEP) problem in a carbon-constrained environment from the perspective of a private electricity generating company. The company plans to enter the partially regulated electricity generation market, in which carbon emission permits are traded. Hence, the government requires the company to obey a limit for the total carbon emission. The company determines the amount of installed capacity for diff erent types of power plants, which may or may not include carbon capture and storage (CCS) technology over a predetermined planning horizon. The company's aim is to maximize the net present value of the total pro fit. The market and the company have some restrictions on the investments. The amount of installed capacity is limited by a maximum and a minimum value for each period for all power plant types. The government constrains the market share of the company in order to prevent monopoly. On the other hand, the company aims to reach certain levels of market share at certain time periods. Moreover, the company restricts the percentage of each type of power plant investments in the portfolio by some upper bound to distribute the investment risk. We first formulate the problem as a deterministic mixed integer linear programming model assuming that all data are known in advance and fi xed. Then, we use multi-stage stochastic programming approach to include uncertainties in the parameters. We implement these models for a hypothetical company operating in Turkey. We apply sensitivity analysis in our deterministic model to determine the eff ects of parameters on the optimal decision. Then, in stochastic model, we analyze the problem by creating scenarios for uncertain parameters.