Energy Investment Planning at a Private Company: A Mathematical Programming-Based Model and Its Application in Turkey

We consider a mid-sized private electricity generating company that plans to enter the market that is partially regulated. There is a cap and trade system in operation in the industry. There are nine possible power plant types that the company considers to invest on through a planning horizon. Some...

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Bibliographic Details
Published in:IEEE transactions on power systems 2017-11, Vol.32 (6), p.4180-4187
Main Authors: Agrali, Semra, Terzi, Fulya, Canakoglu, Ethem, Adiyeke, Esra, Arikan, Yildiz
Format: Article
Language:English
Subjects:
Carbon
Carbon dioxide
Companies
Generation expansion planning
Industries
Investment
investment planning
mixed-integer linear programming
Planning
Power generation
stochastic optimization
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Summary:We consider a mid-sized private electricity generating company that plans to enter the market that is partially regulated. There is a cap and trade system in operation in the industry. There are nine possible power plant types that the company considers to invest on through a planning horizon. Some of these plants may include a carbon capture and storage technology. We develop a stochastic mixed-integer linear program for this problem where the objective is to maximize the expected net present value of the profit obtained. We include restrictions on the maximum and minimum possible amount of investment for every type of investment option. We also enforce market share conditions such that the percentage of the total investments of the company over the total installed capacity of the country stay between upper and lower bounds. Moreover, in order to distribute the investment risk, the percentage of each type of power plant investment is restricted by some upper bound. We tested the model for a hypothetical company operating in Turkey. The results show that the model is suitable to be used for determining the investment strategy of the company.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2017.2676819