Title :
A generation expansion planning model of a strategic electricity generating firm
Author :
Hesamzadeh, M.R. ; Amelin, M.
Author_Institution :
R. Inst. of Technol., Stockholm, Sweden
Abstract :
This paper derives a mathematical structure for investment decisions of a profit-maximising and strategic producer in liberalised electricity markets. The paper assumes a Cournot producer in an energy market with nodal pricing regime. The Cournot producer is assumed to have revenue from selling energy to the pool. The investment problem of the strategic producer is modelled through a leader-follower game in applied mathematics. The leader is the strategic producer seeking the optimal mix of its investment technologies and the follower is a stochastic estimator. The stochastic estimator forecasts the reactions of other producers in the market in response to the investment decisions of the producer in question. The stochastic estimator takes the investment decisions of the producer and it calculates the stochastic prices. The mathematical structure is a stochastic linear bilevel programming problem. This problem is reformulated as a stochastic MILP problem which can be solved using the commercially available software packages. Finally, the developed mathematical structure is applied to a six-node example system to highlight the strengths of the whole approach.
Keywords :
integer programming; linear programming; power generation planning; power markets; profitability; stochastic games; Cournot producer; energy market; generation expansion planning model; investment decisions; liberalised electricity market; nodal pricing regime; profit maximising; stochastic estimator; stochastic linear bilevel programming problem; stochastic mixed integer linear programming problem; stochastic price; strategic electricity generating firm; strategic producer; Electricity; Electricity supply industry; Generators; Investments; Mathematical model; Stochastic processes; Wind power generation; Generation expansion planning; Mixed-integer linear programming; Uncertainty;
Conference_Titel :
Power and Energy Society General Meeting, 2011 IEEE
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4577-1000-1
Electronic_ISBN :
1944-9925
DOI :
10.1109/PES.2011.6039563