Valuation of Power Generation Assets: A Real Option Approach
http://www.erasmusenergy.com/articles/120/1/Valuation-of-Power-Generation-Assets-A-Real-Option-Approach/Page1.html
Doug Gardner
By Doug Gardner
Published on 12/13/2007
Keywords:
Published in: Algo Research Quarterly
Publication year: 2000
Co-author 1: Yiping Zhuang
Real options theory is an increasingly popular tool for valuing physical assets such as power generation plants. In this paper, we describe a model for power plant valuation that accounts for such important operating characteristics as minimum on- and off-times, ramp time, nonconstant heat rates, response rate and minimum electricity dispatch level. The power plant values and optimal operating policies are obtained by employing stochastic dynamic programming. Sample numerical results, using electricity price data from the New England power pool, show that operating constraints can have a significant impact on power plant values and optimal operating policies.
Published in: Algo Research Quarterly
Publication year: 2000
Co-author 1: Yiping Zhuang
Real options theory is an increasingly popular tool for valuing physical assets such as power generation plants. In this paper, we describe a model for power plant valuation that accounts for such important operating characteristics as minimum on- and off-times, ramp time, nonconstant heat rates, response rate and minimum electricity dispatch level. The power plant values and optimal operating policies are obtained by employing stochastic dynamic programming. Sample numerical results, using electricity price data from the New England power pool, show that operating constraints can have a significant impact on power plant values and optimal operating policies.
Valuation of Power Generation Assets: A Real Option Approach
Real options theory is an increasingly popular tool for valuing physical assets such as power generation plants. In this paper, we describe a model for power plant valuation that accounts for such important operating characteristics as minimum on- and off-times, ramp time, nonconstant heat rates, response rate and minimum electricity dispatch level. The power plant values and optimal operating policies are obtained by employing stochastic dynamic programming. Sample numerical results, using electricity price data from the New England power pool, show that operating constraints can have a significant impact on power plant values and optimal operating policies.