A Framework Using Two-Factor Price Lattices for Generation Asset Valuation

• Published in: Operations research Vol. 55; no. 2; pp. 234 - 251
• Main Authors: Tseng, Chung-Li, Lin, Kyle Y
• Format: Journal Article
• Language: English
• Published: Linthicum, MD INFORMS 01.03.2007; Institute for Operations Research and the Management Sciences
• Subjects: Analysis; Applied sciences; Approximation; asset pricing; Dynamic programming; Electric power plants; Electric power production; Electricity; Electricity prices; energy; Exact sciences and technology; Expected values; finance; Fuels; Lattice theory; Market prices; Mathematical lattices; Mathematical moments; Methods; Natural gas; natural resources; Operational research and scientific management; Operational research. Management science; Portfolio theory; Power plants; real options; Real options analysis; Sensitivity analysis; Startup costs; Stochastic models; Studies; Valuation; United States; Branching; Natural resources; natural resources: energy; finance: asset pricing; Stochastic model; Electric power plant; Itô equation; Sensitivity analysis; Probabilistic approach; Finance; real options; Optimization; Lattice; Real option; Stochastic integral; Stochastic programming; Energy resources; Electrical network; Refining; Fuel; Pricing; Discrete time; Profit; Feasibility; Dynamic programming
• DOI: 10.1287/opre.1060.0355
• ISSN: 0030-364X

In this paper, we use a real-options framework to value a power plant. The real option to commit or decommit a generating unit may be exercised on an hourly basis to maximize expected profit while subject to intertemporal operational constraints. The option-exercising process is modeled as a multistage stochastic problem. We develop a framework for generating discrete-time price lattices for two correlated Ito processes for electricity and fuel prices. We show that the proposed framework exceeds existing approaches in both lattice feasibility and computational efficiency. We prove that this framework guarantees existence of branching probabilities at all nodes and all stages of the lattice if the correlation between the two Ito processes is no greater than 4/√35 ≈ 0.676. With price evolution represented by a lattice, the valuation problem is solved using stochastic dynamic programming. We show how the obtained power plant value converges to the true expected value by refining the price lattice. Sensitivity analysis for the power plant value to changes of price parameters is also presented.