Many business decisions—including those for power plant development and acquisition—are made on an overly simple decision framework. A stochastic (probabilistic) approach provides better insights to help developers make better decisions, such as the full range of possible results and the likelihood of each.

Power market consultants at WS&Q have helped clients address key decisions with a state-of-the-art analytical approach that is process-oriented yet flexible enough to support most key business decisions around the power plant valuation. It is broken down into three relatively simple phases.  What's more, this approach can be added on top of existing valuation techniques to extract key information with just a little incremental work. There is a link to a graphical presentation of this material at the bottom of this article.

PHASE I

The key to Phase I is defining the problem and building a model—in this case a power plant cash flow model to be used to calculate generating asset value:

a. Identify Issue—Define what it is that we are trying to decide, and identify all of the potential alternatives.

b. Select Decision Criteria—Decide the basis upon which the decision will be made: Financial (IRR, NPV), Operational (LOLP, capacity factor), Safety (number of expected incidents), etc.

c. Plan the Base Model—Build an influence diagram (flow chart) based on in-house expertise and standard business practices to chart the flow of information that results in calculation of the Decision Criteria.

d. Build the Base Model—Develop a core spreadsheet-based model as a basis for the risk analysis, including all parameters identified in the influence diagram and a calculation of the decision criteria.

PHASE II

Phase II is all about adding probabilistic capabilities—and data—to the model:

a. Identify Key Variables—Identify which of the model variables have the greatest impact on the outcome of the decision by developing a tornado diagram.

b. Identify/Quantify Correlations—Identify key market, operational, and financial data correlations that may materially impact the results (e.g., relation of nuclear availability to regional market clearing prices, gas cost to oil cost).

c. Assign Probability Distributions—For the Key Variables, assign probability distributions that best represent the true nature of the forecast, and accounts for any identified correlations.

d. Gather Data—Based on historical data, internal expertise, or outside data forecasts, capture and model the key base and probabilistic data elements that drive the model and the ultimate decision.

PHASE III

Phase III is where you do model runs, interpretation and recommendations.

a. Calculate the Solution—Use powerful analytical tools (e.g. Crystal Ball or @RISK) to simulate the outcome of the probabilistic model.

b. Interpret the Results—Assess the results to gain insights on the issues that effect the decision, and how the decision can effect the organization.

c. Present Findings—Present the results of the risk analysis highlighting the full range of possible outcomes in a clear, concise decision-oriented manner.

d. Make Decisions—Use the results to make the decision, according to how it fits with the organization’s overall risk profile.

PHASE IV

Phase IV is for advanced analytics, and cannot be rigidly defined.  It may consist of hedging, portfolio analysis with an efficianet frontier approach, or other techniques.

WS&Q offers its clients the expertise to address all of their power industry issues, as well as on-site training of staff. WS&Q will lead or support your teams by incorporating the WS&Q Risk Analysis approach.  For a graphical presentation of this material, visit WS&Q’s power plant risk analysis link.

Jim Letzelter

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