Wisconsin Energy to acquire Integrys in a transaction valued at $9.1 billion; Dominion to acquire the CID Solar Project from EDF Renewable Energy; Landis+Gyr to acquire GRIDiant Corp.; PPL...
The Devil in the Deal: Notes From an M&A Practitioner
A look at due diligence for energy transactions, and at what’s driving them.
now just a fraction of what they once were.
The energy-asset market not only is more liquid and active, but more efficient as well. The counterparties and competitors the transactor faces are increasingly sophisticated, and it takes a lot more to establish a market edge in identifying, evaluating, pricing, and structuring successful deals. So while the importance of thoroughly assessing an asset’s value and risk prior to acquiring it never has been greater, neither has the pressure and difficulty in performing appropriate due diligence.
While we do believe that a due-diligence effort should provide a comprehensive assessment of value and risk for the transaction, our practical experience has shown that it pays, literally, to focus more deeply on some issues than others. And the issues that tend to be of greatest concern generally are a function of the types of assets that are involved in the transaction. Therefore, we now will highlight one issue for each asset type that we believe merits special attention.
When they are present, physical assets usually represent the lion’s share of a merchant-energy firm’s valuation. And physical assets, especially power plants, constitute the majority of transactions on a stand-alone basis as well for the types of assets shown in Figure 1 (see p. 34). Unfortunately, they also tend to be the most complex to evaluate. The typical power plant poses a myriad of operational, regulatory, environmental, and legal issues that must all be taken into account when assessing value and risk.
It is market risk, however, that often poses the greatest challenge. Plant valuation and risk assessment require very long-term forward curves. These will determine when and how often the plant is dispatched, and how much margin is realized throughout the entire useful life of the plant. This creates a need for forward prices, volatilities, and correlations for electricity and fuel that extend out for as many as 30 years. Unfortunately, transparency is limited to a maximum of five years for many markets, and may be as little as one or two years for some.
In response to this constraint, many transactors have opted to use forecasts beyond the observable forward-curve horizon. These forecasts usually are generated by simulation models that rely on a myriad of assumptions and expectations regarding future levels of supply and demand for the commodity. They employ a market equilibrium framework, in which price levels are computed such that future supply and demand are in balance.
The resulting “market-clearing” curves indeed may provide a reasonable forecast for future spot prices. Unfortunately, however, these are not forward curves. And therefore, this approach poses several significant problems that can undermine plant valuation and risk analysis. For starters, model-generated curves will not produce valuations and risk measures (value-at-risk, etc.) that represent the asset’s market value. Instead, the results will at best represent the transactor’s proprietary view of the asset’s value and riskiness. Now it legitimately can be argued that both are necessary—a proprietary view of asset value, as well as an estimate of where the market will transact. However, we have found that many