Although today microgrids serve a tiny fraction of the market, that share will grow as costs fall. Utilities can benefit if they plan ahead.

## Default Retail Supply: Making the Call on Market Price and Model Risk

How to set reserve levels for full requirements auctions.

- customers and counterparties responsible for paying for the energy delivered. Auctions serve a diversified set of customers. Therefore, it is reasonable to assume the credit risk is insignificant. (If the bidder were to choose to reduce the Load/Market Risk, it would increase its credit risk); and

**Model Risk**- dependent on the models used. Running multiple back tests and parallel models can help to generate benchmark risk levels to cover this risk.

A number of company specific factors also can affect the bid price:

- Risk Appetite (target Risk Adjusted Return on Capital);

- Hedge strategy; and

- Risk profile of existing assets, loads, and other trades.

#### Incorporating Market Risk Via RAROC

Risk Adjusted Return on Capital (RAROC) can be estimated with any Monte Carlo model using the expected value and risk numbers generated on a transaction. In its simplest form, RAROC=Expected Value/Risk. In our example above, the RAROC on the deal would be zero if the bid price was $42.19/MWh (Expected Net Earnings of $0/$9.8 million in EaR). Obviously, a 0 percent RAROC is not acceptable to most energy merchants. Therefore, a RAROC hurdle rate should be established depending on the organization's risk appetite.

- 25% $9.8 million x 0.25 = $2.45 million / 856 GWh = $2.86/MWh

- 50% $9.8 million x 0.50 = $4.9 million / 856 GWh = $5.72/MWh

- Add this premia to the "zero value bid price" of $42.19 to get the bid price adjusted for market risk (25%: $45.05/MWh, 50%: $47.92/MWh).

#### Incorporating Model Risk Via RAROC

In addition, the bidder must acknowledge that every model has flaws and that the risk that the model does not include all risks properly must be incorporated in the bid price.

Model risk is the risk the model used to value the trade and measure the risk is materially different from the actual outcome. Monte Carlo models generate a distribution of outcomes. Assuming the analysis generates enough outcomes to cover all possible scenarios, the actual results should fall somewhere within the simulated distribution.

In the example below, we used a bid price of $44.09/MWh to perform our Model Risk analysis. Our model estimated that the value of the trade was expected to be $1.6 million ( *see Table 3 *). The actual settled price was determined to be a $1.6 million loss. Based on the distribution of possible results, the actual settlement value fell within 0.61 standard deviations away from the expected value. This is considered well within acceptable limits.

It is not uncommon to hedge a trade like this. One can determine an "optimal" portfolio of forwards to use in hedging this trade, assuming it is appropriate to hedge the transaction as an isolated exposure. This optimal hedge results in different monthly amounts for on-peak and off-peak structures. Table 4 compares the actual outcome to the modeled distributions of the hedge portfolio and the aggregate portfolio of the hedge and the load contract.

In this case, the hedge portfolio's actual net earnings of $2.6 million were 0.5 standard deviations away from the expected value around zero. However, the hedged portfolio was more than 2