Retail Risk-Based Pricing
most regulated entities. These results can then be compared with assessment of risks relative to opportunity costs of the market.
RBP seeks to allocate the cost of energy supply to reflect the relative risk of each customer. For traditional utilities, the variable costs of production are commonly carried across all customers on an average-cost basis that remains independent of time of use. An appropriate incremental step toward full RBP is to begin allocating the variable cost of production to appropriately reflect the risk in supply costs. This leaves the additional opportunity of allocating fixed costs with a risk component. For competitive retail providers, the RBP becomes a strategic necessity and can be a significant differentiating factor in long-term success.
The RBP formula consists of the original retail price developed from the expected value of the cost of service, plus (positive or negative) a risk adjustment factor, plus a term to adjust rates up or down to equal an enterprise gross margin revenue requirement. 9 The risk adjustment factor corresponds to the second component in Equation 2 and increases or decreases rates from the original price. The third component of Equation 2 increases or decreases rates to achieve a target gross margin revenue requirement. This last term would not be applicable for competitive retail offerings because of the lack of revenue requirements in deregulated markets. Before demonstrating the impact of RBP on retail rates and gross margin revenue, the analytical requirements to determine RBP need to be defined further.
Analytic Requirements for RBP
To generate accurate portrayal of the uncertainty in gross margin by customer, we apply an integrated simulation framework. Our framework captures the range in outcomes for each element of the gross margin function along with their covariate relationships. Through a series of linked structural state-space models, the fundamental uncertainties between weather, load, and market prices (and generation costs) can be captured. By capturing this fundamental relationship, the analytical foundation necessary to develop RBP can be developed.
An overview of the simulation engine we have developed to support RBP is presented in Figure 3. 10 The data inputs consist of traditional market fundamentals of demand and supply accounted through variables of system and customer load, supply stack, transmission constraints, fuel prices, forward curves, and reserve margins. The input data flows into the simulation engine to model uncertain electricity market drivers, supply resources, and market prices. The uncertainty in each of the structural variables is represented by the middle row of boxes in the simulation engine. The output of the simulation engine produces realizations of cash flows that are aggregated into distributions of cash flows for gross margin cost of supply and customer load.
By first simulating weather at multiple weather stations across the region, we maintain a key relationship of weather driving regional load and weather driving customer load. Thus, we preserve the temporal structure of a customer's load response to weather and regional load. This relationship of weather and load translates into the cost of service through the effect of regional load as an explanatory factor of market