To better understand the evolving outlook for LNG and its role in the U.S. gas market, Fortnightly assembled a group of LNG specialists with various perspectives on the issues.
Real Green Costs
Valuing risk reduction for renewables and DSM.
follows from its direct and additive monetization of risk under well-known practices applied by insurance companies and option traders. The benefit of being able to directly subtract the RRV from a portfolio’s costs simplifies the resource-selection process by making risk an explicit component of total resource costs.
The inclusion of market and volumetric uncertainty can yield tremendous insight into portfolio risks when done well. However, proper valuation requires an underlying modeling framework that meets rigorous criteria for simulation of future market states. The current leading practices in energy resource modeling apply an integrated framework that incorporates observed market dynamics with system fundamentals to capture critical covariate relationships:
• Implied heat rate distribution;
• Weather > load > DSM > price relationships;
• Weather > wind and solar generation > price relationships; and
• Correlations between commodity prices.
Model validation requires meeting rigorous benchmarks that demonstrate the ability to reflect previous patterns of market and volumetric uncertainty and evolve these relationships forward based on current expectations. The integrated simulation framework serves as the analytical foundation to rendering portfolio valuations.
Resource valuation then follows an hourly time-step process that reflects the coincident value of market prices and resource characteristics. While simplification into larger time steps can be computationally attractive, critical attributes of resource flexibility, such as interruptible load or coincidence of static generation with market prices, become undervalued with simplified analytical approaches. Getting resource valuation right requires adhering to the higher levels of prudent portfolio management by utilizing all available information from the application of leading modeling techniques.
RRV should thus play a critical role in valuing the risk reduction effectiveness of alternative DSM programs. Including RRV supports a more balanced assessment of cost effectiveness of DSM programs by accounting for the direct value attributed to the reduced risk. The RRV for specific DSM measures can be expressed as a percentage of each program’s total cost ( see Figure 3 ). RRV for DSM ranges broadly, from approximately 5 to 35 percent, depending on the utility supply portfolio.
Alternatively, programs can be valued in terms of net benefit per megawatt hour (MWh) of energy conserved. The RRV becomes a direct credit to the avoided cost function as follows ( see Figure 4 ): Net Benefits = Avoided Costs ƒ (generation, fuel, T&D, emissions, and risk) – Program Costs .
The eventual goal of resource analysis is to determine the cost not of a single resource, but the cost of a portfolio of resources. Incremental analysis, in which individual resources are added to a portfolio in order to ascertain their respective economic impacts on the whole, also is a common approach to resource analysis. Figure 5 incorporates DSM and renewable energy RRV into incremental analysis. In this case, four scenarios are presented, representing the various combinations of base case, DSM, and renewable energy resources. The height of each bar represents the nominal total expected cost of each scenario; the orange portion of each column represents the cost of each scenario, net of the RRV created by DSM and renewable resources. The total and