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Accounting for fuel price risk: Using forward natural gas prices instead of gas price forecasts to compare renewable to natural gas-fired generation

Description: Against the backdrop of increasingly volatile natural gas prices, renewable energy resources, which by their nature are immune to natural gas fuel price risk, provide a real economic benefit. Unlike many contracts for natural gas-fired generation, renewable generation is typically sold under fixed-price contracts. Assuming that electricity consumers value long-term price stability, a utility or other retail electricity supplier that is looking to expand its resource portfolio (or a policymaker interested in evaluating different resource options) should therefore compare the cost of fixed-price renewable generation to the hedged or guaranteed cost of new natural gas-fired generation, rather than to projected costs based on uncertain gas price forecasts. To do otherwise would be to compare apples to oranges: by their nature, renewable resources carry no natural gas fuel price risk, and if the market values that attribute, then the most appropriate comparison is to the hedged cost of natural gas-fired generation. Nonetheless, utilities and others often compare the costs of renewable to gas-fired generation using as their fuel price input long-term gas price forecasts that are inherently uncertain, rather than long-term natural gas forward prices that can actually be locked in. This practice raises the critical question of how these two price streams compare. If they are similar, then one might conclude that forecast-based modeling and planning exercises are in fact approximating an apples-to-apples comparison, and no further consideration is necessary. If, however, natural gas forward prices systematically differ from price forecasts, then the use of such forecasts in planning and modeling exercises will yield results that are biased in favor of either renewable (if forwards < forecasts) or natural gas-fired generation (if forwards > forecasts). In this report we compare the cost of hedging natural gas price risk through traditional gas-based hedging instruments (e.g., futures, swaps, and fixed-price physical supply ...
Date: August 13, 2003
Creator: Bolinger, Mark; Wiser, Ryan & Golove, William
Partner: UNT Libraries Government Documents Department

Distributed generation capabilities of the national energy modeling system

Description: This report describes Berkeley Lab's exploration of how the National Energy Modeling System (NEMS) models distributed generation (DG) and presents possible approaches for improving how DG is modeled. The on-site electric generation capability has been available since the AEO2000 version of NEMS. Berkeley Lab has previously completed research on distributed energy resources (DER) adoption at individual sites and has developed a DER Customer Adoption Model called DER-CAM. Given interest in this area, Berkeley Lab set out to understand how NEMS models small-scale on-site generation to assess how adequately DG is treated in NEMS, and to propose improvements or alternatives. The goal is to determine how well NEMS models the factors influencing DG adoption and to consider alternatives to the current approach. Most small-scale DG adoption takes place in the residential and commercial modules of NEMS. Investment in DG ultimately offsets purchases of electricity, which also eliminates the losses associated with transmission and distribution (T&D). If the DG technology that is chosen is photovoltaics (PV), NEMS assumes renewable energy consumption replaces the energy input to electric generators. If the DG technology is fuel consuming, consumption of fuel in the electric utility sector is replaced by residential or commercial fuel consumption. The waste heat generated from thermal technologies can be used to offset the water heating and space heating energy uses, but there is no thermally activated cooling capability. This study consists of a review of model documentation and a paper by EIA staff, a series of sensitivity runs performed by Berkeley Lab that exercise selected DG parameters in the AEO2002 version of NEMS, and a scoping effort of possible enhancements and alternatives to NEMS current DG capabilities. In general, the treatment of DG in NEMS is rudimentary. The penetration of DG is determined by an economic cash-flow analysis that determines adoption ...
Date: January 1, 2003
Creator: LaCommare, Kristina Hamachi; Edwards, Jennifer L. & Marnay, Chris
Partner: UNT Libraries Government Documents Department

New Method and Reporting of Uncertainty in LBNL National Energy Modeling System Runs

Description: This report describes LBNL's approach for assessing uncertainty in any National Energy Modeling System (NEMS)-related analysis. Based on years of experience using LBNL-NEMS for various analyses, LBNL developed an alternative approach that aims to provide a simple yet comprehensive perspective of how the results behave under a given set of what we believe to be some of the issues important to large-scale energy modeling. This project has established a standard set of eight sensitivity cases that can be run overnight and are highly likely to produce stable and interesting results. The goal was to establish a limited number of interesting sensitivity cases that would routinely produce adjunct results to LBNL-NEMS reporting that will be of value to our readers. These cases will be routinely reported together with future LBNL-NEMS results in the form of a standard output table. As an example, this work uses a Government Performance and Results Act (GPRA) analysis run as the baseline, but th e goal is to establish a standardized set of cases that would change little over time and be applicable to other analyses in addition to GPRA. The approach developed here cannot serve as a substitute for a sensitivity analysis tailored to the question at hand, but it can provide a fast review of some areas that have proven to be of interest in the past.
Date: October 1, 2002
Creator: Gumerman, Etan Z.; LaCommare, Kristina Hamachi & Marnay, Chris
Partner: UNT Libraries Government Documents Department