Etan Gumerman

Evaluation Framework and Tools for Distributed Energy Resources

LBNL-52079 ERNEST ORLANDO LAWRENCE B E R K E L E Y NATIONAL LABORATORY Evaluation Framework and Tools for Distributed Energy Resources Etan Z. Gumerman, Ranjit R. Bharvirkar, Kristina Hamachi LaCommare, and Chris Marnay Lawrence Berkeley National Laboratory 1 Cyclotron Road., MS90-4000 Berkeley, California 94720 Environmental Energy Technologies Division February 2003 Download from: http://eetd.lbl.gov/ea/EMS/EMS_pubs.html#RE This work described in this paper was funded by the Assistant Secretary of Energy Efficiency and Renewable Energy, Office of Building Technologies of the U.S. Department of Energy under Contract No. DE-AC03-76SF00098.

Learning and cost reductions for generating technologies in the national energy modeling system (NEMS)

This report describes how Learning-by-Doing (LBD) is implemented endogenously in the National Energy Modeling System (NEMS) for generating plants. LBD is experiential learning that correlates to a generating technologys capacity growth. The annual amount of Learning-by-Doing affects the annual overnight cost reduction. Currently, there is no straightforward way to integrate and make sense of all the diffuse information related to the endogenous learning calculation in NEMS. This paper organizes the relevant information from the NEMS documentation, source code, input files, and output files, in order to make the models logic more accessible. The end results are shown in three ways: in a simple spreadsheet containing all the parameters related to endogenous learning; by an algorithm that traces how the parameters lead to cost reductions; and by examples showing how AEO 2004 forecasts the reduction of overnight costs for generating technologies over time.

Strategies for cost-effective carbon reductions: A sensitivity analysis of alternative scenarios

Analyses of alternative futures often present results for a limited set of scenarios, with little if any sensitivity analysis to identify the factors affecting the scenario results. This approach creates an artificial impression of certainty associated with the scenarios considered, and inhibits understanding of the underlying forces. This paper summarizes the economic and carbon savings sensitivity analysis completed for the Scenarios for a Clean Energy Future study (IWG, 2000). Its 19 sensitivity cases provide insight into the costs and carbon-reduction impacts of a carbon permit trading system, demand-side efficiency programs, and supply-side policies. Impacts under different natural gas and oil price trajectories are also examined. The results provide compelling evidence that policy opportunities exist to reduce carbon emissions and save society money.

Modeling Interregional Transmission Congestion in the NationalEnergy Modeling System

LBNL-59076 E RNEST O RLANDO L AWRENCE B ERKELEY N ATIONAL L ABORATORY Modeling Interregional Transmission Congestion in the National Energy Modeling System Etan Gumerman, Peter Chan, Bernard Lesieutre, Chris Marnay, and Juan Wang Environmental Energy Technologies Division May 2006 http://eetd.lbl.gov/ea/EMS/EMS_pubs.html This work described in this paper was funded by the Assistant Secretary of Energy for Energy Efficiency and Renewable Energy, Planning, Analysis and Evaluation section of Planning, Budget, and Analysis in the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.

Investigation of residential central air conditioning load shapes in NEMS

This memo explains what Berkeley Lab has learned about how the residential central air-conditioning (CAC) end use is represented in the National Energy Modeling System (NEMS). NEMS is an energy model maintained by the Energy Information Administration (EIA) that is routinely used in analysis of energy efficiency standards for residential appliances. As part of analyzing utility and environmental impacts related to the federal rulemaking for residential CAC, lower-than-expected peak utility results prompted Berkeley Lab to investigate the input load shapes that characterize the peaky CAC end use and the submodule that treats load demand response. Investigations enabled a through understanding of the methodology by which hourly load profiles are input to the model and how the model is structured to respond to peak demand. Notably, it was discovered that NEMS was using an October-peaking load shape to represent residential space cooling, which suppressed peak effects to levels lower than expected. An apparent scaling down of the annual load within the load-demand submodule was found, another significant suppressor of the peak impacts. EIA promptly responded to Berkeley Labs discoveries by updating numerous load shapes for the AEO2002 version of NEMS; EIA is still studying the scaling issue. As a result of this work, it was concluded that Berkeley Labs customary end-use decrement approach was the most defensible way for Berkeley Lab to perform the recent CAC utility impact analysis. This approach was applied in conjunction with the updated AEO2002 load shapes to perform last years published rulemaking analysis. Berkeley Lab experimented with several alternative approaches, including modifying the CAC efficiency level, but determined that these did not sufficiently improve the robustness of the method or results to warrant their implementation. Work in this area will continue in preparation for upcoming rulemakings for the other peak coincident end uses, commercial air conditioning and distribution transformers.

Scenarios for Benefits Analysis of Energy Research, Development,Demonstration and Deployment

For at least the last decade, evaluation of the benefits of research, development, demonstration, and deployment (RD3) by the U.S. Department of Energy has been conducted using deterministic forecasts that unrealistically presume we can precisely foresee our future 10, 25, or even 50 years hence. This effort tries, in a modest way, to begin a process of recognition that the reality of our energy future is rather one rife with uncertainty. The National Energy Modeling System (NEMS) is used by the Department of Energy s Office of Energy Efficiency and Renewable Energy (EE) and Fossil Energy (FE) for their RD3 benefits evaluation. In order to begin scoping out the uncertainty in these deterministic forecasts, EE and FE designed two futures that differ significantly from the basic NEMS forecast. A High Fuel Price Scenario and a Carbon Cap Scenario were envisioned to forecast alternative futures and the associated benefits. Ernest Orlando Lawrence Berkeley National Laboratory (LBNL) implemented these scenarios into its version of NEMS, NEMS-LBNL, in late 2004, and the Energy Information Agency created six scenarios for FE in early 2005. The creation and implementation of the EE-FE scenarios are explained in this report. Both a Carbon Cap Scenario and a High Fuel Price Scenarios were implemented into the NEMS-LBNL. EIA subsequently modeled similar scenarios using NEMS. While the EIA and LBNL implementations were in some ways rather different, their forecasts do not significantly diverge. Compared to the Reference Scenario, the High Fuel Price Scenario reduces energy consumption by 4 percent in 2025, while in the EIA fuel price scenario (known as Scenario 4) reduction from its corresponding reference scenario (known as Scenario 0) in 2025 is marginal. Nonetheless, the 4 percent demand reduction does not lead to other cascading effects that would significantly differentiate the two scenarios. The LBNL and EIA carbon scenarios were mostly identical. The only major difference was that LBNL started working with the AEO 2004 NEMS code and EIA was using AEO 2005 NEMS code. Unlike the High Price Scenario the Carbon Cap scenario gives a radically different forecast than the Reference Scenario. NEMS-LBNL proved that it can handle these alternative scenarios. However, results are price inelastic (for both oil and natural gas prices) within the price range evaluated. Perhaps even higher price paths would lead to a distinctly different forecast than the Reference Scenario. On the other hand, the Carbon Cap Scenario behaves more like an alternative future. The future in the Carbon Cap Scenario has higher electricity prices, reduced driving, more renewable capacity, and reduced energy consumption. The next step for this work is to evaluate the EE benefits under each of the three scenarios. Comparing those three sets of predicted benefits will indicate how much uncertainty is inherent within this sort of deterministic forecasting.

A new approach for modeling the peak utility impacts from a proposed CUAC standard

This report describes a new Berkeley Lab approach for modeling the likely peak electricity load reductions from proposed energy efficiency programs in the National Energy Modeling System (NEMS). This method is presented in the context of the commercial unitary air conditioning (CUAC) energy efficiency standards. A previous report investigating the residential central air conditioning (RCAC) load shapes in NEMS revealed that the peak reduction results were lower than expected. This effect was believed to be due in part to the presence of the squelch, a program algorithm designed to ensure changes in the system load over time are consistent with the input historic trend. The squelch applies a system load-scaling factor that scales any differences between the end-use bottom-up and system loads to maintain consistency with historic trends. To obtain more accurate peak reduction estimates, a new approach for modeling the impact of peaky end uses in NEMS-BT has been developed. The new approach decrements the system load directly, reducing the impact of the squelch on the final results. This report also discusses a number of additional factors, in particular non-coincidence between end-use loads and system loads as represented within NEMS, and their impacts on the peak reductions calculated by NEMS. Using Berkeley Labs new double-decrement approach reduces the conservation load factor (CLF) on an input load decrement from 25% down to 19% for a SEER 13 CUAC trial standard level, as seen in NEMS-BT output. About 4 GW more in peak capacity reduction results from this new approach as compared to Berkeley Labs traditional end-use decrement approach, which relied solely on lowering end use energy consumption. The new method has been fully implemented and tested in the Annual Energy Outlook 2003 (AEO2003) version of NEMS and will routinely be applied to future versions. This capability is now available for use in future end-use efficiency or other policy analysis that requires accurate representation of time varying load reductions.