Economic Simulation using the National Energy Modeling System (NEMS)
Economic simulation involves modeling the economic decision-making of an energy-using sector or entire region. The stock of existing buildings and equipment, data on options available, decision procedures, energy prices, etc. need to be available for the model to attempt to realistically simulate the purchase behavior of people. Even with adequate data, there will always be disagreements on some of the more subjective criteria, such as importance of energy efficiency versus other product characteristics, or market inertia of consumers towards changing consumer preferences. However, economic simulation provides significant insights into the future energy market under changes to key parameters such as temperature.
The most widely recognized economic simulation model is the National Energy Modeling System (NEMS). The Energy Information Administration (EIA) developed this model to forecast national and regional energy supply and demand through 2025. The model allows a wide variety of parameters to be altered to determine their impact on overall fuel use. Examples include changes in equipment efficiencies, costs, fuel supplies, economic growth, and consumer preferences. Detailed information on the model can be found on the Website of the National Energy Modeling System: An Overview 2003.
NEMS models the major end-use sectors of the economy: residential, commercial, industrial, and transportation. Within the energy sector, it models electricity, oil, gas, coal, and renewable energy production. It separates the nation into nine geographical regions for analysis, providing regional information on energy and economic results.
ORNL Use of NEMS
We have used the NEMS model for a number of years in a variety of studies:
In 1999-2000 the Clean Energy Futures study analyzed the potential impacts of various national energy policies to lower the emissions of carbon and other pollutants. It involved researchers from five national laboratories (ORNL, Lawrence Berkeley National Laboratory, National Renewable Energy Laboratory, Argonne National Laboratory, and Pacific Northwest National Laboratory) and used a modified version of the NEMS model from the Energy Information Administration and run by Lawrence Berkeley National Laboratory.
In 2001, we used NEMS to analyze the potential savings from energy efficiency programs and technologies for the state of Iowa. The results of the EIA Annual Energy Outlook 2000 were used as the Base case. Two alternative cases were created to simulate energy savings policies. Voluntary, market-related programs in the residential and commercial sectors and standards programs in the residential sector were simulated. Overall, there is a good potential for saving at least 5% of energy use in Iowa through a combination of market programs and standards, representing over $100 million per year.
In 2003, we conducted a similar study for the state of North Carolina as for Iowa, but also included analysis of the potential for renewable energy. The study found that overall, there is a good potential for saving over 6% of electricity use in North Carolina through a combination of market programs and technology advances, representing over $400 million savings per year. Renewable energy growth could be accelerated through technology advancements or incentives to supply several hundred megawatts of additional power as well.
In 2003-2004 we modified the NEMS model to incorporate annually varying heating and cooling loads for each region of the country. We then used this model (DD-NEMS) and data from a global climate simulator PCM-IBIS to analyze the impact of climate change on each U.S. Census region. Besides the standard case based on the EIA Annual Energy Outlook 2003 (which used constant annual heating and cooling loads), the temperature results from two scenarios of climate change were analyzed. The results showed a near-term decline in energy use for northern regions while an increase in energy use in southern regions as increases in cooling needs offset decreases in heating needs.