Grantee Research Project Results
2001 Progress Report: Implications of Climate Change for Regional Air Pollution, Health Effects and Energy Consumption Behavior
EPA Grant Number: R828731Title: Implications of Climate Change for Regional Air Pollution, Health Effects and Energy Consumption Behavior
Investigators: Ellis, Joseph H. , Hobbs, Benjamin F. , Joutz, Frederick L.
Institution: The Johns Hopkins University
EPA Project Officer: Chung, Serena
Project Period: September 1, 2000 through August 31, 2003 (Extended to February 7, 2005)
Project Period Covered by this Report: September 1, 2000 through August 31, 2001
Project Amount: $1,376,739
RFA: Assessing the Consequences of Interactions between Human Activities and a Changing Climate (2000) RFA Text | Recipients Lists
Research Category: Climate Change , Air
Objective:
This research project has four major modeling elements: climate change and variability, electrical energy demand and production, regional air pollution, and human health effects associated with air pollution exposure. Our overall objective is to develop a scientifically credible modeling facility that will help policymakers and analysts understand the effects of human activities on climate change and variability as well as the possible human responses and adaptations to climate change and variability. The overall connections among the modeling elements listed above are shown schematically below.
Progress Summary:
Electricity Demand Modeling and Forecasting
The focus of the energy demand section has been on short-run effects of climate change and volatility on energy consumption; and more specifically, on electricity demand and motor vehicle miles driven. The role of climate change-driven effects on electrical energy demand and production is the focus of this modeling effort. We are undertaking two general tasks. The first requires detailed, disaggregated models that link hypothesized climate change perturbations to specific demand effects in the residential and commercial sectors. This requires consideration of end-use equipment penetration, load shape forecasting, and creation of detailed (1-hour) short-term forecasts. The end-use and load shape forecasts will be used to explore general relationships between climate and energy use on a regional scale. These models will be used in developing long-term projections of human adaptation to climate change and variability. These projections then will be translated into hourly load forecasts that fully reflect the variability of loads and their correlations with the meteorological conditions that also affect pollutant transport and transformation. These forecasts will be used to analyze interactions between climate, pollution, and energy use on a detailed temporal scale. In the second general task, we will consider the impact of unique events and conduct econometric analyses of the effects of pollution alerts on hourly electricity usage.
In general, aggregate analyses have found that commercial and residential uses are much more sensitive to temperature increases than industrial uses, justifying the focus of this proposal on the former. These studies have usually concluded that climate warming would produce an increase in a few percentage points in cooling requirements, and a similar decrease in heating requirements; the canceling of the two effects often implies that the net impact on annual energy use may be relatively small. As an example of such a study, it has been projected that a 1.9°C warming would increase California's annual electricity requirements by 2.6 percent. What is more relevant, however, is how that change is distributed within the year. In particular, the greatest increases are likely to occur during weekdays during the air conditioning season. It is possible that the proportional increase during periods of high ozone may be even higher; however, no studies have analyzed the climate's effect on energy demands on a fine enough temporal scale to consider this possibility. One of this project's purposes is to fill that gap.
The result of using load duration curves in a long-run production simulation model is a set of estimates of average cost and emissions by generating unit type for a given period of time. This will permit a general assessment of the strength of the linkage between regional climate change and emissions of criteria pollutants. However, because of the high correlation between time varying electricity demands (and thus emissions) and the meteorological conditions that influence ambient pollutant concentrations, the impact of that linkage upon concentrations and ensuing health effects must be based on a detailed chronological simulation of both power system operations and pollutant transport and transformation. Utility experience across the United States shows that variations in weekday demands are most strongly associated with temperature; however, wind, humidity, and cloud cover also are factors that are frequently considered in short-term models. Typically, 80 percent or more of the variation is associated with weather, and for systems we have studied, peak electric demands in the summer can easily vary by 25 percent or more because of day-to-day changes in temperature.
This Year 1 progress report describes the progress on modeling the impact of climate change on short-run electricity consumption behavior. The major accomplishment is the construction of an hourly database of electricity loads for the Pennsylvania-New Jersey-Maryland Interconnection (PJM) in the aggregate and by utility control region. A standard set of hourly forecasting models has been estimated for the whole PJM, accounting for autoregressive components, heating and cooling degree temperature effects, and trading day variation for holidays and weekends. These models were estimated by econometric methods using the standard electricity forecasting package METRIX-ND. Short-run elasticities have been calculated for the heating degree day and cooling degree day effects. The forecasting power of the model has been evaluated and the (absolute) percentage errors range from 0.5 percent to 2 percent of hourly loads. A simple simulation over two 3-day events of particularly hot temperatures during July and August 2000 was performed. The experiment examines the impact of a 2°F increase in the daily temperature.
The results of the analysis have several interesting aspects. First, the effects of the temperature increase on demand are highly variable over the day. There is relatively little effect on peak demands, and a larger impact during the periods before and just after the peak in the daily electricity load. This occurs because air conditioning equipment is being used to its fullest extent during the peak periods in any event, so the effect of higher temperatures is to begin the use of that equipment earlier and to prolong its use. This phenomenon is important because high emissions power plants often are the variable sources of power during these "shoulder" periods. A second aspect is that the effect of temperature is highly variable from day to day, with some days showing large effects and others not. This is because of the nonlinear relationship between temperature and appliance use, a relationship that also varies from day to day because of cumulative effects (a series of several hot days will increase the sensitivity to temperature). As a result, temperature sensitivities may be greater during the periods of worse temperature, and perhaps, ozone formation.
Electric Power System Impacts
The objective of this portion of the project for the first year was to establish the linkage between climate change and power-sector emissions in the short term (see Figure 1). The tasks accomplished include: (1) identification of a study region; (2) compilation of a regional database of generator cost and emission characteristics; (3) formulation of transmission-constrained and unconstrained power system dispatch models for calculating emissions from demand scenarios; and (4) quantification of changes in power sector emissions in the short run (2000) from a hypothetical climate warming scenario.
Given our assumptions about climate change, our analysis shows that a respective average increase of 6.83 percent and 6.13 percent for NOx and SOx during typical 3 consecutive summer days for the region considered (Pennsylvania-Jersey-Maryland system, Virginia, West Virginia, and Ohio). The percentage impacts are relatively high in New Jersey and relatively low in West Virginia.
Figure 1. Schematic of Linkages Among Project Modules.
To estimate the impact of climate change on total emissions of the power sector, we constructed a least-cost dispatch model using linear programming (LP) framework. The model requires extensive data inputs concerning individual generation unit characteristics, such as heat rates, forced outage rate, NOx and SO2 emissions, and provides estimates of power sector output over time and space. Such detailed outputs are required by the pollutant transport and transformation analyses. Our initial database covers eight states and contains 1,700 generating units with a total capacity of 130,000 MW; in Year 2, it will be expanded to include portions of two additional states whose emissions are important to mid-Atlantic tropospheric ozone levels.
In the short run, climate change would affect power system emissions in two different ways. First, it changes the consumption amount and pattern of temperature-sensitive appliances, such as air conditioners. Based on published analyses of short-run demand responsiveness to temperature, we assume an increase of 1 percent in load corresponding to an increase of 1°F in temperature in an initial set of analyses. We will use more refined estimates developed by the George Washington University team. Second, climate change affects the thermal capacity and energy conversion efficiency of power plants. These effects are estimated based upon a literature review, published data, and Carnot efficiency calculations. For instance, average heat rate increases 0.07 percent and 0.06 percent per 1°F rise in temperature for gas turbine and steam plants, respectively.
For the purposes of this progress report, we consider a 3-day period (July 31-August 2, 2000) with a peak load of 97,000 MW. A hypothetical temperature rise of 4.5°F is assumed as a consequence of climate change. We first generate base-case total emissions. We then adjust demands, capacities, and thermal efficiencies to reflect assumed climate warming, and use the dispatch model to estimate shifts in total emissions. Figure 2 describes the total emission impact on a system-wide level. In comparison with the base case, the impact of load alone reveals a super-linear relation, a 4.5 percent increase in load, translating to 6.25 percent and 5.63 percent increase in NOx and SOx emission, respectively. In contrast, the generator performance accounts for only 0.5 percent and 0.4 percent of NOx and SOx emission. The joint impact is a 6.83 percent increase in NOx and a 6.13 percent increase in SOx, roughly equal to sum of two effects.
Figure 2. Emission Impacts of a 4.5°F Increase During 3 Midsummer Days, Decomposed Into Demand and Generator Performance
The above impacts are disaggregated to the state level in Figure 3. While comparisons of tonnage figures show, for instance, that Ohio has higher changes in tonnages of emissions, it is also useful to examine percentage change. A comparison of relative impacts shows that the effect of climate warming is relatively high in New Jersey (21.5 percent and 15.1 percent increases for NOx and SOx), while West Virginia is relatively low (4.04 percent and 3.771 percent for NOx and SOx). Nearly 90 percent of the changes are due to changes in steam coal generators that comprise up to 50 percent of generating capacity of our study region. The changes are relatively greater in New Jersey, because marginal shifts in loads are met by changes in the peaking capacity, which is relatively predominant in that state.
Figure 3. Location Matters: Emissions Changes Vary Over Space
Regional Air Pollution Modeling and Health Effects Characterization
Installation and testing of the Models-3 Framework (air pollution modeling system) and MM5 Version 3-4 (meteorological model) were completed. Modifications were made to the meteorological model to allow one-way nesting and four-dimensional data assimilation (FDDA). Several 3-day episodes of high ozone were simulated using multiple time periods for the Baltimore/Washington, DC region. The nested spatial domains used are shown in Figure 4 (the largest 108 km domain is used only in MM5, the inner domains used in both MM5 and CMAQ have 36, 12, and 4 km grid cells).
Figure 4. MM5 and CMAQ Modeling Domains
Extensive model assessment using graphical and statistical measures, including those recommended by the U.S. Environmental Protection Agency (EPA), showed consistency between model estimates and measurements. The modeling system also fared well when compared with other assessments of tropospheric ozone modeling. An example comparison with ozone measurements at a Maryland location is shown in Figures 5 and 6.
Figure 5. Comparison of Measured and Modeled Ground Level Ozone
Figure 6. Comparison of 12 km and 4 km Results.
We also consistently found that there were relatively small differences between results generated at the 12 and 4 km cell resolutions, an example of which follows for the Suitland, MD, monitoring location.
Numerous epidemiological studies were reviewed and selected for further analysis based on specific criteria (e.g., location of study). A subset of the studies was used to generate concentration-response functions that can be used to assess changes in health outcomes corresponding to changes in tropospheric ozone levels. Total and cause-specific mortality, hospital admissions, and emergency room visits were examined.
Future Activities:
Electricity Demand Modeling and Forecasting. For several research areas, we intend to work on testing and improving the models. The first area involves the collection of hourly temperature data for PJM and the utility control areas. At present, we only have daily high and low temperatures. These two measures may not provide adequate resolution of the impact of temperature variability and its impact on electricity loads. Second, the specification of the model can be modified to try and capture more of the seasonal and temperature dynamics. The current set of hourly models covers PJM as a whole; the goal is to develop hourly models for each of the control regions. Thus far, we have not been able to obtain loads by sector (residential, commercial, industrial, and other). This is another area for data collection and modeling that we expect to report on in the future. The literature and our own experience suggest that the residential and commercial sectors are more sensitive to climate variation than the industrial and other sectors.
Electric Power System Impacts. The analysis performed thus far disregards the transmission constraints, which are likely to have important effects on interregional shifts in demand due to climate warming. Other relevant environmental regulations, especially the NOx cap-and-trade program, were not included in the analysis. Furthermore, the assumption of perfect competition needs to be relaxed, given the presence of large generating firms and transmission constraints that isolate markets. Therefore, in the next year, our primary tasks will involve the:
· Incorporation of transmission constraints in our model, along with strategic generator behavior (Cournot model).
· Integration of the regional NOx cap-and-trade program and NOx control option variable in the model.
· Determination of least-cost control options, such as low NOx burner (LNB), selective catalytic reduction (SCR), or purchases of allowances to meet environmental requirements.
· Refinement of boundary conditions: power import and export from regions adjacent to our study region; we also will expand the database to include eastern Indiana and Kentucky, whose emissions are important to the mid-Atlantic region.
· Development of a long-run market model for capacity mix to simulate response of the structure of the power generation system to climate warming-induced changes in demand and generator characteristics.
· Development of scenarios for energy technology availability and economics, which is necessary data for creating a long-term market model.
Regional Air Pollution Modeling and Health Effects Characterization. We now are in the final stages of making operational the latest standalone CMAQ version (released July 2002), including V1.4 of the SMOKE emissions processing system. We also are using gridded spatial surrogates obtained from the Unified Grid developed by Alpine Geophysics. An important related issue involves the use of precomputed, gridded spatial surrogates; we no longer need ARC/Info and SAS. With this updated system, we then will be able to initiate longer-term simulations that will be driven by the electrical demand and dispatch modeling results described above. We also have a lead on a novel method for introducing climate change inputs from a general circulation model (GCM) to MM5, through the specification of MM5 initial and boundary conditions. We are attempting to obtain the code that translates GCM output to MM5 input.
Journal Articles on this Report : 3 Displayed | Download in RIS Format
Other project views: | All 24 publications | 10 publications in selected types | All 9 journal articles |
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Type | Citation | ||
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Bell M, Ellis H. Comparison of the 1-hr and 8-hr National Ambient Air Quality Standards for ozone using Models-3. Journal of the Air & Waste Management Association 2003;53(12):1531-1540. |
R828731 (2001) R828731 (2002) R828731 (Final) |
Exit Exit |
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Bell M, Ellis H. Sensitivity analysis of tropospheric ozone to modified biogenic emissions for the Mid-Atlantic region. Atmospheric Environment 2004;38(13):1879-1889. |
R828731 (2001) R828731 (2002) R828731 (Final) |
Exit Exit Exit |
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Munson T, Leyffer S, Chen Y, Hobbs B. Comparisons of MPEC algorithms for a leader-follower market equilibrium problem: electric power and NOx allowance markets. Computational Optimization and Applications. |
R828731 (2001) R828731 (2002) |
not available |
Supplemental Keywords:
regional air pollution, electricity demand forecasting, electrical system dispatch, health effects, ozone, particulate matter, climate change., RFA, Scientific Discipline, Health, Air, Geographic Area, particulate matter, air toxics, Health Risk Assessment, climate change, State, Risk Assessments, Atmospheric Sciences, tropospheric ozone, integrated assessments, electrical energy, PM10, environmental monitoring, exposure and effects, stratospheric ozone, policy making, Virginia (VA), Delaware (DE), human activities, PM 2.5, energy generation, climate variations, climate models, Maryland (MD), emissions inventory, human exposure, DC, PM, ecosystem sustainability, human activity, climate variability, ambient air pollution, Global Climate ChangeProgress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.