Air-quality and Health Impacts of Electricity Congestion

Transcription

1 Air-quality and Health Impacts of Electricity Congestion Erik P. Johnson and Juan B. Moreno-Cruz June 2015 Abstract We examine the effects of electricity transmission capacity constraints on emissions and ambient concentrations of criteria pollutants and health outcomes in three electricity markets in Northeastern United States. Electricity system congestion decreases the emissions of sulfur dioxide and carbon dioxide while increasing emissions of nitrogen oxides; ozone and PM 2.5 concentrations increase in New England and New York and decrease in the western portions of the Pennsylvania-New Jersey-Maryland electricity market. These changes in pollution create overall health gains in New England, New York, and Atlantic states that outweigh the health loses in Illinois, Ohio, and Western Pennsylvania. Keywords: Air quality, Electricity, Congestion, Health impacts JEL Codes: H23, I10, L94, Q4, Q5 Corresponding author: School of Economics, Georgia Institute of Technology, Atlanta, GA USA. We want to thank, without implicating, David Laband, Shatakshee Dhongde and seminar participants at Georgia Tech and Morehouse College. 1

2 1 Introduction Electricity generation from fossil fuels like coal, natural gas, and petroleum releases air pollutants that significantly affect human health. Health outcomes not only depend on the source of emissions, but on the time and location of these emissions. The benefits of any policy geared towards reducing air-pollution outcomes depend on the electricity generators that are displaced. Some recent studies have identified this effect and focus primarily on aggregate electricity generation and to a lesser extent on temporal patterns of emissions. We add to this growing body of literature by showing that the emissions and health outcomes associated with electricity generation is greatly affected by the electricity transmission system. Moreover, we show that the spatial distribution of these outcomes are significantly changed by the constraints of the transmission system. The electricity transmission system is a complex web of electricity transmission lines that connects supply and demand nodes across long distances. The power lines that make up the transmission system are subject to capacity constraints, limiting the amount of power they can transfer from node to node. For the system to operate safely and reliably, operators curtail power along the lines that are at full capacity or constrained. When a transmission line is constrained, local demand must be sourced by local power instead of more distant possibly cheaper sources. Typically in wholesale electricity markets electricity service providers and electricity generators submit demand and supply bids to an independent system operator that aggregates the bids into supply and demand curves. If there were no physical constraints on the transmission system, the intersection of the supply and demand curve would determine the electricity price across the entire market. However, since each transmission line has a fixed transmission capacity this scarce capacity must be allocated across generators. One way to efficiently allocate line capacity to electricity generators is to charge congestion prices. A congestion price is determined at every node in the market by the independent system operator by calculating the difference in the cost of generating an additional unit of 2

3 electricity given the current transmission constraints in the system and what the price at that node would be in a system that does not have physical constraints. Therefore, congestion prices send signals about the value of transmission capacity at each of the nodes in the electricity grid. 1 We use these congestion prices to design a new measure of congestion within the entire electricity market instead of congestion at a particular node in the market. Electricity system congestion also affects the environmental performance of the system as a whole. The emissions profiles of the plants that would be dispatched based on marginal production cost are not the same as the emissions profiles of the plants that are actually dispatched due to system congestion. If environmental externalities are not fully priced into the cost of electricity generation, congestion prices will not reflect the differential environmental costs of using alternative electricity generators. Moreover, the environmental costs will depend quite delicately on variations across time and space. In this paper we explore specific environmental and health impacts associated with electricity transmission system congestion. We use variation in time and space to empirically examine the impacts of electricity system congestion on the environment. We examine emissions levels of sulfur dioxide, nitrogen oxides, and carbon dioxide and ambient concentrations of ground level ozone and small particulate matter (PM 2.5 ) in the three wholesale electricity markets in the northeastern US (PJM, NYISO, and ISO-NE). We combine data on congestion prices with both hourly emissions from the EPA s Continuous Emissions Monitoring System (CEMS) and the EPA s detailed Air Quality System data (AQS). The CEMS data captures hourly emissions from all electricity generators with a capacity of 25 megawatts or greater allowing us to capture hourly variation in generator emissions due to congestion. We then aggregate these hourly emissions to both the electricity market level and to the whole of the northeastern US since congestion in different areas of the northeast may lead to different changes in emission pat- 1 The price at each node also includes a line-loss price which is the cost of electricity that is lost due to electrical resistance of the transmission lines. 3

4 terns. We then quantify the impacts that changes in ambient concentrations induced by congestion have on health outcomes using the tool that EPA uses to conduct portions of regulatory impact analyses to estimate damages from pollution, BenMAP. In order to measure congestion within an area of the grid, we need a measure that is broad enough to capture system wide congestion, but effectively discriminate between an hour where there are small congestion prices in many locations with small changes in dispatch and large congestion prices in a few locations with large changes in dispatch. We propose a measure of system congestion that is equal to the square root of the sum across all nodes within an area of the square of the congestion price at a particular point divided by the number of nodes. Using these data and our definition of system congestion, we regress the hourly emissions from electricity generators within an area on the congestion within that area, system load, and a set of month level and day of week fixed effects to estimate the marginal emission changes due to congestion. Our estimates suggest that an additional 37 tons of nitrogen oxides (NOx) are emitted, 71 fewer tons of sulfur dioxide (SO 2 ) are emitted, and 22,200 fewer tons of carbon dioxide (CO 2 ) are emitted on the highest congestion days, relative to a world with no congestion. When we examine congestion in each of the individual wholesale markets in our sample, much of the increase in NOx levels is due to high congestion levels in New York while much of the reductions in SO 2 are due to high congestion levels in mid- Atlantic states (PJM wholesale market). Since both SO 2 and NOx emissions levels generally have large effects in areas other than where they are emitted due to air-transportation dynamics and atmospheric chemistry, we turn to the EPAs Air Quality System to examine how measured concentrations of ground level ozone and PM 2.5 change in response to congestion in the electricity grid. Using hourly readings from monitors located in these wholesale markets, we regress pollution concentrations on grid congestion for each monitor in our sample. Consistent with our emissions results, we find a distinct geographical pattern in pollution 4

5 concentration changes due to congestion and these patterns are different across seasons. There is a significant decrease in PM 2.5 and ground-level ozone in up-wind counties of our study in Illinois, Ohio, and western Pennsylvania and New York. There is a significant increase in these pollutants in the areas of New England, New York, and PJM that are located further east. We then simulate counterfactual pollution distributions with less congestion in the electricity grid. We find that there are clear winners and losers based on geographic location, but overall there is a net health benefit of congestion. That is, electricity system congestion precludes the use of electricity generating plants that cause more health damages. Many studies have assessed the general impact of electricity generation on the environment (for recent examples see Cullen [2013], Denny and O Malley [2006], and Graff Zivin et al. [2012]). The environmental and health impacts of electricity transmission, however, have received little attention. 2 To the best of our knowledge, we are the first ones empirically studying the impacts of grid congestion on the environment. Our results show a non-negligible amount of pollution change results from congestion and the effects are likely to be time, area, and pollutant specific. This suggests a role for environmental considerations during the electricity grid planning and expansion. This also suggests that distributed generation, such as solar PV, may have additional environmental value since it indirectly reduces pollution and likely reduces congestion within the grid. Our paper contributes to the recent literature on the value of transmission and electricity market integration. Mansur and White [2012], Wolak [2014], and Ryan [2013] analyze the competitive effects of transmission in Eastern US, Alberta and India, respectively. The closest paper to what we are doing is Davis and Hausman [2014], which estimates of the value of electricity transmission using a natural experiment generated by the closure of the San Onofre Nuclear Generating Station (SONGS) in February They find that because of limits in the transmission system, this closure could not be met by low cost alternatives and they also find an increase in CO 2 emissions, incurring extra environmental costs of $320 2 See Hitaj [2013] for a recent example using theory and simulations. 5

6 million, in 2013 dollars. Unlike in our study, they do not analyze the spatial distribution of environmental impacts. Previous studies have also integrated air pollution impacts in the analysis of energy systems. The Air Pollution Emission Experiments and Policy analysis model (APEEP) of Muller and Mendelsohn [2006] and Muller et al. [2011] links air emissions data to monetary and non-monetary damages. Using this model, Muller and Mendelsohn [2006] and Muller et al. [2011] find that there is substantial heterogeneity in the distribution of economic damages across space. Siler-Evans et al. [2013] use the APEEP model to test the environmental benefits of wind and solar. They calculate the change in emissions and damages associated with changes in the generation output induced by the introduction of wind and solar electricity generation. While we do not make use of the APEEP model, our study finds an empirical relation between electricity systems and environmental outcomes differential across space and time. A link between air pollution and mortality and morbidity has been identified before (e.g. Chay and Greenstone [2003]; Currie and Neidell [2005]; Currie et al. [2014]). We expand this literature with our work by linking the effects of transmission restrictions to mortality and morbidity by showing that electricity system integration can have unexpected environmental impacts. While over 90% of the monetized health impacts are related to mortality, other morbidity impacts are also accounted for in the literature, such as ER visits, doctor visits, respiratory symptoms and lung function reductions. (Pope III et al. [2002]) The rest of the paper proceeds as follows. In section 2, we set up a simple electricity model to showcase the impacts of congestion on the supply of electricity and how this is linked to environmental outcomes. In section 3, we present data on emissions and concentrations and define our measure of system wide congestion. In section 4, we estimate the impacts of congestion on emissions of pollutants and in section 5, we do the same for concentrations and health impacts. A short conclusion in section 6 closes the paper. 6

7 2 Conceptual Framework Our analysis examines the effect of electricity transmission congestion on pollution. To illustrate the conditions under which electricity congestion alters the spatial and temporal distribution of pollution, we consider a simple electricity system where electricity system congestion is interpreted as an increase in the marginal costs of operation across nodes in the system. We illustrate this idea with a simple system in Figure 1. The electricity system has 4 nodes. There are two generators in nodes 1 and 3; we refer to them as G 1 and G 3. Electricity production costs are C 1 (w) and C 3 (w), where w is kwh generated. There are two demand loads in nodes 2 and 4; we refer to them as L 2 and L 4 and are also measured in kwh. There are three transmission lines. One connects nodes 1 and 2 with a flow y 12, a second one connects nodes 3 and 4 with flow y 34, and a third line connects nodes 2 and 4 with directional flow y 24. The operating costs c ij (y ij ) are assumed to be increasing and convex in the power transmitted, y ij. This captures the capacity constraints associated with each line. Finally, there are two regions A and B. The only way these two regions are connected is via electricity transmission line y 24. To deliver electricity, the operator of the system chooses the combination of output in generators 1 and 3 that minimizes the costs of operation min C 1 (w 1 ) + C 3 (w 3 ) + c 12 (y 12 ) + c 34 (y 34 ) + c 24 (y 24 ) 7

8 subject to the demand constraints and the balance of flows equations: L 2 = y 12 y 24 L 4 = y 34 + y 24 w 1 = y 12 w 3 = y 34 (1) Replacing the constraints, we can write the objective function as: min C 1 (L 2 + y 24 ) + C 3 (L 4 y 24 ) + c 12 (L 2 + y 24 ) + c 34 (L 4 y 24 ) + c 24 (y 24 ) (2) {y 24 } 2.1 Congestion Assume the load in the second node increases. The first order condition associated to the problem above is given by C 1(w 1 ) C 3(w 2 ) = c 34(y 34 ) c 12(y 12 ) c 24(y 24 ) (3) It follows directly from this equation that an increase in L 2 translates in an increase in c 12(y 12 ). To keep the system operating at minimum costs, a larger fraction of the demand L 4 needs to be supplied by generator 3, therefore increasing C 3(w 2 ) and reducing c 24(y 24 ). The increase in the costs c 12 associated to the increase in L 2 translates in a higher electricity price differential between nodes 1 and 2. This price differential is defined as the congestion price between nodes 1 and 2. When the congestion price between nodes 1 and 2 increases, the flow y 12 increases and the flow y 24 decreases. In order to keep the system balanced, the decrease in the flow y 24 is compensated by an increase in the flow y 34. As a result, there is more generation in G 1, to satisfy the increase in L 2, but also more generation in G 3, to satisfy the fraction in the 8

9 demand L 4 that is not supplied by G 1 under the new demand profile. 2.2 System-wide congestion In practice, we do not have a detailed description of the electricity transmission system, nor we are able to identify which generator responds to an increase in a particular load point. In the empirical approach we take below, we analyze responses in terms of emissions and concentrations to changes in system wide congestion. We can use our simple illustration to characterize system wide congestion. Assuming a competitive market, as in the situation we are analyzing empirically, the price charged in each load point will be equal to the marginal costs of generation and the marginal costs of transmission. The marginal costs associated with the transmission of electricity are meant to capture line losses and congestion prices. Suppose line losses are low so that the marginal costs only capture congestion. With this assumption in mind, the congestion price will be equal to the average of the marginal costs of transmission, weighted by the changes in transmission flows. In our simple set-up, the congestion price in node 2 is p 2 = c 12(y 12 ). The congestion price in node 4 is given by p 4 = y 24 y 24 + y 34 c 24(y 24 ) + y 34 y 24 + y 34 c 34(y 34 ), where represents a change in flows due to increased load at L 2. These two prices, p 2 and p 4, are the inputs to our measure of aggregate system congestion. Our challenge, then, is how to aggregate them in a way that best conveys the congestion state of the system as whole. The most straightforward measure is the simple average of the prices, Congestion = p 2+p 4 2. A problem with this measure is that it doesn t account for the differential impacts of the individual price changes. For example, suppose the congestion prices doubles in both nodes, then effectively doubling our congestion measure. This result would be the same as leaving p 2 unchanged and increasing p 4 4 times. But those two scenarios are very different from a congestion perspective. The first scenario leads to curtailment, but not necessarily changes in generation. The second scenario implies G 3 increased substantially its generation to deal with congestion along y 24. To account for this, we use the average of 9

10 the sum of the squares. That is Congestion = p2 2 +p2 4 2 which would be equal to 4 times the measure of congestion under the first scenario, but 16 times the measure under the second scenario. This is the measure we adopt in the analysis below. 2.3 Environmental outcomes Assume that the emissions associated with each generator are given by E 1 (w) and E 3 (w). As a direct result of balancing supply, we find the emissions in zone A and B have increased as a result of the increase in the load L 2. While emissions in zone A have increased directly due to an increase in demand, the increase in emissions in zone B are due to the increase in the congestion of the system. Suppose the emission intensity in generator 1 of any given pollutant is less than the emissions intensity in generator 2. Thus, we can say that the total emissions of this pollutant have increased in the system as a whole. The example above highlights the fact that congestion in one part of the system has environmental implications in different parts of the system and different regions. Moreover, the emissions profile of the system under various operating conditions depends on the emission intensity of each generator. Consider the case where G 1 is fueled with coal while G 3 is fueled with natural gas. Thus, an increase in the congestion price between nodes 1 and 2 translates in a reduction in the emissions of SO 2 and CO 2, but an increase in the emissions of NO x. These changes in emissions will translate into increased concentrations of pollutants like PM 2.5 and ozone. 3 Resulting damages are not only a function of the population and age distribution of individuals in each region, but also the seasons. If region A is more populous than region B it may suffer higher damages in terms of monetized mortality rates even if the emissions are not as high or lower than in region B. Moreover, damages may be quite different in a cold January versus a very hot July. This is precisely why understanding 3 Ground-level ozone is formed by a chemical reaction of nitrogen oxides and volatile organic compounds, facilitated by heat and sunlight. 10

11 congestion is important, as it not only causes changes in the overall composition of pollution, but also a spatial redistribution of pollution across the system as a whole. In the next sections, we empirically test for the environmental and health impacts associated to the congestion of the electricity system using this definition. 3 Data and Preliminaries This section describes the various data sets used in our analysis, how we aggregate these data sets, and provides summary statistics to give background and context for the interpretation of our results. Our analysis consists of two primary parts: examining how emissions from electricity generating units (EGUs) change as system congestion changes and examining how ambient air quality changes as emissions change. Therefore we first discuss the construction of our primary independent variable of interest in both parts of the analysis, grid level congestion, and then discuss the construction of the other two data sets measuring generating unit emissions and local pollution concentrations. 3.1 A general measure of congestion We are interested in understanding the environmental effects of system congestion within a region. To the best of our knowledge there is not a current methodology to measure congestion in a region, only at a specific point. Therefore, we create a new measure of the total amount of congestion within the region by aggregating location-specific congestion prices. Our measure of congestion uses the congestion price component at each location as a proxy for the severity of the congestion at that location. As we discussed above in Section 2, our measure of congestion at time t is given by: Congestion t = 1 N N i=1 ( P c it) 2 (4) 11

12 where P c it is the congestion price at location i at time t and N is the number of nodes in the system. 3.2 Data on Congestion Our aggregate measure of system congestion, described above, uses the sum of the squared congestion price within a region of the electricity grid. Ideally, we would like to have congestion prices for all of the three electric interconnections within the United States to accurately measure congestion. However, since large sections of both the Eastern and Western Interconnections do not have transparent, competitive wholesale electricity markets with publicly available pricing data, we restrict our attention to the three markets in the Eastern Interconnection that have transparent data. These markets are the ISO-NE, which includes all of the states in New England, NYISO, which is covers the state of New York, and PJM, which covers all of Pennsylvania, New Jersey, Maryland, Delaware, Virginia, West Virginia, and Ohio, as well as parts of Kentucky, Indiana, Illinois, and Michigan. Each market has adopted a system of locational marginal pricing whereby the price for electricity is distinct at potentially thousands of different locations within the market. The price of electricity at each location is the sum of an energy price, a price for line losses, and a price for congestion. The energy price is the portion of the price that allows for the electricity market to clear given the level of demand and the supply bids from the generating units across the entire market. If there were no constraints on the transmission system and no line losses, this would also be the total electricity price. In practice there are often constraints that cause deviations at particular locations from this energy price. These constraints are either due to particular transmission lines having a capacity constraint such that more electricity cannot be safely transmitted over them (congestion) or due to the loss of electricity due to transmission distance (line losses). The total price of electricity then is the energy price plus the congestion and line loss price. 12

13 The congestion and line loss prices vary widely and can cause potentially large deviations in the total price of electricity within the wholesale market. Given the market clearing energy price, the congestion and line loss prices are set such that supply and demand balance at every node within the wholesale market. This happens by choosing a different dispatch order than the cost minimizing one that produces the market clearing electricity price. Higher marginal cost units are dispatched closer to congested areas to relieve the congestion. Figure 2 shows that the congestion portion of the price can be quite important and cause the total price of electricity to vary by up to an order of magnitude within a state. Within each of these markets, we collect electricity prices at every location available within the system after the market introduced locational marginal pricing. These data are taken from each Independent System Operator (ISO) or Regional Transmission Organization (RTO) that operates the wholesale electricity market. In addition to collecting the hourly locational price, we also collect data on the total load (demand) within the wholesale electricity market. Our data from ISO-NE and PJM begin in 2007 and data from NYISO begin in 2002 with all of the data running through We combine the data across all three markets into an aggregate measure of hourly congestion. This allows us to capture potentially important changes in generation across markets due to congestion in an adjacent market. The first row of Table 1 shows the summary statistics for our measure of regional congestion from June 2007 to December There are important daily patterns to congestion that are shown in Figure 3 as well as different mean levels of congestion within each market. Congestion patterns tend to follow load patterns with the least congestion in the early morning and late evening hours and a peak in the afternoon. On average, NYISO has a higher level of congestion than PJM, which in turn has a higher mean level of congestion than ISO-NE. As can be seen, though the average amount of congestion over our three wholesale electricity markets is about 25, it varies dramatically from 0 up to nearly 1,

14 3.3 Data on Emissions by Electricity Generating Units For the first portion of our analysis, we examine how aggregate congestion affects emissions from electricity generating units. We obtain hourly emissions data on the emissions of sulfur dioxide (SO 2 ), nitrogen oxides (NO x ), and carbon dioxide (CO 2 ) taken from the EPA s Continuous Emissions Monitoring System (CEMS) from 2002 to The CEMS data contains hourly information on total emissions of these three pollutants from all fossil-fuel EGUs 4 in the United States with a capacity over 25 megawatts. These data also include the location of the power plant, which we will use to assign the EGU to a particular market. Table 1 displays the average emissions level for all electricity generating units in our sample across all of the hours in our sample, expressed both as a rate (pounds or tons per megawatthour (MWh)) and as a total volume. 3.4 Data on Pollution Concentrations Ideally, we would like to map changes in output at a particular electricity generating unit to changes in congestion and then use an air quality model to examine how local pollution concentrations change within our region. However, since we cannot attribute the output changes at any particular power plant to congestion without knowing the supply and demand bids for all power plants, we instead turn our attention to pollution monitor data within our region of analysis. We use data from the EPA s Air Quality System on the ambient concentration of ground level ozone and fine particulate matter (PM 2.5 ). Ozone is not directly emitted into the atmosphere. Ambient concentrations are predicted using a model that considers NO x and VOC as direct precursors to ground level ozone, O 3. Ground level ozone can cause major health impacts and has been linked to asthma incidence (Muller and Mendelsohn [2006]). Electricity generation emits large amounts of nitrogen oxides, which are one of the two 4 A generating unit typically consists of a boiler, generator, and smoke stack. Often one power plant will have multiple boilers, generators, and smoke stacks. 14

15 primary precursors to the formation of ground level ozone and is the primary health concern from nitrogen oxide emissions. PM 2.5 concentrations are emitted directly but often include a large contribution from secondary aerosols formed by their precursors SOx and NOx (Grosjean and Seinfeld [1989], Muller and Mendelsohn [2006]). We focus on PM 2.5 due to the large health impacts that exposure to PM 2.5 can have. For example, increased PM 2.5 concentrations are associated with increases in mortality, asthma rates, non-fatal heart attacks, emergency room (ER) visits, and hospital visits (Pope III et al. [2002]). The Air Quality System data that we use consists of over 5,000 monitors distributed nation-wide and is used to determine if each county in the country is in compliance with the National Ambient Air Quality Standards. We restrict our sample to air quality monitors within the two North American Electric Reliability Corporation (NERC) regions that roughly correspond to the three wholesale electricity markets discussed above, the ReliabilityFirst Corporation (RFC), and the Northeast Power Coordinating Council (NPCC). Summary statistics for these monitors are shown in Table 2 as well as the thresholds for counties to be in attainment with the current National Ambient Air Quality Standards. The ozone readings are taken hourly while the PM 2.5 readings are a daily average. As we can see in Table 2, the amount of ozone changes seasonally, not only due to atmospheric chemistry and changes in air transportation dynamics, but because the sun is required to activate the reactions that lead to the formation of ozone we observe higher levels during the spring and summer months. For consistency, we also present PM 2.5 concentrations seasonally, but a pattern does not emerge in this case. We combine these data with the same electricity congestion data discussed above to leave us with 265,435 daily monitor observations for PM 2.5 and 12,638,308 hourly monitor observations for ozone. 15

16 4 Congestion Related Emissions We begin by examining the relationship between emissions and electricity congestion. We are interested in the marginal emissions from generating units due to a change in the amount of congestion in the electricity system. Since each type of generator has different emissions profiles, increasing congestion within the system will cause a different set of generators to sell electricity, potentially changing aggregate emissions. Table 3 displays the average emission rates for coal, oil, and natural gas fired generators. As can be seen in the table, coal fired generation produces more SO 2, NO x, and CO 2 than either oil or natural gas with natural gas emitting less of each pollutant than oil. Our general approach to examine the relationship between congestion and emissions is to regress total emissions across wholesale electricity markets on the total amount of congestion each hour across wholesale electricity markets. We choose to restrict our attention to emissions from EGUs within the wholesale electricity markets. While EGUs outside of the footprint of the wholesale markets may be used to meet demand within the wholesale markets, much of the demand is met from generators inside the market. Constraining our attention to these EGUs will ensure that they are likely responding to system constraints within their primary market instead of constraints in other areas of the interconnection. The general form of the equation we estimate is E t = β Congestion t + γx t + ϕ m + ψ d + ϵ t (5) where E t are the total emissions of either SO 2, NO x, or CO 2 at time t, Congestion t is our measure of congestion in the electricity system at time t, X t contains both load and line losses, ϕ m is a month fixed effect and ψ d is a day of week fixed effect. We choose to include only weekdays in our sample since the load and therefore generation profiles are substantially different on weekends than weekdays. We estimate equation 5 using ordinary least squares and report Newey-West standard errors based on a 24-hour lag to account for any serial 16

17 correlation. In most product markets the quantity demanded, and in this case therefore marginal emissions, would depend on price and our specifications would be problematic. However, we treat the quantity demanded as exogenous because wholesale electricity prices are not borne by consumers, who typically face an average price of electricity over a month or more. Therefore, we treat demand as perfectly inelastic and include it as a right hand side variable. We first estimate equation 5 treating the whole of New England, New York, and PJM as one unit of observation in each hour. This is done since there are flows of electricity between the markets and load or congestion in one market may affect the other markets. However, we also estimate equation 5 separately for each market to examine how the effects may differ within each system. Table 4 displays the results from the regressions aggregating the congestion in all of the wholesale markets together. The results show that, conditional on demand, an increase in congestion decreases sulfur dioxide emissions by 31 pounds per unit of congestion (though statistically insignificantly) and carbon dioxide emissions by 4.76 tons per unit of congestion. Contrastingly, an increase in congestion increases nitrogen oxide emissions by pounds per unit of congestion. Though we cannot attribute changes in output, and therefore emissions, at any particular generator in our data to congestion in these wholesale markets, this pattern of changing emissions is consistent with a shifting of generation from coal-fired generators to oil- and natural gas-fired generators. Since coal-fired generators tend to be further from population centers and therefore load, this is an intuitive explanation for the patterns. Table 5 displays the results from estimating the regression separately for each wholesale market using only the congestion and emissions within the corresponding market. This table makes clear that there is a distinct geographic pattern to changing emissions with a decrease in the emissions of all three pollutants in the PJM region, an increase in all three in New York, and a similar pattern to the pooled results in New England. 17

18 These results follow a pattern of substituting between distant, coal-fired generation and more local oil- and gas-fired generation. Since PJM has substantially more coal-fired EGUs than either New York or New England, we would expect emissions to decrease the most in that region. Similarly since most of the EGUs in New England are natural gas generators, we would not expect to see a large change in emissions only considering New England generators. As shown in Figure 3, congestion has a pronounced pattern across the day, with most of the congestion happening during the peak hours of demand (noon - 6pm). This has important implications for the changing emissions patterns. Nitrogen oxides react in heat and sunlight with volatile organic compounds to form ground-level ozone. If congestion is causing an increase in NO x during the warmest and sunniest times of the day the emissions have more deleterious effects than if they were emitted at other times of the day. Taken together, the results above suggest that congestion reduces emissions in the western portion of our sample region, an aggregate decrease in carbon dioxide emissions, and an increase in nitrogen oxides in the eastern portion of the sample region. In the next portion of the analysis we will look specifically at how congestion affects local pollutions concentrations and examine the spatial and temporal pattern of those changes. 5 Local Pollution and Health Costs Due to Congestion As the results above make clear, there are shifts in total emissions from generating units in response to congestion in the electricity system. Moreover, since congestion usually occurs near population centers and the primary way to reduce congestion is to use higher cost generators near load centers, we expect that there will be a pattern to the changes ambient pollution concentrations with more pollution being emitted near load centers and less pollution being emitted further away from load centers. We use data from the EPA s network of air quality monitors located within the three wholesale electricity markets under consideration. We examine the local concentrations of 18

19 ground level ozone and small (less than 2.5 micrometers) particulate matter (PM 2.5 ). We choose to focus on these pollutants since according to the EPA s 2011 National Emissions Inventory EGU s are responsible for 51.5% of national emissions that are precursors to ground level ozone formation (nitrogen oxides and volatile organic compounds) and 39.6% of national emissions of PM 2.5. To examine this we regress the ambient concentration registered at each monitor in every hour on the system congestion. These regressions follow a similar form to the emission related regressions, but here we allow for the effects to change quarterly. The estimating equation takes the form c it = 4 β iq Congestion t + γ i X t + α i + ϕ im + ψ id + ν it (6) q=1 where c it is the concentration of either ground level ozone or PM 2.5 at monitor i at time t, Congestion t is the amount of congestion in the electricity system at time t, X t is the load, α i is a monitor fixed effect, ϕ im is a monitor month fixed effect and ψ id is a monitor day of week fixed effect. We limit our sample to weekdays for similar reasons to the emissions specifications in section 4. These regressions produce a separate marginal effect of congestion for each pollution monitor in our sample. The marginal effects follow a spatial pattern and at least for ozone the results are also different across seasons. We show this in Figures 4-5. In these two figures we show only the values of the β iq that are statistically different from zero. For the two pollutants, we see eastern monitors showing positive marginal effects of congestion, while the most western monitors in our sample show negative marginal effects. The seasonal patterns are clearer for the ozone, as we were expecting, but we maintain the symmetry with PM 2.5 to calculate aggregate health impacts. 19

20 5.1 Health Costs and Benefits of Pollution Changes We use these coefficient estimates to simulate a counterfactual pollution concentration scenario. We then value the costs and benefits from these changes in pollution concentrations. To estimate the benefits and costs we use the Environmental Protection Agency s GIS based software, Benefits Mapping and Analysis Program - Community Edition (BenMAP), to estimate the distribution of benefits and costs to human health at the county level. Ben- MAP includes information on baseline health and population data calibrated using data from 2010, concentration-response relationships drawn from published epidemiological literature, economic valuation and estimates health outcomes. BenMAP translates pollution monitor concentration readings into local damages based on the selected concentration-response functions and then converts these health damages into an economic valuation using common valuations from the literature. 5 In addition to the software, EPA makes configuration setup files available for both ozone and PM 2.5 that allow users to conduct similar analyses to EPA using the same epidemiological studies and weighting of estimated concentration-response functions across studies. We use both of these configuration files to estimate the value of reducing congestion in the electricity grid. Since every county does not have a pollution monitor in it for every pollutant, we construct pollution concentrations for each pollutant by interpolating between monitors, weighting each monitor reading by the inverse of it s distance to the interpolated point. We also extrapolate monitor readings by creating a 100 kilometer buffer around the monitors within the wholesale markets. Before we construct our counterfactual scenario, we establish a baseline level of pollution by averaging the pollution level in each hour of the year across all of the complete years of data in our dataset ( ). We also construct the average level of congestion in each 5 Following EPA s calibrated valuations, our simulations in BenMAP use a distribution of the value of a statistical life with a mean of $5.5 million in year 2000 dollars. This translates to approximately $7.3 million in year 2014 dollars. 20

21 hour of the year the same way. This methodology naturally reduces extreme congestion observations from particularly high demand days and gives us a baseline expectation of congestion for each hour. In our counterfactual scenario we simulate a shock equivalent to a one unit reduction in congestion in every hour of the year in which congestion is non-zero by using the estimated monitor-specific coefficients. After constructing these pollution concentration counterfactuals, we estimate the health benefits and costs of this new distribution of pollution. Throughout the reported results of the simulations we set the monitor coefficients to zero if they are not statistically different from zero in the regression. Moreover, there is uncertainty built into the damage estimates for each county. We therefore set all damage estimates to zero for counties that have a damage estimate of zero between the 2.5th percentile estimate and 97.5th percentile estimate. 6 Figure 6 displays the changes in PM 2.5 concentrations in our counterfactual scenario. The figure makes clear there is a distinct spatial pattern of areas that have an increase in pollution and areas that have a decrease in pollution levels. PM 2.5 levels are reduced in the western portions of PJM from congestion while PM 2.5 levels are increased substantially along the Atlantic Ocean. A similar pattern emerges when examining changes in ozone, as shown in Figure 7. The results of this geographic analysis can be seen in Figure 8 and Table 6. The light blue and green counties in Figure 8 are the counties that have health benefits from congestion in the electricity grid. Those counties see a reduction in PM 2.5 and ozone levels and corresponding health gains. The counties shown in dark blue are counties that have an increase in health costs due to congestion. Naturally, a similar pattern emerges in the value of congestion to the changes in pollution concentrations with the areas along the Atlantic 6 Our damage estimates do not change significantly if we instead use the point estimate for both the monitor-specific coefficients and damage estimates. The results are presented in the appendix. 21

22 Ocean incurring significant costs from congestion. Table 6 sums the valuation of the change in congestion for each state in our sample region. The first column displays the estimated health benefits in each state under the counterfactual scenario where just one unit of electricity congestion is removed in every hour of the year. 7 States with positive estimates would benefit from removing congestion while states with negative estimates would be harmed by removing congestion. Overall, states in the western portion of our data are damaged by removing congestion since congestion reduces concentrations in those states while states in the eastern portion are benefited from removing congestion since generation (and therefore emissions) are shifting to the western portion of the area. In aggregate, our estimates suggest that removing congestion from the grid could cause a net benefit of more than $1.2 billion and damages of around $1 billion. Overall, there is a net gain of reducing congestion in the grid of approximately $180 million in present value terms. 6 Conclusion This paper sets out an example of an electricity system to present the relation between electricity grid congestion and environmental and health outcomes. We showed that by affecting the emissions profile of the system, congestion prices have a discernible impact on emissions, pollution concentrations, and health outcomes that vary over time and space. We then empirically examine the impacts of electricity congestion on emissions from generators, pollution concentrations, and health outcomes on the New England, New York and PJM wholesale electricity markets and surrounding areas. We find a marginal change in congestion conditional on the amount of electricity demand causes a decrease in the amount of sulfur dioxide and carbon dioxide and an increase in nitrogen oxide emissions. 7 The damage estimates are the present discounted value of the stream of health benefits the population receives from this one time change. 22

23 These changes in emissions vary across wholesale markets with a decrease in the amount of emissions of all three pollutants in the PJM market and an increase in the amount of emissions of all three pollutants in the New York market. We then examine the effect of congestion on pollution concentrations and find a strong geographic pattern. Counties in the western portion of our study area have a large and significant reduction in the amount of ground level ozone and PM 2.5 while counties in the eastern portion of our study area have a large and significant increase in the amount of ground level ozone and PM 2.5. We a construct counterfactual estimate of pollution levels with less congestion in the electricity grid. Using BenMAP, we estimate the value of reducing congestion to be negative (i.e. the gains in pollution reduction from congestion in the western portion of the sample area outweigh the costs of more pollution in the eastern areas). We estimate the net benefit to society measured in terms of monetized reduction in mortality and morbidity of the current average level of congestion compared to reduction in congestion by one unit from the current level would result in a net gain of around $180 million, with the benefits being up to $1.2 billion and the costs being approximately equal to $1 billion. The electricity transmission system not only transfers electricity across regions, it also determines who benefits and who pays from pollution. When planning for the expansion of the electricity transmission system, it is important to account for the spatial heterogeneity of environmental externalities associated with electricity generation. One way to do this is by increasing the rate of penetration of distributed generation. More generally, the benefits of any environmental policy aimed at reducing pollution and health impacts from electricity generation needs to account for the current and future electricity transmission infrastructure. 23

24 References Kenneth Y Chay and Michael Greenstone. The Impact of Air Pollution on Infant Mortality: Evidence from Geographic Variation in Pollution Shocks Induced by a Recession. Quarterly Journal of Economics, 118(3): , Joseph Cullen. Measuring the Environmental Benefits of Wind-Generated Electricity. American Economic Journal: Economic Policy, 5(4): , Janet Currie and Matthew Neidell. Air pollution and infant health: what can we learn from Californias recent experience? Quarterly Journal of Economics, (August): , Janet Currie, Joshua Graff Zivin, Jamie Mullins, and Matthew Neidell. What Do We Know About Short- and Long-Term Effects of Early-Life Exposure to Pollution? Annual Review of Resource Economics, 6(1): , Lucas Davis and Catherine Hausman. The Value of Transmission in Electricity Markets : Evidence from a Nuclear Power Plant Closure Lucas Davis and Catherine Hausman. Working Paper #248, (March), Eleanor Denny and Mark O Malley. Wind Generation, Power System Operation and Emissions Reduction. IEEE Transactions aon Power Systems, 21: , Joshua S Graff Zivin, Matthew Kotchen, and Erin T Mansur. Spatial and Temporal Heterogeneity of Marginal Emissions: Implications for Electric Cars and Other Electricity- Shifting Policies. NBER Working Paper 18462, Daniel Grosjean and John H Seinfeld. Parameterization of the Formation Potential of Secondary Organic Aerosols. Atmospheric Envrionment, 23(8): , Claudia Hitaj. Renewable Power Effects on Electricity Transmission Congestion and Emissions. Mimeo, pages 1 35,

25 Erin T. Mansur and Matthew W. White. Market Organization and Efficiency in Electricity Markets. Mimeo, pages 1 56, Nicholas Z. Muller and Robert Mendelsohn. The Air Pollution Emission Experiments and Policy Analysis Model (APEEP). Technical report, URL nordhaus/resources/muller JEEM Appendix.pdf. Nicholas Z Muller, Robert Mendelsohn, and William Nordhaus. Environmental Accounting for Pollution in the United States Economy. American Economic Review, 101(5): , C. Arden Pope III, Richard T. Burnett, Michael J. Thun, Eugenia E. Calle, Daniel Krewski, Kazuhiko Ito, and George D. Thurston. Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution. Journal of the American Medical Association, 287(9): , Nicholas Ryan. The Competitive Effects of Transmission Infrastructure in the Indian Electricity Market. Mimeo, pages 1 59, Kyle Siler-Evans, Inês Lima Azevedo, M. Granger Morgan, and Jay Apt. Regional variations in the health, environmental, and climate bene fi ts of wind and solar generation. Proceedings of the National Academy of Sciences of the United States of America, 110(29): , Frank A. Wolak. Measuring the Competitiveness Benefits of a Transmission Investment Policy: The Case of the Alberta Electricity Market. Mimeo, pages 1 37,

26 7 Tables Table 1: Congestion and emissions summary statistics Mean Std. Dev. Min Max N Congestion ,968 SO 2 (lbs/mwh) ,968 NO x (lbs/mwh) ,968 CO 2 (tons/mwh) ,968 SO 2 (lbs) 508, , ,853 1,135,714 34,968 NO x (lbs) 160,279 66,271 60, ,985 34,968 CO 2 (tons) 80,730 16,693 40, ,996 34,968 26

27 Table 2: Pollution concentration summary statistics Mean Std. Dev. Min Max N Ozone Q1 (ppm) ,360,226 Ozone Q2 (ppm) ,225,578 Ozone Q3 (ppm) ,789,636 Ozone Q4 (ppm) ,262,868 PM 2.5 Q1 (µg/m 3 ) ,826 PM 2.5 Q2 (µg/m 3 ) ,058 PM 2.5 Q3 (µg/m 3 ) ,609 PM 2.5 Q4 (µg/m 3 ) ,942 Ozone standard is 0.12 ppm in one hour and 0.08 ppm averaged over 8 hours, PM 2.5 standard is 15.0 µg/m 3 27

28 Table 3: Average emissions rate by fuel type Pounds per MWh SO 2 NO x CO 2 Coal ,249 Natural Gas ,135 Oil ,672 Source: EPA egrid 28

29 Table 4: Marginal emissions from congestion SO2 (lbs) NOx (lbs) CO2 (tons) Congestion *** -4.76*** (19.59) (3.71) (1.01) Line Losses *** (0.77) (0.15) (0.04) Load (Mwh) 3.88*** 1.29*** 0.67*** (0.05) (0.01) (0.00) Observations 34,968 34,968 34,968 OLS estimates. Newey-West standard errors with 24 hour lag are reported. Estimates include day of week, and month of year fixed effects. * p<0.10 ** p<0.05, ** p<

30 Table 5: Marginal emission from congestion by region Pooled New England New York PJM SO 2 (lbs.) *** (19.59) (5.97) (1.96) (47.30) NO x (lbs.) 16.12*** *** *** (3.71) (2.76) (1.19) (11.10) CO 2 (tons) -4.76*** *** *** (1.01) (0.80) (0.20) (3.04) Observations 34,968 37,583 68,880 34,967 OLS estimates. Newey-West standard errors with 24 hour lag are reported. Estimates include day of week, and month of year fixed effects. * p<0.10 ** p<0.05, ** p<

31 Table 6: Benefit and Damage Estimates Benefits Damages CT 82,831 DE 40,095 DC 15,634 IL 393,548 MA 202,661 IN 174,486 MD 40,985 KY 43,439 ME 32,130 MI 95,507 NC 2,700 OH 211,745 NH 23,371 VT 12,349 NJ 356,576 WI 104,099 NY 324,402 WV 7,962 PA 92,054 RI 42,922 VA 46,952 Total 1,263,218 Total 1,083,230 Benefits and damages are listed in thousands of 2000 USD. 31

32 8 Figures 1 y 12 2 A G 1 L 2 y 24 G 3 y 34 L 4 3 Figure 1: Electricity Network 4 B 32

33 Figure 2: Price gradient in PJM on February 28,