The Environmental Impacts of Fuel Switching Electricity Generators.

• Author: Holladay, J. Scott

1. INTRODUCTION

   Electricity generation is one of the largest sources of air pollution in the United States. However, emissions have fallen consistently since 2009. Some of this reduction in pollution is because of regulations designed to reduce emissions and limit externalities, but a significant portion can be traced to unrelated price changes in other markets. Sustained low prices for natural gas have led many electricity generators to switch their fuel source from coal or oil to natural gas. Natural gas combustion produces lower levels of carbon dioxide, sulfur dioxide, and nitrogen oxides than the competing fuel sources. Therefore, the shift to natural gas has the ancillary benefit of reducing pollution emissions from the electricity sector.

   This paper is an effort to determine the impact of the price changes in natural gas on the pollution emissions from electricity generators. We evaluate the environmental impacts of fuel switching at two margins: individual generators switching fuels from oil to natural gas (which we term the intensive margin) and reduced dispatch of pollution intensive power plants that continue to use oil as their primary fuel (the extensive margin). The flexibility of generators to burn multiple fuels has been widely studied. (1) Pettersson et al. (2013) analyze fuel switching in Western Europe and assess the impact of the carbon market on the incentives to switch fuels, but they do not look at the environmental impact of the switch. Maria et al. (2014) finds that fuel choices did not change between the announcement and implementation of the U.S. Environmental Protection Agency's (EPA) Acid Rain Program. Rather than focus on the impact of environmental regulation on fuel choice, we analyze the impact of fuel choice on environmental outcomes. For this analysis, we use the plausibly exogenous fall in natural gas prices caused by the widespread adoption of hydraulic fracturing and horizontal drilling for extraction.

   A significant amount of literature has emerged that predicts the environmental impact of energy policy and highlights the importance of evaluating policy by using marginal emissions rather than average emission rates. Cullen (2013) and Kaffine et al. (2013) estimate the environmental impacts of expanding wind generation. Graf Zivin et al. (2014) estimate marginal emissions across space and time and apply the results to evaluate the environmental impact of plug-in hybrid cars. This literature typically attempts to control for fuel prices using fixed effects and estimates a short-run marginal emission curve. In this paper, we estimate a medium-term emission rate with varying input prices but with a constant set of generators. This method highlights the importance of fuel costs in estimating marginal emission levels.

   Fuel sources are imperfectly observed in various public data sets describing the electricity generating industry. To address this limitation, we build a unique data set that describes the electricity market in New York City (NYC) hour-by-hour from 2005-2010. We also take advantage of detailed generation and pollution emissions data reported to the EPA in conjunction with the S[O.sub.2] and N[O.sub.x] markets implemented under the Clean Air Act. These data and the unique pollution signature of the various fuels allow us to use a series of semi-parametric regressions to identify the fuel-price spread associated with generators switching to natural gas.

   We then build an hourly panel of fuel type, electricity output, and pollution emissions for every generator in the five boroughs of NYC from 2005-2010. Over this period, we observe the oil-fired generators' transitions to natural gas and generation moving from oil to natural gas-fired generators. We estimate that when diesel prices exceed natural gas prices by more than around $4 per million British thermal units (mmBTU), the fuel switching generators switch to natural gas. Using the panel nature of our data, we are able to estimate the pollution emissions averted by the shift to natural gas and natural gas-fired generators. These estimates can be used to evaluate the environmental impact of new and existing energy and environmental policies. To illustrate the use of these estimates, we simulate the impact of implementing real time pricing for electricity in NYC over the study period. The results suggest that the changing fuel-price spreads reduce the environmental benefits of the real time pricing by around one-third. While we focus on real time pricing in our application, these results can be used to assess the environmental impact of any energy policy that has a heterogeneous impact across the demand profile such as plug-in hybrids, bulk electricity storage or energy efficiency building codes.

   The paper proceeds as follows: in section 2, we provide background and describe the data; we describe the methodology for identifying the generators' fuel type in section 3; in section 4, we report the results of the semi-parametric estimation of the generation levels, fuel inputs, and pollution emissions. Section 5 presents the results of a simulation of a real time electricity pricing program in NYC. Section 6 describes the implications of the results and the final section concludes and provides some suggestions for future research.

2. BACKGROUND AND DATA

   New York City has a large and dynamic electricity market. For reliability reasons, the New York systems operator has instituted a local capacity requirement that mandates that within-city generators have the installed capacity to meet approximately 85% of NYC's forecasted peak load. The capacity requirement is enforced primarily through a monthly capacity auction. (2) The auction winning units are required to be available to provide power each day of the contracted month. This requirement helps reduce the risk of catastrophic blackouts by lessening the city's reliance on imported electric power.

   This process forces a significant amount of generation capacity to be located inside the city. This proximity exposes city residents to the pollution associated with electricity generation. The New York State regulations on acceptable emissions levels in the city have essentially banned coal-fired generation. This ban leaves a mix of oil- and natural gas-fired generation to meet the reliability requirement. Figure 1 describes the location, size, and pre-2005 fuel type of the generators in NYC. New York City is an ideal location to study the impacts of fuel switching on pollution because the city's changes in demand will affect the marginal generator that is also located in the city because of the reliability requirements. Any environmental or energy policy that affects demand differently across hours of the day or levels of generation will have an impact on the emissions of local generators.

   Joskow (2013) describes how the twin technological innovations of hydraulic fracturing and horizontal drilling have led to huge decreases in the price of natural gas that have coincided with increased oil prices. Figure 2 describes the evolution of oil and natural gas prices. Natural gas and oil prices both fell during the 2008 recession, but oil prices quickly recovered and began to slowly increase by early 2009. Natural gas prices continued to fall after the recession and have hovered near historic lows of less than $2 per million btu since the spring of 2012. This combined effect is a significant relative price change in favor of natural gas.

   In the long run, oil- and natural gas-fired capacity are near perfect substitutes, but once electric generators are constructed their ability to switch fuels is limited. For the most part, the boilers of electric generators cannot switch between coal and natural gas without cost once they are constructed Soderholm (2001). Switching from oil to natural gas as the primary generator fuel is a significant investment that requires one and half to two years of downtime (IEA (1988)) suggesting intensive margin impacts of fuel switching will require significant and stable changes in fuel prices. (3) In the short run, fuel switching can occur at the extensive margin by bringing oil and natural gas generators online at different times in response to the changing marginal cost of production. No fuel type has enough generating capacity in NYC to meet the city's entire load so changing dispatch patterns cannot lead to complete fuel switching. (4)

   An analysis of the fuel switching of flexible-fuel generators across both margins requires information on electricity generation, fuel prices, electricity markets, and the potential drivers of demand at high frequency. We construct a generator hour-level panel for every generator in NYC's five boroughs. We collect the data for our panel from the EPA's Clean Air Markets database. Their data is collected by the Continuous Emissions Monitoring Systems (CEMS) installed in every generator in the country with a generating capacity above 25 megawatts. The data is used to ensure compliance with the EPA's Acid Rain Program implemented under the Clean Air Act Amendments. The CEMS data reports hourly emission levels of S[O.sub.2], N[O.sub.x], and C[O.sub.2]for each smokestack at every generator. The data also includes hourly measures of fuel inputs and electricity generation along with a host of time-invariant information on the stack's location, ownership, and reported fuel sources. The fuel-source data lists a primary and secondary fuel source, but does not indicate which fuel is being used in which hour.

   We collect fuel-price data from Thompson Reuters Datastream. The data include the daily closing prices for natural gas at Henry Hub and the prices of low sulfur 500 parts per million (ppm) diesel from NY Harbor. (5) We combine this detailed generator and fuel-price data with a host of potential covariates. Weather is one of the largest drivers of variation in electricity demand. To control for that source of...