Potential Energy Load Requirements of an Electrified Transportation Sector in Los Angeles: Impacts on Greenhouse Gas Emissions

Jae D. Kim University of Southern California, jaedkim@usc.edu Mansour Rahimi University of Southern California, mrahimi@usc.edu

Abstract. Using plug-in electric vehicles (PEVs) has become an important component of greenhouse gas (GHG) emissions reduction strategy in the transportation sector. A substantial increase in the use of PEVs results in a significant decrease in the amount of GHG emissions versus conventional gasoline vehicles on a “wells-to-wheels” basis. This study examines the potential impacts on energy load and GHG emissions of a large-scale adoption of PEVs in the City of Los Angeles (LA). A large-scale adoption of PEVs and their resultant emission reduction potentials are dependent on factors such as type and scale of electricity generation sources, electric battery technologies, and consumer behavior in terms of adoption and charging patterns. By integrating these factors, this study creates a comprehensive model that estimates the energy load and GHG emission impacts from PEVs for the years 2020 and 2030. Model simulations for 2020 show that the PEV charging loads will be modest with negligible effects on the overall generation profile. Contrary to popular belief, we found that the average marginal carbon intensity is significantly higher during off-peak hours. Model simulations for 2030 show that the PEV charging loads will become more significant with potential generation shortages for peak demand periods. For 2030, the average marginal carbon intensity for all hours decreases significantly mainly due to the removal of coal from the power generation sources by 2025. In this scenario, off-peak charging becomes preferable versus peak charging. These results suggest that the current economic incentives to encourage off-peak charging may actually result in greater GHG emissions. A greater understanding of a region’s specific electricity generation and dispatching of resources are imperative to creating the best strategies for PEV adoption and charging behavior that would maximize emissions mitigation.

Proceedings of the International Symposium on Sustainable Systems and Technologies (ISSN 2329-9169) is published annually by the Sustainable Conoscente Network. Melissa Bilec and Jun-Ki Choi, co-editors. ISSSTNetwork@gmail.com.

Copyright © 2013 by Jae D. Kim, Mansour Rahimi. Licensed under CC-BY 3.0.

Cite as: Potential Energy Load Requirements of an Electrified Transportation Sector in Los Angeles: Impacts on Greenhouse Gas Emissions. Proc. ISSST, Jae D. Kim, Mansour Rahimi. http://dx.doi.org/10.6084/m9.figshare.930735. v1 (2013)

Potential energy load requirements of an electrified transportation sector in Los Angeles: Impacts on greenhouse gas emissions

Introduction. U.S. policymakers have been increasingly promoting the adoption of plug-in electric vehicles (PEVs) as a means to remedy both environmental and energy security concerns arising from the transportation sector’s high reliance on fossil fuels. PEV adoption has been aided by favorable policy incentives, technological advancements, and improving economic conditions. Government incentives in the form of tax credits have lowered the economic barrier for new car buyers. Specific mandates directed at the automotive industry have also played a major role in the development and introduction of new PEV models. Furthermore, rising gas prices have prompted consumers to turn to PEVs as an alternative to conventional gasoline vehicles. These favorable trends for the PEV market will inevitably put greater pressure on the electricity grid from increasing vehicle charging requirements. However, determining the actual energy load requirements resulting from PEV charging is complex and assessing the net environmental effects is uncertain because the variety of electricity generation sources, battery attributes, and charging patterns all significantly affect the outcome.

Determining the energy load from PEV charging has been a popular topic of research. Previous studies have concluded that under ideal charging scenarios, the existing electricity grid infrastructure has enough capacity to meet PEV charging demand in the near term (Parks et al., 2007; Kitner-Meyer et al., 2007; Schneider et al., 2008; Stephan and Sullivan, 2008). However, there are major factors that can change the energy load and resulting greenhouse gas (GHG) emissions. The change in the electricity grid mix plays a significant role because the type and scale of the energy input sources to the generation facilities dictates the emissions rate of the power supplied to the consumer. The consumer adoption rate of PEVs determines how quickly the emissions mitigation potential on a per-vehicle basis is actualized over time. Increase in battery energy density will inevitably lower costs and extend the distance range for PEVs that may accelerate adoption. The timing of charging will change the energy loads because charging during the daytime or “peak” hours puts greater pressure on the electrical grid that is already at or near its maximum output.

A deep understanding of the complex interlinks across PEV adoption, electricity grid, and charging behavior is imperative to fully assess the net GHG emission impacts of PEVs. Previous studies have attempted to quantify the PEV-related GHG emissions at the national or state level, each study with its own set of assumptions on grid energy mix and PEV adoption. Bankdivadekar et al. assumed a constant U.S. grid energy carbon intensity rate and a linear increase in the PEV market penetration level (2008). Yang et al. considered alternative vehicle options in California and concluded that PEVs have the highest emission mitigation potential assuming that the grid’s carbon intensity is 94% below the 1990 level and PEVs make up 77% of all vehicle miles traveled in 2050 (2009). Numerous other studies have shown that PEVs result in significantly lower GHG emissions based on the assumption that the average electric grid carbon intensity is low and constant (Campbell et al., 2009; CARB, 2009). However, the intrinsic low carbon grid intensity assumption can be problematic because the electricity grid is not homogeneous; each geographic region has varying energy mix comprising of coal, natural gas, and other power generating sources. Consequently, the consumption of electricity in different regions may result in significant differences in the type and amount of emissions. There is also growing consensus that the true assessment of PEV emission impacts requires the consideration of the marginal grid intensity, which has hourly variance depending on the system load (McCarthy and Yang, 2009; Jansen et al., 2010; Elgowainy et al., 2012). Consequently, the time of charging directly affects emissions from PEVs.

Research Question. This study poses the simple question: what are the potential impacts of a large-scale adoption of PEVs in the City of Los Angeles (LA) on grid energy demand and GHG emissions? We create various scenarios and consider the energy load requirements and

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resulting GHG emissions. LA is an important case study because its structure differs significantly from previous studies such that previous assumptions on grid energy and PEV adoption are inapplicable. LA’s electric grid is owned and operated by a single vertically integrated public utility that controls the type and scale of renewable and nonrenewable energy sources as well as the dispatching process. LA’s unique car culture makes early PEV adoption disproportionately higher than the rest of the country suggesting that previous results may be significantly underestimating the short run effects.

Investigative Method. The focus of this study is the development of a comprehensive model that assesses the full GHG emission impacts of a large-scale deployment of PEVs in LA. The model aims to capture the complex relationships across PEV adoption, electricity grid changes, and consumer charging behavior. The model is created and simulated in MATLAB while the results are tabulated in Excel. The use of MATLAB gives us great flexibility in varying parameter values such as the energy discharge rate and charge power. We first model the supply side by projecting the changes to the region’s hourly GHG emission rate or “grid carbon intensity” based on planned energy generation projects and resource dispatching. We then model the demand side by setting certain charging patterns, technology attributes, and adoption scenarios. We create charging scenarios based on idealized peak and off-peak situations. Technological attributes such as the PEV battery discharge rate and the charge power are also examined in the model to see potential effects on emissions. The PEV fleet size is modeled based on working age population changes, vehicle scrap rates, and the region’s historical vehicle sales data. We make an assumption that the PEV is primarily composed of passenger vehicles (i.e., excludes large trucks, buses, etc.) based on technological limitations as well as recent sales trends indicating changing consumer preferences for smaller vehicles.

Supply Side The Los Angeles Department of Water and Power (LADWP) is a publicly-owned utility company that is entirely responsible for the generation, transmission, and distribution of the city’s power needs. LADWP owns in-state natural gas, hydropower, and renewable generation sources and out-of-state coal and nuclear power generation units. We model the changes in LADWP’s grid energy based on the agency’s Power Integrated Resource Plan that contains information on the agency’s short and long-term system load projections as well as generation capacities. LADWP expects the annual net energy demand to increase from 25,688 to 30,101 GWh by 2030 (LADWP, 2012). Some of this increasing demand is attributed to PEV charging, which is expected to be 151 and 526 GWh in 2020 and 2030, respectively. Despite the growing energy demand, LADWP has adopted a plan to decrease its generation capacity from dependable sources over the next 20 years mainly from the removal of coal generation sources in 2015 and 2025 (LADWP, 2012). Rather than acquiring greater conventional generation capacity, LADWP expects to meet increasing peak energy demand with less baseload generation capacity through improving energy efficiency, gains from agency’s peak demand management programs, and greater integration of distributed generation from solar.

Two major changes in LADWP’s future nonrenewable generation capacity are the higher dependency on natural gas and a complete removal of coal power (see Figure 1). Natural gas is expected to make up 41% of the energy consumption by 2030 (LADWP, 2012). LADWP is phasing out its stake in the coal-fired thermal plants in Arizona (Navajo Generation Station) by 2015 and Utah (Intermountain Power Project) by 2025. The phase out of these coal-fired generation sources is a major shift considering that coal generation accounted for 13,142 GWh or 39% of total energy consumption in 2010 (LADWP, 2012).

Potential energy load requirements of an electrified transportation sector in Los Angeles: Impacts on greenhouse gas emissions

LADWP’s ongoing and planned renewable energy projects for new generation sources include those from wind and solar. Currently, combined net generation capacity of wind and solar is approximately 1,013 MW (LADWP, 2012). The actual net dependable capacity, however, is far below at approximately 234 MW (LADWP, 2012). This is mainly because of the intermittent nature of wind and solar – the sun doesn’t always shine and the wind doesn’t always blow. Consequently, future wind and solar generation sources will also suffer from a lower net dependable capacity because of intermittency. For this study, we assume a fixed utilization rate (i.e., net dependable capability versus capacity) for future generation sources.1 For wind generation, the utilization rate is assumed to be 10% for all planned generation. For solar, the utilization rate is assumed to be 27% for photovoltaic (PV) generation and 68% for solar thermal. Expansion of geothermal and biomass generation sources is also crucial in LADWP’s future energy supply. Current projects already expand the capacity from these sources from 27.5 MW to 239 MW and 329 MW by the end 2020 and 2030, respectively (LADWP, 2012).

Figure 1. LADWP's projected generation sources and capacity

Dispatching of Generation Sources The electricity grid is a complex network of power plants, transmission lines, and distribution facilities. Since electricity typically cannot be stored, the grid must generate and deliver electricity to meet real-time demand. For most of the year, LADWP meets real-time demand primarily using its own power plants and distribution resources. Each power plant operates differently because of various factors such as the technology, generation capacity, type of energy resource, and economics. Large coal and nuclear plants typically are the baseload plants that operate continuously at the lowest cost. Peaking or “peaker” power plants do not operate continuously and are dispatched when demand is highest typically during the summer months. In California, the most common peaking plants are natural gas-fired power plants. For LADWP, however, its peaking plants are primarily hydro power plants. The actual dispatching of LADWP’s generation resources is further complicated by its participation in the power markets. That is, any supply shortage is satisfied with import purchases through the electric

1 The given utilization rates for future wind and solar generation sources are equivalent to those in LADWP’s Integrated Resource Planning.

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power market in the larger Western Electricity Coordinating Council (WECC) region. Any excess supply is sold as export through the WECC network. As a result, the actual dispatching of generation resources is a complex process dependent on plant availability, contractual agreements, and market spot prices. Prior studies have used grid models based on least cost (Parks et al., 2007; Sioshansi and Denholm, 2009). Jansen et al. created a grid model using the correlation in the historical data on system load and resource capacity factors for the Western U.S. region (2010). The results were highly accurate and demonstrated that the complexity of the resource dispatch process can be captured without the consideration of market prices and other economic influences (Jansen et al., 2010). McCarthy and Yang also analyzed the Western U.S. grid but used a simplistic modeling approach where the resource dispatching of power plants follow a specific order as the energy load increases (2010). This approach is most similar to our modeling method.

In this study, we model LADWP’s resource dispatching on an hourly basis using a simplistic approach. Every hour, the system load (i.e., system load) is checked against the primary generation resource, namely any available solar and wind energy sources. If the system load exceeds the resource’s net capacity, then the next generation resource (i.e., nuclear then coal) is called upon to meet the demand. The next generation resources (i.e., geothermal, biomass, other renewables then natural gas) are called up. Any residual system load is satisfied by hydro power plants until the resource runs out. If excess system load remains, then it is satisfied using energy imports from other power plants within the WECC.

Emission Factors The generation of electricity has an embedded GHG emission rate or “carbon intensity” that varies for each type of power plant. The nonrenewable energy sources such as coal and natural typically have higher carbon intensities than those of renewable sources. The list of carbon intensity values for each power generating source used in this study is shown in Table 1.

Table 1. Carbon intensity of energy generation sources Energy Source kg CO2e/MWh Source Coal2 961 EPA (2013) Natural Gas3 418 EPA (2013) Solar 50 Fthenakis et al. (2008) Wind 14 World Energy Council (2004) Small Hydro 11 Bergerson and Lave (2002) Geothermal 120 Energy Center of Wisconsin (2009) Biomass and Waste 31 Spitzley and Keoleian (2004) Nuclear 25 Fthenakis and Kim (2008) Large Hydro 240 Pacca (2007)

Demand Side The PEV demand side is the hourly electricity requirement as a result of vehicle charging. Modeling the demand requires integrating the charging scenarios, PEV energy consumption, and the size of the PEV fleet.

PEV Charging Scenarios The energy and emissions impacts from PEVs depend on the daily electric load patterns resulting from PEV recharging. Recent studies have made different assumptions on the daily

2 Emission rate calculated based on EPA data on LADWP’s two coal plants (EPA, 2013) 3 Emission rate calculated based on EPA data on LADWP’s four natural gas plants (EPA, 2013)

Potential energy load requirements of an electrified transportation sector in Los Angeles: Impacts on greenhouse gas emissions

PEV recharging patterns (EPRI, 2007; Kintner-Meyer et al., 2007; Stephan and Sullivan, 2008; Kang and Recker, 2009; Sioshani and Denholm, 2009; Axsen, 2011; Weiller, 2011; Kelly et al., 2012). Many studies assume an ideal scenario where recharging primarily occurs during the night in the “off-peak” hours (EPRI, 2007; Kintner-Meyer et al., 2007; Stephan and Sullivan, 2008). This ideal scenario is often referred to as “valley-filling” because PEV charging increases the energy load of the off-peak hours closer to peak-hour levels resulting in a more leveled energy load profile. A major advantage of this scenario is that electricity costs are lowest during these hours since electricity is generated from baseload plants that would otherwise be underutilized. These studies demonstrate that under such ideal off-peak charging scenario, the current grid infrastructure can accommodate large number of PEVs without any new generation capacities or service disruptions (Kintner-Meyer et al., 2007).

In this study, we consider charging scenarios during off-peak and peak hours. In the off-peak charging scenario, we assume that all PEV charging occurs during the hours between 8 p.m. and 7 a.m. In the peak charging scenario, we assume that all PEV charging occurs between 9 a.m. and 6 p.m. We further assume in both scenarios that the total PEV charging load is constant such that the load is “smooth” with respect to the overall system load profile, therefore, does not cause significantly ramping up and down of generation sources.

PEV Electric Consumption Rate The PEV electric consumption rate is the amount of energy exhausted per mile traveled during driving. The energy consumption depends on many factors such as vehicle size, weight, body shape, driving habit, and climate conditions. Therefore, values for electric consumptions vary in the literature. The U.S. EPA’s urban dynamometer driving schedule (UDDS) and the highway fuel economy test (HWFET) rate the typical energy consumption of a mid-size vehicle as 0.27 kWh/mi for urban and 0.22 kWh/mi for highway driving (Young et al., 2013). EPA’s US06 standard, which assumes more aggressive driving, rates the energy consumption as 0.40 kWh/mi (Young et al., 2013). Some other values reported in the literature are 0.21 kWh/mi (Kang and Recker, 2009), 0.30 kWh/mi (Sioshani and Denholm, 2009), and 0.41 kWh/mi (Stephan and Sullivan, 2008). In this study, we set the default electric consumption rate at 0.35 kWh/mi. Using this discharge rate, the average electric consumption rate is assumed to be 11.55 kW per day based on previous SCAG studies have shown that the average vehicle miles traveled in LA is approximately 33 miles (SCAG 2008).

PEV Adoption We model the PEV adoption with respect to LA’s overall vehicle fleet population by integrating previous new vehicle sales data, the Bass technology diffusion model, and projected changes in the working population. We use the changes in the working-age population as a surrogate to forecast overall vehicle sales and fleet size.

In order to model PEV adoption, we first need to analyze past new vehicles sales data to calculate the potential upper bound on the number of new PEVs entering LA’s vehicle fleet each year. Unfortunately, past new vehicle registration data at the city level is limited and difficult to access. Therefore, we overcome this limitation by creating a model using sales data at the multi-county level that are more readily available to researchers. California New Car Dealers Association (CNCDA) publishes sales data for the entire state as well as Los Angeles and Orange County. CNCDA’s data categorizes vehicle sales broadly into cars and trucks as well as specific vehicle types such as compact sedan, midsize, sports-utility-vehicle, etc. CNCDA’s published reports indicate that Los Angeles and Orange County typically account for approximately 600,000 new vehicle sales annually. Passenger vehicle sales have risen in proportion from about 50% in 2005 to over 65% of all sales in 2012 (CNCDA, 2013). We

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estimate vehicles sales in LA by disaggregating this sales data based on the working-age population. “Working-age” population is defined as residents between the ages 18 and 64. The main assumption is that vehicle sale is a function of the changes in the working-age population. Therefore, as the region’s working-age population increases, new vehicle sales also increase. Detailed census data and population projections for each county are provided by the California Department of Finance (CADF, 2013). Data specific to the City of LA is obtained from studies published by SCAG that provides detailed census data and population forecasts (SCAG, 2012). Since city population projections are based on 10-year or longer increments, we use linear regression to estimate all other years until 2050. Based on these assumptions, the projected new vehicle sale is approximately 100,000 vehicles per year because the working-age population projections remain steady (see Table 2). Again, this sales projection serves as the upper bound of PEV sales projection.

Using the Bass technology diffusion model, we forecast the number of PEVs operating in LA over the next 30 years.4 In the “baseline” case, the maximum market penetration is estimated as 75% of new vehicle sales in LA, which captures all historical LA vehicle sales in the light duty vehicle (LDV) category except for large trucks, vans, and sports utility vehicles (SUVs). In the “low” case, the maximum market penetration is estimated at 65%, which is approximately the historical percentage of passenger vehicle sales in LA (CNCDA, 2013). In the “high” case, the PEV market extends the entire LDV category except for full size pickups. The forecast for the three PEV adoption scenarios are shown in Table 2. In the baseline case, PEV sales reach 11% and 38% of all vehicles sales in LA by 2020 and 2030, respectively. In the high case, PEV market share reach 22% and 70% by 2020 and 2030, respectively. PEV sales remain minimal in the low case until 2025 when sales exceed 20,000 units.

Table 2. Projected PEV sales in LA using projected changes in the working-age population PEV Sales Cases

Working Age (18-64) New Vehicle Registrations Low Baseline High

2012 2,505,648 102,619 1,758 3,819 7,042 2015 2,523,454 103,349 4,442 10,378 20,462 2020 2,518,767 103,157 12,101 30,818 62,804 2025 2,503,196 102,519 25,541 63,726 114,712 2030 2,484,844 101,767 45,365 97,273 145,806

To assess the impact of PEV sales on the fleet composition, the vehicle scrap rate is integrated into the model. There is yet to be any existing data available on the actual scrap rate of PEVs because of the technology’s relative infancy. However, estimates can be made using past studies and published data on scrap rates of all motor vehicles. The National Highway Traffic Safety Administration (NHTSA) used data from the National Vehicle Population Profile (NVPP) to create a linear regression model estimating the vehicle survival rate based on vehicle age (2006). The NHTSA model predicts a rapid decline in the survival rates, which implies a faster fleet turnover. PEVs are dependent on advanced battery technology with potentially limited lifespan. Most manufactures (e.g., Toyota) rate the battery lifespan of their popular PEV models to 100,000 miles (about 10 years). Field tests, however, suggest that PEV owners can use their vehicles beyond the rated lifespan without experiencing any significant drop in performance (Smith et al., 2011). Therefore, the PEV survival rate for later years (beyond 10 years) is still significant, albeit possibly lower than conventional ICEVs. Based on these characteristics of the

4 See Bass (1969) for a full description on Bass technology diffusion modeling.

Potential energy load requirements of an electrified transportation sector in Los Angeles: Impacts on greenhouse gas emissions

two models, this study adopts the results from the NHTSA study based on current status of advanced vehicle battery technology.

Based on new PEV sales and scrap rates, we model the changes in LA’s overall vehicle fleet population. The working-age population is again used as a surrogate to model the changes in LA’s fleet population. That is, the fleet size changes based on the percentage change in the working-age population. The initial passenger vehicle fleet size at base year 2010 is calculated based on the proportion of the city’s working age population with respect to that of the entire LA County. We use population data from the state and local agencies for calculations (CADF, 2013; SCAG, 2012). Based on these assumptions, the number of passenger vehicles in LA County at base year 2010 is estimated at 5,859,407. The corresponding number of vehicles in the City of LA is estimated at 2,263,739. Within the fleet population, the number of PEVs in operation increase as the sales increase. However, the diffusion of PEVs into the entire fleet is not entirely cumulative because of scrap rates. By incorporating the scrap rate based on the NHTSA model described previously, the total number of PEVs with LA’s vehicle fleet increases but at a slower rate than the annual sales. The results for the three PEV sale scenarios are shown in Figure 2.

Figure 2. Projected number of PEVs in operation in Los Angeles

Results. For each charging scenario, we focus on the following variables: daily system load profile and marginal carbon intensity. As described in the previous sections, utility agencies like LADWP build and plan for the summer months when energy demand is highest. Therefore, we present all modeling results with respect to LADWP’s average system load profile for the summer months (June to August) (FERC, 2013). We present results for the years 2020 and 2030.

Year 2020 LADWP’s system load profile for the off-peak charging scenario for the three different levels of PEV adoption cases (i.e., low, baseline, and high) are shown in Figure 1. The result shows that PEV charging loads have minimal effect on LADWP’s daily load profile at least in the short run. Even in the high adoption scenario, the incremental PEV energy load has negligible effect on the overall profile. This result verifies previous studies’ finding that in the short run, PEV adoption does not require any major additional electric generation sources (Parks et al., 2007;

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Kitner-Meyer et al., 2007; Schneider et al., 2008; Stephan and Sullivan, 2008). In the ideal peak charging scenario, the three PEV adoption forecasts also yield minimal impacts on the overall energy load profile (Figure 2).

Figure 3. Off-peak charging effects on LADWP’s system load profile for 3 levels of PEV adoption

Figure 4. Peak charging effects on LADWP’s system load profile for 3 levels of PEV adoption

The effects of PEV charging on GHG emissions are captured by analyzing the marginal carbon intensity of LADWP’s generation mix. We examine the average marginal carbon intensity on an hourly basis. The average grid emission intensity changes because the changing hourly energy load determines the type and amount of energy generation mix. Since electricity generated from a particular power plant cannot be assigned to a specific load, the same grid emission intensity is attributed to every load that is operating during a given hour. Therefore, the additional demand from PEV charging increases the overall emissions but can either increase or decrease the average grid emission intensity for a given hour. In the smooth off-peak charging scenario, greater PEV energy loads lower the hourly carbon intensity relative to the reference load during the off-peak charging hours (see Figure 5). However, the carbon intensity is significantly higher for these hours relative to intensity in the peak hours. In the peak charging scenario, the reverse

Potential energy load requirements of an electrified transportation sector in Los Angeles: Impacts on greenhouse gas emissions

is true (Figure 6). This implies that PEV charging during peak hours results in a lower emissions since the incremental generation has lower carbon intensity.

Figure 5. Off-peak charging effects on LADWP’s average marginal carbon intensity

Figure 6. Peak charging effects on LADWP’s average marginal carbon intensity

Year 2030 In 2030, the energy load profile shows a sharp increase during the charging periods due to the greater number of PEVs in operation. Figure 7 shows the smooth ideal off-peak charging scenario for the three PEV adoption forecasts (i.e., low, baseline, and high). In the high adoption scenario, the incremental PEV energy load may be problematic during peak demand periods during the summer. The sharp spike at 8 p.m. from vehicles arriving home and charging may exceed the upper generation capacity limits. In the ideal peak charging scenario, the three PEV adoption forecasts show that PEV charging pushes the energy load to significantly higher levels (Figure 8). In 2030, LADWP’s planned dependable generation capacity is approximately 6,482 MW so there is enough capacity to meet demand on an average day. However, the energy load from PEV may push the energy load beyond the limits during extremely hot days. For example, during some of hottest summer days, LADWP’s peak demand can reach over 6,000 MW. With

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additional load from PEV charging during peak hours, there may simply be not enough generation capacity causing blackouts or orders of energy imports that may be worse carbon intensities.

Figure 7. Off-peak charging effects on LADWP’s system load profile for 3 levels of PEV adoption

Figure 8. Peak charging effects on LADWP’s system load profile for 3 levels of PEV adoption

In 2030, the marginal carbon intensity changes significantly from 2020. In the off-peak charging scenario, the average marginal carbon intensity is lower for the off-peak charging hours (Figure 9). In the peak charging scenario, the rate is even lower for the off-peak charging hours (Figure 10). The lowering of the carbon intensity can be attributed to the complete phase out of the coal power plants from LADWP’s generation sources. Since coal has the highest carbon intensity, the additional PEV load during off-peak hours is now supplied by a less carbon intensive energy source. In the peak hours, the additional PEV load is still supplied by the same generation source keeping the carbon intensity low.

Potential energy load requirements of an electrified transportation sector in Los Angeles: Impacts on greenhouse gas emissions

Figure 9. Off-peak charging effects on LADWP’s average marginal carbon intensity

Figure 10. Peak charging effects on LADWP’s average marginal carbon intensity

Discussion. As PEV adoption increases, there are concerns over the PEV charging effects on peak demand of power in Los Angeles. In extreme cases, the additional load from uncontrolled PEV charging may push the overall system to a level beyond the grid’s capacity. The negative results would be potential blackouts, damages to grid components (e.g., transformers), higher supply costs, energy imports with greater emissions, etc. Our analysis indicates that prior to the year 2020, these concerns are inconsequential. Across all charging scenarios, the effects from PEVs on the overall system load profile are minimal and are unlikely to cause major supply disruptions. In the year 2030, however, these concerns become relevant as PEV charging requirements increase significantly beyond the grid’s capacity.

Many have proposed off-peak charging as the primary means to power PEVs because of the apparent minimal impacts on peak demand. For LADWP, however, there is a clear tradeoff between off-peak charging for peak demand management and GHG emissions. The high

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percentage of coal power in LADWP’s baseload results in a significantly higher hourly average marginal carbon intensity during off-peak hours. Therefore, additional PEV load during the off-peak period results in higher marginal emissions compared to charging during peak hours. In other words, in the short run, peak charging is preferable to off-peak charging in terms of emissions. This results runs counter to previous marginal emission studies for California that showed significantly higher carbon intensity during peak periods. Unlike the rest of California, LADWP has a high percentage of coal power and relies on hydro power for peak power demand management rather than natural gas. These differences in generation resources cause the divergence in marginal carbon intensity between California and LA. For these reasons, the current incentives to encourage off-peak charging may not be optimal in terms of GHG emission reductions. In terms of policy, the current “time-of-use” (TOU) pricing may be more economical for the power agency but results in greater GHG emissions. Furthermore, the focus on off-peak charging may actually hinder greater PEV adoption because of increasing perception of the unavailability and high costs of daytime charging. Considering its minimal impacts on peak demand in the short run, a greater push for availability and better pricing for peak charging may create greater GHG emission reductions and provide further behavioral incentives for early adopters.

In the long run, however, LADWP’s marginal carbon intensity becomes similar to the rest of California’s power systems. The complete removal of coal in 2025 changes the hourly marginal carbon intensity significantly and makes off-peak PEV charging more preferable. In such a scenario, off-peak charging lead to benefits for both peak demand management and lower marginal carbon intensity.

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Potential energy load requirements of an electrified transportation sector in Los Angeles: Impacts on greenhouse gas emissions

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