Measures which aim to reduce energy consumption and greenhouse gas (GHG) emissions by promoting the increase in energy efficiency of end-use applications, the temporal shifting of electricity use, or the use of electricity over other fuels have an impact in the grid load profile. That is the case with the introduction of electric vehicles (EVs) in a fleet, which increases electricity demand by shifting the energy source used for transportation from mainly petroleum-based fuels to electricity.
Most life-cycle assessment (LCA) studies on EVs assumed that EVs are part of the total load of the system and used average emission factors for electricity supply to assess EV environmental impacts (e.g., [1
]). These studies have found that vehicle use dominates over the vehicle production phase regarding energy consumption and GHG emissions, particularly for fossil-based electricity mixes [8
]. Some studies also addressed the influence of the charging profile in the GHG emissions of EVs, with some reporting that EV charging during off-peak hours results in lower emissions than in peak hours [9
]. On the other hand, a smaller group of studies looked at EVs as a new load added to the electricity system and assessed how the system would respond to this change by determining the marginal electricity supply and corresponding emissions (e.g., [12
]). The different approaches used to determine electricity emissions often lead to very distinct results, which is problematic because of the importance of electricity emissions to the overall EV impacts [16
In this article, we assessed both marginal and average GHG emission factors for the Portuguese electricity system using historical data for recent years (2012–2014) and applied them to the assessment of the introduction of EVs in Portugal in subsequent years (2015–2017). Marginal emission factors (MEFs) describe the GHG intensity of the marginal generators in the system, i.e., the last generators to follow demand at a given time and the first to respond to a change in demand [17
]. MEFs for electricity generation in Portugal were assessed following an empirical approach based on regression of historical data that implicitly accounts for operation constraints and allows for a flexible temporal resolution. This approach was proposed by Hawkes [18
] for estimating marginal emission factors for Great Britain to determine the CO2
reduction performance of demand-side interventions. The author calculated linear regression coefficients of change in the system CO2
emission rate versus the change in total system demand [18
]. Based on this approach, Siler-Evans et al. [17
] calculated MEFs for CO2
, and SO2
for the U.S. and further estimated the share of marginal generation from fossil-fired generators. Zivin et al. [12
] took the calculations from Siler-Evans et al. [17
] further by accounting for the effects of electricity trade within U.S. regions. Although these studies only assessed direct emissions, the empirical approach to derive MEFs is valid for determining the short-term marginal technologies in LCA without requiring sophisticated simulation models [12
The empirical approaches to derive MEFs have been applied to assess the impacts of several demand-side interventions, such as efficiency improvements in lighting systems [17
], utilization of microgeneration technologies for residential heating [18
], and the deployment of distributed solar systems [12
]. MEFs have also been used to assess the impacts of EVs, mainly in the U.S. [12
]. It was found that depending on the time of day, the response of the electricity system to EV charging can be distinct, thus influencing EV GHG emissions [12
]. In order to identify charging strategies that minimize environmental impacts from EVs, it is important to understand how marginal emissions from electricity generation vary over time. Because electricity generation also varies geographically (e.g., due to different technology portfolios, availability of renewables) [19
], the assessment of marginal emissions needs to be performed considering the specific electricity system affected by the intervention.
This article aims to assess the change in GHG emissions resulting from: (i) increasing electricity demand by 1 MWh in Portugal; and (ii) introducing battery electric vehicles (BEVs) in the Portuguese light-duty fleet in 2015–2017. Portugal was chosen as a case study for its favorable conditions for EV deployment (charging network in place and policy incentives for buying EVs). Historical hourly generation data and corresponding emissions from 2012 to 2014 were used to estimate marginal GHG emissions. Trends in marginal emissions regarding electricity demand, time of day, and month were explored, and a comparison between average and marginal emissions provided. Marginal emission factors for electricity generation were then applied to assess the effects of the introduction of BEVs in Portugal from 2015 to 2017 for a range of displacement and charging scenarios. The marginal emission factors provided are suitable for assessing changes in the operation of the electricity system in the near term beyond the applications presented in this article.
Marginal greenhouse gas (GHG) emissions of electricity generation in Portugal were assessed, aiming to understand the impact of activities that affect electricity demand in the near term, namely the introduction of BEVs in the Portuguese light-duty fleet for a range of displacement and charging scenarios.
Coal and natural gas were identified as the marginal energy sources, but their contribution to the margin depended on the hour of the day, time of year, and system load, causing marginal emission factors to vary significantly. Increasing electricity consumption during off-peak hours was found to induce a higher increase in GHG emissions than in peak hours, due to a higher contribution of coal to the margin. In periods of low demand or high hydro availability, coal is often the marginal technology to the detriment of NGCC, as a result of the currently lower fuel operation costs.
For an electricity system with a high share of non-dispatchable renewable power and excess capacity in the near term, such as the Portuguese system, marginal emissions are considerably higher than average emissions. Increasing electricity generation generally means increasing fossil-based generation (either coal or natural gas), resulting in higher emissions than the renewable-based average. For the Portuguese system, marginal GHG emissions can be up to 58% higher than average emissions, considering the time of day. When the goal is to assess the GHG emissions of implementing a technology which entails a change in electricity consumption (may it be increasing or decreasing consumption), marginal emission factors should be used. Because marginal effects have a distinct and larger magnitude than the average behavior of the electricity system, using average emission factors to assess the impacts of implementing a new technology which uses or displaces electricity can underestimate the burdens or the savings achieved.
The application of the model to assess the GHG effects of BEVs in Portugal showed that BEVs induce, in the near term, a much higher burden than an average approach can depict. Even considering the displacement of ICEVs, BEVs increase overall GHG emissions in the majority of scenarios. However, BEV effects on GHG emissions are very dependent on the time of charging and on the assumptions about the displaced technology, including the activity level of both BEVs and displaced ICEVs.
As a result of the temporal variability in the marginal electricity supply, the time of charging can have a major influence on the GHG benefits of BEVs in the near term. What has been considered, in general, the most favorable charging time from the economic standpoint and operation of the electricity system perspective (off-peak hours), may not be so from an environmental standpoint. In Portugal, simply encouraging charging during the night may result in a higher increase in GHG emissions from the electricity system as a result of the coal-based marginal electricity supply. Therefore, understanding how marginal emissions from electricity generation vary over time is crucial in the design of charging strategies that minimize environmental impacts from EVs.