Primary Outcomes (explanation)
A driver's share of total charging on campus is calculated as the ratio of energy consumed from campus charging to the expected energy consumed from total driving. The latter is calculated per driver-reported odometer readings and Department of Energy vehicle efficiency estimates. Odometer readings depend on responses from divers within our on campus EV club; because readings are not reported by all drivers we have a sample size smaller than the group size. The remaining five variables are collected directly from charging session data collected by chargers and encompass all drivers on campus who charge. However our sample is largely restricted to drivers within our EV club where we can observe demographic variable in addition to all of their on campus charging behavior. We can also observe the time for each of these five remaining variables allowing us to also look for changes in timing of sessions.
Our primary interest is how the expansion of an EV charging network changes drivers' charging behavior as well as the policy implications of such changes. “Behavior” has three main components: 1) decisions about where to charge, measured as the share of charging done on campus; 2) when on campus, decisions about when to charge, measured as the hours of the day over which charging occurs; and 3) the “depth” of charging sessions, measured as the energy consumed relative to the EV's battery capacity. These three components of charging behavior have implications for the greenhouse gas emissions from charging, cost recovery to the EV network host, and congestion on the electric grid.
In California, daytime charging is associated with substantially fewer emissions compared to overnight charging as well as lower grid congestion. During the night, California electricity is derived primarily from natural gas power plants, while solar generation peaks during the daytime. Yet the majority of EV charging nationally and in California occurs at home overnight. Shifts toward greater campus charging, induced for example by installing new chargers, therefore generate social benefits through avoided damages from emissions. As with any such positive externality, a common policy response is to subsidize the good in question.
Workplace charging generates revenues for the site host through the sale of electricity and, in California, through the California's Low Carbon Fuel Standard (LCFS) program. At the same time, shifting charging to hours in the day when site's electric load is low reduces the site's demand charges. The size of these private benefits depends on the total charging done at the site as well as the timing and depth of charging. Drivers who recoup energy via fewer longer charging sessions (compared to more frequent shallower sessions) increase overall network efficiency because they minimize the time that vehicles sit idle in EV stalls and prohibit others from charging.
Lastly, shifting charging to hours in the day when system electric demand is low reduces grid congestion both locally, potentially deferring costly capacity upgrades, as well as on the transmission system, thereby reducing congestion costs in the wholesale electricity market.
Our interest in the effects of network expansion therefore cover both the intensive and extensive margins of charging. The intensive margin measures if drivers who currently charge on campus increase their use of the network post-expansion; the extensive margin measures if drivers who did not previously charge on campus are induced to do so. From these shifts in behavior, we can calculate social benefits derived from avoided emissions and the social cost of carbon. Furthermore, we can calculate private benefits to the network host using equivalent annual costs of installing and maintaining charger infrastructure to calculate the net annual benefits/costs of the expansion.