A researcher at the University of San Diego has used energy meter-level data from the San Diego region to analyze the energy load profiles of residential customers under the time-of-use (TOU) rate with and without EV charging requirements.

Unlike previous forecasts on the effects of EV charging loads, the energy load profile of TOU customers with EVs shows a “twin demand peak” where there is a peak demand during the evening hours and another at midnight. The results, published in a paper in the journal Energy Policy, reveal potential issues for grid operations with greater EV adoption and the importance of careful TOU rate design.

City of San Diego average hourly load for the EVTOU residential rate group (Q1: Jan–Mar, Q2: Apr–Jun, Q3: Jul–Sep, Q4: Oct–Dec). Kim (2019)

Governments around the world have taken direct action to promote the adoption of the plug-in electric vehicle (EV) using new legislation, tax incentives, and other policy instruments. California’s Governor Brown has initiated the zero-emission vehicle (ZEV) mandate that targets the deployment of 1.5 million ZEVs by 2025. Rapid integration of such a large number of EVs will inevitably cause uncertainty and variability on the operation of the existing electric power system. There is high uncertainty on not only the speed and scale of EV adoption but also the EV energy and power requirements. Furthermore, there is an additional unknown human factor in the anticipated mass adoption of EVs that clouds the forecasted charging load requirements: when will EV drivers actually charge their vehicles?

These uncertainties have contributed to a condition where there is no clear roadmap on building the appropriate EV charging infrastructure that must be strategically constructed to foster a rapid, seamless transition to transportation electrification.

… The vast majority of the EV charging is expected to take place at people’s homes (i.e., residential). Therefore, the effect from residential EV charging (L1 & L2) is expected to be significant in both the total energy load and the shape of the load profile. One key takeaway from this study is that the forecast of EV charging loads primarily hinges on the assumption that EV drivers charge their vehicles based on travel patterns. That is, most EVs begin charging when EV drivers arrive at home after work during peak hours (approximately 5–8 p.m.). This study attempts to shed light on this assumption using actual meter-level energy data from EV owners. Strong evidence against such charging behavior assumption would have significant impact on the validity and accuracy of the EV charging load forecasts, which would consequently have an impact on energy policy.

—Kim (2019)

For the study, Jae Kim focused on the San Diego Gas & Electric (SDGE) service territory. SDGE is one of the three major investor-owned utilities (IOU) in California and supplies power to roughly 1.4 million businesses and residential customers in a 4,100 square-mile service area.

There are roughly 30 different billing rate groups within the residential customers in the SDGE service territory. The majority of the residential customers fall under the “DR” rate group—the default for residential customers. The DR plan is a tiered-pricing system with increasing rates with increasing usage. Each customer is given a monthly baseline usage allowance based on their location. If a customer exceeds the baseline allowance by a certain threshold, then the rates increase from Tier 1 to Tier 2. There is a range of allowable usage within Tier 2. Once a customer exceeds the Tier 2 range, then the rates increase to the high usage charge (HUC) level.

The baseline allowance and the high usage charge threshold vary depending on the region and time of year.

SDGE also offers time-of-use (TOU) pricing plans to its residential customers. In a standard TOU plan, each day is broken into on-peak and off-peak time zones with energy costing less during the off-peak hours. Similar to the standard DR plan, the TOU plans are also tiered plans so if a customer exceeds the baseline allowance by a certain threshold, the rates increase.

Within the TOU group, there are also specific rate groups that are designated explicitly for residential customers who charge an EV(s) on the premise (EVTOU).

Kim found that in the standard TOU load profiles, there is a single peak load during evening hours across all regions similar to the region’s non-TOU load profile. However, in the EVTOU load profiles, there are twin peak loads with one of the peaks occurring at midnight when the super off-peak period begins.

The load also decreases rapidly from midnight to a leveled minimum load within a couple of hours. The explicit choice of midnight as the time when the super off-peak period begins has a direct impact on the occurrence of the second peak demand. Since most EV chargers are controlled easily using a smartphone app, it is highly plausible that EV owners would simply shift the charging activity to the changes in the TOU rate structure. For example, if SDGE were to change its super off-peak period start time from midnight to 2 a.m., then the demand peak would simply shift as well to 2 a.m. Therefore, careful consideration of the TOU rate design is critical to shaping the load profile as more EVs are adopted and charged at homes. Poor TOU rate structure design would lead to unintended consequences such as new peak demand and rapid ramping requirements.

… Results from this study clearly show the importance of TOU rate structure design with respect to EV charging loads. Poor design of the rate structure can lead to the rise of new energy peaks and other unintended consequences. The necessary next research step is the modeling and simulation of the EV charging loads as a function of different TOU rate structure design and technology adoption rate.

For example, the SDGE service territory is expected to experience a significant growth of EVs over the next decade. As more EV drivers are on the road, there will be greater energy demand at the residential sector. An analysis on the potential energy load profiles based on different TOU rate structures across regions and EV charging load requirements would give insights on how to best design the rate structure to induce the best charging behavior for better grid operations.

—Kim (2019)

Resources

• Jae D. Kim (2019) “Insights into residential EV charging behavior using energy meter data,” Energy Policy, Volume 129, Pages 610-618 doi: 10.1016/j.enpol.2019.02.049