Quantifying the Electric Grid Cost Savings of Increasing E-Bike Mode Share
Research Team: Michael Hyland (lead), Brian Tarroja, and Kate Forrest
UC Campus(es): UC Irvine
Problem Statement: Electrifying vehicles to meet transportation demands is critical to support the transition away from fossil fuels. The replacement of fossil-fuel powered vehicles with battery electric vehicles (BEVs) is expected to play a major role. The mass deployment of BEVs without reducing vehicle-miles-traveled, however, will increase the peak load on the electric grid, requiring more costly upgrades to electrical system supply and distribution capacity. While improved smart grid management can mitigate these expenses, the real-world acceptance of smart charging programs by BEV drivers is uncertain. Since most vehicle trips are less than five miles, an alternative method to mitigate increases in peak electric loads would be substituting more energy-efficient transportation modes, such as electric bikes (e-bikes), for future BEV trips. While prior studies have analyzed various benefits of increasing the market share of e-bikes – including shorter travel times in congested cities, mental and physical health benefits, and lower emissions – no studies have quantified the electric grid infrastructure cost saving benefits of increasing e-bike mode share.
Project Description: This project will develop a model to quantify the extent to which substituting e-bikes for BEVs reduces grid peak loads imposed by vehicle electrification and analyze the equipment needs and monetary costs associated with needed upgrades. To accomplish this, the researchers are focusing on an analysis for the San Diego region. First, they will analyze vehicle trips in the San Diego region using the San Diego Association of Government’s agent-and-activity-based travel model system. Using this data, they will identify when, where, and for how long BEVs are parked and available for charging to ensure that these vehicles can meet all their intended trips. Second, the team will develop an in-house electric vehicle charging model to translate trip data to produce hourly electric grid load profiles for different vehicle types and infrastructure scenarios, over an entire year. Lastly, the researchers will determine how substituting e-bikes for BEVs for different trip types and distances affects the annual peak electric grid load and estimate the cost savings in upgrading the electric distribution infrastructure in two representative neighborhoods—one single-family oriented and one multi-family oriented.
Status: In Progress