Grossmont Union High School District (GUHSD), one of the largest school districts in San Diego, California, partnered with ENGIE Impact to plan the electrification of their school bus fleet. Through the partnership, GUHSD’s technical and financial needs were assessed including the analysis of suitable grants to support with the transition. This complex project will be deployed progressively over the next 20 years.
Building on its leadership in environmental responsibility, GUHSD decided that the time is now to initiate the process of fleet electrification. The District partnered with ENGIE Impact to collaborate on addressing technology and market barriers. To start, electric school bus and charging station technologies are complex and rapidly evolving. Developing a zero-emission fleet involves many considerations including, but not limited to:
Furthermore, the school must deal with a very fragmented market of different manufacturers, suppliers and vendors selling different components of the required full solution.
The need to transition from a conventional to an electric school bus fleet is complex, requiring the coming together of several technologies, business solutions, and partners. ENGIE Impact tapped into our global experience, to define three key criteria to develop a blueprint for the electrification of GUHSD’s fleet at the minimal cost.
Our experts worked with the school district to identify a diverse and representative group of stakeholders, whose input was necessary to ensure a successful fleet transition. Examples of the internal stakeholders included the heads of energy services, fleet services, as well as student representation. External stakeholders included representatives from the district’s local utility and City Council members.
We created a plan to transition the bus fleet from diesel to electric over 20 years, a suitable period to allow the district to stretch out costs, implement innovative technology, and refine best practices from an iterative process. The plan calls for the fleet to transition to electric vehicles (EVs) in four phases, with each phase taking from two to 10 years. The first phase, for example, calls for 19 buses swapped out for electric replacements between 2021 and 2023. The last phase stretches over 10 years, from 2030 to 2040, and includes a final group of 20 buses.
The school district’s priorities were to make the transition as seamless as possible for students while keeping operating costs under control. Our team embarked on a detailed market scan and developed an extensive database of electric vehicles, charging stations and distributed energy resources assets, to select the most cost-competitive options. While the 20-year period did offer important advantages, it also meant the district would have to manage a changing mix of vehicles with different fueling needs on rigid timetables and schedules.
ENGIE Impact helped GUHSD develop an actionable step-by-step blueprint that minimizes the total cost of ownership and maximizes GHG emissions reductions. The plan will enable the district to cut Scope 1 and Scope 2 emissions by nearly 90%.
The analysis first required collecting and analyzing operational data specific to the School District fleet.
Each fleet route is assigned a dedicated bus, and the assigned bus size is dependent on the typical number of students that this route has historically served. An hour-by-hour weekly schedule is simulated for each bus route, throughout both the summer and winter seasons. A one-hour buffer after each trip as well as a one-hour buffer before the afternoon trip is assumed, in order to account for potential delays in the trip start and end times. This conservative assumption reduces the time available for bus charging. Furthermore, we assume that buses are required to be at least 90% charged by 5:00 am each day Monday through Friday, and they should be at least 10% charged at any point in time. These constraints on state-of-charge (SOC) are imposed both to ensure robust operations and to adhere to manufacturers’ recommendations around the battery’s healthy SOC. When buses are not serving their route, they are available to charge at the bus depot. To avoid operational complexity, each bus is assumed to have a dedicated parking spot and charging spot.
When evaluating any diesel-to-electric transition, it is essential to benchmark the performance of different electric buses. We compared performance factors like battery capacity, charging rate, charging compatibility, and the expected lifetime of each bus. For this analysis, ENGIE Impact leverages its extensive database of electric vehicles, including electric school buses, updating information where needed to track the evolution in product offerings and specifications in the market. Overall, we considered 19 different electric bus models, which can be used over the next 20 years to replace existing diesel buses within GUHSD’s fleet. All bus models are assumed to be compatible with Alternating Current (AC) Level 2 and Direct Current Fast Charging (DCFC), which is predominantly true today. Bus models are continuously and rapidly improving to accommodate both options. The lifetime of each bus is based on the duration of the warranty, this is a conservative approach to estimating the operational lifetime of each vehicle. Furthermore, a constant annual battery degradation is assumed for each bus.
Electric bus fleets require different fueling infrastructure than diesel buses, notably charging stations at a depot or along routes. Charging stations vary in type and charging capacity. Since the range, and corresponding energy consumption of electric buses varies from one vendor to another, selecting charging stations would be contingent on the chosen buses. Similar to the buses we leveraged our extensive database of electric vehicle charging infrastructure for the School District, considering 22 different EV charging station models with a wide range of maximum charging rates, which can be used over the next 20 years for bus charging. The lifetime of each charging station is based on the duration of the warranty. With most charging stations between one and six years. In terms of economics, a clear distinction should be made between the cost of the charging station itself (box) and the associated EPC (engineering, procurement, and construction cost). Furthermore, the operational cost of each charging station should consider service and maintenance, as well as networking fees for smart/managed charging.
We considered a wide range of technologies and economic variables to ensure cost-effective and reliable energy supply to recharge the buses. When investigating consuming electricity from the local utility grid, it’s important to accurately identify the utility billing rate that will be used. In this case, we consider a special time-of-use (TOU) rate that is specific for commercial EV charging. In addition, the analysis considers six options for distributed energy resources (DER): four on-site solar PV technologies, and two stationary battery storage technologies. Cost estimates related to both on-site solar PV systems and battery storage systems are based on ENGIE’s market estimates and informed by actual project experience. These estimates include full installation costs, which is important to compare the fully loaded cost of energy from DER vs. utility rates from the grid. Furthermore, to ensure accurate valuation of energy costs, we model the detailed accounting rules associated with net energy metering (NEM). NEM is a utility-billing accounting mechanism that compensates a utility customer for excess electricity exports to the grid from on-site DERs. NEM rules vary widely across utilities and across states.
One of the primary drivers for transitioning to an electric bus fleet is reducing greenhouse gas (GHG) emissions and their associated environmental and health impacts. The total reduction in GHG emissions is analyzed over the 20-year lifetime period for each fleet electrification phase, by comparing the carbon intensity of the original diesel buses to those of the new electric buses powered by the grid and on-site renewable energy. The GHG emissions from the electric fleet are computed by multiplying the hourly energy consumption with an hourly average carbon intensity factor for the electricity mix fueling the buses. The average carbon intensity factor for the electricity mix should take into account the fraction of electricity coming from the grid as well as the fraction of electricity coming from on-site solar.
The analysis was conducted using a digital tool developed in-house by ENGIE Impact, called Prosumer. Prosumer is a multi-objective optimization tool that comes with a library of reference data. Through a robust platform, Prosumer considers the existing energy and mobility infrastructure, the new energy and mobility demand profiles that need to be satisfied, and the list of energy and mobility hardware technologies to consider. From that, Prosumer calculates the optimal investment strategy in mobility and energy assets for a predetermined project lifetime.
The analyses seek to minimize the total cost of ownership and CO2 emissions for the whole fleet over a total project lifetime of 20 years. This means having the optimal number and model of electric buses, as well as determining the optimal number of chargers, mix of charger types, and integration of charging with energy supply from both the grid and/or DERs.
While rooted in and based on real technological options for buses, charging stations, and distributed energy resources, the modeling emphasizes conceptual technological and economic specifications, e.g., bus battery range, bus seating capacity, and EV station charging power; the specific product brand or vendor identity is deprioritized. The vendor selection is less relevant to the fleet’s planning and blueprinting effort and becomes more relevant during the subsequent procurement process.
The analysis identifies the optimal number and sizing of the electric buses, charging stations, on-site solar PV, and battery storage systems to fulfill all mobility needs under the lowest total cost of ownership. Most notably, transitioning the GUHSD fleet to electric buses reduces its total GHG emissions by 86%.
For buses, the work shows that all routes can be served with electric buses using models that are commercially available today. The buses’ size range from 24 to 83 seats, with a driving range between 75 miles (84 kWh battery) and 138 miles (226 kWh battery). To power these buses, Prosumer selected three types of chargers, with a maximum charging rate at 22.5 kW; only 10% of the routes needed DC Fast Charging stations beyond 19.2 kW.
The analysis shows that charging the buses is mostly done during the day and late at night, using both the grid and on-site DERs. Roughly 24% of the total energy used to charge buses comes directly from on-site solar PV, while 76% comes from the grid; however, even when charging from the grid, the vast majority of energy is from solar credits. So long as the accounting for on-site solar generation follows the utility NEM rules, the investment in on-site battery storage is not favorable. In essence, NEM allows using the grid as a limited virtual storage system: credit surplus accumulated during weekends and in the summer is used to cover energy deficits and charge the buses on weekdays and in the winter.
Adding the nameplate capacity of all required charging stations shows that the theoretical grid capacity needed to charge the buses is about 1 MW. However, the maximum grid peak load is only about 50% of the theoretical capacity. This is because Prosumer models the optimal charging behavior of the whole fleet to minimize TCO, including demand charges, so not all buses will be charging at the maximum rate at the same time.
The capital expenses (CAPEX) consist of 91% electric bus, 3% charging stations, 6% on-site solar PV. The operating expenses (OPEX) consist of 71% energy supply from the grid, 13% maintenance and networking fees for the charging stations and 16% maintenance for solar PV.
Visualization of the hourly charging profile for the 3rd phase of fleet transition (10 buses total) on their respective routes during the summer season.
The model optimizes charging hours for the whole fleet, to meet GUHSD goals of minimizing TCO and GHG emissions.
The model identified the lowest maximum charging level required and subsequently, created a schedule to charge the buses at or below this level whenever possible. In the middle of the day, solar is used to charge the buses directly, when the buses are in the depot between their morning and afternoon trips. Grid electricity is used to fulfill remaining charging needs after midnight, and in the evening when needed. The energy needed by the various routes would be first sourced during super off-peak charging time periods (early mornings), followed by off-peak (mid-days and late nights) and, as a last resort, on-peak time periods (mornings and evenings).
The School District was advised to procure a portfolio of buses and charging stations that meet the fleet’s mobility needs. The District was also advised to install on-site solar PV, which helps fulfil the majority of charging needs in the middle of the day.
Visualization of solar energy generation and consumption associated with the 3rd phase of fleet transition (10 buses total).
To minimize the cost of energy, and therefore minimize total cost of ownership, the model shifts some of the optimal charging behavior to ensure maximum utilization of solar energy. Electricity produced from solar is either directly consumed by the buses or is fed back to the grid to generate energy credits. Optimal charging requires energy credits to offset energy expenses as closely as possible.
Of the total 302 MWh of electricity generated by the PV system each year, about 66 MWh are used to directly charge the buses, and 236 MWh are fed into the grid to generate NEM credits. Those credits fall under the three tiers. All three tiers of credits seem to be effectively balanced ensuring maximum utilization of credits throughout the year. Only 8% of the credits are not utilized and lost at the end of each year.
The School District was advised to optimize the size of the solar system such that the accounting of NEM credits can significantly offset the fleet energy costs. The District was also advised to collaborate closely with the local utility to ensure long-term visibility into utility billing and crediting. Furthermore, the School District was discouraged from installing battery storage due to the lack of economic appeal, given the utility rate structure.