Cost Reduction of School Bus Fleet Electrification With Optimized Charging and Distributed Energy Resources Preprint William Becker, Eric Miller, Partha Pratim Mishra, Rishabh Jain, Dan Olis and Xiangkun Li National Renewable Energy Laboratory Presented at the 2019 North American Power Symposium Wichita, Kansas October 13–15, 2019 NREL is a national laboratory of the U.S. Department of Energy Conference Paper Office of Energy Efficiency & Renewable Energy NREL/CP-7A40-74187 Operated by the Alliance for Sustainable Energy, LLC March 2020 This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. Contract No. DE-AC36-08GO28308 Cost Reduction of School Bus Fleet Electrification With Optimized Charging and Distributed Energy Resources Preprint William Becker, Eric Miller, Partha Pratim Mishra, Rishabh Jain, Dan Olis and Xiangkun Li National Renewable Energy Laboratory Suggested Citation Becker, William, Eric Miller, Partha Pratim Mishra, Rishabh Jain, Dan Olis and Xiangkun Li. 2020. Cost Reduction of School Bus Fleet Electrification With Optimized Charging and Distributed Energy Resources: Preprint. Golden, CO: National Renewable Energy Laboratory. NREL/ CP-7A40-74187. https://www.nrel.gov/docs/fy20osti/74187.pdf NREL is a national laboratory of the U.S. Department of Energy Conference Paper Office of Energy Efficiency & Renewable Energy NREL/CP-7A40-74187 Operated by the Alliance for Sustainable Energy, LLC March 2020 This report is available at no cost from the National Renewable Energy National Renewable Energy Laboratory Laboratory (NREL) at www.nrel.gov/publications. 15013 Denver West Parkway Golden, CO 80401 Contract No. DE-AC36-08GO28308 303-275-3000 • www.nrel.gov NOTICE This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy and Eaton Corporation.. The views expressed herein do not necessarily represent the views of the DOE or the U.S. Government. 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Cost Reduction of School Bus Fleet Electrification With Optimized Charging and Distributed Energy Resources William Becker Eric Miller Partha Pratim Mishra National Renewable Energy Laboratory National Renewable Energy Laboratory National Renewable Energy Laboratory Golden, Colorado Golden, Colorado Golden, Colorado Email: [email protected] Email: [email protected] Email: [email protected] Rishabh Jain Dan Olis Xiangkun Li National Renewable Energy Laboratory National Renewable Energy Laboratory National Renewable Energy Laboratory Golden, Colorado Golden, Colorado Golden, Colorado Email: [email protected] Email: [email protected] Email: [email protected] Abstract—Considerations for electrifying school buses are coincides with the peak building loads, increased demand presented with an analysis of battery sizing to match bus-driving charges are incurred. There is an opportunity to reduce energy requirements. The charging and vehicle-to-building dispatch of and demand charges by implementing smart-charging controls the electric school buses were optimized to evaluate the potential and by integrating distributed energy resources (DERs) along to reduce the impact of the bus charging on the school’s electric with vehicle electrification. utility bill. Distributed energy resources and flexible building loads were also considered with the school bus electrification to REopt™ is a techno-economic optimization model devel- evaluate the further reductions in energy costs with enhanced oped by the National Renewable Energy Laboratory (NREL). systems integration and optimized dispatch. The effect of degra- It is a planning tool formulated as a mixed-integer linear dation on the school bus batteries was analyzed to determine program to optimally size and dispatch the DERs and storage- if the smart-charging and vehicle-to-building battery operation based assets given the historical building loads and rate tariffs decreases the life of the battery. The results show that there is an opportunity to mitigate the increase in electric utility bill for a specific site [3]. The range of loads, technology resources, with improved charging controls and bi-directionally operating drivers, and outputs considered in the model are shown in the school bus batteries. The battery degradation analysis using Fig. 1. The most common objective function of the model dispatch with optimized charging and discharging shows that is to minimize the life-cycle cost (LCC) of energy. In this acceptable battery life remains. work, solar photovoltaics (PV), stationary battery, and chilled water tank thermal energy storage (TES) technologies are Keywords—Electric Vehicle, School Bus, Fleet Electrification, Distributed Energy Resources, smart-charging, Vehicle-To-Grid. modeled. The addition of the electric school bus fleet into the REopt model is a key innovation of this work that enables an integrated optimization of DER technologies and smart- I. INTRODUCTION charging EV batteries to minimize life-cycle energy costs. The electrification o f m edium- a nd h eavy-duty vehicles has numerous societal benefits. W hen c ompared t o their diesel counterparts, electric vehicles (EVs) are quieter, reduce combustion product emissions to zero, and are mechanically simpler, making them easier to maintain. Diesel school buses have been shown to increase the exposure of school children to aerosol pollutants including diesel exhaust particulates [1]. Ex- posure to these particulates is linked to weezing and decreased lung function [2]. Additionally, reduced maintenance needs benefits s chool d istricts, w hich o ften l ack t he infrastructure to make major repairs to their fleets. Although the electrification o f v ehicles p rovides savings by eliminating diesel fuel and reducing maintenance costs, the electricity required for charging is an added cost. Commercial scale buildings such as schools typically have utility rates that include both energy and demand charges (potentially dependent on the time of use). If charging occurs during peak Fig. 1: REopt techno-economic optimization tool. Source: [3] electric rate times, this energy can be expensive. If charging 1 This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. In any EV application, it is important to understand how TABLE I: Drivetrain Model Parameters long the batteries will last given the specific usage scenario because batteries are by far the costliest component in an Symbol Value Units Description EV [4]. Significant advances have been made on lithium- m 15195 kg Vehicle mass Crr 0.008 - Rolling resistance coefficient ion (Li-ion) battery life-degradation modeling and analysis C 3.8 N Drag coefficient dl (m=s)2 with the underlying literature spanning research topics from rtr 4.5/1.0 - Transmission gearing (2-speed) understanding battery-aging mechanisms [5] to developing ηtr 0.98 - Transmission efficiency mathematical models for health-conscious control applications rdf 4.5 - Differential gearing ηdf 1 - Differential efficiency [6]. In this paper, one objective is to analyze the impact on the long-term health of the EV batteries from controlling and dispatching them in ways to reduce the school’s electric utility school anywhere in the United States. The GPS coordinates bill. and elevation of each bus were logged every second. From The case study presented is a school in New Jersey. Various these measures, vehicle speed, acceleration, and road grade scenarios are analyzed as illustrated in Fig. 2. The analysis were calculated. The vehicle drivetrain model is a variant assumes that buses are parked at the school when they are not of NREL’s FastSim vehicle simulation tool [7]. This type of driving. While buses are often parked at depots for fueling and model is called backward-looking, because it uses recorded maintenance, the results of this analysis show that there may be driving profiles to calculate the energy expenditure of the an economic incentive to park EV buses at schools so that they vehicle working backward through the drivetrain. The benefit can be used to manage utility bills and potentially contribute of these models is that the they do not involve any iteration, to resilience. Scenario 1 represents the business as usual case, making them faster to execute. The drivetrain model is de- where the school purchases all electricity from the utility, and scribed in (1), and further in [1], with vehicle parameters listed uses diesel buses. Scenario 2 considers opportunities for utility in Table I: bill reduction through DERs, but still uses diesel buses. In θ dv 2 −!