Co-Simulation of Optimal EVSE and Techno-Economic System Design Models for Electrified Fleets

Abstract

As the transition to electric mobility is expanding at a rapid pace, operationally feasible and economically viable charging infrastructure is needed to support electrified fleets. This paper presents a co-simulation of optimal electric vehicle supply equipment (EVSE) and techno-economic system design models to investigate the behaviors of various EVSE configurations from cost and technical aspects. While the system design optimization is performed for a grid-tied PV system, the optimal EVSE model considers all EVSE options which are currently installed at workplaces. To investigate the impact of EV utilization rate, three fleet sizes are considered that are generated based on real EV fleet data. Furthermore, the impact of electricity rates is also explored through an innovative EV-specific (BEV) rate and a conventional timeof-use (ToU) tariff. It is shown that investing in grid-tied renewable energy technologies for workplace charging infrastructure supply can lower charging costs. Cost savings differ from EVSE types and fleet size under the BEV rate while EVSEs display similar cost-saving behavior under the ToU tariff irrespective of fleet size. DC Fast Charging (DCFC) EVSE is found to be highly sensitive to fleet size as compared to AC EVSEs. Moreover, DCFCs make better use of the BEV rate which makes their economics competitive as much as AC EVSEs. Finally, it is found that the fleet size and AC EVSE types have a minor effect on the use of renewable energy in contrast to the DCFC case.

A Hybrid Power Heronian Function-Based Multicriteria Decision-Making Model for Workplace Charging Scheduling Algorithms

“…4 (b). The next three variables form all the cost elements of charging at workplaces, which are considered in the objective function in (3). C2 is the energy charge, representing the daily electricity consumption cost to charge all EVs as in (4).…”

“…In this respect, workplace charging stations can play an important role in ensuring the smooth integration of electric vehicle (EV) charging loads into power systems. Thanks to their relatively longer dwell times and predictable mobility, EVs at workplaces can provide ancillary services to the grid and aid in the integration of variable renewable energy sources for electricity generation [3]. However, smart charging strategies and scheduling are required to develop in order for EVs to provide such services.…”

EV Fleet Charging Load Forecasting Based on Multiple Decomposition With CEEMDAN and Swarm Decomposition

“…EV forecasting studies can be categorized into two groups: mobility or charging pattern identification and charging load forecasting [3]. Based on probability density functions such as the kernel [4] or Gaussian distributions [5], most of the studies have focused on modeling EV transportation mobility or charging behavior, which are inputs to smart charging algorithms [6]. As such, EV charging loads are better managed from both the grid perspective and EV user convenience.…”

Modeling and Data Analysis of Electric Vehicle Fleet Charging

“…Fleet electrification is expanding around the world in respond to global zero-emissions mandates. In addition to government commitments to convert public transport fleets to electrify, major companies have declared the transition to electric mobility by shifting their conventional fleets to electric vehicles (EVs) [1]. Due to this transition, the charging demand from EV fleets is expected to drive increments in peak power generation and transmission capacity [2].…”