Operation Pattern Analysis of Electric Vehicle Taxis
considering Electricity Market Price
Raiki Morisako, Masahiro Mae, Ryuji Matsuhashi
Department of Electrical Engineering and Information Systems
The University of Tokyo
Tokyo, Japanraikimorisako@gmail.com, {mae, matu} @enesys.t.u-tokyo.ac.jp
Abstract— Demand response plays a vital role in the optimal useof renewable energy resources. This paper aims to identify themost efficient operational patterns for taxi operatorstransitioning to electric vehicles (EVs). To achieve this, this paper
compares the fuel expenses of traditional vehicles against the costsof electricity, optimized for charging opportunities. The resultshows that employing night shifts and operating at half capacity
are the most effective strategies for minimizing energy costs.
Consequently, the adoption of EVs for demand response not only
provides financial incentives for transportation operators but
also supports demand management and enhances the efficientutilization of renewable energy.
Index Terms—Electric vehicles, Demand Response, Electricity
Market, Numerical simulation, Feasibility Study

TABLE I TYPES OF ENERGY COSTS
Types ofEnergy CostsLPG
SimpleCharging
Optimized
Charging

Description
Fuel cost for traditional internal combustion engine
vehicles running on LPG
Charging electricity cost when charging is done after theoperation hours
Charging electricity cost minimized through optimization

TABLE II COMPARISON CONDITIONS
Comparison Conditions
Vehicle Operational Patterns
Optimization Target PeriodI.

INTRODUCTION

This paper investigates the adoption of electric vehicles(EVs) for demand response to reduce the curtailment of
renewable energy production. Renewable energy output
fluctuates significantly throughout the day and in response toweather conditions, often leading to excess supply over demandduring daylight hours, which necessitates the reduction ofoutput [1]. Utilizing EV battery storage for demand response
emerges as a crucial strategy to mitigate such reductions [2] [3].
This research focuses on the application of EVs bytransportation operators managing large fleets.
Previous studies have concentrated on specifictransportation operators, examining demand response andVehicle to Grid (V2G) systems. For example, research by
Yoneda [4] simulate an EV charging and discharging energy
management system within a car-sharing service, forecasting
electricity prices based on the Japan Electric Power Exchange(JEPX) and evaluating a preference model for car-sharing. Thereasons why this study focused on taxis are: there are manystudies on V2G, but few studies that focus on specifictransportation providers; the taxi fleet has a high utilization rateof approximately 50%, so there are many waiting vehicles,
which is likely to be effective for demand response; and there
are few cost comparisons with Liquefied Petroleum Gas (LPG)
used for taxis, rather than with electricity or oil costs.
The contribution of this paper is verification of energy costs
linked to the use of EVs by taxi operators, considering theunique traits of the taxi industry. Characterized by diverse

979-8-3503-8174-0/24/$31.00 ©2024 IEEE

Vehicle Utilization Rates Twopatterns

Description
Three patterns: day shift, night shift,
and alternate day shifts
Three months starting from February,
May, and August 2023
100% or 50%

operational patterns, our verification process considers thesevariations [5]. Moreover, the study incorporates dynamic
pricing analysis, where electricity rates are subject to change
based on supply and demand equilibrium [6]. This paper
calculates and verifies different energy costs, including LPG
fuel prices and electricity rates, following various operational
patterns, target periods, and utilization rates.
The outline is as follows: In Section II, the problem that is
considered in this study is formulated. In Section III, thedeveloped approach for verification is presented. In Section IV,
the results of the verification are verified by the numericalsimulation. In Section V, conclusions are presented.
II.

PROBLEM FORMULATION

A. Energy Costs
The main goal of this paper is to determine the most costefficient operational strategies for taxi operators transitioning toEVs, focusing on energy expenses. We formulated anoptimization problem designed to reduce the charging
electricity costs for a single EV taxi, considering electricity
market prices. Through this, we simulate the potential savingsin energy costs that can be achieved by optimizing charging

Figure 1 Variable Electricity Retail Rates in May 2023

Figure 2 SOC transition during simple recharging in daytime day shift
with full utilization rate

TABLE III OPERATIONAL PATTERNSOperational Patterns

Description

Day Shift

7:00 ~ 16:00 with 1 hour breakNight Shift

18:00 ~ 27:00 with 1 hour break

Alternate Day Shift

8:00 ~ 28:00 with 3 hours breakpractices. The energy costs we compared are presented inTABLE I .
Additionally, the scenarios under comparison are outlinedin TABLE II, with details on each scenario to be discussed
subsequently. We will perform a comparative analysis ofenergy costs for each scenario.
B. Electricity Market Prices
In this paper, the electricity rates for EV charging aredetermined by dynamic pricing, which is tied to the market
prices at JEPX. Specifically, the electricity rates for EV
charging were calculated as the area prices from JEPX plus atransmission charge, assumed to be 5 JPY/kWh per kilowatthour (kWh). For rapid charging scenarios, our cost calculations
were based on existing service charge models. These models do
not rely on electricity market prices but instead use a systemthat calculates charges based on the duration of use.
The illustration below depicts the trend in electricity rates
for February 2023. A horizontal line in Figure 1 indicates theprice per kWh for using rapid charging services.
C. Operational Patterns
Typical taxi operators utilize a variety of vehicle operational
patterns; therefore, this paper examines different patterns toevaluate how variations in vehicle operation impact theelectricity costs associated with EV driving. Taxi operation
patterns can generally be divided into three categories: day shift,night shift, and alternate day shifts. In smaller cities and rural
areas, day shifts are predominant, whereas in larger cities,
where street-hailing is more frequent, alternate day shifts are
more common. Although the exact operating and break times
might differ across operators, this paper characterizes each
operational pattern as TABLE III.:
Figure 2 illustrates the transitions of the State of Charge(SOC) throughout a day shift (full operation) when using
simple charging methods. Horizontal segments of the graphsignify "idle" periods, downward slopes denote "driving"
periods, and upward slopes indicate periods of "regularcharging" or "rapid charging."

TABLE IV SYMBOLS IN CHARGE OPTIMIZATION
Name
𝑠𝑙𝑜𝑡𝑠_𝑝𝑒𝑟_𝑑𝑎𝑦
𝑡𝑜𝑡𝑎𝑙_𝑠𝑙𝑜𝑡𝑠
𝑆𝑖𝑑𝑙𝑒 [𝑖]𝑆𝑟𝑢𝑛 [𝑖]𝑆𝑟𝑒𝑔𝑢𝑙𝑎𝑟 [𝑖]𝑆𝑞𝑢𝑖𝑐𝑘 [𝑖]𝑆𝑂𝐶 [𝑖]
𝑆𝑂𝐶𝑚𝑎𝑥𝑆𝑂𝐶𝑚𝑖𝑛𝑆𝑂𝐶𝑠𝑡𝑎𝑟𝑡
𝑇𝑠𝑡𝑎𝑟𝑡𝑇𝑒𝑛𝑑
𝑇𝑏𝑟𝑒𝑎𝑘𝑃 [𝑖]𝑃𝑞𝑢𝑖𝑐𝑘𝐶𝐸𝑉
𝐸𝑟𝑒𝑔𝑢𝑙𝑎𝑟𝐸𝑞𝑢𝑖𝑐𝑘𝐸𝑟𝑢𝑛𝑃𝐿𝑃𝐺𝑉

Description
Number of steps per day
Total number of steps in the optimization period
Boolean variable for idle state (0 or 1)
Boolean variable for driving state (0 or 1)
Boolean variable for normal charging (0 or 1)
Boolean variable for rapid charging (0 or 1)
Charging rate at step i [%]Upper limit of charging rate [%]Lower limit of charging rate [%]Lower limit of charging rate at the start of operation [%]Number of steps at the start of the day's operation (0 ~48)
Number of steps at the start of the day's operation (0 ~48)
Number of break steps per operation
Electricity rate at step i [JPY/kWh]
Rapid charging service charge [JPY/min]
EV battery capacity [kWh]Normal charging output [kW]Rapid charging output [kW]Power consumption during driving [kW]LPG fuel economy based on distance [JPY/km]
Average speed of taxi [km/h]

TABLE V CONSTANTS IN CHARGE OPTIMIZATION
Description
Average Speed during Taxi Operation
EV Taxi Battery Capacity [7]
EV Taxi Energy Consumption [7]Standard Charger Output [8]Fast Charger Output [9]Fast Charging Usage Fee [9]Additional Charges such as Wheeling
Charges on JEPX Electricity Price
LPG Mileage [10]LPG Price [11]Value
25 [km/h]
60 [kWh]
0.13 [kWh/km]
6 [kW]
50 [kW]
25 [JPY/min]
5 [JPY/kWh]16.8 [km/L]
110 [JPY/L]

Owing to uniform operating and break times, the graphexhibits a consistent pattern. Charging begins right after theoperation period concludes with regular charging (depicted ingreen), and the vehicle stays idle until the commencement ofthe next operation period.

𝑖𝑓 { 𝑇𝑠𝑡𝑎𝑟𝑡 ≤ 𝑖 (mod 𝑠𝑙𝑜𝑡𝑠_𝑝𝑒𝑟_𝑑𝑎𝑦) ≤ 𝑇𝑒𝑛𝑑 }𝑆𝑟𝑒𝑔𝑢𝑙𝑎𝑟 = 0
TABLE VI VEHICLE STATUSES
StatusDescription
The vehicle is idle, with noincrease or decrease in SOC

Idle
Driving
NormalCharging
RapidCharging

The vehicle is driving, with adecrease in SOC
The vehicle is connected to anormal charger, with anincrease in SOC
The vehicle is connected to a
rapid charger, with an increase
in SOCIII.

Possible Time Periods
Outside operating hours,
During breaks in operatinghours
During operating hours

Equation (6) imposes a constraint on the minimum SOC at
the start of operation, as a certain level of SOC is deemednecessary for normal operations.
𝑖𝑓 { 𝑇𝑠𝑡𝑎𝑟𝑡 = 𝑖 (mod 𝑠𝑙𝑜𝑡𝑠_𝑝𝑒𝑟_𝑑𝑎𝑦)}
𝑆𝑂𝐶[𝑖] ≥ 𝑆𝑂𝐶𝑠𝑡𝑎𝑟𝑡Outside operating hours
During breaks in operating
hours∑(𝑆𝑞𝑢𝑖𝑐𝑘 [𝑖] + 𝑆𝑖𝑑𝑙𝑒 [𝑖] ) = 𝑇𝑏𝑟𝑒𝑎𝑘Outside operating hours, since the statuses "driving" and"rapid charging" are not taken, the constraints are as follows:

CHARGE OPTIMIZATION APPROACH

B. Objective function
The objective function of this paper is to minimize thecharging electricity price for EV taxi driving, as shown inEquation (1).
𝑡𝑜𝑡𝑎𝑙_𝑠𝑙𝑜𝑡𝑠( 𝑃 [𝑖] × 𝐸𝑟𝑒𝑔𝑢𝑙𝑎𝑟 × 𝑆𝑟𝑒𝑔𝑢𝑙𝑎𝑟 [𝑖] + 𝑃𝑞𝑢𝑖𝑐𝑘 × 𝑆𝑞𝑢𝑖𝑐𝑘 [𝑖] × 30 )(1)

𝑖=0C. Constraints
This paper presents constraint conditions applicable acrossall steps as follows. Equation (2) imposes constraints on theState of Charge (SOC) within upper and lower limits.
𝑆𝑂𝐶𝑚𝑖𝑛 ≤ 𝑆𝑂𝐶 [𝑖] ≤ 𝑆𝑂𝐶𝑚𝑎𝑥(2)

Equation (3) ensures that at step i, an EV can only assume
one of the following statuses: idle, driving, normal charging, orrapid charging. The specific time periods each status can occur
are detailed in TABLE VI.
𝑆𝑖𝑑𝑙𝑒 [𝑖] + 𝑆𝑟𝑢𝑛 [𝑖] + 𝑆𝑟𝑒𝑔𝑢𝑙𝑎𝑟 [𝑖] + 𝑆𝑞𝑢𝑖𝑐𝑘 [𝑖] = 1(7)

𝑖𝑖𝑓 𝑛𝑜𝑡 { 𝑇𝑠𝑡𝑎𝑟𝑡 ≤ 𝑖 (mod 𝑠𝑙𝑜𝑡𝑠_𝑝𝑒𝑟_𝑑𝑎𝑦) ≤ 𝑇𝑒𝑛𝑑 }𝑆𝑟𝑢𝑛 = 0
𝑆𝑞𝑢𝑖𝑐𝑘 = 0IV.

∑(6)

Equation (7) relates to constraints during break times. As thestatus during these times can only be "idle" or "rapid charging,"
a constraint is set so the number of steps assuming status during
the day's operating hours equals Tbreak.

A. Parameters and Constants
The symbols in this problem are listed in TABLE IV. The
constants in this problem are listed in TABLE V.

min(5)(3)

Equation (4) constrains the SOC at step i+1, calculating thenext step's SOC using boolean variables Sregular, Squick, and Srun.
𝐸𝑟𝑒𝑔𝑢𝑙𝑎𝑟𝐸𝑞𝑢𝑖𝑐𝑘1
1× + 𝑆𝑞𝑢𝑖𝑐𝑘 [𝑖] ×
×
𝐶𝐸𝑉
2
𝐶𝐸𝑉
2𝐸𝑟𝑢𝑛
1
(4)
− 𝑆𝑟𝑢𝑚 [𝑖] ××
𝐶𝐸𝑉
2𝑆𝑂𝐶 [𝑖 + 1] = 𝑆𝑂𝐶 [𝑖] + 𝑆𝑟𝑒𝑔𝑢𝑙𝑎𝑟 [𝑖] ×Normal charging is not performed during operating hours,
hence the constraint during these times is as follows:(8)

CASE STUDY

After adjusting the target period and utilization rate, theoutcomes of the energy cost calculations for LPG gas, simplecharging, and optimized charging are depicted in Figure 3.
Independent of the utilization rate and target period, the energy
costs associated with employing EVs during night shifts prove
to be the lowest. This outcome is attributed to the opportunity
to charge the vehicles during the day, when the reduction ofenergy output is more likely in Figure 4.
Additionally, under many conditions, the energy costs foralternate day shifts emerge as the highest. This is believed toresult from the extended operation times characteristic ofalternate day shifts, which require the utilization of rapidcharging. Rapid charging incurs a higher cost per charge in
Figure 5.
Regardless of the utilization rate and the period considered,energy costs are lowest when EVs are used during night shifts(Figure 6 and Figure 7). This is likely because night shifts cancharge the vehicles during the daytime, when output
curtailment is more likely to occur. Moreover, under mostconditions, the energy costs for alternate day shifts are thehighest. This is thought to be due to the necessity of using fastcharging for alternate day shifts, which has a higher unit
charging cost.
The reduction in energy costs achieved by introducing EVs
in place of traditional internal combustion engine vehicles
fueled by LP gas is believed to result from the differences
between the fuel costs of traditional vehicles and the electricity
costs of EVs. The rate of energy cost reduction from
optimizing charging operations is greater for patterns with a
50% utilization rate. This is thought to be because the longer
available time for charging allows for charging during periods
when electricity rates are lower.

Figure 3 SOC and electricity rates at optimum charging of Day shift with 100% utilization in May 2023

Figure 4 SOC and electricity rates at optimum charge of Night shifts with 100% occupancy in May 2023

Figure 5 SOC and electricity rates at optimal charging of Alternate Day Shifts with 100% utilization rate in May 2023

Figure 6 Energy cost calculation results with 100% utilization rate

Figure 7 Energy cost calculation results 50% utilization rate

V.

CONCLUSION

Switching from LPG gas to simple charging resulted inapproximately a 60% reduction in costs, while transitioning
from simple charging to optimized charging yielded about a
30% reduction in costs. Therefore, utilizing EVs for demand
response can simultaneously secure incentives fortransportation operators and promote demand adjustment andthe effective use of renewable energy. Moreover, the cost
reduction rate of night shifts was superior across almost all
operational patterns, and a 50% utilization rate demonstrated
greater cost savings due to optimized charging operations.
Future research directions include analysis considering thevariation in EV energy consumption across different seasons,analysis accounting for battery degradation due to rapid
charging, and comprehensive evaluation analysis that includes
initial costs and running costs associated with EV adoption.
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