FleetPower: An Intelligent Trading Strategy to form
Virtual Power Plants in Sustainable Smart Energy
Markets
(Authors’ names blinded for peer review)
A changing energy landscape with intermittent and distributed renewable energy sources puts pressure on
the balance of the electric grid. To alleviate this pressure, we design and evaluate an intelligent trading
agent that turns fleets of electric vehicles into virtual power plants. The agent draws on real-time locational
information from 1,100 electric carsharing cars and reacts to energy market prices (ancillary service markets)
to charge or discharge the electric cars. We analyze the economic feasibility of providing regulation power
from these cars to offset the increased volatility caused by the large scale adoption of renewable energy
sources. The study compares the agents ability to offset volatility from these energy sources across San
Diego, Amsterdam, and Stuttgart because they have a different energy mix. We find limits on the charging
infrastructure density, battery technology, and energy market price which are required to economically offer
balancing capacity. We show that virtual power plants of electric vehicles create sustainable new revenue
streams for electric vehicle rental companies without compromising their rental business. These enhanced
revenue streams arise mostly from charging electric vehicles at a cheaper rate to absorb excess energy.
Key words : Decision Support Systems, Electric Vehicles, Energy Informatics, Green IS, Smart Grid, Smart
Markets, Vehicle-to-Grid, Virtual Power Plants.
1.
Introduction
In its World Energy Investment Outlook 2014, the International Energy Agency estimates that
over the next 20 years a cumulative investment of $53 trillion is required worldwide to limit the
concentration of greenhouse gases in the atmosphere to 450 parts per million of CO2 (International Energy Agency 2014). According to the United Nations Intergovernmental Panel on Climate
Change (IPCC) failing to do so bears the key risks of increasing exposure to coastal flooding, variable and extreme precipitation, inland flooding, frequency and intensity of extreme heat, drought,
rising ocean temperature, ocean acidification, loss of arctic sea ice, and rising land temperatures
(Field and et al. 2014). The majority of the anthropogenic caused greenhouse gas emissions come
from fossil fuels. Therefore, many stakeholders and countries around the world pursue a higher
share of renewable energy sources. These large scale penetrations of volatile renewable energy
sources pose a challenge to the stability of the electrical grid as their energy output is weather
dependent and difficult to forecast (Kassakian and Schmalensee 2011). The grid is the backbone
of a highly perishable supply-chain for electricity, where supply and demand has to be in balance
at all times. With the phasing out of fossil fuel-based power plants and increasing amounts of
renewable energy sources, balancing becomes increasingly difficult. In practice, this means that the
1
2
chance of brownouts and, worse yet, blackouts, increases with potentially disastrous consequences.
However, we are able to demonstrate that the presented information system in this study mitigates
this effect in the current situation and by 2030 will alleviate it entirely while making a profit.
Smart grids offer an information-based solution to keep the grid in balance. With electricity
consumption information one can incentivize electricity consumers to act on imbalances locally.
As electric vehicles (EV) consume more electricity than most other devices and even have large
storage capacities, they can contribute to solving the imbalance problem substantially (Ausubel
and Cramton 2010); this is why Elon Musk from Tesla Motors is building the largest battery factory in the world. EVs can be charged when wind and solar sources produce energy, and they can
make energy available to the grid (vehicle-to-grid, V2G) when those energy sources are unavailable. Additionally, EV’s can generate power locally at the distribution level, decreasing the need
for expensive transmission infrastructure. Several EVs together constitute a virtual power plant
(VPP), a collection of distributed power sources that are centrally coordinated by an information
system to offset energy imbalances (Pudjianto et al. 2007). Demand for VPPs is typically high
during peak load (high demand for electricity). The control mechanism of the VPP that decides
how to allocate EVs is an IS artifact in the design science paradigm (Hevner et al. 2004). Through
intelligent planning, we can reach synergies from complementing or replacing conventional, fossil
fuel-based balancing sources with EVs yielding system-wide efficiencies. We research the role that
EVs play in contributing to resolving balancing complications in the grid by designing and optimizing an auction market to resolve imbalances. This smart market (McCabe et al. 1991, Gallien
and Wein 2005, Bichler et al. 2010) enables renewable energy sources to be deployed cost effectively at a larger scale replacing fossil fuels without compromising grid stability. Our contribution
is a cornerstone in enabling a renewables-based solution to grid imbalances, alleviating balancing
problems resulting from renewables intermittency. We study how intelligently coordinated virtual
power plants of electric vehicle fleets contribute to enabling a primarily renewables-based energy
mix for users (drivers), the environment (CO2 emissions), and carsharing operators (profits). The
theoretical importance of this study to the IS community is emphasized by Watson et al. (2010).
They formulate an energy informatics framework with the aim to create an ecologically sustainable
society. The framework formulates the need for IS research’s role in managing demand and supply
for energy efficiency.
We create an intelligent agent, which we call FleetPower that combines real-time location, usage,
and battery storage from EVs with price information from energy markets to optimally allocate
EVs within an EV rental fleet. The agent decides autonomously for fleet owners which EVs should
be deployed as part of a VPP, so which EVs should be charged or provide V2G services, and which
EVs should be made available for rental in real-time. Energy (ancillary service) markets provide
3
information about the magnitude of imbalances over time, which is the basis for decisions to turn
EVs into VPPs. The real-time agent considers both the very short term (seconds to minutes)
integration of demand and supply on the energy market, as well as socio-technical factors, such as
implications for the mobility of renters. The system makes an explicit trade-off between benefits
from offering cars for rental and using them for balancing the grid in real-time. It optimizes the
allocation of EVs to renters that need to drive with EVs on the one hand and the balancing
market that pays for services to balance the grid on the other hand. This allocation is done with
”regime” models from Ketter et al. (2012) of prices, availability, and time to forecast revenues
from rentals and energy markets. This yields a viable business model for carsharing operators, but
also reduces the balancing problem associated with large shares of renewable energy sources. By
turning worthy causes into viable business models, we create a stimulus for people and businesses
to behave sustainable and provide ”an opportunity to create shared value – that is, a meaningful
benefit for society that is also valuable to the business” (Porter and Kramer 2006).
To the best of our knowledge this is the first study that uses real driving, charging, and locational
data of 1,100 EVs and makes an international comparison among three major cities in the USA,
the Netherlands, and Germany with different energy mixes. The unique driving data for this study
was provided by Daimler AG subsidiary Car2Go. In our research we assume that driving patterns
are unknown a priori, which is a groundbreaking advancement in EV balancing research and was
left open to future research by Vytelingum et al. (2011), Tomic and Kempton (2007). We approach
this challenge from a sociotechnical perspective by approximating valuations for mobility from
price and probabilistic demand for the EVs in a carsharing context.
2.
Theoretical background
This section summarizes relevant research and outlines the general setting of balancing renewable
energy sources. Firstly, the embedding in the information systems literature on sustainability will
be given. Subsequently, we describe the energy market in detail to explain the operation of a VPP
with price signals. Finally, we position our research within the literature on EVs and the carsharing
context.
2.1.
Information based Sustainable Society
Information systems can be both a contributor to climate change and a remedy for negative environmental impact. Similar to Loock et al. (2013)’s use of information to align individual interests
with sustainability, we use information to align organizational goals with sustainability by means of
a decision support system. As a result, financial and environmental goals are brought in harmony
to foster carbon neutrality (Malhotra et al. 2013). Knowing when and where people prefer to rent
EVs, puts EV fleet owners in a position to make inferences about the rentals of these people (Seidel
4
et al. 2013), and to make sociotechnical trade offs (Geels 2004) between their need to drive and
the need of storage on the energy market market.
2.2.
Balancing the Electrical Grid: Ancillary Services
In the energy sector, electricity is sold on energy markets hours or days before it is actually produced
in order to let electricity generators plan their production operations. This is efficient for planning purposes, but not for offsetting real-time differences in actual consumption and production.
Therefore, an energy market exists to maintain grid stability, which is called an ancillary service.
In this market power plants are paid to be on standby so that they can produce (or consume)
electricity when needed. The market is coordinated by electronic auctions, in which participants
make offers or issue bids. The clearing mechanism is a multiunit first-price sealed-bid auction,
which is settled on a ”pay-as-bid” basis (International Grid Control Cooperation 2014). Offered
asks refer to the generation of electricity on short notice, called up regulation, while bids pertain to
the consumption of electricity on short notice, called down regulation. The balancing responsible
party settles these offers and bids as needed in merit order (cheapest resources are used first) to
make the market efficient. Increasing levels of intermittent renewable energy and the decommissioning of conventional power plants exposes the ancillary service market to the risk that at some
point the need for regulation power exceeds the offered or issued quantity. This is important, for
instance, when photovoltaic cells are covered with clouds and suddenly stop producing energy.
Figure 1 shows exemplary that the electricity production of solar panels is erratic with extreme
variations in output per minute. Notice how the panel runs at its maximum output at 13.30 and
only minutes later production drops by more than 50%, which is in stark contrast to fossil fuel
generators that produce electricity at a constant rate. These drops in energy output need to be
offset within seconds to avoid blackouts.
The present study focuses on the secondary operating reserve market with an extremely fast
response time requirement between 0.5 and 15 minutes. We focus on this market because the energy
prices are higher than day ahead energy markets and the trading volume is significantly higher
than markets with an even faster response requirement. Ramp-up cost and the time it takes to start
up generators are significant for units such as nuclear power plants. EVs possess large electrical
batteries whose energy is almost instantly accessible, which make them well suited for regulation
purposes.
2.3.
Carsharing with Electric Vehicles
Carsharing, and especially free float carsharing, where the car can be picked up and dropped
off anywhere, contribute to more sustainable transport. Firnkorn and Müller (2011) find that
even under pessimistic assumptions, free float carsharing has a significantly positive environmental
5
4
3
0
1
2
Output [kW]
5
6
7
Solar panel electricity production over a day
2:00
7:00
12:00
17:00
22:00
Time of the day
Figure 1
Solar panel electricity output that illustrates the erratic behavior in photovoltaic electricity production
with extreme variations in output within minutes.
effect, reducing carbon emissions by 6%. By combining carsharing with electric vehicles carbon
emissions are reduced even more. EVs have lower carbon emissions than combustion engine cars;
when they are charged with renewable energy, they are emission neutral. Yet charging remains
a critical issue as charging infrastructure for EVs is not available everywhere and charging time
is significant. Mak et al. (2013) and Avci et al. (2014) suggest an optimal spatial infrastructure
design of battery swapping stations, that addresses the first problem and makes the second one
obsolete. The reason that we focus on conventional charging stations instead is that they are more
established in practice and the key supporter of the battery swapping idea Better Place (Wolfson
et al. 2011) went bankrupt. Yet the problem in both cases is that charging many EVs (or batteries
only) in the same neighborhood at the same time will quickly overload transformers and substations
(Kim et al. 2012, Sioshansi 2012). Previous research has addressed this issue by proposing smart
charging, meaning that EVs are charged at times when the grid is less congested to complement
peaks in electricity consumption without creating new peaks. With smart charging EV owners get
financial incentives to shift charging times, yielding significant peak reductions (Valogianni et al.
2014a,b). An extension of smart charging is the V2G concept1 . A study by Vytelingum et al.
1
Regarding the technical feasibility it should be noted that the standard of the International Electrotechnical Commission IEC 62196 supports V2G.
6
(2011) considers the savings a household can make with a battery exposed to dynamic pricing
at the energy wholesale market and found that efficient use of the battery would save 14% in
utility costs and 7% in carbon emissions. However, the study made the unrealistic assumption
that individual households trade on the energy market. Therefore, Ketter et al. (2013) introduced
the notion of electricity brokers (a.k.a aggregators) to conform to minimum quantity requirements
from the energy market. Also, Vytelingum et al. (2011) considers static batteries, not driving EVs.
Other studies related to EVs find yearly benefits per EV of $10-120 (Peterson et al. 2010) and
$176-203 (Schill 2011). Two contrasting opinions regarding the feasibility of the V2G concept.
Peterson et al. (2010) contend that relatively low yearly benefits per EV do not justify a widespread
roll out. Kahlen et al. (2014), however, argues that V2G would enable volatile renewable energy
sources to be economically suitable for mainstream usage. Tomic and Kempton (2007) show that
the profitability depends on the target market: the larger the variations in the electricity price,
the higher the profitability. A shortcoming of the existing studies is that trips are assumed to be
known in advance. In reality trips are not always known in advance. This is problematic when
an EV is committed to either charge or V2G at the same time as someone needs to drive it. We
approach this challenge from a sociotechnical perspective by approximating the cost of not being
able to drive an EV from the carsharing revenues. Also, previous studies have used either small
fleets or data from combustion engine vehicles, which have a longer range and are not subject to
range anxiety, the fear of stranding with an empty battery.
3.
Model Description
At the core of this research is the development of an IS artifact (Hevner et al. 2004), which is
a decision support system that decides when and which EVs to turn into VPPs. The system is
evaluated in a simulation environment based on real driving data from 1,100 EVs. A simulation
is most suitable for this purpose, as we deal with a complex system that would be prohibitively
expensive to build and difficult to manipulate market parameters in. A simulation helps to refine
the IS artifact, which is driven by business needs from its environment (relevance), and applying
a decision theoretic approach (rigor). In this section, we outline the selected approach and the
reasoning behind it.
3.1.
Virtual Power Plant Decision Support: FleetPower
Fleet operators need to decide how to deploy EVs by deciding which EVs should be charged, which
should provide V2G services, and which EVs should be made available for rental for one time
interval ahead in real-time. Figure 2 illustrates the allocation of EVs at the example of San Diego.
Charging and discharging (V2G) applies only to EVs that are parked at a charging spot. Making
real-time deployment decisions in this complex environment requires automated decision making
7
Figure 2
EV’s from Car2Go in San Diego, USA. FleetPower committed strategically placed EV’s as virtual
power plants to charge or discharge (Vehicle-2-Grid).
by an intelligent trading agent (Collins et al. 2010). We call this intelligent trading agent, which
acts on behalf of the fleet owner, FleetPower. Bichler et al. (2010) have outlined the design of Smart
Markets (markets that rely on optimization techniques to create an efficient market) and provide
a generic decision making process consisting of three phases. We will tailor these three phases to
FleetPower, which is a Smart Electricity Market. In the first phase, the driving behavior of renters
are elicited and represented in a model. In the second phase, the driving behavior model from the
first phase is updated to anticipate renters’s driving behavior and enriched with energy market
price information. This information leads to actionable decisions on the rental or energy market.
In the third phase, the model is enriched with probabilistic availability of EVs at the fleet level,
which updates the model in real-time to coordinate allocations within a fleet and places asks and
bids for electricity on the energy market.
3.1.1.
Phase 1: Renter Preference Elicitation. First of all, a model is needed that cap-
tures the preferences and behaviors of renters. The model is shaped by observations from rental
transactions of idle EV’s coupled with their locational and temporal dimensions. The locational
dimension gives insights into the likelihood that EVs are rented out given that they are parked in a
certain district, which is represented by a zip code. The temporal dimension discloses the likelihood
that EVs are rented out during a certain time. We represent time as discrete 15 minute intervals for
each day of the week. Based on this information, we create a four dimensional model that provides
8
us with knowledge on the likelihood that a car is rented out within the next 15 minutes based on
the day of the week, the 15 minute time interval during the day, and the zip code where the car
is parked. In contrast to day and time, zip code is a categorical variable meaning that the spaces
between the zip codes have no natural interpretation.
3.1.2.
Phase 2: Prediction Model Update and Decision Support at the individual
EV level. The rental likelihood model as an indicator for future rentals serves as an input to
forecasting rentals and ultimately decide on a deployment. For this decision FleetPower calculates
a profit performance indicator (PPI ) for a specific EV i over time interval t, and location l (see
ˆ ), the opportunity
Equation 1). It maximizes the expected profits over renting (rental benefit, RB
of charging cheaper than under the industry average retail tariff (charging benefit, CB ), and using
the car for V2G applications (discharging benefit, DB ). With benefit we refer to the gross profit
(the difference between the revenues and fuel cost but excluding capital costs to purchase the EV),
which can be earned from each respective allocation. The system allocates the EV to the most
valuable option:
ˆ t,i,l , CB t,i,l , DB t,i,l )
PPI t,i,l = max(RB
(1)
For a table of notation including measurement units see Table 1.
ˆ ) car i during interval t
Expected rental gross profit: The expected profit for renting (RB
parked at location l is determined with a regression analysis:
ˆ t,i,l = β1 + β2 ct,l + β3 qt,i + ϵRB
RB
(2)
where β1 , β2 , andβ3 are regression coefficients, c is the likelihood that an EV gets rented out from
the rental likelihood model based on the time (t), and location (l), q is the remaining capacity in
the battery, and ϵ is the error term. As rental benefits are generally much higher than benefits
from DB and CB a car that was predicted not to be rented out when it actually was rented out
will be much costlier than the other way around. To account for these asymmetric misclassification
ˆ . The level of confidence depends on
costs, we use the upper confidence bound as predictor for RB
the magnitude of the asymmetric misclassification costs. Note that most conventional carsharing
companies require cars to be fully charged upon return; this is not the case for carsharing EVs as
charging takes much longer relative to the rental duration.
Charging: EVs can be parked anywhere in the city but only if an EV is parked at a charging
station FleetPower has the option to turn the EV into a VPP. FleetPower then bids in the energy
market for a cheap electricity rate. Figure 3 shows the bid quantity and bid price with its components that is submitted to the first-price sealed-bid auction for ancillary services relative to bids by
9
−20
Demand curve: issued bids for charging
Retail electricity price
P
0
d
VP
Expected Rental E[RBt,i,l]
Bi
neg
20
Margin µt
Excess Energy
40
Reserve Price [$ / MWh]
Bid 3
Bid Price
Opportunity Cost −C/ε
Bid 9
Bid 63
I
∑Ψ
neg
− RPE
Bid 18
neg
1
60
Bid Quantity
0
500
1000
1500
Quantity [MWh]
Figure 3
The graph shows the demand curve for getting rid of excess energy; in this example the grid needs to
get rid of 1450 MWh within seconds. As our VPP bid offers to absorb this electricity cheaper than bid 18, which
is the last (partially) cleared bid, the market agrees to sell the electricity to the fleet owner. The graph also shows
how our VPP bid is constructed from the opportunity cost, expected rental revenues, and a margin.
other parties. The first component of the bid price, opportunity cost, serves as a reference point;
FleetPower would not purchase electricity on the energy market if the normal electricity tariff is
cheaper. The second component, the expected rental gross profits, ensures that EVs are less likely
to charge when it is probable that the EV will be rented out. A last consideration, the margin,
allows the fleet owner to make a profit, which makes a trade-off between payoff and likelihood of
bid acceptance. In the following we will describe the bid in more detail; the bid quantity will be
readjusted in Phase 3.
The quantity issued to charge the EV offers benefits (CB ) which are determined by the following
identity:
neg
CB t,i = Ψneg
t,i (Pt,i,l −
C
)
ξ
(3)
where (Ψ) is the quantity with which the battery of car i at interval t can still be charged with
and (P neg ) the bid price to charge, which differs per EV i and time interval t. More specifically
the quantity that the battery can still be charged with (Ψneg ) in time interval t takes into account
the amount of electricity stored in the battery Q and the charging speed γ, which is defined by
10
Ψneg
t,i,l = min((Ωi − Qt,i,l ), γ∆t), where Ω is the maximum energy that can be stored in the battery.
As this option would replace the costs for charging, we add the opportunity benefit of not having
to pay for charging fees (C), the retail electricity tariff including ξ, the charging inefficiency. In
other words the CB is the difference between what one would pay with a common electricity tariff
and energy market price.
The issued bidding price P neg for charging is determined as follows:
neg
Pt,i,l
=−
ˆ t,i,l
C
RB
+ µneg
+
t
ξ
∆Q∆t,i,l
(4)
where:
∆Q∆t,i,l = β4 + β5 ct,l + β6 qt,i + ϵQ
(5)
ˆ is the expected rental benefit from Equation 2 per unit of
where ξ is the charging inefficiency, RB
energy, and µneg is the profit margin which maximizes overall profits for all previous time intervals t
(of the training data set). β4 , .., β6 are regression coefficients. Equation 5 can be interpreted similar
to Equation 2 only that it predicts the quantity of energy used during the rental rather than the
rental revenues. Note, that if P neg is too high, the market will instead choose a cheaper bid and
consequently there is no CB for that time period.
Discharging (V2G): In similar vein to charging, an EV can also contribute to a VPP if parked
at a charging station with V2G. FleetPower then has the option to sell electricity with V2G
submits an offer to the energy market. Figure 4 shows the offer quantity and offer price with its
components that is submitted to the first-price sealed-bid auction for ancillary services relative
to bids by other parties. The first component of the offer price to sell electricity, the energy cost,
reimburses the fleet owner for the electricity price that he paid to charge the EV in the first place.
The second component, battery depreciation, compensates the fleet owner for using and wearing
out the battery. The third component, the expected rental gross profit, ensures that EVs are less
likely to discharge (V2G) when it is probable that the EV will be rented out, which includes time
to recharge the EV to its previous fuel state. The last consideration, the margin, allows the fleet
owner to make a profit, which makes a trade-off between payoff and likelihood of offer acceptance.
In the following we will describe the offer in more detail; the offer quantity will be readjusted in
Phase 3.
The quantity issued to discharge the EVs (V2G) offers benefits (DB ) which are determined by
the following identity:
[
DB t,i,l =
Ψpos
t,i
pos
Pt,i,l
(
)]
C
− D+
ξ
(6)
11
200
Supply curve: offered asks for discharging (V2G)
Energy Shortage
150
I
∑Ψ
− RPE
pos
Offer 3
pos
1
100
Offer 16
Offer 5
pos
Expected Rental
pos
Ψt,i
δt
pos
∑ E[RB](t+j),i,l | (Qt − Ψt,i ) | <∆Qt,i
1
ffe
Battery Depreciation D
O
Offer 1
rV
PP
50
Margin µt
0
Energy Cost C/ε
Offer Price
Reserve Price [$ / MWh]
Offer Quantity
−500
−1000
−1500
Quantity [MWh]
Figure 4
The graph shows the supply curve for purchasing energy to bridge a deficit; in this example the grid
needs to acquire additional of 1200 MWh within seconds. As our VPP bid offers to provide this electricity
cheaper than offer 16, which is the last (partially) cleared offer, the market agrees to buy the electricity from the
fleet owner. The graph also shows how our VPP offer is constructed from the energy cost, battery depreciation,
expected rental revenues and a margin.
where (Ψpos ) is the electricity stored in EV i that can be accessed within time interval t. P pos is
the offered selling price, which is defined in Equation 7. The electricity available for V2G (Ψpos ) at
time interval t based on the amount of stored electricity Q and the discharging speed δ is expressed
by Ψpos
t,i,l = min(Qt,i,l , δ∆t). Moreover, the cost for battery wear out is depreciated (D) over its
effective use and the costs for charging C, including inefficiency ξ, are taken into account. η is the
discharging inefficiency which plays an indirect role as the amount of electricity consumed from
the battery Ψpos is larger than the amount sold by
Ψpos
.
η
The offered selling price (P pos ) is determined as follows:
C ∑
ˆ (t+j),i,l , CB t+j,i,l ) ∗ λ) + µpos
((max(RB
+
t
ξ
j=1
h
pos
Pt,i,l
=D+
(7)
pos
where the summation term refers to recharging the EV after V2G. h = round(
Ψt,i,l
) is the time
ˆ and CB
interval it takes to recharge the EV rounded to the closest time interval (15 minutes), RB
δt
are opportunity costs of not being able to rent out the EV due to its commitment to a VPP in the
current interval t and recharging it thereafter. The dummy variable λ = (Qt,i,l − Ψpos
t,i,l ) < ∆Qt+j,i,l
12
states that opportunity cost only apply if the next renter cannot complete his expected trip with
the residual capacity from V2G.
ˆ and CB opportunity costs for rental and λ the recharging time if the battery has insufficient
RB
capacity for the renters’s purposes, and µpos margin which maximizes overall profits for the time
intervals t in the training data set similarly to the margin in Equation 4.
To summarize, we have created an agent that autonomously determines for each EV in the
upcoming 15 minutes, depending on the parking time and location and whether it is connected
to a charging pole, for which EVs it is more profitable to make them available for rental or for a
VPP. This decision making process is based on the highest of the expected profits of 1) renting
ˆ ), 2) savings from cheaper charging rates on the energy market compared to the
the EV out (RB
industry average retail electricity price (CB ), and 3) profits from selling V2G electricity on the
energy (ancillary services) market (DB ).
3.1.3.
Phase 3: Real-time Virtual Power Plant at the Fleet Level. In the final phase
the system acts on the information from phase 1 and 2 to deploy EV’s for rental and purchases and
sells electricity on the energy market based on the model described in this section. It incorporates
feedback from the market and coordinates within the fleet which cars to allocate to rental and
VPPs. While it is important for renters to rent a car at a specific location, the precise location
within a city is less relevant for energy markets. Rather than deciding for each EV individually
where it should be deployed, we estimate an overall quantity to charge and discharge. This quantity
comes from different EVs across the city and could switch within a time interval (t) from one EV
to another if it is rented out.
Therefore we do not simply sum up the storage potential and energy stored of each individual
EV (CB and DB ) that could be committed to a VPP, but we exploit the risk pooling effect (RPE ).
The risk pooling effect refers to accuracy improvements when predicting EV storage potential and
energy stored for a whole fleet rather than for each individual EV. In the context of this study the
RPE effect is the difference between the predicted sum of the storage potential and energy stored
per individual EV (CB and DB ) and the quantity predicted for the whole fleet, the offer or bid
quantity. This improvement also explains why a VPP outperforms several EVs in isolation.
We determine the respective RPE ’s for charging and discharging (V2G) in the fleet with a linear
regression analysis with two independent variables.
Charging: The RPE for storage potential for charging is given by:
RPE neg
t
= β + βnneg
I
∑
(j(x) ∗ Ψneg
t−1,i,l )
i=1
+ βrneg
I
∑
i=1
(k(x) ∗ Ψneg
t−1,i,l ) − s
(8)
13
Table 1
Variable
PPI
ˆ
RB
RB
CB
DB
RPE
D
d
C
c
j(x)
i
I
k(x)
m(x)
l
n(x)
P
Q
q
s
t
∆t
β
γ
δ
ϵ
η
λ
µ
ξ
Ψ
Ω
Table of notation.
Description
Unit
ˆ , CB and DB
Profit Performance Indicator, maximum of RB
$
Rental benefit, expected profits from rentals over given time interval
$
Observed rental revenues
$
Charging benefit, savings from purchasing electricity
$
on reserve market as compared to the electricity price C
Discharging benefit, profits from selling
$
V2G electricity on reserve market
Risk pooling effect
kWh
Battery depreciation cost for wear off over battery lifetime
$/kWh
Distance EVi to closest EV
km
Electricity price (industry average)
$/kWh
%
Rental probability by day, time, and location (Rental Likelihood Cube)
100
Indicator function, see Equation 9
Specific EV
tag
∑
Total number of EVs ( i)
Indicator function, see Equation 10
Indicator function, see Equation 12
Location
zip code
Indicator function, see Equation 13
Bid/offer price to buy or sell electricity from reserve market
$/kWh
Amount of electricity stored in an EV
kWh
%
Remaining battery capacity (Q/Ω)
100
Safety stock to reserve EVs for rental
kWh
Time interval
index
Duration of a time interval
15 minutes
Regression parameter
Charging speed
kW
Discharging speed
kW
Regression error term
%
Discharging inefficiency
100
Dummy to account for opportunity costs from recharging
boolean
Margin on the bid/offer price, to optimize bidding price
$
%
Charging inefficiency
100
Electricity accessible within time interval, depends on γ and δ
kWh
Maximum battery capacity
kWh
where j(x) and k(x) are defined as:
{
j(x) =
1,
0,

 1,
k(x) =
 0,
if PPI t−1,i,l = CB t−1,i,l
otherwise
(9)
if PPI t−1,i,l = CB t−1,i,l
and RBt−1,i,l > CB t−1,i,l
otherwise
(10)
where β, βn , βr are regression coefficients, I is the total number of EVs, and RB are the actual
rental benefits as observed from the data.
14
Discharging: The RPE for energy stored for discharging (V2G) is given by:
RPE pos
t
= β + βnpos
I
∑
(m(x) ∗ Ψpos
t−1,i,l )
i=1
+ βrpos
I
∑
(11)
(n(x) ∗ Ψpos
t−1,i,l ) − s
i=1
where k(x) and m(x) are defined as:
{
m(x) =
1,
0,

 1,
n(x) =
 0,
if PPI t−1,i,l = DB t−1,i,l
otherwise
(12)
if PPI t−1,i,l = DB t−1,i,l
and RBt−1,i > DB t−1,i,l
otherwise
(13)
The first independent variable is the storage potential or the energy stored after the previous
interval in the EVs (Ψneg /Ψpos ) where the performance indicator suggested to turn an EV into a
VPP, by either charging (Equation 8) or V2G (Equation 11). We chose this variable because it is
an indicator for the total availability of regulation power in the respective directions. The second
independent variable is the storage potential or the energy stored in EVs where the performance
indicator suggested turn EVs into a VPP in the previous time interval, but where it would have
been more profitable to rent out the EV based on observed rentals. This variable is an indicator
for the trend of rentals, so whether the demand for rentals is rather high or low. s refers to safety
stocks that determine the trade-off between the risk of not renting out a car and missing out on
potential profits on the reserve markets. The safety stocks (in combination with Equation 8 and 11)
determine how many cars should not be committed as reserves. The optimization of the safety stock
was done over the 7 week training period with the objective to maximize the overall profit. The
safety stock grows with the asymmetric misclassification costs discussed in the previous section.
The actual allocation of individual cars is done in the order of profitability according to PPI , until
I
I
∑
∑
the respective overall quantities
CB − RPE neg and
DB − RPE pos are reached. Each quantity
i=1
i=1
is submitted to the energy market at the average price from Equations 4 and 7 weighted by the
quantities for the individual prices respectively. Figures 3 and 4 show exemplary auction bids and
offers including the quantities for bids and offers from the third phase.
4.
Evaluation: Evidence from a Real World Setting
In order to assess the economic viability of the above described decision support system in practice,
we apply it to the carsharing service Car2Go of the Daimler AG with 1,100 EVs at three reference
sites in Stuttgart (Germany), Amsterdam (Netherlands), and San Diego (USA). We chose Car2Go
because they are the only carsharing company that is globally present with a large fleet of EVs
15
and the same cars to make it comparable across countries. The sites are particularly suited for
this research purpose because they are homogeneous in terms of their energy mix. Germany and
California are both at the forefront of renewable energy standards with an energy mix that consists
of 24%, and 19% renewable energy respectively in 2013, but with the distinct difference that CA
additionally sources 8% hydro energy from neighbouring states. The Netherlands rely on a more
conservative, fossil fuel-based economy with 4% renewable energy content in 2013.
We consider a period of 11 weeks from the 2nd of May 2014 till the 19th of July 2014. The
first eight weeks from the 2nd of May 2014 till the 27th of June 2014 serve as a training data set
(bootstrap period). From this training set values for the rental likelihood cube, expected driven
kilometers, rental time, and rental profits are used to train the model. The last three weeks from
the 28th of June till the 19th of July are used to evaluate the model.
4.1.
Carsharing Operator: Car2Go
The free floating fleet of Car2Go in Stuttgart consists of 500 EVs, and in Amsterdam and San
Diego of 300 EVs. These EVs are distributed over 54 zip code districts around Stuttgart, 65 around
Amsterdam, and 23 around San Diego. Even though these districts are formed differently across
countries they give us a reasonable estimate on the car usage in this area. We cannot use the raw
GPS data because there are insufficient observations to estimate the rental probability (Rental
Likelihood Cube) for each GPS location. Minor adjustments were made for Amsterdam where the
zip codes identify single streets; we only took the numeric zip code to identify the district and
left out the letters that identify the street for comparisons sake. Members pay for the carsharing
service on a use basis only (per minute/hour/day and an extra km fee above a threshold of 50 km).
They are incentivized to return EVs to a charging station when the battery status is below 30% by
rewarding them with 10 free driving minutes (not applicable for San Diego). The prices across the
different locations are similar but not uniform (see Table 2), so we will use the arithmetic mean of
the prices for easier comparison between the locations.
The driving data was retrieved from information that is openly available on the internet. From
Car2Go we got access to a private application programming interface to harvest the data. We
wrote a web scraper that automatically retrieves a list of EVs that are available for rental right
now from the Car2Go website www.car2go.com. We download, add a time stamp, and store it it
in a database every 15 minutes. This information contains the unique car name, the geographic
coordinates where the car is parked, the street name and zip code of that location (l), the state of
charge of the battery (Q), and whether the EV is currently charging. Based on this information we
can infer how long the EV was rented, how many kilometers were driven, and how much revenues
ˆ ).
were earned as rental benefit (RB
16
Table 2
Description
EV type
Max. battery capacity (Ω)
Battery depreciation cost (D)
Environmental variables Car2Go.
Stuttgart
Amsterdam
San Diego
Smart Fortwo Electric
16.5 kWh
0.13 $/kWh (2015)
0.067 $/kWh (2020)
0.034 $/kWh (2030)
Charging speed (γ)
Type 2: 3.6 kW (linear)
Discharging speed (δ)
Type 2: 3.6 kW (linear)
Charging inefficiency (ξ)
96% (Reichert 2010)
Discharging inefficiency (η)
97.4%∗
EV fleet size
500
300
300
Charging Stations
220
1500
100
Electricity costs (industry tariff) (C)∗∗ $0.1155
$0.1002
$0.0977
Rental per minute∗∗
$0.37
$0.39
$0.41
Rental per hour∗∗
$18.92
$18.92
$14.99
Rental per day∗∗
$74.93
$87.63
$84.99
$
$
$
0.39 km
0.45 km
Extra fee after 50 km
0.37 km
Min/Avg/Max Min/Avg/Max Min/Avg/Max
EVs available
23%/75%/89%
29%/82%/98%
28%/69%/85%
Rental revenues∗∗
$4/$17/$134
$4/$15/$133
$4/$18/$133
∗
Value is not known, assumption based on our best estimate.
∗∗
Exchange rate is 1 Euro = 1.34 U.S. dollars.
Electricity retail prices are an important cost factor for Car2Go because it is a large electricity
consumer. Extrapolating the electricity consumption to a whole year, they would use more than 500
MWh per city to charge the EVs which gives them access to the tariffs of medium sized industries
in the respective regions. For an overview of the environmental variables see Table 2.
4.2.
Secondary Operating Reserves: Transnet BW
As illustrated in the section 2.2, EV storage is particularly suited for the secondary operating
reserve, due to its fast response requirement. Therefore we use auction data on secondary operating
reserves (in the US market it is called ancillary service market) to determine the prices, for balancing
(charging as well as discharging) at each point in time. We use the data from the respective
energy market operators in Stuttgart, Amsterdam, and San Diego. In Stuttgart, we use data from
regelleistung.net which is the German energy market operator, in Amsterdam from Tennet, and
in San Diego from California ISO. These prices determine whether the energy market operator
settles the asks and bids placed by FleetPower if the price P from the model is below the market
price and significantly influence the charging and discharging (V2G) regulation benefits (CB , DB ).
The low renewable energy content in the energy mix is responsible for relatively low prices with
little variation for regulation prices. This reduces the revenues for VPPs in the Netherlands. In
contrast, Germany’s and California’s high renewable energy content of the energy mix leads to
higher prices and higher volatility respectively. This increases the revenues for VPPs because they
17
can charge cheap and sell large volumes of energy at higher prices (Stuttgart), or smaller amounts
of at extremely high prices (San Diego).
4.3.
Fitting the Model to the Case
We train the model for the expected rental benefit (Equation 2) from 8 weeks of Car2Go rental
data. The fitted model suggests that being at a place and time where EVs frequently get rented out
has a strong positive impact, and lower battery levels have a negative impact on the expected rental
profits for the upcoming 15 minutes. To account for the asymmetric payoff from rental (average
benefit per rental is $20.04) and selling electricity (average benefit is $0.40) we use the upper 99%
confidence level to overstate the rental benefit accordingly.
5.
Analysis and Discussion
Over the four week test out period the decision support system made over 284,000 EV deployment
decisions for Stuttgart, Amsterdam, and San Diego. For the exact allocation to rental and VPP
(charging and discharging (V2G)) see Figure 5, 6, and 7. Although Amsterdam and San Diego
both have the same number of rental EVs, FleetPower makes much more deployment decisions for
Amsterdam, even more than for Stuttgart. The large difference across the cities is mostly related
to the number of charging stations available and regulation energy prices. With fewer charging
stations EVs are less likely to be parked at one and therefore are less likely to be turned into a VPP.
Regardless of city it appears that VPPs based on charging is much more attractive than discharging
(V2G) because of the high battery depreciation costs. This is expressed in the comparatively low
number of decisions that lead to commitments for discharging on the energy market. It is striking
that Amsterdam has the highest ratio of discharging usage, even though prices are lower and less
volatile. Again the intuitive explanation is the difference in charging infrastructure. With fewer
charging stations they are relatively more used for charging and less time is left to discharge (V2G)
EVs. But in general the trend remains consistent throughout the cities that the low market prices
for discharging from ancillary services hardly compensate for the battery depreciation costs. The
leaves of the trees from Figure 5, 6, and 7 give an intuition about refused renters and its comparison
to the additional benefits from trading energy on the energy market as VPP. We define refused
renters as rentals that we observed in the test data, but that we could not honor because too many
EVs were committed to a VPP and no alternative EV was in walking distance. In 16, 9, and 2 of the
cases a renter wanted to rent an EV while it was committed as a VPP for Stuttgart, Amsterdam,
and San Diego respectively; but in 8, 4, and 1 cases there was a car in walking distance. That
means that we had to refuse renters in 8, 5, and 1 instances over the four week time period which
translates into a loss in gross profits of 0.23%, 0.03%, and 0.08%. At the same time the decision
support system traded 47, 32, and 15 MWh on the energy market, increasing the gross profits of
18
Stuttgart
102,645 EV deployment decisions
Rental:
40,579 or 40%
VPP:
62,066 or 60%
Discharged (V2G):
3,366 or 5%
Charging:
58,700 or 95%
Traded: 3.4 MWh Refused rentals: 1 Traded: 43.7 MWh Refused rentals: 7
↑ 0.3% GP
↓ 0.01% GP
↑ 4.3% GP
↓ 0.22% GP
Figure 5
Tree illustrating FleetPower’s decision distribution for Stuttgart (GP=Gross Profit).
Car2Go by about 4.6%, 3.4%, and 3.8% assuming capital costs for EVs are 60% of rental revenues
(industry average). This increase is significant at the 0.01 significance level (p-values: 0.002, 0.001,
0.002). Therefore, charging EVs with cheap excess capacity from the energy market is already
under current circumstances economically worthwhile and helps in reducing fluctuations in the
grid. However, the difference in profitability seems to depend more on the energy prices, which
are higher and more volatile in Stuttgart and San Diego as in Amsterdam, than on the number of
charging stations. Even though Amsterdam made relatively more decisions to turn EVs into VPP
than the other cities, Car2Go Amsterdam earned less money with it because the revenues for doing
so are lower.
5.1.
Benchmark: comparing FleetPower to allocations with perfect information
When we compare the decisions of FleetPower to a case in which Car2Go has perfect information
on the trips of EVs in advance, we see that FleetPower makes almost optimal decisions. With
perfect driving information fleet operators would increase gross profits to about 4.8%, 4.5%, and
3.7%. The relatively high accuracy of FleetPower can mostly be attributed to the risk pooling
by substituting committed EVs for ancillary services with other available EVs if a renter wants
to rent a specific EV. If we were to make this decision for each EV individually, we would make
a loss in all cities. One can conclude that using FleetPower is significantly more profitable then
only renting out EVs and comes close to a scenario where the fleet owner has perfect information.
Figure 8 illustrates FleetPowers decisions to turn EVs into VPPs over the course of a representative
day (28th of June 2014) in Stuttgart. For most of the day the EVs charge, but at 16.00 PM the
market price is high enough for the VPP to discharge (V2G) EVs. In addition, the figure also shows
19
Amsterdam
136,600 EV deployment decisions
Rental:
90,738 or 66%
VPP:
45,862 or 34%
Discharged (V2G):
4,238 or 9%
Charging:
41,624 or 91%
Traded: 3.5 MWh Refused rentals: 1 Traded: 28.9 MWh Refused rentals: 4
↑ 0.4% GP
↓ 0.01% GP
↑ 3.0% GP
↓ 0.02% GP
Figure 6
Tree illustrating FleetPower’s decision distribution for Amsterdam (GP=Gross Profit).
San Diego
44,764 EV deployment decisions
Rental:
25,832 or 58%
VPP:
18,932 or 42%
Discharged (V2G):
359 or 2%
Charging:
18,573 or 98%
Traded: 0.3 MWh Refused rentals: 0 Traded: 14.8 MWh Refused rentals: 1
↑ 0.9% GP
↑ 2.9% GP
↓ 0.08% GP
Figure 7
Tree illustrating FleetPower’s decision distribution for San Diego (GP=Gross Profit).
that the FleetPowers decisions are extremely accurate compared to the benchmark, with perfect
information about future rentals. The accuracy of the model lies slightly above 99%.
6.
Conclusion and Future Work
We have proposed and evaluated the decision support system FleetPower, which enables companies with electric vehicle fleets to participate in the energy (ancillary service) market as well as
continuing their traditional rental business. We continuously extract value from information about
electric vehicles, such as GPS data and fuel status, to overcome inefficiencies in energy systems.
20
0
Discharging (V2G) with perfect trip information
Discharging (V2G) with FleetPower
Charging with FleetPower
Charging with perfect trip information
−200
−100
Ouput [kW]
100
200
VPP electricity production over a day in Stuttgart
02:00
07:00
12:00
17:00
22:00
Time of the Day
Figure 8
Virtual power plant output for the 28th of June 2014.
This is done with an intelligent agent that decides whether an electric vehicle at a specific location
should be available for rent, or charged, or discharged in form of a virtual power plant as ancillary
services. The system makes this decision based on forecasts for revenues from rental, charging, and
discharging with an accuracy of above 99%. We show that using electric vehicles for ancillary services enhances gross profits of the electric vehicle fleet owner consistently between 3.4% and 4.6%,
depending on the location. By analyzing the agent’s performance across Stuttgart, Amsterdam,
and San Diego, we are able to show the relevance of profitability on the charging infrastructure
availability and energy prices at the regulation markets. Most of these gross profits (99.8%) are due
to getting a cheaper charging tariff when using excess energy, while only vehicle-2-grid currently
accounts for only a marginal proportion of these additional profits. With this decision support
system, it is possible to replace carbon intensive generation capacity with clean energy storage in
the future, which will increase system efficiency.
References
Ausubel, L. M., P. Cramton. 2010. Virtual power plant auctions. Util. Policy 18(4) 201–208.
Avci, B., K. Girotra, S. Netessine. 2014. Electric vehicles with a battery switching station: Adoption and
environmental impact (articles in advance). Management Sci. .
Bichler, M., A. Gupta, W. Ketter. 2010. Designing smart markets. Inform. Systems Res. 21(4) 688–699.
21
Collins, J., W. Ketter, N. Sadeh. 2010. Pushing the limits of rational agents: the trading agent competition
for supply chain management. AI magazine 31(2) 63.
Field, C.B., et al. 2014. Change 2014: Impacts, adaptation, and vulnerability. part a: Global and sectoral
aspects. contribution of working group ii to the fifth assessment report of the intergovernmental panel
on climate change. Technical summary, Cambridge University Press, Cambridge, United Kingdom and
New York, NY, USA.
Firnkorn, J., M. Müller. 2011. What will be the environmental effects of new free-floating car-sharing
systems? the case of car2go in ulm. Ecological Econom. 70(8) 1519–1528.
Gallien, J., L. Wein. 2005. A smart market for industrial procurement with capacity constraints. Management
Science 51(1) 76–91.
Geels, F. 2004. From sectoral systems of innovation to socio-technical systems: Insights about dynamics and
change from sociology and institutional theory. Res. policy 33(6) 897–920.
Hevner, A. R., S. T. March, J. Park, S. Ram. 2004. Design Science in Information Systems research.
Management Inform. Systems Quarterly 28(1) 75–105.
International Energy Agency. 2014. World Energy Investment Outlook . OECD/IEA, Paris, France.
International Grid Control Cooperation. 2014. Information on grid control cooperation and international
development. IGCC price model - transmission system operator market information 1–19.
Kahlen, M., W. Ketter, J. van Dalen. 2014. Balancing with electric vehicles: A profitable business model.
22nd European Conference on Inform. Systems. Tel Aviv, Israel, 1–16.
Kassakian, J. G., R. Schmalensee. 2011. The future of the electric grid: An interdisciplinary MIT study.
Tech. rep., Massachusetts Institute of Technology. ISBN 978-0-9828008-6-7.
Ketter, W., J. Collins, M. Gini, A. Gupta, Paul Schrater. 2012. Real-time tactical and strategic sales
management for intelligent agents guided by economic regimes. Inform. Systems Res. 23 1263–1283.
Ketter, W., J. Collins, P. Reddy. 2013. Power TAC: A competitive economic simulation of the smart grid.
Energy Economics 39(0) 262–270.
Kim, E., R. Tabors, R. Stoddard, T. Allmendinger. 2012. Carbitrage: Utility integration of electric vehicles
and the smart grid. The Electricity J. 25(2) 16–23.
Loock, C., T. Staake, F. Thiesse. 2013. Motivating energy-efficient behavior with green is: an investigation
of goal setting and the role of defaults. Management Inform. Systems Quarterly 37(4) 1313–1332.
Mak, H., Y. Rong, Z. M. Shen. 2013. Infrastructure planning for electric vehicles with battery swapping.
Management Sci. 59(7) 1557–1575.
Malhotra, A., N. Melville, R. Watson. 2013. Spurring impactful research on information systems for environmental sustainability. Management Inform. Systems Quarterly 37(4) 1265–1274.
McCabe, K., S. Rassenti, V. Smith. 1991. Smart computer-assisted markets. Science 254(5031) 534–538.
22
Peterson, S., J. Whitacre, J. Apt. 2010. The economics of using plug-in hybrid electric vehicle battery packs
for grid storage. J. of Power Sources 195(8) 2377–2384.
Porter, M. E., M. R. Kramer. 2006. The link between competitive advantage and corporate social responsibility. Harvard Business Review 11.
Pudjianto, D., C. Ramsay, G. Strbac. 2007. Virtual power plant and system integration of distributed energy
resources. Renewable power generation, IET 1(1) 10–16.
Reichert, S. 2010. Considerations for highly efficient bidirectional battery chargers for e-mobility. E-Mobility.
Technologien, Infrastruktur, Maerkte. VDE-Verlag, Leipzig, 5.
Schill, W. 2011. Electric vehicles in imperfect electricity markets: The case of Germany. Energy Policy
39(10) 6178–6189.
Seidel, S., J. Recker, J. Vom Brocke. 2013. Sensemaking and sustainable practicing: functional affordances
of information systems in green transformations. Management Inform. Systems Quarterly 37(4) 1275–
1299.
Sioshansi, R. 2012. OR Forum-Modeling the impacts of electricity tariffs on plug-in hybrid electric vehicle
charging, costs, and emissions. Oper. Res. 60(3) 5060–516.
Tomic, J., W. Kempton. 2007. Using fleets of electric-drive vehicles for grid support. J. of Power Sources
168(2) 459–468.
Valogianni, K., W. Ketter, J. Collins, D. Zhdanov. 2014a. Effective management of electric vehicle storage
using smart charging. 28th AAAI Conference on Artificial Intelligence. Quebec, Canada, 472–478.
Valogianni, K., W. Ketter, J. Collins, D. Zhdanov. 2014b. Enabling sustainable smart homes: An intelligent
agent approach. 35th International Conference on Inform. Systems (ICIS) 1–20.
Vytelingum, P., T. Voice, S. Ramchurn, A. Rogers, N. Jennings. 2011. Theoretical and practical foundations
of Large-Scale Agent-Based Micro-Storage in the smart grid. J. of Artificial Intelligence Res. 42 765–
813.
Watson, R. T., M. Boudreau, A. J. Chen. 2010. Information systems and environmentally sustainable development: energy informatics and new directions for the IS community. Management Inform. Systems
Quarterly 34(1) 4.
Wolfson, A., D. Tavor, S. Mark, M. Schermann, H. Krcmar. 2011. Better place: A case study of the reciprocal
relations between sustainability and service. Service Sci. 3(2) 172–181.