Transcript Coping with the Emerging Energy Demand for Charging Plug-in Electric Vehicles background lecture.

Coping with the Emerging Energy
Demand for Charging Plug-in
Electric Vehicles
background lecture
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Electric Vehicles (EVs) over the next few
years…
64-86% U.S. sales of new light vehicles by 2030
Why?
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electric vehicles benefits (1)
• Economic saving in fueling
40% saving of primary energy with
respect to the ICE
but there is another benefit of paramount
importance for society..
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have you heard about
Global Warming?
• increasing temperatures in
various regions
• increasing extremities in weather
patterns
• almost 100% is due to the
increase in the atmosphere of …
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greenhouse gases
• greenhouse gases are those gases that
contribute to the greenhouse effect…
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the largest contributing source of greenhouse
gases is the burning of fossil fuels leading to
the emission of carbon dioxide
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conventional vehicles emissions
• when a combustion vehicle operates carbon dioxide
is produced
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some data…
• an average passenger vehicle produces 5.2 metric
tons of carbon dioxide per year
• considering 800 million of cars circulating worldwide,
this means 4 billion 160 million (4,160,000,000) Metric
Tons per year!
it is more than 15 percent of total CO2
emissions!!
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Electric Vehicles benefits (2)
• electric vehicles may contribute to reduce the greenhouse gas
emissions:
no combustion= no pollutants
Indeed if electric energy they require comes from RENEWABLE
SOURCES (wind, solar, geothermal, hydropower, wave/tidal
etc…)
Zero emissions!!!
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EV anatomy
Electronic
control
Electrical
engine
Battery pack
Dashboard
the engine is an electric motor
energy is supplied by a battery pack
an electronic system controls the motor operation and the
state of charge of the battery pack
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main advantages
•
•
•
•
•
•
Clean (low or zero emissions)
Higher driving comfort
More convenient in terms of cost per mile
Reduced engine maintenance
Higher Safety in case of car accidents
Reduced dependence on Oil
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main disadvantages
•
•
•
•
•
Expensive
Limited autonomy
Battery maintenance
“Refueling” time (battery charging)
The risk of a new dependence on rare metals
the major concerns are just about batteries..
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the battery is the vehicle “tank” !
• the tank capacity is now the ENERGY that can
the battery can store for the propulsion.
• it is expressed in Wh (Watt hour)
• typical range in commercial EVs: 5-50kWh (for
a range autonomy of 100-200 miles)
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how much fast we “fill” our battery:
The “speed” of the battery charging process is related to the charging rate.
It is a measure of ELECTRICAL POWER, expressed in Watts!
energy required
charge time
chargingrate
a 15kWh battery charged at 15kW, 10 and 5kW…
1 hour
1,5 hours
3 hours
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the energy stored in the battery and used
for propulsion is supplied by the electric grid..
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the problem we have to cope with:
when a vehicle is connected for battery recharging, it is an electric
load for the grid
to understand the problem, let’s consider the load trend during a
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typical day…
typical load trends during the day*
The energy request is low during the night
It starts to increase as people wake up..
Slowly decreases during the evening…
quite stable during the day
Increases when people come back home
can you imagine the effect of a diffusion of electric vehicles?
When people will charge their electric vehicles?
just when they’ll come back at home!!
* based on the daily winter weekday maximum demand on the GB transmission system in 2005/06 from
http://www.nationalgrid.com/uk/sys_06/print.asp?chap=2
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effect of the EVs contemporary load
on the grid
higher energy request
during peak hours
overload!!
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existing grids are not able to manage this
overload, because..
 the charging process starts just when the vehicle is plugged in,
provided that the available power on the grid is enough
 the charging process is performed at a fixed rate (usually the
maximum charging rate allowed by the battery)
 if the power request exceeds the available power at the time of plugin, the charging request is rejected and the user cannot charge its
battery
this is the so-called “superdumb” approach:
• no communication between users and service supplier
• no supply/demand adjustments
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a practical example
Available
power
30kW
Ms. Gray 20kWh
Arrival 18:00
Depart 22:00
Max Rate 20kW
at 18.00 Ms. Gray arrives
her charging begins, at a rate of 20kW
10kW are still available at the node
at 18.15 Mr. White arrives
Mr. White 20kWh
Arrival 18:15
Depart 22:00
Max Rate 20kW
the available power at the node
does not suffice
his charging is not possible!
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a practical example
Available
power
30kW
Ms. Gray 20kWh
Arrival 18:00
Depart 22:00
Max Rate 20kW
at 18.00 Ms. Gray arrives
her charging begins, at a rate of 20kW
10kW are still available at the node
at 18.30 Mr. Red arrives
Mr. Red 20kWh
Arrival 18:30
Depart 20:00
Max Rate 20kW
the available power at the node
does not suffice
his charging is not possible!
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at 22.00…
Ms. Gray
Mr. White
State of charge
100%
State of charge
0%
Mr. Red
State of charge
0%
average satisfaction degree= (100%+0%+0%) / (3 users) = 33.3%
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a first possible development…
we could “schedule” the energy
demand of the users
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how time scheduling works…
 if the available power on the grid suffices, the charging
process starts just when the vehicle is plugged in
 if it not possible to satisfy the user request, the user is
queued in a queue containing the users still to be
charged, sorted in accord with their arrival time
 as soon as enough power becomes available, the users
of the queue are served
clearly users must communicate to a control entity
their expected departure times, as well as the instant
at which they are fully charged
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in the same case now..
at 18.00 Ms. Gray arrives
Ms. Gray 20kWh
Arrival 18:00
Depart 22:00
her charging begins, at a rate of 20kW
10kW are still available at the node
Max Rate 20kW
at 18.15 Mr. White arrives
the available power at the node is not enough
Mr. White
20kWh
Arrival
18:15
Depart 22:00
Max Rate 20kW
his charging is not possible!
however he is queued
at 18.30 Mr. Red arrives
the available power at the node is not enough
Mr. Red
20kWh
Arrival
18:30
Depart 20:00
Max Rate
20kW
his charging is not possible!
however he is queued
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at 19.00…
at 19.00 Ms. Gray is fully charged
Ms. Gray 20kWh
her charging ends
Arrival 18:00
Depart 22:00
30 kW are now available at the node
Max Rate 20kW
so, at 19.00 Mr. White can be served
his charging begins, at a rate of 20kW
Mr. White
20kWh
Arrival
18:15
10 kW are now available at the node
Depart 22:00
Max Rate 20kW
however, at 19.00 the available power at the node
does not suffice also for Mr. Red
Mr. Red
20kWh
Arrival
18:30
Depart 20:00
Max Rate
20kW
his charging is not possible!
he is still queued
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at 20.00…
Ms. Gray 20kWh
Ms. Gray is fully charged
Arrival 18:00
Depart 22:00
Max Rate 20kW
Mr. White is fully charged
his charging ends
Mr. White
20kWh
Arrival
18:15
30 kW are now available at the node
Depart 22:00
Max Rate 20kW
the charging of Mr. Red could start
Mr. Red
20kWh
Arrival
18:30
however, he departs
Depart 20:00
so, Mr. Red is not satisfied
Max Rate
20kW
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at 22.00
Ms. Gray
Mr. White
State of charge
100%
State of charge
100%
Mr. Red
State of charge
0%
Average satisfaction degree= (100%+100%+0%) / (3 users) = 66.7%
It’s better than the “superdumb” approach, but…
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it is still a “dumb” approach
Ms. Gray 20kWh
Arrival 18:00
Mr. White
20kWh
Arrival
18:15
Depart 22:00
Depart 22:00
Max Rate 20kW
Max Rate 20kW
Mr. Red
20kWh
Arrival
18:30
Depart 20:00
Max Rate
20kW
Mr. Red has less time at disposal. His charging is more urgent…
using a fixed charging rate of 20 kW with a maximum available
power of 30 kW does not allow performing more than one
charging process at the same time
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summarizing the “dumb” approach :
 the users are arranged in the queue solely on the basis
of their arrival time
 this scheduling process does not take into account that
some users could have less time at disposal to complete the
charging process
the charging process is performed at a fixed charging rate,
without any adapting mechanism to the grid load conditions
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what if…
Ms. Gray 20kWh
Arrival 18:00
Mr. White
20kWh
Arrival
18:15
Depart 22:00
Depart 22:00
Max Rate 20kW
Max Rate 20kW
Mr. Red
20kWh
Arrival
18:30
Depart 20:00
Max Rate
20kW
can you imagine what happens if you could sort the charging
processes by assigning to Mr. Red a higher priority?
can you imagine what happens if you could adjust the charging
rate of each user during the charging process to fully exploit
the 30 kW available from the grid?
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a “smart” approach
 the charging process of each PEV can be scheduled on the
basis of a priority criterion rather than just considering the
arrival times of the users
 the charging rate of each PEV can be adapted to the grid load
condition during the charging process
users must communicate to a control entity
their
arrival and departure times, as well as
their
current state of charge
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how to assign the priority?
you can consider how much energy an user requires
and how much time is still available for its charging
process before the user drive-off
power required
priority=
maximum grid available power
do you think there should be something else
to be taken into account?
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..and the utility profit?
in a realistic scenario, it is presumable that
users will have different electricity rates
this can be an important parameter for the
utility profit
utility would like to give a higher priority to users
which are disposed to pay higher electricity rates
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a more complete priority function
we can also account for the electricity rate of the user
priority= a
power required
electricity rate
+b
maximum grid available power
maximum electricity rate
the influence of the power required and the electricity
rate on determining the user priority can be adjusted by
tuning the weighting coefficients a and b (it is
convenient to set always a+b=1)
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how to decide the charging rate?
you can “spread” the required energy over the entire remaining
time period before the user drive-off
charging rate=
energy required
time before expected user departure
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updating priority and charging rate
priority= a
power required
electricity rate
+b
maximum grid available power
maximum electricity rate
charging rate=
energy required
time before expected user departure
for each user, both the energy required and the time still
available change during the charging process. Therefore,
priority and charging rate must be updated at regular
intervals (time resolution). How do you choose the time
resolution?
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what about the grid structure?
smart grid
manager
two-way
communication
devices
system users, operators and automated devices need to exchange
information about grid operating conditions, so to dynamically
respond to changes in grid condition and user requests
the grid becomes “smart”
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also the vehicle..
Electronic
control
Electrical
engine
Battery pack
Dashboard
Smart charging
manager/grid
interface
on-board control system needs to be developed to support both
data exchange with the grid and advanced charging strategies
in your experiments…
you’ll simulate the charging processes of Electric
Vehicles by using different charging strategies
measure of the effectiveness:
• users satisfaction degree (it is a measure of the
success the system has to satisfy the requests)
• utility profit (it is a measure of how effective is for the system
owner its resource management policies)
you’ll also evaluate the impact of each charging strategy
on the system complexity …
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how to “measure” the system complexity?
you can measure:
1)
simulation time
2)
number of communication
events
the larger the simulation time, the more complex (and then costly) the system
the larger the number of communication events, the more complex (and then
costly) the system
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the tool
you will use a very intuitive Graphical User Interface (GUI)
implemented in Matlab environment
the main Graphical User Interface (GUI) will allow you to
navigate easily through all the tasks to be performed 42
you will create different scenarios…
in the different tasks you will analyze different situations by fixing the grid
available power, the number of users, the user parameters (battery capacity,
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max. charging rate, electricity rate, etc.), the energy dispatching strategy…
… and you will see the system evolution
The tool allows to visualize the satisfaction degree and
the communication events for each user of the system,
as well as the utility profit and the simulation time
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summarizing your tasks
Task 1: you will be familiarized with the basic concepts about
battery charging parameters: available power from the grid,
battery charging rate and capacity, state of charge
Task 2-5: you will compare the superdumb, the dumb, and your
different smart approaches in a small-scale scenario (a local
node of the grid with 6 vehicles at most)
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Part 2 (optional)
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Task 6:
extension to a large scale system
• real grids consists of hundreds of local nodes
• experiment the impact of different strategies
in a large scale scenario rather than focusing
solely on a local node
• in this case thousands of users will be involved
in your experiments..
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statistical analysis
thousands of users will have a random
behavior:
•random arrival and departure times
•random electricity rates
the simulation tool provides you the possibility
to carry out a statistical analysis of a large
scale scenario
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statistics distribution
to perform large-scale analyses, you will decide statistical user profiles.
you can choose uniform or gaussian statistical distributions for the user
arrival and departure times, as well as for their electricity rate
uniform
the probability that a user arrives in a
given time instant x is the same within
the time interval [a, b]
gaussian
the probability that a user arrives in a
given time instant x is higher around the
instant m (mean). However, about 68% of
users will arrive in the time interval [m-s,
m+s]. s is the “standard deviation” 49
for further details please refer to the
project assignment document
and the GUI manual
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useful references
• [1] http://www.youtube.com/watch?v=xKg7EYThJ0c
• [2] R. Pratt et al., “The Smart Grid: An Estimation of the Energy and CO2
Benefits”, Pacific Northwest National Laboratory Report, 2010.
• [3] Emission Facts: Greenhouse Gas Emissions from a Typical Passenger
Vehicle http://www.epa.gov/oms/greenhousegases.htm
• (http://timeforchange.org/cause-and-effect-for-global-warming)
• [4] http://energycenter.org/index.php/technical-assistance/climate-change
• [5] Bureau of Transportation, number of vehicles and vehicle classification
http://www.bts.gov/publications/national_transportation_statistics/html/table_01
_11.html.
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Images credits
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Slide 2. Left: Image by Joe Ross, http://www.fotopedia.com/items/flickr-2254098564. Right: Image by saebaryo,
http://www.fotopedia.com/items/flickr-3455146732
Slide 3. Image by tobym, http://www.fotopedia.com/items/flickr-466229969
Slide 4. Image by Martha de Jong-Lantink , http://www.fotopedia.com/items/flickr-2889561921
Slide 6. Image by net_efekt, http://www.flickr.com/photos/wheatfields/4688140998/
Slide 7. Image by wwf_france, http://www.flickr.com/photos/wwf-france_footage/2916082821/
Slide 9. Image by United Nations Photo, http://www.flickr.com/photos/un_photo/5410822714/
Slide 15. Power plant image by Bruno D Rodrigues, http://www.flickr.com/photos/davipt/164341428/ . Transmission
substsation image by ykanazawa1999, http://www.flickr.com/photos/27889738@N07/4860145679. High voltage
transmission lines image by Henrik Johansson, http://www.fotopedia.com/items/flickr-5515505114. Power
substation image by Iris Shreve Garrott, http://www.fotopedia.com/items/flickr-2310447664. Transformer image by
Paul Chernikhowsky, http://www.flickr.com/photos/pchernik/4099436471/. Low voltage line image by Paul Joseph,
http://www.fotopedia.com/items/flickr-3110129640. Home image by James Thompson,
http://www.flickr.com/photos/jwthompson2/139445633/sizes/l/in/photostream/.
Slide 20. Ms. Gray car image by Gioxx, http://www.fotopedia.com/items/flickr-3095669284. Mr. White car image by
harry_nl, http://www.fotopedia.com/items/flickr-3444637211.
Slide 21. Mr. Red car image by Nadir Hashmi, http://www.fotopedia.com/items/flickr-2227623451.
Slide 38. Two-way communication devices image by HEA Engineering Subject Centre,
http://www.flickr.com/photos/22760956@N08/. Smart grid manager image by npslibrarian,
http://www.flickr.com/photos/npslibrarian/2104253867/.
Slide 49. Uniform distribution, http://en.wikipedia.org/wiki/File:Uniform_distribution_PDF.png . Gaussian
distribution, http://en.wikipedia.org/wiki/File:Normal_Distribution_PDF.svg.
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