Dr. Salvador Acha – Imperial College Research Associate
Modelling Electric Vehicles in
Optimal Power Flow Problems
9th January, 2012
Presentation Contents
• Introduction, context and research
questions
• Agent based models
• Optimal power delivery to EVs
• Case study
• Conclusions
• Q&A
2
Introduction
• EV deployment represents a challenging task
in power systems since its intrinsic mobility
characteristic means loads can appear and
disappear in different parts of the network.
• Addressing this issue will require thorough
research in driving patterns and vehicle
usage; thus providing utilities the capacity to
forecast when and where EVs may require
energy for their travelling needs while also
guaranteeing they are capable of supplying
stochastic demand whenever required.
3
Context
• Commercial EVs signal that the electrification
of the transport sector is imminent.
• This represents a good opportunity to reduce
carbon emissions from transport activities.
• However, EVs can only be effective mitigating
carbon if the electricity used to charge the
batteries are through low-carbon technologies.
• EVs offer great load flexibility potential:
– Vehicles are idle 95% of the time;
– 40 miles/day average urban travel (EU data);
– Spatial and temporal considerations must be taken.
4
Context
• Demand response refers to “deliberate load
control during times of system need, such as
periods of peak demand or high market prices,
thus creating a techno-economical effective
balance between supply and demand”.
• In order for demand response programs to be
effective and unbiased it is necessary to apply
a holistic approach in assessing and
quantifying the tradeoffs EVs bring to power
systems.
• In this work an ABMEVOPF framework is
5
formulated.
Energy W2W Efficiency
• Comparing km/lt vs. km/kWh is elemental.
• The energy W2W equation follows the energy
content of the fuel from its original source up
to its point of consumption. EVs are more energy efficient
than ICE units even when fuelled
with power generated from coal
6
Carbon W2W Efficiency
• No electric car is carbon free.
• The carbon W2WCO2 equation translates the
W2W efficiency into carbon emissions per
vehicle model (kg/km). Based on the calculations EVs when fuelled
with power generated from coal will pollute
more than ICE vehicles
7
The UK Fuel Mix
• Today the UK fuel mix is composed from 48%
gas, 26% coal, 18% nuclear, 6% low carbon.
The increased presence of wind power
will naturally decrease the carbon
content of the fuel mix in the UK
Figure. Exemplifies the differences in the carbon emitted for each MWh of generated once 8
wind power is prominent.
The UK Power Market
• Renewable sources not only affect emissions,
but also wholesale prices of electricity.
Wind power will displace marginal
plant, thus if the wind blows spot
prices mostly at peak times will be
reduced
Figure. The incursion of intermittent energy sources in the UK fuel mix will have an
increasing influence in the bids and offers of the spot market.
9
Research Questions
• How can we model this complex problem that
merges transport and power systems?
• How can EVs and network operators
coordinate their needs for the overall benefit
of the infrastructure?
• How can EVs become incentivised to charge
when electricity is cheaper and less dirty?
• Is it possible to manage EV profiles in order
to minimise negative network impact such as
peak demand?
10
Power Market Interactions
Figure. Illustrates the interactions a global coordinator should consider in order to
provide optimal load control signals to EV users.
11
Modelling Framework
• A combined formulation is presented in this
work in order to represent and address the
research questions at hand.
Figure. Integration of agent-based and optimal power flow models to analyse the effects of12
EVs on an electricity network.
Agent Based Model
• The ABM describes the behaviour of EV
owners as their activities bring them to
various parts of the city at different times.
• Random distribution is employed to
determine departure times and next activity.
• At each interval plugged EVs are aggregated
to give SOC and estimated rate of charge.
• ABM was implemented in Repast Symphony
– Input: driver profiles, EV capacity and travelling
efficiency, city layout (building, road, and network)
– Assumption: EV parked = EV plugged
– Result: EV energy forecast to coordinator
13
EV Power Flow
• A coordinated optimal power flow is used to
determine when, where and by how much
EVs should charge in a local network.
• EV demand is estimated based on the data
given by ABM simulation.
• The multi-objective function problem tries to
consider key drivers that look to influence
how EV technology can be charged efficiently
from day to day. These are represented in
monetary terms as follows:
– Day ahead spot electricity market prices
– Cost from charging with carbon electricity
– Network operating costs for energy delivery
14
Multi-objective Function
• OPF model was implemented in gPROMS.
• For total energy cost minimisation
min EVcos ts  minCEV ,  CDNO , 
(1)
• For spot and carbon market costs
n
min C EV
 n

 min   PEV ,   P   P 
 1  1

(2)
• For operating network costs
n
min C DNO ,
 n
2
 min   aPDNO ,  bPDNO , 
 1  1

(3)
OPF Constraints
Case Study
• Goal: demonstrate that through ABM the
(temporal and spatial) load flexibility on electrical
networks can be estimated and exploited.
• Driver profiles (14 profiles):
– People with a job, either at an office or leisure centre,
who may or may not have kids of school-going age
(10).
– People without a job, but who have kids of schoolgoing age (2).
– Pensioners and other people who do not have to go
to work and/or to a school (2).
Case Study
• EV characteristics (250
agents):
– EV unit is similar to the
Nissan Leaf; 24 kWh
capacity, 100 miles on a
full charged battery.
– Max charging rate per EV
is 3.84 kW, 16 A, 240 V
• City layout (3 areas):
– All stored geographical
data is stored using GIS.
Figure. Map of the urban area considered in
the case study.
Case Study
• Network features and load profiles (4 nodes):
MW
– 11 kV level with a radial topology, operating under
balanced conditions and represented by its positive
sequence network. The load in each node is assumed
to be a 3 phase balanced load
Figure. Residential and commercial load profiles used for the urban area.
Case Study Scenarios
• The case study explores 2 scenarios:
– 1st scenario takes the current power generation
portfolio in the UK to depict how optimal EV charging
would look like within the present context
– 2nd scenario attempts to look at a future in which
power is more expensive, renewable energies have
an important presence (20%) and carbon emissions
are taxed as well
– In all scenarios the same load profiles and number of
EVs are used, the only changing variables are fuel
mix and energy costs/emissions
– Simulation runs from Thursday 6 am to Sunday 6 am
ABM Results
Aggregated EV State of Charge and Load Flexibility in Node 2
Aggregated EV State of Charge and Load Flexibility in Node 1
4
4
Node 2 MaxSOC
Node 1 MaxSOC
Node 1 SOC
3.5
Node 1 LEV
3
Node 2 LEV
3
2.5
MWh
2.5
MWh
Node 2 SOC
3.5
2
2
1.5
1.5
1
1
0.5
0.5
0
06:00 10:00 14:00 18:00 22:00 02:00 06:00 10:00 14:00 18:00 22:00 02:00 06:00 10:00 14:00 18:00 22:00 02:00
0
06:00 10:00 14:00 18:00 22:00 02:00 06:00 10:00 14:00 18:00 22:00 02:00 06:00 10:00 14:00 18:00 22:00 02:00
Time
Time
Aggregated EV State of Charge and Load Flexibility in Node 4
Aggregated EV State of Charge and Load Flexibility in Node 3
4
4
Node 4 MaxSOC
Node 3 MaxSOC
Node 3 SOC
3.5
Node 3 LEV
3
Node 4 LEV
3
2.5
MWh
2.5
MWh
Node 4 Soc
3.5
2
2
1.5
1.5
1
1
0.5
0.5
0
06:00 10:00 14:00 18:00 22:00 02:00 06:00 10:00 14:00 18:00 22:00 02:00 06:00 10:00 14:00 18:00 22:00 02:00
Time
0
06:00 10:00 14:00 18:00 22:00 02:00 06:00 10:00 14:00 18:00 22:00 02:00 06:00 10:00 14:00 18:00 22:00 02:00
Time
Figure. ABM allows DNOs to estimate the influence EVs can play in local networks –
penetration and SOC levels give load flexibility.
EV OPF Results
Electric Load Profile at Supply Point - Scenario 1
10
Total Mobile Load
Total Static Load
8
MW
6
4
2
0
06:00 10:00 14:00 18:00 22:00 02:00 06:00 10:00 14:00 18:00 22:00 02:00 06:00 10:00 14:00 18:00 22:00 02:00
Time
Electric Load Profile at Supply
Point - Scenario 2
10
Total Mobile Load
Total Static Load
8
MW
6
4
2
0
06:00 10:00 14:00 18:00 22:00 02:00 06:00 10:00 14:00 18:00 22:00 02:00 06:00 10:00 14:00 18:00 22:00 02:00
Time
Figure. Load profiles seen from the supply point will evolve over time as EV penetration
increases and random prices occur – the solver is quite efficient in indicating ideal
charging times.
EV OPF Results
Nodal Comparison of EV Charging Profile - Scenario 1
1.0
N1 Mobile Load
0.9
N2 Mobile Load
0.8
N3 Mobile Load
0.7
N4 Mobile Load
MW
0.6
0.5
0.4
0.3
0.2
0.1
0.0
06:00 10:00 14:00 18:00 22:00 02:00 06:00 10:00 14:00 18:00 22:00 02:00 06:00 10:00 14:00 18:00 22:00 02:00
Time
Nodal Comparison of EV Charging Profile - Scenario 2
1.0
N1 Mobile Load
0.9
N2 Mobile Load
0.8
N3 Mobile Load
0.7
N4 Mobile Load
MW
0.6
0.5
0.4
0.3
0.2
0.1
0.0
06:00 10:00 14:00 18:00 22:00 02:00 06:00 10:00 14:00 18:00 22:00 02:00 06:00 10:00 14:00 18:00 22:00 02:00
Time
Figure. The solver provides valuable granular detail on nodal charging of EVs.
Conclusions
• The ability to determine optimal charging profiles of EVs
is paramount in developing an efficient and reliable
smart-grid.
• This work has attempted to merge transport related
modelling concepts from EV travel with traditional
optimal power flow issues in order to identify ideal EV
charging strategies that enhance power network
performance.
• Power system engineers need to work hard to tackle the
many issues surrounding EVs, but we must be confident
we can model such complicated problems. A simplified
EV + OPF model was presented here in order for
engineers to grasp key concepts.
Conclusions
• The work presented makes new formulations to address
what energy spot and carbon markets bring to future
power systems.
• Coordination between power and transport sectors will
be paramount. In this work ABM and OPF worked hand
in hand.
• Stakeholders need to address carbon issues both in ‘real
costs’ and W2W analysis, otherwise EV technology will
not actively contribute to reduce GHG emissions.
• Results suggest incentivised EV charging via power
markets can yield meaningful results if methodologies
such as the ones presented here are continued to be
explored.
Thank you for your
attention!