Modelling Electric Vehicles in Optimal Power Flow Problems

9th January, 2012

Dr. Salvador Acha – Imperial College Research Associate

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Presentation Contents

• Introduction, context and research questions

• Agent based models

• Optimal power delivery to EVs

• Case study

• Conclusions

• Q&A

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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.

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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.

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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 formulated.

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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 fuelledwith power generated from coal

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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 pollutemore than ICE vehicles

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The UK Fuel Mix

• Today the UK fuel mix is composed from 48% gas, 26% coal, 18% nuclear, 6% low carbon.

Figure. Exemplifies the differences in the carbon emitted for each MWh of generated once wind power is prominent.

The increased presence of wind powerwill naturally decrease the carboncontent of the fuel mix in the UK

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The UK Power Market

• Renewable sources not only affect emissions, but also wholesale prices of electricity.

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.

Wind power will displace marginalplant, thus if the wind blows spotprices mostly at peak times will bereduced

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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?

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Power Market Interactions

Figure. Illustrates the interactions a global coordinator should consider in order toprovide optimal load control signals to EV users.

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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 of EVs on an electricity network.

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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

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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

Multi-objective Function

• OPF model was implemented in gPROMS.

• For total energy cost minimisation

• For spot and carbon market costs

• For operating network costs

,,cos minmin DNOEVts CCEV

n n

PPEVEV PC1 1

,minmin

n n

DNODNODNO bPaPC1 1

2,,, minmin

(1)

(2)

(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 school-going 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):– 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.

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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

Figure. ABM allows DNOs to estimate the influence EVs can play in local networks – penetration and SOC levels give load flexibility.

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Aggregated EV State of Charge and Load Flexibility in Node 1

Node 1 MaxSOC

Node 1 SOC

Node 1 LEV

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Aggregated EV State of Charge and Load Flexibility in Node 2

Node 2 MaxSOC

Node 2 SOC

Node 2 LEV

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Aggregated EV State of Charge and Load Flexibility in Node 3

Node 3 MaxSOC

Node 3 SOC

Node 3 LEV

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Aggregated EV State of Charge and Load Flexibility in Node 4

Node 4 MaxSOC

Node 4 Soc

Node 4 LEV

EV OPF Results

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.

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Electric Load Profile at Supply Point - Scenario 1

Total Mobile Load

Total Static Load

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Electric Load Profile at Supply Point - Scenario 2

Total Mobile Load

Total Static Load

EV OPF Results

Figure. The solver provides valuable granular detail on nodal charging of EVs.

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Nodal Comparison of EV Charging Profile - Scenario 1

N1 Mobile Load

N2 Mobile Load

N3 Mobile Load

N4 Mobile Load

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Nodal Comparison of EV Charging Profile - Scenario 2N1 Mobile Load

N2 Mobile Load

N3 Mobile Load

N4 Mobile Load

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!