The role of electric mobility in future Energy Systems
Dr. ir. Zofia Lukszo
With collaboration with dr. Remco Verzijlbergh
Section Energy and Industry
Technology, Policy and Management
@: Z.Lukszo@tudelft.nl
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Content
• Why electric mobility?
• Responsive demand
• Are the goals of many actors involved the same?
• What about the environment?
• Why EVs can be compared to cold storage warehouses?
• What can we learn from looking at different price scenario’s?
• Future work
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Future energy systems
Old schedule generation to meet demand
New schedule demand to meet generation e.g. electric mobility

Electric mobility
How can electric mobility contribute to a more sustainable transportation & electrical power system and on the same time align the interests of its relevant actors?
See: Remco Verzijlbergh, The Power of Electric Vehicles,
PhD Thesis TU Delft, 2013, http://repository.tudelft.nl/

Why electric mobility - CO
2 air quality, noise polluttion emission

Energy usage households +/- 10 kWh
7
4
3
2
6
5
1
0
Energy demand (kWh/day)

Power sector complex socio-technical system

Standard Household Profile

Estimation of the expected energy usage of EVs
Data from Mobility Research Netherlands
Average: ~34 km
~ 90% < 100km
Ministry of Transport, Public Works and Water Management, “Mobiliteitsonderzoek Nederland (in Dutch)”
Available: www.mobiliteitsonderzoeknederland.nl

Charging scenario's and network load
Based on real life data

Network load:100 houses and 50 EVs
Price control
Load Control
Imbalance Control
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Separate EV demand profiles

Electric mobility in a city
– city of Utrecht
Load flow analysis shows:
• 10% electric mobility 
24% overloaded
• Reference case (merely organic growth)
 19% overloaded
See E.J. Kleiwegt , Electric Mobility: on the Road to Energy Transition:
A technical and actor assessment of social costs of electric mobility , Master Thesis, TU Delft, 2011 http://repository.tudelft.nl/

Example – city of Utrecht
Use calculations for critical component map
Green / Yellow /
Red locations for installing charging stations

Merit order vs emission – two cases
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D A
14

CO
2 emissions of EV charging as a function of CO
2 price
15
A
D
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Dispatch profiles for different vehicles scenarios
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Charging strategy based on predicted price
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Negative price?
Conventional, wind and solar power and spot prices for the German system on
June 16th
2013.

Responsive demand – cold storage
Old schedule generation to meet demand
New schedule demand to meet generation e.g. with a cold storage warehouse

Matching renewable energy and demand response through price
System model:
• Cold store has PV generation on site
• PV production known in advance
• Pays price C in
(t) for energy, receives C
• Temperature upper bound T max out
(t)
Goal: Investigate relations between demand response strategy of a cold store and electricity prices & Evaluate different pricing regimes on optimal energy use

Physical model of cold store
Heat balance
Incoming heat
Outgoing heat
Resulting equation for T dynamics
Discretized in time

System model
• Cold store has PV generation on site
• PV production known in advance
• Pays price C in
(t) for energy, receives C out
(t)
• Temperature upper bound T max

Optimization formulation
Objective function constraints

Compare cold store with EV optimization problem
Optimization problem
State dynamics

Price scenarios
A: flat tariff
B: flat double tariff
C: day-night tariff
D: APX based real time tariff
E: APX based real time tariff, high solar penetration

Comparison
• Optimal cooling trajectory depends strongly on tariff structure.
• Local use of PV energy depends on tariffs
• Most 'value' of control in case with high solar penetration.
• The effective use of demand response requires the right tariff structure

New plans

NWO URSES - CaPP Project
• Design, Management and Control Systems for multimodal, detachable decentral sustainable energy systems
• Car as Power Plant as a multi-modal system (power, transport, gas/hydrogen, heat)
• ICT and business models for CaPP
• Detachable decentral = fuel cell cars

NWO URSES – CaPP Project
• design, assess and analyse the fuel cell car as power plant (CaPP) in integrated transport and energy systems
• investigate and design robust control systems of CaPPbased smart energy systems
• explore effective incentive and organizational structures for the emergence of CaPP integrated energy and transport systems

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Most urgent question
• How to reduce uncertainty for actors in the energy chain by developing the science and tools that are needed for smart energy systems?
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