1 ChargeFlex – Increased grid capacity for EV charging through smart management of available flexibility nordic SMART electric transport conference, Oslo November 20 th 2015 Stig Ødegaard Ottesen, Head of R&D eSmart Systems/PhD Candidate NTNU
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ChargeFlex – Increased grid capacity for EV chargingthrough smart management of available flexibility
nordic SMART electric transport conference, Oslo November 20th 2015
Stig Ødegaard Ottesen, Head of R&D eSmart Systems/PhD Candidate NTNU
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Presentation outline§ Problem description§ The ChargeFlex project§ Smart Charging strategies§ eSmart Systems
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Problem description§ Simultaneous charging of EVs may lead to problems in
the power system: Violation of capacity limits
§ Most probable in the local distribution grid and at the end-‐users’ premises• Overload of transformers, lines/cables or fuses• Economic problem: Power/capacity based tariffs
§ Possible solutions• Reinforce the grid • Introduce battery banks/local generation• Control the charging process -‐ “Smart Charging”• Or a combination
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The ChargeFlex project
§ 3 years, started 2015, app. 12 MNOK§ Funded by RCN Pioneering research and innovation§ «Increased grid capacity for EV charging through smart management of available flexibility»
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Process and research need
§ Big data§ Uncertainty§ Dynamics
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Pilots
Ladetorget, Moss Remmen kunnskapspark, Halden
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Charging site model
§ Our problem: How to schedule• Power to each charging point• Charging/discharging each storage unit• So that
• The capacity limit is not violated• The charging demand is met
§ eSmart Systems has developed two methods1. A rule-‐based algorithm 2. An optimization-‐based method
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Common for both methods§ We split planning horizon into periods (time slots)§ Each EV
1: arrives to the charging site and connects to a charging point, starts charging, 2: gets fully charged, 3: disconnects and departs.
§ Each time we enter a new period, we receive new information and we decide new schedules (iterative planning)
§ We base our decision only on available information
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Rule-‐based method
§ Simple rules, easy to implement§ Myopic, only see “here-‐and-‐now”, does not
require additional information
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Optimization-‐based method
§ EV drivers enter information about charging demand and preferred/expected time for departure
§ We have information to postpone some charging and still deliver the demand § We have flexibility and formulate the problem as a MIQP§ Objective: To deliver as much of the charging demand as possible – if not possible:
to distribute the not delivered demand
§ Subject to not violating the charging site capacity limit
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Case study
§ We have implemented the two scheduling methods§ They are tested/verified in a case study
• A projected charging site outside office building• Charging data from a similar type of building• We have sampled datasets
§ We analyze several combinations of technology and control method§ Research questions:1. If we are going to deliver all EV charging demand, what is the least maximum imported power needed?2. If the maximum imported power is given, how will the different methods deliver the EV charging
demand?
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Summary of findings
§ We perform a case study to evaluate the methods:• Largest reduction in maximum power by introducing
either Smart charging or storage• Combination of Smart charging and storage further
decreases max power, but only slightly• Optimization-‐based method outperforms Rule-‐based in
all situations except when situation is very limited or only slightly limited
• Difference between the two methods decrease when introducing storage and generation
§ Methods will be tested in pilot site in the ChargeFlexproject
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eSmart Systems
§ Startup-‐company, established 2013§ «Child» of NCE Smart Energy Markets§ Located in Halden§ Funded partly through Innovation Norway, utilities and employees§ App. 30 employees, 3 full-‐time researchers§ Business idea based on 2 mega trends
The Nexus of ForcesSource: Gartner 2013
Disruptive Innovation the Way ForwardFor High Value Adding Economy
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Thank you for your [email protected] +47 90973124