Understanding and managing the impacts of PEVs on the electric … · Daily travel costs ($/day)...
Transcript of Understanding and managing the impacts of PEVs on the electric … · Daily travel costs ($/day)...
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IEEE GMS PES 150219
Understanding and managing the impacts of PEVs on the electric grid
Jeff Frolik
University of Vermont
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The next ~30 minutes § The PEV “problem”
§ Cause & Effect – Adoption Heterogeneity – Infrastructure
§ Charge Management – Problem with optimized approaches – The Packetized Charging approach – Results to date – Ongoing work
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The PEV Problem (!?)
§ Models/data indicate PEV charging can put 2x load on neighborhood infrastructure – Level 2 Charging: ~5-7 kW @ 4-5 hrs = ~25
kWh – My house ~ 18.5 kWh/day
§ Premise: Existing neighborhood and substation power distribution infrastructure will not handle significant PEV adoption rates without charge management (CM)
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Pinch Points
5
Substation ~1000 homes
1MVA
Transformer ~12 homes
25 kVA
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Heterogeneity of Adoption Examples: § ½ of Teslas sold in CA (Bloomberg, 2014) § ½ of EVs are sold in 5 cities: SF, LA, Seattle, NY, Atlanta
(GreenCar Reports, 2013) § Fremont, CA has 2x the EVs as the county average
(CleanTechnica, 2014) § Neighborhood effect
Adoption influenced by income, education, politics, geography and incentives
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Infrastructure
Cables and Transformers Excess Current Exceeding Rated Operation Temps
Accelerated Aging Or
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Cable Aging How does rainfall influence the aging of buried cables?
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Modeling Heating in Transformers § Annex G is the IEEE standard
(albeit complicated and intended for distribution transformers)
§ A 25 kVA service transformer is a simple device
§ GA uses real data to develop a simpler and customized model
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Load Data
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Model Fits
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Why Charge Management?
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Reduces stress on existing infrastructure
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Centralized & Optimized CM Approaches
§ Utility collects data pertaining to all PEV customers’ needs – State of Charge (SOC) – Amount of charge needed – Expected arrival/departure times
§ Using this data, an optimized charging schedule (vs. time or power) is developed for each PEV – Ensures ‘fairness’ – Ensures resources are used most efficiently/safely
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Issues as we see them Provider side: - Utilities may not want to deal with lots of data in real time - Need approaches that prevents overload throughout
system – distributed control Customer side: - User behavior is dynamic; advance scheduling may not
work - Users want simplicity - Users want privacy - What is ‘fair’?
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Packetized Charging
§ Treat EV charging as we do data – i.e., as discrete packets in a “net neutral” environment
§ EV’s objective is then to receive the requisite number of packets to complete a charge – charging need not be continuous
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Packetized Charge Management
§ The distribution system can only provide a fix number of power packets during each epoch
§ Task is to manage the demand in an egalitarian manner; i.e., fairness as a metric
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Limit%
Load%
#%of%PEV
s%charging%
PEV%ID%%%%%%
(Frolik, IEVC 2012)
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Inspiration for Packetization
§ Problem is analogous to that found in data communications – leverage those methods
§ Problem is also analogous to other distributed participation control methods – leverage those too
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What can we assume? § Power distribution capacity will vary day to day § PEV charging requirements will vary day to day
and user to user § PEV plug in/plug out times will vary day to day
and user to user
PEV CM problem - random supply and random demand - robust (and simple) and fair CM techniques are
needed that maintain anonymity 21 IEEE GMS PES
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Random Demand Analogy
Problem: How does one share a single resource when the demand is distributed and random?
Objective: Want the overhead to be low. Analogy: Medium Access Control (MAC) in random
access, packet communication channels
- Examples: ALOHA, slotted-ALOHA, CSMA
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Limit%
Load%
#%of%PEV
s%charging%
PEV%ID%%%%%%
(Frolik, IEVC 2012)
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Random Supply Analogy
Problem: Desire to control the number of active participants in a distributed random network
Objectives: Fairness and little communications
Analogy: Quality of Service (QoS) Control in wireless sensor networks using a distributed, automaton-based approach
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Automaton-based design
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§ EV requests a charge during any particular epoch with probability Pk
§ P1 > P2 > P3
§ Successful requests moves EV to a higher automaton state § Failed requests moves EV to a lower automaton state § Controller accepts/denies requests blind to the identity of the requesting EV – privacy maintained
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Let’s test with an experiment § Can the packetized approach keep loads below
limits?
§ How much would one day of travel cost under packetized charging, relative to an “ideal” optimization scenario in which the operator has perfect information about travel patterns?
§ Compare also to a very simple first-come-first served charging scenario
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(Razaei, TSG 2014)
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Assumptions § 320 GM-Volt-like PHEVs § 320 homes (i.e., 50% penetration). § Level-2 charging
§ Travel patterns from US survey data. § 500 kVA load limit § Customers are charged on a time-of-use (peak:
$0.14/kWh / off-peak: $0.10/kWh) rate § Use gasoline if batteries run low.
§ Three methods: FCFS, Optimal, Packetized 27 IEEE GMS PES
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USF 20-Oct-08 28
Daily travel costs ($/day) Costs Travel costs are similar, despite the unrealistic information requirements for optimal approach.
off- peak elec.
peak elec.
gas.
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FCFS
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Charging pattern for 20 of the 320 EVs Green – Charging Red – Charge mitigated Blue – Charge not needed Note: Higher ID cars are not getting full charge
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Opt
imiz
ed
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All EVs charge when plugged in but at variable rates (0-6)
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Pac
ketiz
ed
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Again: Green – Charging Red – Charging mitigated Note: Charging is randomly distributed across EVs
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All Methods Work But…. § Optimized case assumes all EV data is known in
advance so that a fair and efficient charging schedule can be developed
§ The simple first-come, first-served (FCFS) approach requires no advanced data but results in customers not being treated fairly – LCLS!
§ Packetized CM approaches the fairness and efficiency found in optimized schemes and has the simplicity and adaptability of FCFS.
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Implementation
§ Unidirectional and Localized § Bidirectional and Aggregated
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Uni-directional Implementation § Listen only § A EV will randomly listen to a broadcasted signal
to ascertain whether they can take a ‘packet’
§ Broadcast could be from the local service transformer and/or substation
§ No data / requests are sent by EV § Broadcast can be from multiple points
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Bi-directional Implementation Request to Charge / Clear to Charge - A EV will randomly send a request to a local
aggregator to ascertain whether a ‘packet’ can be supported
- Aggregator would have working knowledge of service transformer and substation loads and overall system capacity to support charge
- Similar to anonymous RTS/CTS* from wireless communications. No data is transferred.
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*RTS/CTS – request to send / clear to send
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Caveats § Packetized charging does not guarantee
everyone will receive the full charge desired.
§ Users will need incentives to play fairly in this scheme based on chance (green zone) of service
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§ Users with urgent needs should have the option to by-pass approach if they are willing to pay the full rate (red zone)
§ EV chargers could be equipped with an urgency dial that indicates the price/kWh for a given guarantee of service
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Ongoing Work
§ Results to date are based on simulations
§ Hoping to implement a pilot implementation in partnership with a start up and VT concerns
§ Formalizing the analysis of the approach and considering other automaton actions
§ Understanding consumer reaction to charge management and developing appropriate incentives
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Questions & Feedback
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