energy, power & intelligent control Intelligent Grid Interfaced Vehicle Eco‐charging ‐ iGIVE 1 Prof Kang Li, Prof Yusheng Xue, Prof Shumei Cui, Prof Patrick Luk Queen’s University Belfast Cranfield University Harbin Institute of Technology State Grid Electric Power Research Institute 20‐21 April 2016
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Prof Kang Li, Prof Yusheng Xue, Prof Shumei Cui, Prof Patrick Luk
Queen’s University BelfastCranfield University
Harbin Institute of TechnologyState Grid Electric Power Research Institute
20‐21 April 2016
energy, power & intelligent control
Project overview2
EP/L001063/1 NSFC51361130153
energy, power & intelligent control
Aim: to develop an intelligent grid interfaced vehicle eco‐charging(iGIVE) system for more reliable, more flexible and efficient, and moreenvironmental friendly smart gird solutions for seamless integration ofdistributed low‐carbon intermittent power generation and largenumber of EVs.
Challenges: Real‐time estimation of SOC, SOH, etc On‐board charging apparatus piecemeal and non‐systematic Environmental acceptance: harmonics, EMI, etc. Dispatch for coordinated EV charging/discharging Behaviours of different actors affect whole system, e.g. reliability Information platform
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Project overview
energy, power & intelligent control
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WP1) Development of energy and information flow framework for iGIVEWP2) Power flow control‐based battery modelWP3) Bi‐directional traction drive charging systemWP4) Model for environment friendly EV battery chargingWP5) Optimal dispatching strategy for EV charging and dischargingWP6) Holistic system model for the impact of EV actors
G‐Wiz electric car• DimensionsL 2.6m, W 1.3m, H 1.6m• Motor power: 6kW continuous• Weight: 400 kg excl batteries• Battery: 200AH, 48V, Lead‐acid• Range: 48 miles
Retrofit: Li‐ion battery, BMS, wireless communication, standard charging plug Electric DeLorean
energy, power & intelligent control
Test dataHPPC tests at different temperatures: [0, 10, 23, 32, 39, 52] on LiFePO4 (10Ah)
Thermal test
energy, power & intelligent control
Simplified Thermoelectric Battery Model
∗ ∗∗ ∗ ∗
Battery electrical model and SOC estimation.
[1] Cheng Zhang, Kang Li, Lei Pei, Chunbo Zhu. "An integrated approach for real‐time model‐based state‐of‐charge estimation of lithium‐ion batteries." Journal of Power Sources 283 (2015): 24‐36
: battery internal thermal capacity: battery shell thermal capacity: internal temperature :shell temperature: environment temperature
Battery thermal model and internal temperature estimation.
[2] Cheng Zhang, Kang Li, Jing Deng. "Real‐time estimation of battery internal temperature based on a simplified thermoelectric model." Journal of Power Sources 302 (2016): 146‐154
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energy, power & intelligent control
Battery modelling results
Part of the electrical modelling resultsThermal modelling results
energy, power & intelligent control
SOH estimation12
State of Health (SOH) estimation method
loss _LLI LAM liNEQ Q Q
32
246 72
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,0
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amb
Ah thr Ah thrdis
cap capdis
kkAh thrt
losscell
Q Qkk k IQ QI Ah thr
EOD EOCcap dis
QQ k e
I
Q kk U U e eQ I
Degradationcycle
Temp (°C)
Measured SOHP (%)
Est.SOHP (%)
Measured SOHE (%)
Est.SOHE (%)
0 50 100.0 100.6 100.0 100.6
100 30 98.4 99.8 95.4 98.7
200 10 92.2 94.7 87.7 91.8
300 −10 71.3 70.9 75.2 76.7
energy, power & intelligent control
Battery management system13
A battery management system has been setup to control
charging and discharging of lithium‐ion battery pack
energy, power & intelligent control
Battery Temperature ControlConsider the developed battery thermal model, an objective function is proposed
which is a trade‐off between cooling performance (characterized by battery aging), and cooling parasitic energy consumption, i.e.,
Then different controller can be designed and optimized.
Single Phase PWM AD/DC LCL Filter with a small resistor
Bi-directional DC/DC Dual Active Bridge
load
ACi
ACU
2L 1L
C CU1C
DCI
DCU
1Q
3Q
2Q
4Q
2i
2L1L
C1i
inU outUfR
-200
-100
0
100
200
Mag
nitu
de (d
B)
102
103
104
105
106
-270
-225
-180
-135
-90
Phas
e (d
eg)
Bode DiagramGm = Inf , Pm = 90 deg (at 278 rad/s)
Frequency (rad/s)
LCL滤波
改进的LCL滤波
L型滤波
Vin
C1
Vo
CoA
B
C
D
Q1T
Q2 Q5 Q6
Q7 Q8Q3 Q40.4 0.5 0.6 0.7 0.8 0.9 1 1.13
3.5
4
4.5
5
5.5
6
6.5
Po(kW)
THD
(%)
控制 流加 法电 权PR +控制 流加 法电 权PI +控制PI控制PR
THD using different control
energy, power & intelligent control
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Interface and Integration of Microgrid and EVs
energy, power & intelligent control
Hierarchical Management for Integrated Community Energy System
Group-based DR management• Thermostatically controlled loads• EVs considering eco-charging• EVs and TCLs coordination.
Integrated optimal power flow• Heat, gas, and electricity flows• Three-phase electrical system• Microgrids and distribution networks.
energy, power & intelligent control
Flowchart of the three-phase DR.
Three‐phase Demand Response Dispatch
energy, power & intelligent control
A hybrid experimental‐economics‐based simulation platform
A questionnaire design method proposed. Probabilisticcharacteristics of potential EV users’ purchase and traveldecisions extracted from questionnaires
A multi‐agent model was built to reflect multi‐dimensionaljoint probability distribution of the respondents’ willingness
A verification method designed – potentially used for modelingof other group decision behaviors
A framework to study the Interactivity of EV and power gridoperation proposed
energy, power & intelligent control
archives
SCADAinquiry
experi.sim.
mathmodel
Stat. data caus. data behav. data
stat. anal caus. anal
Multiagentsmodelling
experi. econ. interact. hybrid simul.real person
caus.find
knowl.ext.
knowl.ext.
raising eff.
adaptivity
cros. fielddecis. sup.
Multi‐source heterogeneous data
Collection, analysis, knowledge extraction and decision support of big data
energy, power & intelligent control
• Difficulties of experimental economics to study group behaviors– The number of participants is limited– Repeated trials may be poorly comparable with each other
• Extracting correlation inf. from big data to model group behavior – Historical data, real‐time data acquisition, questionnaire– Matching probability distribution of respondents’ behaviors – Verifying the validity of multi‐agent by comparing the multi‐agent Monte‐
Carlo simulation results and the questionnaire results
Modeling and Simulation Methods of EV Users’ Behaviors
Dynamic Simulation
Real Participants Computer Agents
Knowledge Extraction
Information Collection
Decision Support
HMI Data
Transfer
System Models
Decision Modes
• A hybrid experimental‐economics‐based simulation– Using computer agents to
replace a certain group of experts
– Using real participants to play several key roles
energy, power & intelligent control
Behavior‐involved Projects Studied by Using Hybrid Simulations
Mathematical Models
Experimental Economics Simulations Platform
Multi-agentsActual participants
19. 04. 2016 XUE Yusheng, SGEPRI, China 27
energy, power & intelligent control
ActualSystem
Disturbance
Researcher
BehavioralAgents
HumanParticipants
SimulationPlatform
Mathematical Model
Hybrid Simulation
Multi-agent
Hybrid SimulationPlatform
Hybrid simulations including game behaviors
Mathematical Model
Mathem. Model
Mathematical Model
SimulationPlatform
SimulationPlatform
Computer Technology
HumanPartic.
Disturbance Disturbance
Researcher
Disturbance Disturbance
Researcher
Researcher
Disturbance Disturbance DisturbanceDisturbance
Researcher
19. 04. 2016 XUE Yusheng, SGEPRI, China 28
energy, power & intelligent control
DSMES supports dynamic interactive simulations of cross‐domain to study interactions among physical power system,power market, emission trading, etc.
Dynamic Simulation platform for Macro‐Energy Systems (DSMES)
19. 04. 2016 XUE Yusheng, SGEPRI, China
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energy, power & intelligent control
Experimental Economics Research on EV Purchase Willingness
Verification of multi‐agent model accuracy
Agent‐1 ...
Ranking of factors’ importance
Factors affecting purchase decisions
Psychological thresholddistribution of a single factor
Extract information
The joint distribution of all factors’ psychological thresholds
Agents’ probabilistic model based on the joint distribution
Tested EV types
Agent‐n
StatisticsQuestionnaire‐1 ... Questionnaire‐n
Statistics
Hybrid simulation with true participants and verified agents
Questionnaires
Passed
Extract information
Extract information
Creating agents
energy, power & intelligent control
Flow Chart for Generating Multi‐Agents Reflecting the Purchase Willingness of EV Potential Users
i = 1
Mapping the distribution of factors’ importance
N
Y
Comparing the simulation results
i < the total number of agents
Mapping the distribution of each factor’s threshold
Multi‐agents modeling
Decision of the ith agent
i = i+1
Extracting deep information fromanswer sheets
Generating individual agents as many as needed
Questionnaires data
energy, power & intelligent control
PMU Telecoms Framework
ApplicationInertial EV response
energy, power & intelligent control
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Wind Power Integration
Time Source Receiver(GPS)
Data Acquisition(GPS Disciplined)
Signal Processing(Embedded PC)
Data Representation
(IEEE / IEC Std.)
Input Signals
Output Synchrophasor
energy, power & intelligent control
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Electric Vehicles with Smart Grid
f
df/dt
P
t
t
t
charge
discharge
2-3 s
t=0 s
0
10%
Simulation of inertial response
energy, power & intelligent control
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Wind and Gas Integration
• Ramping carried out per unit generated• Wind resulted in an increase from 54 MW/GWh to 57 MW/GWh• Flexibility is key for the future generation portfolio• Overall total gas generation costs fell 13% with the inclusion of wind• This fall is masked by less generation• Similar costs on a per unit basis
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35
45
55
65
75
85
95
01/01/2011 11/04/2011 20/07/2011 28/10/2011
Ramp pe
r Unit G
enerated
(M
W/G
Wh)
Wind No Wind
40
45
50
55
60
65
70
31/12/2010 10/04/2011 19/07/2011 27/10/2011
Cost per Unit G
enerated
(Eur/G
Wh)
Wind No Wind
energy, power & intelligent control
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Wind and storage• Counters wind variability
– Reduces the requirement of thermal generation to fulfil residual demand• Increases system flexibility
– Fast acting response to system status• Can provide arbitrage opportunities
– Peak Load Shaving– Charge low, Discharge High– Unit given a SRMC of 150 €/MWh
• Reduce wind power curtailment– Reduction of 6 GWh to 1.7% of total available capacity
energy, power & intelligent control
Add: No.19 Chengxin Avenue, Nanjing 211106, Jiangsu Province, China
This 3‐day workshop is featured with keynote speeches, industrial exhibitions andknowledge transfer training, technical presentations and posters, panel discussions,break‐out brain‐storming, academic visits, formation of consortium and specific taskgroups, and networking events and social visits