2017 atlanta regional user seminar - using opal-rt real-time simulation and hil system in power and...

Opal-RT Regional User Seminar

Using Opal-RT Real-Time Simulation and HIL System in Power and Energy

Systems Research

Shuhui Li

Department of Electrical & Computer Engineering

The University of Alabama

Presented on

February 15, 2017

Atlanta, GA


Contents

1. Opal-RT system at UA

2. Solar energy conversion, generation and grid integration

3. Charging stations and transportation integration

4. Microgrid control and management

5. IPM motor control: EV and automation

6. NSF I/UCRC Center for efficient vehicles and sustainable

transportation systems (EV-STS)


Renewable Energy Systems Laboratory (RESyL)

RESyL

Science & Engineering Quad

Science and Engineering Quad


Opal-RT HIL and compatible hardware facilities

Target computer #1

Target computer #2

Target computer #3

Target computer #4

Hardware interface #1

HardwareFacilities


PCs connected to Opal-RT system

High-performance PC

Local area network

Opal-RT system


Contents






6. NSF I/UCR Center for efficient vehicles and sustainable



Solar Photovoltaic Power Generation Systems

Grid connected PV system


Problems- Uneven Solar Irradiation Conditions

Clouds will cause a shading problem


Central dc/ac and dc/dc converters

Overall

0 100 200 300 400 5000

5

10

15

20

Vs (V)

Pow

er

(kW

)

None

50%

100%

0 100 200 300 400 500-300

-200

-100

0

100

Vs (V)

Pow

er

(W)

None

50%

100%

Shaded cell


String converter based PV system

Central dc/ac inverter and string dc/dc converters

String inverter configuration


Micro converter based PV system

dc/dc optimizers per module and a central inverter

Microinverter PV system


PV Module with Bypass Diode

Vs

Is

0 100 200 300 400 5000

5

10

15

20

Vs (V)

Pow

er

(kW

)

full-sun

n=1

n=2

n=3

n=4

n=6

n=9

n=12

n=18

n=36


Computational and Hardware Experiments

0 4 8 12 16 20 2450

60

70

80

90

Tem

pera

ture

(F)

Time (Hour)

0 4 8 12 16 20 240

200

400

600

800

1000

Sol

ar Ir

radi

atio

n(W

/m2)

Temp

Irra

0.5 1 1.5 2 2.50

5

10

15

20

Time(s)

Out

put

Pow

er (

kW)

Max IC SF S-PI

CPU 1

CPU 3 CPU 2CPU 4


Grid-Connected PV and Energy Storage System

Pref

Qref


Artificial Neural Network for Control and Grid Integration of Residential PV Systems

MPPT Control for dc/dc converter

ANN Control for dc/ac inverter


0 0.5 1 1.5 2 2.5200

220

240

260

280

300

320

Time (s)

(a) d

c-lin

k volt

age (

V)

Vdc

0 0.5 1 1.5 2 2.5 3

Time (s)

Vdc

2.66 2.68 2.7 2.72 2.74

-20

0

20

40

Time (s)

(b) g

rid cu

rrent(

A)

Igrid

2.66 2.68 2.7 2.72 2.74

Time (s)

Igrid

0 0.5 1 1.5 2 2.50

1k

2k

3k

Time (s)

(e) P

V po

wer(W

)

Ppv

0 0.5 1 1.5 2 2.5 3

Time (s)

Ppv

0 5 10 150

5

10

Harmonic order

(f) M

ag(%

of F

unda

men

tal)

THD=4.67%

0 5 10 15

Harmonic order

THD=12.98%


Contents









Energy Storage for Grid Power Leveling

Help make energy sources, whose power

output cannot be controlled, smooth and

dispatchable.


Grid Integration: EV characteristics

Three different kinds of vehicles make up the EV fleet:

• Plug-in Hybrid Electric Vehicles (PHEVs)

– Hybrid vehicles that run on an internal combustion engine with batteries that can be recharged by connecting a plug to an external power source.

– Larger batteries than traditional hybrid vehicles (e.g., 5-22 kWh).

– Unlimited driving range because of hybrid engines

• Extended Range Electric Vehicles (EREVs)

– Electric vehicles with relatively large batteries (e.g., 16-27 kWh)

– capable of relatively long all electric ranges (e.g., 40-60 miles).

– An on-board internal combustion engine provides an unlimited driving range by recharging the battery when needed.

• Battery Electric Vehicles (BEVs)

– Pure electric vehicles with no internal combustion engine

– Require recharging at the end of their designed driving range.

– Have the highest all-electric range (e.g., 60-300 miles) and the largest battery capacity (e.g., 25-35 kWh)


Grid Integration: Driving Characteristics

• Transportation data for U.S. driving patterns indicates

– 60% of domestic average daily driving is 30 miles or less

– Approximately 70% of driving is 40 miles or less.

– Upcoming EREVs:

• designed to drive 40 miles in all-electric mode.

• could accommodate 70% of driving in all-electric mode with a single over-night charge.

• daytime charging using public charging or at-work charging obviously extends vehicles’ effective all-electric driving ranges.

– BEVs have a limited driving range before extended charging is required (e.g., a 40-60 mile battery, or even a 100-mile battery), urban and close-in suburban areas are the ideal target market.


Grid Integration: Charging Characteristics

Charge level Utility Service Charge Power (kW)

Time to charge

AC Level 1 120V, 20A 1.44 > 8 hours

AC Level 2 240V, 15-30A 3.3 4 hours

DC Level 3 480V, 167A 50-70 20-50 min

• The total energy required to charge a battery, and the average energy required per day, depend on the miles driven and the vehicle energy consumption per mile.

• Additional power may be required for accessories and air conditioning during summer months.

• EVs will have onboard communications, computing capabilities, and the other functionality in the near term that will enable them to be "smarter" than most end-use loads.

3 levels charging schemes


Charging Stations with Other Renewables


Charging Stations with Built-in Energy Storage

• Lower power loss caused by converters• Lower cost• Efficient energy management


Real time simulation implementation

Real-time model structure of the EDV charging station


Simulation results (1)

4 6 8 10 12 14 16 18 20-50-30-10103050

Time (s)

Iref

/Iba

tt (

A)

Iref

Ibatt

4 6 8 10 12 14 16 18 20200

400

600

Time (s)

Vre

f/V

batt

(V

)

Vref

Vbatt

4 6 8 10 12 14 16 18 20

69.5

70

70.5

Time (s)

Sta

te o

f C

harg

e (

%)


Smart Transportation Grid Integration

• Battery remaining capacity

• Charging station locations

• Price information

• How much energy charging station can provide.


Contents









Microgrid in Power Distribution System

• A typical microgrid:

– a low-voltage distribution network

– distributed generation (DG) units

– distributed storage (DS) units

– controllable loads.

– Grid-tied mode, islanded mode

• Control and management of a renewable-based microgrid:

– a renewable source level

– a microgrid central control (MGCC) level

– a utility distribution management system (DMS) level.


ANN-ADP vector controller at DG Level

ADP: approximate dynamic programming ANN: artificial neural network trained to implement ADP-based

optimal control ANN-ADP: has potential to integrate optimal, predictive, PI, and PR

control advantages together


Types of DER (distributed energy resources) inverters

• Grid-following inverter:

– PQ inverter DER: operates by injecting active and reactive power into the microgrid

– PV inverter DER: operates by injecting active power into the microgridwhile simultaneously maintaining the PCC bus voltage at a desired value

• Grid-forming inverter:

– V-f inverter DER: Operates based on the conventional droop control concept, which is a necessary requirement in the microgrid islanding operating condition.

– Droop control:

0 0 0 0,s s f ac ac ac ac V ac acf f r P P V V r Q Q


A benchmark LV network with microgrid

• Grid connected• Islanding


Tracking variable reference commends (Ts=1ms)

4 6 8 10 12 14 16 18 204

6

8

10

12

Win

d S

peed

(m/s

)

Time (s)

0 2 4 6 8 10 12 14 16

-400

-200

0

200

Cur

rent

s (A

)

Time (sec)

Id Iq Id* Iq*


Connecting to the grid without synchronization control

0.95 0.975 1 1.025 1.051.05-300

-200

-100

0

100

200

300

abc

cu

rre

nts

(A

)

Time (sec)

1.95 1.975 2 2.025 2.05-200

-100

0

100

200

abc

cur

rent

s (A

)

Time (sec)


Hardware Experiment System


Hardware Experiment Results

Grid d-axis current waveform dc link voltage

Grid q-axis current waveform Three-phase PCC voltage

0 20 40 60 80 100 120 140 160 180 20030

40

50

60

70

Time (sec)

Voltage (

V)

0 20 40 60 80 100 120 140 160 180 200-0.5

0

0.5

1

1.5

Time (sec)

d-a

xis

curr

ent (A

)

Id Id-ref

0 20 40 60 80 100 120 140 160 180 200-2

-1

0

1

Time (sec)

q-a

xis

curr

ent (A

)

Iq

Iq-ref

100.3 100.32 100.34 100.36 100.38 100.4100.4-20

-10

0

10

20

Time (sec)

Voltage (

V)


Contents









PM motor control with standard three-leg inverter in EVs

Speed or torque commend


P I

e

*sqv

*, ,a b cv

*di

*qi

di

qi

, ,a b ci

P I*sdv

P I

*r

r

00

10

dsd sd sd sdq

s e e PMsq sq sq sqd

q

dLv i i iLdt

Rv i i iLd

Ldt

Issues: Conventional Standard PMSM Control

Motor Controller


+-

Vdc

PWM

PI+

+

+

-

--

e

*1v

*1v

_sq refi

mech

*mech

+

+

+

NN structure

Motor

Encoder

/d dtmech

sai

sbi

sci

eje

eje

2/3

*

1, 1, 1a b cv

savsbvscv

2/3

,i

PI

PImech *

rd

mech *rd

++

rd

PI_sd refi-

+

+

_sd compv

_sq compv

+

-

-

InputHidden

Output

+

+_sd refi

_sq refi

sde

sqe

sds

sqs

*sdv

*sqvsdi

Conventional standard current-loop control

Outer speed-loop control

Outer rotor flux control

P

sqi

sqisdi

tanh

tanh

tanh

tanh

tanh

tanh

tanh

tanh

tanh

tanh

tanh

tanh

tanh

tanh

tanh

tanh

tanh

tanh

1/Gain2

1/Gain

Input Preprocess

Output layer

V*sd

V*sqsde

sqe

sds

sqs

Hidden layer

NN

struct

ure

NN controller

-

*

sqv

*sdv

2/3

How to address the issues: ANN-ADP Solution

o Replace the conventional controller by a Neural Network Motor Controller (NNMC)

o Our NNMC uses Artificial Intelligence techniques that adapt quickly and efficiently in real-time


Simulation for IPM Operating in Linear Over modulation Conditions (d- and q-axis currents)


HIL Dyno System for Motor Control Evaluation


Operation of IPM Motors in Linear and Over Modulation Regions

0 10 20 30 40 50 60 70 80 90 100-10

-5

0

5

10

15

20

Time (A)

Curre

nt (A

)

Ref

ADP

Conv

0 10 20 30 40 50 60 70 80 90 100-10

-5

0

5

10

Time (Sec)

Curre

nt (A

)

Ref ADP Conv

0 10 20 30 40 50 60 70 80 90 1000.5

1

1.5

2

Time (Sec)

Mod

ulatio

n Ind

ex

ADP

Conv

(a)

(b)

(c)


Investigate Applying Real-Time Simulation in Robotics and Automation

From Solidworks to Simulink to Opal-RT


Contents






6. NSF I/UCRC Center for efficient vehicles and sustainable



Focusing Areas of NSF I/UCRC Center

The Grid