Distributed Renewable Energy Sources Integration and Smart Grid Control

Distributed Renewable Energy Sources

Integration and Smart Grid Control

Jianwu Zeng

Power and Energy Systems Laboratory

Department of Electrical & Computer Engineering

University of Nebraska-Lincoln

Background – Smart Grid

Sustainability

High efficiency

Reliability

Flexibility

[1] http://solutions.3m.com/wps/portal/3M/en_EU/SmartGrid/EU-Smart-Grid/

3M Solution [1]

Distributed

Renewable Energy

Sources Integration

Grid Control

Background – Outline

DC-DC Converter

Short-time Wind/Solar

Nonlinear Control

DC-AC Inverter

Distributed

Renewable Energy

Sources Integration

Grid Control

Two Stage

DC-DC-AC

One Stage

DC-AC

DC Microgrid

Scheduling

Background – Distributed RES Integration



Two stage: DC-DC-AC

Source 1

DC Link

DC-AC

Grid

Source m

Source 2

P

2

P

1

P

m





DC-DC



One-stage: DC-AC

DC-AC

Grid

Source 1

Source m

Source 2

P

2

P

1

P

m





Functions of converter:

1. Maximum power point

tracking (MPPT)

Background – Traffic Signal Light System

Lincoln, NE

418 intersections (2009)



Traffic Poles at One Intersection

137,940 kWh/Month

$2.5 million (15 years)

Background – Traffic Signal Light System



A Roadway Wind/Solar Hybrid Power Generation and Distribution Systems

(RHPS) Towards Energy-plus Roadway

Sponsor: Department of Transportation (DOT) Federal Highway Administration

RHPS Microgrid Utility Grid Substation Circuit Breaker Transformer

Unit

Background – Traffic Signal Light System

C a b in e t Roadway Microgrid LPMC Wind Turbine Generator

PV Panel Control Signals from SPMC AC Load DC Load Power Electronics Interface Battery

AC,60 Hz

AC-DC

DC

DC



Unit: Energy-plus Roadway/Traffic-Signal Light (EPRTL)

Test site: Hw2 & 84

th

_ST

AC,60 Hz

Annually (1 intersection)

Background – Challenges of Converter Design

1. Integrating different RES

4. High efficiency

2. High power density

0 100 200 300 400 500 600 700 0 100 200 300 400 500 600 700 800 900 1000 Rotor speed (rpm) M e c h n ic a l p o w e r (W ) 7 m/s 8 m/s 9 m/s 11 m/s MPP 0 5 10 15 20 25 30 35 40 0 20 40 60 80 100 120 140 PV panel voltage (V) P V p a n e l p o w e r (W ) 200 W/m2 400 W/m2 600 W/m2 800 W/m2 MPPs

WTG

PV panel

3. High voltage conversion ratio

5. Deal with intermittence of RES

Compact

Isolated: transformer

Soft-switching

Energy storage

Cost-effective

Soft-switched

Bidirectional

Isolated Multiport

Converter

Multiple inputs

Distributed

Renewable Energy

Sources Integration

Outline

DC-DC Converter

Distributed

Renewable Energy

Sources Integration

Two Stage

DC-DC-AC

MPPT

Controller

PWM

1

+

–

L

₁

S

₁

i

₁

C

s

C

+ –

v

_p

v

+_s –

i

_p

TX

+ –

v

_cs

L

L

₂

S

₂

v

_dc

i

₂

v

₁

v

₂

D

₁

D

₂

D

_s1

_D

_s2

D

_s3

D

_s4

P

_o N

L

₃

i

₃

D

3 –

v

₃

S

₃ + + + + – WTG PV1 PV2

i

₁

v

₁

i

₂

v

₂

i

₃

v

₃

PWM

2

PWM

3

n=N

p

/

N

s

R

C2

C

3 C1

Distributed Renewable Energy Integration



Cost-effective

Isolated Multiport

DC-DC

Converter

Simple topology:

m

input port,

m

switches

J. Zeng

, W. Qiao, L. Qu, and Y. Jiao "An isolated multiport DC-DC converter for simultaneous

power management of multiple different renewable energy sources,"

IEEE Journal of Emerging

Selection Topics in Power Electronics

, vol. 2, no. 1, pp. 70-78, Mar. 2014

Source 1 DC Link DC-AC Grid Source m Source 2 P2 P1 Pm   DC-DC



ZCS

Isolated Multiport

DC-DC

Converter

+ – L1 S₁ Cs C + – v_p ip TX + – v_cs Vdc Cr Ds1 Ds2 D_s3 D_s4 Pout+ Pout– n=Np:Ns i_r1 L_r1 + – v Lp1 LCL-Resonant Circuit + – vds1 – D1 L₂ i₂ v_s2 D2 P2 L_m i_m D_m Pm v_sm  +



+



i₁ P1 + S2



S_m C2 Cm C1 vs1



L_M iT _R L iDs2 Lr2 Lrm ir2 irm i_M L'_p Lp + – vds2

Zero-current Switching (ZCS)

m

ports,

m

switches

Low voltage stress

High efficiency

+ – L1 S1 + – i1 Cs C + – vp + v_–T2 ip TX + – vcs vdc v1 Cr Ds1 D_s2 Ds3 Ds4 P1+ Pout+ Pout– P– n=Np:Ns i Lr + – v iT2 Lp C1 + – L2 S2 PV P2+ S3 vbat + – ibat C2 – + ev V*dc vdc Voltage PI I*bat Saturation MPPT Controller v1 i1 ton T PWM generator P W M1 I*bat> 0 _Discharge PI Charge PI ibat – + + – ibat d2 I*bat≤ 0 d3 d3 d2 P W M2 PWM3 1 2 K Lm im



ZCS

Isolated Multiport

Bidirectional

DC-DC

Converter

Power flows

Battery PV Panel Load

Mod

_{e 1}

Mod

e 3

Mo

de

2

M

o

d

e

1

M

od

e 1

Mode 1: Daytime

Mode 2: Night

Mode 3: Battery is unavailable

Distributed Renewable Energy Integration

J. Zeng

, W. Qiao, and L. Qu, “An isolated three-port bidirectional dc-dc converter for PV

systems with energy storage,”

IEEE Trans. Industry Applications

, accepted for publication.

5 10 15 20 25 30 35 40 45 20 40 60 80 100 120 140 Voltage (V) O u tp u t p o w e r o f P V p a n e l (W ) 3:00 4:00 5:30 6:00 6:30 15 20 25 30 35 40 45 O u tp u t p o w e r o f P V p a n e l (W ) 3:00 4:00 5:30 6:00 6:30 PV curve Operating points

PV

₁

PV

₂ 0 5 10 15 20 25 30 35 40 45 2 3 4 5 6 7 Time (sec) W in d s p e e d ( m /s ) 10 20 30 40 50 60 70 80 90 P o w e r (W ) Ideal MPP Measurement



MPPT Results

WTG



Soft-switching

i

r1 (5A/div)

i

r2 (1A/div)

v

ds2 (20V/div)

v

ds1 (20V/div) Time (2 us/div)



Efficiency

ZCS

Soft-switched converter has higher efficiency

than that of hard-switched converter

Distributed Renewable Energy Integration

10 20 30 40 50 60 70 80 90 100 110 85 86 87 88 89 90 91 92 93 94 Output power (W) E ff ic ie n c y ( % ) Hard switching Soft switching



Bidirectional Power Flow

Distributed Renewable Energy Integration

p1 v1 4V 12V 20V 0W 20W 40W p1 v1 i1 36.86W vdc 50.18V ibat – 0.87A 2.67A 13.79V 36.86W p1 v1 4V 12V 20V 0W 20W 40W p1 v1 i1 37.96W

Charge battery

Operating points

MPP

Three-port isolated DC-DC Converter Load PV Battery p1 p2 pout MPPT Battery PV Panel Load Mod_{e 1}Mode 3 Mode 2 M o d e 1 Mod e 1



Bidirectional Power Flow

Distributed Renewable Energy Integration

Three-port isolated DC-DC Converter Load PV Battery p1 pout p2 MPPT p1 v1 4V 12V _20V 2W 10W 18W 13.33W 49.91V 2.67A 13.33W 0.92A 14.28V

Time: (5 us/div) vdc: (10 V/div) p1: (2 W/div) ibat: (1 A/div) i1: (0.5 A/div) p1 v1 i1 vdc ibat p1 v1 4V 12V 20V 2W 10W 18W 14.5W

Time: (2 ms/div) i₁: (0.5 A/div) p₁: (2 W/div) v₁: (2 V/div) p1 v1 i1

Discharge battery

Operating points

MPP

Battery PV Panel Load Mod_{e 1}Mode 3 Mode 2 M o d e 1 Mod e 1

Outline

DC-AC Inverter

Distributed

Renewable Energy

Sources Integration

One Stage

DC-AC



ZVS

Isolated Multiport

DC-AC

Inverter

Distributed Renewable Energy Integration

L2 + – vp ip TX PV C2 Np S3 C1 S2 S1 + – + –v1 i1 v2 ibat Port 1 Port 2 MPPT Controller PWM generator v1* IVC d1 v1 OVC vo d3 P W M2 P W M3 P W M1 + – + – i2 Lm Lk S42 + – Co vo Lo io Ns Ns S52 S41 S51 RL Port 3 rb Cb + –vbat sign(•) PWM generator PWM41~PWM52 Sine wave 1 + – d4 d5 vo *

J. Zeng

, W. Qiao, and L. Qu, "An isolated multiport single-stage microinverter for the distributed

power generation systems,"

IEEE Trans. Industrial Electronics

(in review)

Zero-voltage Switching (ZVS)

Single-Stage: high efficiency

No electrolytic capacitor (<15 years)

PV panel: 25 years

DC-AC Grid Source 1 Source m Source 2 P2 P1 Pm  



ZVS

v

ds3 (50V/div)

v

ds2 (20V/div) ZVS PWM2 PWM3

v

o (100V/div) PWM3 PWM41 PWM51



Output voltage

AC voltage, 60 Hz

Distributed Renewable Energy Integration

Smart Grid Control

Standalone Mode

_{Grid-connected, Island Mode}

RHPS Microgrid Utility Grid Substation Circuit Breaker Transformer

Scheduling

DC Microgrid

Outline

Nonlinear Control

Grid Control

Nonlinear Control for DC Microgrid System



DC Microgrid System

Source #n DC Bus DC-DC Converter #n P1 CPL #m Source #1 DC-DC Converter #1 Source #2 DC-DC Converter #2 Pn P2 CPL #1

⁞

⁞

⁞

Inverter/ Rectifier Utility Grid DC/DC Converter DC/DC Converter Load DC Bus Output Voltage

Controller Reference _Voltage

Output Voltage P=Const. Control Signal Source

Constant Power Load (CPL):

cause instability due to its negative

incremental impedance

i

v

Resistor

dv/di >

0

dv/di <

0

CPL

Nonlinear Control for DC Microgrid System



Interconnection and Damping Assignment

Passivity-Based Controller (IDA-PBC)

i v -10 -5 0 5 10 15 190 192 194 196 198 200 202 204 206 208 210 10 20 30 40 50 60 70 80 90 L D S C + C1 i -v + -E r1 ' CPL P r2'

Energy Contour

o -- current point

* -- desired point



_*



2



_*



2 1 1 2 2 1 1 ( ) 2 2 d H x x x x x L C     2 2 1 2 1 1 ( ) 2 2 H x x x L C  

Hamiltonian Energy Function

Desired Energy Function

Energy Reshape

                   2 2 2 1 2 * 2 2 1 1 1 * 1 1 ) 1 ( 1 1 x CP x C r x L d r C x E x C d x L r r L x

J. Zeng

, Z. Zhang, and W. Qiao, "An interconnection and damping assignment passivity-based

v

i

E

P

r

E

v

d







1

(

/



)

0.1 0.15 0.2 0.25 0.3 0.35 0.4 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 Time (s) In d u c to r c u rr e n t (A ) r 1 ' _{= 0.25} r₁' = 0.42 r₁' = 0.6



Experimental Results

Outline

Grid Control

Short-time Wind/Solar

Short-term Wind/Solar Power Prediction

Normalization (Sigmoid Function, Transmissivity) Prediction (SVM, AR, RBFNN) Denormalization

)

(

ˆ

_t

_h

y



Feature Representation

X

t DOY TOD DOY TOD



) 1 ( t X

y

t ) 1 (m t X

x

t

)

(

ˆ

t

h

y



ȳ

t



Short-term solar power prediction (SPP)

J. Zeng

and W. Qiao, "Shot-term solar power prediction using a support vector machine,"

Renewable Energy

, vol. 52, pp. 118-127, Apr. 2013.



Normalization: transmissivity

t t t

y

R





y

_t

R

_t

y

_t

:

Time series of ground radiations

Short-term Wind/Solar Power Prediction

Normalization Feature Representation WSVM Denormalization Power Curve

P

ˆ

y

ˆ

x

v

v

ˆ

y













































Preprocessing

Wind speed-to-wind power conversion



Short-term wind power prediction (WPP)

, , 1 1 , , * 1 1 (LS-WSVM) ˆ( ) ( ) (ε-WSVM ) N _D t j i j i j i i t N _D t j i j i i j i _i x x h b a y x x x h b a



 

           _ _  _{ }       _{  }    _{ }   







Wavelet Support Vector Machine (WSVM)

Short-term Wind/Solar Power Prediction



SPP Results

Short-term Wind/Solar Power Prediction



WPP Results

Summary

High efficiency

Three novel converters

Energy-based control

SVM



SPP

WSVM



WPP

MPPT

Low cost

Distributed

Renewable Energy

Sources Integration

Grid Control

Two Stage

DC-DC-AC

One Stage

DC-AC

DC Microgrid

Scheduling

Summary



Other Experience

Wind Energy Conversion Systems (ACC 2013)

Mechanical, Transmission System (M.S. Thesis)

Fault Detection and Diagnosis

Control

Computational Intelligence

Sensorless Control: PV system (ECCE 2011)

Direct Torque Control: PMSM (ECCE 2014)

Neural Networks (PESGM 2011)

Rough Set (M.S. Thesis)

Future Research

Electric Power & Energy Generation Systems

Power

Electronics

Smart Grid Control

& Optimization

Energy Storage Systems Sustainable Energy Computational Intelligence

Control

Electric Vehicle

Systems Energy Generation Energy Integration Energy Efficiency Energy Hub High Voltage Large Current

Fault Detection & Diagnosis Conditional Monitoring Big Data Distributed Control Intelligent Control Energy Hub High Frequency

New Materials (SiC, GaN) Biomedical Applications Vehicle to Grid (V2G)

Grid to Vehicle (G2V) Motor Drive

Teaching

Power & Energy

Electronics

Linear Systems

Digital Signal Processing

Electric Power Systems

Power Electronics

Controlled Electric Drives

Control

Renewable Energy Systems

Computational Intelligence

Advanced Topics in Power Electronics

Real-Time Computer Control Systems

Teaching

Power & Energy

Electronics

*

Control Systems

Digital Signal Processing

*

Electric Power Systems

Power Electronics

*

Controlled Electric Drives

*

Computational Intelligence in Power and Energy Systems

**

Control

Renewable Energy Systems

Computational Intelligence

Advanced Topics in Power Electronics

**

Real-Time Computer Control Systems

Control of Electrical Power Conversion Systems

Teaching experiences

*



TA (including labs)

**



Lecture

Students Mentoring

Andrew He

(decoupling control)

Jackson Olson & Brandon Guenther

(Wireless communication)