Energy Internet An open and transactive electricity market · Energy Internet: An open and...

Energy Internet: An open and transactive electricity market


Dr. Alex Q. Huang, [email protected] Energy Distinguished Professor

FREEDM Systems Center, NC State University

1

mailto:[email protected]


Outline

• Energy Internet Concept

• Key Enabling Technologies for Energy Internet

– Power delivery system

– Software platform

– Market framework

2


Energy Cells are to

operate and manage local generators,

energy storage, and dispatchable load

compete with each other to maximize

their own profits

Analogous to internet consumer-to-

consumer commerce (eBay) or stock market

Utilities are expected to make more

profit on providing ancillary services to

ensure the residential distribution

system security and reliability, in

addition to electricity transaction.

Energy Internet Concept

[1] W. Su, and A.Q. Huang, “A Game Theoretic Framework for a

Next-generation Retail Electricity Market with High Penetration of

Distributed Residential Electricity Suppliers” Applied Energy,

vol.119, pp.341-350, April 2014.

[2] W. Su, “The Role of Customers in the U.S. Electricity Market:

Past, Present, and Future”, The Electricity Journal, 2014. (invited)


Grid Infrastructure Needs: A Resilient Grid to support plug-and-play

Power

Factor

Correction

DC/DC

Converter

CPU

Power

System

Power

Memory

Power

Graphics

Power

400V (DC)

< Front end AC/DC >

< On board DC/DC >

60-264V (AC)(DC)

(DC)

(DC)

(DC)

(DC)

•Plug-and-play of power

•Unprecedented power quality

•Isolation of load dynamics by POL

(Point of Load) converters

4

POLs


Distribution Grid of the Future

Power

Factor

Correction

DC/DC

Converter

Load

Renewable

Energy

Generation

Energy

Storage

Others

MV DC

< Front end AC/DC >

4160-69 kV (AC))

LV DC

Direct MV Power Conversion:

•Ability to achieve power conversion

without 60 Hz transformer

•Silicon Carbide technology is within

reach in the 4160-15 kV class range

Inverter LV AC

5


Voltage (kV)

6

SiC p-

ETO

15

20

2 20 40

10

Silicon

Switching frequency (kHz)

SiC MOSFET

SiC n-IGBT

300 MHz-V

600 MHz-V(ZVS)

5 MHz-V

40 MHz-V

Medium Voltage SiC Technology

Using SiC MOSFET

• 60 to 120 X improvements in

V-f capability

Enables MV & HF power

conversion


FREEDM System for Energy Internet

Bi-directional flow

2. Solid State Transformer

(Energy Router)

380 VDC Bus

7

Proposed by Dr. Huang in 2007

Storage DG

1. Plug & play interface

1. Plug & play interface

Plug-and-play DC Microgrid

(Energy Cell)

software

Energy Internet: An open and transactive electricity market 8

DC FREEDM System

Plug-and-play DC Microgrid

(Energy Cell)

Storage DG


DC Microgrid (Energy Cell) Advantage

AC & DC microgrid

AC microgridSST interfaced DC microgrid

SST

1) higher efficiency;

2) better to power homes’ DC loads

3) no harmonic and reactive power factors

AC busDC bus

DC/AC DC/AC


Resilient Grid: Autonomous and Distributed Power Management Control

10

Mode I:

FREEDM system

in grid-connected

Mode II:

FREEDM system

in islanding

Mode III:

SST in

islanding

Substation SST detects

grid faults and disconnects

MVAC bus under frequency

Substation SST synchronizes MVAC bus to

grid and connects

Substation SST restores MVAC bus and SSTs

reconnects

Rectifier stage

DC/DC

BatterySST2

FREEDM MV Feeder

droop

SST1

MPPT Controller

AC Load

droop droop

AC

Load

DC

Microgrid

Transmission Grid

Substation SST

DC/ACAC/DC

IPM Command

via DGI

QHVref

VMVref

fMVref

VMV, fMV

PSST1

QSST1

VMV fMV

QSST1 PSST1

PSST2

QSST2

QMVref

VhdcrefVMV

VHV, fHV

Vhdc

fMV

PMVref

Vldcref

droop

droop

Vldc

ωref

Vlac_dref

Vlac

droop

Vldc Pbat

Pref(IPM)

IEM Command

via DGI

Pbatref

Pref(IEM)SOC

PV

droop

Vldc

Pdroop

PPV

PMPPTVPV

IPV

Vldc

Load

D: Load shedding

PLoad

DC Microgrid FeederBlue: sampling signals for IPM

Green: controllable/reference

signals for IPM Inverter

Min

FREEDM SYSTEM

C: DRER control

OR

B: DESD control

A: SST droop

f

no

shedding

vldc vldc vldc vldc

P P P0

380

Pbat_max0 0

A:SST B:DESD C:DRER D:Load

PPV_max

MPPT

360

400

Pbat_min

385

375

390

0 PL_maxPL_min

370

365

I E M382

378

fmin fmax60


11

Solid State Transformer

Functional Diagram

SST

Medium Voltage AC

(or Medium Voltage DC)

LV DC

LVAC 2LVAC 1

Software Communication

•Power Management:

•Change/Control the customer

voltage levels

•Provide DC power

•Control power factor

•Disconnect from grid under fault

•Limiting fault current

•Eliminate customer side harmonics

•Low voltage ride through

•Supports multiple islanding modes

•Energy Management

•Monitor energy usage

•Smart agent for energy transaction

•Can control/dispatch power via

microgrids (Energy Cell)

•Demand side management


MV AC voltage sag operation: 25% voltage sag, 5KW

Input voltage, current, PWM voltage, high voltage DC link 3Output DC voltage, AC voltage and current

Gen-I SST

120V AC

3.6 kV AC

5 kW LOAD


SST Integration with a Non-linear Load: Harmonic Mitigation

13

Gen-II SST

120V AC

1.8 kV AC

Non linear LOAD

LV AC load current

HV AC load current

Note: Output AC phase is 180 shifted with the HV AC

LV AC Voltage

0 1000 2000 3000 4000 5000 6000

HV AC Current Harmonics


Energy Internet Technology Needs

1) Plug-and-Play Interface

(RJ45, USB)

2) Router

3)Open Standard Protocol and

Operation System

(TCP/IP, HTML)

4) Data Storage

5) WWW/eBay/Twitter/Facebook

Internet/Computer

1) Plug-and-play DC and AC

Energy Cell protocol

2) Solid State Transformer

(with embedded software)

3) Software platform?

4) Distributed storage

5) Market/business framework

Energy Internet

14

http://images.google.com/imgres?imgurl=http://www.slipperybrick.com/wp-content/uploads/2007/09/linksys-wrt100-rangeplus-router.jpg&imgrefurl=http://www.slipperybrick.com/2007/09/linksys-rangeplus-networking/&usg=__7SZK8n1VnOb4IR1CDT4d_oel-tU=&h=505&w=500&sz=26&hl=en&start=2&um=1&tbnid=dBCriU7EVtLGjM:&tbnh=130&tbnw=129&prev=/images?q=router&hl=en&rls=com.microsoft:en-us&um=1

http://images.google.com/imgres?imgurl=http://www.slipperybrick.com/wp-content/uploads/2007/09/linksys-wrt100-rangeplus-router.jpg&imgrefurl=http://www.slipperybrick.com/2007/09/linksys-rangeplus-networking/&usg=__7SZK8n1VnOb4IR1CDT4d_oel-tU=&h=505&w=500&sz=26&hl=en&start=2&um=1&tbnid=dBCriU7EVtLGjM:&tbnh=130&tbnw=129&prev=/images?q=router&hl=en&rls=com.microsoft:en-us&um=1


Key Challenge: Software Platform

High-volume

High-velocity

Expensive

Single point of failure

Not easy to expand

Highly complex

……

Centralized Data Storage and

Processing

……

Energy Cell #1

SST1 SST2 SSTn

Energy Cell #2

Energy Cell #n


……

Energy Cell #1

SST1 SST2 SSTn

Energy Cell #2

Energy Cell #n

Operation View

1. Real-time Energy Cell Data Collection: Open Standard and Plug-and-Play

2. Distributed Data Storage and Management

3. Open-source Programming & Open API

Cost-effective

Scalable

Fast

Resilient to failure

Flexible

Easy to use

……

Distributed Data Storage and Processing


Proof-of-Concept Platform

…… ……

16 of low-cost single

board PCs (e.g.,

CubieBoard)

• High-availability distributed object-oriented platform

• Google’s MapReduce and Google File System

Simulated PV data is implemented into Lego

Mindstorm EV3 Intelligent Brick (ARM 9 processor

with Linux-based operating system)

Energy Cell

Layer

SST

Software

Layer

……

…


Efficient

Allows parallel data processing over a large amount of PV data

Reduce overall processing time through parallel & faster execution

Efficient Data Management

0

100

200

300

400

500

600

700

Pro

cess

ing

tim

e (S

ecs)

Data Size (MB)

trial 1 trial 2 trial 3 trial 4

0

1000

2000

3000

4000

5000

6000

7000

Tota

l Pro

cess

ing

Tim

e (S

)

Data Size (MB)

Distributed Conventional

Total processing times using the distributed data management systems over different data size and

trials

Comparison on average data processing time

using distributed and conventional data

management systems


Energy Cell #1:

Cost functions Comfort level

Customer preference

Load demand

Local regulation and policy

Local operational constraints Bidding/offer strategy

……

Utility Cell:

Cost functions (supply curve)

Electricity transaction

Ancillary services

Power flow (global) constraints

Environment:

Social impact

Government policy

Environmental incentives

Renewable energy forecast

……

...

Energy Cell #2 Energy Cell #N

min ( )i iF U

Market Clearing House:

Collect bid and offer from cells

Find Nash equilibrium points

Clear the market price

Regulate the deregulated

electricity market

Pi is the net power demand

of the i-th energy cell

1 1

( )N N

i i

i i

P P

λ is the retailed electricity price

for customers within a same

distribution system

( )i i iP f U

19

Energy Internet Market Framework: Energy eBay


The Proposed Market Clearing Scheme

Start

Initialization with PLoss and x0

Iter1 = Iter2 = k = 0

Max ѱ(xk,y) in to find yi subject to local constraints

Run power flow in OpenDSS with yi and calculate the new PLoss

Calculate and update PLoss

Iter2 = Iter2+1

End

No

Yes

ΔPLoss< ɛ

Iter1 = Iter2 = 0

Apply relaxation algorithm to obtain new xk

Z(x) < ɛNo

Yes

k = k +1

Check solution feasibility

yi (Iter1) = yi (Iter1-1)+ΔW

Iter1 = Iter1+1No

Yes

A Modified IEEE Distribution Test Feeder

20

Need game-theoretical algorithms to clear the

electricity market price

Energy Internet Clearing House


21

Matlab/Simulink-based Testbed

Energy Internet Market Framework


Objective Values $

Type 1 Energy cell Type 2 Utility Cell

Step k Bus 645 Bus 671 Bus 675 Bus 634 Bus 650

1 -165.04 -3403.7 -4667.2 -1794.1 4007.9

2 420.09 -2632.7 -3642.7 -1684.2 6514.7

3 707.89 -2226.7 -3342.3 -1605.4 7673.6

4 834.51 -2020.9 -3162.5 -1580.9 8179.4

5 908.73 -1901.0 -3157.0 1556.9 8444.7

: : : : : :

15 993.86 -1726.5 -3328.6 -1529.5 8808.9

16 993.96 -1726.0 -3330.3 -1529.4 8810.1

17 994.05 -1725.8 -3331.5 -1529.3 8810.8

Nikaido-Isoda Function Values

Pay-off Function Values 22

*

*

,

max , 0.x y X

x y

Energy Internet Market Framework

Nikaido-Isoda function reaches zero, no

player can increase its payoffs by

unilaterally changing its strategy (demand

or supply). Therefore, the Nikaido-Isoda

function can approximately approach a

Nash equilibrium point.


One example of industry implementing evolutionary tech:

www.grid-bridge.com


ConclusionsEnergy Internet Concept is discussed

– A totally open energy market supports the energy transaction of all types of Energy Cells

– A resilient and plug-and-play capable grid is a prerequisite for realizing energy internet. Power electronics plays a key role in achieving this goal

– Distributed and secure software platform to support large and distributed data monitoring and application

– Business model of the energy internet will be more and more like an Energy eBay. Example of using game theory to clear every Energy Cell’s bidding is shown

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Energy Internet An open and transactive electricity market · Energy Internet: An open and...

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