Agustín IrizarryCarlos Torres Manuel RodríguezIdalides Vergara José CedeñoJuan Jim é nez

Intelligent Power Routers for Distributed Coordination in Electric Energy

Processing Networks Progress Report

Agustín Irizarry Carlos TorresManuel Rodríguez Idalides VergaraJosé Cedeño Juan JiménezBienvenido Vélez Marianela SantiagoMiguel Vélez-Reyes Noel FigueroaEfraín O’Neill-Carrillo Alma EstremeraAlberto Ramírez

[email protected]

October 23-24 2003 EPNES: Intelligent Power Routers 2

Outline

• Background and Problem Statement

• Analogy: IPRs and Data Networks

• Report on project activities

• Year 1 Accomplishments Summary

• Year 2 Proposed activities


State-of-the-Art Power Delivery

ProducersP1 P2

Pn

P3

Consumers

C1 C2 C3 C4

GOAL:De-centralized System

Reconfigurationwith

Minimal Human Intervention


Re-routing in Response to Failures

ProducersP1 P2

Pn

P3

Consumers

C1 C2 C3 C4

x

x

System MTTR Limited by Operator

Response Time


Re-routing in Response to Major Disturbances

ProducersP1 P2

Pn

P3

Consumers

C1 C2 C3 C4

Slow Operator Response

May Cause Cascading

Failures



ProducersP1 P2

Pn

P3

Consumers

C1 C2 C3 C4

IPRSRespondPromptlyto AvoidFurther

Deterioration


Our approach

• De-centralized control in response to major disturbances

• Intelligent Power Routers (IPR):– modular building blocks– strategically distributed over entire network– embedded intelligence – information exchange allows neighboring IPRs to

coordinate network reconfiguration– improve network survivability, security, reliability,

and re-configurability


Outline

Background and Problem Statement

• Analogy: IPRs and Data Networks





Distributed Data Routing

S3

C1

S1

S2

C2

DataConsumer

Data Network

DataServers

Multiple redundant paths to move data between computers

R1

R3

R4

R2

Routers



S3

C1

S1

C2

DataConsumer

DataServers

R1

R3

R4

R2

S2

Data Packets

MajorDisturbance

Data Network



S3

C1

S1

C2

DataConsumer

DataServers

R1

R3

R4

R2

S2

Data Packets

Major Disturbance

Data Network


How are power delivery systems different from computer networks?

– Energy transmission (not data)

– Must match generation to demand at all times

– No buffers

– Hard to get rid of excess energy

We must deal with the laws of Physics!


Outline

Background and Problem StatementAnalogy: IPRs and Data Networks





RestorationModels

IPRProtocols

DistributedControlModels

IPRArchitecture

Project Organization

Economics

Education

EducationEd

uca

tion E

du

catio

nRisk Assessment


Restoration Models and IPR Protocols

• Use the Power System Restoration (PSR) problem, an extreme condition, as starting point to address the system reconfiguration problem.– Use PSR problem global (centralized)

solution as benchmark– Develop communication and data protocols

that allow the implementation of different de-centralized restoration strategies

RestorationModels

IPRProtocols


Power System Restoration (PSR)

• Goal:– rebuild a stable electric system– restore all unserved loads

• Approach:– Apply particle swarm optimization (PSO) to solve PSR

• Optimization problem:– minimize the amount of unserved loads at each stage– power flow constraints– feasible bounds on state and control variables – capacity limits on lines and transformers – only one switching operation per stage

RestorationModels


Particle swarm optimization (PSO) method

• Emerging Evolutionary Computation (EC) technique [Kennedy 1995]

• Based on "flocking behavior" of animals• In PSO individuals move around in a search space looking

for an optimal solution based on their current position and on the best position within the flock.

kiv

1k

iv

kis

1kis

gbestv

pbestv

IF ii vSrand ()

THEN 1kis = 1,

ELSE 1kis = 0

kiiki

ki spbestrandcvv

111 () kii sgbestrandc 22 ()

11 ki

ki

ki vss Continuous variables

Binary variables

RestorationModels


Power System Restoration:Example

Test System and Results:

Restoration Path

Generation Units and

Transformers Transmission Lines Loads

G1 & G2 & G3 &

Stag

e T1-4 T2-7 T3-9

L4-5 L4-6 L5-7 L6-9 L7-8 L8-9 L5 L6 L8

0 X 1 X X 2 X X X 3 X X X X 4 X X X X X 5 X X X X X X 6 X X X X X X X 7 X X X X X X X X 8 X X X X X X X X X 9 X X X X X X X X X X 10 X X X X X X X X X X X 11 X X X X X X X X X X X X

Total load served increased through the stages.At each stage, all the control and state variables

remained within their feasible limits and the power balance constraints were satisfied.

The restoration path was established and all loads were successfully served.

50%50% 25%50%100%

75%100%

100%

RestorationCompleted

RestorationModels

WSCC Nine-Bus Test System


• Goal:– Develop Communication Protocols to

implement a System Restoration Algorithm

• Approach:– Use a graph model for the power network

with IPRs

• Optimization problem:– minimize the amount of unserved loads based on

priority [Nagata et. al. 2002]

De-Centralized Communication & Control Protocols

IPRProtocols


Modeling a Power Network As a Graph

Link 1 Link 2 Link 3


Link 7 Link 8

Bus 1 Bus 2

Bus 4Bus 3 IPR 4IPR 3

IPR 1 IPR 2

Src 1 Src 3Src 2

Snk 2Snk 1

IPRProtocols

•IPR model:•Vertices – IPRs on buses•Edges – branches between buses•Weight – power flow•Edges have Priority/Reliability measure

ControlMessages


Restoration in Electrical Energy Network Featuring Intelligent Power Routers (IPRs)



Link 7 Link 8

Bus 1 Bus 2

Bus 4Bus 3 IPR 4IPR 3

IPR 1 IPR 2

Src 1 Src 3Src 2

Snk 2Snk 1

PR Link Priority Reliability

Pr1 1 - 1

4 1 -

Pr2 2 - 1

3 - 2

5 2 -

6 1 -

Pr3 4 - 1

5 - 2

7 1 -

Pr4 6 - 1

8 1 -

Normal State

— Normal State Message

System going down

— Request Power

— Deny Request

— Request Status— Response Status

— Affirmative Response

Restoration Process Table 1. Priority and Reliability

IPRProtocols


Risk Assessment

• Goal:– Measure the change in reliability of a

power system operated with and without IPRs.

• Approach:– Use an existing method

• Well-Being indices [Billinton et.al.]

• Risk Framework [McCalley et.al.]

– Need failure probability data

RiskAssessment


IPR failure mechanism• No data available on

IPR failure probability • Need to understand

failure mechanisms – Computer Hardware– Power Hardware

• Literature search well under way for both

– Software

• Data Routers info will be used to make an initial estimate on failure probability.

Data RouterComp Hardware

Switch

Power Hardware

Intelligence

Software

RiskAssessment

IPR


Education Educa

tionE

duca

tion

EducationYear-to-Date Accomplishments

• Proposed:– Development of economics and ethics modules

• Achieved:– Developed a module on ethics– Offered two ethics seminars

• Ethical and Social Implications in Engineering• Integrating Ethics to the Curriculum

– Proposed a new EE Course on economic issues – Started collaboration with Social Sciences (modules to assess

student perceptions)– Introduced IPR concept in graduate courses – Offered IPRs seminars

• integration of research into undergraduate education• recruit students• disseminate our results


DC Zonal Electric Distribution System

(DCZEDS) with Centralized Controller

Z o n e 1 Z o n e 2 Z o n e 3u y u y u y

1 5 k W

P S

1 5 kW

P S

Star tboardBus500 V

Por tBus500 V

Central Controller

Controller Characteristics

• Global State Information

• Controller decisions can achieve global optimality.

• Reliability issues.



Z o n e 1 Z o n e 2 Z o n e 3u y u y u y

1 5 k W

P S

1 5 kW

P S

Star tboardBus500 V

Por tBus500 V

Controller Characteristics

• Local State Information

• Quality is an issue in controller decisions.

• Potential to improve survivability and reliability.

Controller Controller

Controller

DCZEDS with Distributed Control



Intelligence in the IPR

• Flat system: no supervisory control

• Solving a dynamic optimization (or control) problem

• Different Concepts to be Explored– Agents– Biologically collaborative schemes



Proposed architecture for the Intelligent Power Router

IPRArchitecture

Power NetworkEnergy

Sensors andFlow Control

Devices

ICCUInterfacing

Circuits

SensorInput

SwitchingCommands

Intelligent Power Router

ProgrammableIntelligent

Communication and Control Unit


A Simple Switch-based IPR System

IPRArchitecture

IPR

Source Source

Load Load

sensor sensor

TransferSwitch

TransferSwitch

1. Upon failure of a source, IPR decides which load to serve based on latest dynamic priorities

2. Decision can be any computable function

3. More complex configurations possible by modular composition of multiple levels of IPRs

4. Future IPRs based on more complex power flow control devices (e.g. FACTS)


Simulating the Simple IPR SystemIPR

Architecture

Source

CurrentSensor

PowerLines

TransferSwitch

Load


Outline

Background and Problem StatementAnalogy: IPRs and Data NetworksReport on project activities




What we accomplished in year 1

Developed first generation IPR software modelsDeveloped first generation communication and data exchange mechanism for IPRStudied the DC Zonal Electric Distribution System (DCZEDS)Studied the power system restoration problem, using particle swarm optimizationStarted to determine IPR failure modes thru analogy to data routersDeveloped economics and ethics modulesOne accepted paper, two under review


What we promise for year 2 …

• Disseminate results from iteration 0• Design of alternative IPR control algorithms• Perform simulations for preliminary reliability

assessment on IPR-based system• Design of second generation of IPR software model• Evaluate alternative IPR control algorithms• Use economics and ethics modules in electrical

engineering courses and use assessment tools• Develop a short course for non-power engineering

majors


Documents

Agustín IrizarryCarlos Torres Manuel RodríguezIdalides Vergara José CedeñoJuan Jim é nez