state estimation - a presentation at grid analytics europe 2013

© 2013: Smarter Grid Solutions Ltd. (SC344695). All rights reserved.

State EstimationA Joint Presentation at Grid Analytics Europe 2013London, 24 October 2013

David MacLeman

Distribution R&D Manager

SSEPD

Euan Davidson

Senior Engineer

Smarter Grid Solutions

Grid Analytics:

State Estimation

David MacLeman

Distribution R&D Manager

Overview

• SSE and SSEPD

• The Grid Analytics Challenge

• The Orkney Smart Grid

• Distribution State Estimation on Orkney

• Bringing Advanced Analytical Functions into the

Business

• Lessons Learned

SSE

* As at 11 January 2013

• 30th largest company in the FTSE 100*

• £13.8 Bn market capitalisation

• Around 19,500 employees

• Delivered a real dividend increase every year since 1999

• UK’s broadest-based energy company

• Electricity generation, transmission, distribution, supply and services

• Gas production, storage, distribution, supply and services

• Telecoms networks and data storage

• Ireland’s fastest-growing energy company

• Electricity generation

• Electricity and gas supply

• Street lighting maintenance

5

• We own

• one electricity transmission

• two electricity distribution networks

• 106,000 substations

• 130,000 km of overhead lines

and underground cables

• 100+ submarine cable links

• Across one third of the UK

landmass.

• Serving 3.5Million Customers

SSEPD


The Orkney Smart Grid

Mainland .

South

Ronaldsay

BurrayHoy

Flotta

Shapinsay

Stronsay

Sanday

Eday

Westray

Rousay

Core

Zone 3

Zone 4

Zone 2a

Zone 2b

Scorradale

Load8 MW

Flotta26

Load1 MW

Shapinsay33

Load1 MW

Sanday32

FG1.5 MW

Sigurd3

FG4.2 MW

Thorfinn4

FG5 MW

Burgar Hill Wind8

FG7 MW

Billia Croo10

FG4 MW

Eday9

FG2.7 MW

Bu Farm1

FG7.5 MW

Spurness7

NNFG2.5 MW

Spurness II14

NNFG4.5 MW

Hammars Hill15

NNFG0.9 MW

Rousay16

NNFG0.9 MW

Stronsay17

NNFG0.9 MW

Shapinsay18

NNFG1.8 MW

Gallowhill19

NNFG0.9 MW

Ore20

NNFG0.9 MW

Hatston21

NNFG0.9 MW

Thorkell22

NNFG0.9 MW

Eday Wind23

NNFG0.9 MW

Dounby12

Load12.5 MW

Flotta Elf27

FG10.5 MW

Flotta5

Load4 MW

St Mary34

Load1 MW

Lyness29

FG2 MW

Westfield11

Load0.1 MW

North Hoy30

Load2x24MW MW

Kirkwall28

Load8 MW

Burgar Hill24

NNFG2.3 MW

Burgar Hill Ren.13

Load1 MW

Westray37

Load1 MW

Stronsay36

Load2x8 MW

Stronmness35

FG1 MW

Northfield2

Load1 MW

Rousay31

Load1 MW

Eday25

FG15 MW

Kirkwall6

Zone 1a

Zone 1

Zone 2

Traditional Power flow

National Grid

Reverse Power Flow



ChallengeSSEPD seeking cost-effective alternative to grid upgrades to provide connections for new renewable generators: network already at ‘full capacity’

Approach

Non-firm actively managed grid connections for distributed generation

Delivered Benefits

• 12 Generators and 6 constraint locations deployed to date – more

to follow:

o Demand 6-31 MW

o Additional 26 MW of generation contracted to connect (14 MW connected already)

Connected >20 MW new renewable generation capacity to 33 kV grid previously considered to be full

Estimated savings of £30million for SSEPD


New TechnologyA New Type of Distribution NetworkNew Operational Challenges and Opportunities?


An increased requirement for visibility:- For the control room- For the Active Network Management system- For supporting system planning


Prerequisites forDistribution State Estimation

1. Measurements

+

2. Knowledge of the accuracy of those measurements

3. A model of the network

4. Pseudo-measurements

5. Knowledge of the accuracy of those pseudo measurements


Distribution State Estimation

Two Families of Techniques:

1) WLS-style State Estimators with Pseudo-measurements

2) Load Estimators


Requirements-driven Distribution State Estimation

Observability Analysis


Bad Data Detection and Removal

Error Estimation

Cleave network into Observable Islands:Areas of the network where a state estimate can be

calculated.

Island 1

Island 2

Island 3

Island 5

Island 4

Future

Future

Future

Future

Future

Future

Future

Future

Known branch and bus state variables before state estimation

Known branch and bus state variables after state estimation

Island 1

Island 2

Island 3

Future

Future

Future

Future

Future

Future

Future

Future

Known branch and bus state variables before state estimation

Known branch and bus state variables after state estimation






Error Estimation

Calculate State Estimate using advanced numerical methods






Error Estimation

Detect bad data and remove from the scope






Error Estimation

Use optimisation methods to calculate the upper and lower bounds of each estimated

parameter

70 bus network involves solving 140 LPs


Error Estimation

Error accumulates down 11kV feeders

Tightening of Bounds were there are multiple measurements at 33kV


Other Challenges:Integration with other systemsEmbedding knowledge within the business


“The data itself is not enough”

Lessons Learned

• Distribution State Estimation can increase network visibility on

Orkney

• Systems integration is challenging (but not insurmountable)

• A key area of development are the analytical methods themselves

• These methods are domain specific –power systems data

presents challenges that need domain specific solutions.

• How to you make best use of the additional data?

• Essential to engage all stakeholders in the business to understand

both the positive impact of Grid Analytics and the change in

working practices and corporate knowledge required to bring that

into the business.


www.smartergridsolutions.com

http://www.smartergridsolutions.com/

state estimation - a presentation at grid analytics europe 2013

Business

grid upgrades

kv grid

bus state variables

mw kirkwall load

mw flotta load

mw sanday load

mw eday load

grid analytics europe