Long Term Network Development Demand Forecast for a Distribution Network

Long Term Network Development Demand Forecast

for a Distribution Network

Long Term Network Development Demand Forecast

for a Distribution Network

David Spackman

Dr. Nirmal Nair

David Spackman

Dr. Nirmal Nair

David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair22

Summary:

Vector needed a long-term electricity demand forecast

This will feed into their long-term plans

Designed a new long-term forecast methodology:

the ‘policy-guided model’

Tested on Vector’s Auckland network and obtained

promising results

Long Term Network Development Demand Forecast for a

Distribution Network

Long Term Network Development Demand Forecast for a

Distribution Network


Background Forecast Model Results Future work Conclusions

OutlineOutline


Vector Electricity NetworkVector Electricity Network

Largest distribution company in NZ

Auckland, Northern, Wellington

660,000 connections

Zone substations: 123

Distribution substations: 24,000

Planning for demand growth

10-15 year forecasts

Long-Term Forecasting:

Strategic long-term (30-70 years)

Network asset investment

Purchasing land


Designing a Forecast ModelDesigning a Forecast Model

Many existing methods considered

Econometric

Artificial Neural Networks

Cellular Automata: Computer based Land

Use Simulations

New methodology designed


Designing a Forecast ModelDesigning a Forecast Model

Consider saturation of land

From Willis, H.L., Spatial Electric Load Forecasting


Basis for Forecast ModelBasis for Forecast Model

More customers More demand per customer


Forecast ModelForecast Model


Future Land Use: A Policy-Guided ApproachFuture Land Use: A Policy-Guided Approach

ARC 2050 Growth Strategy

Auckland Regional Council sets land use rules


Processing District Plan ZoningProcessing District Plan Zoning

Zoning information readily available from Councils

Processing of this data was required

Simplification into classes defined by ‘electricity demand’

Policy-guided model: 19 ClassesPolicy-guided model: 19 Classes

Auckland City Council: 36 Classes

Papakura District Council: 25 Classes

Manukau City Council: 138 Classes

Total: 199 Classes




Electricity Demand for each Customer/Land Use Class

Electricity Demand for each Customer/Land Use Class

The 19 simplified zone classes need to be assigned load densities


Load Densities: Approach 1Load Densities: Approach 1

1. Select feeders with one simplified zone class

2. Remove feeders not fully developed

3. Record area for each useful feeder (m2)

4. Record peak load for each useful feeder (W)

Open Space

Res Low


Determining Peak LoadDetermining Peak Load


Calculated Load DensitiesCalculated Load Densities

Land use class Load density (W/m2)

Open Space 0

Residential – Low Intensity 3.99

Residential – Medium Intensity 5.82

Business – High Intensity 86.4

Industrial – Light Intensity 11.54

… …

Land use class Load density (W/m2)

Open Space

Residential – Low Intensity

Residential – Medium Intensity

Business – High Intensity

Industrial – Light Intensity

… …



Further simplify zone classes More areas to work with

Res High

Res Med-High

Res Med

Res Low

Res


Approach 2 ResultsApproach 2 Results

0

2

4

6

8

10

12

0 10 20 30 40 50 60 70

Sample Feeders

LoadDensity(W/ m2)

Residential Load Densities



Smart Metering data Finer resolution of load densities Applicable now to some Commercial and Industrial customers




CombiningCombining

Applying load densities to zone classes

x 107,886 m2x 107,886 m2430.5 kW430.5 kW3.99 W/m23.99 W/m2




Scenario AnalysisScenario Analysis

Long-term horizon causes forecast to be scenario-dependent

A ‘Business-as-usual’ scenario to begin

Scenarios modify one or more variables of the model



Examples: New transport links Rezoning of land DSM, DG Intelligent Buildings: EMCS



Scenarios classified as:

1. End-use change scenarios eg. All Industrial peak demand increases by 5%

2. Re-zoning scenarios eg. Tank Farm redevelopment

Industrial Commercial + Residential

3. Micro-scale Creation of new ‘zone’ for specific development

4. Macro-scale Selection of areas based on other variables


Forecast Model CompletedForecast Model Completed


Case Study ResultsCase Study Results

Auckland Region


Case Study ResultsCase Study Results

Scenario Analysis: Residential Growth High infill of zones

near a majortransport corridor

Height = Peak Demand


VerificationVerification

Found no other small area study to directly compare with, during our literature survey

However, small area should be consistent with larger area

Electricity Commission forecasts to 2040 for major industry investments

By obtaining their data we can align our forecast and check…


VerificationVerification

0

500

1000

1500

2000

2500

3000

3500

4000

2000 2010 2020 2030 2040 2050 2060 2070

Year

Peak Load (MW)

Electricity Commission forecast, 2006 Policy-guided forecast


ApplicationApplication

Vector’s Long-term Strategic Network Development Plan

Australasian Universities Power Engineering Conference (AUPEC) Perth, Australia; December 2007

Provisionally accepted, paper to be made available through IEEE Explore


Future WorkFuture Work

Update with new data as it becomes available

Include CBD method

Cross-checking ARC 2050 plan

Amendments current and future

Extend to:

Northern region

North Shore, Waitakere, Rodney

Wellington region

Wellington City, Lower Hutt, Upper Hutt, Porirua


Future WorkFuture Work

Compare summed CAU results with an econometric model at CAU level:

Use population, GDP forecasts (2-20 years max- extrapolate?)

Need residential/commercial breakdown


ConclusionsConclusions

Investigated various forecasting methods for a long-term forecast

Designed new long-term forecast methodology

Completed a forecast for Vector’s Auckland Region

Sum of Auckland Region forecast results compare well with Electricity Commission forecast

AcknowledgementsAcknowledgements

Vector

Guhan Sivakumar

Auckland City Council

Manukau City Council

Papakura District Council


Long Term Network Development Demand

Forecast for a Distribution Network

Long Term Network Development Demand

Forecast for a Distribution Network

David Spackman

Dr. Nirmal Nair

David Spackman

Dr. Nirmal Nair

Documents

Long Term Network Development Demand Forecast for a Distribution Network