long term network development demand forecast for a distribution network david spackman dr. nirmal...
Post on 17-Dec-2015
219 Views
Preview:
TRANSCRIPT
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
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair33
Background Forecast Model Results Future work Conclusions
OutlineOutline
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair44
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
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair55
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
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair66
Designing a Forecast ModelDesigning a Forecast Model
Consider saturation of land
From Willis, H.L., Spatial Electric Load Forecasting
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair77
Basis for Forecast ModelBasis for Forecast Model
More customers More demand per customer
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair99
Future Land Use: A Policy-Guided ApproachFuture Land Use: A Policy-Guided Approach
ARC 2050 Growth Strategy
Auckland Regional Council sets land use rules
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair1010
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
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair1212
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
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair1313
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
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair1414
Determining Peak LoadDetermining Peak Load
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair1515
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
… …
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair1616
Load Densities: Approach 2Load Densities: Approach 2
Further simplify zone classes More areas to work with
Res High
Res Med-High
Res Med
Res Low
Res
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair1717
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
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair1818
Load Densities: Approach 3Load Densities: Approach 3
Smart Metering data Finer resolution of load densities Applicable now to some Commercial and Industrial customers
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair2020
CombiningCombining
Applying load densities to zone classes
x 107,886 m2x 107,886 m2430.5 kW430.5 kW3.99 W/m23.99 W/m2
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair2222
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
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair2323
Scenario AnalysisScenario Analysis
Examples: New transport links Rezoning of land DSM, DG Intelligent Buildings: EMCS
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair2424
Scenario AnalysisScenario Analysis
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
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair2525
Forecast Model CompletedForecast Model Completed
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair2626
Case Study ResultsCase Study Results
Auckland Region
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair2727
Case Study ResultsCase Study Results
Scenario Analysis: Residential Growth High infill of zones
near a majortransport corridor
Height = Peak Demand
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair2828
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…
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair2929
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
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair3030
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
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair3131
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
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair3232
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
David Spackman, Dr. Nirmal NairDavid Spackman, Dr. Nirmal Nair3333
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
top related