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Electricity demand forecasting and the problem of embedded - - PowerPoint PPT Presentation

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Electricity demand forecasting and the problem of embedded generation Place your chosen image here. The four corners must just cover the arrow tips. For covers, the three pictures should be the same size and in a straight line. John Young

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Place your chosen image here. The four corners must just cover the arrow tips. For covers, the three pictures should be the same size and in a straight line.

Electricity demand forecasting

and the problem of embedded generation

John Young 6th March 2013

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Operating the system

 Electricity National Control Centre

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50.7 50.6 50.5 50.4 50.3 50.2 50.1 50.0 49.9 49.8 49.7 49.6 49.5 49.4 49.3 49.2 49.1 49.0 48.9 48.8 50.0 Normal operating frequency 50.5 Upper statutory limit 52.0 Generators tripping 49.5 Lower statutory limit 48.8 Demand disconnection starts 47.0 Demand disconnection complete Hz

Generation Demand

Operating the system

50.0 49.5 50.5

Frequency

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Demand profile shapes

 Shape of demand curves – in terms of turning points and points of inflections - remains fairly constant from day to day  Exact position of turning points, both in vertical (Demand) and horizontal (Time) directions varies, at least partially because of weather and non-weather variables  Shape evolves slowly over time, with some abrupt discontinuities

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GB National Demand A Typical Daily Profile: January

Winter Peak ~ 56,000 MW Winter Minimum ~ 32,000 MW

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GB National Demand A Typical Daily Profile: January

Winter Peak ~ 56,000 MW Winter Minimum ~ 32,000 MW

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GB National Demand A Typical Daily Profile: February

Winter Peak ~ 56,000 MW

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GB National Demand A Typical Daily Profile: March

Winter Peak ~ 56,000 MW

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GB National Demand A Typical Daily Profile: April

Winter Peak ~ 56,000 MW

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GB National Demand A Typical Daily Profile: May

Winter Peak ~ 56,000 MW

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GB National Demand A Typical Daily Profile: June

Winter Peak ~ 56,000 MW

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GB National Demand A Typical Daily Profile: June

Winter Peak ~ 56,000 MW Summer Minimum ~ 20,000 MW Summer Maximum ~ 40,000 MW

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Forecasting electricity demand

 Typical demand profile shape  2 distinct shapes: GMT and BST

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What Else Affects Demand?  Time of Day  Bank Holidays  School Holidays  Day of Week

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Day of week impact

Friday Demand curve for weekday in GMT Monday Demand curve for Saturday in GMT Demand curve for Sunday in GMT

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What Else Affects Demand?  Time of Day  Day of Week  Bank Holidays  School Holidays  Weather  Special Events  TV

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Temperature Demand Effect (MW)

COLD High Demand MILD Low Demand HOT Quite High Demand

Temperature

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18 Radiation Demand Effect (MW)

DULL High Demand BRIGHT Low Demand

Illumination

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19 Strong Wind: High Demand Low Wind: Low Demand Wind Speed Demand (MW)

The Impact of Weather Cooling Power of the Wind

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The Impact of Weather Rain

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Temperature (1°C fall in cold conditions) Cloud cover (clear sky to thick cloud) Precipitation (no rain to heavy rain) Temperature (1°C rise in hot conditions)

+ 500 MW + 500 MW + 1,000 MW + 1,500 MW

Cooling power (10 mph rise in cold conditions)

+ 1,000 MW

The Impact of Weather Some Numbers

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Weather Variables

 4-Hourly Average Temperature [TO]  Effective temperature [TE]  Wind Speed [WS]  Cooling Power of the Wind [CP] – a function of Wind Speed and Temperature (TO)  Effective illumination of the Sky [EI] - A derived quantity calculated from radiation levels and measurements of cloud type and cover

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Non-weather variables

 Day of week  Year Effect – indicator variable for different years: mostly owing

to different economic conditions

 Time of year – seasonality  Time of Sunrise and Sunset  School Holidays - % of schools on holiday  Annual Holidays – indicator variable from common August

holiday weeks

 Bank Holidays – excluded from data set for purposes of

modelling, then deal with on an ad hoc basis

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GB National Demand Cardinal Points

1B 2B DP

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 Model Inputs:  Historic Demands  Historic Weather – Heathrow, Glasgow, Manchester, Bristol, Leeds, Bimingham  Additional Effects – School Holidays, Day of Week, Time of Year

Basic Demand Day of Week Component Weather Component

Standard Linear Regression Conventional Models

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Modelling

 Construct different models for each of the Cardinal Points (CPs)  Construct different models for GMT and BST  Construct models of two different types for each CP:

Standard linear regression models (Conventional Models) Time series models with linear regression (Trend models)

 Depending on the CP we construct 7 day models, 5 day models, Saturday models and Sunday models  On any day of the week there are at least two (and up to four) models that we forecast with

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2B Demand (12:30)

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2B Model On Day Of Week Effect

Actual Demand Vs Fitted Values

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Including A Seasonal Effect

Actual Demand Vs Fitted Values

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Including A Weather Effect

Actual Demand Vs Fitted Values

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Including A School Holiday Effect

Actual Demand Vs Fitted Values

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32 2A 2B 3B 3C/DP 4B

Time Series with Linear Regression

Trend Models

1F

Yesterday Today

1A 04 1B 1F 1A 04 1B

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Including A School Holiday Effect

Actual Demand Vs Fitted Values

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Including A Trend Component

Actual Demand Vs Fitted Values

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The Model Symbolically

      CP EI SCH DWK L B L 04 ~ 2

Day of Week Effect Weather Effect Error term Trend term

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Modelling CP Demand

 Construct forecast models using variables that make sense  Use best model possible with variables that reduce the residual error significantly

Basic Demand Day of Week Component Weather Component

 Track ‘Basic Demand’, non-model component of demand

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Basic Demand

 Manually track and forecast basic element of demand

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Profile Matching

 Check how well CP forecasts match historic days

Daily Demand Profile

06-5 06-9 06-13 06-17 06-21 06-25 06-29 06-33 06-37 06-41 06-45 20110301 20110309 20110316 20100309 Actual_20130306 Forecast_20130306 Eview s

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Choosing Basics

 Forecast basic demand  Aim is to reduce risk of error

2B model: Jan-Feb 2013

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Embedded PV Generation

200 400 600 800 1,000 1,200 1,400 1,600 1,800 20100331 20100629 20100927 20101226 20110326 20110624 20110922 20111221 20120320 20120618 20120916 20121215

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Embedded PV Generation

Daily Demand Profile

28.5 33.5 38.5 43.5 48.5 05-1 05-5 05-9 05-13 05-17 05-21 05-25 05-29 05-33 05-37 05-41 05-45 20120306 20110308 20100302 20130304 Actual_20130305 Forecast_20130305 Eview s

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Embedded Generation

 ‘Invisible’, non-metered  Connected directly into distribution networks  Effectively reduces demand on the system  Not just PV…

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National Demand Embedded Generation

 True GB Demand is higher than National Grid observe  Not a new phenomenon, but an increase in more variable technologies means it is a more significant effect  Wind Power ~ 2,000 MW  Solar Power ~ 1,500 MW

The Impact of Embedded Generation

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Virtual Demand National Demand Embedded Generation

Virtual Demand: A True National Demand

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Basic Demand Week Day Component Weather Component

Model Using Virtual Demand

Virtual Demand

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Basic Demand National Demand Embedded Generation Week Day Component Weather Component

Forecast Virtual Demand; Adjust for Embedded Generation

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The Forecasting Process

National Demand Embedded Generation Basic Demand Week Day Component Weather Component National Demand Embedded Generation Basic Demand Week Day Component Weather Component

Model using Virtual Demand Forecast Virtual Demand; Adjust for Embedded Generation

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Wind Power Forecasting System

  • Metered Wind ~ 5,800 MW
  • Embedded ~ 2,000 MW

National Demand Forecast Metered Wind Power Forecast Embedded Wind Farms Metered Wind Farms

Forecasting Embedded Wind Generation Existing Forecasting Methods

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Standard Wind Power Curve

Wind Farm

10 20 30 40 50 0.0 0.2 0.4 0.6 0.8 1.0 Forecast Wind Speed / mph Load Factor

 Decile wind speed forecast applied to a load curve  Load curves for each wind generator, optimised using actual metering

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Wind Power forecast probabilistic view for next 5 days from Mon 3rd Dec 2012

Wind Power Forecast - Probabilistic View for Next 5 Days 1,000 2,000 3,000 4,000 5,000 03-DEC-2012 5:00 8:00 12:00 17:00 21:00 04-DEC-2012 5:00 8:00 12:00 17:00 21:00 05-DEC-2012 5:00 8:00 12:00 17:00 21:00 06-DEC-2012 5:00 8:00 12:00 17:00 21:00 07-DEC-2012 5:00 8:00 12:00 17:00 21:00 20% confidence 40% confidence 60% confidence 80% confidence Mean Forecast Excluding Cut-out Mean Forecast Wind Cut-out Forecast 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000 20% confidence 40% confidence 60% confidence 80% confidence Mean Cut-out

Wind cut out forecast

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Metered wind generation forecast

 Use same process to forecast embedded wind  Have information

  • n location and

capacity for all embedded wind generators above 2MW

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Forecasting Embedded Generation Simulated National PV Output

200 400 600 800 1,000 1,200 1,400 1,600 1,800 20100331 20100629 20100927 20101226 20110326 20110624 20110922 20111221 20120320 20120618 20120916 20121215

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Forecasting Embedded Generation Forecasting PV

David Lenaghan - Load Factor Vs Odiham Radiation 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 500 1,000 1,500 2,000 2,500 3,000 Mark Holland - Load Factor Vs Heathrow Radiation 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 500 1,000 1,500 2,000 2,500 3,000

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 500 1,000 1,500 2,000 2,500 3,000 National Average RA Load Factor

 National average radiation forecast  Generic power curve  National capacity

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The challenges of PV

Embedded Wind  Individual locations and capacities  Wind speed forecasts for various locations  Experience forecasting metered wind Embedded PV  Overall capacity for whole country  National Average radiation  No operational experience yet

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Forecasting Embedded Generation Forecasting PV

10 20 30 40 50 53000 54000 55000 56000 57000 Index X3B_Basic 10 20 30 40 50 55000 56000 57000 58000 59000 Index X3C_Basic 53000 54000 55000 56000 57000 58000 0e+00 2e-04 4e-04 6e-04

density.default(x = X3B_Basic + Output_1500, bw = 300)

N = 57 Bandwidth = 300 Density 54000 55000 56000 57000 58000 59000 60000 0e+00 2e-04 4e-04 6e-04

density.default(x = X3C_Basic + Output_1700, bw = 300)

N = 57 Bandwidth = 300 Density

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The Problem with Embedded Generation

~ 3,500 MW installed capacity Variable output dependant on weather effects Changing capacity levels Reliant on estimates of output No means of directly testing forecast models Increases the volatility of National Demand

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Including A Trend Component

Actual Demand Vs Fitted Values

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Including Embedded Wind Generation

Actual Demand Vs Fitted Values

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Including Embedded Solar Generation

Actual Demand Vs Fitted Values

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Thanks for listening

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Demand and Wind Forecasting