load forecasting for the grid integration of renewable · 2020-03-18 · load forecasting, jethro...
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Load forecasting for the grid integration of renewable poweran eclectic Overview
Jethro Betcke
MeteoRES Workshop 6 Oct 2013
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
About me
1997 M.Sc. Theoretical Physics at Utrecht University Master thesis on defect states in a-Si solar cells
1997 E-connection Delft Long term prediction of wind turbine yield using WaSp 1998-2002 Science, Technology and Society at Utrecht University
Monitoring, Modelling, and feasibility studies of Photovoltaic systems.
Different aspect of solar irradiance
2003-2006 Solar energy meteorology group at Oldenburg University(Accuracy) improvement of satellite based irradiance measurements
Performance check of photovoltaic systems
2006-2009 Forwind at Oldenburg UniversityUtility scale load forecasting
2009-Present Solar energy meteorology group at Oldenburg UniversitySpectral distribution of solar irradiance
Synthetic irradiance data for grid-integration studies
Load forecasting for low voltage grids
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Why load forecasting?sp
atia
l sc
ale
forecast time scale
transmission grid
low voltage grid
large industrialplant
utility grid
small/mediumcompany
house holds
decenium+<hour day week year several years
gridplanning
balance market
hour
spotmarket
day-aheadmarket medium to
long termmarket
grid control:demand and/or production
curtailment
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Why load forecasting?sp
atia
l sc
ale
forecast time scale
transmission grid
low voltage grid
large industrialplant
utility grid
small/mediumcompany
house holds
decenium+<hour day week year several years
gridplanning
balance market
hour
spotmarket
day-aheadmarket medium to
long termmarket
grid control:demand and/or production
curtailment
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Why load forecasting?sp
atia
l sc
ale
forecast time scale
transmission grid
low voltage grid
large industrialplant
utility grid
small/mediumcompany
house holds
decenium+<hour day week year several years
gridplanning
balance market
hour
spotmarket
day-aheadmarket medium to
long termmarketoptimise self-consumption
of renewable power
optimise self-consumption of renewable power
grid control:demand and/or production
curtailment
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Why load forecasting?sp
atia
l sc
ale
forecast time scale
transmission grid
low voltage grid
large industrialplant
utility grid
small/mediumcompany
house holds
decenium+<hour day week year several years
gridplanning
balance market
hour
spotmarket
day-aheadmarket medium to
long termmarketoptimise self-consumption
of renewable power
optimise self-consumption of renewable power
grid control:demand and/or production
curtailment
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Topics discussedsp
atia
l sc
ale
forecast time scale
transmission grid
low voltage grid
large industrialplant
utility grid
small/mediumcompany
house holds
decenium+<hour day week year several years
gridplanning
balance market
hour
spotmarket
day-aheadmarket medium to
long termmarketoptimise self-consumption
of renewable power
optimise self-consumption of renewable power
grid control:demand and/or production
curtailment
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Day ahead for utility gridsp
atia
l sc
ale
forecast time scale
transmission grid
low voltage grid
large industrialplant
utility grid
small/mediumcompany
house holds
decenium+<hour day week year several years
gridplanning
balance market
hour
spotmarket
day-aheadmarket medium to
long termmarketoptimise self-consumption
of renewable power
optimise self-consumption of renewable power
grid control:demand and/or production
curtailment
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Utility scale load forecasting: the EWE-DEMS project
• DEMS: Decentral Energy management system, project of North German utility EWE
• Goal: Tool to develop tool to automate/support:
– Technical grid management (mainly medium voltage grid),
– Optimised purchasing of electricity
– Optimised use of own production means
• Including:
– Wind load forecasting
– Demand load forecasting
– Grid modeling
– Grid measurements/monitoring
– Grid switches
– Price forecasting/Strategies for purchasing and use of own production means
– IT platform to bring everything together
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Pre-conditions and requirements to load forecasting
• Preceeding project showed commercial software,
– could not beat human expert
– could not deal very well with delayed influences
– had too little options to deal with external influences
– was computationally intensive
• Requirements to load forecasting software:
– Day ahead forecast for utility and medium voltage grid with hourly resolution
– Computatinonally light
– Robust, i.e should work for all days of the year and avoid large errors
– Should be able to deal with delayed influences
• Pre-conditions
– Two years of utility grid data (one for learning one for validation), without large costumers, peak power: several GW.
– Increase in annual load
– Measurements of medium Voltage were not usable, due to connected wind parks
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Influences on load to consider
• Day of the week (typeday)
• Hour of the day
• Weather, mainly temperature and irradiance
• Calendar influences:
– School holidays
– National holidays
– Religious holidays
– Bridge days
• Important, but not considered:
– important football games
– local events
– one time events
– ...
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Reference method: Comparison day method
• For normal days:
– Take diurnal pattern of previous occurance of same typeday, e.g. prediction for monday
use previous monday, prediction for thursday use previous tuesday
• For special days (holidays, bridge days):
– select previous occurance of that special day
• After clock adjustment:
– use diurnal pattern of previous year
Both Fridays assumed to be the
same
Day nr
P
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Results of Validation for EWE gridMAE(% of Max Power)
RMSE(% of mean Power)
MBE(% of mean Power)
Comparison Day method 1.65 3.60 -0.11
ProLa without PC
ProLa with PC
Weather corrected comparison day method
Error (% of max. P.)
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
ProLa basics: Linear regression
• Historic demand data is split up in sub datasets depending on time of day, typeday, and winter/summertime,
• Relationship between Power and external variables is determined for each sub set seperately:
• P(T,G,CV1,..CV
N)=P
0+c
TT + c
T2T2 +c
Gln(G) + ∑
j ∆P
j CV
j ,
where: T=Temperature, G= irradiance, CV= binary calendar variable
• Note 1: coefficients c, and ∆Pj are different for each sub set
• Note 2: One year of learning data means 78 datapoints in each subset for midweek days, and 26 points for the other days
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Linear Regression
P
T(°C)
Power demand at 20:00 on Friday is related to temperature at 20:00 on Friday
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Results of Validation for EWE gridMAE(% of Max Power)
RMSE(% of mean Power)
MBE(% of mean Power)
Comparison Day method 1.65 3.60 -0.11
ProLa without PC 1.78 3.63 1.52
ProLa with PC
Weather corrected comparison day method
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Delayed influences on Power demand
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Delayed influences on Power demand
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
How to deal with influence of past weather?
• Considering the weather variables at different time points would mean too much coefficients to fit → unstable results
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Summarising weather data using Principal Component Analysis
Normalised Temperature at 9:00
Nor
mal
ised
Te m
pera
t ure
at
1 0:0
0
General recipy:• Normalise each variable with
standarddeviation
• Calculate covariance matrix of normalised variables
• Determine eigenvectors of covariance matrix
• Eigenvectors form axis of PC-space.
• The PC corresponding with the hightest eigenvalue contains the most information
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Only a few PCs are needed
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1,0
Temperatur
Einstrahlung
Log(Einstrahl.)
Niederschlag
Windgeschwin-digkeit
Windrichtung
Number of principal components
Des
crib
ed v
ari a
nce
Example: diurnal pattern of temperature at 19th May 2005
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
New approach: relate power to PCs describing a period
• P(PC1, PC
M CV
1,..CV
N)=P
0+ ∑
ic
i PC
i + ∑
j ∆P
j CV
j
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Results of Validation for EWE gridMAE(% of Max Power)
RMSE(% of mean Power)
MBE(% of mean Power)
Comparison Day method 1.65 3.60 -0.11
ProLa without PC 1.78 3.63 1.52
ProLa with PC 1.49 2,84 -1.37
Weather corrected comparison day method
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Correction of comparison day method
• Carry out the analysis of historic data as before, but use relationship to correct the values of the comparison day:
P(PC1, PC
M CV
1,..CV
N)=P
comparison day+ ∑
ic
i ∆PC
i + ∑
j ∆P
j ∆CV
j
where: ∆PCi = change in Principal Component i between
comparison day and forecast day
∆CVj = change in Calendar Variable j between
comparison day and forecast day
•
•
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Results of Validation for EWE gridMAE(% of Max Power)
RMSE(% of mean Power)
MBE(% of mean Power)
Comparison Day method 1.65 3.60 -0.11
ProLa without PC 1.78 3.63 1.52
ProLa with PC 1.49 2,84 -1.37
Weather corrected comparison day method
1.49 3.05 -0.04
Error (% of max. P.)
Combination of both methods reduces bias, but widens spread
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Get your calendar straight!
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Day ahead forecast for low voltage gridsp
atia
l sc
ale
forecast time scale
transmission grid
low voltage grid
large industrialplant
utility grid
small/mediumcompany
house holds
decenium+<hour day week year several years
gridplanning
balance market
hour
spotmarket
day-aheadmarket medium to
long termmarketoptimise self-consumption
of renewable power
optimise self-consumption of renewable power
grid control:demand and/or production
curtailment
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Belgian medium Voltage grid
• Research part of the MeteoRES project• Peak demand: Several tens of MW• Four years of data available• 15 minute resolution• Stable demand
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Results of Validation for Infrax gridMAE(% of Max Power)
RMSE(% of mean Power)
MBE(% of mean Power)
Comparison Day method
2.81 6.67 0.27
ProLa without PC 3.60 7.37 0.02
ProLa with PC 3.53 7.32 0.05
Weather corrected comparison day method
To be done
• Comparison: – RMSE of wind forecast for single site 18% (of Pmax)– RMSE for solar power forecast for Germany ~30% (of Pmean)
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Why can't ProLa beat the comparison day method in Belgium
• In Infrax grid different holidays have different effects
Start of winter holiday (Krokus vakantie)
ProLa expects drop in demand, but there is none
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Long term forecast for low voltage gridsp
atia
l sc
ale
forecast time scale
transmission grid
low voltage grid
large industrialplant
utility grid
small/mediumcompany
house holds
decenium+<hour day week year several years
gridplanning
balance market
hour
spotmarket
day-aheadmarket medium to
long termmarketoptimise self-consumption
of renewable power
optimise self-consumption of renewable power
grid control:demand and/or production
curtailment
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
DiGASP: How much PV can a low Voltage grid endure?
• Grid operators determine safe PV penetration levels based on (unrealistic?) worst case scenarios→ Grid connections may be unnecessarily refused
• Approach of the DiGASP project:
– Use a Monte Carlo approach to cover the whole range of possible grid states by creating
a large number of household power demand and PV production time series as basis for
grid simulations.
– Both types of time series require high resolution synthetic weather data as input
– Should also work in the absence of historic demand data.
• Demand load generator and grid simulation by Christof Bucher of ETH Zürich/ Basler & Hofmann
V Norm V }
No PV With PV, worst case: maximum PV output, no demand
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Bucher - Andersson Procedure: Analysis
• Analyse historic data of typical household types: family, pensioner etc.
• If you don't have historic data: produce it (with a bottom-up procedure)
• For every fifteen minutes determine:
• power probability distribution function (PDF) and mean load duration
Image credit: Christof Bucher ETH Zürich/ Basler & Hofmann AG [1]
8:00 to 8:15
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Bucher - Andersson Procedure: creating new timeseries
1. At 0:00 randomly draw demand value according to known PDF2. Get load duration from table3. Goto 1 after end of load duration
Resulting synthetic power demand timerseriesNOT considering load duration
Resulting synthetic power demand timerseriesconsidering load duration
Image credit: Christof Bucher ETH Zürich/ Basler & Hofmann AG
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Synthetic high resolution Irradiance data
Where: NMC = number of Monte Carlo runs
Nsys = number of PV systems
k* = clear sky index= cloud transmittance
G = irradiance
[3]
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
DiGASP results
• For a testcase in Switzerland with 100 households:
– If the correlation between PV production and power demand are taken into consideration 55% more PV power can be connected to the grid than based on the usual conservative approach.
– Reactive power control can add over 100%.
– Demand side management can add 91%
Image credit: Christof Bucher ETH Zürich/ Basler & Hofmann AG [2]
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Conclusions
• There is no “one size fits all“ in load forecasting• Before weather influences can be considered the
calendar influences must correctly be dealt with • Human insight is irreplacable• Simple methods may be accurate enough for grid
integration of renewables• In the absence of measurements synthetic data in
combination with a Monte Carlo method can deliver valuable insights in grid stability
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
Acknowledgements
• We wish to thank:– EWE, the European Commission, and the Federal Ministry
for the Environment, Nature Conservation and Nuclear Safety for respectively supporting the DEMS, MeteoRES and DiGASP project.
– EWE and Infrax for providing data– Christof Bucher for information on the Bucher-Andersson
procedure.
Load forecasting, Jethro Betcke, Oldenburg, University, MeteoRES workshop, 7 Oct 2013 Brussels
References
[1] Christof Bucher and Göran Andersson: Generation of Domestic Load Profiles - an AdaptiveTop-Down Approach. Proceedings of PMAPS 2012, Istanbul, Turkey, June 10-14, 2012.
[2] Christof Bucher, Jethro Betcke, Göran Andersson, Benoît Bletterie, Lukas Küng: Simulation of Distribution Grids with Photovoltaics by means of Stochastic Load Profiles and Irradiance data. 27th EUPVSEC Frankfurt, 24. - 28. September 2012.
[3] Jethro Betcke, Jan Kühnert, Thomas Scheidsteger: Development and Validation of the DiGASP weather generator. Technical Report, Energy and Semiconductor Laboratory, Carl von Ossietzky University of Oldenburg, August 2013.
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