Load forecasting for the grid integration of renewable · 2020-03-18 · Load forecasting, Jethro...

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


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


Why load forecasting?sp

atia

l sc

ale

forecast time scale

transmission grid

low voltage grid


utility grid

small/mediumcompany

house holds


gridplanning

balance market

hour

spotmarket


long termmarketoptimise self-consumption

of renewable power

optimise self-consumption of renewable power


curtailment


Topics discussedsp

atia

l sc

ale

forecast time scale

transmission grid

low voltage grid


utility grid

small/mediumcompany

house holds


gridplanning

balance market

hour

spotmarket



of renewable power



curtailment


Day ahead for utility gridsp

atia

l sc

ale

forecast time scale

transmission grid

low voltage grid


utility grid

small/mediumcompany

house holds


gridplanning

balance market

hour

spotmarket



of renewable power



curtailment


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


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


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

– ...


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


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.)


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


Linear Regression

P

T(°C)

Power demand at 20:00 on Friday is related to temperature at 20:00 on Friday






ProLa without PC 1.78 3.63 1.52

ProLa with PC



Delayed influences on Power demand


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


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


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


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







ProLa with PC 1.49 2,84 -1.37



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

•

•







ProLa with PC 1.49 2,84 -1.37


1.49 3.05 -0.04

Error (% of max. P.)

Combination of both methods reduces bias, but widens spread


Get your calendar straight!


Day ahead forecast for low voltage gridsp

atia

l sc

ale

forecast time scale

transmission grid

low voltage grid


utility grid

small/mediumcompany

house holds


gridplanning

balance market

hour

spotmarket



of renewable power



curtailment


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


Results of Validation for Infrax gridMAE(% of Max Power)



Comparison Day method

2.81 6.67 0.27


ProLa with PC 3.53 7.32 0.05


To be done

• Comparison: – RMSE of wind forecast for single site 18% (of Pmax)– RMSE for solar power forecast for Germany ~30% (of Pmean)


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


Long term forecast for low voltage gridsp

atia

l sc

ale

forecast time scale

transmission grid

low voltage grid


utility grid

small/mediumcompany

house holds


gridplanning

balance market

hour

spotmarket



of renewable power



curtailment


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


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


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


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]


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]


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


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.


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.

Load forecasting for the grid integration of renewable · 2020-03-18 · Load forecasting, Jethro...

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