Simulation of Electricity Demand in a Remote Island for Optimal … · MSc thesis, 95 pages, 2016....
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Simulation of Electricity Demand in a Remote Island for Optimal Planning of a Hybrid Renewable Energy System European Geosciences Union General Assembly 2017, 23-28 April, 2017 Vienna, Austria ERE3.7/HS5.11: Renewable energy and environmental systems: modelling, control and management for a sustainable future Aristotelis Koskinas*, Eleni Zacharopoulou, George Pouliasis, Ioannis Engonopoulos, Konstantinos Mavroyeoryos, Ilias Deligiannis, Georgios Karakatsanis, Panayiotis Dimitriadis, Theano Iliopoulou, Demetris Koutsoyiannis, and Hristos Tyralis Department of Water Resources, School of Civil Engineering, National Technical University of Athens, Greece INTRODUCTION The purpose of this study is to find correlations between the variables renewable energy systems depend on, in order to better simulate the electrical energy demand in the remote island of Astypalaia, Greece. To this end, we first obtain information regarding the local socioeconomic conditions and energy demand needs. Secondly, the available hourly demand load data are analyzed at various time scales (hourly, daily, weekly, seasonal). The cross-correlations between the electrical energy demand load and the mean daily temperature as well as other climatic variables for the same time period are computed. Also, we obtain data from numerous observations around the globe for various climatic variables, in order to investigate the cross-correlation between them, and assess the impact of each one on a hybrid renewable energy system. An exploratory data analysis including all variables is performed with the purpose to find hidden patterns on a yearly and monthly basis. Finally, the demand is simulated, considering all the periodicities found in the analysis. This simulation will be used in the development of a framework for planning a hybrid renewable energy system in Astypalaia. LOCATION -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 H=0.5 H=0.6 H=0.7 H=0.8 H=0.9 Zero lag Cross-Corelation -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 Rain-Temp Rain-Wind Rain-Dew Temp-Wind Temp-Dew Wind-Dew Zero lag Cross-Corelation To examine the validity of the cross- correlations, a test is conducted using timeseries of normal random values. Through Monte Carlo analysis and the SMA generation algorithm [7], 1000 timeseries of 20 years each are generated, and their cross-correlations are calculated, for various values of the Hurst parameter. Next, using SMA, 10000 annual synthetic timeseries for each process with a 32-year length are generated based on the historical data from the Samos and Kos stations and with Hurst parameters as indicated in [8] and references therein. As is shown in the upper box plot, even cross correlations between uncorrelated datasets follow the Gaussian distribution. Therefore, it is proven that a high cross- correlation value has a very low probability to occur randomly. The actual historical cross-correlations are compared with the synthetic timeseries in the lower box plot (shown as points). 1.Climatic Variables Methodology 4.Validation of the Results Temperature Dew Point Wind Speed Precipitation (3 hour basis) (3 hour basis) (3 hour basis) (daily basis) Create timeseries (annual and monthly basis) Calculate cross-correlations Ensure accuracy (with a data filter ) more than 20 years of data more than 300 days/year more than 25 days/month 1 2 3 2.Global Cross-Correlations Analysis Exception: certain areas with negative or close to zero correlation Strong positive correlation is the most consistent 180 ° W 135 ° W 90 ° W 45 ° W 0 ° 45 ° E 90 ° E 135 ° E 180 ° E 90 ° S 45 ° S 0 ° 45 ° N 90 ° N -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 Map 2: Zero-lag Cross Correlations between mean annual Temperature and Dew Point These areas seem to be arid (large deviation between daily minimum and maximum temperatures) We cross-correlated the average temperature difference between minimum and maximum values with their temperature-dew point cross-correlation coefficient. A negative relationship (-0.4) was calculated. The island of Astypalaia Aegean Sea, Greece 3.Astypalaia Cross-Correlations Methodology Comparison of Cross Correlations Rain-Temp Rain-Wind Rain-DewTemp-Wind Temp-Dew Wind-Dew Samos-Kos 0,50 -0,30 0,36 -0,23 0,64 -0,28 Heraklion-Santorini 0,04 0,33 0,13 -0,35 0,54 -0,45 Chania-Santorini 0,11 -0,29 -0,10 -0,32 0,51 -0,40 For the example of the remote island of Astypalaia, in the absence of meteorological stations at the exact location, several nearby stations are analyzed. Specifically, precipitation measurements on a daily basis are taken from stations in Samos, Heraklion, and Chania, whereas temperature, wind speed, and dew point measurements on a three hourly basis are taken from Santorini and Kos. Figure 2: Cross correlations of climatic variables from stations close to Astypalaia and the global mean on a monthly basis Fig. 3: Box plots of cross-correlations among sets of normal random values (top) and annual Samos-Kos data (bottom) Figure 1: Zero-lag Cross Correlations between Temperature, Wind Speed, and Dew Point (Annual basis, a sample of 100 stations) Table 1: Zero-lag cross-correlation coefficients of annual timeseries CONCLUSIONS • Concerning the cross-correlations of climatic variables around the globe, the only strong and consistent pattern is a positive correlation between mean temperature and dew point. • Comparisons among other climatic variables show a mild cross-correlation consistency on a global scale (the average global mean is close to zero for both annual and monthly scales). • Due to the relatively short lengths of the sample timeseries, any small correlation values could be statistically non- significant. • In the example of Astypalaia, on an annual scale, a moderately positive correlation is found between two processes: precipitation and mean temperature (approx. +0.5), and mean temperature and dew point (approx. +0.6). • By examining the nearby Samos and Kos stations on a monthly scale, observed annual cross-correlations become more prominent during the winter, and less during the summer. • Through Monte Carlo analysis, it is evident that high cross- correlation values have a very low probability of random occurrence (<5%). Thus, the cross-correlation between precipitation-temperature and temperature-dew point as estimated from the meteorological stations close to Astypalaia can be considered statistically significant. • The historic electricity demand timeseries includes periodicities derived in part from the earth’s rotation around the sun, but mostly occur due to consumer habits. Those periodicities should be taken into account for the simulation. • Significant demand peaks in the evening are observed. The designed system must have the capability to cover these needs. • Increased demand during the summer due to tourism. • Significant positive correlation between energy consumption and temperature. 5.Demand Analysis Methodology Visualize and cluster the data in 2 time scales (hourly and daily) to better understand the behavior of electricity demand and phenomena that are directly related to it 1 2 Calculate the main statistical characteristics and search for hidden periodicities. If any recurrences are found, they will be taken into account in the simulation 3 Analyze temperature measurements and search for correlations between them and electricity demand 4 Fit a stochastic model to historical data to create a simulation, and confirm the appropriate behavior of statistical characteristics of the historic timeseries Goal: Analyze electricity demand data in Astypalaia in order to create a simulation with the use of synthetic timeseries, 100 years long, for future demand load. Further Use: Design of a hybrid renewable energy system. Accurate forecasts will lead to substantial savings in operation and maintenance costs, increased reliability of the power supply, and will act as guidance for future development Data: Hourly demand data from the years 2014-2015 6.Demand Data Fig. 4: Variance of electricity demand load in years 2014- 2015. The timeseries have an annual periodicity and a positive asymmetry (more values above the average) 7.Clustering Data Hourly basis Daily basis Fig. 5,6,7,8: Hourly (top) and daily (bottom) variance of electricity demand loads in the years 2014-2015, and their corresponding autocorrelograms Demand load increase during summer due to tourism and increased temperature in both time scales • Strong evidence of daily periodicity, on an hourly basis • No periodicities on a daily basis [1],[2] (Results confirmed from the autocorrelograms) Hourly basis : • Local maximum in the evening • Local minimum around 07:00 Daily basis : • Weak (50 KW) increases/decreases during the week • No minimums or maximums 8.Energy Demand and Temperature Figure 9: Total day consumption related to mean daily temperature data from the island of Kos, Greece Energy demand peaks follow temperature peaks [1],[3] Use of air conditioning systems Temperature peaks during population peaks 1 2 Figure 10: Simulation of demand in the first 10 years (2016-2025) Simulation with the tsboot function of the software R [1],[4-6] Simulation REFERENCES: [1] Η. Τyralis, Spatio-temporal analysis of the electrical energy demand in Greece, MSc thesis, 95 pages, 2016. [2] H. Tyralis, G. Karakatsanis, K. Tzouka and N. Mamassis, Analysis of the electricity demand of Greece for optimal planning of a large-scale hybrid renewable energy system, EGU General Assembly 2015, Geophysical Research Abstracts, Vol. 17, Vienna. [3] H. Tyralis, Karakatsanis G, Tzouka K, Mamassis N (2017) Exploratory data analysis of the electrical energy demand in the time domain in Greece. Energy. In review. [4] AC Davison, Hinkley DV (1997) Bootstrap methods and their application. Cambridge University Press,Cambridge. [5] A. Canty, Ripley BD (2015) boot: Bootstrap R (S-Plus) Functions. R package version 1.3-17 [6] T. Hayfield, Racine JS (2008) Nonparametric Econometrics: The np Package. Journal of Statistical Software 27(5) [7] D. Koutsoyiannis, A generalized mathematical framework for stochastic simulation and forecast of hydrologic time series, Water Resources Research, 36 (6), 1519–1533, 2000. [8] P. Dimitriadis, K. Tzouka, H. Tyralis, E. Feloni, T. Iliopoulou, O. Daskalou Y. Markonis, and D. Koutsoyiannis, Long Range Dependence of hydro- meteorological processes under climate variability, (www.researchersnight.gr/), 2016 The poster will be uploaded to www.itia.ntua.gr Acknowledgement: This research is conducted within the frame of the undergraduate course "Stochastic Methods in Water Resources" of the National Technical University of Athens. The School of Civil Engineering of NTUA provided moral support for the participation of the students in the Assembly. Land Based Station Data (https://www.ncdc.noaa.gov/data-access/land-based-station-data) *Contact Author: [email protected]
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Simulation of Electricity Demand in a Remote Island for Optimal Planning of a Hybrid Renewable Energy SystemEuropean Geosciences Union General Assembly 2017, 23-28 April, 2017 Vienna, Austria
ERE3.7/HS5.11: Renewable energy and environmental systems: modelling, control and management for a sustainable future
Aristotelis Koskinas*, Eleni Zacharopoulou, George Pouliasis, Ioannis Engonopoulos, Konstantinos Mavroyeoryos, Ilias Deligiannis, Georgios Karakatsanis, Panayiotis Dimitriadis, Theano Iliopoulou, Demetris Koutsoyiannis, and Hristos Tyralis
Department of Water Resources, School of Civil Engineering, National Technical University of Athens, Greece
INTRODUCTIONThe purpose of this study is to find correlations betweenthe variables renewable energy systems depend on, in orderto better simulate the electrical energy demand in theremote island of Astypalaia, Greece.
To this end, we first obtain information regarding the localsocioeconomic conditions and energy demand needs.Secondly, the available hourly demand load data areanalyzed at various time scales (hourly, daily, weekly,seasonal). The cross-correlations between the electricalenergy demand load and the mean daily temperature aswell as other climatic variables for the same time period arecomputed.
Also, we obtain data from numerous observations aroundthe globe for various climatic variables, in order toinvestigate the cross-correlation between them, and assessthe impact of each one on a hybrid renewable energysystem. An exploratory data analysis including all variablesis performed with the purpose to find hidden patterns on ayearly and monthly basis.
Finally, the demand is simulated, considering all theperiodicities found in the analysis. This simulation will beused in the development of a framework for planning ahybrid renewable energy system in Astypalaia.
LOCATION
-0.8
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H=0.5 H=0.6 H=0.7 H=0.8 H=0.9
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Rain-Temp Rain-Wind Rain-Dew Temp-Wind Temp-Dew Wind-Dew
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To examine the validity of the cross-correlations, a test is conducted usingtimeseries of normal random values.Through Monte Carlo analysis and theSMA generation algorithm [7], 1000timeseries of 20 years each are generated,and their cross-correlations are calculated,for various values of the Hurst parameter.Next, using SMA, 10000 annual synthetictimeseries for each process with a 32-yearlength are generated based on the historicaldata from the Samos and Kos stations andwith Hurst parameters as indicated in [8]and references therein.As is shown in the upper box plot, evencross correlations between uncorrelateddatasets follow the Gaussian distribution.Therefore, it is proven that a high cross-correlation value has a very low probabilityto occur randomly.The actual historical cross-correlations arecompared with the synthetic timeseries inthe lower box plot (shown as points).
1.Climatic Variables Methodology
4.Validation of the Results
Temperature Dew Point
Wind Speed Precipitation
(3 hour basis) (3 hour basis)
(3 hour basis) (daily basis)
Create timeseries(annual and monthly
basis)
Calculate cross-correlations
Ensure accuracy (with a data filter)
more than 20 years of data
more than 300 days/year
more than 25 days/month
1
2
3
2.Global Cross-Correlations Analysis
Exception: certain areas with negative or close to zero
correlation
Strong positive correlation is the most consistent
180° W 135° W 90° W 45° W 0° 45° E 90° E 135° E 180° E
90° S
45° S
0°
45° N
90° N p p
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
Map 2: Zero-lag Cross Correlations between mean annual Temperature and Dew Point
These areas seem to be arid(large deviation between daily
minimum and maximum temperatures)
We cross-correlated the average temperature
difference between minimum and maximum values with
their temperature-dew point cross-correlation coefficient.A negative relationship (-0.4)
was calculated.
The island of AstypalaiaAegean Sea, Greece
3.Astypalaia Cross-Correlations Methodology
Comparison of Cross Correlations
Rain-Temp Rain-Wind Rain-Dew Temp-Wind Temp-Dew Wind-Dew
Samos-Kos 0,50 -0,30 0,36 -0,23 0,64 -0,28
Heraklion-Santorini 0,04 0,33 0,13 -0,35 0,54 -0,45
Chania-Santorini 0,11 -0,29 -0,10 -0,32 0,51 -0,40
For the example of the remote island of Astypalaia, in the absence ofmeteorological stations at the exact location, several nearby stationsare analyzed. Specifically, precipitation measurements on a dailybasis are taken from stations in Samos, Heraklion, and Chania,whereas temperature, wind speed, and dew point measurements on athree hourly basis are taken from Santorini and Kos.
Figure 2: Cross correlations of climatic variables from stations close to Astypalaia and the global mean on a monthly basis Fig. 3: Box plots of cross-correlations among sets of normal random values (top) and annual Samos-Kos data (bottom)
Figure 1: Zero-lag Cross Correlations between Temperature, Wind Speed, and Dew Point (Annual basis, a sample of 100 stations)
Table 1: Zero-lag cross-correlation coefficients of annual timeseries
CONCLUSIONS• Concerning the cross-correlations of climatic variables around
the globe, the only strong and consistent pattern is a positivecorrelation between mean temperature and dew point.
• Comparisons among other climatic variables show a mildcross-correlation consistency on a global scale (the averageglobal mean is close to zero for both annual and monthlyscales).
• Due to the relatively short lengths of the sample timeseries,any small correlation values could be statistically non-significant.
• In the example of Astypalaia, on an annual scale, amoderately positive correlation is found between twoprocesses: precipitation and mean temperature (approx. +0.5),and mean temperature and dew point (approx. +0.6).
• By examining the nearby Samos and Kos stations on amonthly scale, observed annual cross-correlations becomemore prominent during the winter, and less during thesummer.
• Through Monte Carlo analysis, it is evident that high cross-correlation values have a very low probability of randomoccurrence (<5%). Thus, the cross-correlation betweenprecipitation-temperature and temperature-dew point asestimated from the meteorological stations close to Astypalaiacan be considered statistically significant.
• The historic electricity demand timeseries includesperiodicities derived in part from the earth’s rotation aroundthe sun, but mostly occur due to consumer habits. Thoseperiodicities should be taken into account for the simulation.
• Significant demand peaks in the evening are observed. Thedesigned system must have the capability to cover theseneeds.
• Increased demand during the summer due to tourism.• Significant positive correlation between energy consumption
and temperature.
5.Demand Analysis Methodology
Visualize and cluster the data in 2 time scales (hourly and daily) to betterunderstand the behavior of electricity demand and phenomena that aredirectly related to it
1
2Calculate the main statistical characteristics and search for hiddenperiodicities. If any recurrences are found, they will be taken into accountin the simulation
3 Analyze temperature measurements and search for correlations betweenthem and electricity demand
4Fit a stochastic model to historical data to create a simulation, and confirmthe appropriate behavior of statistical characteristics of the historic timeseries
Goal: Analyze electricity demand data in Astypalaia in order to create asimulation with the use of synthetic timeseries, 100 years long, for futuredemand load.Further Use: Design of a hybrid renewable energy system. Accurateforecasts will lead to substantial savings in operation and maintenancecosts, increased reliability of the power supply, and will act as guidance forfuture developmentData: Hourly demand data from the years 2014-2015
6.Demand Data
Fig. 4: Variance of electricity demand load in years 2014-2015. The timeseries have an annual periodicity and apositive asymmetry (more values above the average)
7.Clustering DataHourly basis
Daily basis
Fig. 5,6,7,8: Hourly (top) and daily (bottom) variance of electricity demand loads in the years 2014-2015, and their corresponding autocorrelograms
Demand load increase during summer due to tourism and increased
temperature in both time scales
• Strong evidence ofdaily periodicity, onan hourly basis
• No periodicities on a daily basis [1],[2]
(Results confirmed fromthe autocorrelograms)
Hourly basis:• Local maximum in
the evening• Local minimum
around 07:00Daily basis:• Weak (50 KW)
increases/decreases during the week
• No minimums or maximums
8.Energy Demand and Temperature
Figure 9: Total day consumption related to mean daily temperature data from the island of Kos, Greece
Energy demand peaks followtemperature peaks [1],[3]
Use of air conditioning
systemsTemperature peaks during
population peaks
1
2
Figure 10: Simulation of demand in the first 10 years (2016-2025)
Simulation with the tsboot function of the
software R [1],[4-6]
Simulation
REFERENCES:[1] Η. Τyralis, Spatio-temporal analysis ofthe electrical energy demand in Greece,MSc thesis,95 pages, 2016.[2] H. Tyralis, G. Karakatsanis, K. Tzoukaand N. Mamassis, Analysis of theelectricity demand of Greece for optimalplanning of a large-scale hybridrenewable energy system, EGU General
Assembly 2015, Geophysical ResearchAbstracts, Vol. 17, Vienna.[3] H. Tyralis, Karakatsanis G, Tzouka K,Mamassis N (2017) Exploratory dataanalysis of the electrical energy demandin the time domain in Greece. Energy. Inreview.[4] AC Davison, Hinkley DV (1997)Bootstrap methods and their application.Cambridge University Press,Cambridge.[5] A. Canty, Ripley BD (2015) boot:
Bootstrap R (S-Plus) Functions. R packageversion 1.3-17[6] T. Hayfield, Racine JS (2008)Nonparametric Econometrics: The npPackage. Journal of Statistical Software27(5)[7] D. Koutsoyiannis, A generalizedmathematical framework for stochasticsimulation and forecast of hydrologictime series, Water Resources Research, 36(6), 1519–1533, 2000.
[8] P. Dimitriadis, K. Tzouka, H. Tyralis, E.Feloni, T. Iliopoulou, O. Daskalou Y.Markonis, and D. Koutsoyiannis, LongRange Dependence of hydro-meteorological processes under climatevariability, (www.researchersnight.gr/),2016
The poster will be uploaded to www.itia.ntua.gr
Acknowledgement: This research is conducted within the frame of the undergraduate course "Stochastic Methods in Water Resources" of the National Technical University of Athens. The School of Civil Engineering of NTUA provided moral support for the participation of the students in the Assembly.
Land Based Station Data (https://www.ncdc.noaa.gov/data-access/land-based-station-data)
*Contact Author: [email protected]
