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ๅ็ซ่บ็ฃ็งๆๅคงๅญธ
ๅทฅๆฅญ็ฎก็็ณป
็ขฉๅฃซๅญธไฝ่ซๆ
ๅญธ่๏ผ M10701849
็จๆผ็ผ้ป้้ ๆธฌ็็ญๆๅคช้ฝ่ผป็
งๅบฆๅฏฆ็จ
้ ๆธฌไน็ ็ฉถ
Pragmatic Short -Term Solar
Irradiance Prediction for Power
Generation Prediction
็ ็ฉถ ็๏ผSiti Bariroh Maulidyawati
ๆๅฐๆๆ๏ผ Shuo -Yan Chou ้ญไผฏๅณ ๅ
ๅฃซไธญ่ฏๆฐๅไธไธ้ถๅนดไธๆ
2
3
4
ABSTRACT
Owing to its essential contribution to... |
Solar irradiation estimation is a critical
component for renewable energy systems such as photovoltaic (PV) systems to be built. It may
also help reduce energy costs and provide high energy quality in distributed solar photovoltaic
generation electricity grids. Thus, this study aims to forecast one -step and multi ... |
His ideas, kindness, advice, and
passion always inspire and motivate me to further enhance my work and achieve a great outcome. I would also like to acknowledge Prof. Po -Hsun Kuo and Prof. Tiffany Yu as my thesis defense
committee for their encouragement, insightful comments, evaluation and suggestions for my
resea... |
.................... 14
2.4. Research on Solar Irradiance Prediction ................................ .............................. 15
3 CHAPTER 3 METHODOLOGY ................................ ................................ ................. 17
3.1 Pre-analysis Method ................................ ............. |
...................... 11
Figure 3.1 Research Framework ................................ ................................ .............................. 17
Figure 3.2 Framework Analysis Procedure ................................ ................................ |
................... 22
Figure 4.2 One -Minute Feature Correlation ................................ ................................ ............ 23
Figure 4.3 Feature Correlation 10 -minutes Granularity ................................ .......................... 24
Figure 4.4 Correlation Between Variable ........... |
.................... 26
Figure 4.8 Auto -Correlation Solar Irradiance ................................ ................................ .......... 27
Figure 4.9 ANOVA Test Monthly Irradiance ................................ ................................ .......... 28
Figure 4.10 Monthly Irradiance Boxplot ....... |
............................... 33
Figure 4.15 Percentage Error and Actual Irradiance Relationship ................................ .......... 34
Figure 4.16 Relationship between Absolute Error and Percentage Error ................................ 35
Figure 4.17 Pragmatic Error for Month of May ..................... |
....................... 36
Table 4.6 Improvement Model Average Results ................................ ................................ ..... 39
Table 4.7 Moving Average Improvement in the Chance of Getting Error .............................. 39
9
1 CHAPTER 1
INTRODUCTION
1.1 Background
According... |
11
Figure 1.1 Organization of the Thesis
12
2 CHAPTER 2
LITERATURE REVIEW
2.1. Renewables Issues
Recently, renewable energy has been the most resilient energy source to the Covid -19
lockdown measures. Renewable electricity has been mostly unaffected, while demand for other
uses of renewable... |
3.1.2. Auto -Correlation Test
Auto -correlation tests are often carried out in the process of handling time -series data. Auto -
correlation test performed to see the correlation of data in time (t) with its past -data (denoted
as time lags x (t -1), x (t -2) and so on), a time window could be calculated. The magnit... |
The weather data
collected by the sensor is inevitable, so the missing value . Since the irradiance data consists of
a few missing values, filling out the missing value of the prediction is another problem since it
will pro duce bias in the prediction results. Masking informs sequence -processing layers that
there ... |
Feature Correlation
One of the mechanisms for seeing the characteristics of the data is feature correlation. Input
from the prediction method would be based on the predictor variable, which correlates
positively with the expected value. The way to find the correlation between two or more
variables may be done thro... |
4.1.2. Autocorrelation
Auto -correlation is a way to find the required time -lag configuration in the prediction model's
parameter settings. To find out th e explicit dependency, time series analysis requires auto -
correlation analysis. The effects of auto -correlation have been used to extend independent
27
... |
Fei Wang, Zhao Zhena, Yu Lie, Kangping Lia, Liqiang Zhaof, Miadreza Shafie -
khahh, Joรฃo P.S. Catalรฃoi, "A minutely solar irradiance forecasting method based on
real-time sky image -irradiance mapping model," Energy Conversion and Management,
vol. 220, 2020. |
A. Rahim, "Effect of high irradiation on pho tovoltaic power and
energy," International Journal of Energy Research, 2017. [11] "30 Innovative Solutions Show Path to Renewable -Powered Future." International
Renewable Energy Agency (IRENA). https://www.irena.org/newsroom/articles/2020/Jul/30 -Innovative -Solutions -Sh... |
I. PVPS, "TRENDS 2016 IN PHOTOVOLTAIC APPLICATIONS," 2016. [16] S. F. Giorgio Graditi, Giovanna Ad inolfi, Giuseppe Marco Tina, Cristina Ventura,
"Energy yield estimation of thin -film photovoltaic plants by using physical approach
and artificial neural networks," Solar Energy, vol. 130, pp. |
ๅ็ซ่บ็ฃ็งๆๅคงๅญธ
ๅทฅๆฅญ็ฎก็็ณป
็ขฉๅฃซๅญธไฝ่ซๆ
ๅญธ่๏ผM10801866
ๅ็่ฝๆบ้ ๆธฌไธ็ขบๅฎๆงๆผๅบๅนๅธๅ ดไธญไน
ๅฒ่ฝๅฎน้่ฃๅๆธฌๅฎ
Battery capacity determination for the
compensation of renewable energy
forecast uncertainty in a bidding -based
power market
็ ็ฉถ ็๏ผDavid Wacker
ๆๅฐๆๆ๏ผๅจ็ขฉๅฝฅ
ไธญ่ฏๆฐๅไธไธ้ถๅนดไธๆ
ๆ่ฆ
ๅฏๅ็่ฝๆบ่ขซ่ช็บๆฏๆๅฐๅ
จ็ๆๅ ๅๅ
ถๅพๆ็ๆ้่ฆ่ฝๆบไนไธใ็ก่ซๅฎ
ๅ็ๆฝๅๅฆไฝ๏ผๅจๅฎๅๅฎๅ
จๅไปฃๅณ็ตฑ็็ผ้ปๆนๅผๅ
... |
One o f these issues is that both solar and wind energy are not
available on demand but rather depend on the current weathe r. But to ensure grid
stability demand and supply always must be matched , which requires the grid operator
to known the available amount of power ahead of time. This research propose s a
p... |
Using the
composition, a simulation -based approach on determining storage capacity is
presented. The approach considers buffers, conversion losses, cycle life, maximum
depth of discharge and self -discharge. The result is an estimate for required battery
capacity to fully compensate any forecast errors made inclu... |
Without a doubt this sharing of wisdom is the most significant
source of motivation for me . Further I w ould like to thank the other committee member of my Thesis: Prof. Po-Hsun Kuo, and Prof. Loke Kar Seng, for their insightful comments and questions
that elevate the contents of my writing. Also, I thank my fello... |
65
Appendix 1. Forecast Composition โ MAPE Distributions ................................ ......... 68
Appendix 2. Forecast Composition โ Maxim um PE Distribution ............................... 70
Appendix 3. Forecast Composition โ Share PE over 5% ................................ ............ |
.. 54
Table 4: Forecast Composition โ MAPE Distributions ................................ ............... 68
Table 5: Forecast Composition โ Maximum PE Distributions ................................ .... 70
Table 6: Forecast Compos ition โ Distribution of PE over 5% ................................ .... 72
Li... |
.. 42
Figure 9: Cumulative Error over Time (Adjusted data) ................................ ............... 43
Figure 10: Forecast Composition โ Percentage Error Progression .............................. 44
Figure 11: Forecast Composition โ Maximum Percentage Error Progression ............ 46
Figure 12: Forecas... |
...... 56
Nomenclature
๐ธ๐ก Energy stored in BESS at time ๐ก
๐ต๐ธ๐๐ ๐๐๐ Storage Capacity
๐ฟ๐ท๐๐ท Maximum depth of discharge (%)
๐๐๐ก Power charged at time ๐ก
๐๐๐ก Power discharged at time ๐ก
๐๐ Charging efficiency
๐๐ Discharging efficiency
๐ด๐ก Actual Power at time ๐ก
๐น๐ก Forecasted Po... |
One of the measurements to be
taken is the significant reduction or even complete elimination of greenhouse gases . As
production of electricity with fossil fuels is one of the main contributors to CO 2-
emission [Boden, Marland et al. 2009] , production from renew able energy sources have
come into focus . Most cou... |
But as exac t demand is uncertain, the operator must still be able
to adjust power supply within short time (less than 30 seconds). To do so operating
reserve is necessary, which are basically forms of power production that can be started
or shut down almost immediate ly. As aforementioned, renewable energy sources... |
The
first reason that the model architectures are and have been extensively explored and
summarized in other research [Lai, Chang et al. 2020] . The second reason is that
according to โNo free lunch theoremโ there is no single model which will perform best
for every problem. The theorem states : โthat all optimiza... |
1.2.1. Super grid
One solution the European Union is currently investing in a so called โSuper
gridโ, which links European countries and surrounding countries through an additional
high-voltage power grid that sits on top of the existing grids [Cole, Vrana et al. 2011] .The idea is that by covering a wider area t... |
Battery Storage Systems
Due to drawbacks in regards of cost but also considering the aspect of time that
is required to realize super and smart grids, do not deliver an immediate solution for
solving intermittency issues of renewable energy. Electrical Storage Systems (ESS)
instead can be integrated into existing... |
Therefore, the followingly describe d research, also mainly evolves around this topic as
well. The effectiveness of a battery system in national grids ha s been demonstrated
through the Hornsdale Power Reserve in Australia, a 150 MW battery built by Tesla. As
presented by the โAurecon Hornsdale Power Reserve Impac... |
Al. consider battery sizing for
the behind the meter application, with the goal of reducing electricity cost of
commercial and industrial customers with high consumption in markets where
electricity rates changes depending on time of use [Wu, Kintner -Meyer et al. 2017] . |
Al. provides a comprehensive comparison of a pproaches for storage sizing
in hybrid power systems, where solar, wind and a battery are coupled to satisfy demand Introduction
23 requirements. These approaches optimize cost while having a set constraint on demand
mismatch [Hatata, Osman et al. |
Arnold and Andersson present a research closest
to the idea of this thesis, which is using BESS to counter forecast errors. They use a
Monte -Carlo simulatio n to simulate errors and determine required battery capacity to
counter these errors. The approach is optimized around a cost metric, meaning it allows
to th... |
Data Origin
The data used within this research all stems from a single provider. In total 3 6
datasets were considered (one dataset per i nverter) , stemming from three different
locations. All datasets range from the 13th of December 2019 to the 30th of June 2020,
a total of 2 01 days with one datapoint every ... |
Research design and methodology
2.2. Forecast Modelling
As mentioned earlier the intention of this paper is not to explore and explain
forecasting models and their performance. Therefore, also few explanations or
justification for the choices made during the modelling process is given , as it would
divert from... |
The layer has no additional configuration,
all settings are d efault. The reason for this choice, is previous research on the data that
adding layers, regulations or dropout does not significantly improve performance or
even does worsen it. Again, the chosen architecture and configuration might not be the
most opt... |
After the modelling process, post -processing is
applied the forecast in results in following ways:
1. All forecasts that predict power generation during known night are set to
zero. 2. The pow er value of the original dataset has the property of only occurring
in a fixed interval size of 0.06. Since the original ... |
Nevertheless, an improvement should be present. Lastly it should be addressed why the data is not combine first and only a
singular forecast is created. T he reason why there is not only singular forecast for all
these systems together is that each system has their own unique characteristics that
influence their ... |
2.4. Derivin g Storage Cap acity
The goal is to calculate the minimal required battery size that can compensate
all forecasting error of the composition at any given point in time ๐ก. Besides an initial
charge the battery can only be charged by the system s considered in the composition. |
Multiple factors impact the required battery capacity, which will be laid out
followingly. From these factors a set of conditions is derived. These conditions are
applied to a simulation of the battery charge over time from which the battery capacity
is derived as a result.Research design and methodology
2.4.... |
2.4.1.3. Permanent Capacity Loss
Permanent ca pacity loss, refers due losses in capacity that are not recoverable
through charging. The permanent capacity loss is mainly affected by the number of full
charge and discharge cycles, battery load/voltage and temperature. This capacity loss
is unavoidable but can be re... |
While these factors and their influence actual capacity loss are not be evaluated
in numerical terms , they are still be considered within the model. Ensuring partial
charging and discharging, is inherently covered as the power will only be charged or
discharged to equal out forecast error. Therefore, unless a cont... |
Constraints
Based on the set goal and relevant factors explained before, a set of constraints
can be derived. Some of these constraints are necessary to be ful filled while other are
only optional, which will be outlined clearly in their respective description. The final
required capacity of the BESS is equal to ... |
If Equation (7) must be
fulfilled without any exception than it must consider the highest possible error, even if
it is a single outlier. โ(๐ต๐ธ๐๐ ๐๐๐โ ๐ฟ๐๐ ๐ข๐)>๐๐๐ฅ (๐๐) (12)
โ(0 โ ๐ฟ๐๐ ๐๐๐ค)>๐๐๐ฅ (๐๐) (13) Research design and methodology
37
Lastly as an optional constraint the desired cycl... |
๐ต๐ธ๐๐ ๐๐๐ = โ๐๐๐ก๐ก + โ๐๐๐ก๐ก
๐ถ๐ฟ(๐ก) (14)
2.4.3. Simu lation Method
To determine the required capacity for a set of systems in a composition a
simulation -based approach will be employed. This method is inspired by dam capacity
planning, where demand (the required water flow out) and the water flow... |
Determine the values for ๐ฟ๐๐ ๐ข๐ and ๐ฟ๐๐ ๐๐๐ค. This simply depend on the
buffer size (๐ฝ), which can be freely determined. ๐ฟ๐๐ ๐ข๐๐๐๐ = ๐ต๐ธ๐๐ ๐๐๐ โ(1 โฮฒ)
๐ฟ๐๐ ๐๐๐ค๐๐ = ๐ต๐ธ๐๐ ๐๐๐ โ๐ฝ (16)
5. Define a battery management policy. To keep the size at a minimum , an
active component ... |
Zero -Mean Adjustment
Followingly the effect of the zero -mean adjustment is visualized and explained. Below in Figure 6 a series of boxplot showing the true error based on hour of the day
of the forecast composition with original unadjusted data is displayed . Generally, the
boxplot displays the distribution ... |
The issue resulting from that is that is easier to understand
when looking at Figure 7. Consistent underprediction also would mean the battery
needs to be consistently charged and would only be rarely discharged, which is not a
practical scenario. Analysis
41 Figure 6: Error based on dayti me
Figure 7: Cumulativ... |
Each step along the x -axis
represents an increase of the set size , while the y -axis represents the magnitude of error . The first datapoint therefore is distribution of the individual datasets. For example, t he
second datapoint describes the error distribution of all sets consisting of two combined
datasets, in... |
A ๐๐๐ท of
2% per month is equal to about 0.000463 % per ten minutes. The numerical inputs and results of the simulation are displayed in
1https://www.tesla.com/sites/default/files/pdfs /powerwall/Powerwall%202_AC_Datasheet_en_northam
erica.pdf
2https://sonnenusa... |
Due to the employed loa d adjustment policy, both stay well within the limit of the total
capacity and above zero. Analysis
53 Table 2: Capacity Simulation
Stag
e Parameters Tesla Powerwall 2 sonnen eco
0 ๐ฟ๐๐๐ (For 10min) 16.6667% 15%
ฮท๐= ฮท๐ 94.868% 90.0333%
1 ๐ต๐ธ๐๐ ๐๐๐๐๐๐๐ก 389 W 433 W
๏ฟฝ... |
Zero-Mean Adjustment
As shown the zero-mean adjustment is a simple but effective method for
correcting forecasts with an inherent tendency to either under - or overpredict. In Table
3 a comparison of the error distrib ution between the composition of the non -adjusted
data and adjusted data is given. The overall... |
The fluctuation of the max imum and minimum value of each metric between
set-sizes can be explained through the chosen approach of randomly drawing datasets
to create a limited number of combinations within each set. The number of samples
obtained is extremely small compared to the number of possible combinations. |
Therefore, the samples within this set do not accurately reflect the distribution of all
possible combinations of the respective set size. But this also highlights that the chance
of experiencing a significant improvement w ithin a given error metric increases with
an increasing number of individual forecasts, corr... |
This condition though is not a hard
condition, meaning not fulfilling it will not break the operation of the BESS. As
mentioned earlier exceeding the maximum DoD can cause permanent reduction of the
batteryโs capacity. While the relative excess does not seem to be major, n umerically
evaluating the exact conseque... |
The main drawback of the presented approach is the relative long period of data
needed to ensure reliability . The zero -mean adjustment already relies on a split of the
data, meaning the simulation can only be run with the second set. But the simulation
itself also needs to be validated, meaning it requires two set... |
5. Conclusion and Discussion
The goal of the research to provide a solution to determining battery capacity in
such a way that it is capable of fully compensating all forecast errors and thereby
indirectly making forecast reliable. Or in other words from the viewpoint of an operator
in the bidding market, making ... |
Whatever the reason might be, systems with forecast that have overall worse
performance are compensated through the more well performing systems. The
subsequent impact for the battery capacity is a reduction in required capacity , and
prolonged life time. Th e lower maximum errors impact the capacity as the maximum ... |
Future Work
The presented work provides merely a fundamental idea. From the discussion
in last chapter a few potential ideas arise, which could extend this research are
followingly provided. Translating the findings into an economic model would be of benefit to a
potential operator of such a battery system. Opp... |
Pode (2015). "Potential of lithium -ion batteries in renewable
energy." Renewable Energy 76: 375 -380. EPRI (2011). "Estimating the Cost s and Benefits of the Smart Grid: A Preliminary
Estimate of the Investment Requirements and the Resultant Benefits of a Fully
Functioning Smart Grid ." Electric Power Research Ins... |
(2004). "Self -discharge losses in lithium -ion cells." Aerospace and
Electronic Systems Magazine, IEEE 19: 19-24. 70 Appendix 1. Forecast Composition โ MAPE Distributions
Table 4: Forecast Composition โ MAPE Distributions
n forecasts
combined MAPE Distribution
Maximum (%) Mean (%) Median (%) Minimum (%) ... |
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