=Paper=
{{Paper
|id=Vol-2732/20200270
|storemode=property
|title=Open Data in Electrical Energy Balancing of Ukraine: Green Deal and
Security Aspects
|pdfUrl=https://ceur-ws.org/Vol-2732/20200270.pdf
|volume=Vol-2732
|authors=Svitlana Kolosok,Iuliia Myroshnychenko,Liudmyla Zakharkina
|dblpUrl=https://dblp.org/rec/conf/icteri/KolosokMZ20
}}
==Open Data in Electrical Energy Balancing of Ukraine: Green Deal and
Security Aspects==
https://ceur-ws.org/Vol-2732/20200270.pdf
Open Data in Electrical Energy Balancing of Ukraine:
Green Deal and Security Aspects
Svitlana Kolosok1[0000-0002-5133-9878], Iuliia Myroshnychenko1[0000-0002-0463-0347] and
Liudmyla Zakharkina1 [0000-0003-1002-130X]
1
Sumy State University, 2 Rymskogo-Korsakova st., 40007, Sumy, Ukraine
kolosok@management.sumdu.edu.ua
Abstract. The implementation of The European Green Deal is a modern driver
of changes in the energy sector for transition to a clean economy and energy se-
curity. Access and sustained consumption of clean energy sources, reduction of
greenhouse gas emissions and environmental pollution are important initiatives
for overall socio-economic development. This all implies the need to develop
models for the analysis the state of the energy system in real time as well as pre-
dict general energy consumption based on available open data and balance data-
bases. In this manuscript, we investigate the importance of open data for energy
security and the development of effective energy policy and institutional frame-
works. Analysis of the electrical energy balance of Ukraine (the 2019 calendar
year) on the base of the open data shows the existence of volatility between the
production of electricity from renewable energy sources and its consumption,
which may directly affect the country's security.
Keywords: open data in energy sector, electrical energy balancing, energy se-
curity in Ukraine.
1 Introduction
In order to analyze the state of the energy system in real time, to predict internal process,
and, especially, to develop energy policies at all levels of governance, the availability
of open energy data is important. Currently, energy systems in most countries are being
modernized and developed based on the concept of deep integration of electric power
grids and computer information and communication networks. Developing access to
modern energy database for users is not a main challenge, but also strongly multidisci-
plinary linked to other aspects such as geography, health, education and equality. Using
Open data gives fresh perspectives for scientific community and policy-makers to cre-
ate efficient energy systems. Furthermore, extension of Open data, grids databases such
as electricity generation capacities, consumption, electrical loads, geo-referenced data
promote to fill knowledge gaps and contributes to energy SDG targets and Green Deal
agenda [1]. Ukraine also have obligations in several environmental, energy and climate
partnerships [2].
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Special attention in the research made on interconnection among availability of Open
electrical energy data and credibility of research for energy systems development based
on energy balancing methodology. The electrical energy balance lets to study the do-
mestic electrical energy situation, to monitor effects of national electrical energy policy
of a country [3] and to compare balancing activities at the international energy market.
2 Recent Research Analysis
2.1 Related Works on Energy Balancing
The problem of energy balance has been studied extensively for the past decade. Actu-
ally, there are many theoretical and practical approaches developed in the field of elec-
tric energy balance. Fig. 1 shows the number of publications with TITLE-ABS-KEY
“Open Data&Energy” per year in accordance with Scopus database of peer-reviewed
literature. The exponentially growing number of publications per year proves that the
open data in the context of electric energy balance is currently a trendy topic of re-
search.
80
PUBLICATIONS/YEAR
60
40
20
0
2010 2012 2014 2016 2018 2020
PUBLICATION YEAR
Fig. 1. Number of publications per year under the topic “Open Data&Energy” listed in the source
index of the Scopus (Source: Conducted by authors based on Scopus database [4])
However, the peculiarities of application open data in electrical energy balancing
faces a lack of methodological approaches. This is mainly because of significant com-
plexity of the problem. Researchers as well as policy makers in energy sector generally
emphasizes the importance and urgency of the issue, but also its multiplicity and diffi-
culty. Pfenninger et al. [1] prove the need of open and high-quality big data to develop
quantitative energy models, which is the basis of well-thought-out energy policy at all
levels of government. In Wiese et al. [6] the lack of transparency of energy models as
well as lack of an open source energy system for developing a sustainability strategy
are discussed. The author emphasizes the importance of expertise from different fields
on technical, economic, environmental, and social issues for modeling a complex sys-
tem as renewable energy pathways simulation system.
It should be noted about some ethical and security concerns around open data. Col-
lecting data, formulating models might encounter problem with access to sensitive com-
mercial data or to data containing individual households’ information. Costs of security
failures in smart grid deployments have analyzed by McDaniel and McLaughlin [7].
Simmhan et al. [8] note that security and privacy concerns inherent in an information-
rich Smart Grid environment. Such situation can be exacerbated by their deployment
smart grid software architectures on clouds.
Energy security and current transformations in the economy are highlighted in the
literature. Effects of energy convergence are analyzed for example in Rui et al. [9].
Authors in [10, 11] shows importance of balanced resource allocation at all levels of
governance. Vasylyeva and Pryymenko in [12] focused on the concept of "energy de-
pendence" through energy security of all types of energy resources by using Jewell
model. In addition, authors in [13] develop methodical approach that allows to assess
the overall level of energy security of the country with further minimization of integral
specific discounted environmental and economic costs. By using IEA Model of short-
term energy security, the energy security of Ukraine is assessed. Karakasis in [14] focus
on policy paradigms for energy security matters. By conducting open-ended interviews
with the opinion-makers, author come to conclusion that there is difference between
the academic and the policy world in energy fields.
Many authors in their papers [15] confirm that biogas is the most perspective alter-
native resources and expected to bring benefits for the environment and economy [16].
Effects that take place through reduction of the natural gas consumption and replace-
ment by alternative fuel types are analyzed for example in [17], which finds additional
budget stimulation for local energy market transformation. From a different perspec-
tive, a methodological approach to electricity pricing on local level is developed by
Mentel et al. [18] in the form of accounting a balance of electricity production and its
consumption by the population of a particular territory. It is important to highlight the
implementation of the Net-zero building concept to gain energy security. In [19] re-
searchers make the economic assessment of the different power of solar collectors and
energy consumption by the households taking into account principles of Net-zero build-
ing.
Several empirical studies indicate that green investments have a positive effect on
economy and energy security. Lyeonov et al. in [19] estimates that green investment
could provoke the growth of GDP per capita by 6.4% and the increase of renewable
energy by 5.6% in the total final energy consumption. Similar arguments are presented
by Marcel in [21]. Author by using Stationery and Johansen cointegration tests, VAR
model, and Granger causality determines relationship between electricity consumption
and economic growth. In manuscript, bidirectional causality is empirically proven.
Some authors [22, 23] emphasize on impotence of green bonds as incentive instrument
for realizing green and renewable projects.
2.2 Open Data for Quality of Science in Energy Fields and Energy Policy
According to European data portal [24], “Open data” is data that anyone can access,
use and share. In other words, it’s a tool for the digital age that brings social, economic
and environmental benefits. The Open Knowledge Foundation [25] specify Open data
on two characteristics to openness: legal openness (legal to build on and to share it -
open license, placing into the public domain, etc.) and technical openness (no technical
barriers to using data - machine readable, available in bulk, etc.). Moreover, Foundation
defines principles of Open data: availability and access; re-use and redistribution; uni-
versal participation.
Importance of open data for energy security are determined by the following factors:
─ the constantly growing demands both on environmental friendliness and on improv-
ing the efficiency of energy systems and networks;
─ the unstable nature of wind and solar power generators that increases the require-
ments for efficient and fast load management in power systems;
─ the need for effective management of distributed generation systems with a large
number of sources;
─ the need for accurate load energy forecasting and efficient management of network
elements to save energy and prevent congestion.
Increasing the relevance of the topic of the energy balance and the availability of open
data requires creation of a specific online collaboration platform across science and
policy. The interconnected factors that influence quality of science in energy fields and
energy policy are presented in Fig. 2.
Fig. 2. The importance of open data for quality of science in energy fields and energy policy
(Source: Conducted by authors)
The role played by Open Data and Big data as a tool for research in different fields
of science has been intensely debated in the academic literature over the last years [26,
27, 28, 29]. In Beyi [30], the link between the individual and the digital network is
explained through new market mediator tools for creating global social dialogue.
Summarized literature review on place of Open and Big data in the contemporary
world is discussed in [31].
3 Data and Methodology
3.1 Data
Our research based on the course of the hour-by-hour electrical energy balance of the
Integrated power system (IPS) of Ukraine. The data were upload from the Open data
portal of Ukraine (that fully integrated into the European data portal). The electrical
energy balance databases on the portal available from the 2016 year, but we used elec-
trical energy statistics refer to the 2019 calendar year only (from January to December
on the hour-by-hour basis). The daily cycle of electrical energy balance stats at
01:00 a.m. and ends at 12:00 p.m. The data in electrical energy balances were presented
in megawatt-hours (МW). The accuracy of the electrical energy balance data in some
cases not very good. Firstly, in the 2019-year database, we found missing data for two
observation hours (see Fig. 3). For the purpose of research, we had to make corrections
of empty cells with the values above (empty cells at 4:00 p.m., 17.12.2019) and below
(at 5:00 p.m., 17.12.2019). Secondly, we observed some errors in volumes of computed
index ‘Used for the internal market’ electrical energy. The “statistical difference” in
energy balance not high but indicates “that some reported elements are inaccurate (or
alternatively, some elements are not reported)” [3]. Thirdly, this database has two dif-
ferent names on the portal (‘Hour-by-hour balance of the IPS capacity of Ukraine’ and
‘Electrical energy production and consumption balance (forecast and actual)’).
3.2 General Model
The electrical energy statistics collected by the National power company ‘Ukrenergo’
[32] according to the statistical methodology of Ukraine. The presented approach not
fully harmonized with Eurostat’s energy statistics approaches. We tried to apply defi-
nitions of Regulation (EC) No 1099/2008 on energy statistics [33], but not all data were
covered. ‘Ukrenergo’ does not report these data points: geothermal, wind power pro-
duction (but publish the value of renewables); total fuel consumption in main activity
producer plants. Electrical energy balance of Ukraine possible to describe by following
equations (1-6):
ETEP i = ENPP i + ETHPP i + ETPP i + Eother renewables i + εld i (1)
ETHPP i = EHPP i + ECHPP i + EPSP i + εld i (2)
ETNEP i = ETEP i – EConsumption i + εld i (3)
EUIM i = ETNEP i + ETNEI i - EusedPSP i + εld i = 0 (4)
ETNEI i = ETEI i - ETEE i + εld i (5)
ETNEI i = ENEI EU i + ENEI R-B i + ENEI M i + εld i (6)
where ETEP i – total electricity production (МW) in the i-balance (i = 1, …, N), ENPP i –
nuclear power production (МW), ETHPP i – total hydro power production (МW), ETPP i
– conventional thermal power production (МW), Eother renewables i – other renewables (not
including hydro power production, МW), εld i – losses (transformation, distribution and
transmission losses) and statistical difference (МW) in the i-balance, EHPP i – hydro
power production (МW), ECHPP i – combined heat and power plant production (МW),
EPSP i – part of hydro produced from pumped storage (МW), ETNEP i – total net electricity
production (МW), EConsumption i – electricity consumption (МW), EUIM i – used for the
internal market (МW), ETNEI i – total net electricity imports (МW), EusedPSP i – electricity
used for pumped storage (МW), ETEI i – total electricity imports (МW), ETEE i – total
electricity exports (МW), ENEI EU i – net electricity imports to EU (МW), ENEI B-R i – net
electricity imports to Belarus and Russia (МW), ENEI M i – net electricity imports to
Moldova (МW).
Fig. 3. Hour-by-hour electrical energy balance of the IPS of Ukraine in 2019 (Source: Conducted
by authors based on the data of the Open data portal (ODP) of Ukraine [32])
The general regression model of electrical energy balance based on equations (1-6)
is as follow (7):
Y i = ꬵ (X i )+ εld i (7)
where Y i – dependent variable, X i – independent variables in the i-balance (i = 1, …,
N).
To test differences between electricity consumption and renewables power produc-
tion, we set the hypothesis is as follows:
H0: No difference between means of electricity consumption and renewables power
production.
Ha: Difference between means (means of electricity consumption and renewables
power production is not equal to another).
The data set of electrical energy balance has a high range of values. To apply statis-
tical testing, we used a normalization procedure in Python to change the values to a
common scale. The general statistics were computed in Python with Pandas, SciPy, the
Scikit-learn, Statsmodels modules.
4 Results
Ukrainian electricity production generally orients on the internal market. The peak and
the most significant variation values of electrical energy production and consumption
were observed in January 2019. But the highest mean values of production and con-
sumption were in February 2019. The lowest level of electrical energy production and
consumption occurred during the warmest seasonal period in Ukraine: from May to
October. But if we look at renewable power production, are visible differences between
electrical energy consumption and renewable power production (see Fig. 4). This situ-
ation arises in connection with the production of electricity from new renewable energy
sources (other renewables in Ukrainian case). Operators and agents in the energy mar-
ket balance the production and consumption of electricity not only through pumped
storage but within combined heat and power plant production (ECHPP, Table 1). Export-
import operations are not the main source of the electricity market balancing in Ukraine
due to existing restrictions in this area. The Ukrainian electricity market is only by 6 %
synchronized with the EU market. The rest of the energy is flowing between Ukraine
and Belarus, as well as between Ukraine and Russia, although Russia is proclaimed a
military adversary of Ukraine. And therefore, the uncontrolled and unsystematic pro-
duction of electricity from new renewable energy sources strengthens Ukraine’s de-
pendence on Russia through the problem of balancing the production and consumption
of electricity. And may worsen the energy security of Ukraine.
The OLS regression on electrical energy balance analysis results on following as:
─ the total hydro power production is explained by 54,2 % of the variation in electrical
energy consumption (Table 2); the 94,7 % of the variation in electrical energy con-
sumption is explained by total electricity production (Table 3);
─ there is a significant difference in means (Table 2 and Table 3), so we should reject
the H0 hypothesis, and accept the Ha hypothesis;
─ The F-statistic = 1.036 for the total electricity production and F-statistic = 1.559 for
the total hydro power production, which is indicating that there is a significant effect
of electrical energy production on consumption; this indicates that the overall re-
gressions are meaningful.
a) consumption b) renewables
c) total electricity production d) total net electricity production
Fig. 4. Box and Whisker charts of electrical energy balance of Ukraine in 2019 (Source: Con-
ducted by authors based on the data of the ODP of Ukraine [32])
Table 1. Correlation matrix between electricity consumption (EConsumption) and other electrical
energy aggregates (Source: Conducted by authors based on the data of the ODP of Ukraine [32])
renewables
0.448 EusedPSP
-0.044 ENEI R-B
-0.034 ENEI EU
ENEI M
ECHPP
ETHPP
ETNEP
ETNEI
Eother
EHPP
ENPP
EUIM
ETPP
ETEP
EPSP
-0.239
-0.498
-0.107
-0.005
0.658
0.496
0.748
0.422
0.736
0.699
0.035
0.973
Table 2. OLS Regression results: E_Consumption and E_THPP in 2019 (Source: Conducted by
authors based on the data of the ODP of Ukraine [32])
Dep. Variable: E_Consumption R-squared: 0.542
Model: OLS Adj. R-squared: 0.542
Method: Least Squares F-statistic: 1.036e+04
No. Observations: 8759 Prob (F-statistic): 0.00
Df Residuals: 8757 Log-Likelihood: -77454.
Df Model: 1 AIC: 1.549e+05
Covariance Type: nonrobust BIC: 1.549e+05
coef std err t P>|t| [0.025 0.975]
Intercept 1.283e+04 41.960 305.836 0.000 1.28e+04 1.29e+04
E_THPP 2.3577 0.023 101.788 0.000 2.312 2.403
Table 3. OLS Regression results: E_Consumption and E_TEP in 2019 (Source: Conducted by
authors based on the data of the ODP of Ukraine [32])
Dep. Variable: E_Consumption R-squared: 0.947
Model: OLS Adj. R-squared: 0.947
Method: Least Squares F-statistic: 1.559e+05
No. Observations: 8759 Prob (F-statistic): 0.00
Df Residuals: 8757 Log-Likelihood: -68023.
Df Model: 1 AIC: 1.360e+05
Covariance Type: nonrobust BIC: 1.361e+05
coef std err t P>|t| [0.025 0.975]
Intercept -740.9104 44.577 -16.621 0.000 -828.291 -653.530
E_TEP 1.0022 0.003 394.872 0.000 0.997 1.007
5 Conclusions
Our study showed a significant interest increase in the energy sector, especially in re-
newable energy matters. Open data should help researchers conduct analysis. But the
presented databases in the energy sector are not always available, relevant, and meet
the requirements for the quality of research. All these shortcomings correspond to
Ukrainian open data in the energy sector. The data in the energy sector is published
only by 18 percent on the Ukrainian open data portal; the same databases have several
names; some data are absent in the databases, and there are errors in the calculations.
However, open data is quite necessary for balancing electrical energy. An analysis
of the production and consumption of electricity allows us to see the problems of bal-
ancing, which can affect the energy security of the country as a whole and individual
contractors in particular.
The data test displays that stimulating the electricity production from renewable
sources in Ukraine not only leads to green energy consumption but also significantly
increases the volatility between production and electricity consumption, the depend-
ence on neighboring countries to balance the electrical market. And since one of the
neighboring countries is Russia, the current situation causes the need for electrical en-
ergy balancing therapy in a short period and conceptual changes the source of renewa-
ble power production in a long period in Ukraine.
Acknowledgment
This work was supported by the Ministry of Education and Science of Ukraine (the
projects No. 0119U100766 ‘The optimization model of smart and secure energy grids
building: an innovative technologies of enterprises and regions ecologisation’ and No.
0117U003922 ‘Innovative drivers of national economic security: structural modeling
and forecasting’).
References
1. EUR-Lex Website. https://eur-lex.europa.eu/legal-con-
tent/EN/TXT/?uri=COM%3A2019%3A640%3AFIN
2. Energy Community Website. https://energy-community.org/dam/jcr:f2d4b3b8-de85-41b2-
aa28-142854b65903/Decision_2012_04_MC_RE.pdf
3. European Commission – Eurostat Website. https://ec.europa.eu/euro-
stat/cache/metadata/en/nrg_bal_esms.htm
4. Scopus Website. https://www.scopus.com
5. Pfenninger, S., DeCarolis, J., Hirth, L., Quoilin, S., & Staffell, I.: The Importance of open
data and software: is energy research lagging behind?. Energy Policy. 101, 211–215 (2017).
https://doi.org/10.1016/j.enpol.2016.11.046
6. Wiese, F., Bökenkamp, G., Wingenbach, C., Hohmeyer, O.: An open source energy system
simulation model as an instrument for public participation in the development of strategies
for a sustainable future Wiley Interdiscip. Rev.: Energy Environ. 3(5), pp. 490-504 (2014)
7. McDaniel, P., McLaughlin, S.: Security and Privacy Challenges in the Smart Grid. IEEE
Security & Privacy. 3, 75-77 (2009). http://dx.doi.org/10.1109/MSP.2009.76
8. Simmhan, Y., Kumbhare, A., Cao, B., Prasanna, V.: An analysis of security and privacy
issues in smart grid software architectures on clouds. In: IEEE International Conference on
Cloud Computing (CLOUD), pp. 582–589. IEEE, Italy (2011)
9. Rui, L., Sineviciene, L., Melnyk, L., Kubatko, O., Karintseva, O., Lyulyov, O.: Economic
and environmental convergence of transformation economy: The case of China. Problems
and Perspectives in Management. 17(3), 233–241 (2019).
https://doi.org/10.21511/ppm.17(3).2019.19
10. Sokolenko, L., Tiutiunyk, I., Leus, D.: Ecological and economic security assessment in the
system of regional environmental management: A case study of Ukraine. International Jour-
nal of Ecology and Development. 32(3), 27–35 (2017)
11. Matvieieva, Y., Myroshnychenko, I., Valenkevych, L. Optimization model of the socio-eco-
logical-economic development of the administrative territory. Journal of Environmental
Management and Tourism. 10(8), 1874–1899 (2019)
12. Vasylyeva, T., Pryymenko, S.: Environmental economic assessment of energy resources in
the context of Ukraine’s energy security. Actual Problems of Economics. 160(1), 252–260
(2014)
13. Salihaj, T., Pryimenko, S. Modification of the international energy agency model (the IEA
Model of short-term energy security) for assessing the energy security of Ukraine. SocioEc-
onomic Challenges. 1(4), 95–103 (2017). doi: 10.21272sec.1(4).95-103.2017
14. Karakasis, V.: The impact of “policy paradigms” on energy security issues in protracted
conflict environments: the case of Cyprus. SocioEconomic Challenges. 1(2), 5–18 (2017).
doi: 10.21272/sec.1(2).5-18.2017
15. Yevdokimov, Y., Chygryn, O., Pimonenko, T., Lyulyov, O.: Biogas as an alternative energy
resource for Ukrainian companies: EU experience. Innovative Marketing. 14(2), 7–15
(2018). doi: 10.21511/im.14(2).2018.01
16. Cebula, J., Chygryn, O., Chayen, S.V., Pimonenko, T.: Biogas as an alternative energy
source in Ukraine and Israel: current issues and benefits. International Journal of Environ-
mental Technology and Management. 21(5-6), 421–438 (2018). doi:
10.1504/IJETM.2018.100592
17. Vakulenko, I., Myroshnychenko, Iu.: Approaches to the organization of the energy efficient
activity at the regional level in the context of limited budget resources during the transfor-
mation of energy market paradigm. Environmental and Climate Technologies. 15(1), 59–76
(2015). doi: 10.1515/rtuect-2015-0006
18. Mentel, G., Vasilyeva, T., Samusevych, Y., Pryymenko, S.: Regional differentiation of elec-
tricity prices: Social-equitable approach. International Journal of Environmental Technol-
ogy and Management. 21(5-6), 354–372 (2018). doi: 10.1504/IJETM.2018.100583
19. Pimonenko, T.; Prokopenko, O.; Dado, J.: Net zero house: EU experience in Ukrainian con-
ditions. International Journal of Ecological Economics & Statistics. 38 (4), 46-57 (2017)
20. Lyeonov, S., Pimonenko, T., Bilan, Y., Štreimikiene, D., Mentel, G.: Assessment of green
investments' impact on sustainable development: Linking gross domestic product per capita,
greenhouse gas emissions and renewable energy. Energies. 12(20), 1–12 (2019). doi:
10.3390/en12203891
21. Marcel, D.: Electricity Consumption and Economic Growth Nexus in the Republic of Benin.
SocioEconomic Challenges. 3(2), 63–69 (2019). doi: 10.21272/sec.3(2).63-69.2019
22. Chygryn, O., Pimonenko, T., Luylyov, O., Goncharova, A.: Green bonds like the incentive
instrument for cleaner production at the government and corporate levels: Experience from
EU to Ukraine. Journal of Environmental Management and Tourism. 9(7), 1443–1456
(2018). doi: 10.14505//jemt.v9.7(31).09
23. Pimonenko, T., Bilan, Y., Horák, J., Starchenko, L., Gajda, W.: Green brand of companies
and greenwashing under sustainable development goals. Sustainability. 12(4), 1679 (2020).
doi: 10.3390/su12041679.
24. European data portal. https://www.europeandataportal.eu/elearning/en/module1/#/id/co-01
25. Open Knowledge Foundation Website. http:// opendefinition.org/od/2.1/en/
26. Giebe, C., Hammerström, L., Zwerenz, D.: Big Data & Analytics as a sustainable customer
loyalty instrument in banking and finance. Financial Markets, Institutions and Risks. 3(4),
74–88 (2019). doi: 10.21272/fmir.3(4).74-88.2019
27. Njegovanović, A.: Digital financial decision with a view of neuroplasticity / neurofinancy /
neural networks. Financial Markets, Institutions and Risks. 2(4), 82–91 (2018). doi:
10.21272/fmir.2(4).82-91.2018
28. Kiss, L.: The examination of the appearance of CSR in on-line scientific databases. Business
Ethics and Leadership. 2(2), 56–65 (2018). doi: 10.21272/bel.2(2).56-65.2018
29. Vasilyeva, T., Kuzmenko, O., Bozhenko, V., Kolotilina, O.: Assessment of the dynamics of
bifurcation transformations in the economy. In: CEUR Workshop Proceedings 2422, pp.
134–146. CEUR, Ukraine (2019). doi: 10.1051/shsconf/20196504006
30. Beyi, W.: The trilogy of a digital communication between the real man, his digital individual
and the market of the digital economy. SocioEconomic Challenges. 2(2), 66–74 (2018). doi:
10.21272/sec.2(2).66-74.2018.
31. Karaoulanis, A.: Big Data, what is it, its limits and implications in contemporary life. Busi-
ness Ethics and Leadership. 2(4), 108-114 (2018). doi: 10.21272/bel.2(4).108-114.2018.
32. Open data portal. https://data.gov.ua/en/dataset/31199018-e15e-4e87-bf5e-
2a4293151f5c/resource/bdf0229b-e7af-41ee-94bf-529cb060ed7a
33. EUR-Lex Website. https://eur-lex.europa.eu/legal-con-
tent/EN/TXT/PDF/?uri=CELEX:32008R1099&from=EN