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 security. 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 predict general energy consumption based on available open data and balance databases. In this manuscript, we investigate the importance of open data for energy security and the development of effective energy policy and institutional frameworks. 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.
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
multidisciplinary linked to other aspects such as geography, health, education and equality. Using
Open data gives fresh perspectives for scientific community and policy-makers to
create 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 [
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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
domestic electrical energy situation, to monitor effects of national electrical energy policy
of a country [
The problem of energy balance has been studied extensively for the past decade. Actually, there are many theoretical and practical approaches developed in the field of electric 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 research.
80 R A YE60 / S N IO40 T A C IL20 B U P
However, the peculiarities of application open data in electrical energy balancing
faces a lack of methodological approaches. This is mainly because of significant
complexity 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
difficulty. Pfenninger et al. [
It should be noted about some ethical and security concerns around open data.
Collecting data, formulating models might encounter problem with access to sensitive
commercial data or to data containing individual households’ information. Costs of security
failures in smart grid deployments have analyzed by McDaniel and McLaughlin [
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. [
Many authors in their papers [
Several empirical studies indicate that green investments have a positive effect on
economy and energy security. Lyeonov et al. in [
According to European data portal [
Importance of open data for energy security are determined by the following factors: ─ the constantly growing demands both on environmental friendliness and on improving the efficiency of energy systems and networks; ─ the unstable nature of wind and solar power generators that increases the requirements 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.
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 [
Summarized literature review on place of Open and Big data in the contemporary world is discussed in [31]. 3 3.1
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
electrical 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)” [
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 definitions 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 production (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
ETHPP i = EHPP i + ECHPP i + EPSP i + εld i
ETNEP i = ETEP i – EConsumption i + εld i EUIM i = ETNEP i + ETNEI i - EusedPSP i + εld i = 0
ETNEI i = ETEI i - ETEE i + εld i ETNEI i = ENEI EU i + ENEI R-B i + ENEI M i + εld i (1) (2) (3) (4) (5) 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).
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 production, 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 statistical 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
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 consumption 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 situation arises in connection with the production of electricity from new renewable energy sources (other renewables in Ukrainian case). Operators and agents in the energy market balance the production and consumption of electricity not only through pumped storage but within combined heat and power plant production (ECHPP, Table 1). Exportimport 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 production of electricity from new renewable energy sources strengthens Ukraine’s dependence 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 consumption 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 regressions are meaningful.
No. Observations: Df Residuals: Df Model: Covariance Type:
E_THPP coef 8759 8757 1 nonrobust std err Our study showed a significant interest increase in the energy sector, especially in renewable 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 balancing, 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 dependence 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 energy balancing therapy in a short period and conceptual changes the source of renewable power production in a long period in Ukraine.
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’). 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. Business 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-bf5e2a4293151f5c/resource/bdf0229b-e7af-41ee-94bf-529cb060ed7a 33. EUR-Lex Website. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32008R1099&from=EN