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Statistical Modeling of the Energy Market Development in the Regions of Ukraine
Chala T. G., Priadko Y. V., Slavuta O. I.

Chala, Tеtyana G., Priadko, Yuliia V., and Slavuta, Olena I. (2021) “Statistical Modeling of the Energy Market Development in the Regions of Ukraine.” Business Inform 1:151–157.
https://doi.org/10.32983/2222-4459-2021-1-151-157

Section: Regional Economy

UDC 311.21:[339.13.021-027.541:620.9](477)

Abstract:
The article is aimed at modeling the energy market of Ukraine on the meso-level, identifying its features and development problems, forming a system of indicators for monitoring the development of electricity production at the regional level. It is determined that Ukraine produces electrical power mainly from non-renewable energy sources, namely fossil and mineral fuels, using nuclear power plants and thermal power plants, which comprises 54% of the total electricity production. Wind, solar sources of electrical power, biofuels and hydroelectric power plants make up a smaller part of production – 7%. It is noted that Ukraine annually consumes about 92 million tonnes of oil equivalent (toe) of energy, has a high level of energy intensity of the economy, therefore, in order to reach the world average level, it is necessary to reduce energy consumption by 50 million toe. The level of losses of the produced and imported energy during its transformation and transportation to the end user is 44%. The potential for reducing energy consumption during consumption in sectors such as housing, budget-based and energy supply is about 19 million toe. To assess the state of the energy sector in Ukraine in 2019, a grouping of regions of Ukraine was carried out using cluster analysis. The regions that have entered the 1st cluster, namely: Dnipropetrovsk, Donetsk and Zaporizhzhia regions, have the best prospects for the rapid innovative development of the energy market. The regions that are included in the 3rd cluster have the lowest values of energy sector development indicators among other clusters and require special attention to the development of new, cost-effective energy technologies. The system of indicators for the analysis of the energy market development on meso-level is substantiated. The proposed indicators are adapted to the indicators of monitoring the achievement of sustainable development goals, namely: sustainable development goals 7 «Ensuring access to inexpensive, reliable, sustainable and modern energy sources for everyone».

Keywords: electrical power market, renewable sources, non-renewable sources, cluster analysis, meso-level, statistical modeling.

Fig.: 3. Tabl.: 3. Bibl.: 14.

Chala Tеtyana G. – Candidate of Sciences (Economics), Associate Professor, Associate Professor, Department of Statistics, Accounting and Auditing, V. N. Karazin Kharkiv National University (4 Svobody Square, Kharkіv, 61022, Ukraine)
Email: [email protected]
Priadko Yuliia V. – Student, V. N. Karazin Kharkiv National University (4 Svobody Square, Kharkіv, 61022, Ukraine)
Email: [email protected]
Slavuta Olena I. – Candidate of Sciences (Economics), Associate Professor, Department of Economics, O. M. Beketov National University of Urban Economy in Kharkiv (17 Chornohlazivska Str., Kharkіv, 61002, Ukraine)
Email: [email protected]

List of references in article

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Iria, J., and Soares, F. “A sluster-based optimization approach to support the participation of an aggregator of a larger number of prosumers in the day-ahead energy market“. Electric Power Systems Research, vol. 168 (2019): 324-335. DOI: https://doi.org/10.1016/j.epsr.2018.11.022
Schneider, I., and Roozbehani, M. “Energy Market Design for Renewable Resources: Imbalance Settlements and Efficiency-Robustness Tradeoffs“. IEEE Transactions on Power Systems, vol. 33, no. 4 (2018): 3757-3767. DOI: 10.1109/TPWRS.2017.2782638
Siano, P., and Sarno, D. “Assessing the benefits of residential demand response in a real time distribution energy market“. Applied Energy, vol. 161 (2016): 533-551. DOI: https://doi.org/10.1016/j.apenergy.2015.10.017
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Wang, J., and Wang, J. “Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations“. Energy, vol. 102 (2016): 365-374. DOI: https://doi.org/10.1016/j.energy.2016.02.098

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