Economic efficiency of Russian renewable energy projects in the context of state support of the sector

Economic efficiency of Russian renewable energy projects in the context of state support of the sector

Galina S. Chebotareva

Academic Department of Energy and Industrial Enterprises Management Systems, Ural Federal University, Russia

Page: 
226-244
|
DOI: 
https://doi.org/10.2495/EQ-V7-N3-226-244
Received: 
N/A
| |
Accepted: 
N/A
| | Citation

© 2022 IIETA. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).

OPEN ACCESS

Abstract: 

A current global trend in the development of renewable energy (RES) is the phasing out of state support and the transition of this sector to an exclusively competitive market. The question however is, when, among other things, it would be possible for such projects to achieve self-sufficiency. Therefore, the main goal of this work is to study the economic efficiency of Russian RES projects as a prospect for their functioning outside of state support programs. Fifty-two solar, wind, and hydropower projects, which have received support in the form of a capacity-based support scheme in 2018–2020, were selected as the objects of research. The methodological basis of this work is the classical method of investment analysis, supplemented by an industry-specific approach. The efficiency assessment was carried out for the 15-year period of projects’ state support, as well as for the entire designed operation period of power plants. The dependence of the projects’ economic effect on a combination of factors, including the type of project, the commissioning period, regional affiliation, capital expenditures, etc., were studied. Based on the results of the analysis, the conclusions about the current unpreparedness of the Russian RES sector to operate in a competitive market were substantiated; proposals for the development of programs to support the sector were formulated. A unique factor that has a significant impact on the achievement of a positive economic effect by such projects – the value of specific capital expenditures – was identified. The obtained research results are of practical and methodological significance. They will be used in the development of a methodological approach to assess the effectiveness of the rejection by the Russian RES market of state support tools at certain stages of the projects.

Keywords: 

capacity-based support scheme, capital expenditures economic efficiency, energy market, hydroelectric power, investment analysis, renewable energy, solar power, state support, wind power

  References

[1] Ministry of Energy of Russian Federation, System of state stimulation of electricity storage in Russia [Sistema gosudarstvennogo stimulirovaniya hraneniya elektroenergii v Rossii], 2021. https://minenergo.gov.ru/node/489. Accessed on: 20 December 2021.

[2] Chebotareva, G., Strielkowski, W. & Streimikiene, D., Risk assessment in renewable energy projects: A case of Russia. Journal of Cleaner Production, 269, p. 122110, 2020. https://doi.org/10.1016/j.jclepro.2020.122110

[3] Energy Policy [Energeticheskaya politika], RES 2.0: A new program for the development of “green” energy in Russia [VIE 2.0: Novaya programma razvitiya «zelenoj» energetiki v Rossii], 2020. https://energypolicy.ru/a-maksimov-vie-2-0-novaya-programma-razvitiyazelenoj-energetiki-v-rossii/energetika/2020/17/13/. Accessed on: 10 January 2022.

[4] Kommersant, Green energy is eroding [Zelenaya energetika vyvetrivaetsya], 2017. https://www.kommersant.ru/doc/3342654. Accessed on: 11 January 2022.

[5] Vedomosti, Ministry of Economic Development proposed to cut the renewable energy support program in half [Minekonomrazvitiya predlozhilo urezat’ vdvoe programmu podderzhki VIE], 2020. https://www.vedomosti.ru/economics/articles/2020/10/20/843976-minekonomrazvitiya-predlozhilo. Accessed on: 11 January 2022.

[6] Landi, D., Castorani, V. & Germani, M., Interactive energetic, environmental and economic analysis of renewable hybrid energy system. International Journal on Interactive Design and Manufacturing, 13(3), pp. 885–899, 2019. https://doi.org/10.1007/s12008-019-00554-x

[7] Ghiasi, M., Esmaeilnamazi, S., Ghiasi, R. & Fathi, M., Role of renewable energy sources in evaluating technical and economic efficiency of power quality. Technology and Economics of Smart Grids and Sustainable Energy, 5(1), p. 1, 2020. https://doi.org/10.1007/s40866-019-0073-1

[8] Diemuodeke, E. O., Addo, A., Oko, C. O. C., Mulugetta, Y. & Ojapah, M. M., Optimal mapping of hybrid renewable energy systems for locations using multi-criteria decision-making algorithm. Renewable Energy, 134, pp. 461–477, 2019. https://doi.org/10.1016/j.renene.2018.11.055

[9] Herbes, C., Roth, U., Wulf, S. & Dahlin, J., Economic assessment of different biogas digestate processing technologies: A scenario-based analysis. Journal of Cleaner Production, 255, p. 120282, 2020. https://doi.org/10.1016/j.jclepro.2020.120282

[10] Trovato, V. & Kantharaj, B., Energy storage behind-the-meter with renewable generators: Techno-economic value of optimal imbalance management. International Journal of Electrical Power and Energy Systems, 118, p. 105813, 2020. https://doi.org/10.1016/j.ijepes.2019.105813

[11] Zhang, Y., Yuan, J., Zhao, C. & Lyu, L., Can dispersed wind power take off in China: A technical & institutional economics analysis. Journal of Cleaner Production, 256, p. 120475, 2020. https://doi.org/10.1016/j.jclepro.2020.120475

[12] Karmaker, A. K., Ahmed, M. R., Hossain, M. A. & Sikder, M. M., Feasibility assessment and design of hybrid renewable energy based electric vehicle charging station in Bangladesh. Sustainable Cities and Society, 39, pp. 189–202, 2018. https://doi.org/10.1016/j.scs.2018.02.035

[13] Decree of the Government of the Russian Federation No. 449, On the Mechanism for Stimulating the Use of Renewable Energy Sources in the Wholesale Electricity and Capacity Market [O mekhanizme stimulirovaniya ispol’zovaniya vozobnovlyaemyh istochnikov energii na optovom rynke elektricheskoj energii i moshchnosti], 2021. https://base.garant.ru/70388616/#block_45. Accessed on: 01 November 2021.

[14] AtsEnergo, Project selection results [Rezul’taty otborov proektov], 2021. https://www.atsenergo.ru/vie/proresults. Assessed on: 01 September 2021.

[15] Association NP “Market Council”, Geography of the Russian electricity market [Geografiya rossijskogo elektroenergeticheskogo rynka], 2021. http://ais.np-sr.ru/iasen/information/IASE_0V_R0_OVERALL#0/0/OVERALL. Accessed on: 12 December 2021.

[16] InterRAO, Methodological features of evaluating the effectiveness of projects in the electric power industry [Metodicheskie osobennosti ocenki effektivnosti proektov v elektroenergetike], 2008. https://library-full.nadzor-info.ru/doc/51123. Accessed on: 01 September 2021.

[17] Association NP “Market Council”, Regulations for determining the parameters necessary for calculating the price under contracts for the provision of capacity of qualified generating facilities operating on the basis of the use of renewable energy sources [Reglament opredeleniya parametrov, neobhodimyh dlya rascheta ceny po dogovoram o predostavlenii moshchnosti kvalificirovannyh generiruyushchih ob”ektov, funkcioniruyushchih na osnove ispol’zovaniya vozobnovlyaemyh istochnikov energii], 2016. https://www.np-sr.ru/sites/default/files/sr_regulation/reglaments/SR_0V050259/r19_4_01092016_27092016.pdf. Accessed on: 01 September 2021.

[18] AtsEnergo, Forecasts of free (unregulated) prices for electric energy (capacity) for the subjects of the Russian Federation for 2021 and the initial data for making forecasts [Prognozy svobodnyh (nereguliruemyh) cen na elektricheskuyu energiyu (moshchnost’) po sub”ektam Rossijskoj Federacii na 2021 god i iskhodnye dannye dlya postroeniya prognozov], 2020. http://www.atsenergo.ru/sites/default/files/prognoz/20201229_anpsr_ishodnye_dannye_i_prognoz_na_2021.pdf. Accessed on: 01 September 2021.

[19] Fox, E. B., Dunson, D. B. & Airoldi, E. M., Bayesian nonparametric covariance regression. Journal of Machine Learning Research, 16, pp. 2501–2542, 2015.

[20] Mokhov, V.G., Chebotareva, G.S. & Khomenko, P.M., Modelling of green investments risks. Bulletin of the South Ural State University, Series: Mathematical Modelling, Programming and Computer Software, 11(2), pp. 154–159, 2018. https://doi.org/10.14529/mmp180213