Influence of RES Integrated Systems on Energy Supply Improvement and Risks
Vladimir Ivanovich Velkin
Ural Federal University named after B. N. Yeltsin, Yekaterinburg, Russian Federation (Russian Federation)
Sergei Evgenevich Shcheklein
Ural Federal University named after B. N. Yeltsin, Yekaterinburg, Russian Federation (Russian Federation)
Abstract
RES (renewable energy sources) plays a very important role in the context of sustainable development, as an alternative to fossil fuels and nuclear power.
This paper presents the description of a RES technology cluster – an integrated system, which consists of equipment using different types of RES. It demonstrates that the stochasticity of renewable energy input influences the energy supply reliability. The work considers the influence of diversification of different RES sources on the improvement of energy supply reliability and reduction of risks connected with energy loss. Based on mathematical simulation using the convex optimization method, the authors propose a novelty solution to determine the most effective equipment configuration of an integrated energy system – a RES cluster. Effective computer programs have been developed and registered in order to calculate the optimal integrated renewable energy system in the Russian Federation.
The optimization criterion is the minimal cost of generating 1kWh of electricity of the whole complex of renewable energy sources. The feature of calculating the optimal combination of renewable energy sources is based on the variance of random variables, climatic characteristics unlike average for the year. This approach improves the accuracy of calculations by 25-40%. This leads to a reduction in capital equipment costs and reducing the cost of production of 1kWh of electricity.
Keywords:
renewable energy sources, RES technology cluster, renewable power supply system, energy supply reliabilityReferences
KNIGHT F., 1921, Risk, Uncertainty and Profit, Houghton Mifflin, Boston and New York.
Google Scholar
BENDAT J., PIERSOL A., 1971, Random data: Analysis and measurement processes, Wiley Interscience, New York.
Google Scholar
ESIPOV Y.V., SAMSONOV F.A., CHEREMISIN A.I., 2008, Monitoring and evaluation of system risks, Publishing house LKI, Moscow.
Google Scholar
SHARPE W.F., 1963, A Simplified Model for Portfolio Analysis, in: Management Science, vol. 9(2), p. 277-293.
Google Scholar
VELKIN V., 2013, The use of the graphical model for the RES cluster for determining the optimal composition of the equipment of renewable energy sources, in: World Applied Sciences Journal, vol. 29(9), p. 1343-1348.
Google Scholar
TOBIN J., 1965, The Theory of Portfolio Selection, in: The Theory of Interest Rate, eds. Hahn F.N., Brechling F.R.P., Macmillan, London.
Google Scholar
MARKOWITZ H.M., 1952, Portfolio selection, in: Journal of Finance, vol. 7(1), p. 77-91.
Google Scholar
MARKOWITZ H.M., 1990, Mean-Variance Analysis in Portfolio Choice and Capital Markets, Blackwell, Cambridge, MA.
Google Scholar
FULZELE J.B., DUTT S., 2012, Optimium Planning of Hybrid Renewable Energy System Using HOMER, in: International Journal of Electrical and Computer Engineering, vol. 2(1), p. 68-74.
Google Scholar
LAKHOUA M.N., 2014, System Analysis of a Hybrid Renewable Energy System, in: International Journal of Electrical and Computer Engineering, vol. 4(3), p. 343-350.
Google Scholar
Authors
Vladimir Ivanovich VelkinUral Federal University named after B. N. Yeltsin, Yekaterinburg, Russian Federation Russian Federation
Authors
Sergei Evgenevich ShchekleinUral Federal University named after B. N. Yeltsin, Yekaterinburg, Russian Federation Russian Federation
Statistics
Abstract views: 8PDF downloads: 3
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
