ASSESSMENT OF THE POWER BALANCE OF COMBINED ENERGY SYSTEMS
The choice of renewable energy in the combined power system should ensure the balance-sheet reliability of generation due to the likely consumption regimes. It is also necessary to ensure the rational use of energy produced. The adequacy of the power system is estimated using a series of indices, such as the probability of loss of load or the appearance of excessive power. One way to determine reliability indicators is to use a retrospective analysis of statistical data. An analytical determination of the probability distribution functions allows us to calculate the desired indexes, simplify calculations for a large number of the energy objects possible combinations. The distribution function of the balancing power process can be determined by statistical processing of historical data. Statistical analysis requires synchronous comparison of generation and consumption to determine the energy balance as a time series. For simulation of generation and load, instantaneous power is represented as the sum of averaged value, a random average daily value, and current short-term changes. The main problem for wind and solar energy is the impact of the stochastic component. For its modeling, actual synchronized data on weather conditions, energy characteristics of wind and solar installations and the level of electricity consumption by a single consumer (population) and a group of such consumers have been used. The characteristics of the distribution of probability are calculated, their normal character is confirmed. It is shown that small relative capacities of RES have practically no effect on the general variability; their role becomes noticeable at the implementation levels of more than 20% of the total power. The influence of the weather and loads forecast accuracy on the general level of uncertainty is estimated. It is shown that the prediction of average daily power can significantly reduce the probability of exceeding the specified levels of imbalance, reducing the need for additional compensating capacities. Comparison of calculated and actual values confirms the adequacy of the proposed model. References 8, tables 4, fig. 3.
techniques in optimal sizing of hybrid renewable energy systems.
International Journal of Research in Engineering and
Technology. eISSN 2319-1163 | pISSN 2321-7308. Vol. 04,
Issue 11. рp. 153–163. (Eng.)
2. Kuznietsov M. and Uzheyko S. (2016) Probabilistic aspects
of renewable energy using in the Chernobyl
Zone, Vidnovluvana Energetika, No. 3 (46). pp. 6–12. Available
at: http://ve.org.ua/index.php/journal/article/view/128 (Ukr.)
3. Kuznietsov M. (2017) Construction of a mathematical
model of electricity consumption mode, Vidnovluvana Energetika,
No. 4 (51). pp. 33–42. Available at:
4. Kuznetsov N.P. (2014) Features of wind power modeling.
Alternative Energy and Ecology (ISJAEE). No. 23. pp. 44–48. (Rus.)
Available at: https://www.isjaee.com/jour/article/view/741
5. Kuznietsov M. (2016) Modeling common work for wind
and solar power plants. Vidnovluvana energetіka. No. 1. pp. 12–
6. Kuznietsov M., Lysenko О. (2017) Statistical analysis of
energy indices of solar radiation (Based on the data of Tokmak
Solar Power Station). Problemele Energeticii Regionale.
No. 2 (34). pp. 140–148 (Rus.)
7. Lysenko O. (2018) Estimation of the random properties
of electricity consumption levels, Vidnovliuvana Enerhetyka,
No. 1 (52), pp. 26–35. (Ukr.) Available at:
8. Lew, Debra & Milligan, Michael & Jordan, G & Freeman,
L & Miller, N & Clark, Kara & Piwko, R. (2009) How do
Wind and Solar Power Affect Grid Operations: The Western
Wind and Solar Integration Study; Preprint. Conference Paper.
Bremen, October 14–15, 2009. 7 p. Available at:
Abstract views: 106 PDF Downloads: 36