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 The Bulletin of Irkutsk State University. Series Mathematics, 2018, Volume 26, Pages 76–90 (Mi iigum358)

Volterra equation based models for energy storage usage based on load forecast in EPS with renewable generation

D. N. Sidorovabc, A. V. Zhukovbd, I. R. Muftahovbe

a Irkutsk State University, Irkutsk, Russian Federation
b Melentiev Energy Systems Institute SB RAS, Irkutsk, Russian Federation
c Irkutsk National Research Technical University, Irkutsk, Russian Federation
d Institute of Solar-Terrestrial Physics SB RAS, Irkutsk, Russian Federation
e Irkutsk Computing Center of JSC Russian Railways, Irkutsk, Russian Federation

Abstract: High penetration of renewable energy under condition of the free electricity market leads to the need of creating new methods for maintaining balance between load and generation, in particular, energy storage usage in modern power systems. However, most of the proposed models of energy storage do not take into account some important parameters, such as the nonlinear dependence of efficiency on life time and changes in capacity over time, the distribution of load between several independent storages and others. In order to solve this problem models based on Volterra integral equations of the first kind with kernels presented in the form of discontinuous functions are proposed. Such models allows to determine the alternating power function at known values of load and generation. However, to effectively solve this problem, an accurate forecast of the electrical load is required, therefore, several forecasting models based on machine learning was exploited. Forecasting models use different kind of features such as average daily temperature, load values with time shift, moving averages and others. In the paper comparison of the forecasting results is provided, including random forest, gradient boosting over the decision trees, the support vector machine, and also multiparameter linear regression. Effectiveness of the proposed forecasting models and storage model is demonstrated on the real data of Germany power system.

Keywords: Volterra equation, machine learning, forecasting, electric power systems, energy storage.

 Funding Agency Grant Number Russian Foundation for Basic Research 18-31-00206

DOI: https://doi.org/10.26516/1997-7670.2018.26.76

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Bibliographic databases:

UDC: 51-74
MSC: 45D05, 68T05

Citation: D. N. Sidorov, A. V. Zhukov, I. R. Muftahov, “Volterra equation based models for energy storage usage based on load forecast in EPS with renewable generation”, The Bulletin of Irkutsk State University. Series Mathematics, 26 (2018), 76–90

Citation in format AMSBIB
\Bibitem{SidZhuMuf18} \by D.~N.~Sidorov, A.~V.~Zhukov, I.~R.~Muftahov \paper Volterra equation based models for energy storage usage based on load forecast in EPS with renewable generation \jour The Bulletin of Irkutsk State University. Series Mathematics \yr 2018 \vol 26 \pages 76--90 \mathnet{http://mi.mathnet.ru/iigum358} \crossref{https://doi.org/10.26516/1997-7670.2018.26.76}