Optimal Capacity of a Battery Energy Storage System Based on Solar Variability Index to Smooth out Power Fluctuations in PV-Diesel Microgrids

Julius Susanto and Farhad Shahnia ()
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Julius Susanto: Australian Energy Market Commission (AEMC), Perth 6000, Australia
Farhad Shahnia: School of Engineering and Energy, Murdoch University, Perth 6150, Australia

Energies, 2023, vol. 16, issue 15, 1-21

Abstract: Battery energy storage systems can be integrated with photovoltaic (PV)-diesel microgrids as an enabling technology to increase the penetration of PV systems and aid microgrid stability by smoothing out the power fluctuations of the PV systems. This paper focuses on this topic and aims to derive correlations between the optimal capacity of the smoothing batteries and variabilities in the daily solar irradiance. To this end, the two most commonly used moving average and ramp rate control techniques are employed on a real solar irradiance dataset with a 1-min resolution for a full calendar year across 11 sites in Australia. The paper then presents the developed empirical model, based on linear regressions, to estimate the batteries’ optimal capacity without requiring detailed simulation studies, which are useful for practitioners at the early stages of a project’s feasibility evaluation. The performance of the developed technique is validated through numerical simulation studies in MATLAB ® . The study demonstrates that the empirical model provided reasonably accurate estimates when using the moving average smoothing technique but had limited accuracy under the ramp rate control technique.

Keywords: PV-diesel microgrid; power smoothing battery; solar irradiance variability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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