A statistical approach for hybrid energy storage system sizing based on capacity distributions in an autonomous PV/Wind power generation system


A statistical analysis and random generation of meteorological data are determined.

An improved management of the intended power for the storage system to assimilate high fluctuations is developed.

A statistical approach is used to elaborate the capacity of HESS.

The capacity distribution of HESS is defined using Monte Carlo Simulation.

The sizing results of HESS are established at different cumulative probability levels.


Hybrid Energy Storage System (HESS) integration with autonomous PV/Wind hybrid power system becomes, currently, an interesting option for the improvement of the storage units’ reliability and the life cycle assessment. In this paper, a new method for optimally sizing of HESS based on a statistical approach is proposed. This approach aims to exploit the capacity distribution of hybrid supercapacitor-battery system in an autonomous PV/Wind power generation system. This hybridization, of both slow and fast dynamics, aims to eliminate the power peaks caused by the load consumption. For the power distribution for all coordinated components of the storage system, a frequency management control and a hysteresis strategy are used in order to accomplish two goals: firstly, to delimit this exchanged power for not exceeding the maximum value and, secondly, to keep the States Of Charge (SOC) of the batteries-supercapacitors in a suitable range. Moreover, statistical analysis of several cumulative levels was performed to examine their contribution on the HESS optimal sizing. The obtained results prove that the integration of supercapacitor takes advantage of the complementary characteristics of the batteries, improves the exchanged power flow, extends the battery life cycle and affects on storage system sizing through accommodate the fast power fluctuations.


  • Optimal sizing;
  • Hybrid energy storage system (HESS);
  • Probability distributions;
  • Frequency management;
  • Capacity distributions

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