A majority of remote power systems are going to be supplied by diesel-renewable resources such as wind and photovoltaic energy in the future. However, the unpredictable nature of wind generation increases the concern about the reliable operation of these isolated microgrids. Using energy storage systems (ESSs) is recently accepted as an efficient solution to the volatility and intermittency of renewable energy sources. In this paper, a stochastic programming based on the Monte Carlo approach is introduced for optimal planning of remote systems. So far, most literatures have focused exclusively on the energy storage initial sizing. However, capacity expansion of ESS through the time span can result in significant cost saving and will be illustrated in this paper. Factors such as reliability criteria together with the investment and the operation costs are taken into account in the proposed methodology. This method utilizes practical operational constraints of ESS including efficiency and life cycle. Considering life cycle constraint reinforces the proposed method to completely investigate the difference between ESS technologies. The results of case study demonstrate that the proposed capacity expansion algorithm could lead to about 10% more profit over the traditional energy storage sizing. Index Terms-Energy storage system (ESS), MonteCarlo, planning, wind generation. NOMENCLATURE A. Variables e s ess (t) Stored energy of energy storage system (ESS) at time t. E y ess Energy capacity of ESS in year y. I y Investment cost in year y. IC s y Interruption cost in year y for scenario s. LS s (t) Load shedding at time t for scenario s. LSINDX s (t) Load shedding index at time t for scenario s. M s y Maintenance cost in year y for scenario s. O s y Operation cost in year y for scenario s. OC W Operation cost of wind units. OMC ESS Operation and maintenance cost of ESS. P s ch (t) ESS power charging at time t for scenario s. P s dch (t) ESS power discharging at time t for scenario s. ). M. Bozorg is with the Power

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