This paper aims at utilizing energy storage systems for two purposes at the same time including smoothing the uncertainties of wind-solar units as well as reduction of network losses. In order to achieve these objectives, IEEE 24-bus test system is considered as case study. This network is integrated with wind turbine and solar system. The output powers of wind and solar units are modeled by probability distribution function. The energy storage systems are installed on the network to smooth out the uncertainty as well as loss reduction. The network is modeled by AC power flow including both active-reactive power. The problem of finding location, power, capacity, and charging-discharging pattern of energy storage systems is expressed as nonlinear mixed integer optimization stochastic programming. The uncertainties are handled by Monte-Carlo simulation and the proposed stochastic programming is solved by modified particle swarm optimization algorithm. The results demonstrate that the proposed stochastic programming can efficiently install energy storage systems on the network. The problem finds optimal siting, sizing, and hourly operation pattern for all energy storage systems, while it minimizes the losses. It is worth mentioning that number of predefined locations for energy storage systems and renewable resources are limited to simplify mathematical formulation of the planning. As well, the proposed methodology can successfully improve network operation by reliving flow in transmission lines and improving voltage on buses. A sensitivity analysis is also carried out to indicate the impacts of the parameters on the planning. All simulations including modeling, solution, and sensitivity analysis are carried out in MATLAB software. (C) 2018 Elsevier Ltd. All rights reserved.