Abstract The minimization of the power losses in the distribution grid is one of the main issue for the Distribution System Operator in order to reduce the management costs of the grid. The recent development of the battery technologies has introduced new possible applications for these systems within the radial distribution grid. In fact, batteries can be opportunely integrated in the grid and managed in order to reduce the power losses, and consequently increasing, for instance, the penetration of the renewable distributed generation and contributing to voltage regulation through reactive power production from battery inverter. In this context, one of the main research interest is the definition of the optimal siting and sizing of the energy storage solutions, considering a battery management capable to reduce network power losses and taking into account battery installation costs. Since network losses are expressed by means of quadratic function, an approach based on Mixed Integer Quadratically Constrained Quadratic Programming is proposed here to identify possible batteries optimal management strategies capable to minimize the power losses. Evaluation of the grid variables is obtained by the iterative Backward/Forward Sweep method implemented within the formulation of the optimization problem. The reactive power generated by the battery inverter is modeled as well by introducing quadratic constraints in order to further contribute in the network power losses reduction, taking into account the power factor limitation due to the inverter capability curve. An optimization procedure, called D-XEMS13 , based on the formulation of the optimization problem, is implemented within a single loop optimization algorithm in order to identify the best size of the connected BESSs units, capable to maximize the reduction of the power losses in test grids. BESS optimal placement is also identified through a proposed approach based on a nodal sensitivity analysis of the network power losses. Validation of the proposed approach is then obtained by means of a cost/benefit comparison from energy point of view. Finally, the single loop optimization algorithm is further implemented on two test grids with optimal BESSs sizes and siting, assuming different capability curves for the BESS inverters. The results of simulations with the corresponding benefits are then presented and discussed.