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Energy storage systems may represent a viable solution to tackle over- and undervoltages arising in low voltage networks due to the increasing penetration of low carbon technologies. An algorithm for siting and sizing energy storage systems in radial low voltage networks was proposed in previous work. Siting exploits the voltage sensitivity matrix of the network, while sizing is performed by solving a multi-period optimal power flow problem. In this paper, we discuss the sizing step of the aforementioned algorithm for a low voltage network featuring overvoltages due to the high penetration of photovoltaic generation. Since the considered decision problem is affected by uncertainty on photovoltaic generation, a scenario-based approach, coupled with suitable scenario reduction techniques, is analyzed. While this approach is useful to keep the computational burden affordable, we investigate whether the energy storage system sizes found after scenario reduction can guarantee the solution of overvoltages with a priori defined confidence level. This is done by testing the overall procedure on a real Italian low voltage network provided by the main Italian distribution system operator

Over recent years there has been a general consensus about the necessary changes towards modernizing the existing power grid to meet environmental and socio-economic objectives. The adoption of low carbon technologies is a milestone in this process. On the other hand, the massive and uncoordinated connection of distributed generators (e.g. solar or wind) is making the operation of electrical distribution networks more challenging, e.g. causing energy balancing problems or voltage violations. Energy storage systems represent a possible means to cope with these issues. In this paper, we consider the problem of sizing the energy storage systems installed in a low voltage network with the aim of preventing voltage violations along the feeders. Since the problem is solved at the planning stage, when future realizations of demand and generation are unknown, we adopt a two-stage stochastic formulation where daily demand and generation profiles are modelled as random processes. The cost function to be minimized takes into account installation and operation costs related to storage use. By taking a scenario-based approach, the two-stage problem is approximated via a multi-scenario optimal power flow. To reduce the computational burden of the latter problem, a heuristic strategy consisting of solving separately a sizing problem for each scenario, and then combining the solutions of the single problems through a worst-case criterion, is proposed. The multi-scenario approach and the heuristic strategy are compared in terms of both computation time and quality of the solution using real data from an Italian low voltage network with photovoltaic generation

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