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.

Bucciarelli, M., Giannitrapani, A., Paoletti, S., Vicino, A., Zarrilli, D. (2017). Sizing of energy storage systems considering uncertainty on demand and generation. In Proc. of 20th IFAC World Congress (pp.8861-8866). Amsterdam : Elsevier B.V. [10.1016/j.ifacol.2017.08.1543].

Sizing of energy storage systems considering uncertainty on demand and generation

Bucciarelli, Martina;Giannitrapani, Antonio;Paoletti, Simone;Vicino, Antonio;Zarrilli, Donato
2017-01-01

Abstract

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.
2017
Bucciarelli, M., Giannitrapani, A., Paoletti, S., Vicino, A., Zarrilli, D. (2017). Sizing of energy storage systems considering uncertainty on demand and generation. In Proc. of 20th IFAC World Congress (pp.8861-8866). Amsterdam : Elsevier B.V. [10.1016/j.ifacol.2017.08.1543].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/1025251