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Energies 2016, 9(7), 528;

Optimal Placement of Energy Storage and Wind Power under Uncertainty

Power and Energy Analysis and Research Laboratory (PEARL), Universidad de Castilla-La Mancha, Ciudad Real 13071, Spain
Author to whom correspondence should be addressed.
Academic Editor: João P. S. Catalão
Received: 29 April 2016 / Revised: 4 July 2016 / Accepted: 5 July 2016 / Published: 11 July 2016
(This article belongs to the Special Issue Distributed Renewable Generation)
Full-Text   |   PDF [1840 KB, uploaded 11 July 2016]   |  


Due to the rapid growth in the amount of wind energy connected to distribution grids, they are exposed to higher network constraints, which poses additional challenges to system operation. Based on regulation, the system operator has the right to curtail wind energy in order to avoid any violation of system constraints. Energy storage systems (ESS) are considered to be a viable solution to solve this problem. The aim of this paper is to provide the best locations of both ESS and wind power by optimizing distribution system costs taking into account network constraints and the uncertainty associated to the nature of wind, load and price. To do that, we use a mixed integer linear programming (MILP) approach consisting of loss reduction, voltage improvement and minimization of generation costs. An alternative current (AC) linear optimal power flow (OPF), which employs binary variables to define the location of the generation, is implemented. The proposed stochastic MILP approach has been applied to the IEEE 69-bus distribution network and the results show the performance of the model under different values of installed capacities of ESS and wind power. View Full-Text
Keywords: optimal location; energy storage systems (ESS); mixed integer linear programming (MILP); wind power; optimal power flow (OPF) optimal location; energy storage systems (ESS); mixed integer linear programming (MILP); wind power; optimal power flow (OPF)

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Meneses de Quevedo, P.; Contreras, J. Optimal Placement of Energy Storage and Wind Power under Uncertainty. Energies 2016, 9, 528.

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