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Model predictive control for optimal power flow in grid-connected unbalanced microgrids

Abstract:

This paper presents a model predictive control (MPC) designed to solve the optimal power flow (OPF) in grid-connected unbalanced microgrids. The controller considers the modeling of distributed renewable generators, storage units, unbalanced loads, voltage regulators, tap transformers and capacitor banks with tap controls. Active and reactive power flows are optimized within the same optimization problem, thus allowing renewable generators to produce reactive power. A predictive OPF is modeled as a mixed integer non-linear programming problem and a convenient linearization is proposed, so that, the OPF can be implemented through commercial solvers. Testing is performed in a 24 h simulation in modified versions of the IEEE 13 and IEEE 123 bus test feeders, and the results are compared with recent controllers. The controller is capable of finding optimum operation points according to predictable changes updated online, while still maintaining a high execution speed required for real-time applications. This tool performs an accurate OPF with reduced computational effort, with the aim of saving resources in control systems sizing and operation.