In this paper a Power Hardware in the loop simulation has been realized to test in a safely way the performances and reliability of a device called “PowerCorner” used to supply an islanded microgrid. A real-time model has been developed in order to simulate the microgrid, batteries and photovoltaic panels. Some modeling criterions have been proposed to reduce time-step simulation and enhancing the Power Hardware in the loop simulation stability. Power Hardware in the loop simulation is used to emulate the AC and DC environments around the power inverters. On the DC side, DC power amplifier is used to emulate photovoltaic power plants and storage devices made on Lithium batteries. On the AC side, AC power amplifier is used to emulate the behavior of the microgrid. These two power amplifiers are controlled by a digital real time simulator which embeds the dynamic behavior of both DC and AC sides.

This paper proposes a two-layer microgrid supervisor based on Model Predictive Control (MPC). The supervisor in the upper layer relies on an economical optimization that considers the cost of energy and the load and production forecasts to define the State of Charge (SoC) targets for each storage device on a timescale of 15 minutes. The lower layer displays a shorter timescale and aims to control the equipment to ensure the stability of the overall system and SoC tracking while satisfying the economic constraints specified by the upper layer. These two layers require an uniformization of the timestep and of the references in order to behave properly. The main contributions of this paper are the microgrid network modelling embedded in the optimization routine of the lower layer and a discretization for integrating upper-layer references.

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An indirect iterative learning algorithm has shown to be able to update the parameters of an acceleration feedback controller for flexible manipulators. The fine estimation of the masses of a chain of mass-spring-dampers units allows the simultaneous tuning of both the feedback controller and the trajectory generation. This algorithm has been validated on an industrial robot arm.