Interconnecting microgrids in LV power system presents appealing features such as self-healing or power quality. When networked microgrids are not connected to a strong utility grid, their Point of Common Coupling (PCC) voltage and their power reserves vary with the operating point. An external droop control architecture is proposed that allows active and reactive power sharing among the different microgrids, thereby stabilizing the system frequency and PCC voltage, and the maximum achievable droop gains are supplied. Next, the design of appropriate primary controllers for the Distributed Energy Resources inside each microgrid allows to achieve a specified aggregated external droop controller at the connection point with little communication requirements. This methodology is applied to a modified CIGRE benchmark and shows good results while keeping a standard decentralized control architecture.

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.

With the integration of power converters into the power grid, it becomes crucial for the Transmission System Operator (TSO) to ascertain whether they are operating in Grid Forming or Grid Following modes. Due to intellectual properties, classification can only be performed based on non-intrusive measurements and models, such as admittance at the PCC. This classification poses a challenge as the TSO lacks precise knowledge of the actual control structures and algorithms. This paper introduces a novel classification algorithm based on Convolutional Neural Networks (CNN), capable of detecting patterns in sequential data. The proposed CNN utilizes a new architecture to separate admittances along the d and q axes, and a decision layer allows to determine the correct converter mode. The performance of the proposed CNN model was assessed through two tests and compared to the traditional feedforward model. The proposed CNN architecture demonstrates significant classification capabilities, as it is able to identify the control mode of the converter even when its control structure is not part of the training dataset.

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 to optimize the real time operation of a microgrid controlled with a two-layer Model Predictive Controller supervisor. Based on the classical decomposition of control level, the proposed supervisor tracks long-term economic references from a classical economic optimization routine. The optimization problem is formulated as a Mixed Integer Quadratic Problem and uses the different power references as levers to reach this optimum and maintain the state of the microgrid within limitations. In addition, it is able to minimize the grid losses.