Towards Optimal Management in Microgrids: An Overview

A microgrid is a set of decentralized loads and electricity sources, mainly renewable. It can operate connected to and synchronized with a traditional wide-area synchronous grid, i.e., a macrogrid, but can also be disconnected to operate in “island mode” or “isolated mode”. When this microgrid is able to manage its own resources and loads through the use of smart meters, smart appliances, control systems, and the like, it is referred to as a smart grid. Therefore, the management and the distribution of the energy inside the microgrid is an important issue, especially when operating in isolated mode. This work presents an overview of the different solutions that have been tested during the last few years to manage microgrids. The review shows the variety of mature and tested solutions for managing microgrids with different configurations and under several approaches

OPTIMAL OPERATION AND PLANNING OF MICROGRIDS CONSIDERING FREQUENCY STABILITY

In modern power systems, the traditional power plants consuming fossil fuels are gradually being replaced by renewable plants which bring about several challenges and issues for the safe operation of these systems. Higher penetration of renewable plants weakens the grid’s frequency control capability. The frequency of a power system is an important indicator of any load-generation imbalance. If proper measures are not taken, the frequency-related events as a result of the contingencies might have devastating consequences such as unintentional load shedding, generator tripping, equipment damage, and blackout. The consequences have significantly higher impacts in a small-scale power system such as a microgrid where the penetration level of renewable generation is high, the inertia of the system is low, and the resources capable of providing reserve are limited. Battery energy storage systems (BESSs) have suitable characteristics to provide a wide range of high-power and high-energy services and they can have a significant contribution to frequency control if they are optimally scheduled. In a microgrid setting, the main goal of the frequency stability constraints is to prevent such consequences after the occurrence of credible contingencies and ensure the microgrid can ride through these events. This thesis presents optimization models to take frequency stability constraints into account for microgrid operation and planning studies. In the operation stage, frequency stability constraints are integrated into the day-ahead scheduling model of a grid-connected microgrid considering the unit commitment constraints and the sudden islanding as the contingency. The goal of the proposed algorithm is to ensure that when a grid-connected microgrid suddenly and unintentionally disconnects from the main grid, the microgrid’s frequency stability metrics remain within their safe ranges and the resources can provide sufficient primary frequency response to this end. Also, for the planning stage, optimal sizing of a BESS as a source of power and energy services is studied considering the frequency stability constraints for grid-connected and islanded microgrids. For realistic modeling, a detailed frequency response model of the microgrid elements under different contingency types is developed based on a discretized model of the swing equation. The developed optimization models are linear and relatively fast in obtaining the globally optimum solution with the proposed reformulated mathematical models. A k-means clustering algorithm is also used for scenario reduction purposes and identification of representative days. For all the studies, a microgrid test case comprised of conventional generators, a PV plant, BESS, and loads is used. The discretized swing equation and time-domain model of the primary frequency response of microgrid units are used without sacrificing accuracy. Also, the accelerated decomposition techniques ensure the computational burdens are reasonable and they improve the performance of the algorithms. All the simulations and codings were done in the GAMS and MATLAB environments