A Two-Layer Control Architecture for Islanded AC Microgrids with Storage Devices

Optim Control Appl Methods

Klaus

Ferrari-Trecate

et al. 2021

Self Cite

This article describes a control approach for large-scale electricity networks, with the goal of efficiently coordinating distributed generators to balance unexpected load variations with respect to nominal forecasts. To mitigate the difficulties due to the size of the problem, the proposed methodology is divided in two steps. First, the network is partitioned into clusters, composed of several dispatchable and nondispatchable generators, storage systems, and loads. A clustering algorithm is designed with the aim of obtaining clusters with the following characteristics: (i) they must be compact, keeping the distance between generators and loads as small as possible; (ii) they must be able to internally balance load variations to the maximum possible extent. Once the network clustering has been completed, a two layer control system is designed. At the lower layer, a local model predictive controller is associated to each cluster for managing the available generation and storage elements to compensate local load variations. If the local sources are not sufficient to balance the cluster's load variations, a power request is sent to the supervisory layer, which optimally distributes additional resources available from the other clusters of the network. To enhance the scalability of the approach, the supervisor is implemented relying on a fully distributed optimization algorithm. The IEEE 118-bus system is used to test the proposed design procedure in a nontrivial scenario.

Section: Numerical Resultsmentioning

confidence: 99%

Section: Two‐layer Control Of Network Clustersmentioning

confidence: 99%

Supervised model predictive control of large‐scale electricity networks via clustering methods

Optim Control Appl Methods

Klaus

Ferrari-Trecate

et al. 2021

Self Cite

“…Among the proposed solutions, the three-layer control architecture for DC-MGs, designed during a visiting period at the Automatic Control Laboratory of EPFL, is here described. In case the reader is interested to the designed control architectures for islanded AC-MGs, please refers to [6,17,18]. The schematic of the proposed control architecture for islanded DC-MGs is depicted in Fig.…”

Section: Hierarchical Model Predictive Control Architectures For Islamentioning

confidence: 99%

Optimization-Based Control of Microgrids for Ancillary Services Provision and Islanded Operation

Special Topics in Information Technology

2021

Self Cite

The ongoing environmental crisis is pushing the electrical sector towards a radical transformation, as the wide diffusion of renewable sources requires the power system to be more distributed, cooperative, and flexible, being each portion of the grid now able to produce and absorb power. This poses much more coordination challenges with respect to the traditional centralized system, largely sustained by fully controllable fossil-based power plants. In this context, microgrids, i.e. intelligent small-scale grids equipped with distributed energy resources and smart loads, are considered as the fundamental bricks of this future paradigm. This is due to the opportunity of coordinating co-located sources and loads, and to the microgrids extreme flexibility, as they can be operated either connected to the main grid or in islanded mode. The contribution of this doctoral research consists in the design of dedicated control architectures for ensuring the efficient and secure operation of microgrids in these two modes, characterized by different challenges and opportunities.

“…Microgrids (mGs) are electric networks comprising different devices such as distributed generation units (DGUs) interfaced with power-electronic converters, energy storage units (ESSs), and loads. MGs can operate in grid-connected and islanded modes, and are compatible with both AC and DC operating paradigms [1], [2], [3]. In particular, DC microgrids, due to their ability to interface naturally with renewable energy sources (for instance PV modules), batteries, and electronic loads (various appliances, LEDs, electric vehicles, etc), have gained traction in the recent years [4], [5].…”

Section: Introductionmentioning

confidence: 99%

A Supervisory Control Structure for Voltage-Controlled Islanded DC Microgrids

2019 IEEE 58th Conference on Decision and Control (CDC)

Ferrari-Trecate

2019

In this work, we propose a supervisory control structure in islanded DC microgrids such that a well scheduled and balanced utilization of various resources is achieved. Our supervisory control layer rests on top of a voltage-controlled primary layer and comprises a secondary layer, which receives power references from an energy management system. The secondary layer translates these power into appropriate voltage references by solving an optimization problem. These references act as an input for the primary voltage controllers. We show that the unconstrained secondary optimization problem is always feasible. Moreover, since the voltages can only be enforced at the generator nodes, we provide a novel condition to guarantee the uniqueness of load voltages and power injection of the generation units. Indeed, in the absence of uniqueness, for fixed generator voltages, the load nodes and power injections may be different than planned. This can result in violation of operational limits causing damage to the connected loads. Moreover, this uniqueness condition can be verified at each load node by utilizing local load parameters, and does not require any information about microgrid topology. The functioning of the proposed architecture is tested via simulations.