This paper presents a hierarchical control architecture for the regulation of frequency and nodal voltages of a microgrid in islanded operation. Considering systems with both dispatchable and nondispatchable generation, as well as noncontrollable loads, the suggested approach allows to coordinate the MG devices in order to maintain the network variables inside the desired operational ranges. Moreover, the proposed algorithm, based on model predictive control, introduces the possibility to define different resource management strategies while taking into account the constraints of the available devices. Simulation examples are reported and described in the final part of this paper.

This article addresses the data-based modeling and optimal control of district heating systems (DHSs). Physical models of such large-scale networked systems are governed by complex nonlinear equations that require a large amount of parameters, leading to potential computational issues in optimizing their operation. A novel methodology is hence proposed, exploiting operational data and available physical knowledge to attain accurate and computationally efficient DHSs dynamic models. The proposed idea consists in leveraging multiple recurrent neural networks (RNNs) and in embedding the physical topology of the DHS network in their interconnections. With respect to standard RNN approaches, the resulting modeling methodology, denoted as physics-informed RNN (PI-RNN), enables to achieve faster training procedures and higher modeling accuracy, even when reduced-dimension models are exploited. The developed PI-RNN modeling technique paves the way for the design of a nonlinear model predictive control (NMPC) regulation strategy, enabling, with limited computational time, to minimize production costs, to increase system efficiency and to respect operational constraints over the whole DHS network. The proposed methods are tested in simulation on a DHS benchmark referenced in the literature, showing promising results from the modeling and control perspective.

Hierarchical architectures stacking primary, secondary, and tertiary layers are widely employed for the operation and control of islanded dc microgrids (dcmGs) composed of distribution generation units (DGUs), loads, and power lines. However, a comprehensive analysis of all the layers put together is often missing. In this work, we remedy this limitation by setting out a top-to-bottom hierarchical control architecture. Decentralized voltage controllers attached to DGUs form our primary layer. Governed by an MPC-based energy management system (EMS), our tertiary layer generates optimal power references and decision variables for DGUs. In particular, decision variables can turn DGUs on/off and select their operation modes. An intermediary secondary layer translates EMS power references into appropriate voltage signals required by the primary layer. More specifically, to provide a voltage solution, the secondary layer solves an optimization problem embedding power-flow equations shown to be always solvable. Since load voltages are not directly enforced, their uniqueness is necessary for DGUs to produce reference powers handed down by the EMS. To this aim, we deduce a novel uniqueness condition based only on local load parameters. Our control framework, besides being applicable for generic dcmG topologies, can accommodate topological changes caused by EMS commands. Its functioning is validated via simulations on a modified 16-bus dc system.

This paper proposes a novel sufficient condition for the incremental input-to-state stability of a class of recurrent neural networks (RNNs). The established condition is compared with others available in the literature, showing to be less conservative. Moreover, it can be applied for the design of incremental input-to-state stable RNN-based control systems, resulting in a linear matrix inequality constraint for some specific RNN architectures. The formulation of nonlinear observers for the considered system class, as well as the design of control schemes with explicit integral action, are also investigated. The theoretical results are validated through simulation on a referenced nonlinear system.

Microgrids (MGs) are an interesting solution to locally manage distributed generation, but they are not usually designed to provide significant contribution in terms of external ancillary services to the main grid. In this brief, we propose a framework for the aggregation, coordination, and day-ahead scheduling of interconnected MGs to provide external services, such as the power-reserve provision. The framework is structured in three phases. In the first one, a distributed optimization algorithm computes the day-ahead profile of the active power production of the MGs based on the available forecasts. Scalability of the optimization problem and confidentiality requirements are guaranteed. In the second phase, reactive power flows are scheduled while ensuring that the active power trends planned in the first phase do not compromise voltage/current limitations. The third phase is used to schedule the active and reactive power profiles of the generation units of each MG to make them consistent with the requirements and results of the previous two phases.