Multi-energy systems as enablers of the flexible energy transition

The growing deployment of renewable sources is having a relevant impact on the whole energy chain, and much more flexibility is required to continuously ensure the power balance between generation and demand. In line with this, the paper focuses on the analysis of Multi-Energy Systems, recognized as a key solution to efficiently match final users’ needs by exploiting flexible interactions between different energy vectors

An incremental input-to-state stability condition for a generic class of recurrent neural networks

This paper proposes a novel sufficient condition for the incremental input-to-state stability of a generic 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

Switching nonlinear model predictive control of collaborative railway vehicles in catenary grids

This article contributes to the railway control field by proposing a novel approach capable of making trains collaborate, while also minimizing both traction energy and power line losses in catenary grids. The train dynamics are captured by a combination of four operating modes, so that the formulation of a switched control problem naturally applies. This model is interfaced with that of the catenary grid, consisting of the electrical substations and transmission lines over the track. Relying on these models, an eco-drive control system is proposed based on an original switching nonlinear model predictive control (SNMPC). Being collaborative-conceived, the new SNMPC is compared and evaluated against a noncollaborative version of the controller by means of simulation case studies relying on real-world test data, a validated train model, and measured track topology. We obtain that the proposed SNMPC outperforms the noncollaborative counterpart both in terms of traction energy and energy losses on the train rheostats and over the electrical lines. Thus, we demonstrate that the proposed SNMPC for collaborative eco-drive, based on the energy exchange between trains, has a potential positive impact on railway systems in catenary grids

Design of aggregators for the day-ahead management of microgrids providing active and reactive power services

The increasing diffusion of distributed energy generation systems requires the development of new control paradigms for the coordination of micro-generators, storage systems, and loads aimed at maintaining the efficiency and the safe operability of the electricity network. MicroGrids (MGs) are an interesting way to locally manage groups of generation devices, but they cannot singularly provide a significant contribution to sustain the main electricity grid in terms of ancillary services, such as the availability of a minimum amount of power reserve for the frequency regulation. For these reasons, in this paper we propose a framework for the aggregation and coordination of interconnected MGs to provide ancillary services to the main utility. The proposed framework is structured in three main 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 of the renewable sources production and the loads absorption. In this phase, scalability of the optimization problem and confidentiality requirements are guaranteed. In the second phase, reactive power flows are scheduled and it is ensured that the active power trends planned in the first phase do not compromise the voltage/current limitations. A final 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. The developed method is used for control of the IEEE 13-bus system network and the results achieved are thoroughly discussed in terms of performance and scalability properties.Comment: (under revision

Physics-informed Neural Network Modelling and Predictive Control of District Heating Systems

This paper addresses the data-based modelling 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 modelling methodology, denoted as Physics-Informed RNN (PI-RNN), enables to achieve faster training procedures and higher modelling accuracy, even when reduced-dimension models are exploited. The developed PI-RNN modelling 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 operative 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 modelling and control perspective

Model predictive control of battery-powered trains for catenary-free operation

Rail transportation is recently making a comeback with stunning results from technological viewpoint. While the efficiency of trains is well known, many aspects related to energy management still need to be tackled, including sustainability and optimization issues. These issues are central to the control community, and in this context model predictive control (MPC) is a powerful control approach for its capability of guaranteeing constraints satisfaction, while minimizing a predefined cost function. In this article, we exploit these advantages to provide more efficient control of the electric equipment inside railway vehicles. More precisely, the proposed MPC approach is capable of addressing the challenging scenario of partially catenary-free tracks for trains. These are equipped with batteries, which have to supply traction motors and parallel-connected auxiliary loads in an efficient manner, while reducing the amount of losses over the electric lines. Simulation results, based on real data provided by the industrial partner Alstom rail transport, show the effectiveness of the proposal

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements