• Energy Research

This paper presents the first results of a study, carried out in the framework of an industrial collaboration project, whose aim was the development of a Non-Intrusive Load Monitoring system of electrical device activities to be implemented in an IoT platform for Smart Grid. A load disaggregation algorithm has been developed which implements original strategies to deal with some limits of the methods proposed in the literature.

Non-Intrusive Load Monitoring (NILM) allows providing appliance-level electricity consumption information and decomposing the overall power consumption by using simple hardware (one sensor) with a suitable software. This paper presents a low-frequency NILM-based monitoring system suitable for a typical house. The proposed solution is a hybrid event-detection approach including an event-detection algorithm for devices with a finite number of states and an auxiliary algorithm for appliances characterized by complex patterns. The system was developed using data collected at households in Italy and tested also with data from BLUED, a widely used dataset of real-world power consumption data. Results show that the proposed approach works well in detecting and classifying what appliance is working and its consumption in complex household load dataset.

Wendelstein 7-X, the world largest superconducting advanced stellarator, aims to demonstrate high-performance steady-state experiments lasting up to 30 min. To this purpose, high heat flux (HHF) divertors capable of withstanding steady-state heat fluxes up to 10 MW/m2 are being installed on the machine, in preparation for the next experimental campaign (OP2.1). The real-time heat flux estimation is pivotal for controlling the divertor heat loads during the experiments. Currently, the THEODOR (Thermal Energy Onto DivertOR) code computes the heat flux offline by numerically solving the heat equation, but the computation time does not allow the application of this approach to the real-time operation of the device. In this work, a new approach based on Physics Informed Neural Networks (PINNs) is proposed for solving the heat equation and estimating the heat fluxes on the divertor tiles in real time. PINN models are Neural Networks that learn Partial Differential Equations (PDEs) by minimizing the PDE loss. The inputs of a PINN are the independent variables of the PDE, e.g., spatiotemporal coordinates, while the loss of the model is designed to make the neural network satisfy the PDE and related initial and boundary conditions. Hence, the model can be trained without any experimental data. Only the initial and boundary conditions of the PDE are necessary for constructing the model. First intermediate results are discussed considering a normalized tile and starting from a constant thermal diffusivity.

Electric power systems are experiencing relevant changes involving the growing penetration of distributed generation and energy storage systems, the introduction of electric vehicles, the management of responsive loads, the proposals for new energy markets and so on. Such an evolution is pushing a paradigm shift that is one of the most important challenges in power network design: the management must move from traditional planning and manual intervention to full “smartization” of medium and low voltage networks. Peculiarities and criticalities of future power distribution networks originate from the complexity of the system which includes both the physical aspects of electric networks and the cyber aspects, like data elaboration, feature extraction, communication, supervision and control; only fully integrated advanced monitoring systems can foster this transition towards network automation. The design and development of such future networks require distinct kinds of expertise in the industrial and information engineering fields. In this context, this paper provides a comprehensive review of current challenges and multidisciplinary interactions in the development of smart distribution networks. The aim of this paper is to discuss, in an integrated and organized manner, the state of the art while focusing on the need for interaction between different disciplines and highlighting how innovative and future-proof outcomes of both research and practice can only emerge from a coordinated design of all the layers in the smart distribution network architecture.