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In this paper, we present a Hardware-in-the-Loop (HIL) co-simulation framework to test multi-modal energy systems. The framework has been tested using as an example a section of the Living Lab Energy Campus (LLEC) of the Forshungszentrum Julich (DE). A master algorithm has been developed to orchestrate the two components of the co-simulation platform: the real-time simulation of the power network running on OPAL-RT, and the real-time simulation of the low-temperature district heating (LTDH) network using Functional Mock-up Units (FMU) on a custom cluster. The master algorithm also coordinates the exchange of information between the real-time simulators and the device under test. The device under test is composed of a cloud-based model predictive control (MPC) -that operates on the heat pumps in the LTDH network - and an MQTT broker. The results show the co-simulation is successful and the framework can validate the control algorithm.

Nodes in electric distribution networks are greatly differentiated and are very often nonlinear and/or unbalanced. They can create significant harmonic pollution, with harmonics that inevitably spread along the grid. Monitoring harmonic propagation and correlated power quality phenomena requires specific measurement devices and methodologies. Nevertheless, because of the unavailability of a rapid diffusion of synchronized and dedicated devices (due to technical and economic reasons) on every node and branch of the network, estimating the harmonic status of the entire grid by means of a complete or even redundant monitoring system can be practically unfeasible. A more feasible, though always meaningful, goal can thus be pursued, that is estimating the main harmonic sources in the network, rather than its complete harmonic status. This approach, of course, can be based on a simpler and cheaper upgrade of the distributed monitoring system. Even more, by considering the common scenario where the number of significant harmonic sources is lower than the number of loads connected to the grid, specific estimation procedures can be defined to further reduce the complexity of the monitoring system. In this scenario, this paper presents an efficient compressive sensing harmonics detector (CSHD) for the identification and the estimation of the principal pollution sources. The proposed CSHD method is validated by means of appropriate tests performed on an example of distribution grid.

Non-intrusive load monitoring (NILM) is a process of determining the operating states and the energy consumption of single electric devices using a single energy meter providing aggregate load measurements. Due to the large spread of power electronic-based and nonlinear devices connected to the network, the time signals of both voltage and current are typically non-sinusoidal. The effectiveness of a NILM algorithm strongly depends on determining a set of discriminative features. In this paper, voltage and current signals were combined to define, according to the definitions provided in Standard IEEE 1459, different power quantities, that can be used to distinguish different types of appliance. Multi-layer perceptron (MLP) classifiers were trained to solve the appliance detection problem as a multi-class event classification problem, varying the electric features in input. This allowed to select an optimal set of features guarantying good classification performance in identifying typical electric loads.

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.

The harmonic pollution generated by non-linear loads and generators in distribution networks could lead to serious consequences for the customers. The identification of the polluting sources is thus necessary to act directly on the origin of the problems, in order to reduce the corresponding effects. As well as the other Power Quality phenomena, the harmonic analysis requires specific measurement devices, which are not widespread in distribution systems. Consequently, specific algorithms and methodologies are required, in order to overcome the lack of measurements. In this regard, Compressive Sensing (CS) represents a valid solution for the analysis under study, since it allows to recover sparse signals, when only few measurements are available.In this paper, two of the most common algorithms for CS applications, the Orthogonal Matching Pursuit and the l 1 -minimization, implemented in the framework of Harmonic Source Estimation procedures, are tested under different measurement uncertainty conditions. The tests, performed on a small medium voltage network, underline the different impact brought by the measurement uncertainties, of both magnitude and phase angle, on the two algorithms.

In this paper, an approach to perform model reduction of distribution grids for phasor measurement unit (PMU)-based state estimation (SE) purposes is presented. The method is based on ac load flow equations. An arbitrary number of lines and nodes can be selected and reduced to an equivalent one. The performance of the method is tested using the IEEE34 and IEEE123 test cases. An evaluation of the computational performance of the SE process is also reported. To conclude, the SE process based on the reduced model is tested against uncertainty in the parameters of the system and in the measurements so to evaluate how the performance of the SE is deteriorated by these phenomena.

The paper presents a study focused on the modelization of measurement uncertainty in fault location methods and on the evaluation of its impact on the performance. In particular, algorithms based on a compressive sensing methodology are considered. Fault detection applications are very important in power systems control and protection, nevertheless, most of the methods proposed in the literature do not appropriately consider the overall accuracy of the measurement chain involved in the process both in the definition and testing. In this context, this paper faces the problem of the accuracy actually available in the measurement devices working under the dynamic conditions, which occur during faults, and its impact on fault location applications. The paper will discuss, by means of the results obtained on a small distribution network, how the operating conditions during fault and their effects on the accuracy performance of both instrument transformers and measurement devices affect the results.