Capturing the dynamic behavior of the power distribution grids, especially under high penetration of renewables, is of high interest for grid operators. The distribution power grids are not fully observable due to lack of sufficient metering infrastructure, especially downstream of medium-voltage substations. Therefore, fusion of data recorded at significantly different reporting rates is proposed to increase the situational awareness of the system with non-negligible effect on the accuracy of the monitoring tool. Higher reporting rates are possible for next generation smart meters (SM), but they raise higher concerns about data privacy, already an issue for SM rollout. This article proposes a framework for knowledge extraction from high reporting-rate SM data. The process takes place at SM level and with low computation and communication costs and preserving user privacy, with the scope to increase the accuracy of the monitoring tools for distribution power grids. The methodology makes use of statistical metrics able to capture system dynamics relevant for network diagnosis. The proposed approach is validated on a three-phase low-voltage power flow model applied to a realistic testbed microgrid and real field measurements synchronized at 1 s.

The effectiveness of the measurement process hinges on the harmony between the information extracted from the analysed phenomena and the capabilities of the measurement devices. Typically, in power systems, this alignment is related to assuming a steady-state phenomenon during the measurement process. Consequently, this entails working with a model characterized by a handful of parameters that need identification throughout the measurement process. Thus, assigning a metric to the assumed signal model helps to obtain additional information on the discussed phenomenon. In this paper we propose the Goodness of Fit (GoF) indicator modified as to highlight the model inadequacy to the measured quantity. We applied the methodology to the active power measurements in low voltage (LV) networks, where the variability within legacy measurement windows today is significant. Results are compared with another variability indicator, Coefficient of Variation (CV) of the Root Mean Square Deviation (RMSD), for the same recorded signals to enable labelling different processes using the variability of the same quantity (in our case active power).

The electric power systems together with the entire energy sector are rapidly evolving towards a low-carbon, secure, and competitive economy facing revolutionary transformations from technical structure to economic value chain. Pathways to achieve sustainability led to the development of new technologies, accommodation of larger shares of unpredictable and stochastic electricity transfer from sources to end-users without loss of reliability, new business models and services, data management, and so on. The new technologies and incentives for local energy communities along with large development of microgrids are main forces driving the evolution of the low voltage energy sector changing the context and paradigm of rigid contractual binding between utilities and end-user customers (now progressing to flexible prosumers with generation and storage capabilities). The flexibility and operation of a prosumer can be enhanced by a non-intrusive time-frequency analysis of distorted power quality waveforms for both generation and demand at the point of common connection. Therefore, it becomes of importance to discriminate among successive quasi-steady-state operation of a given local system using only the aggregated waveforms information available in the PCC. This paper focuses on the Hilbert–Huang method with modifications such as empirical mode decomposition improved with masking signals based on the Fast Fourier Transform, Hilbert spectral analysis, and a post-processing method for separating components and their amplitudes and frequencies within distorted power signals for a low-voltage prosumer operation. The method is used for a time-frequency-magnitude representation with promising localization capabilities enabling efficient operation for prosumers.

It is widely accepted that future internet is developing grace to the availability of platforms (cloud-based) enabling deployment of a large range of applications, including connecting to Internet of Things (IoT), big data analysis, handling data context, monitoring applications and services etc. Recently, European Commission and major European ICT companies launched a program in order to define a platform that would be an open option for the development of applications in the Future Internet. FIWARE technology is an open source infrastructure which allows the development of such applications and create services on the Internet. This technology provides a set of open source and API-enabled tools, and has been chosen for development in the power engineering area. In this paper, first results of FIWARE handling information delivered by Phasor Measurement Units deployed on wide area in the Romanian power system are presented, as part of the ongoing project FISMEP. From the large number of potential practical applications, in this paper results from frequency and voltage monitoring are detailed.

The measurement process generates information by aligning the observed signal with its assumed implicit model. Recognizing the challenges in accurately assessing, reporting, and using information from low inertia power systems, especially when dealing with highly variable quantities during both the measurement and the reporting windows, we address in this work the overall uncertainty associated with classical measurements in power systems. The focus is on quantifying the information uncertainty derived from the variability of parameters describing the energy transfer which are usually silently assumed constant during the measurements reporting window. To this, we use statistical measures and propose a monitoring approach able to reveal - for the considered observation time, depending on the application - the particularity of energy transfer in a selected node/branch of power network, directly impacting the choice of measurement system.

Due to the increasing complexity of the global energy system and the amount of data transmitted by smart devices, there is an urgent need to unlock smart technologies and sendees with investments on the scalability of computational resources. This objective can be achieved by means of a standardized software platform that can support interoperability and behave as the main vehicle for the rapid implementation of innovative energy sendees. One possibility to enhance computational power on demand is cloud computing, which forms the Internet of Things (IoT). In this work, a customizable open-source IoT platform setup using the FIWARE framework is deployed, which exploits the advantages for a smart energy domain. The term platform comprehends a set of software tools that allow quick integration of various devices. This study presents the implemented platform and analyses its functionality in different use cases on the European level.