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.

One of the main challenges in smart city models is consumer behaviour, namely guiding the efforts to promote optimal use of energy in the dynamics of the developing cities, through lower energy consumption without impact on the comfort level. This relates to all energy vectors and can be done through different means. The uptake stage of smart metering and information and communication technologies (ICT) varies in different countries, creating the need for tailored innovative approaches in motivating behavioural change. This paper presents the work carried out within the joint research project ITCity (an ICT platform for sustainable energy ecosystem in smart cities) between the European Union and Latin American Countries. Cooperation of energy researchers and experts in ICT and application developers facilitates adoption of a multipurpose interdisciplinary approach. This work aims at providing the smart city and energy research and innovation community broader understanding of factors influencing adoption of energy efficiency technologies. Different ways of guiding optimal energy use are addressed elaborating on the building blocks for a smart city which consider load patterns based on high resolution, high reporting rate measurements, extensive consumer surveys, available communication solution analysis and end-user interaction with the ICT tool based on gamification principles.

The smart grids paradigm includes newly defined real time control strategies, and therefore detailed models for the energy transfer are required together with new algorithms for information retrieval. Although today measurement infrastructure (including the high reporting rate measurement devices, like Phasor Measurement Units) is capable to give insight to such details, the measurement information has to be structured in a new way. Moreover, new quantities - like rate of change of frequency - rocof - become relevant for both monitoring and control purposes. Presently there are in use aggregation algorithms inherited from power quality standards like the IEC 61000-4-30 set, but no algorithm is yet specified for rocof reporting. In this paper is presented a discussion of the time- and space- aggregation of frequency (including its rate of change) using phasor data concentrators' framework.

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).

In many power system applications it is required to assess the type and frequency of occurrence of power quality (PQ) events. Voltage waveform is one of the most regulated PQ - related issue. Although PQ is dealing essentially with steady-state energy transfer, the emerging power networks require faster control algorithms and therefore it is important to discriminate between steady-state, quasi-steady state and dynamic phenomena. Rapid voltage change (RVC) appears as a key condition in LV grid control algorithms. The methodology to assess and discriminate between RVC events and other voltage variations is presented in the newest version (2015) of IEC 610004-30 [1] where a reporting rate for the measurement set-up of 100 frames/s is indicated and some values for the threshold are suggested. This paper addresses the impact of the measurement set-up both in terms of reporting rate and measurement uncertainty on detection and assessment of this type of events.

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.