During the last few years, Smart Electricity Meters have been deployed in several countries all around the world, replacing the trustable Electromechanical meter and even other electronic meters. Since the early deployments, many concerns and complaints from customers which do not trust Smart Meters accuracy have appeared. As a result, researchers of different institutions have been testing electricity meters under distorted waveform conditions and proposing methods to calibrate such meters in a more representative real world operative conditions. Applicable accuracy standards and regulations indicate a maximum distortion factor of 3% of the sinusoidal waveform for voltage and current during the calibration, which is not representative of many modern dynamic power quality scenarios. New tests and recommendations have been issued by regulatory bodies, but they are still not mandatory for meters to be certified. With many changes upcoming in the near future for the electrical Smart Grid like the inclusion of renewables, increasing non-linear loads, electric charging vehicles and other emerging technologies, the power quality conditions of the grid is expected to be significantly affected. In this paper, a review of the current and upcoming challenges for the smart meters is presented.

Wireless sensor networks (WSNs) are technologies that play an important role in Internet of Things (IoT) systems. In most applications, sensor nodes are expected to operate for extended periods without maintenance. Therefore, minimizing power consumption and self-sustainability based on energy harvesting (EH) still represent important research challenges. In this article, a power management strategy (PMS) based on the weighted order statistics (WOS) classification technique is proposed to dynamically adapt the duty cycle of the sensor node according to historical data measurements. Unnecessary acquisition and transmission of slow-varying signals are reduced, improving the power consumption of the node. As traditional renewable sources (sunlight, wind, and vibration) are scarce in indoor scenarios, an array of Dypsis Lutescens plants is used as a power bioenergy cell, providing a clean and cost-effective alternative to power indoor sensor nodes. The WOS technique is programmed into an nRF52840 microcontroller, and an ultralow-power BQ25570 harvesting circuit harnesses the energy from the array of plants. Experimental results include the energy performance analysis of the wireless sensor node and the measured power generation capacity of the bioenergy source, showing an energy-autonomous behavior for IoT applications. Average power consumption of 15.6 mW per transmitted data packet of 14 bytes is achieved, which represents the ability to perform at least 693 data transmissions per day considering an 8 F supercapacitor as a storage device.

The design of digital hardware controllers for the integration of renewable energy sources in DC microgrids is a research topic of interest. In this paper, a Kalman filter-based maximum power point tracking algorithm is implemented in an FPGA and adapted in a dual active bridge (DAB) converter topology for DC microgrids. This approach uses the Hardware/Software (HW/SW) co-design paradigm in combination with a pipelined piecewise polynomial approximation design of the Kalman-maximum power point tracking (MPPT) algorithm instead of traditional lookup table (LUT)-based methods. Experimental results reveal a good integration of the Kalman-MPPT design with the DAB-based converter, particularly during irradiation and temperature variations due to changes in weather conditions, as well as a good-balanced hardware design in complexity and area-time performance compared to other state-of-art FPGA designs.

Numerous changes in electrical grid schemes, like the inclusion of renewable energy, the rise of non-linear loads and the emergence of electric vehicle charging, increases variable power quality conditions of the grid. In this dynamic scenario where energy could flow in both directions and the waveforms could be highly distorted, accuracy becomes a crucial factor for the correct measurement of electrical energy and power values. Errors in the assessment of these values have significant ramifications for revenue, billing and/or control. This non-ideal power quality scenario produces an error in electricity meters, that is not yet well known since there is no standardised procedure to calibrate meters under typical or emerging distorted waveform conditions. Current standards relevant for revenue energy meters like EN 50470-3:2006 allows measurements error up to ±2.5% while local regulations could be even more permissive. In order to establish an electricity fair trade market and meet expectations from consumers and utilities, electricity meters should arguably comply with higher accuracy standards. In this paper, the pertinence and possible impact of including tests under distorted waveform conditions, as well as new accuracy requirements on standards applicable to electricity meters for billing purposes will be discussed.

During the last few years, the accuracy of static electricity meters (SEM) has been questioned. Significant metering deviations with respect to a reference meter have been observed at customer premises, and laboratory experimental tests results support such findings. The root cause of such errors remains unknown, as there are multiple elements that could affect the accuracy of electricity meters. Furthermore, standard compliant meters exposed to distorted signals may produce negligible, positive or negative relative error depending on the instrument design. Distorted current signals with fast amplitude transitions have produced the highest error in SEMs reported in the literature. In this paper, the accuracy of an energy metering Integrated Circuit (IC) is evaluated beyond the limits of the standards requirements employing a selection of distorted signals from the standards, real-world captured signals and a set of waveforms designed to test the IC under fast changing currents conditions, which are representative of the waveforms resulting from power electronic devices. The experimental results reveal an accuracy boundary imposed by Gibb’s phenomenon for fast changing current signals and a strong relationship between the IC’s measurement error and two key parameters of the measured waveform: signal slope and phase angle. This paper therefore provides a methodology for the comprehensive analysis of SEMs in future power systems which are dominated with power electronic-controlled electrical demand and contributes to the search for the root cause of error in SEMs exposed to distorted waveforms.