The increasing penetration of Distributed Energy Resources in modern power grids necessitates more sophisticated power quality analysis. Specifically, there is an urgent need to ensure the compliance of waveform distortions with established emission limits. To address these challenges, this paper introduces two key innovations to the widely adopted CIGRE/CIRED C4.109 harmonic compliance assessment methodology, which necessitates both the magnitude and phase angle of spectral components. The first contribution expands the spectral range of interest beyond traditional harmonics to include supraharmonics, covering the 2 kHz to 150 kHz frequency band. Since analyzing such a broad spectrum requires maximum precision —particularly regarding amplitude and phase angle assessment — we propose implementing cosine windows within an Interpolated Discrete Fourier Transform (IpDFT) framework. This approach replaces the standard Discrete Fourier Transform (DFT) suggested by IEC 61000-4-7 and IEC 61000-4-30, effectively minimizing spectral leakage. The second innovation involves performing phase angle calculations of spectral components prior to grouping, specifically within each consecutive 10/12-cycle window. This refinement further ensures high accuracy in the characterisation of spectral components. The efficacy of these proposed enhancements was rigorously validated through controlled laboratory measurements on a simulated low-voltage system that incorporated realistic operating conditions, such as existing background distortion from the utility. The implementation of a custom test network and its extensive validation represent a further valuable contribution of our research. The paper concludes with a comprehensive discussion of the results, demonstrating how these methodological improvements facilitate a more reliable emission compliance of waveform distortion in modern power systems.

The fundamental frequency is one of the parameters that define power quality. Correctly determining this parameter under the conditions that prevail in modern power grids is crucial. Diagnostic purposes often require an efficient estimation of this parameter within short time windows. Therefore, this article presents the results of numerical simulation studies that allow the assessment of errors in various fundamental frequency estimation methods, including the standard IEC 61000-4-30 method, when the analyzed signal has a form similar to that found in modern power grids. For the purposes of this study, a test signal was adopted recreating the states of the power grid, including the simultaneous occurrence of voltage fluctuations and distortions. Conclusions are presented based on conducted research.

The most common instruments currently measuring active/reactive energy and power quality indicators are smart energy meters. Unfortunately, the verification of such meters is currently performed under ideal conditions or with simple signal models, which do not recreate actual states occurring in the power grid and do not ensure the verification of the properties of their signal chains. This paper presents challenges in the proper metrological verification of smart energy meters. It presents existing legal and normative requirements and scientific research directions regarding these meters. Selected test results are presented, which show that although the tested meters meet the normative and legal requirements because they have been approved for sale, numerous imperfections in the signal and measurement chains of the analyzed instruments are revealed for the selected test signal. On the basis of the presented research results, further directions of research in the field of smart energy meters have been determined.

In this paper, the aggregated modelling of large-scale wind farms for harmonic studies is considered. The limitations of the harmonic emission assessment based on the IEC 61000-3-6 summation rule are analysed in a probabilistic framework. An iterative probabilistic procedure based on the Quasi Monte Carlo technique is proposed and applied to a simplified electromagnetic transient model of an exemplary wind power plant. Then, detailed results obtained are used to quantify the above mentioned limitations of the summation rule. Keywords. Aggregated Inverter-Based Resources, Harmonics, IEC summation rule, IEEE St. P2800, Power Quality.

Inverter-based resources (IBRs) are a new type of generators entering power systems. Industries are concerned about the harmonic impact caused by IBR plants. How to model IBRs for harmonic studies has, therefore, become an important topic. This industry application-oriented tutorial paper presents a comprehensive review and analysis of the harmonic behaviors of IBR units, covering their harmonic characteristics, advanced and practical harmonic models, methods for model parameter determination and more. Lab test results on a physical IBR unit are presented to substantiate the findings. It is hoped this paper will clarify the confusion between the harmonic models of VSC (voltage source converter) based IBR units versus LCC (line commutated converter) based loads. Furthermore, a comparison between IBRs and synchronous generators reveals many similarities in their harmonic behaviors.

Forecasts of waveform harmonic distortions can be used by operators to implement corrective actions for complying with suggested limits, to refine the components' dynamic rating predictions, and to run scheduling strategies for controllable resources in smart/micro grids. Nevertheless, developing a unified forecasting methodology for all the harmonic components is challenged by their different time-varying characteristics. To tackle this problem, this paper proposes to integrate clustering techniques in probabilistic current and voltage harmonic forecasting methodologies. Identifying common characteristics in the harmonic component patterns enables building the underlying forecasting models in a diversified manner, with a model for each cluster. Since true memberships of unobserved patterns to clusters are unknown to the forecaster, the individual forecasts must be optimally recombined to get the final predictions. This paper proposes the combination through an optimized Beta-transformed Linear Pool (BLP), mitigating the lack of calibration that can occur when combining probabilistic forecasts. To assess the clustering effectiveness, parametric and nonparametric underlying forecasting models are considered, and different combination benchmarks are taken as reference. Applications based on actual measurements in commercial, office, or residential sites confirm the suitability of the proposal, with relative improvements of the forecast accuracy over persistence benchmarks from 2 to 48%.

The problem of road tunnel energy consumption and sustainability is considered in this paper. To reduce energy costs and to improve sustainability, a Microgrid (MG) with distributed energy resources including renewable generators and storage systems is proposed to supply the typical loads of road tunnels. The proposed MG is modular in the sense that sets of individual modules for the solar and wind generators, as well as the batteries, are considered for the installation, while the number of modules is obtained from the resolution of a long-term planning optimization problem. A new optimal probabilistic approach that calculates the number of modules maximizing the Net Present Value (NPV) to evaluate the profitability of the MG investment is proposed. The proposed procedure includes an advanced long-term Markov Chain (MC) based methodology to generate scenarios of stochastic processes modeling all the uncertain variables involved in the planning procedure, satisfies technical and contractual constraints selectable by the tunnel owner, and implements the decision criteria most frequently applied for the selection of the optimal solution. In the numerical applications, the results obtained in different case studies show the benefits, the flexibility and the effectiveness of the proposed procedure. In fact, the modular solution allows the application of the proposed approach to a generic road tunnel located in any geographical area optimizing the use of the distributed resources.

The probabilistic short circuit analysis provides relevant information for power system planning and power quality assessment tasks. Traditional Monte Carlo methods (TMCMs) are usually applied to consider the randomness affecting short circuit operating conditions, but they require numerous iterations to properly characterize the network conditions. This paper proposes a mixed Taguchi-based method (MTBM) as a new alternative tool to account for the uncertainties affecting the inputs of probabilistic short circuit analysis. The MTBM significantly reduces the number of iterations required to properly address the randomness of inputs (environmental conditions, pre-fault conditions, fault characteristics), and allows diversifying the representation of inputs through a quantile-based selection of their levels. The proposed method is applied to unbalanced three-phase four-wire low-voltage (LV) distribution systems with photovoltaic systems (PVSs) operating in low voltage ride-through (LVRT) during the fault. Numerical applications related to a test system are presented, and the proposed MTBM is compared with the TMCM, the unmixed Taguchi-based method (UTBM), and the point estimate method (PEM). The proposed MTBM returns values very close to those of the TMCM (with average deviations ranging from 0.01% to 3.12%) and enables a fast and accurate analysis of faulted LV distribution systems with PVSs operating in LVRT.

This paper presents a sensitivity analysis of the Power Oscillation Damping (POD) service provided by an Inverter-Based Resource (IBR) to modern transmission systems. A POD control system based on a double-input second-order Lead-Lag Compensator (LLC) is considered to adapt the IBR reactive power injection to the Low-Frequency Oscillation (LFO) measured at the Point of Common Coupling (PCC). The sensitivity analysis on the parameters is provided in terms of the resulting damping factors at the target LFO. The estimation of the effective damping is performed on a test network in a simulation environment through different signal processing methods that are developed aiming to fit the time-domain data of system variables (such as frequencies and active power flows) to single- or multi-modal damped cosine waves, extracting the estimation of the damping factor at the LFO mode. Extensive numerical results are presented to compare the signal processing methods with classical optimization techniques and to assess the impact of the POD control system parameters on the resulting damping factor at the target LFO.

In this paper the aggregated modelling of large-scale wind farms for harmonic studies is considered. It analyzes the limitations of the IEC 61000-3-6 summation rule (IEC-SR) for harmonic emission assessment using a probabilistic framework. A new simplified analytical model is proposed to estimate the harmonic current emissions of a wind farm including several wind turbines (WTs) that are Inverter-Based Resources (IBRs). A sensitivity analysis of the WT’s harmonic current emissions, considering the main operation and design parameters of WTs, allows for ascertaining the main uncertain variables to be included in the probabilistic framework. The analytical model is validated using a simplified electromagnetic transient model (EMT) of a complete wind power plant. The proposed probabilistic procedure was applied to a typical radial topology of WF to evaluate the harmonic emissions and the IEC-SR exponents comparing the results obtained by the new analytical model and the EMT time domain simulations.

In modern power systems voltage source converters (VSCs) are often used for interfacing renewable distributed generation units. To evaluate the impact on power quality of the widespread use of VSCs proper models and methods have to be developed and investigated. In this paper, a review of harmonic models in the frequency domain is carried out and Norton-based models are applied and compared in terms of computational complexity and accuracy. For this purpose, a 12 kW photovoltaic plant (of the type typically found in low voltage distribution networks) is analysed for a range of operating conditions.

Secondary and primary substations of networks with electric vehicle (EV) chargers and photovoltaics (PVs) experience net loads characterized by uncertainty. Accurate characterization of EV, PV and net load energy profiles is necessary to plan new installations and to develop forecasting methodologies. This paper provides a novel contribution to the energy profile characterization of EVs and PVs, exploiting clustering techniques in a hierarchical framework to eventually characterize the overall net load profiles. In the proposal, the lower levels of the hierarchy identify clusters of EV load and PV generation profiles at individual installations, alternatively using one clustering technique among DBSCAN, Gaussian mixture models (GMMs), K-means algorithm (KMA), and spectral clustering (SC). The intermediate levels of the hierarchy reconstruct the overall EV load and PV generation profiles through a proposed frequentist combination of the lower-level profiles. The upper level of the hierarchy characterizes the overall net load through a novel approach based on the quantile convolution of the intermediate-level EV and PV profiles. Real EV load and PV generation data are used to evaluate the performance of the presented hierarchical methodology, with relative fitting improvements between 1% and 8% (compared to a two-level hierarchical benchmark) and between 16% and 29% (compared to a direct, non-hierarchical benchmark).

The integration of smart grids and energy communities in modern distribution systems drives the need for robust power quality analyses, particularly regarding waveform distortions. The distortions pose challenges in responsibility assessment between utilities and customers, due to their complex spectral characteristics and time-varying nature. To address this issue, this paper presents a comparative analysis of single-point measurement metrics (indices) derived from powers in non-ideal conditions, expanding their scope to incorporate supraharmonics (SHs). SHs are voltage or current frequency components that extend beyond the traditional harmonic range, typically ranging up to 150 kHz. Also, a new variation index useful to quantify the incidence of supraharmonics on the power-based responsibility indices is provided. Utilizing laboratory measurements from an emulated low-voltage system, the metrics are assessed under conditions involving disturbing loads and utility background distortion. The paper provides a detailed critical analysis of power-based metrics, describes the laboratory setup, and presents and discusses the results of numerical applications.

This paper presents a methodology for the synthetic generation of heterogeneous random variables that affect the decision-making process in planning electric components and Distributed Energy Resources (DERs) for smart road tunnels. To ease the task of the decision maker, the methodology is developed in a generalized form in order to tackle variables of different sources and extractions (e.g., electrical, weather and economic variables). The methodology allows the generation of long-term synthetic scenarios of these heterogeneous variables through the development of Markov Chains (MCs), assuming that each variable can be represented by states with memory. The proposed methodology is applied and tested on actual data collected at the locations of two smart road tunnels in Italy, that are under evaluation to be equipped with Photovoltaic (PV) and wind generation systems and with a Battery Energy Storage System (BESS). Numerical experiments confirm the validity of the proposal for the synthetic generation of variables like solar irradiance, wind speed and electricity prices, as confirmed through the evaluation of their relevant statistics in a twenty-year period.

The proliferation of photovoltaic (PV) systems connected to low voltage (LV) distribution networks can have detrimental impacts on waveform distortion. This is caused by the power electronic interface, with voltage source converter (VSC) technology being by far the most prevalent. As such, proper emission models, which can account for the non-linear operation of VSCs used in PV systems have to be developed and investigated for the assessment of harmonic distortion in LV networks. This paper compares different frequency domain models (FDMs), specifically, methods based on frequency coupling matrices and an analytical method based on a harmonically coupled impedance matrix, for the type of single-phase PV systems typically found in LV distribution networks. Two case studies are presented to compare the models in terms of computational complexity and accuracy, with results showing that models accounting for the interaction between same order harmonics are sufficiently accurate.

Waveform distortion is one of the most common disturbances in power quality in real power grids. Accurate assessment of the distortion level for voltage and current is a challenge for researchers, as a result of simultaneous occurrence of a large number of time-varying spectral components and the problem of proper synchronization of measurement window. The article presents a new method for assessing low-frequency harmonics (LFH) and high-frequency harmonics (HFH), which are supraharmonics with a frequency that is a multiple of fundamental frequency. The proposed method is based on discrete Fourier transform analysis using a “flat-top” window, where the sliding measurement window is synchronized with the short-term fundamental period by using empirical Fourier decomposition. The presented approach focuses on increasing the accuracy of assessment of LFH and HFH in power grids only by modifying the synchronization of measurement window. The presented approach makes significant modifications to the current IEC standard unnecessary, which in turn allows its faster implementation on a large scale. The proposed approach is also compared with other synchronized methods presented in recent literature and with methods suggested by IEC standards. The presented research results confirm the accuracy of the proposed solution.

An electrical distribution system with several dispersed generation (DG) units interconnected to the AC grid through PWM static converters is considered.The DG units can provide both energy and system ancillary services (e.g.voltage regulation, voltage harmonic distortion and unbalance compensation).The DG unit converters are coordinated through a centralized control system that provides in real time the reference signals to the control systems of the DG static converters allowing them to provide the aforementioned services.The centralized control system calculates the reference signals using an optimization procedure whose inputs are measurements from the distribution network busbars and whose outputs are the reference signals; thus, the compensation action is depending on the unavoidable time delays introduced in the whole process by data acquisition, digital processing and data communication.With particular reference to the waveform distortion compensation, the paper proposes a new procedure to compensate them.Time domain simulations on an actual distribution system are reported in order to analyze the delay effects and show the effectiveness of the proposed compensation procedure.

The analysis of power quality disturbances in distribution systems has gained significance with the diffusion of electric vehicles (EVs). Waveform distortions are interesting since EV currents introduce distortions with spectral components in both low and high-frequency bands. This paper develops specific indices to assess cumulative emissions from single-phase EV on-board chargers, extending the aggregation and diversity factors to the supra-harmonic range. The methodology accounts for variables such as EV charging powers, upstream network impedance, and number of EVs. A simplified time-domain model of a low-power unidirectional converter, commonly used for EV battery charging, is employed to balance circuit complexity and computational effort. This model allows for sensitivity analyses of key parameters influencing charger emissions. Numerical applications are carried out for both individuals and groups of EV chargers at a charging station. Results highlight the need for careful quantification of aggregated EV emissions, showing that supra-harmonic emissions are highly sensitive to variations in the power absorbed by EV chargers. Notably, their cumulative impact is much lower when chargers operate at different power levels than when all chargers operate at the same power level. These findings underscore the importance of accurately assessing the impact of EV charging on power quality.

Waveform distortion disturbances deteriorate Power Quality (PQ) in power systems and affect electrical installations and equipment. In modern Low-Voltage (LV) distribution systems, electrical signals are characterized by the contemporaneous presence of low-frequency and High-Frequency (HF) spectral components (i.e., up to 150 kHz). This paper focuses on HF voltage distortions, also known as supraharmonics, that can create losses and reduce the efficiency of the equipment. Accurate predictions of future HF voltage distortions help distribution system operators and regulators in taking timely actions to define and withstand PQ limits or to analyze the residual capacity of networks toward the additional installation of power electronic converters. This paper provides a probabilistic approach to forecast HF voltage distortions by applying a methodology based on Quantile Regression Forests (QRFs). Results of numerical experiments based on publicly available actual data collected at a LV distribution system are presented to assess the performance of the proposed methodology.

Hydrogen refueling stations that produce hydrogen on-site from renewable sources are an interesting solution to guarantee green hydrogen with zero CO2 emissions. The main limit in developing these stations is the hydrogen production cost, which depends on the plant size (hydrogen production capacity) and on the availability of renewable energy sources. In this study, a techno-economic evaluation of an on-site Hydrogen Refueling Station (HRS) based on a grid-connected photovoltaic (PV) plant integrated with an electrolysis unit is performed. In particular, an optimization problem based on an economic objective function that includes installation and operation costs is formulated in order to choose the size of the HRS components that minimize the unitary cost of hydrogen production through the plant's lifetime.