• Energy Research

Distribution networks are undergoing radical changes due to the high level of penetration of dispersed generation. Dispersed generation systems require particular attention due to their incorporation of uncertain energy sources, such as wind farms, and due to the impacts that such sources have on the planning and operation of distribution networks. In particular, the foreseeable, extensive use of wind turbine generator units in the future requires that distribution system engineers properly account for their impacts on the system. Many new technical considerations must be addressed, including protection coordination, steady-state analysis, and power quality issues. This paper deals with the very short-term, steady-state analysis of a distribution system with wind farms, for which the time horizon of interest ranges from one hour to a few hours ahead. Several wind-forecasting methods are presented in order to obtain reliable input data for the steady-state analysis. Both deterministic and probabilistic methods were considered and used in performing deterministic and probabilistic load-flow analyses. Numerical applications on a 17-bus, medium-voltage, electrical distribution system with various wind farms connected at different busbars are presented and discussed.

In this paper, we propose a new method for the accurate estimation of frequencies and magnitudes of harmonics and interharmonics caused on the supply-side currents by an adjustable-speed drive. The proposed method consists of a three-step procedure in which Estimation of Signal Parameters by Rotational Invariance Technique (ESPRIT) and Prony high-resolution methods are properly applied together with theoretical formulas. The method guarantees high accuracy with acceptable computational effort. Numerical applications were made on a hardware PWM adjustable-speed drive, and comparisons were made between the results of the proposed method, results obtained by applying the traditional IEC standard approach, and results obtained by another high-resolution method proposed in the relevant literature. The numerical applications and comparisons demonstrated the superior effectiveness and efficiency of the proposed method.

IEC Standards characterize the waveform distortions in power systems with the amplitudes of harmonic and interharmonic groupings (subgroups and groups) calculated by using the waveform spectral components obtained with a 5 Hz frequency resolution DFT. In some cases the power system waveforms are characterized by means of spectral signal components that the DFT with 5 Hz frequency resolution is unable to capture with sufficient accuracy. In this paper a new Prony method is proposed to calculate the harmonic and interharmonic subgroups. This method is based on an adaptive technique that acts with the aim of minimizing the mean square relative error of signal estimation.

A primary problem in waveform distortion assessment in power systems is to examine ways to reduce the effects of spectral leakage. In the framework of DFT approaches, line frequency synchronization techniques or algorithms to compensate for desynchronization are necessary; alternative approaches such as those based on the Prony and ESPRIT methods are not sensitive to desynchronization, but they often require significant computational burden. In this paper, the signal processing aspects of the problem are considered; different proposals by the same authors regarding DFT-, Prony-, and ESPRIT-based advanced methods are reviewed and compared in terms of their accuracy and computational efforts. The results of several numerical experiments are reported and analysed; some of them are in accordance with IEC Standards, while others use more open scenarios.

AbstractPhotovoltaic (PV) systems are widely spread across MV and LV distribution systems and the penetration of PV generation is solidly growing. Because of the uncertain nature of the solar energy resource, PV power forecasting models are crucial in any energy management system for smart distribution networks. Although point forecasts can suit many scopes, probabilistic forecasts add further flexibility to an energy management system and are recommended to enable a wider range of decision making and optimization strategies. This paper proposes methodology towards probabilistic PV power forecasting based on a Bayesian bootstrap quantile regression model, in which a Bayesian bootstrap is applied to estimate the parameters of a quantile regression model. A novel procedure is presented to optimize the extraction of the predictive quantiles from the bootstrapped estimation of the related coefficients, raising the predictive ability of the final forecasts. Numerical experiments based on actual data quantify an enhancement of the performance of up to 2.2% when compared to relevant benchmarks.

The ability to forecast the production of power by photovoltaic (PV) systems accurately and reliably is of major importance for the appropriate management of future electrical distribution systems. Several forecasting methods have been proposed in the relevant literature, and many indices have been used to quantify the quality of the forecasts. The methods can provide either deterministic or probabilistic forecasts; the latter seem to be the most appropriate to take into account the unavoidable uncertainties of PV power production. Similarly, indices were used to quantify the quality of both deterministic and probabilistic forecasting methods, but they usually do not account for the economic consequences of forecasting errors. In this paper, two advanced probabilistic forecasting approaches based on the Bayesian inference method are applied to the short-term forecasting of PV power production. Moreover, new probabilistic indices were proposed with the aim of comparing the probabilistic forecasting methods in such way that the value of the forecast is not included only by the users in their decision-making process; instead, it is partially anticipated by the forecasters in their quality-assessment process. Numerical applications also are presented to provide evidence of the performances of the Bayesian-based approaches and the probabilistic indices that were considered.

Modern Smart Grids (SGs) are characterized by the simultaneous presence of time-varying and non-linear loads as well as distributed energy sources with power converters which contribute to wide spectra waveform distortions. Moreover, one of the crucial device of the SG architectures is the smart metering systems which utilize high frequencies for the power line communication (PLC) transmission. In particular, the presence of spectral components in the range of 2\div 150kHz (supraharmonics) has recently become an issue of great interest due to the interaction between widespread diffusion of high-spectral emission devices (e.g., end-user devices and converter-interfaced distributed generation systems) and power line communication systems. The complexity of these waveforms distortions makes their assessment a challenge. This paper critically analyses and compares two methods proposed in recent literature which seem particularly suitable for the spectral analysis of waveforms with wide spectra. One of the methods is based on the sliding-window discrete Fourier transform (SWDFT) referring to IEC standard harmonic estimation. The second method utilizes the sliding-window wavelet-modified ESPRIT-based method (SWWMEM). This is a companion document to a paper of the same title, Part II, where the methods are tested on synthetic and measured waveforms in terms of accuracy and computational efforts.