A new short-term probabilistic forecasting method is proposed to predict the probability density function of the hourly active power generated by a photovoltaic system. Firstly, the probability density function of the hourly clearness index is forecasted making use of a Bayesian auto regressive time series model; the model takes into account the dependence of the solar radiation on some meteorological variables, such as the cloud cover and humidity. Then, a Monte Carlo simulation procedure is used to evaluate the predictive probability density function of the hourly active power by applying the photovoltaic system model to the random sampling of the clearness index distribution. A numerical application demonstrates the effectiveness and advantages of the proposed forecasting method.

Photovoltaic systems are expected to play a key role in the planning and operation of future distribution systems due to the benefits associated with their use. Unfortunately, a great problem is involved in photovoltaic power utilization, i.e., the unpredictability of the solar source. Thus, many forecasting methods have been developed in order to provide tools with adequate consistency, quality, and value. The methods can provide either deterministic or probabilistic forecasts; the latter seem to be the most appropriate for taking into account the unavoidable uncertainties of the solar source. In this paper, a new probabilistic method based on a competitive ensemble of different base predictors is proposed for the short-term forecasting of photovoltaic power. Three probabilistic methods were selected and trained as base predictors in order to obtain an ensemble of the predictive distribution with optimal characteristics of sharpness and reliability. Numerical applications based on actual data were performed to test the effectiveness of the proposed method with respect to single predictors and to a benchmark method.

Short-term probabilistic load forecasting is essential to power systems management and optimization of power flows across transmission networks. Developing forecasting tools capable of providing accurate predictions must comply with their practical implementation in short-term operations, mainly in terms of fast computing and high efficiency. Many data-driven forecasting solutions are often unnecessarily verbose, thus making their practical value limited. This problem occurs more frequently when multiple loads have to be predicted simultaneously, as in power transmission system analysis and optimization. In this paper, we propose a new cooperative forecasting system that refines probabilistic forecasts of individual loads online. The refining procedure is based on a multivariate quantile regression, which is dynamically applied to the individual forecasts as new observations become available. The proposal is validated on the load data published by ISO New England for eight regions, covering six States of the United States. The quality of probabilistic forecasts is assessed in terms of reliability and sharpness, comparing the results to three benchmarks. The proposed method outperforms the best benchmark by up to 6% w.r.t. the reduction in pinball loss.

Future distribution networks are undergoing radical changes, due to the high level of penetration of dispersed generation and information/communication technologies, evolving into the new concept of the Smart Grid. Dispersed generation systems, such as wind farms and photovoltaic power plants, require particular attention due to their incorporation of uncertain energy sources. Further and significant well-known uncertainties are introduced by the load demands. In this case, many new technical considerations must be addressed to take into account the impacts of these uncertainties on the planning and operation of distribution networks. This paper proposes novel Bayesian-based approaches to forecast the power production of wind and photovoltaic generators and phase load demands. These approaches are used in a probabilistic short-term steady-state analysis of a Smart Grid obtained by means of a probabilistic load flow performed using the Point Estimate Method. Numerical applications on a 30-busbar, low-voltage distribution test system with wind farms and photovoltaic power plants connected at different busbars are presented and discussed.

The International Electrotechnical Commission (IEC) standards characterize the waveform distortions in power systems with the amplitudes of harmonic and interharmonic groups and subgroups. These groups/subgroups utilize the waveform spectral components obtained from a fixed frequency-resolution discrete Fourier transform (DFT). Using the IEC standards allows for a compromise among the different goals, such as the needs for accuracy, simplification, and unification. In some cases, however, the power-system waveforms are characterized by spectral components that the DFT cannot capture with enough accuracy due to the fixed frequency resolution and/or the spectral leakage phenomenon. This paper investigates the possibility of a group/subgroup evaluation using the following advanced spectrum estimation methods: adaptive Prony, estimation of signal parameters via rotational invariance techniques, and root multiple-signal classification (MUSIC). These adaptive methods use variable lengths of time windows of analysis to ensure the best fit of the waveforms; they are not characterized by the fixed frequency resolution and do not suffer from the spectral leakage phenomenon. This paper also presents the results of the applications of these methods to three test waveforms, to current and voltage waveforms obtained from simulations of a real DC arc-furnace plant, and to waveforms measured at the point of common coupling of the low-voltage network supplying a high-performance laser printer.