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Electrical power system robustness is defined as the intrinsic capacity of an electrical power system to maintain assigned disturbance levels when external conditions change. This paper compares two methods for estimating voltage dip severity: the critical distance method and the fault position method. Their differences are not linked to the type of network but to the type of results which we are searching for. This paper proposes a graphical color visualization of the during fault voltage matrices that immediately correlates dip severity with colors defined by a proper scale. New robustness indices are also proposed to synthesize bus performance in terms of affected and exposed areas. Numerical applications refer to real transmission and real distribution systems. Index Terms--Power distribution, power quality (PQ), power system reliability, power transmission, voltage dips.

DC are furnaces are more and more applied in large industrial systems and represent one of the major sources of perturbations for the feeding system. This paper deals with the problem of the dc arc modeling using three well-known chaotic attractors (Rossler, Chua, and Lorenz attractors). A new tuning procedure is adopted to determine the most adequate parameters of the attractors to model the dc arc furnace. Waveform distortions and voltage fluctuations indexes are calculated from the simulation results of a whole existing plant and compared with measured data. The proposed models of dc arc furnace can be used to assess the impact in terms of power quality of the dc arc furnace when planning new plants or evaluating the performances of compensating devices. Index Terms--Chaotic models, DC arc furnaces, power quality.

The ac/dc/ac double-stage converter, frequently used nowadays to provide ac adjustable frequency and magnitude voltages in the adjustable-speed drives, consists of a voltage source inverter (VSI) fed by a three-phase diode bridge rectifier; this rectifier generates high line current harmonics in the supply system. This paper presents a complete modelling of the VSI-fed drives, when energized by nonsinusoidal supply voltages, based on the analytical form of the current harmonics injected in the supply system; the equations of the current harmonics are obtained for both the continuous and discontinuous current modes and are derived by Fourier analysis of steady-state current time evolution. The high accuracy of the proposed models is demonstrated by comparison with the results of time-domain simulation. The modelling is useful to study the effects of supply voltage distortions on current harmonics injected in the supply system, both in continuous and discontinuous mode, with a very short computing time. Index Terms--Harmonic modelling, voltage source inverter-fed drive.

Due to their particular feature, direct current (dc) electric arc furnace (EAF) installations are peculiar loads that cause moderate to severe power quality (PQ) disturbances in the feeding power systems. Among them, voltage fluctuations and waveform distortions are the most impactful ones, and they should be adequately addressed in order to mitigate the detrimental effects. Several types of models have been developed in order to evaluate the effects of EAFs on networks, and chaotic models have been specifically recognized as suitable tools to evaluate the impact of EAFs in terms of PQ disturbances. This article compares the performance of three chaotic models (Chua, Lorenz, and Rössler) aiming at estimating PQ indices values of dc EAFs. The procedure exploits a block diagramming tool of the dc EAF installation and minimizes the deviation of estimated PQ indices from the actual ones through a new Monte Carlo optimization procedure, performed upon the parameters of the three chaotic models. To comply with the time-varying nature of the current and voltage waveforms in a dc EAF installation and with the wide presence of interharmonics, traditional, and advanced PQ indices are considered in this article. Actual data collected at an Italian dc EAF installation are used to conduct a numerical comparative analysis and to validate the effectiveness of the models in estimating the PQ disturbances. [ABSTRACT FROM AUTHOR]

Operating electrical networks by Dynamic Transformer Rating (DTR) unlocks the capacity of grids and allows to increase the exploitation of distributed generation and renewables. However, there is risk associated with the operation of transformers by DTR. Energy dispatch and/or load curtailment must be scheduled before the actual energy delivery, thus DTR and load should be predicted prior the actual transformer loading. Since both are random variables, this problem is prone to be addressed by stress-strength analysis. In this paper, the novel comprehensive probabilistic tool “SmarTrafo” is presented. It allows predicting the probability of the DTR to be greater than the load (i.e., the probability of success) through an exact analytic stress-strength model, and formulating an alarm-setting strategy in order to establish a warning if the expected probability is greater than a threshold. Specifically, the threshold is optimized in a multi-objective formulation, exploiting three different indices which differently evaluate the predictive skill of alarm-setting strategy. Numerical experiments based on actual data confirm the suitability of the proposal in predicting the probability of success and in establishing high-performance alarms based on such predictions. [ABSTRACT FROM AUTHOR]

Summary: Dispatching energy in transmission and distribution networks and bidding on electricity markets require probabilistic wind power forecasts available several hours before the actual occurrence. The volatility of the wind over large time horizons complicates the generation of skilled, reliable wind power forecasts. Exploiting numeric weather predictions (NWPs) is generally considered mandatory to increase the skill of probabilistic predictions, and forecasts may further be enhanced by adding several spatially distributed predictions. However, feature selection becomes a more complicated and time consuming as the number of NWPs increases. In this paper, we predict the power generated by a wind farm developing a new technique on the basis of ranking and combining spatially distributed NWPs, easing the feature selection and reducing the computational efforts, as well as maintaining high the skill of probabilistic forecasts. Several spatially distributed NWPs, provided for the area surrounding the wind farm, are ranked for each individual generator, and the ranked NWPs are combined to form an ensemble set of predictors for the probabilistic forecasting model. This ensemble is obtained using three different weighted combination approaches. Gradient boosting regression tree models and quantile regression neural networks generate probabilistic wind power forecasts. The proposed methodology is applied for day‐ahead wind power forecasting of individual generators and of the entire wind farm. Numerical experiments carried out on an actual wind farm in southern Italy suggest that ranking NWPs may keep the skill of forecasts at high levels even under a simplified, less computationally intensive procedure. The performance is also enhanced up to 1.8% with respect to standard techniques. [ABSTRACT FROM AUTHOR]

The overall growth of electrical energy consumption and the spread of distributed generation pose severe problems to system planners and operators, mainly due to the difficulties in expanding and upgrading electrical distribution systems. These problems can be partially overcome through the smart operation of power system components. In particular, under the new smart grid paradigm, lines and transformers could be loaded beyond their nameplate rating in favorable thermal conditions, without loss of rated life or breakdown. This paper deals with the loading of oil-immersed distribution transformers, and it proposes a probabilistic approach based on the monitoring of electrical and environmental conditions. The approach consists in forecasting the predictive distribution of the transformer dynamic rating, and then in forecasting the transformer allowable current. The probabilistic approach takes into account the unavoidable uncertainties involved in the thermal modeling of the transformer; indeed, it selects the allowable current through an index that takes into account both the probability of the allowable current to be higher than the predicted dynamic rating and the corresponding expected load curtailment. Numerical applications are performed on real data to evaluate the effectiveness of the proposed procedure. [ABSTRACT FROM PUBLISHER]

This paper deals with the problem of optimal allocation (siting and sizing) of storage resources in unbalanced three-phase low voltage microgrids. The siting and sizing problem is formulated as a mixed, non-linear, constrained optimization problem whose objective function deals with economic issues and whose constraints involve technical limitations of both network and distributed resources. Emphasis is given to the power quality issue with particular attention to unbalance reduction and voltage profile improvement. Technological issues, such as those related to the preservation of batteries' lifetime, were also taken into account. The planning problem is solved by means of a genetic algorithm which includes an inner algorithm based on sequential quadratic programming. In order to limit the processing time while maintaining reasonable accuracy, the genetic algorithm search space is significantly reduced identifying a subset of candidate buses for the siting of the storage resources. The Inherent Structure Theory of Networks and the Loading Constraints Criterion were used to identify the candidate buses. The proposed method has been applied to a low voltage test network demonstrating the effectiveness of the procedure in terms of computational burden while also preserving the accuracy of the solution. [ABSTRACT FROM AUTHOR]

This paper details an approach for the optimal scheduling of microgrids, including different distributed resources such as datacenters, electric vehicles, and distributed generation units. Management of both loads and generation systems is considered to be a strategic approach to optimally operate the grid. At this purpose, a multi-objective strategy was formulated, including several objectives that are considered essential for the efficient grid operation. Numerical simulations were performed to test the effectiveness of the proposed approach and the results were compared with those obtained through uncontrolled scenarios. [ABSTRACT FROM PUBLISHER]

This paper analyzes the influence of technology uncertainties on the sizing of battery systems. The sizing is based on the minimization of the costs incurred by the end customer and is performed considering demand response applications in the frame of time of use tariffs. The randomness of i) battery round trip efficiency, ii) life time duration, iii) unit costs related to battery capacity, power conversion system, operation and maintenance and replacement is taken into account in order to identify the most convenient solution from an economic and technical point of view. Based on the load requests of actual industrial and residential loads, numerical applications have been performed. The results provided useful information regarding the influence uncertainties have in the choice of a battery energy storage system. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

Waveform distortions are an important issue in distribution systems. In particular, the assessment of very wide spectra, that include also components in the 2-150 kHz range, has recently become an issue of great interest. This is due to the increasing presence of high-spectral emission devices like end-user devices and distributed generation systems. This study proposed a new sliding-window wavelet-modified estimation of signal parameters by rotational invariance technique (ESPRIT) method, particularly suitable for the spectral analysis of waveforms that have very wide spectra. The method is very accurate and requires reduced computational effort. It can be applied successfully to detect spectral components in the range of 0-150 kHz introduced both by distributed power plants, such as wind and photovoltaic generation systems, and by end-user equipment connected to grids through static converters, such as fluorescent lamps. [ABSTRACT FROM AUTHOR]

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 Bayesianbased approaches and the probabilistic indices that were considered. [ABSTRACT FROM AUTHOR]

Voltage stability (VS) has become a fundamental issue in the new, liberalised markets due to the fact that the new power systems are approaching more and more the stability limits. Then, several approaches were proposed in the relevant literature to find the critical conditions and recently the problem was faced also with reference to unbalanced three-phase power systems. The unbalances, in fact, can be responsible of more critical stability conditions than in balanced power systems. Continuation power flow and optimal power flows were applied to analyse such conditions. This study deals with VS analysis in unbalanced power systems and proposes a new optimisation model to determine the critical point based on the use of complementarity constraints. In particular, the maximum stability margin is calculated by a single-stage or a multistage procedure that accounts for the relationship between the actual operating point and the maximum loading point. In addition, the multistage maximum stability margin problem is formulated also in a probabilistic framework to account for the uncertainties affecting the input data (e.g. load powers). An application is presented on a test system highlighting the feasibility and the goodness of the proposed technique. [ABSTRACT FROM AUTHOR]