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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.

Selection of cable size in the nonsinusoidal conditions is only based on ampacity considerations without any attention to the cost of the losses that will be suffered in the cable life. Since the cost of these losses (fundamental plus harmonics) can assume significant values, the selection of a cross section higher than required for ampacity considerations can result in a large reduction of cost. This paper proposes a method which allows the optimal economic selection of medium-voltage cables in nonsinusoidal operating conditions; it takes into account the initial investment costs and the Joule losses costs, including the additional costs due to current harmonics. It employs simplified expressions similar to those adopted by the IEC Standard in sinusoidal conditions, being the harmonic presence taken into account by a proper definition of a harmonic loss factor and by the introduction of harmonic coefficients to be predicted. Numerical applications to medium-voltage cables are developed and discussed in order to show the sensitivity of the cable optimum size to variations in the coefficients that characterize the harmonic presence. Index Terms--Cables, economics, harmonic distortion.

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]

The increasing application of adjustable speed drives dictates that the effect of waveform distortions on other equipment in industrial systems must be seriously considered. In this paper, we proposed new, advanced methods for the accurate and fast estimation of waveform distortions caused on the supply-side currents by an adjustable-speed drive. The methods consist of a four-step procedure based on proper combinations of Prony, ESPRIT, and DFT methods as well as analytical formulas. The methods properly combine the accuracy of the parametric methods (ESPRIT or Prony) with the speed of both the DFT method and analytical formulas. Experiments and comparisons were made based on both synthetic and measurement data. The results showed that the proposed methods were effective and efficient. Copyright © 2015 John Wiley & Sons, Ltd. [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]

A larger penetration of plug‐in electric vehicle (EV) fleets and of data centres in the microgrids (µGs) is foreseeable in the near future. Their presence and the presence of distributed generation (DG) units represent a challenge for system operators in that they must identify approaches for using these distributed resources that allow them to operate the grid in a more efficient and economical way. With particular reference to EVs and data centres, it must be noted that they are equipped with storage devices that potentially could result in the modification of the daily load profile of µGs by shifting peak demand away from the high‐cost periods. This study is focused on the use of data centres and EV aggregators as demand‐response resources, and a new model for the operation of a µG that includes DG units, EV fleets and the data centre's storage systems has been proposed. The procedure is based on a non‐linear, constrained, optimisation model with the aim of minimising the total cost of energy and simultaneously shaving peak power. The effectiveness of the proposed strategy was demonstrated by means of numerical applications to a low‐voltage, test µG. [ABSTRACT FROM AUTHOR]

Capacitors and series voltage regulators are used extensively in distribution systems to reduce power losses and improve the voltage profile along the feeders. This paper deals with the problem of contemporaneously choosing optimal locations and sizes for both capacitors and series voltage regulators in three-phase, unbalanced distribution systems. This is a mixed, non-linear, constrained, multi-objective optimization problem that usually is solved in deterministic scenarios. However, distribution systems are stochastic in nature, which can lead to inaccurate deterministic solutions. To take into account the unavoidable uncertainties that affect the input data related to the problem, in this paper, we have formulated and solved the multi-objective optimization problem in probabilistic scenarios. To address the multi-objective optimization problem, algorithms were used in which all the objective functions were combined to form a single function. These algorithms allow us to transform the original multi-objective optimization problem into an equivalent, single-objective, optimization problem, an approach that appeared to be particularly suitable since computational time was an important issue. To further reduce the computational efforts, a linearized form of the equality constraints of the optimization model was used, and a micro-genetic algorithm-based procedure was applied in the solution method. [ABSTRACT FROM AUTHOR]

Distribution systems are undergoing significant changes as they evolve toward the grids of the future, which are known as smart grids (SGs). The perspective of SGs is to facilitate large-scale penetration of distributed generation using renewable energy sources (RESs), encourage the efficient use of energy, reduce systems' losses, and improve the quality of power. Photovoltaic (PV) systems have become one of the most promising RESs due to the expected cost reduction and the increased efficiency of PV panels and interfacing converters. The ability to forecast power-production information accurately and reliably is of primary importance for the appropriate management of an SG and for making decisions relative to the energy market. Several forecasting methods have been proposed, and many indices have been used to quantify the accuracy of the forecasts of PV power production. Unfortunately, the indices that have been used have deficiencies and usually do not directly account for the economic consequences of forecasting errors in the framework of liberalized electricity markets. In this paper, advanced, more accurate indices are proposed that account directly for the economic consequences of forecasting errors. The proposed indices also were compared to the most frequently used indices in order to demonstrate their different, improved capability. The comparisons were based on the results obtained using a forecasting method based on an artificial neural network. This method was chosen because it was deemed to be one of the most promising methods available due to its capability for forecasting PV power. Numerical applications also are presented that considered an actual PV plant to provide evidence of the forecasting performances of all of the indices that were considered. [ABSTRACT FROM AUTHOR]

Demand response (DR) can be very useful for an industrial facility, since it allows noticeable reductions in the electricity bill due to the significant value of energy demand. Although most industrial processes have stringent constraints in terms of hourly active power, DR only becomes attractive when performed with the contemporaneous use of battery energy storage systems (BESSs). When this option is used, an optimal sizing of BESSs is desirable, because the investment costs can be significant. This paper deals with the optimal sizing of a BESS installed in an industrial facility to reduce electricity costs. A four-step procedure, based on Decision Theory, was used to obtain a good solution for the sizing problem, even when facing uncertainties; in fact, we think that the sizing procedure must properly take into account the unavoidable uncertainties introduced by the cost of electricity and the load demands of industrial facilities. Three approaches provided by Decision Theory were applied, and they were based on: (1) the minimization of expected cost; (2) the regret felt by the sizing engineer; and (3) a mix of (1) and (2). The numerical applications performed on an actual industrial facility provided evidence of the effectiveness of the proposed procedure. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

In recent years, modern distribution networks have rapidly evolved toward complex systems due to the increasing level of penetration of distributed generation units, storage systems, and information and communication technologies. In this framework, power quality disturbances such as waveform distortions should be minimized to guarantee optimal system behavior. This article formulates the planning problem of passive filtering systems in a multi-convertor electrical distribution system as a probabilistic multi-objective optimization problem whose input random variables are characterized with probability density functions. A heuristic simplified approach including trade-off analysis issues is applied to solve the planning problem with the aim of optimizing several objectives and meeting proper probabilistic equality and inequality constraints. The approach is able to quickly find solutions on the Pareto frontier that can help the decision-maker to select the final planning alternative for practical operation. The proposed approach is applied to a 17-busbar distribution test system to evidence its effectiveness. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel scheme that jointly employs sliding-window (SW) Prony and the discrete Fourier transform (DFT) for estimating power-quality indices based on the time-frequency analysis of non-stationary waveforms in power-system disturbances. These indices, recently proposed in the relevant literature, require the separation of the waveform in two parts, i.e., the fundamental component and the disturbance waveform. In the proposed scheme, Prony's method was applied to calculate only the fundamental component from low-pass, filtered data, while the disturbance waveform is obtained by using DFT. The application of Prony's method only to the fundamental component reduces the computational effort required by this method significantly and allows the size of the window of the DFT to be set as an integer multiple of the estimated fundamental period, which minimizes the spectral leakage of the DFT. The main advantages of the proposed, new approach include high resolution and accuracy in the estimation of indices and significant reduction of the computational cost. Experiments were conducted and comparisons were made using both synthetic and measured data. The results confirmed the effectiveness and efficiency of the proposed scheme. [ABSTRACT FROM AUTHOR]

The problem of developing a definition that adequately assesses power-quality (PQ) levels in the presence of distributed generation (DG) is addressed by using proper probabilistic indices for distribution networks. In the planning of new DG installations, these indices take the variation of PQ level into account and use weighting functions properly. They are useful in quantifying the impact of the installation of DG units because they can consider several PQ disturbances simultaneously. Also, they indicate how each PQ disturbance may affect the decision concerning the installation of DG units. Several tests on real distribution networks were performed and discussed in order to show the usefulness of these indices in evidencing the impact of DG on PQ levels. The analysis of the electrical distribution systems on the basis of the considered indices is valuable also to help decide the best allocation and size of distributed generators. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: This paper applies the point estimate method to account for the uncertainties that affect the evaluation of the steady state operating condition of an unbalanced three-phase power system. Moreover, since the point estimate method requires that the input random variables are uncorrelated, a suitable adjustment to take into account the correlation is applied. Different point estimate schemes (2m, 2m +1 and 4m +1 schemes) are presented and tested. The accuracy of the proposed techniques is tested on a three-phase unbalanced IEEE 34-bus test system; the results obtained applying the Monte Carlo simulation are assumed as reference . Both correlated and uncorrelated input random variables are considered, and multimodal probability density functions are tested. The conclusion is that the 2m +1 scheme gives the best solution in terms of accuracy and computational efforts; in the case of correlated input random variables, an adequate procedure to take into account the correlation must be applied. [Copyright &y& Elsevier]

In this paper, a probabilistic method is proposed to analyze the very short-term steady-state performance of an unbalanced distribution electrical system characterized by the presence of wind farms. This method, which can take into account the uncertainties of loads and wind productions, is based on a Monte Carlo simulation procedure applied to the non-linear three-phase load flow equations, including wind farm models. Bayesian time series models are used to predict the next hour's wind speed probability density functions, making possible a predictive evaluation of the very short-term system steady-state behavior. Numerical applications are presented and discussed with reference to the three-phase unbalanced IEEE 34-bus test distribution system in the presence of wind farms connected at different busbars. [ABSTRACT FROM AUTHOR]

The steady state thermal rating of overhead transmission lines is limited by the conductor's maximum design temperature, which is related to the maximum sag and the loss of tensile strength of the conductors. Traditionally, this overhead transmission lines rating is computed using a deterministic approach, with reference to severe weather conditions. Thus, the application of this method leads to conservative results resulting in under-utilization of conductors. In this paper, a new method based on an hourly probabilistic index is proposed to predict the line thermal rating for each hour of the day; this index is evaluated using the conductor current limit probability density function (pdf). The method uses Bayesian time series models for the weather parameters (ambient temperature, wind speed and wind direction) and calculates the conductor current limit pdf using a Monte Carlo simulation. The probabilistic index is applied by considering measured weather data of both hot and cold seasons; the corresponding lines ratings are reported and analyzed. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of contemporaneously choosing optimal locations and sizes for both shunt capacitors and series voltage regulators in three-phase unbalanced distribution systems. The sizing and placement procedure not only minimizes the power losses along distribution feeders but also makes sure that both capacitors and series regulators will have the minimum possible impact on the harmonic distortion of bus voltages in the system. The fact that distribution systems can operate under unbalanced loading conditions means that the optimization will have to account for any unbalances in the system. A genetic algorithm, which successfully solves the above-described problem, is developed and tested. This paper presents the results of simulations for a 34-bus IEEE distribution test system. [ABSTRACT FROM AUTHOR]

Accurate probabilistic forecasts of renewable generation are drivers for operational and management excellence in modern power systems and for the sustainable integration of green energy. The combination of forecasts provided by different individual models may allow increasing the accuracy of predictions; however, in contrast to point forecast combination, for which the simple weighted averaging is often a plausible solution, combining probabilistic forecasts is a much more challenging task. This paper aims at developing a new ensemble method for photovoltaic (PV) power forecasting, which combines the outcomes of three underlying probabilistic models (quantile k-nearest neighbors, quantile regression forests, and quantile regression) through a weighted quantile combination. Due to the challenges in combining probabilistic forecasts, the paper presents different combination strategies; the competing strategies are based on unconstrained, constrained, and regularized optimization problems for estimating the weights. The competing strategies are compared to individual forecasts and to several benchmarks, using the data published during the Global Energy Forecasting Competition 2014. Numerical experiments were run in MATLAB and R environments; the results suggest that unconstrained and Least Absolute Shrinkage and Selection Operator (LASSO)-regularized strategies exhibit the best performances for the datasets under study, outperforming the best competitors by 2.5 to 9% of the Pinball Score. [ABSTRACT FROM AUTHOR]

The article presents a commentary on the paper titled "A Method for Short-Term Wind Power Prediction With Multiple Observation Points." Topics discussed include time frames for wind power forecasting, proposed methods for wind power forecasting, and the selection of historical data points. Also mentioned is the use of Persistence and Grey model for predicting the wind direction.