Search

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]

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. [ABSTRACT FROM AUTHOR]

In this paper, we analysed the growing penetration of generating units from renewable energy sources in transmission power systems. Among the possible effects of the interaction of distributed generation (DG) with transmission systems, we considered the abnormal operating conditions caused by short circuits in the transmission systems and the resulting voltage sags that occur in the network. A systematic method is presented for analysing the voltage sag behaviour of any transmission system in which large DG units are connected to the transmission system by means of High Voltage/Medium Voltage (HV/MV) stations. The method proposed for obtaining the voltage sags is the fault position method (FPM), from which we derived a graphical visualization of the during-fault voltage (DFV) matrix as a valuable tool to obtain an immediate measure of the propagation of voltage sags in the network. The results obtained were based on a portion of a real transmission system. The system that we considered is an actual portion of the Italian transmission system, and all of the quantities we used were obtained from the data of the Transmission system operator (TSO). [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]

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]

In this paper, a probabilistic method is proposed to analyze the steady-state operating conditions of an active electrical distribution system with Wind (WD) and Photovoltaic (PV) generation plants. This method takes into account the uncertainties of power load demands and power production from renewable generation systems and combines Monte Carlo simulation techniques and multi-linearized power flow equations. The power flow equations include models of wind turbine and PV generation units and multi-linearization is accomplished by applying a criterion based on the total active power of the system. The method properly extends a probabilistic method proposed in the relevant literature for traditional passive electrical distribution systems to the field of an active electrical distribution system with WD and PV generation units. Numerical applications are presented and discussed with reference to a 17-bus test distribution system characterized by WD and PV systems connected at different busbars. The results obtained with the proposed algorithm are compared with the results obtained using a Monte Carlo simulation algorithm that included non-linear power flow equations. [ABSTRACT FROM AUTHOR]

• A multivariate approach to probabilistic industrial load forecasting is proposed. • Univariate quantile regression and quantile regression forests generate individual forecasts. • A multivariate quantile regression model combines individual forecasts of active and reactive power. • Multivariate quantile regression on quantile regression forests is the best pick for aggregate load. • Multivariate quantile regression on univariate quantile regression is the best pick for a single load. Industrial load takes a big portion of the total electricity demand. Skilled probabilistic industrial load forecasts allow for optimally exploiting energy resources, managing the reserves, and market bidding, which are beneficial to transmission and distribution system operators and their industrial customers. Despite its importance, industrial load forecasting has never been a popular subject in the literature. Most existing methods operate on the active power alone, partially or totally neglecting the reactive power. This paper proposes a multivariate approach to probabilistic industrial load forecasting, which addresses active and reactive power simultaneously. The proposed method is based on a two-level procedure, which consists of generating probabilistic forecasts individually for active and reactive power through univariate probabilistic models, and combining these forecasts in a multivariate approach based on a multivariate quantile regression model. The procedure to estimate the parameters of the multivariate quantile regression model is posed in this paper under a linear programming problem, to facilitate the convergence to the optimal solution. The proposed method is validated using actual load data collected at an Italian factory, under comparison with several probabilistic benchmarks. The proposed multivariate method enhances the skill of forecasts by 6% to 13.5%, with respect to univariate benchmarks. [ABSTRACT FROM AUTHOR]

This paper presents a probabilistic model for supporting the process of decision making about the value of new lighting systems in existing road tunnels when some data and parameters are affected by uncertainty. The proposed model, which we have called Probabilistic Energy Screening of Tunnel (PrEST), accounts for both the technical performance and the economic objectives of the new lighting systems. The technical performance is described on an adequate (x, y) plane that was defined by two indices. The first index measured the consumption of electricity per kilometre of tunnel lengths; the second index measured the performance of the lighting systems per unit of illuminated area. The economic results were measured by the net present value of the savings and by the payback period. Both the terms account for initial capital investments, energy and maintenance costs. PrEST was applied to two real road tunnels in service in Italy showing that the statistics of the results can support a final decision in function of the business strategy. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of the probabilistic steady-state analysis of an electrical distribution system that includes wind and photovoltaic power plants. We propose a new method that takes into account the uncertainties due to the time variations of power load demands and the random nature of solar and wind energy. The new method extends a probabilistic approach proposed in the relevant literature for balanced electrical distribution systems to unbalanced electrical distribution systems, and it also takes into account the correlation among the random input variables. The method is based on Taguchi's orthogonal arrays. Numerical applications based on an IEEE test system provide evidence of the probabilistic performance of the proposed approach. The results obtained with the proposed method are compared with the results obtained using non-linear Monte Carlo simulation and the Point Estimate Method. [ABSTRACT FROM AUTHOR]

• The features of a real trolley-bus that crosses over the center of Naples are studied. • Electrical measurements were recorded on the actual bus. • The main BESS technologies for the electric vehicles are also presented. • The substitution of the conventional ICE with an on-board BESS is proposed. • To design the unit, a new two steps optimized procedure is proposed and validated. The paper investigates the renewal of a hybrid trolley-bus, powered by a 600 V DC overhead electrical grid. The analysis focuses on replacing the on-board internal combustion engine (ICE) with a battery-based power unit. A novel two-step optimization procedure is proposed for this purpose. The procedure compares the solutions in terms of the total cost sustained by the ownership. By means of an iterative method, the optimal size of the battery unit is designed as a function of power and energy requirements, taking into account cycle life, depth of discharge, working temperature, replacement, and the requirements of the transport service operator. Using the real measurements taken on a trolley-bus operating in the city center of Naples (Italy), several numerical simulations are performed. The simulations examine three alternative Lithium high specific power and energy batteries. The comparison of the results allows to select the best solution among the different technologies for the proposed application. [ABSTRACT FROM AUTHOR]