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The study introduces a novel method for optimizing surge arrester placement in the distribution line to mitigate lightning-induced overvoltages, employing a single-objective optimization algorithm. The study assesses the effectiveness of two types of surge arresters concerning discharge energy in reducing lightning overvoltage. Moreover, the comparative analysis takes into account the transfer of overvoltage performance from protected to unprotected towers, highlighting its significance in estimating flashover rates under different protection alternatives. Decision theory analysis is employed in the present study to identify optimal arrester locations. The study aims to minimize the expected flashover rate and identify the most effective protection strategy by considering futures/scenarios associated with different values of peak current and lightning locations. The results of numerical applications also showed the substantial impact of lightning surge transfer within the network, emphasizing the imperative to incorporate this phenomenon into lightning protection modelling. • Focuses on indirect lightning protection for medium-voltage networks. • Analyses common surge arrester types for network protection. • Studied transfer voltage effects on adjacent towers. • Studies transfer voltage effects on adjacent towers. • Evaluates effectiveness of combining surge arresters and overhead shieldwire. • Considers optimal surge arrester placement using decision theory for cost-effectiveness. [ABSTRACT FROM AUTHOR]

• Probabilistic design of battery storage systems in AC/DC microgrids is proposed. • An analytical approach is proposed based on a closed-form method. • Uncertainties of AC and DC loads, and renewable power generation are considered. • The probabilistic approach makes use of the Taguchi arrays. • Comparison with Monte Carlo approach allows demonstrating accuracy and feasibility. The transformation of electrical networks in the context of the new smart grid paradigm unavoidably involves new challenges for electrical component planning. This paper provides a tool for the probabilistic sizing of a battery energy storage system in a hybrid AC/DC microgrid (i.e., a microgrid including AC and DC subsections with AC and DC loads and renewable power generation). The procedure is based on an analytical formulation to assess the total costs sustained by the microgrid for the inclusion of the battery energy storage system. The total costs depend on the random nature of the load demand, the electrical energy prices, and the renewable power generation, therefore, a probabilistic approach has been used. Furthermore, to account for uncertainties that affect the input data, the Taguchi orthogonal arrays are applied which allow significantly reducing the computational efforts while guaranteeing the desired accuracy of the results. The proposed analytical formulation along with the use of the Taguchi orthogonal arrays allows limiting the computational complexity even in presence of a large number of random inputs and discretization levels. A case study based on an industrial hybrid microgrid is presented to analyse the results obtained in terms of the optimal sizing of the battery energy storage system and to investigate the sensitivity with respect to some inputs. [ABSTRACT FROM AUTHOR]

This paper deals with the modelling of the 2 × 25 kV – 50 Hz high speed railway supply system. The proposed approach extends the traditional multi-conductor transmission line approach typically used for high speed railway systems, which is based on the assumption of overhead conductors. More specifically, this approach models the system by considering the presence of both overhead and buried conductors. Analytical formulations to calculate self and mutual line parameters are used. A numerical simulation allows evidencing the different results when the simplified and the detailed modelling approaches are adopted. A sensitivity analysis to point out the influence of the finitely conductive ground is also proposed. [ABSTRACT FROM AUTHOR]

Demand response (DR) is a useful tool for end users, since it allows noticeable reductions in the electricity bill. However, some customers have stringent constraints in terms of hourly active power, which makes DR attractive only when performed with the contemporaneous use of battery energy storage systems (BESSs). When this option is used, it is desirable to optimally size the BESSs, since their high investment costs could make their use impracticable for DR purposes. The uncertainties related to many of the inputs required by the BESS-sizing procedure make it necessary to use a probabilistic framework for sizing. In this paper, a probabilistic approach is proposed for the optimal sizing of BESSs when time-of-use (ToU) pricing is applied. The sizing procedure takes into account the uncertainties that unavoidably affect the evaluation of the total cost incurred by the customer such as load demand, energy prices, and economic factors. [ABSTRACT FROM AUTHOR]

• PV power intra-hour forecasting is dealt with. • A novel method is proposed based on fractional calculus, more specifically the Caputo derivative has been used. • The use of Caputo derivative allows considering information on the trend of the forecasting variable. • The prediction tool allows to provide a closed form for the predictor which is particularly suitable for online applications. • A sensitivity analysis shows that the inclusion of information on the trend of the forecasting variable in recent past could lead to accurate results. Intra-hour photovoltaic power forecasting provides essential information for real time optimal control of microgrids. At this purpose, a critical issue is the selection of the forecasting method. The choice of a forecasting method depends on many factors such as the availability of historical data, the time horizon, the lag period, and the time available for the forecast. Persistence based methods are particularly tailored for real time forecasting which require fast information and are typically a good trade-off choice when dealing with real time operation of microgrids. Their accuracy, however, could be not satisfactory in some cases such as when it appears critical to consider the trend of the power output in the last few minutes rather than only the last measured value. Derivatives help reach this goal, but fractional derivatives seem to be a more accurate choice in order to take into account the history of the variable to be forecasted as they are a promising tool for describing memory phenomena. In this paper, a novel intra-hour forecasting method is proposed based on the Caputo derivative. Numerical applications are carried out to show the efficacy of the proposed approach. Also, the accuracy of the proposed approach is tested through comparison with three models namely, persistence, derivative-persistence and auto regressive moving average models. The strength of the proposed forecasting tool is strictly related to its low computational burden without compromising accuracy. This makes of it an interesting means for real time grid operation strategies and can be of interest for the grid operators especially in vision of the changes distribution grids are witnessing with the transition to the smart grid paradigm. [ABSTRACT FROM AUTHOR]

• The paper explores Bayesian bootstrap in probabilistic photovoltaic power forecasting. • Bayesian bootstrap is applied to three probabilistic forecasting models. • The optimal quantile is extracted from the sample bootstrap distribution. • Bayesian bootstrap is compared with traditional bootstrap and with other benchmarks. • Numerical experiments validate the Bayesian bootstrap performance on real data. Modern distribution systems are characterized by increasing penetration of photovoltaic generation systems. Due to the uncertain nature of the solar primary source, photovoltaic power forecasting models must be developed in any energy management system for smart distribution networks. Although point forecasts can suit many scopes, probabilistic forecasts add further flexibility to any energy management system, and they are recommended to enable a wider range of decision making and optimization strategies. Real-time probabilistic photovoltaic power forecasting is performed in this paper by using an approach based on Bayesian bootstrap. Particularly, the Bayesian bootstrap is applied to three probabilistic forecasting models (i.e., linear quantile regression, gradient boosting regression tree and quantile regression neural network) to provide sample bootstrap distributions of the predictive quantiles of photovoltaic power. The heterogeneous nature of the selected models allows evaluating the performance of the Bayesian bootstrap within different forecasting frameworks. Several benchmarks and error indices and scores are used to assess the performance of Bayesian bootstrap in probabilistic photovoltaic power forecasting. Tests carried out on two actual photovoltaic power datasets for probabilistic forecasting demonstrates the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper focuses on the design stage of an electrical energy storage system which is intended to be used to level the power required by ships for propulsion when sailing in irregular seas. Particularly, a preliminary analysis has been carried out aimed at choosing, between two storage technologies namely battery and ultracapacitor, the more adequate storage system for the levelling action with respect to the period of the sea waves. A sizing procedure based upon a Hilbert transform decomposition of load demand is used. More specifically, a specified threshold frequency has been identified such that, power fluctuations related to irregular seas which are characterized by frequencies higher than the specified threshold, indicate the ultracapacitor as the more appropriate device for load levelling. For sizing the storage device, the load demand has been decomposed into two signals without overlapping frequencies. An optimal control strategy is proposed, by exploiting the decomposition of the load power, which allows simulating ultracapacitor real-time operation within the previously determined size constraints. Numerical results have been carried out referring to a Ro-Pax ferry powered by an electrical motor. • Onboard electrical energy storage is used for load levelling. • The use of the storage refers to a ship sailing in irregular sea states. • A threshold frequency is identified for the choice of storage technology. • A proper decomposition of the load request signal is performed. • An optimal control strategy based on an autoregressive model is used. [ABSTRACT FROM AUTHOR]

Highlights: [•] Two multi-objective optimization methods are proposed. [•] Exponential Weighted Criterion (EWC) and Compromise Programming Method (CPM). [•] Suitable when computational time is an important issue, as in case of optimal control. [•] The EWC enables better exploration of the Pareto frontier by capturing more points. [•] The EWC and the CPM are robust with reference to the constraints. [ABSTRACT FROM AUTHOR]

• The paper explores hierarchical probabilistic electric vehicle load forecasting. • Electric vehicle load is forecasted at low-level and high-level regions. • Probabilistic baseline models are coupled with principal component analysis. • A robust model is applied to forecast the load at the hierarchical higher level. • Numerical experiments validate the hierarchical performance on real data. Transportation electrification is a valid option for supporting decarbonization efforts but, at the same time, the growing number of electric vehicles will produce new and unpredictable load conditions for the electrical networks. Accurate electric vehicle load forecasting becomes essential to reduce adverse effects of electric vehicle integration into the grid. In this paper, a methodology dedicated to probabilistic electric vehicle load forecasting for different geographic regions is presented. The hierarchical approach is applied to decompose the problem into sub-problems at low-level regions, which are resolved through standard probabilistic models such as gradient boosted regression trees, quantile regression forests and quantile regression neural networks, coupled with principal component analysis to reduce the dimensionality of the sub-problems. The hierarchical perspective is then finalized to forecast the aggregate load at a high-level geographic region through an ensemble methodology based on a penalized linear quantile regression model. This paper brings, as relevant contributions, the development of hierarchical probabilistic forecasting framework, its comparison with non-hierarchical frameworks, and the assessment of the role of data dimensionality refduction. Extensive experimental results based on actual electric vehicle load data are presented which confirm that the hierarchical approaches increase the skill of probabilistic forecasts up to 9.5% compared with non-hierarchical approaches. [ABSTRACT FROM AUTHOR]

• Analysis of lightning-induced voltages on overhead lines is carried out. • Exact analytical solutions are used for parametric and a statistical analysis. • Role of various parameters affecting the induced voltages is analyzed. • Parametric fitting distributions are checked to fit peak voltage simulated data. In this paper, which is an extended version of the paper presented by the authors at the 34th International Conference on Lightning Protection (ICLP 2018), a parametric and a statistical analysis of lightning-induced voltages on overhead lines has been carried out by means of exact analytical solutions, for both step-function and linearly rising channel-base currents. The role played by the various parameters affecting the induced voltages has been analyzed. Further, the statistical investigations has allowed us to identify the parametric statistical distributions which best fit the data obtained by simulations. Based on this statistical investigation, an analysis of the lightning performance of an overhead line has been carried out too. [ABSTRACT FROM AUTHOR]