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The availability of charging infrastructure is essential for large-scale adoption of electric vehicles (EV). Charging patterns and the utilization of infrastructure have consequences not only for the energy demand by loading local power grids, but influence the economic returns, parking policies and further adoption of EVs. We develop a data-driven approach that exploits predictors compiled from Geographic Information Systems data describing the urban context and urban activities near charging infrastructure to explore correlations with a comprehensive set of indicators that measure the performance of charging infrastructure. The best fit was identified for the size of the unique group of visitors (popularity) attracted by the charging infrastructure. Consecutively, charging infrastructure is ranked by popularity. The question of whether or not a given charging spot belongs to the top tier is posed as a binary classification problem and predictive performance of logistic regression regularized with an $\mathit {l}_{1}$ penalty, random forests and gradient boosted regression trees is evaluated. Obtained results indicate that the collected predictors contain information that can be used to predict the popularity of charging infrastructure. The significance of predictors and how they are linked with the popularity are explored as well. The proposed methodology can be used to inform charging infrastructure deployment strategies.

This paper deals with the problem of the optimal rating of mineral-oil-immersed transformers in large wind farms. The optimal rating is derived based on the probabilistic analyses of wind power generation through the Ornstein–Uhlenbeck stochastic process and on thermal model of the transformer through the integration of stochastic differential equations. These analyses allow the stochastic characterization of lifetime reduction of the transformer and then its optimal rating through a simple closed form. The numerical application highlights the effectiveness and easy applicability of the proposed methodology. The proposed methodology allows deriving the rating of transformers which better fits the specific peculiarities of wind power generation. Compared to the conventional approaches, the proposed method can better adapt the transformer size to the intermittence and variability of the power generated by wind farms, thus overcoming the often-recognized reduced lifetime.

This paper deals with the use of distributed energy storage systems in microgrids, and proposes a planning method which accounts for the uncertainties of load and distributed generation. Objectives of the planning method are the reduction of the energy costs, while providing the supply of ancillary services as a technical support to the network. The energy costs are evaluated considering a hourly varying pricing scheme and optimizing the storage systems charging/discharging stages. The technical support is devoted to the restraint of bus voltage amplitudes, and of network components' currents/powers within admissible ranges. The input data uncertainties are managed through three decision theory criteria (i.e., the minimization of expected costs; an approach based on the weighted regret felt by the design engineer; and a stability area criterion), which allow considering the multiple design alternatives and futures (i.e., possible values of uncertain input data) in an accurate and feasible way. The design alternatives refer to the size and location of the distributed storage systems, while each future is associated with a different level of load demand and power production of distributed generation over the whole planning period. The results of numerical applications are reported and discussed with reference to a Cigré test network.

In this paper, a probabilistic direct method to assess the waveform distortion levels of low voltage distribution systems including plug-in hybrid electric vehicles charging stations is proposed. The method takes into account uncertainties in number of vehicles being charged, charging voltage and current levels, power battery status and capacity, and charging time duration. Numerical applications are performed by applying the proposed probabilistic harmonic analysis to assess the harmonic distortions of a test low voltage unbalanced distribution network.

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.

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.

The optimal allocation of capacitors in unbalanced distribution systems can be formulated as a mixed integer, non-linear, constrained optimisation problem. Fuzzy-based approaches, simulated annealing, tabu search and genetic algorithms are some of the techniques used for solving the problem in deterministic scenarios. However, distribution systems are probabilistic in nature, leading to inaccurate deterministic solutions. As a result, a probabilistic optimization model is required to take into account the unavoidable uncertainties affecting the problem input data, primarily the load demands. Of the various techniques for the solution of the problem, one of the most frequently used is the genetic algorithm. However, the application of simple genetic algorithms to solve the probabilistic optimization model involves tremendous computational effort. To reduce the computational effort, this paper proposes a new single-objective probabilistic approach based on the use of a micro-genetic algorithm. Two different techniques, one based on the linearised form of the equality constraints of the probabilistic optimisation model and one based on the point estimate method, were tested and compared. The proposed approaches were tested on the IEEE 34-node unbalanced distribution system to demonstrate the effectiveness of the procedures in generating reduced computational efforts and increased accuracy of the results.

A 2×25-kV 50 Hz traction power system was analyzed and modeled in the time domain in order to simulate short-circuit conditions and to attain a practical method to identify the short circuit behavior of the traction system. In particular, due to the difficulty in assessing the track-line parameters which mainly depend on changing environmental conditions, the possibility of neglecting the capacitive parameters was analyzed. A complete model of the 2×25-kV 50 Hz traction power system was developed and validated through comparison with the results of referenced models and experimental tests performed on real systems. Simulations, addressed to reduce the short-circuit modeling complexity, demonstrate that neglecting the capacitive effects between conductors and to ground does not affect the calculation accuracy for standard analyses. The obtained results show that the proposed model can be remarkably useful for the traction system design as well as for investigating the effects of short-circuit conditions on other circuits, such as telecom circuits, power-supply equipment, and signaling track circuits.

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.

Radiation forecast accounting for daily and instantaneous variability was pursued by means of a new bi-parametric statistical model that builds on a model previously proposed by the same authors. The statistical model is developed with direct reference to the Liu-Jordan clear sky theoretical expression but is not bound by a specific clear sky model; it accounts separately for the mean daily variability and for the variation of solar irradiance during the day by means of two corrective parameters. This new proposal allows for a better understanding of the physical phenomena and improves the effectiveness of statistical characterization and subsequent simulation of the introduced parameters to generate a synthetic solar irradiance time series. Furthermore, the analysis of the experimental distributions of the two parameters' data was developed, obtaining opportune fittings by means of parametric analytical distributions or mixtures of more than one distribution. Finally, the model was further improved toward the inclusion of weather prediction information in the solar irradiance forecasting stage, from the perspective of overcoming the limitations of purely statistical approaches and implementing a new tool in the frame of solar irradiance prediction accounting for weather predictions over different time horizons. © 2016 by the authors.

Industrial systems are usually characterized by high energy consumption and presence of sensitive loads which require high levels of Power Quality. Based on the grid architecture of an actual Italian industrial facility, this paper proposes the installation of a hybrid AC/DC microgrid. The hybrid microgrid includes dispatchable and non-dispatchable renewable generation units, battery storage systems and controllable loads. An optimization model is formulated to solve the problem of the day-ahead optimal scheduling of the microgrid. The model aims to optimize the operation of dispatchable resources, controllable loads and storage systems minimizing the daily costs of the energy imported from the AC grid and the costs required by the dispatchable generators while satisfying operational constraints such as those related to the production process of the industry facility. Different case studies are investigated in order to show the feasibility and effectiveness of the proposed procedure. © 2015, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

Battery energy storage systems (BESSs) are considered a key device to be introduced to actuate the smart grid paradigm. However, the most critical aspect related to the use of such device is its economic feasibility as it is a still developing technology characterized by high costs and limited life duration. Particularly, the sizing of BESSs must be performed in an optimized way in order to maximize the benefits related to their use. This paper presents a simple and quick closed form procedure for the sizing of BESSs in residential and industrial applications when time-of-use tariff schemes are applied. A sensitivity analysis is also performed to consider different perspectives in terms of life span and future costs.

This study considers a distribution system with a number of dispersed generation (DG) units interconnected to the AC grid through power electronic interfaces. Some selected DG units were able to provide energy service and system ancillary services (in particular, voltage regulation and partial compensation or elimination of some power quality disturbances, such as waveform distortions and voltage unbalances) through a proper centralised control system that provides the reference signals to the converters of the DG units in real time. In addition, the problem of time delays owing to data acquisition and digital signal processing for reference signal calculations and the effects of these delays on compensation actions were studied. Several time-domain simulations of an actual distribution system are reported, taking into account different DG units and time-delay scenarios.

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.

Genetic algorithms (GAs) are widely used for optimal allocation of capacitors in distribution systems. When dealing with large-scale systems, such as in case of unbalanced multi-converter distribution systems, these algorithms can require significant computational efforts, which reduce their effectiveness. In order to reduce processing time for GAs and simultaneously maintain adequate levels of accuracy, methods based on the reduction of the search space of GAs or based on micro-genetic algorithms have been proposed. These methods generally guarantee good solutions with acceptable levels of computational effort. In this paper, some fast, GA-based methods are compared and applied for solving the problem of optimal sizing and siting of capacitors in unbalanced multi-converter distribution systems. The algorithms have been implemented and tested on the unbalanced IEEE 34-bus test distribution system, and their performances have been compared with the performance of the simple genetic algorithm technique.

The problem of choosing the optimal location and size for shunt capacitors in unbalanced distribution systems can be formulated as a mixed, non-linear, constrained multi-objective optimization problem, and is usually solved in deterministic scenarios. However, distribution systems are stochastic in nature, which leads to inaccurate deterministic solutions.

– An electrical distribution system with several dispersed generation (DG) units interconnected to the AC grid through PWM static converters is considered. The DG units can provide both energy and system ancillary services (e.g. voltage regulation, voltage harmonic distortion and unbalance compensation). The DG unit converters are coordinated through a centralized control system that provides in real time the reference signals to the control systems of the DG static converters allowing them to provide the aforementioned services. The centralized control system calculates the reference signals using an optimization procedure whose inputs are measurements from the distribution network busbars and whose outputs are the reference signals; thus, the compensation action is depending on the unavoidable time delays introduced in the whole process by data acquisition, digital processing and data communication. With particular reference to the waveform distortion compensation, the paper proposes a new procedure to compensate them. Time domain simulations on an actual distribution system are reported in order to analyze the delay effects and show the effectiveness of the proposed compensation procedure.

In this paper, the problem of the optimal allocatio n and sizing of capacitors in unbalanced distribution sys tems is formulated as a multi-objective optimization proble m. This problem is solved by means of the e-constrained technique. Besides, some methods for determining a reduced fea sible region and a micro genetic algorithm are used. The solving method proposed is tested on the IEEE 34-node unbal anced distribution system in order to demonstrate the eff ectiveness of the approaches in terms of reduced computational efforts and accuracy of the results.

The increasing penetration of dispersed generation (DG) influences the power quality (PQ) levels in the distribution networks. Then, an accurate assessment of PQ disturbances in presence of DG is needed and adequate probabilistic indices should be adopted to properly quantify the random nature of the disturbances. In this paper, a tool for the calculation of slow voltage variations, waveform distortions, unbalances, voltage fluctuations and voltage sag probabilistic indices has been developed by using DigSilent Power Factory software. Numerical applications are performed on a 120-bus distribution network whose DG units are wind generators and gas turbine units.