Auxiliary battery-based substations (ABSs) can enhance conventional railway feeder systems. In particular, ABSs make DC feeders located in areas far from the AC grid, able to power high-performance passenger and freight trains and store their braking energy. The paper proposes a techno-economic method to define size, position along the track, and control parameters of an ABS, with the goal to minimize the annual cost of energy (ACOE). Our approach takes into account the replacements of battery modules within the ABS expected lifetime in order to reduce costs. Numerical simulations are carried out assuming a new generation high-performance train on a real Italian 3 kV DC railway system equipped with one ABS. Although in some cases ABSs are already cheaper solutions compared to new traditional substations, the proposed sizing method allows obtaining a further reduction in the ABS cost.

Energy demand associated with the ever-increasing penetration of electric vehicles on worldwide roads is set to rise exponentially in the coming years. The fact that more and more vehicles will be connected to the electricity network will offer greater advantages to the network operators, as the presence of an on-board battery of discrete capacity will be able to support a whole series of ancillary services or smart energy management. To allow this, the vehicle must be equipped with a bidirectional full power charger, which will allow not only recharging but also the supply of energy to the network, playing an active role as a distributed energy resource. To manage recharge and vehicle-to-grid (V2G) operations, the charger has to be more complex and has to require a fast and effective control structure. In this work, we present a control strategy for an integrated on-board battery charger with a nine-phase electric machine. The control scheme integrates a fuzzy logic controller within a voltage-oriented control strategy. The control has been implemented and simulated in Simulink. The results show how the voltage on the DC-bus is controlled to the reference value by the fuzzy controller and how the CC/CV charging mode of the battery is possible, using different charging/discharging current levels. This allows both three-phase fast charge and V2G operations with fast control response time, without causing relevant distortion grid-side (Total Harmonic Distortion is maintained around 3%), even in the presence of imbalances of the machine, and with very low ripple stress on the battery current/voltage.

Power systems are facing increasing strain due to the worldwide diffusion of electric vehicles (EVs). The need for charging stations (CSs) for battery electric vehicles (BEVs) in urban and private parking areas (PAs) is becoming a relevant issue. In this scenario, the use of energy storage systems (ESSs) could be an effective solution to reduce the peak power request by CSs in PAs to the grid. Moreover, II-Life battery modules are a potential approach for cutting costs and implementing sustainable solutions. We propose a method to size ESSs coupled to CSs by using II-Life battery modules. Our methodology is based on the estimation of the residual cycles and the decrease in the supplied power due to the battery aging for defining the number of EV battery packs required for an ESS use case. Then, economic evaluations are presented to compare II-Life with the equivalent I-Life storage system.

This study introduces a novel hybrid forecasting model for wind power generation. It integrates Artificial Neural Networks, data clustering, and Particle Swarm Optimization algorithms. The methodology employs a systematic framework: initial clustering of weather data via the k-means algorithm, followed by Pearson’s analysis to pinpoint pivotal elements in each cluster. Subsequently, a Multi-Layer Perceptron Artificial Neural Network undergoes training with a Particle Swarm Optimization algorithm, enhancing convergence and minimizing prediction discrepancies. An important focus of this study is to streamline wind forecasting. By judiciously utilizing only sixteen observation points near a wind farm plant, in contrast to the complex global numerical weather prediction systems employed by the European Center Medium Weather Forecast, which rely on thousands of data points, this approach not only enhances forecast accuracy but also significantly simplifies the modeling process. Validation is performed using data from the Italian National Meteorological Centre. Comparative assessments against both a persistence model and actual wind farm data from Southern Italy substantiate the superior performance of the proposed hybrid model. Specifically, the clustered Particle Swarm Optimization-Artificial Neural Network-Wind Forecasting Method demonstrates a noteworthy improvement, with a reduction in mean absolute percentage error of up to 59.47% and a decrease in root mean square error of up to 52.27% when compared to the persistence model.

The integration of Electric Vehicles (EVs) equipped with Vehicle-to-Grid (V2G) technology has been recognized as a promising solution to enhance the flexibility of modern power distribution systems. However, to make this a reality, there is a need for flexible, reliable, and highly scalable computing architectures that can coordinate the charging and discharging process of EV fleets while identifying the best trade-offs between the vehicle charging expectations and the grid requirements. This challenging problem has been formalized in this paper as a welfare maximization problem, by defining proper utility functions to quantify the marginal cost/benefits of the charging and discharging process. Then, a self-organizing solution scheme is proposed in the task of enabling the EVs fleet to solve the welfare maximization problem by only exchanging local non-sensitive information. The insight is to define a formal connection between the charging/discharging scheduling problem and the economic dispatch of thermal generators, hence exploiting all the vast literature on the theory of the marginal cost for the decentralized solution of the scheduling problem, which is one of the main contributions of this paper. Simulation results obtained on two case studies are presented and discussed in order to assess the effectiveness of the proposed technique in solving complex scheduling problems in realistic operation scenarios.