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Railway industry is experiencing a strong need of renewal and smart technology. Hence, advanced approaches are needed to improve efficiency and sustainability of railway operations. In urban contexts, the public railway sector is object of an inevitable process of revamping, which involves expectations of end-users, railway operators, local supply distributors or local authorities. This paper examines an urban railway power system, where a smart scenario is envisaged. Particularly, an Italian AC/DC railway power infrastructure is considered, and an energy efficiency strategy is assumed. Aim of the paper is to evaluate the voltage quality in the supplying distribution network for any operating conditions of the railway system. Also, the energy efficiency strategy is arranged to improve voltage quality. More specifically, the strategy exploits electrical energy storage systems recharged by regenerative braking of rolling stocks in order to increase efficiency of the whole system. Also, the storage system allows shaving the peak load demand aimed at improving the voltage quality. Numerical simulations are carried out, they provide power quality analysis by examining nodal voltages as a function of the efficiency measure. The behavior of the railway power system has been analyzed under different operating conditions.

È proposta una strategia di controllo per la partecipazione flessibile dei collegamenti HVDC alla regolazione di frequenza basata sulla variazione adattativa della potenza sincronizzante e versatile rispetto alle condizioni di carico del sistema.

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.

Combined heat and power (CHP) generation is a fundamental practice to reduce primary energy consumptions and mitigate the related greenhouse gas emissions. Cogeneration plants are even more important in the Hospital sector, as they give the opportunity to operate regardless of the electric power grid, so ensuring energy availability also during grid faults while providing significant cost savings to the public healthcare system. The integration of a battery energy storage with the CHP system could further increase the expected advantages by enabling more flexible operating strategies, further energy reliability and lower operating costs. Therefore, this research paper addresses the development of a specific methodology for the energetic and economic assessment of a grid connected CHP plant assisted by a battery energy storage. More specifically, an evaluation algorithm has been developed and then coupled to a genetic evolutionary algorithm. Then, a vector optimization problem has been solved to find the optimal configurations of a modular cogeneration plant (i.e. size and number of CHP gas engines; battery size) which maximize the primary energy saving while minimizing the payback period. Results based on electricity and heat demand of a hospital facility demonstrate how the battery energy storage system allows the shifting of the Pareto frontier towards better economic results if compared with Pareto solutions found when the optimization problem does not consider a battery storage supporting the CHP plant. The proposed methodology provides an effective and flexible procedure for the optimal configuration and detailed analysis of CHP plants integrated by an electrochemical based energy storage system. In fact, the effects of a high number of interacting energetic and economic parameters are considered together with the technical constraints required to extend battery lifetime in real applications.

The forced switching converters are the kernel of the modern paradigm of VSC-HVDC system. It is widely recognized that appropriate control laws make these converters more advantageous than the traditional line commutated ones. They can play a versatile role by properly controlling active and reactive powers between electrical networks, improving the dynamic performances of the whole system. Besides this is more and more motivated by the wide spreading penetration of renewable resources. In this paper after a few references to basic concepts, a possible control strategy of VSC-HVDC transmission system is discussed. With respect to a two-area system with AC-DC link, a combined control law is investigated for improving inertial and primary frequency response. The control law is based upon the equivalent HVDC tie link synchronization coefficient which allows determining the reference value of the AC voltage reference angles of the converters. A damping action is further added for improving the power swings damping. The simulations performed with a simple but quite general case study permit to confirm the validity and the goodness of the proposed control strategy.

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.

The optimal exploitation and management of electrical energy passes through the possibility to store energy. Battery energy storage systems offer new, important potentialities to optimally manage electrical energy and to exploit renewables in a microgrid. Sizing the battery energy storage systems is however a critical point, as their total costs are still expensive. Researchers have tackled this aspect in deterministic and probabilistic frameworks due to the many sources of randomness that influence the battery sizing. Probabilistic sizing is, in principle, computationally intensive, as it requires several scenarios to be run to individuate the size ranges left to the decision maker. This paper provides a methodology for sizing battery energy storage systems, considering the randomness of load profiles in distribution networks, where scenarios are considered based on clustered load profiles which make the sizing procedure less computationally intensive. Particularly, load profiles are clustered through a dedicated methodology based on k-means algorithm, aiming at reproducing groups of days that share similar features. The clustered load profiles are used as inputs of the sizing methodology. Numerical experiments based on actual data show the effectiveness of the proposal.

Due to the liberalization of the railway sector, Railway System Operators (RSOs) are involved in the renewal of railway systems in terms of both technological and managing improvements. They are evaluating how new strategies can improve energy efficiency and operating sustainability. This paper proposes the optimal management of a fleet of metro trains in terms of power key performance indicator (KPI). To control the regularity of the railway service assuming a constant Headway value, there were examined how the optimal assignment of the Layover time between the two terminal stops can be managed concerning the maximum power requirements at the Traction Power Substations (TPSs). The numerical application focused on the traction power system of Metro Line 1 of Naples (Italy). The results highlighted the evaluation of the power peaks required to the distribution grid as a function of the Layover time assignment.

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.

This paper deals with the inertia estimation of power systems in presence of significant contribution provided by renewable energy sources. The efforts to decommission conventional power plants and replace them with renewable resources could affect the robust and reliable operation of future power systems. The main issue related to the integration of renewable-based generation in the networks deals with the substitution of conventional synchronous generators connected to the grid and equipped by rotating masses, with converter-based generator which have not rotating masses, or they are decoupled by the grid due to the presence of interfacing power converters. Since the system inertia is the inherent ability of the online synchronous machines to oppose sudden changes in generation or load, the increasing share of renewable generators, such as wind and solar, the overall system inertia could decrease. This leading network operators to introduce new tools for monitoring the system inertia values, to contain them within admissible values, and to better prepare for different kinds of operational scenarios with undesired reduction of the system inertia. At this purpose, in this paper a tool for the analysis and prediction of inertia is proposed. The proposed method is based on a minimum variance harmonic finite impulse response and can consider the actual penetration of renewable energy sources. The proposed approach is tailored for online monitoring and exploits the real time measurements of relevant features of the network. The method is tested with actual data of the Italian Transmission Network and its effectiveness is studied by comparisons with some popular and well-established method of analysis and prediction.

Worldwide expectancy of larger fleets of PEVs requires an increasing number of available fast and ultra-fast charging stations. In order to reduce the impact on the distribution grid of the power demand of large fleets of PEVs, the coordination of charging in stations equipped with multiple charging slots is mandatory. The paper suggests a smart charging profile of electrical vehicles based on an optimization problem in order to minimize the charging time. Constraints in term of limits on the power supplied by DSO, the available state of charge (SOC) of the BESS and the energy requested by the PEVs are taken into account. Some simulations are presented to validate the suggested charging strategy.

This paper presents a novel approach for the optimal design of the control system for battery energy storage systems adopted in power systems in the framework of the primary frequency control. The challenge issued by the use of batteries for frequency control in modern power systems is related to the need of the repeatedly deep charge and discharge phases. The presence of intermittent loads and generators, in fact, imposes very stressful power profiles to the battery which imply high rate of degradation to its lifetime. Based on the stress model of the battery, the proposed approach aims to design a control system which allows for containing the state of charge time variation within intervals able to match the requirements of frequency profile improvements and lifetime preservation. At this aim, the typical control scheme for batteries adopted for primary frequency control is analyzed and elaborated to address stable dynamic behavior of the regulator. Moreover, starting from the observation of the statistical nature of the power request for the pure frequency regulation service, a statistical study is conducted to perform an optimal design of the regulator in terms of both lifetime duration and primary frequency regulation service.

This paper focuses on very-short-term photovoltaic power forecasting based solely on power measurement. The approach proposed in the paper is tailored for real time operation of smart grids and microgrids, that deals with energy management and control of the various resources of the grid and, particularly, on those based on renewable energy sources. These resources are characterized by variable power production, thus constituting a challenge for the effectiveness of strategies and tools of management and control. This paper refers to photovoltaic power production and presents a new real time forecasting method which improves the classical persistence model. The proposed method is based on the idea of conveniently weighting information on past data by imposing continuity of the function, of its first derivative and of the second derivative so obtaining three estimates of the function in the forecast interval which are then opportunely weighted to provide the forecast. The effectiveness of the proposed approach is shown in the numerical application based on actual measured data. The accuracy of the proposed approach is also tested through comparisons with two models based on the persistence and auto regressive moving average models.

This paper deals with the optimal operation of microgrids in presence of uncertainties. Loads and distributed energy resources such as renewable-based generation units and electrical energy storage systems are supposed to be clustered and managed by a microgrid operator. The proposed approach allows the operator to optimally schedule the microgrid’s control variables by properly managing the uncertainties affecting the input parameters. Different forecasting methods are adopted within an optimal day ahead scheduling procedure which is devoted to the total cost minimization while preserving a secure operation of the network. The proposed approach accounts for uncertainties affecting the active power of loads and renewable generators, the reactive power of loads, and the electricity prices. Comparison of different forecasting methods allows evidencing the impact of uncertainties on the operation of the microgrid.

Large-scale penetration of renewable energy sources in power systems is essentially related to the need of reducing the environmental impact caused by the fossil-fuel. As well known, the interface between the power grid and this kind of energy resource is achieved by power converters, with a consequent dynamic behavior quite different from the synchronous generators. This matter involves negative impacts on the operating conditions of power systems. In this context, it is crucial to individuate estimation techniques able to predict promptly critical conditions which could, in extreme cases, compromise the stability of whole system. In this paper the authors employ an auto-regressive model which can describe the dynamic evolution of the power system inertia. The core of the procedure relies on an inertia model conceived as the sum of a periodic component and a noise stochastic process distributed according a Logistic model. The robustness of a novel estimation procedure, able to capture the dynamic evolution of the inertia, is investigated by testing two scenarios of Italian Transmission Network. The assumptions in terms of the obtained numerical results show the validity of the estimation technique and of the probabilistic characterization of the noise.

Recent improvements in shipboard systems focus on the use of new technological systems and the adoption of innovative operating strategies for both increasing energy efficiency and reduce gas emissions. In such a context, this article studies a ship power system, where an energy storage system and a tailored energy management system are applied. It focuses on a detailed model of the on-board power system and points out a scenario of analysis larger than those usually adopted in the literature. The model is carried out with reference to DC, AC and hybrid AC/DC power grid architectures, by means of a power flow approach. The strategy of the energy management system optimally controls the on-board power system integrated with a battery storage device. The strategy aims to minimizes fuel consumption of the prime movers, while satisfying time-varying load demand under different operating scenarios. The procedure, tested on actual operative conditions of a supply vessel, shows feasibility and effectiveness of the power flow approach, giving evidence to the reduction of fuel consumption, while the correct operation of the power system is guaranteed.

Extreme Wind Speed modeling, i.e. the probabilistic characterization of extreme values of wind speed, is a key tool for properly understanding the destructive wind forces which may affect mechanical safety and reliability of wind power systems, but it is also extremely useful for the purpose of achieving accurate wind energy production estimations. Indeed, the need of more accurate wind estimations has been often highlighted in the literature, especially for Extreme Wind Speed, since classical adopted models, such as the Weibull distribution, behave poorly in the range of Extreme Wind Speed values. In the paper, a new model, generated by a proper mixture of the established Inverse Weibull distribution, is proposed and illustrated. The proposal is the “Inverse Log-logistic” distribution, whose adequacy in interpreting some sets of real Extreme Wind Speed data is shown after giving some hints to its identification. This study also develops a peculiar Bayesian statistical inference approach for the estimation of the above model from available data, using different prior distributions, i.e. the Lognormal, the Generalized Gamma and the Uniform distribution. Extensive numerical simulations confirm that the proposed estimation technique constitutes a very fast, efficient and robust method for the Extreme Wind Speed modeling.

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.

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.

In this paper, statistical analysis of lightning-induced voltages on distribution lines accounting for lossy ground is proposed. The analysis is performed by means of a Monte Carlo-based probabilistic approach. It aims at deducing parametric distributions that best fit the probability density functions of the induced voltages. A lightning model based on step-function channel-base current is employed.

This paper examines the management of ship power systems equipped by energy storage systems. Energy storage in the on-board power system can increase the efficiency of prime movers in order to reduce fuel consumption and pollutant emissions. In this paper, a management strategy for the on-board electrical network is proposed. The strategy optimally balances the charge/discharge of the storage device, the power of the generation system, the loads’ demand of hotels, auxiliaries, and electrical machineries used for propulsion and maneuvering purposes. The optimal approach aims at minimizing the fuel consumption, while technical constraints of the on-board system are considered in terms of both generation system and energy storage system. A case study of a real off-shore supply vessel is proposed. The results of the numerical application are discussed showing the effectiveness of the proposed strategy.

This paper deals with the design and control aspects of modern ship power systems within the paradigm of an all-electric ship. The widespread use of power electronic converters is central in this context due to the technological advances in automation systems and the integration of the electrical propulsion systems and other components, such as electrical energy storage systems and renewable energy sources. The issue to address in this scenario is related to the request of increased performances in dynamic operation while pursuing advantages in terms of energy savings and overall system security. In addition, the presence of large load changes requires providing robustness of the control in terms of system stability. This paper is focused on medium voltage direct current (MVDC) ship power systems and the design and control of coupled inductor DC&ndash, DC converters. The load is handled in terms of a constant power model, which generally is considered the most critical case for testing the stability of the system. The robustness of the design procedure, which is verified numerically against large and rapid load variations, allowed us to confirm the feasibility and the attractiveness of the design and the control proposal.

This study is devoted to a proper modeling and estimation method of battery capacity fading under the widely adopted exponential decay model, in view of an efficient lifetime assessment of battery. The method is based upon recursive online Bayes estimation of capacity fading. It is also shown how a degradation-based modeling of battery reliability can be adopted, leading to a Bernstein lifetime distribution. The performances of the proposed method are successfully evaluated by means of extensive Monte Carlo simulations based upon available literature and experimental data. A brief account is also given of a robustness analysis of the proposed methodology with respect to departures from the assumption of Gaussian noise.

This paper proposes a new methodological approach in the field of studies devoted to proper and accurate selection of a reliability model for battery systems. The study is performed with particular emphasis on the modeling and estimation of the Conditional Reliability Function, conceived as a key analytical tool in predicting the "Remaining useful life" of the battery, which is in turn an important information in order to identify the best maintenance strategy selection, or for inspection optimization, and also spare parts provision. Estimation of the Conditional Reliability Function is developed by means of a method based on the Inverse Gaussian Distribution and its Bayes Estimation. The performances of this estimation are developed and validated by means of extensive simulations and available experimental data. A brief account is reported of robustness analyses of the method with respect to the assumed prior Distribution.

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.

This paper proposes a frequency analysis of the High-Speed Railway System. The analysis focuses on the currents flowing into the system's conductors when a harmonic source is applied. Different power line configurations are observed with the aim of verifying the potential presence of resonance frequencies in the range Hz. These last indeed could be interference signals for system internal equipment and other external proximity victim circuits.