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The improvement of the lightning performance of overhead distribution lines, hit by direct strokes, is investigated considering the use of additional shield wires beneath the phase conductors. The frequency-domain theory of multiconductor transmission lines with periodical grounding is adopted permitting a rigorous evaluation of the phase and shield wire currents and of the overvoltages across insulators. These can be minimized by careful positioning of the underbuilt shield wires. A sensitivity analysis on the overvoltages is conducted by varying either the position of the underbuilt shield wires and the grounding impedance.

This work is devoted to the analysis of voltages produced on overhead distribution lines hit by direct lightning. We will consider arbitrary terminations at both line ends and, also, arbitrary positioning of the pole being struck. We will deal with lines protected by shield wires, placed either above or below the phase conductors. The analytical tools chosen to deal with such complex system are the transfer matrix method and the theory of multiconductor transmission lines with periodic grounding. The innovation of an articial symmetrical unit cell has been introduced to eliminate the need of two characteristic wave impedance matrices and to ease computation ow.

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 modern revolutionary changes in power delivery systems with the advent of smart and flexible grids require systems interoperability, seamless integration of technologies and functionalities. This scenario is paving the way to new prospective of energy clusters that call for new methodologies for the dynamic energy management of distributed energy resources. This paper proposes a new management scheme of energy clusters (e.g., smart building, energy community or virtual power plant) that is based on a data driven decision-maker that daily deploys the activities of the day ahead, providing an optimized scheduling which will be the base for the operations for the next day. The new aggregator relies on some innovative features. It leverages an optimization process based on the elastic net regularization that proves to be an effective support for finding the best scheduling of the distributed resources according to specific key performance indicators, that presently is the unbalance. The decision process works on predicated data obtained through a recently assessed short term forecasting based on long short-term memory neural networks properly adapted to distributed environments. The method allows to extract insight from data and run what-if scenarios to define the best scheduling of resources. The technique uses a set of feasible rules to optimally aggregate the distributed resources and demand-side management programs. We tested the proposed aggregator on a real energy cluster making use of real measured data over two years period. The results show the effectiveness of the proposed approach that is able to predict any sort of unit and manage any sort of program. The significance of this work is to approach the energy management as an optimization and decision problem in a more robust way of reasoning, by employing forecasting/optimization over all the quantities in the community, resulting in a thoroughly complex and efficient method.

In this paper we consider the problem of restoring the voltage for stand-alone inverter-based Microgrids despite the effects of the time-delays arising with the information exchange among the electrical busses. To guarantee that all Distributed Generators (DGs) reach in a finite-time and maintain the voltage set-point, as imposed by a virtual DG acting as a leader, we suggest a novel robust networked-based control protocol that is also able to counteract both the time-varying communication delays and natural fluctuations caused by the primary controllers. The finite-time stability of the whole Microgrid is analytically proven by exploiting Lyapunov-Krasovskii theory and finite-time stability mathematical tools. In so doing, delay-dependent stability conditions are derived as a set of Linear Matrix Inequalities (LMIs), whose solution allows the proper tuning of the control gains such that the control objective is achieved with required transient and steady-state performances. A thorough numerical analysis is carried out on the IEEE 14-bus test system. Simulation results corroborate the analytical derivation and reveal both the effectiveness and the robustness of the suggested controller in ensuring the voltage restoration in finite-time in spite of the effects of time-varying communication delays.

In this paper, that is an extended version of a work [1] presented at the 2019 International Symposium on Lightning Protection (XV SIPDA), a survey of analytical solutions available in the literature for the evaluation of the lightning-induced voltages in case of both step and linearly rising channel-base currents is presented. Two tools, CiLIV and LIV, based on some of the above mentioned analytical solutions are also presented. Finally, a validation of both analytical solutions and tools is presented.

Here, we propose a new deep learning scheme to solve the energy time series prediction problem. The model implementation is based on the use of Long Short-Term Memory networks and Convolutional Neural Networks. These techniques are combined in such a fashion that inter-dependencies among several different time series can be exploited and used for forecasting purposes by filtering and joining their samples. The resulting learning scheme can be summarized as a superposition of network layers, resulting in a stacked deep neural architecture. We proved the accuracy and robustness of the proposed approach by testing it on real-world energy problems.

The Multiple Microgrids (MMGs) concept has been identified as a promising solution for the management of large-scale power grids in order to maximize the use of widespread renewable energies sources. However, its deployment in realistic operation scenarios is still an open issue due to the presence of non-ideal and unreliable communication systems that allow each component within the power network to share information about its state. Indeed, due to technological constraints, multiple time-varying communication delays consistently appear during data acquisition and the transmission process and their effects must be considered in the control design phase. To this aim, this paper addresses the voltage regulation control problem for MMGs systems in the presence of time-varying communication delays. To solve this problem, we propose a novel hierarchical two-layer distributed control architecture that accounts for the presence of communication latencies in the information exchange. More specifically, the upper control layer aims at guaranteeing a proper and economical reactive power dispatch among MMGs, while the lower control layer aims at ensuring voltage regulation of all electrical buses within each MG to the desired voltage set-point. By leveraging a proper Driver Generator Nodes Selection Algorithm, we first provide the best choice of generator nodes which, considering the upper layer control goal, speeds up the voltage synchronization process of all the buses within each MG to the voltage set-point computed by the upper-control layer. Then, the lower control layer, on the basis of this desired voltage value, drives the reactive power capability of each smart device within each MG and compensates for possible voltage deviations. Simulation analysis is carried out on the realistic case study of an MMGs system consisting of two identical IEEE 14-bus test systems and the numerical results disclose the effectiveness of the proposed control strategy, as well as its robustness with respect to load fluctuations.

We study the shielding performance of shield wires in overhead power lines. We propose a novel chain matrix formulation, that employs longitudinally symmetric transmission-line cells to more efficiently, and correctly, simulate the propagation behaviour over the line accounting for the periodical grounding. The analysis is performed in the frequency domain and results in time domain are obtained through inverse Fourier transform. In the case of direct lightning, we can easily compute overvoltages across insulator strings for insulation coordination purposes.

Surge arresters may represent an efficient choice for limiting lightning surge effects, significantly reducing the outage rate of power lines. The present work firstly presents an efficient numerical approach suitable for insulation coordination studies based on an implicit Crank–Nicolson finite difference time domain method; then, the IEEE recommended surge arrester model is reviewed and implemented by means of a local implicit scheme, based on a set of non-linear equations, that are recast in a suitable form for efficient solution. The model is proven to ensure robustness and second-order accuracy. The implementation of the arrester model in the implicit Crank–Nicolson scheme represents the added value brought by the present study. Indeed, its preserved stability for larger time steps allows reducing running time by more than 60 % compared to the well-known finite difference time domain method based on the explicit leap-frog scheme. The reduced computation time allows faster repeated solutions, which need to be looked for on assessing the lightning performance (randomly changing, parameters such as peak current, rise time, tail time, location of the vertical leader channel, phase conductor voltages, footing resistance, insulator strength, etc. would need to be changed thousands of times).

In smart grids and microgrids, time series prediction is a fundamental tool for enabling intelligent energy resource management and advanced interactions between heterogeneous agents. In this work, we propose a solution to the energy forecasting problem based on two machine learning techniques: Convolutional Neural Network and Long Short-Term Memory Network. These techniques are combined with a new embedding format to appropriately feed the time series to the stacked network architecture. The resulting novel deep learning scheme is able to retrieve information from the data by inferring time dependent correlation structures. The model is validated using real-world examples, showing good performances with a 3-days forecasting horizon.

The large-scale deployment of pervasive sensors and decentralized computing in modern smart grids is expected to exponentially increase the volume of data exchanged by power system applications. In this context, the research for scalable, and flexible methodologies aimed at supporting rapid decisions in a data rich, but information limited environment represents a relevant issue to address. To this aim, this paper outlines the potential role of Knowledge Discovery from massive Datasets in smart grid computing, presenting the most recent activities developed in this field by the Task Force on “Enabling Paradigms for High-Performance Computing in Wide Area Monitoring Protective and Control Systems” of the IEEE PSOPE Technologies and Innovation Subcommittee.

The probability of flashovers due to indirect lightning on overhead lines is us ually calculated assuming a statistical distribution for the induced voltages, and a deterministic and specified value for the insulation level of the line. However, more accurate practices of insulation coordination suggest a statistical treatment for the insulation level too, in order to relate the probabilistic nature of both electrical stress (induced voltage) and the electrical strength (insulation level). In this paper, a statistical insulation coordination method is applied for the assessment of induced-voltage flashovers and, more in general, for the indirect lightning performance of the line. Such a statistical approach, which is inherently more accurate than the traditional approach, allows also to discriminate among probability of occurrence of flashovers on one, two, or on all phases.

In the present work we assess the lightning performance against direct strokes of overhead distribution lines, making use of the theory of multiconductor transmission lines with periodical grounding, recently developed by the authors. We assume that the lightning protection is provided by shield wires that are placed both above and below (underbuilt construction) the phase conductors. We adopt the transmission line model to include the propagation effects on the poles. We investigate the effects of geometrical parameters, and, by the analyses of different arrangements, we evaluate the impact on the lightning performance. The core of the work is the demonstration that addition of underbuilt ground wires can be a viable solution for improving the lightning protection of distribution lines.

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.

Modern power distribution systems require reliable, self-organizing and highly scalable voltage control systems, which should be able to promptly compensate the voltage fluctuations induced by intermittent and non-programmable generators. However, their deployment in realistic operation scenarios is still an open issue due, for example, to the presence of non-ideal and unreliable communication systems that allow each component within the power network to share information about its state. Indeed, due to technological constraints, time-delays in data acquisition and transmission are unavoidable and their effects have to be taken into account in the control design phase. To this aim, in this paper, we propose a fully distributed cooperative control protocol allowing the voltage control to be achieved despite the presence of heterogeneous time-varying latencies. The idea is to exploit the distributed intelligence along the network, so that it is possible to bring out an optimal global behavior via cooperative distributed control action that leverages both local and the outdated information shared among the devices within the power network. Detailed simulation results obtained on the realistic case study of the IEEE 30-bus test system are presented and discussed in order to prove the effectiveness of the proposed approach in the task of solving complex voltage control problems. Finally, a robustness analysis with respect to both loads variations and hard communication delays was also carried to disclose the efficiency of the approach.

A novel deep learning approach is proposed for the predictive analysis of trends in energy related time series, in particular those relevant to photovoltaic systems. Aim of the proposed approach is to grasp the trend of the time series, namely, if the series goes up, down or keep stable, instead of predicting the future numerical value. The modeling system is based on Long Short-Term Memory networks, which are a type of recurrent neural network able to extract information in samples located very far from the current one. This new approach has been tested in a real-world case study showing good robustness and accuracy.

The large-scale integration of renewable power generators in power grids may cause complex technical issues, which could hinder their hosting capacity. In this context, the mitigation of the grid voltage fluctuations represents one of the main issues to address. Although different control paradigms, based on both local and global computing, could be deployed for online voltage regulation in active power networks, the identification of the most effective approach, which is influenced by the available computing resources, and the required control performance, is still an open problem. To face this issue, in this paper, the mathematical backbone, the expected performance, and the architectural requirements of a novel decentralized control paradigm based on dynamic agents are analyzed. Detailed simulation results obtained in a realistic case study are presented and discussed to prove the effectiveness and the robustness of the proposed method.

In the modern scenario of smart-grids, the concept of virtual power plant (VPP) is undoubtedly a cornerstone for the smooth integration of renewable energy sources into existing energy systems with a high penetration level. A VPP is the aggregation of decentralized medium-scale power sources, including photovoltaic and wind power plants, combined heat and power units, as well as demand-responsive loads and storage systems, with a twofold objective. On one hand, VPP relieves the stability and dispatchability problems on the external smart grid since it can be operated on an individual basis, appearing as a single system on the whole. On the other hand, VPP improves flexibility coming from all the networked units and enable traders to enhance forecasting and trading programs of renewable energies. This paper proposes a novel distributed decentralized prediction method for the management of VPPs. The novelty of the proposed technique is to effectively combine the concepts of neural networks and machine learning with a distributed architecture that is suitable for the aggregation purposes of the VPP.

We propose a new channel-base current model, which is particularly well suited for lightning-induced voltage calculations. We will show that this new proposal meets a significant number of characteristics required to these applications, and favorably compares to existing solutions.

A novel deep learning approach in proposed in this paper for multivariate prediction of energy time series. It is developed by using Convolutional Neural Network and Long Short-Term Memory models, in such a way that several correlated time series can be joined and filtered together considering the long term dependencies on the whole information. The learning scheme can be viewed as a stacked deep neural network where one or more layers are superposed, feeding their output in the sequent layer’s input. The new approach is applied to real-world problems in energy area to prove robustness and accuracy.

The large-scale deployment of pervasive sensors and decentralized computing in modern smart grids is expected to exponentially increase the volume of data exchanged by power system applications. In this context, the research for scalable and flexible methodologies aimed at supporting rapid decisions in a data rich, but information limited environment represents a relevant issue to address. To this aim, this paper investigates the role of Knowledge Discovery from massive Datasets in smart grid computing, exploring its various application fields by considering the power system stakeholder available data and knowledge extraction needs. In particular, the aim of this paper is dual. In the first part, the authors summarize the most recent activities developed in this field by the Task Force on &ldquo, Enabling Paradigms for High-Performance Computing in Wide Area Monitoring Protective and Control Systems&rdquo, of the IEEE PSOPE Technologies and Innovation Subcommittee. Differently, in the second part, the authors propose the development of a data-driven forecasting methodology, which is modeled by considering the fundamental principles of Knowledge Discovery Process data workflow. Furthermore, the described methodology is applied to solve the load forecasting problem for a complex user case, in order to emphasize the potential role of knowledge discovery in supporting post processing analysis in data-rich environments, as feedback for the improvement of the forecasting performances.

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.

Protection against lightning-induced voltages is a particularly critical issue, especially for smart grids, due to the presence of electronic-based equipment, as well as control and monitoring devices. Analysis of the severity of the induced voltages is then imperative; on the other hand, the random nature of the lightning phenomenon cannot be disregarded. In this paper, the severity of lightning-induced voltage is analyzed by means of a probabilistic approach which, starting from closed-form solutions, uses a Monte Carlo procedure. Parametric distributions that best fit the distributions of the induced voltages are investigated as well. The results show that the lognormal and the generalized extreme value distributions are the best candidates.

We propose an improved procedure to identify the lightning return stroke current characteristics. This procedure follows the inverse approaches, presenting a new expansion basis able to widen the class of function which can be reconstructed.

An inverse remote-sensing procedure is presented for reconstructing the spatial waveform of the lightning return stroke current throughout a numerical-field synthesis procedure, based on working regularization methods. The approach uses as input data the acquisition of time-domain recordings of the electric and/or magnetic field generated by the lightning current, at various locations on the ground and converts these signals into harmonics by Fourier decomposition. This combination between the proposed solving procedures and harmonic filtering yields numerical results that are in good agreement with the testing functions.

In the companion paper (Part I), an analytical approach for calculating lightning-induced voltages for the case of lossy ground has been developed. In this paper (Part II), predictions of the new formulation are compared to those based on other approaches found in the literature.

Some inverse sensing procedures combined with numerical regularization techniques are presented for identifying and reconstructing the spatial and temporal waveform of the lightning return stroke current. The novelty of the approach relies on simulation of real time acquisition of the electric and/or magnetic field generated by the discharge channel, at various locations on the ground and at various frequencies and evaluation of these field values by specific field synthesis methods.