ON THE INFLUENCE OF CHANNEL TORTUOSITY ON ELECTRIC FIELDS GENERATED BY LIGHTNING RETURN STROKES AT CLOSE DISTANCE

In this paper the results of the estimated electric field associated with tortuous lightning paths at close distance (50 m to 500 m) are shown. Such results are compared with experimental data available in the literature and are illustrated along with a quantitative analysis of the field waveforms and their frequency spectra. The limits of the usual straight-vertical channel assumption and the influence of tortuosity at different azimuth and distances from the lightning channel base are also highlighted

2-D convolutional deep neural network for the multivariate prediction of photovoltaic time series

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 interdependencies 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

A review of the enabling methodologies for knowledge discovery from smart grids data

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 “Enabling Paradigms for High-Performance Computing in Wide Area Monitoring Protective and Control Systems” 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

decision theory criteria for the planning of distributed energy storage systems in the presence of uncertainties

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 Cigre test network

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Decentralized Smart Grid Voltage Control by Synchronization of Linear Multiagent Systems in the Presence of Time-Varying Latencies

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

Hierarchical Two-Layer Distributed Control Architecture for Voltage Regulation in Multiple Microgrids in the Presence of Time-Varying Delays

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