Low-Carbon Operation of a Multi-Energy System with Hydrogen-Based Vehicle Applications

In order to cope with the challenges of improving energy efficiency, increasing the integration of renewable energy, and achieving carbon emission reduction, multi-energy systems have received more and more attention in recent years and have been developing rapidly. Traditionally, different energy infrastructures are usually scheduled and operated independently, which leads to inefficient use of energy and waste of resources. By integrating into a multi-energy system, different energy infrastructures can be coupled and optimized into one unit. In this article, from a low-carbon point of view, the optimal scheduling of a real multi-energy system with hydrogen-based vehicle applications is proposed. The simulation results show that the proposed optimal scheduling can help quantify the daily operation cost and carbon emissions and achieve considerably operation cost saving and carbon reduction by reasonably arranging and utilizing all the devices in the system.

Low-Carbon Operation of a Multi-Energy System with Hydrogen-Based Vehicle Applications

In order to cope with the challenges of improving energy efficiency, increasing the integration of renewable energy, and achieving carbon emission reduction, multi-energy systems have received more and more attention in recent years and have been developing rapidly. Traditionally, different energy infrastructures are usually scheduled and operated independently, which leads to inefficient use of energy and waste of resources. By integrating into a multi-energy system, different energy infrastructures can be coupled and optimized into one unit. In this article, from a low-carbon point of view, the optimal scheduling of a real multi-energy system with hydrogen-based vehicle applications is proposed. The simulation results show that the proposed optimal scheduling can help quantify the daily operation cost and carbon emissions and achieve considerably operation cost saving and carbon reduction by reasonably arranging and utilizing all the devices in the system.

Chaos-guided neural key coordination for improving security of critical energy infrastructures

In this paper, chaos-guided artificial neural learning-based session key coordination for industrial internet-of-things (IIoT) to enhance the security of critical energy infrastructures (CEI) is proposed. An intruder might pose several security problems since the data are transferred across a public network. Although there have been substantial efforts to solve security problems in the IIoT, the majority of them have relied on traditional methods. A wide range of privacy issues (secrecy, authenticity, and access control) must be addressed to protect IIoT systems against attack. Owing to the unique characteristics of IIoT nodes, existing solutions do not properly address the entire security range of IIoT networks. To deal with this, a chaos-based triple layer vector-valued neural network (TLVVNN) is proposed in this paper. A chaos-based exchange of common seed value for the generation of the identical input vector at both transmitter and receiver is also proposed. This technique has several advantages, including (1) it protects IIoT devices by utilizing TLVVNN synchronization to improve CEI security. (2) Here, artificial neural coordination is utilized for the exchange of neural keys between two IIoT nodes. (3) Using this suggested methodology, chaotic synchronization can be achieved, enabling the chaos-based PRNG seed exchange. (4) Vector-valued inputs and weights are taken into consideration for TLVVNN networks. (5) The deep internal architecture is made up of three hidden layers of the neural network and a vector value as input. As a result, the attacker would have great difficulty interpreting the internal structure. Experiments to verify the performance of the proposed technique are conducted, and the findings demonstrate that the proposed technique has greater performance benefits than the existing related techniques.

Multiset-based assessment of vulnerability of energy infrastructures to destructive impacts

This paper is dedicated to the application of the multigrammatical framework to the assessment of vulnerability of energy infrastructures affected by impacts destroying (reducing capabilities of) their facilities (power plants, fuel producing plants, power transmission lines, fuel transporting pipes, as well as networking devices of both electricity and fuel subsystems of an energy infrastructures). A basic graph representation of energy infrastructures is considered, and technique of their multigrammatical representation is introduced. Criterial base for recognition of the energy infrastructures vulnerability, being a generalization of the similar criterial base developed regarding industrial infrastructures is proposed. Techniques of multigrammatical modelling reservation of energy infrastructures and their recovery after impacts is proposed. Directions of future research in this area are announced.

Energy Worlds in Experiment

Energy Worlds in Experiment is an experiment in writing about energy and an exploration of energy infrastructures as experiments. Twenty authors have written collaborative chapters that examine energy politics and practices, from electricity cables and energy monitors to swamps and estuaries. Each chapter proposes a unique format to tell energy worlds differently and to stimulate energy imaginaries: thesis, propositions, interviews, stories, card games, and a graphic novel. The book offers practitioners, students, and scholars a range of new tools to help think, engage and critique energy politics, practices and infrastructures.