The climate change crisis, exacerbated by the global dependency of fossil fuels, has brought significant challenges. In the medium to long term, extensive renewable-energy-based electrification is considered to be one of the most promising development paths to address these challenges. However, this is tangible only if the energy infrastructure can accommodate renewable energy sources and distributed energy resources, such as batteries and heat pumps, without adversely affecting power grid operations. To realize renewable-energy-based electrification goals, a new concept—the Energy Internet (EI)—has been proposed, inspired by the most recent advances in information and telecommunication network technologies. Recently, many measures have also been taken to practically implement the EI. Although these EI models share many ideas, a definitive universal definition of the EI is yet to be agreed. Additionally, some studies have proposed protocols and architectures, but a generalized technological overview is still missing. An understanding of the technologies that underpin and encompass the current and future EI is very important to push toward a standardized version of the EI that will eventually make it easier to implement it across the world. In this paper, we first examine and analyze the typical popular definitions of the EI in scientific literature. Based on definitions, assumptions, scope, and application areas, the scientific literature is then classified into four different groups representing the way in which the papers have approached the EI. Then, we synthesize these definitions and concepts, and keeping in mind the future smart grid, we propose a new universal definition of the EI. We also identify the underlying key technologies for managing, coordinating, and controlling the multiple (distributed or not) subsystems with their own particular challenges. The survey concludes by highlighting the main challenges facing a future EI-based energy system and indicating core requirements in terms of system complexity, security, standardization, energy trading and business models and social acceptance.

This article surveys emerging technologies related to pervasive edge computing (PEC) for industrial internet-of-things (IIoT) enabled by fifth-generation (5G) and beyond communication networks. PEC encompasses all devices that are capable of performing computational tasks locally, including those at the edge of the core network (edge servers co-located with 5G base stations) and in the radio access network (sensors, actuators, etc.). The main advantages of this paradigm are core network offloading (and benefits therefrom) and low latency for delay-sensitive applications (e.g., automatic control). We have reviewed the state-of-the-art in the PEC paradigm and its applications to the IIoT domain, which have been enabled by the recent developments in 5G technology. We have classified and described three important research areas related to PEC—distributed artificial intelligence methods, energy efficiency, and cyber security. We have also identified the main open challenges that must be solved to have a scalable PEC-based IIoT network that operates efficiently under different conditions. By explaining the applications, challenges, and opportunities, our paper reinforces the perspective that the PEC paradigm is an extremely suitable and important deployment model for industrial communication networks, considering the modern trend toward private industrial 5G networks with local operations and flexible management.

Industrial activities consume a large portion of the total energy demand worldwide and thus significantly contribute to greenhouse gas emissions. One of the most effective ways to reduce energy consumption in the industrial sector is to implement an energy management system. Current research into Industrial Energy Management System (IEnMS) remains insufficient, and to the best of our knowledge, a holistic framework for an IEnMS using the Internet of Things (IoT) and big data does not exist. This paper provides a comprehensive systematic literature review of the existing academic publications on IEnMS from where the main requirements and components of an IEnMS are identified. We further verify this study by conducting a detailed survey with specialized employees of ten (10) large companies to acquire expert opinion about using the modern technologies like IoT, big data, and data analytics in IEnMS. We have then proposed a theoretical framework for the IEnMS using IoT, big data and data analytics to construct an effective cyber-physical system architecture including steps from data acquisition to the end-user decision-making process. These findings demonstrate how the suggested framework provides an objective methodology for selecting the most appropriate IEnMS for various businesses based on their specific needs.

Distributed intelligence is a well-known approach for optimizing interactions among numerous smart devices that interconnect and operate together as Internet of Things (IoT) systems. A modern form of human-machine collective intelligence emerges when humans interact with IoT systems in sociotechnical environments such as smart homes. Fifth-generation (5G) communication networks are designed for high-speed reliable wireless connectivity and expected to boost IoT and (distributed) collective intelligence by revolutionizing human–device–human interactions. In this paper, we contribute a comprehensive review of state-of-the-art sociotechnical environments that exhibit collective intelligence, supported by 5G-enabled IoT. We discuss the latest developments in 5G and their implications for collective intelligence. Further, we explain the key challenges for using 5G to support collective intelligence, e.g., data processing, security, and radio resource management. Finally, we describe four practical applications of collective intelligence to sociotechnical environments—road traffic control, unmanned aerial vehicles, electrical load demand response, and augmented democracy.

Digitalization has led to radical changes in the distribution of goods across various sectors. The tendency is to move from traditional buyer-seller markets to subscriptionbased, on-demand "smart" matching platforms enabled by pervasive information and communications technologies (ICTs). The driving force behind this lies in the fact that assets, which were scarce in the past, are readily abundant, approaching a regime of zero marginal costs. This is also becoming a reality in electrified energy systems because of the substantial growth of distributed renewable energy sources, such as solar and wind; the increasing number of small-scale storage units, such as batteries and heat pumps; and the availability of flexible loads that enable demand-side management (DSM).

This paper aims to introduce a predictive weather-based control policy for the microgrid energy management to improve the resilience of the microgrid. This policy relies on the application of machine learning models for the prediction of microgrid load demand and solar production and supply interruption in the upstream distribution network. The predictions serve as an input to multiobjective chance constraint optimization that balances the microgrid resilience and economic objectives based on the probability of a supply interruption. The interruption predictions are made with a decision-tree-based model that can predict an upcoming interruption in the distribution network with 78% of the maximum accuracy. The case study microgrid consisting of several customers, solar photovoltaic generation, and battery storage is applied to cluster areas located in Finland. Overall, the developed control policy shows an improvement in the daily resilience of the microgrid in regard to an interruption in the main grid when compared with economic dispatch only.