Prerpints.org logo
Preprint
Article

Withdrawn:

A Review of IoT-Enabled Smart Energy Hub Systems: Rising, Applications, Challenges, and Future Prospects

Altmetrics

Downloads

90

Views

266

Comments

0

A peer-reviewed article of this preprint also exists.

Submitted:

12 September 2024

Posted:

14 September 2024

Withdrawn:

09 October 2024

Alerts
Abstract
The 21st century's booming population and escalating energy demands have driven significant efforts to enhance the Energy Hub (EH). The goal is to create a more intelligent and responsive system that can effectively cater to consumer needs while simultaneously improving the reliability and efficiency of contemporary energy infrastructure. The Internet of Things (IoT) has emerged as a key enabling technology for Smart Energy Hubs (SEH). While IoT offers a plethora of innovative solutions across various sectors, including critical infrastructure, it also introduces new security challenges. Since IoT devices inherently connect to the internet, SEH systems become susceptible to a wider range of cyberattacks. This vulnerability heightens as the number of integrated IoT devices increases. A single compromised device can expose the entire hub to cyber threats. Such attacks on energy sources have the potential to cripple entire cities, resulting in significant economic devastation. Therefore, robust security measures must be implemented before the widespread adoption of IoT-based devices in energy systems. This research delves into the architecture and infrastructure of IoT-enabled SEH. Following this, the focus shifts to analyzing the primary challenges and security concerns associated with their deployment. Finally, the research emphasizes cutting-edge approaches and tools that can bolster the security and resilience of IoT-enabled SEH against contemporary physical and cyber threats. Implementing secure and advanced data transmission systems based on blockchain technology holds promise in safeguarding the entire EH from cyber-physical attacks in the future.
Keywords: 
Subject: Computer Science and Mathematics  -   Artificial Intelligence and Machine Learning

1. Introduction

1.2. Emerging Energy Hubs

Energy has perpetually been and remains the utmost essential everyday requirement for individuals. The increasing reliance of humans on energy sources has paralleled the remarkable progress in technology, and these needs have become increasingly urgent. Hence, the main challenge of the twenty-first century is to provide additional energy sources that are clean, secure, renewable, and cost-effective [1]. Fossil fuels have been the primary energy source for the past century. Electricity generated from central power plants served as the main source of energy. The adverse environmental effects and resource depletion caused by fossil fuels have stimulated the advancement of novel, exceptionally efficient techniques for energy production [2]. The hierarchical structure is an essential element of conventional power systems. In addition, energy from major generating stations is transmitted over long distances to consuming sites. Furthermore, the effectiveness of these systems has been significantly hindered by exorbitant investment costs, energy wastage, and poor efficiency. Consequently, it becomes difficult to depend on these technologies for the implementation of energy systems in the future. Other fossil energy sources, such as natural gas, encounter comparable difficulties [3]. Furthermore, these systems operate and are managed autonomously [4]. In recent times, different energy systems have been able to interact and integrate due to the use of advanced technologies such as electric heat pumps, combined heat and power cogeneration [5], and electric vehicles. Hence, it is imperative to advance all energy systems, rather than solely focusing on electric power systems. In order to establish energy systems that are environmentally friendly and can effectively include various energy sources, it is essential to take into account the significance of multi-energy systems [6]. By fully harnessing the capabilities of multi-energy systems, several technical, environmental, and economic benefits can be achieved, such as reduced fuel usage, operational costs, and CO2 emissions. Integrating the management framework is an essential requirement for efficiently managing varied systems and achieving optimal performance. Utilizing energy hubs (EHs) is a practical method to efficiently manage these systems and establish a comprehensive model for sustainable energy systems [7]. Figure 1 illustrates the transition of energy systems from linear and unsustainable models to sustainable and fully integrated circular ecosystems. Various communication technologies, like the Internet of Things (IoT), fog computing, and cloud computing, can be combined with EHs to improve their performance. The IoT platform layer encompasses all the system's sensors responsible for monitoring and modifying an agent's physical or environmental condition. The IoT encompasses a multitude of challenges and a vast amount of data and information. This encompasses limiting access, resolving security issues, and storing and processing data across a wide geographic area. In order to enhance EH's ability to utilize IoT technology for the flow of information and data, it is imperative to establish a communication network that is dependable, fast, secure, and intelligent [8,9]. By incorporating these three methods into EH, it transforms into a smart EH (SEH) that provides the previously mentioned characteristics.
A key component of the SEH is a comprehensive sensor network that is enhanced by a two-way communication system. This network continuously monitors the state of the entire system. The two-way communication system facilitates the transfer of measurement data and control signals among different network elements. This two-way flow of information facilitates the administration and monitoring of both network infrastructure and user assets. Additionally, the intelligent EH system requires sufficient computational resources to efficiently analyze the collected data within designated timeframes. Due to the extensive data collection and geographically distributed nature of the sensors, control and monitoring functions within the network are decentralized. The advantages of SEH compared to traditional EH are outlined in Table 1 [7]. However, it is important to acknowledge that these advancements come at the cost of increased complexity and infrastructure requirements for the hub. Consequently, ongoing efforts are crucial to address these challenges by leveraging integration technologies and solutions [10].

1.3. IoT Devices Rising in Smart Energy Hubs

The IoT concept is a state-of-the-art solution in the realm of telecommunication. Everyday objects are becoming internet-enabled, like having tiny computers inside. These "smart things" can talk to each other and share information, often using the internet. The IoT enables two-way communication and distributed computational capabilities, making it a promising solution for addressing the inevitable challenges of migrating traditional energy networks to updated SEH systems [11]. Within an SEH environment, certain services are necessary. In order to achieve a more efficient energy system, we need to focus on four key areas: large-scale adoption of renewable energy sources, open communication channels between consumers and providers regarding pricing and usage, building data collection and analysis infrastructure, and finally, taking action based on the insights gained from the data.
In order to facilitate intelligent decision-making, the SEH generates a substantial volume of data that must be sent, analyzed, and stored [12]. Given its diverse advantages across several industries, the IoT seems to be a promising option with substantial potential for implementation in the SEH system. The IoT can enhance the accuracy and competency of the system by incorporating intelligent and proactive features. Additionally, it can facilitate a seamless transition from the old EH to a more efficient SEH system [13].
The primary considerations in a conventional EH system are the quality of energy and its reliability, both of which can be effectively managed through the utilization of the IoT, which provides enhanced control over these aspects. Smart meters with intelligent data analysis using advanced metering infrastructure (AMI) can turn a one-way energy delivery system into a two-way system where both consumers and providers benefit (EH to SEH) [14]. The IoT has the ability to greatly optimize and regulate energy use by utilizing both sensing and actuation technologies in the AMI. This comprehensive system gathers a vast amount of data and information from many components of the grid system, encompassing energy consumption, voltage levels, current levels, and phase measurements. State-of-the-art IoT technology has the capability to gather extensive data, transmit it, and analyze it intelligently, hence enabling more efficient control of energy grids [15]. The internet of things (IoT) can drastically improve how we manage different parts of SEH systems, like power plants, control centers for electricity flow, and smart meters. It can also be used to track how much pollution these systems create.
Additionally, advancements in cloud and edge computing offer a path toward decentralized monitoring and management of geographically dispersed energy resources. This not only improves efficiency but also addresses the cybersecurity vulnerabilities inherent in traditional centralized supervisory control and data acquisition (SCADA) systems [16].
This enables the hub to operate and manage more efficiently by connecting with smart appliances, homes, buildings, and even cities. This integration allows for accessing and controlling a greater number of devices over the Internet. Nevertheless, this necessitates the utilization of increasingly sophisticated computational capabilities and resource- allocation algorithms. Although the deployment of IoT-enabled SEH brings improved efficiency in monitoring and operating the energy system, it also presents a range of challenges. IoT cyber adversaries have the ability to implement SEH in many types of attacks, which may be categorized into three primary groups: operational, economic, and system security. The damages are enumerated as follows [17]:
  • Power outages that occur in specific areas and affect a significant number of people.
  • Substantial financial detriment to the utilities and electrical sectors.
  • Publicizing customers' information poses social security dangers.
  • Alteration records of consumption of energy.
  • Disrupting the operation of energy systems.
In response to the issues outlined above, various technologies have been developed, including machine learning (ML) approaches, artificial intelligence, blockchain, and multifactor authentication systems [18].

1.4. Motivational Factors and Contributions

This survey was motivated by the recent advancements in IoT-enabled SEH systems. The IoT provides the framework and standards for the intelligent system's ability to sense, activate, communicate, and process technologies. Furthermore, the rapid advancement of technology in various IoT sectors has opened up new opportunities for the seamless development of intelligent EHs. This study aims to facilitate the understanding of the structure of an IoT-enabled SEH system for researchers, industry professionals, and stakeholders. This text aims to acquaint readers with various uses of IoT technologies, vulnerabilities in terms of security, and techniques to mitigate risks in order to ensure the secure functioning of SEH systems. The study's primary contributions are as follows:
  • This research investigates the idea of a SEH made possible by the IoT and looks at recent advancements in the field. It dives deeper into how communication technologies are used in modern energy systems, focusing on the benefits, challenges, and what's possible with these technologies.
  • This study investigates the utilization of 5G-based IoT technologies in SEH. It specifically focuses on the fast data transfer speed of 5G for remote control, the robust security measures that protect customer privacy, and the high reliability that ensures the effectiveness of SEH.
  • This study addressed the previously unexplored vulnerability landscape of IoT-enabled Smart Energy Hub (SEH- IoT) by examining potential cyberattacks and proposing mitigation strategies. This study examined how hackers could target weaknesses in these systems to launch attacks, putting the security of the entire IoT energy grid at risk. Our research uncovered a range of threats, like stealing energy by manipulating smart meter data, attacking home automation systems, disrupting data analysis platforms, and manipulating energy markets. We looked into lightweight intrusion detection for SEH-IoT systems and ways to minimize device-level risks, emphasizing the importance of these often-ignored issues. Additionally, this research is the first known comprehensive investigation into cybersecurity designed specifically for SEH-IoT systems.
  • The study examined the possibilities for individuals or organizations who utilize distributed ledger systems that are built on blockchain technology. The emphasis was placed on safeguarding data privacy during peer-to-peer energy trading and information sharing. In order to assess the prospective opportunities and uses inside the IoT framework, we also investigated the new ML techniques for energy systems enabled by IoT.
  • Future research proposes methods to develop the effectiveness and dependability of IoT-enabled SEHs through strategies such as extensive data gathering, real-time system monitoring, edge computing, network security measures, and novel business models, with a focus on implementing 5G-based IoT devices and securing related equipment and software.

1.5. Paper Organization

This paper is organized to guide the reader through the key aspects of IoT-enabled SEHs. Section 2 delves into the research methodology, while Section 3 provides an explanation for the concept of EHs. Section 4 provides a concise explanation of the motivations behind implementing these IoT-powered SEHs. Following this, Section 5 offers a brief overview of the relevant IoT technologies, architecture, and protocols. Section 6 and Section 7 then delve deeper, showcasing the applications of IoT-enabled SEHs through practical examples. These sections don't shy away from the challenges, also exploring potential solutions to security concerns. Section 8 illustrates measurements and uncertainties in SEH. Real applications of SEHs are presented in Section 9. Finally, Section 10 summarizes the key findings of this survey, and Figure 2 visually depicts the overall layout of the article, highlighting the paper's main contributions.

2. Search Methodology

To achieve a comprehensive analysis of research trends in IoT-enabled Smart Energy Hubs (SEH), this article employed a rigorous search methodology. The focus was on indexed, English-language publications, encompassing both journal articles and conference proceedings, as well as books. To cast a wide net, the search utilized multiple databases including Scopus and Web of Science, accessed through platforms like IEEE Xplore, Google Scholar, and ScienceDirect. A combination of keywords was strategically chosen to capture relevant research: "smart energy hubs," "renewable energy," "IoT," "Technologies," "Cybersecurity," "attacks," "ML," and "future technologies." This comprehensive search strategy yielded a rich harvest of over 1657 documents, providing a solid foundation for exploring the current landscape of IoT-enabled SEH research. Our search process identified a pool of relevant articles. We started with 1657 documents found through IEEE Xplore, Google Scholar, and ScienceDirect. After reviewing titles, abstracts, keywords, structure, contributions, results, and conclusions, we narrowed this down to 1215 documents relevant to the main topic. We then applied stricter criteria based on publisher reputation, impact factors, and thematic coherence, resulting in a set of 690 articles. Finally, we selected the most recent and pertinent 450 articles published within the last five years.
Utilizing the Scopus database and visualized through VOSviewer [19], researchers are delving into the interconnectedness of various SEH-related terminology, aspects, and keywords in Figure 3. This analysis yields five distinct data clusters, which comprehensively illustrate the current trends in IoT-enabled SEH research.
This research explores five key areas (clusters) related to Smart Energy Homes (SEHs) that leverage the IoT. These clusters focus on:
-
Automation methods and their connection to IoT-based SEH operations (Cluster 1, Yellow).
-
The relationship between wireless sensor networks and SEHs (Cluster 2, Red).
-
Emerging trends in network architecture for IoT-enabled SEHs (Cluster 3, Blue).
-
The link between economic solutions, the energy market, and SEHs (Cluster 4, Purple).
-
The impact of IoT on improving energy efficiency within SEHs (Cluster 5, Green).
The research goes beyond traditional SEH concepts by considering the widespread adoption of IoT technologies and addressing energy demands. It also highlights the growing use of data-driven approaches to optimize SEH operations and control hubs.

3. Energy Hub Concept

Researchers in Power Systems and the ETH Zurich High Voltage Laboratory presented the concept of EH under a project, namely Vision of Future Energy Networks. The project aims to propose a visualization of energy systems in the future, over a long term of 20–30 years, by using the technique of Greenfield [7]. The main features of this project can be outlined as follows:
  • Moving towards EH to take advantage of synergies between various energy carriers.
  • Moving to non-hierarchical structures.
  • Moving directly to interconnected and integrated energy systems.
  • Joint transport of various energy carriers through longer distances in single transmitters.
  • Developing EH concept: an integrated unit converts and stores multi-energy carriers.
The EH concept was presented in [20] as integrating energy storage systems, transmitters, generation, and consumers
in various ways. This integration is done through converting devices or directly by dealing with single or multiple energy carriers. Figure 4 illustrates the matrix model of EH and the connection of different input and output energy vectors across the coupling matrix.
Each element of the matrix defines the internal characteristics of the EH, which contain converters and conversion efficiencies for the internal components of the EH. Geidl and Andersson presented a more accurate definition of EH as follows: “EH is a framework which provides input, output, energy storage systems and conversion for several energy carriers” [21]. EHs can interface between network participants (generation and consumers) and energy infrastructure or among various energy infrastructures, such as district heat, natural gas, and electricity systems. The hybrid word is sometimes used with the EH, which reflects the interaction among various energy carriers in EH [22]. Thus, the EH can be defined, in brief, as: “The place where different energy carriers are received, then converted, stored and consumed”.
Concerning these definitions, different EHs’ structures can be constructed, as shown in Figure 5. In these structures, the EHs are fed by electricity, natural gas, district heat, wind, solar radiation, water, Nitrogen, Hydrogen, and wood chips, while the hub's output gives electricity, heating, cooling, and natural gas. Generally, the converters in the hub include one or more of the following converters: a furnace, solar thermal, heat pump, combined heat and power unit, a transformer, power to the gas unit, electric chiller, photovoltaic panel, fuel cell, a heat exchanger, the electrical battery, natural gas storage, cooling storage, heat storage, and absorption chiller. The hub controls the inputs to the EH to supply the requirements of various loads at the output.
Increasing efficiency and reducing costs are the basic functions of these structures. The structures have to provide high flexibility in meeting demand in different ways, which increases their reliability. It is also possible to carry out various maintenance operations in these structures without affecting the supply of system demand. On the other hand, increasing the degree of flexibility in feeding the loads provides a higher possibility of optimization. The size of EH is flexible and can be applied to any system. Using EHs, a massive number of different energy carriers can be combined, which provides high system flexibility.

4. The Motivation Behind Implementation of IoT-Enabled Smart Energy Hubs

Figure 6 illustrates the main characteristics of IoT technology, highlighting its capacity to effectively address the challenges associated with transforming conventional EHs into upgraded SEHs. The utilization of IoT technology is increasingly gaining Favor for present SEH applications in residential and commercial edifices. Implementing sensors and smart metering in a SEH will enhance operational efficiency across all stages of power generation, transmission, and distribution, effectively addressing the majority of issues faced by the industry [23]. The IoT, supported by the analysis of large amounts of data, has the potential to assist in making important decisions regarding energy sources and meeting the demands of end-users [7]. Similarly, the ability to analyze insights in real-time can impact policymakers and energy- generating service providers in their decision-making process to promptly respond to market variations. This necessitates the establishment of a mechanism to adjust output levels in order to enhance the efficiency of energy. Moreover, these technologies provide the efficient examination of the obtained data for the goal of estimating future conditions. Moreover, users may effectively track current energy prices and effectively control their energy consumption using mobile devices that are integrated with IoT technology [24].
The following are a number of capabilities that can be achieved by IoT-enabled SEH [24]:
  • The self-healing capacity allows hub operators to intelligently identify the precise location of problems, evaluate their effects on the entire system, and rapidly respond.
  • Implementation of renewable energy resources on a large scale [25].
  • Facilitating real-time information transmission between hub providers and consumers through an interactive platform. Furthermore, users would own authority over their energy consumption and the choice of tariff, which would be determined by the time-of-use (ToU).
  • IoT services encompass the management and operation of real-time charging systems while also facilitating the increased adoption of electrification.
  • Mitigating and addressing cyber and physical threats through continuous monitoring of the hub components' behavior in real-time.

5. Describing IoT Technologies: Architecture and Protocols

5.1. Eliminating the ambiguity —IOE and IoT, and IoE

To ensure clarity, this study first defines three key terms: Internet-of-Everything (IOE), IoT, and Internet-of-Energy (IoE). While the all-encompassing IoE (illustrated in Figure 7) includes the IoT, these terms are often used interchangeably despite application-specific nuances across different fields. The IoE utilizes a five-layer architecture for its operation, including the infrastructure layer, energy internet networking, energy router, smart energy management system, and smart terminals [26]. However, this research focuses specifically on the IoT, which plays a critical role in enabling efficient energy system management.

5.2. IoT Technologies Architecture

5.2.1. IoT Layered Slicing

IoT-based systems designing depends entirely on the functioning of the connected components using various technologies in different places. The architecture is commonly identified through a systematic arrangement of layers, with each layer having a specific purpose to fulfil [27]. Figure 8 illustrates a design consisting of four layers that can be used to integrate the IoT with the SEH. This integration is particularly significant in terms of the use of IoT and adherence to energy system rules [28]. The design of the IoT-enabled SEH consists of four layers, as outlined in [29]:
  • The physical layer of the IoT-enabled SEH encompasses the hub's physical infrastructure and components that carry out its operations. This layer is responsible for executing all distributed and decision-making instructions in order to achieve the intended functionality of the system. Furthermore, the exchange of energy in both directions between the processes of generation, transmission, distribution, and clients takes place within this layer.
  • The communication network layer is a crucial component of the IoT-enabled SEH architecture. It acts as a bridge connecting the lower physical layer with the top cyber layer. This encompasses the whole operations of the information network, including the communication between energy facilities and diverse components, as well as the transmission of control instructions from higher layers and the collection of data from lower layers.
  • The system's central intelligence, also called the cyber layer, acts like its mastermind. This cloud-based processing unit, working alongside intelligent devices throughout the system, boosts both computing power and control. As the control center, the cyber layer allows humans to interact with the system through a user-friendly interface. It acts like a conductor, coordinating and managing all the lower levels by issuing commands and essentially running the entire show.
  • The application layer, often referred to as the management and control layer, is the topmost level of decision- making in a system. It includes service providers, marketplaces, and operations. To ensure real-world energy balance, decision-makers weigh economic, social, and environmental factors. This evaluation includes analyzing market regulation, pricing, and incentive measures. This layer is characterized by the efficient execution of processes, which relies on the exchange of information and value between marketplaces and service providers.

5.2.2. IoTs, from the Perspective of Information and Communications Technology

The four factors that facilitate the use of information and communications technologies in the architecture of the SEH based on the IoT are edge computing, cloud computing, physical entities, and communication networks [28]. The definition of each of these components is specifically provided in [30] as follows:
  • Cloud computing: Cloud computing efficiently manages the networking, storage, and processing requirements of huge data and provides a wide range of application services. Cloud computing utilizes virtualization technology to integrate hardware and software resources from multiple locations, creating a virtual platform with robust storage and processing capabilities. Cloud computing is crucial for facilitating convenient and immediate network access to a dispersed set of customizable computer resources. These resources can be easily allocated and released by service providers with minimal effort. The word "cloud" is commonly used to describe distributed data centers that are geographically dispersed and accessible to many customers via the Internet. Cloud computing enables the storing of vast amounts of data and provides highly reliable, scalable, and self-governing processing capabilities. Cloud services are utilized to consolidate data and information from diverse components, including sensors, appliances, and other gadgets. In addition, they engage in the processing and analysis of the gathered data, subsequently delivering the outcomes to consumers and service providers to get further insights. Figure 9 illustrates various aspects of cloud computing [31].
  • Communication network: Communication networks are comprised of data transmission links that connect user terminals, edge devices, and cloud computing resources in order to establish the all-encompassing information network of the SEH, spanning both the physical and cyber layers. Due to the distinct communication, processing, and storage needs of each energy service, setting up dedicated physical facilities for various applications in the SEH design that is enabled by the IoT can be expensive and may hinder the connection and interoperability of the hub [32]. Hence, the meticulous choice of communication technology is a crucial element of IoT-enabled SEH. Table 2 and Table 3 categorize and contrast the commonly employed wired and wireless communication network technologies in SEH [4,31].
  • Edge Computing: Involves deploying intelligent agents near IoT devices to provide computation, storage, and application services in close proximity to data generation points. This approach reduces latency, minimizes communication overhead, and eases traffic on central networks, enhancing responsiveness for various electricity applications and services. To bridge gaps in computing capabilities between IoT devices and the cloud, fog servers are introduced as an intermediary layer. However, to further optimize IoT applications, multi-access edge computing standardizes the relocation of cloud resources to the network edge. The categorizations of edge computing are illustrated in Figure 10 [32]. Utilizing edge computing in SEH applications, such as peak-load shifting and real-time load-demand balancing, offers ideal solutions for power generation scheduling [33].
  • Physical entities in the SHE: Refer to energy components integral to the power system, enabling distributed sensing and action. In an IoT-enabled SEH, these entities can leverage AI techniques to learn from their environment, adapt to new inputs, and execute tasks akin to human capabilities. Furthermore, neighboring entities have the ability to establish direct communication among themselves utilizing device-to-device communication, without the need for a third party, in order to share information directly [34].

5.2.3. Operating Systems for IoT-based Devices

In the IoT, tiny computers (microcontroller units) control various devices (end devices) that talk to each other using different languages (communication protocols). The terminal devices in an IoT framework come in several forms, including sensors, actuators, and switches, each with a limited set of functions. End-devices are often small in size and have limited resources in terms of memory, processing power, and energy. They are capable of communication using low-power short-range protocols [35]. The microcontroller unit’s firmware is crucial for the functioning of IoT operations. Now, it is feasible to install firmware that can enhance the device's functionality and receive automatic security upgrades over-the-air. This firmware has the capability to serve as a complete operating system, hence improving the device's functionality and security. Due to the limited resources of these end devices, data must be collected and transmitted in real-time without any buffering. The term used to describe these operating systems is real-time operating systems. A real- time operating system enhances the productivity of a programmer or system integrator by granting access to a wide range of low-level operations [36].
Gateway devices serve as an intermediary between different IoT devices, enabling communication protocols and possessing enhanced capabilities for data collecting and analysis. Cloud services are often integrated into designs, and in such cases, the devices located at the intersection of the external Internet and the internal local Intranet are referred to as edge gateways. Gateway devices necessitate an operating system capable of managing a wide range of communications. Additionally, they must possess robust security measures and be impervious to external cyber threats. Gateway devices, in contrast to end devices, usually include a user interface that allows for the control of different network characteristics or the visualization of data [36].

5.2.4. Standards and Protocols for IoT Technologies

The wide variety of sensors and devices necessitates numerous international standards (ISO, IEC, IEEE) to ensure compatibility at the physical data gathering layer. The ISO standards are utilized for various Radio Frequency Identification (RFID) applications as follows [37]:
-
ISO 11784: This standard defines how data should be structured to ensure it's organized and understandable by different systems.
-
ISO 15459: This standard establishes a system for identifying and tracking products during transportation.
-
ISO 18000: This set of standards covers various technologies and protocols used in tracking and managing the movement of goods.
-
ISO 18047: This standard outlines methods for testing and evaluating the performance of equipment.
-
ISO/IEC 18092: This standard defines the technical specifications for near-field communication (NFC), a short- range wireless technology used for data exchange.
-
ISO/IEC 20248: This standard provides a framework for fog and edge computing, which are decentralized computing architectures that process data closer to where it's generated.
-
ISO 29182: This standard defines a reference architecture for sensor networks, which are networks of interconnected sensors that collect and transmit data.
-
ISO/IEC 30118: This standard specifies the protocols and architecture for Universal Plug and Play (UPnP), a technology that allows devices to automatically discover and configure themselves on a network.
Furthermore, wireless communication technologies make use of the following IEEE standards [46]:
-
IEEE 802.15: This refers to a family of IEEE standards for short-range wireless communication.
-
IEEE 802.15.4: This is a specific standard within the 802.15 family that defines a low-power, low-bitrate wireless communication protocol for battery-operated devices. It's commonly used in sensor networks, home automation, and industrial automation.
-
IEEE 802.15.1: This standard defines the protocol for Bluetooth, a wireless technology for connecting devices over short distances.
-
IEEE 802.11: This is a family of IEEE standards for wireless local area networks, commonly known as Wi-Fi. Different versions of the standard offer varying capabilities in terms of speed, range, and security:
-
IEEE 802.11a, b, g: These are earlier versions of Wi-Fi with varying speeds and ranges.
-
IEEE 802.11n (Wi-Fi 4): This standard introduced significant speed improvements over previous versions.
-
IEEE 802.11ac (Wi-Fi 5): This standard offered further speed enhancements and introduced features like better multi-device handling.
-
IEEE 802.11ax (Wi-Fi 6): This is the latest Wi-Fi standard, known for even faster speeds, improved efficiency, and better performance in crowded environments.
There are additional standards within the 802.11 families (h, i, ad, af, ah, ai, aj, aq, ay) that focus on specific aspects like security, mesh networking, or millimeter wave frequencies.

6. Applications of IoT Technologies in Smart Energy Hubs

IoT technologies offer significant potential across multiple energy sectors, including efficient power generation management, SCADA-linked transmission networks, AMI integration in distribution systems, emissions monitoring, and deployment of intelligent home and building systems. Fog computing, as an advanced IoT solution, offers numerous opportunities for enhancing and overseeing the SCADA-connected transmission network. In recent years, IoT technology has enabled the complete automation of most smart home products. This section explores several options for SEH applications that have been enabled through the use of IoT technology.

6.1. Implementing SCADA for Fog-Based SEH Systems

Absolutely critical for managing electrical grids, SCADA systems act as the central nervous system, overseeing and controlling the generation, transmission, and distribution of electricity. SCADA supervises automated procedures to control and regulate various system parameters, ensuring smooth operation. The increasing availability of fog computing, a type of IoT solution, has further enhanced SCADA system efficiency [38]. For a detailed illustration of a fog-based SCADA system design for the energy grid, refer to Table 4 [39].

6.2. AMI-Connected Distribution Networks.

AMI allows smart meters at customers' homes to automatically send and receive information with the utility company on a regular basis. This system gives utility companies a more accurate and up-to-date picture of how their customers are using electricity. It is expected that, in the next 5 years, customers will have the ability to make energy-efficient choices using real-time pricing offered by the AMI system [40]. The IoT-based AMI has significant potential to optimize and control consumers' energy use through efficient smart meter connections. AMI can interface with a range of devices including lighting fixtures, ventilation systems, dishwashers, switches, electrical outlets, and water heaters to gather and transmit up-to-the-minute information to utility companies for the purpose of enhancing energy management efficiency [41]. Figure 11 illustrates the various integration layers of IoT devices and their corresponding protocols in distribution networks, as described in [26].

6.3. IoT for Smart Meters

IoT technologies facilitate the intelligent management of homes, communities, and hubs through the use of smart meters. Smart meters act like data collectors for energy use, sending information to utility companies in real-time. This allows for better control of EHs. These meters can not only track things like voltage and current but also temperature and moisture. They can even be used to adjust these settings remotely. They can also track energy usage [42]. Table 5 displays the benefits and drawbacks of utilizing IoT-enabled smart meters [43].

6.4. Utilization of 5G in IoT-Based Demand Response Programs (DRs)

DRPs are all about influencing how customers use electricity (think changing consumption patterns). This can be triggered by various factors: fluctuating prices during specific times, receiving incentive payments to cut back on usage during peak hours, or even unforeseen events that threaten grid reliability [46]. Looking ahead, 5G-enabled IoT devices are expected to revolutionize DR management in future energy networks. Since most new IoT devices are cloud-based, software applications running on these platforms can seamlessly integrate and exchange data [47]. This, combined with the Internet of Everything (IoE) framework, empowers both prosumers (those who consume and produce energy) and utilities to autonomously balance supply and demand. The secret sauce lies in advanced forecasting algorithms that leverage weather forecasts, traffic predictions, and other intelligent features built into IoT-based energy systems [48]. To delve deeper into how 5G networks can enhance DRPs in SEH (Sustainable Energy Systems), a comprehensive analysis is presented in Table 6 [49].

6.5. Virtual Power Plant in SEH

Virtual Power Plants offer a promising avenue for SEHs to aggregate distributed energy resources, such as rooftop solar panels and battery storage systems, into a centralized virtual power plant. This allows SEHs to participate in energy markets and provide grid services, enhancing their economic viability and environmental impact. Ref. [50] proposes a multi-objective optimization model for virtual energy hub plants (VEHPs) integrated with data centers and plug-in electric vehicles. The model aims to optimize the operation of VEHPs while considering various objectives, including cost minimization, energy efficiency, grid stability, and the integration of renewable energy sources. A multi-objective optimization model for a VEHP, incorporating information gap theory to address uncertainties in DR programs and other external factors was proposed in [51]. The model aims to optimize the operation of the VEHP while considering multiple objectives, such as cost minimization, energy efficiency, grid stability, and the integration of renewable energy sources. An optimization model for the self-scheduling of a VEHP participating in energy and reserve markets was presented in [52]. The model aims to maximize the VEHP's profitability while considering various constraints and uncertainties.

6.6. Dynamic Pricing in SEH

Dynamic pricing is a pricing strategy that allows energy prices to fluctuate based on supply and demand conditions. In the context of SEHs, dynamic pricing can be a powerful tool for optimizing energy consumption, improving grid stability, and integrating renewable energy sources. Ref. [53], proposed energy management strategies based on dynamic energy pricing for community-integrated energy systems, considering the interactions between suppliers and users. The proposed strategies aim to optimize energy consumption, reduce costs, and enhance grid stability while ensuring fair and equitable outcomes for all stakeholders. Authors in [54] proposed a bi-level optimization model for the dynamic pricing and energy management of hydrogen-based integrated energy service providers. The model aims to optimize energy allocation, reduce costs, and enhance grid stability while considering the interactions between the integrated energy service providers and their customers. Ref. [55] explored the strategies for the efficient operation of SEH in electricity markets characterized by ToU and dynamic pricing mechanisms. The objective is to optimize energy consumption, reduce costs, and enhance grid stability while leveraging the benefits of these pricing models.

6.5. Machine Learning in SEH

ML is a powerful tool for optimizing the operation of SEHs. By leveraging the vast amounts of data generated by IoT devices and sensors, ML algorithms can extract valuable insights and make intelligent decisions. Ref. [56] proposed a reinforcement learning approach for the optimal energy management of SEH. The proposed model aims to maximize energy efficiency, reduce costs, and enhance grid stability while considering the complex dynamics and uncertainties inherent in energy systems. Authors in [57] proposed a digital twin-based approach for the management and real-time monitoring of interconnected SEH. The proposed approach leverages machine learning techniques to optimize energy allocation, enhance grid stability, and improve operational efficiency.

7. Vulnerabilities and Challenges in Cyber-Physical Security of SHE

7.1. General Definitions, Framework, and Guidelines.

The energy systems have enhanced their intelligence and interactivity through the extensive adoption of IoT-based technologies, resulting in improved consistency, efficiency, and adaptability of the system. Conversely, the prevalence of cybersecurity vulnerabilities is growing. This section will address the security concerns associated with IoT-connected smart energy systems and propose solutions to mitigate these vulnerabilities. Figure 12 illustrates the whole framework of cyber-physical security in SEHs [58]. There are five major factors that contribute to the susceptibility of SEHs to cyberattacks [59]:
  • The proliferation of intelligent electronic devices leads to a corresponding increase in the number of attack sites within the network. Even if the security of a single point is compromised, the entire network system is impacted.
  • Security experts advise against the unregulated installation of third-party components on a network, highlighting the increased vulnerability to hacking such additions can introduce. These components, lacking the same rigorous security protocols as core network devices, might harbor hidden malware like Trojans. Once installed, these Trojans could then spread laterally, infecting other devices on the network and compromising its overall security. Inadequate personal training: Adequate training is necessary to effectively utilize any technology. Insufficient staff training increases their susceptibility to phishing fraud.
  • Inadequate Internet protocols: When it comes to transferring data, not all protocols provide sufficient security. Multiple protocols utilize unencrypted data transfer. Consequently, they are vulnerable to man-in-the-middle attacks that retrieve data.
  • Maintenance: The main goal of maintenance is to guarantee the uninterrupted functionality of equipment, but it can also create vulnerabilities for cyberattacks, often requiring operators to temporarily disable security systems for testing purposes.
The aforementioned five factors may undermine one of the five primary objectives of the cybersecurity framework in SEHs [60,61].
  • Authentication: This makes sure you're dealing with a real SEH device, like how an energy provider verifies a smart meter is genuine before charging the user.
  • Authorization: Even if a device is real (authenticated), it still needs permission to do specific things. Imagine an agent needing approval to manually configure a smart meter.
  • Availability: This ensures users can access data and resources whenever needed. It's like guaranteeing a store's system is always up and running for customers to check their accounts.
  • Confidentiality: Only authorized people should see certain information. Think of smart meter data - only the hub operator or energy provider, not the customer next door, should be able to see your energy use.
  • Integrity: This ensures data hasn't been tampered with. Smart meters, for example, need to make sure software updates are genuine and haven't been modified.
The National Institute of Standards and Technology (NIST) has developed a framework to enhance the cybersecurity of SEH. This framework proposes 14 standards that SEH should meet in order to protect themselves from various cyber- physical threats.
  • Training to increase staff awareness.
  • Managing configurations and access.
  • Thirdly, safeguards for the environment and physical safety.
  • Ongoing audit and responsibility.
  • Evaluation and approval of security measures.
  • Ensuring the uninterrupted functioning of operations at both the individual and systemic levels.
  • Planning for the development and maintenance of a project.
  • Effective implementation of identification and authentication methods.
  • Processes for managing documents and information.
  • A plan for responding to incidents.
  • The control and safeguarding of media.
  • Program for managing security of persons and premises.
  • Evaluation and control of potential hazards and uncertainties.
  • This refers to the purchase of services and protection of communication and information integrity in a SEH information system.

7.2. Main Cyberattack Strategies in IoT-Enabled SEH

Cyber attackers follow a well-worn path to infiltrate devices, employing four main techniques: scanning, surveillance, maintenance, and manipulation. First, in the reconnaissance phase, they gather intel on their target, collecting data to understand the system's vulnerabilities. This might involve scanning exposed ports to identify running services and gleaning details about the operating system and device manufacturer. Next, they exploit these vulnerabilities, aiming for complete system control. If successful, they'll gain administrator access, but their work isn't done. To maintain this access long-term, they'll install a hidden program that grants them easy re-entry. The importance of strong security in SEH becomes clear when you realize attackers follow a similar playbook. At each layer of the SEH system, they'll deploy various tactics to breach defenses [62]. Table 7 offers a detailed breakdown of how different attack types can undermine system security [31], while Figure 13 provides a visual representation of attacker methods for compromising systems [4,31].

7.2.1. Reconnaissance Definition and Strategies

In the initial phase of a cyberattack, known as reconnaissance, attackers gather intel through tactics like traffic analysis and social engineering. Social engineering, a non-technical approach, prioritizes human manipulation. Attackers leverage persuasion and communication to build trust with users, ultimately aiming to steal sensitive information like server access PINs or passwords [68]. Phishing attempts are a common social engineering tactic. Traffic analysis, on the other hand, is a technical approach that involves monitoring and analyzing network data flow. This allows attackers to identify and map machines and devices connected to the network, along with their IP addresses. Both social engineering and traffic analysis pose significant risks to information security [69].

7.2.2. Scanning Strategies

Following the reconnaissance phase, attackers move to scanning, a critical stage for identifying all accessible computers and devices on a network. This scan considers IP addresses, open ports, running services, and potential security weaknesses. Imagine an attacker with a list of IP addresses gleaned from reconnaissance. They'll methodically scan these hosts, first to see if they're even reachable and then to probe each available port to determine what services or devices are running behind them. Finally, they'll delve deeper with a service scan to pinpoint the exact nature of the service or device associated with each open port [70]. This information is gold for attackers, allowing them to identify vulnerabilities they can exploit later. Industrial protocols like Modbus and DNP3 are particularly susceptible to scan attacks due to their inherent vulnerabilities. TCP/Modbus was created as a more secure alternative to the traditional scanning Modbus network method. A common attack technique involves sending a seemingly harmless message to all connected devices on the network to capture their data. The Mods scan is a notorious scanner specifically designed for the SCADA Modbus network. It can not only detect and establish TCP/Modbus connections but also pinpoint the IP addresses and unique identifiers (slave IDs) of the systems on the network [71].

7.2.3. Exploitation Strategies

The third phase, exploitation, marks a shift to aggressive tactics. Here, attackers ruthlessly target vulnerabilities in the IoT-enabled smart energy system to gain control of its components [72]. This can involve malicious software like viruses, worms, and Trojan horses infecting the human-machine interface, potentially leading to a range of security breaches. These breaches could encompass privacy violations, data jamming that disrupts communication, compromised data integrity, denial-of-service (DoS) attacks that overwhelm systems, man-in-the-middle (MitM) attacks where attackers eavesdrop and potentially manipulate data transmissions, and even replay attacks where attackers intercept and resend legitimate messages to deceive the system [73]. Let's break down the malware used in these attacks: viruses can infiltrate and corrupt devices within the smart energy system, replicating themselves and spreading the infection. Worms are similar to viruses but autonomous, capable of self-replication and infecting multiple devices across the network. Trojan horses, unlike the mythical gift, masquerade as legitimate programs, tricking users into installing them, only to unleash their harmful effects once inside the system. These malicious programs pose a significant threat to the security of smart energy systems [74].

7.2.4. Maintaining Access

The final phase is maintenance, where the attacker strives to maintain persistent control over the compromised system. This often involves deploying tools like backdoors, viruses, and Trojan horses. Backdoors, a form of hidden software, are installed discreetly to provide the attacker with easy, ongoing access. Imagine an attacker successfully planting a backdoor in the SCADA server, the control center for the energy grid. With this backdoor, they could launch a series of attacks, potentially causing significant disruptions to the entire power system. This underscores the criticality of robust security measures on the IT network, particularly those focused on confidentiality (protecting sensitive data), integrity (ensuring data accuracy and preventing unauthorized modifications), and availability (guaranteeing authorized access to systems and data). These three principles form the foundation of a secure IT network, and they're especially important in safeguarding critical infrastructure like the smart energy grid.

7.3. Negative Consequences of Cyberattacks on SEH

In the following, we will examine many instances that illustrate the adverse effects of cyberattacks on the secure functioning of IoT-enabled SEH, specifically in terms of their economic and stability implications.

7.3.1. Electricity Market Losses

Smart energy systems, while offering efficiency benefits, are vulnerable to cyberattacks that can have significant economic and physical consequences. This study emphasizes the need to analyze not just the technical aspects of cyberattacks on SEHs, but also their economic impact [75]. These economic challenges are particularly concerning for renewable energy sources, which are increasingly integrated into SEHs. Electricity markets rely on both real-time and day-ahead trading [76]. Day-ahead markets aim to find the most cost-effective solutions for optimization and load prediction [77]. However, cyberattacks using FDI techniques can disrupt this process, leading to inaccurate pricing in the market. Real-time markets, on the other hand, ensure supply meets demand by balancing electricity generation with real- time consumption. Here too, FDI attacks can significantly impact accurate power flow measurement across transmission lines, hindering the assessment of congestion patterns and real-time pricing calculations. In essence, these attacks can manipulate the entire economic engine of the electricity market [78].

7.3.2. Power System Stability

The prevalence of FDI attacks poses a significant threat to the technological and physical well-being of IoT-enabled SEHs. These attacks target both steady-state stability and transient behavior in SEHs. Specifically, FDI attacks can wreak havoc on an SEH's ability to control steady-state voltage, manage demand current, voltage, and power, and optimize energy use [79]. Furthermore, they can disrupt the overall smooth operation of the system, jeopardizing not only dynamic stability but also transient stability, which is critical for handling sudden changes [80]. FDI attacks can even infiltrate the SEH's frequency control system, potentially impacting rotor angle stability. These multifaceted attacks underscore the importance of robust cybersecurity measures to safeguard SEHs from both technical and physical harm [81].

7.3.3. Energy Theft

SEHs leverage the power of IoT-assisted AMI to efficiently manage the entire system. This digital technology replaces the old method of manually reading analog meters. The vast amount of energy data and information collected by AMI is transmitted wirelessly via IoT, enabling further analysis and significantly reducing manual labor. However, a significant challenge has emerged within the energy sector: energy theft [82]. This theft, often perpetrated by manipulating meters to underreport actual consumption, results in substantial financial losses for both energy providers and honest consumers. Common techniques involve bypassing the meter entirely to avoid registering energy use for billing purposes. These thefts highlight the need for robust security measures to protect the integrity of data collected by AMI systems [83].

7.3.4. Disruption of Service in Critical and Non-Critical Facilities

Cyberattacks can be carried out against automation equipment in both critical and non-critical facilities with the intention of achieving the objectives mentioned below [84]:
  • To obtain initial access, such as by exploiting vulnerabilities in smart lights, in order to acquire Wi-Fi authentication and ultimately gain control over Wi-Fi network devices.
  • To induce an indirect disruption of service, such as remotely controlling the building's air conditioning system using a thermostat.
  • The purpose is to acquire and distribute information. Utilize a software program that exploits vulnerabilities in intelligent devices, such as smart TVs, to simulate their powered-off state and subsequently utilize their microphones to surreptitiously record and disclose nearby conversations.
  • In the case of system misuse, such as deliberately creating rapid and repetitive flashes of light that could potentially induce epileptic seizures in susceptible individuals.
  • To launch a more aggressive assault on vital infrastructure such as hospitals using a variety of specifically aimed intelligent gadgets. The objective is to disable SEH automation systems by simultaneously targeting a significant quantity of IoT-enabled SEH automation devices within a brief timeframe.

7.3.5. Disruption of Transactive Energy Systems

The transactive energy system utilizes an integrated approach that combines economic and operational processes to actively manage the balance between energy demand and supply in the hub system. This results in enhanced efficiency and reliability of the SEH. The transactive energy control mechanism for decision-making and DRPs significantly depends on the cyber system of distributed edge computing and IoT-enabled technologies. This system requires a significant volume of data to be transferred across multiple market processes. Cyberattacks can be executed using the following method to intentionally interrupt the secure functioning of transactive energy systems [85]:
  • The introduction of malware into the system can lead to a widespread power outage or the unauthorized acquisition of data.
  • Cybercriminals have the ability to manipulate or harm smart meters for various intentions.
  • To disrupt the transactive system by altering the control signals of the relay and circuit breaker.

7.3.6. Environmental Security

Keeping the environment safe is essential for reliable SEH systems. This means protecting critical equipment from natural disasters and pollution (floods, earthquakes, landslides, falling trees, fires) caused by humans. The key is to be prepared by using data to warn people of dangers and have backup power sources ready. Even though this is considered a non-technical part of SEH, it actually has both technical and non-technical aspects. For example, the system needs to be able to recover from problems (be resilient). This could involve automatically switching to backup power when there's an outage. Many things can make a system less resilient, like natural disasters, extreme weather, high energy needs, running out of fossil fuels, economic problems, or even attacks. However, technology plays a vital role in mitigating these risks. geographic information systems leverage real-time data from IoT devices like smart meters to analyze data and predict natural disasters. These timely and accurate environmental threat alerts empower proactive response measures, safeguarding SEHs and their inhabitants.

7.4. Detection and Mitigation of IoT-Enabled Cyberattacks

SEHs involve a complex web of stakeholders, including residents (both consumers and those who generate their own power), utility companies, power grid operators, and third-party service providers. This complexity creates challenges in managing SEH data, particularly from smart meters. Several frameworks exist to address these challenges, outlining best practices for integrating security and privacy measures across all parties involved. These frameworks typically categorize security into three key areas: communication security, system control security, and secure computing. Communication security itself encompasses cryptography, secure routing protocols, and network privacy measures [86]. A critical objective is to implement end-to-end encryption and multi-hop routing to guarantee data security in transit. Research highlights the core functionalities of smart meters: monitoring energy consumption, voltage, and frequency [87]. These meters securely transmit data to a central hub, and even allow operators to remotely control load switches to prevent outages during emergencies.
Studies have explored various solutions for cybersecurity challenges in SEHs, with recent research suggesting that artificial intelligence approaches hold promise in managing the growing complexity of SEH due to the proliferation of smart devices [88]. However, human error remains a vulnerability, as social engineering attacks can exploit user trust [89]. Recognizing this, this study categorizes the most effective contemporary techniques for protecting IoT-enabled SEHs into two main groups: non-human-centric (focusing on technical solutions) and human-centric (focusing on user education and awareness).

7.4.1. Non-Human-Centric Methods

The non-anthropocentric approaches can be classified into three categories: (1) methods based on ML, (2) methods based on cloud computing, and (3) ways based on blockchain technology. Next, we will provide a concise overview of each of the aforementioned techniques.
A. 
Machine-Learning-Based Methods
SEHs rely on a network of strategically placed sensors that continuously monitor connected devices, generating a vast amount of data (log files and time-series data). This sensor data is typically stored on a cloud server but requires pre- processing before transmission. While local servers offer an alternative for data storage, prioritizing data security through local storage comes at the expense of advanced functionalities like pattern recognition or predictions from complex optimization algorithms [90]. ML has emerged as a powerful tool for cybersecurity in recent years. Unlike rule-based methods, ML utilizes historical data to identify intrusions. For instance, the research described in [91] employed a hybrid JRipper and Adaboost model to predict power system disturbances. This model successfully classified events into three categories: cyberattacks, natural disturbances, and normal system operation. FDI attacks are a major threat to smart energy systems, as they can manipulate smart meter data, leading to financial losses for both utilities and consumers. The study in [92] evaluated the effectiveness of an ensemble-based learning model on an IEEE 14-bus test system, comparing its performance to algorithms like linear regression, naive Bayes, decision trees, and support vector machines. The unsupervised ensemble-based learning model outperformed all other algorithms, achieving an impressive 73% accuracy in attack detection. These findings highlight the potential of ML for safeguarding SEHs from cyberattacks while managing the trade-offs between data security and advanced analytical capabilities.
Building on prior research, several studies have explored advanced detection methods for cyberattacks in SEHs. One study [93] proposed a resilient detection mechanism using a Kalman filter and an exponential weighting function to effectively counter FDI attacks. This method was successful in identifying attacks even under varying noise levels and attack intensities. Another study [93] investigated a deep learning approach utilizing a conditional deep belief network model for real-time detection of FDI attack behaviors. This method not only achieved high accuracy but also identified patterns consistent with energy theft during simulations on large test systems (IEEE 118-bus and 300-bus), demonstrating its scalability. Furthermore, research has addressed Distributed Denial-of-Service (DDoS) attacks, which can disrupt communication within SEHs by overwhelming servers with fake requests. In [94], a DDoS attack detection approach was introduced that utilizes a multilayer auto-encoder structure. This method effectively extracts and generates features from data, leading to a comprehensive detection model with superior prediction accuracy compared to other methods. These studies showcase the ongoing development of sophisticated techniques to safeguard SEHs from a range of cyber threats.
B. 
Cloud-Computing-Based Methods
Continuing the discussion on cloud computing in SEHs, a previous study [95] explored the potential benefits and risks for utility companies. The focus was on how cloud computing characteristics could enhance defense mechanisms against DDoS attacks in smart energy systems. An extensive literature review identified various DDoS defense tactics that could be leveraged through cloud-based solutions. Another study [96] proposed a system for attribute-based online/offline searchable encryption to address challenges associated with encrypted data management. This system separated the encryption and trapdoor algorithms into two distinct phases. The encryption and attribute control policies were then executed offline, improving efficiency. Moreover, the proposed method offered additional security by being resistant to chosen plaintext attacks and chosen keyword attacks. These studies highlight how cloud computing can not only introduce security risks but also offer potential solutions for safeguarding SEHs.
C. 
Blockchain-Based Methods
The integration of blockchain technology is gaining traction as a complex yet essential approach to bolstering security in IoT-enabled SEHs [97]. At its core, blockchain transforms the traditional centralized ledger system into a distributed ledger, leveraging public key cryptography for enhanced security. This distributed processing structure facilitates end-to- end encryption, safeguarding communication integrity and reliability [98]. Research in [99] explores a blockchain-based security solution for secure and authorized access to resources within smart cities. The suggested approach integrates blockchain technology and the object security architecture for IoT, focusing on authentication and authorization. Blockchain enables a dynamic permission system, while object security architecture uses a public ledger to form secure multicast groups for authorized users. Additionally, a meteor-based application offers an intuitive interface for interacting with diverse smart city technologies such as traffic lights, smart energy meters, and security cameras. This application empowers users to control and interact with these resources, showcasing the potential of blockchain technology to enhance security and user experience in SEHs. In [100], a novel blockchain-based energy transaction model for multiple SEH, aiming to enhance the efficiency, security, and transparency of energy trading was proposed. The proposed model leverages blockchain technology to facilitate peer-to-peer energy transactions between SEH, enabling them to optimize energy consumption, reduce costs, and increase grid stability. A hybrid approach for multi-party energy management in SEH, combining Stackelberg game theory and blockchain technology was proposed in [101]. The proposed approach aims to optimize energy allocation, reduce costs, and enhance grid stability while considering the interests of multiple stakeholders. A novel blockchain-based framework for local energy trading among multiple SEH, enabling peer-to-peer transactions was proposed in [102]. The proposed framework aims to optimize energy utilization, reduce costs, and enhance grid stability while considering the unique characteristics of SEH.

7.4.2. Human-Centric Methods Multifactor Authentication

Making your login with two steps in a row makes it much harder to crack your password. This multifactor authentication makes it harder for attackers to get into your stuff by adding an extra layer of security. There are different multifactor authentication methods, like getting a code by text message, email, a special key fob, an app on your phone, or even using your phone itself as a second factor [103].
A. 
Employee Training
Due to technological breakthroughs that have increased the complexity of assaults on smart equipment, hackers are now focusing more on targeting humans. Adversaries are employing ML technologies to identify human behaviours and generate diverse scenarios. Hence, employee training plays a pivotal part in mitigating the hackers' effectiveness in carrying out their nefarious intentions.
B. 
Password Strength
Utilizing robust passwords reduces the probability of a breach in the integrity or confidentiality of data. Weak passwords increase the likelihood of password-guessing attacks. Password guessing is a technique used to gain unauthorized access to a system by making educated guesses about passwords in order to get entry to a specific device. Furthermore, the assailant depletes network resources and bandwidth in order to execute many attacks, hence restricting legitimate users' access to the resources [104].
C. 
The Protection of Operating System
Users are a vulnerable aspect of cybersecurity, and one of the primary difficulties with users is that they cannot be educated in the same manner as employees. Therefore, it is imperative to safeguard intelligent devices like smart meters and smart inverters from potential cyber threats. Securing the internal operating systems of devices is a highly effective method for safeguarding them against cyber criminals [105].
D. 
Customer safeguarding against third-party applications
Users need to be cautious when they encounter applications seeking authorization, as they store sensitive information on their devices and certain third-party apps might demand excessive data access. Approximately 98.5 percent of consumers disregard or only occasionally consent to the permissions sought by applications without giving it much consideration. According to research, 93.6 percent of users agree to the terms and conditions of the applications immediately or within one minute [106].
E. 
Reporting of Malicious Behaviour
Customers should have the ability to promptly report any suspected breach on a platform established by utility companies. The extent of destruction would increase exponentially as the duration between the attack and the time of reporting expands. The failure to promptly notify an attacker not only endangers the confidentiality of a single client but also compromises the privacy of other interconnected consumers inside the network [31].

8. Measurements and Uncertainties in SEH

SEH, as complex systems integrating various energy sources, storage, and distribution technologies, require precise and accurate measurements to ensure efficient operation, reliability, and economic viability. However, these measurements are often subject to uncertainties that can significantly impact the overall performance of the hub. These measurements such as power generation from renewable and conventional sources, state of charge of energy storage systems, electricity demand from various loads, and power flows between the hub and the external grid.
Measurement uncertainties in SEHs can arise from various factors such as sensor inaccuracies, environmental influences, calibration errors, communication issues, and model limitations. These uncertainties can lead to inaccurate readings and affect the overall performance of the hub. Measurement uncertainties can have significant negative consequences such as inefficient energy management, increased costs, reduced reliability, and even safety risks, particularly in energy storage systems.
To address measurement uncertainties in SEHs: select high-quality sensors, calibrate them regularly, use redundant sensors, implement data validation techniques, employ advanced modeling, and quantify uncertainties to assess their impact on system performance. These strategies can help improve the accuracy and reliability of measurements, leading to more efficient and reliable SEH operations.

8.1. Key Measurement Parameters and Their Impact on IoT Devices

  • Fluctuations in voltage, frequency, and harmonics can affect the operation of sensitive IoT devices, leading to malfunctions or premature failure.
  • Interruptions or variations in power supply can cause IoT devices to lose their connection or experience data corruption.
  • The quality of the network connection between the IoT devices and the SEH can influence data transmission rates, reliability, and overall performance.

8.2. Impacts of Measurement Uncertainties on IoT Devices

  • Inaccurate measurements of power quality can lead to voltage surges or dips that damage or cause IoT devices to malfunction.
  • Uncertainties in energy availability can result in unexpected power outages, leading to data loss or corruption in IoT devices.
  • Measurement errors in network parameters, such as bandwidth or latency, can impact the communication between IoT devices and the SEH, leading to slower response times or decreased reliability.
  • Inaccurate measurements can create vulnerabilities in the SEH's security system, potentially exposing IoT devices to cyberattacks.

8.3. Addressing Measurement Uncertainties and Their Impact on IoT Devices

  • Design IoT devices to be tolerant to fluctuations in power quality and network conditions.
  • Implement redundant power supplies and network connections to minimize the impact of measurement uncertainties.
  • Use advanced measurement techniques, such as phasor measurement units, to provide more accurate and reliable data.
  • Continuously monitor the performance of IoT devices to detect and address issues related to measurement uncertainties.
  • Implement data validation and correction algorithms to identify and correct errors in measurements.
  • Use secure communication protocols to protect IoT devices and the SEH from cyber threats.

8.4. Specifications of IoT Devices Used in Smart Grids

IoT devices play a critical role in enabling smart grid functionality. Here are some key specifications to consider:
  • Power Consumption:
    -
    IoT devices in smart grids must be energy-efficient to minimize their impact on the grid and reduce operational costs.
    -
    For battery-powered devices, long battery life is essential to reduce maintenance requirements.
  • Communication Protocols:
    -
    IoT devices in smart grids typically use wireless communication protocols such as Wi-Fi, Bluetooth, Zigbee, LoRaWAN, or cellular (4G/5G) to connect to the network.
    -
    The required data rate depends on the specific application. For example, real-time monitoring of power usage may require higher data rates than infrequent updates of device status.
    -
    The range of the wireless connection is important, especially for devices located in remote areas or underground.
  • Environmental Factors:
    -
    IoT devices must be able to operate in a wide range of temperature and humidity conditions, especially if they are deployed outdoors.
    -
    Devices should be designed to withstand harsh environmental conditions, such as vibration, shock, and dust.
  • Security:
    -
    IoT devices in smart grids should use encryption to protect sensitive data from unauthorized access.
    -
    Strong authentication mechanisms are necessary to prevent unauthorized devices from joining the network.
  • Interoperability:
    -
    IoT devices should comply with relevant industry standards, such as IEC 61850 or IEEE 802.11, to ensure interoperability with other devices and systems.
  • Scalability:
    -
    The IoT network must be able to handle a large number of devices without compromising performance.
    -
    Efficient data management and storage solutions are essential for handling the vast amounts of data generated by IoT devices.
  • Real-time capabilities:
    -
    For applications that require real-time monitoring or control, IoT devices must have low-latency communication capabilities.
  • Cost-effectiveness:
    -
    IoT devices should be cost-effective to enable widespread deployment.

9. Real applications of SEHs

9.1. Amsterdam Smart Energy Hub

The Amsterdam Smart Energy Hub is a pioneering project aimed at transforming Amsterdam into a sustainable and resilient energy city [107]. It is a centralized energy platform that integrates various energy sources, storage technologies, and smart grid infrastructure. The hub combines renewable energy sources like solar and wind power with traditional energy sources and energy storage technologies. The hub incorporates various energy storage technologies, such as batteries and thermal storage, to store excess energy for later use. The hub utilizes AMI and other smart grid technologies to optimize energy distribution and consumption. The hub encourages energy consumers to participate in DR programs, where they can adjust their energy consumption patterns to match supply and demand. The hub actively involves the community in its development and operation, fostering a sense of ownership and participation. The Amsterdam Smart Energy Hub leverages IoT technology to enhance its capabilities and achieve its objectives. Here are some key points of IoT implemented in the hub:
  • IoT sensors are deployed throughout the hub to monitor various parameters, such as energy consumption, grid performance, and the state of energy storage systems. This real-time data enables efficient management and optimization of the energy system.
  • IoT-enabled devices, such as smart thermostats and appliances, can be controlled remotely to adjust their energy consumption based on grid conditions. This helps to balance supply and demand and reduce peak load.
  • IoT sensors and communication technologies facilitate the integration of renewable energy sources, such as solar panels and wind turbines, into the grid. This helps to increase the hub's sustainability and reduce reliance on fossil fuels.
  • IoT devices can monitor the state of charge of energy storage systems and optimize their charging and discharging cycles to ensure maximum efficiency and reliability.
  • IoT-powered smart meters can provide residents with real-time information about their energy consumption, empowering them to make informed decisions and participate in energy-saving initiatives.
Cloud computing plays a crucial role in the Amsterdam Smart Energy Hub, providing a scalable and flexible infrastructure for data management, analytics, and application hosting. Cloud computing offers several advantages for the Amsterdam Smart Energy Hub: it efficiently stores and manages large amounts of data generated by IoT devices, enables advanced data analysis for optimization and decision-making, provides a platform for hosting applications that control various aspects of the hub, offers flexibility to scale resources as needed, and can be more cost-effective than traditional on-premise infrastructure.
In the Amsterdam Smart Energy Hub, fog computing enables local data processing, real-time decisions, improved reliability, reduced network congestion, and enhanced security. Fog nodes can perform edge analytics, control energy systems locally, and provide computing resources for IoT devices, leading to greater efficiency, reliability, and responsiveness in the hub's operations.

9.2. Copenhagen Energy Lab, Denmark

The Copenhagen Energy Lab is a leading-edge research and development facility that serves as a testbed for innovative energy technologies [108]. IoT, fog computing, and cloud computing play pivotal roles in its operations.
The Copenhagen Energy Lab utilizes IoT sensors to gather real-time data on energy consumption, generation, and storage, enabling efficient system management. IoT-integrated smart grids facilitate bidirectional communication between energy producers, consumers, and the grid. Moreover, IoT technologies enable seamless integration of renewable energy sources like solar and wind power.
Fog nodes deployed throughout the lab perform local data processing, reducing latency and enhancing real-time decision-making. Decentralized control through fog computing allows for more flexible and responsive energy system operations. Additionally, fog computing provides redundancy and fault tolerance, improving overall system reliability.
Cloud-based platforms store and manage the vast amounts of data generated by IoT devices and sensors. Advanced analytics tools on these platforms help researchers analyze data to identify trends, patterns, and anomalies. Furthermore, cloud-based applications manage and control various aspects of the lab's energy systems.
The combined use of IoT, fog, and cloud computing in the Copenhagen Energy Lab leads to improved efficiency, enhanced reliability, increased sustainability, and fosters innovation in the energy sector.

10. Conclusions

IoT is about connecting all sorts of devices to the internet, anywhere in the world, no matter how they connect, how powerful they are, or where they are located. The SEH is the most extensive use of the IoT, with intelligent devices spread across the whole energy supply chain, from power generation plants to end-users. The IoT will enhance current intelligent energy networks by enabling immediate management and surveillance of the hub's components. Nevertheless, throughout the last ten years, as examined in scholarly works, cybersecurity has been perceived as a significant obstacle to the widespread use and expansion of IoT in SEH systems worldwide. Safeguarding hub-connected devices is a formidable undertaking, mostly because of the vast number of devices linked to communication networks. This proliferation of devices heightens the danger of cyberattacks and the potential for significant consequences. By 2025, it is estimated that there will be approximately 30.9 billion IoT devices worldwide. Out of these, around 19% will be used in the energy sector. This increase in IoT devices in the energy sector is expected to lead to a 54% rise in cyberattacks targeting this industry. The vulnerable attack surface will significantly increase as more IoT-enabled devices are integrated into SEH. In order to tackle the aforementioned difficulties and challenges, the following suggestions are put up for enhancing IoT- based SEH:
  • Enhancements need be made to the framework and modeling of SEH, and appropriate reconfiguration technologies need to be created for the restoration of EH.
  • Extensive implementation of secure AMI technologies, enhanced cloud and edge-computing capabilities, and the adoption of 5G telecommunications are crucial for enhancing the operational efficiency and security of SEHs.
  • SEH should have robust communication protocols that take into account the diversity of IoT devices. These protocols should allow for the implementation of AI algorithms directly on the devices, rather than relying on remote control, in order to minimize the risk of communication breaches.
  • Implementing blockchain-based technologies is crucial for ensuring advanced security and data exchange in IoT- based SEH.
  • Efficient utilization of game-theoretic models, particularly in the context of energy markets, along with cognitive and deep learning techniques, is crucial for ensuring the seamless and dependable functioning of the energy system.

References

  1. A. Eladl, M. El-Afifi, and M. El-Saadawi, “Optimal power dispatch of multiple energy sources in energy hubs,” 2017 19th Int. Middle-East Power Syst. Conf. MEPCON 2017 - Proc., vol. 2018-Febru, no. December, pp. 1053–1058, 2018. [CrossRef]
  2. Y. Noorollahi, R. Itoi, H. Yousefi, M. Mohammadi, and A. Farhadi, “Modeling for diversifying electricity supply by maximizing renewable energy use in Ebino city southern Japan,” Sustain. Cities Soc., vol. 34, pp. 371–384, 2017. [CrossRef]
  3. M. Geidl and G. Andersson, “Integrated Modeling and Optimization of Multi-Carrier Energy Systems,” Power Syst. Lab., vol. Doctor of, no. 17141, p. 143, 2007.
  4. A. Eladl, M. El-Afifi, and M. El-Saadawi, “Communication Technologies Requirement for Energy Hubs: A survey,” 2019 21st Int. Middle East Power Syst. Conf. MEPCON 2019 - Proc., pp. 821–827, 2019. [CrossRef]
  5. M. El-afifi and M. Saadawi, A. Eladl, “Cogeneration Systems Performance Analysis as a Sustainable Clean Energy and Water Source Based on Energy Hubs Using the Archimedes Optimization Algorithm,” 2022.
  6. M. Shariatkhah, M. Haghifam, M. Parsa-Moghaddam, and P. Siano, “Modeling the reliability of multi-carrier energy systems considering dynamic behavior of thermal loads,” Energy Build., vol. 103, pp. 375–383, 2015. [CrossRef]
  7. A. Eladl, M. El-Afifi, M. El-Saadawi, and B. Sedhom, “A review on energy hubs: Models, methods, classification, applications, and future trends,” Alexandria Eng. J., vol. 68, pp. 315–342, 2023. [CrossRef]
  8. A. Eladl, M. El-afifi, M. El-saadawi, and B. Sedhom, “Distributed Optimal Dispatch of Smart Multi-agent Energy Hubs Based on Consensus.
  9. Algorithm Considering Lossy Communication Network and Uncertainty,” 2023. [CrossRef]
  10. M. El-Afifi, B. Sedhom, A. Eladl, M. Elgamal, and P. Siano, “Demand side management strategy for smart building using multi-objective hybrid optimization technique,” Results Eng., vol. 22, p. 102265, Jun. 2024. [CrossRef]
  11. D. Liu, Z. Qin, H. Hua, Y. Ding, and J. Cao, “Incremental incentive mechanism design for diversified consumers in demand response,” Appl. Energy, vol. 329, p. 120240, Jan. 2023. [CrossRef]
  12. A. Eladl, M. El-Afifi, M. Saeed, and M. El-Saadawi, “Optimal operation of energy hubs integrated with renewable energy sources and storage devices considering CO2 emissions,” Int. J. Electr. Power Energy Syst., vol. 117, no. November 2019, p. 105719, 2020. [CrossRef]
  13. Q. Doan, A. M. Kayes, W. Rahayu, and K. Nguyen, “Integration of IoT Streaming Data with Efficient Indexing and Storage Optimization,” IEEE Access, vol. 8, pp. 47456–47467, 2020. [CrossRef]
  14. L. Xiao, X. Wan, X. Lu, Y. Zhang, and D. Wu, “IoT Security Techniques Based on Machine Learning: How Do IoT Devices Use AI to Enhance Security?,” IEEE Signal Process. Mag., vol. 35, no. 5, pp. 41–49, 2018. [CrossRef]
  15. D. Geneiatakis, I. Kounelis, R. Neisse, I. Nai-Fovino, G. Steri, and G. Baldini, “Security and privacy issues for an IoT based smart home,” 2017 40th Int. Conv. Inf. Commun. Technol. Electron. Microelectron. MIPRO 2017 - Proc., no. 1, pp. 1292–1297, 2017. [CrossRef]
  16. S. Bibri and J. Krogstie, “The core enabling technologies of big data analytics and context-aware computing for smart sustainable cities: a review and synthesis,” J. Big Data, vol. 4, no. 1, 2017. [CrossRef]
  17. W. Li, M. Au, and Y. Wang, “A fog-based collaborative intrusion detection framework for smart grid,” Int. J. Netw. Manag., vol. 31, no. 2, pp. 1–11, 2021. [CrossRef]
  18. J. Lázaro, A. Astarloa, M. Rodríguez, U. Bidarte, and J. Jiménez, “A survey on vulnerabilities and countermeasures in the communications of the smart grid,” Electron., vol. 10, no. 16, pp. 1–15, 2021. [CrossRef]
  19. Sedhom et al., “IoT-based optimal demand side management and control scheme for smart microgrid”, International Journal of Electrical Power & Energy Systems, Vol. 127, May 2021, 106674. [CrossRef]
  20. N. van Eck and L. Waltman, “Software survey: VOSviewer, a computer program for bibliometric mapping,” Scientometrics, vol. 84, no. 2, pp. 523–538, 2010. [CrossRef]
  21. M. Geidl, G. Koeppel, and P. Favre-Perrod, “The Energy Hub–A powerful concept for future energy systems,” Third Annual Carnegie Mellon Conference on the Electricity Industry, 13 – 14 March 2007 [Online]. Available: https://research.ece.cmu.edu/electriconf/2007/2007%20Conf%20Papers/Andersson%20Paper%20final.pdf.
  22. M. Geidl and G. Andersson, “A modeling and optimization approach for multiple energy carrier power flow,” 2005 IEEE Russ. Power Tech, PowerTech, 2005. [CrossRef]
  23. S. Dorahaki, A. Abdollahi, M. Rashidinejad, and M. Moghbeli, “The role of energy storage and demand response as energy democracy policies in the energy productivity of hybrid hub system considering social inconvenience cost,” J. Energy Storage, vol. 33, no. October, p. 102022, 2021. [CrossRef]
  24. M. Saeed et al., “Energy management system in smart buildings based coalition game theory with fog platform and smart meter infrastructure,” Scientific Reports, 13(2023). [CrossRef]
  25. T. Ding, W. Jia, M. Shahidehpour, O. Han, Y. Sun, and Z. Zhang, “Review of Optimization Methods for Energy Hub Planning, Operation, Trading, and Control,” IEEE Trans. Sustain. Energy, vol. 13, no. 3, pp. 1802–1818, 2022. [CrossRef]
  26. M. El-afifi and W. Abdelrazik, “Renewable Energy Sources Applications in Currently Occupied Structures,” Nile J. Commun. Comput. Sci., vol. 0, no. 0, pp. 0–0, 2023. [CrossRef]
  27. Peng, H. Sun, M. Yang, and Y. L. Wang, “A Survey on Security Communication and Control for Smart Grids under Malicious Cyber Attacks,”.
  28. IEEE Trans. Syst. Man, Cybern. Syst., vol. 49, no. 8, pp. 1554–1569, 2019. [CrossRef]
  29. H. Shahinzadeh, J. Moradi, G. B. Gharehpetian, H. Nafisi, and M. Abedi, “IoT Architecture for smart grids,” Int. Conf. Prot. Autom. Power Syst. IPAPS 2019, no. January, pp. 22–30, 2019. [CrossRef]
  30. M. Saleem, M. Usman, M. Yaqub, A. Liotta, and A. Asim, “Smarter Grid in the 5G Era: Integrating the Internet of Things with a Cyber-Physical System,” IEEE Access, vol. 12, no. March, pp. 34002–34018, 2024. [CrossRef]
  31. Y. Kabalci, E. Kabalci, S. Padmanaban, J. B. Holm-Nielsen, and F. Blaabjerg, “Internet of things applications as energy internet in smart grids and smart environments,” Electron., vol. 8, no. 9, pp. 1–16, 2019. [CrossRef]
  32. A. Tufail, A. Namoun, A. Alrehaili, and A. Ali, “A Survey on 5G Enabled Multi-Access Edge Computing for Smart Cities: Issues and Future Prospects Usability of Arabic Interfaces View project Crowd Management View project A Survey on 5G Enabled Multi-Access Edge Computing for Smart Cities: Issues and Futu,” IJCSNS Int. J. Comput. Sci. Netw. Secur., vol. 21, no. 6, pp. 107–118, 2021. [CrossRef]
  33. M. El-afifi and H. Sakr, “Security Issues and Challenges for IoT-based Smart Multi Energy Carrier Systems,” Nile J. Commun. Comput. Sci., vol. 0, no. 0, pp. 0–0, 2023. [CrossRef]
  34. H. Zhang, Q. Cao, H. Gao, P. Wang, W. Zhang, and N. Yousefi, “Optimum design of a multi-form energy hub by applying particle swarm optimization,” J. Clean. Prod., vol. 260, 2020. [CrossRef]
  35. M. Nozari, M. Yaghoubi, K. Jafarpur, and G. Mansoori, “Development of dynamic energy storage hub concept: A comprehensive literature review of multi storage systems,” J. Energy Storage, vol. 48, no. August 2021, p. 103972, 2022. [CrossRef]
  36. S. Savazzi, M. Nicoli, and V. Rampa, “Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks,” IEEE Internet Things J., vol. 7, no. 5, pp. 4641–4654, 2020. [CrossRef]
  37. S. Khan, C. Liu, and J. Ansari, “Centralized Fuzzy Logic Based Optimization of PI Controllers for VSC Control in MTDC Network,” J. Electr. Eng. Technol., vol. 15, no. 6, pp. 2577–2585, 2020. [CrossRef]
  38. D. Mocrii, Y. Chen, and P. Musilek, “IoT-based smart homes: A review of system architecture, software, communications, privacy and security,”.
  39. Internet of Things (Netherlands), vol. 1–2, pp. 81–98, 2018. [CrossRef]
  40. M. Soori, B. Arezoo, and R. Dastres, “Internet of things for smart factories in industry 4.0, a review,” Internet Things Cyber-Physical Syst., vol. 3, pp. 192–204, 2023. [CrossRef]
  41. A. Yousefpour et al., “All one needs to know about fog computing and related edge computing paradigms: A complete survey,” J. Syst. Archit., vol. 98, no. December 2018, pp. 289–330, 2019. [CrossRef]
  42. A. Kumari, S. Tanwar, S. Tyagi, N. Kumar, M. S. Obaidat, and J. J. P. C. Rodrigues, “Fog Computing for Smart Grid Systems in the 5G Environment: Challenges and Solutions,” IEEE Wirel. Commun., vol. 26, no. 3, pp. 47–53, 2019. [CrossRef]
  43. S. Repo, J. Pylvanainen, M. Kauppinen, S. Repo, and P. Jarventausta, “Automatic Meter Infrastructure (AMI) as a part of flexibility market,”.
  44. Int. Conf. Eur. Energy Mark. EEM, vol. 2018-June, 2018. [CrossRef]
  45. Y. Agalgaonkar and D. Hammerstrom, “Evaluation of Smart Grid Technologies Employed for System Reliability Improvement: Pacific.
  46. Northwest Smart Grid Demonstration Experience,” IEEE Power Energy Technol. Syst. J., vol. 4, no. 2, pp. 24–31, 2017. [CrossRef]
  47. J. Al Dakheel, C. Del Pero, N. Aste, and F. Leonforte, “Smart buildings features and key performance indicators: A review,” Sustain. Cities Soc., vol. 61, p. 102328, 2020. [CrossRef]
  48. S. Nižetić, N. Djilali, A. Papadopoulos, and J. J. P. C. Rodrigues, “Smart technologies for promotion of energy efficiency, utilization of sustainable resources and waste management,” J. Clean. Prod., vol. 231, pp. 565–591, 2019. [CrossRef]
  49. M. El-Afifi, M. El-Saadawi, B. Sedhom, and A. Eladl, “An IoT-fog-cloud consensus-based energy management algorithm of multi-agent smart energy hubs considering packet losses and uncertainty,” Renew. Energy, vol. 221, no. June 2023, p. 119716, 2024. [CrossRef]
  50. B. Sovacool, P. Kivimaa, S. Hielscher, and K. Jenkins, “Vulnerability and resistance in the United Kingdom’s smart meter transition,” Energy Policy, vol. 109, no. May, pp. 767–781, 2017. [CrossRef]
  51. M. I. El-Afifi, B. E. Sedhom, and A. A. Eladl, “Smart Building Demand Side Management Using Multi-Objective Archimedes Optimization Algorithms,” 2023 24th Int. Middle East Power Syst. Conf. MEPCON 2023, no. December, 2023. [CrossRef]
  52. M. Waseem, Z. Lin, S. Liu, Z. Jinai, M. Rizwan, and I. A. Sajjad, “Optimal BRA based electric demand prediction strategy considering instance- based learning of the forecast factors,” Int. Trans. Electr. Energy Syst., vol. 31, no. 9, pp. 1–28, 2021. [CrossRef]
  53. M. Waseem, Z. Lin, S. Liu, Z. Zhang, T. Aziz, and D. Khan, “Fuzzy compromised solution-based novel home appliances scheduling and demand response with optimal dispatch of distributed energy resources,” Appl. Energy, vol. 290, no. February, p. 116761, 2021. [CrossRef]
  54. M. Yue et al., “Hydrogen energy systems : A critical review of technologies , applications , trends and challenges,” pp. 0–34, 2023.
  55. H. Yuan, K. Feng, W. Li, and X. Sun, “Multi-objective optimization of virtual energy hub plant integrated with data center and plug-in electric vehicles under a mixed robust-stochastic model,” J. Clean. Prod., vol. 363, p. 132365, Aug. 2022. [CrossRef]
  56. L. Fan, D. Ji, G. Lin, P. Lin, and L. Liu, “Information gap-based multi-objective optimization of a virtual energy hub plant considering a developed demand response model,” Energy, vol. 276, p. 127462, Aug. 2023. [CrossRef]
  57. J. Wang, V. Ilea, C. Bovo, N. Xie, and Y. Wang, “Optimal self-scheduling for a multi-energy virtual power plant providing energy and reserve services under a holistic market framework,” Energy, vol. 278, p. 127903, Sep. 2023. [CrossRef]
  58. T. Ma, W. Pei, H. Xiao, L. Kong, Y. Mu, and T. Pu, “The energy management strategies based on dynamic energy pricing for community integrated energy system considering the interactions between suppliers and users,” Energy, vol. 211, p. 118677, Nov. 2020. [CrossRef]
  59. Q. Wu and C. Li, “Dynamic pricing and energy management of hydrogen-based integrated energy service provider considering integrated demand response with a bi-level approach,” J. Energy Storage, vol. 59, p. 106558, Mar. 2023. [CrossRef]
  60. Kamyab and S. Bahrami, “Efficient operation of energy hubs in time-of-use and dynamic pricing electricity markets,” Energy, vol. 106, pp. 343–355, 2016. [CrossRef]
  61. Z. Yadollahi, R. Gharibi, R. Dashti, and A. Torabi Jahromi, “Optimal energy management of energy hub: A reinforcement learning approach,”.
  62. Sustain. Cities Soc., vol. 102, p. 105179, Mar. 2024. [CrossRef]
  63. Q. He, M. Wu, C. Liu, D. Jin, and M. Zhao, “Management and real-time monitoring of interconnected energy hubs using digital twin: Machine learning based approach,” Sol. Energy, vol. 250, pp. 173–181, Jan. 2023. [CrossRef]
  64. D. Agnew et al., “Implementation Aspects of Smart Grids Cyber-Security Cross-Layered Framework for Critical Infrastructure Operation,” Appl. Sci., vol. 12, no. 14, 2022. [CrossRef]
  65. S. Tufail, I. Parvez, S. Batool, and A. Sarwat, “A survey on cybersecurity challenges, detection, and mitigation techniques for the smart grid,”.
  66. Energies, vol. 14, no. 18, pp. 1–22, 2021. [CrossRef]
  67. A. Agarkar and H. Agrawal, “A review and vision on authentication and privacy preservation schemes in smart grid network,” Secur. Priv., vol. 2, no. 2, 2019. [CrossRef]
  68. K. Shuaib, Z. Trabelsi, M. Abed-Hafez, A. Gaouda, and M. Alahmad, “Resiliency of smart power meters to common security attacks,” Procedia Comput. Sci., vol. 52, no. 1, pp. 145–152, 2015. [CrossRef]
  69. J. Abdella and K. Shuaib, “An Architecture for Blockchain based Peer to Peer Energy Trading,” 2019 6th Int. Conf. Internet Things Syst. Manag. Secur. IOTSMS 2019, pp. 412–419, 2019. [CrossRef]
  70. R. Kvdq et al., “Interconnected energy hubs including DERs targeted by FDI cyberattacks,” pp. 1–6, 2020.
  71. P. Zhao et al., “A Cyber-Secured Operation for Water-Energy Nexus,” IEEE Trans. Power Syst., vol. 36, no. 4, pp. 3105–3117, 2021. [CrossRef]
  72. P. Zhao, C. Gu, and D. Huo, “Coordinated Risk Mitigation Strategy for Integrated Energy Systems under Cyber-Attacks,” IEEE Trans. Power Syst., vol. 35, no. 5, pp. 4014–4025, 2020. [CrossRef]
  73. P. Zhao et al., “Cyber-Resilient Multi-Energy Management for Complex Systems,” IEEE Trans. Ind. Informatics, vol. 18, no. 3, pp. 2144–2159, 2022. [CrossRef]
  74. A. Sawas, H. Khani, and H. Farag, “On the Resiliency of Power and Gas Integration Resources against Cyber Attacks,” IEEE Trans. Ind. Informatics, vol. 17, no. 5, pp. 3099–3110, 2021. [CrossRef]
  75. S. Pazouki, E. Naderi, and A. Asrari, “A remedial action framework against cyberattacks targeting energy hubs integrated with distributed energy resources,” Appl. Energy, vol. 304, p. 117895, Dec. 2021. [CrossRef]
  76. H. Sakr et al., “Al-based Traffic System: A Novel Approach,” 2023 24th Int. Middle East Power Syst. Conf. MEPCON 2023, no. December, 2023. [CrossRef]
  77. M. N. Kurt, Y. Yilmaz, and X. Wang, “Distributed quickest detection of cyber-attacks in smart grid,” IEEE Trans. Inf. Forensics Secur., vol. 13, no. 8, pp. 2015–2030, 2018. [CrossRef]
  78. A. Burg, A. Chattopadhyay, and K. Lam, “Wireless Communication and Security Issues for Cyber–Physical Systems and the Internet-of-Things,”.
  79. Proc. IEEE , vol. 106, no. 1, pp. 38–60, 2017.
  80. W. Wang and Z. Lu, “Cyber security in the Smart Grid: Survey and challenges,” Comput. Networks, vol. 57, no. 5, pp. 1344–1371, 2013. [CrossRef]
  81. M. Aldwairi and D. Alansari, “n-Grams exclusion and inclusion filter for intrusion detection in Internet of Energy big data systems,” Trans. Emerg. Telecommun. Technol., vol. 33, no. 3, pp. 1–27, 2022. [CrossRef]
  82. C. Li et al., “Energy hub-based optimal planning framework for user-level integrated energy systems: Considering synergistic effects under multiple uncertainties,” Appl. Energy, p. 118099, Nov. 2021. [CrossRef]
  83. N. Yan, G. Ma, X. Li, and J. Guerrero, “Low-Carbon Economic Dispatch Method for Integrated Energy System Considering Seasonal Carbon Flow Dynamic Balance,” IEEE Trans. Sustain. Energy, vol. 14, no. 1, pp. 576–586, 2023. [CrossRef]
  84. F. Holik, L. Flå, M. Jaatun, S. Yayilgan, and J. Foros, “Threat Modeling of a Smart Grid Secondary Substation,” Electron., vol. 11, no. 6, pp. 1–21, 2022. [CrossRef]
  85. A. Goudarzi, S. Fahad, J. Ni, F. Ghayoor, P. Siano, and H. Haes Alhelou, “A sequential hybridization of ETLBO and IPSO for solving reserve- constrained combined heat, power and economic dispatch problem,” IET Gener. Transm. Distrib., vol. 16, no. 10, pp. 1930–1949, 2022. [CrossRef]
  86. N. Omase, S. Mittal, S. Palaniraja, P. Guchhait, M. Patil, and P. Mundra, “A comprehensive review of electric vehicle charging infrastructure and associated challenges,” Int. J. Sci. Res. Arch., vol. 10, no. 1, pp. 834–840, 2023. [CrossRef]
  87. S. Khan, M. Wang, W. Su, G. Liu, and S. Chaturvedi, “Grid-Forming Converters for Stability Issues in Future Power Grids,” Energies, vol. 15, no. 14, 2022. [CrossRef]
  88. S. Fahad, A. Goudarzi, and J. Xiang, “Demand management of active distribution network using coordination of virtual synchronous generators,”.
  89. IEEE Trans. Sustain. Energy, vol. 12, no. 1, pp. 250–261, 2021. [CrossRef]
  90. A. Farraj, E. Hammad, and D. Kundur, “On the Impact of Cyber Attacks on Data Integrity in Storage-Based Transient Stability Control,” IEEE Trans. Ind. Informatics, vol. 13, no. 6, pp. 3322–3333, 2017. [CrossRef]
  91. M. Ibrahim, W. Dong, and Q. Yang, “Machine learning driven smart electric power systems: Current trends and new perspectives,” Appl. Energy, vol. 272, no. February, p. 115237, 2020. [CrossRef]
  92. L. Cui, Y. Qu, L. Gao, G. Xie, and S. Yu, “Detecting false data attacks using machine learning techniques in smart grid: A survey,” J. Netw. Comput. Appl., vol. 170, no. February, p. 102808, 2020. [CrossRef]
  93. Nayab et al., “An Efficient Online/Offline Signcryption Scheme for Internet of Things in Smart Home,” Wirel. Commun. Mob. Comput., vol. 2022, 2022. [CrossRef]
  94. Y. Lin and J. Wang, “Realizing the Transactive Energy Future with Local Energy Market: an Overview,” Curr. Sustain. Energy Reports, vol. 9, no. 1, pp. 1–14, 2022. [CrossRef]
  95. G. Kalogridis, M. Sooriyabandara, Z. Fan, and M. A. Mustafa, “Toward unified security and privacy protection for smart meter networks,” IEEE Syst. J., vol. 8, no. 2, pp. 641–654, 2014. [CrossRef]
  96. J. Mühlberg, S. Cleemput, M. Mustafa, J. Van Bulck, B. Preneel, and F. Piessens, “An implementation of a high assurance smart meter using protected module architectures,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9895 LNCS, no. November, pp. 53–69, 2016. [CrossRef]
  97. M. Abdullahi et al., “Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review,” Electron., vol. 11, no. 2, pp. 1–27, 2022. [CrossRef]
  98. F. Aloul, A. Al-Ali, R. Al-Dalky, M. Al-Mardini, and W. El-Hajj, “Smart Grid Security: Threats, Vulnerabilities and Solutions,” Int. J. Smart Grid Clean Energy, no. 971, pp. 1–6, 2012. [CrossRef]
  99. D. Hudani, M. Haseeb, M. Taufiq, M. A. Umer, and N. K. Kandasamy, “A Data-Centric Approach to Generate Invariants for a Smart Grid Using Machine Learning,” SaT-CPS 2022 - Proc. 2022 ACM Work. Secur. Trust. Cyber-Physical Syst., pp. 31–36, 2022. [CrossRef]
  100. R. Borges Hink, J. Beaver, M. Buckner, T. Morris, U. Adhikari, and S. Pan, “Machine learning for power system disturbance and cyber-attack discrimination,” 7th Int. Symp. Resilient Control Syst. ISRCS 2014, no. February 2015, 2014. [CrossRef]
  101. M. Ashrafuzzaman, S. Das, Y. Chakhchoukh, S. Shiva, and F. Sheldon, “Detecting stealthy false data injection attacks in the smart grid using ensemble-based machine learning,” Comput. Secur., vol. 97, p. 101994, 2020. [CrossRef]
  102. C. Pei, Y. Xiao, W. Liang, and X. Han, “A Deviation-Based Detection Method against False Data Injection Attacks in Smart Grid,” IEEE Access, vol. 9, pp. 15499–15509, 2021. [CrossRef]
  103. S. Ali and Y. Li, “Learning multilevel auto-encoders for ddos attack detection in smart grid network,” IEEE Access, vol. 7, pp. 108647–108659, 2019. [CrossRef]
  104. A. Califano, E. Dincelli, and S. Goel, “Using Features of Cloud Computing to Defend Smart Grid against DDoS Attacks,” 10th Annu. Symp. Inf. Assur., no. June, p. 44, 2015.
  105. N. Eltayieb, R. Elhabob, A. Hassan, and F. Li, “An efficient attribute-based online/offline searchable encryption and its application in cloud- based reliable smart grid,” J. Syst. Archit., vol. 98, no. January, pp. 165–172, 2019. [CrossRef]
  106. X. Wang, F. Yao, and F. Wen, “Applications of Blockchain Technology in Modern Power Systems: A Brief Survey,” Energies, vol. 15, no. 13.
  107. pp. 1–22, 2022. [CrossRef]
  108. S. Mahmood, M. Chadhar, and S. Firmin, “Cybersecurity Challenges in Blockchain Technology: A Scoping Review,” Hum. Behav. Emerg. Technol., vol. 2022, pp. 1–11, 2022. [CrossRef]
  109. M. Asif, Z. Aziz, M. Bin Ahmad, A. Khalid, H. A. Waris, and A. Gilani, “Blockchain-Based Authentication and Trust Management Mechanism for Smart Cities,” Sensors, vol. 22, no. 7, pp. 1–26, 2022. [CrossRef]
  110. Y. Yang and L. Jia, “Blockchain-based Energy Transaction Model for Multiple Energy Hubs,” Proc. 2021 IEEE 10th Data Driven Control Learn. Syst. Conf. DDCLS 2021, pp. 1235–1240, 2021. [CrossRef]
  111. N. Liu, L. Tan, L. Zhou, and Q. Chen, “Multi-party energy management of energy hub: A hybrid approach with stackelberg game and blockchain,” J. Mod. Power Syst. Clean Energy, vol. 8, no. 5, pp. 919–928, 2020. [CrossRef]
  112. K. Smart, E. Hub, and S. Theorem, “Blockchain-Based Local Energy Trading Framework Considering Multi-Energy Hub Enabling P2p Transaction,” 2022.
  113. P. Gope, “PMAKE: Privacy-aware multi-factor authenticated key establishment scheme for Advance Metering Infrastructure in smart grid,”.
  114. Comput. Commun., vol. 152, no. January, pp. 338–344, 2020. [CrossRef]
  115. H. Dong, J. Zhao, X. Yang, and K. Yang, “Combination of D-AHP and Grey Theory for the Assessment of the Information Security Risks of Smart Grids,” Math. Probl. Eng., vol. 2020, 2020. [CrossRef]
  116. C. C. Sun, D. J. Sebastian Cardenas, A. Hahn, and C. C. Liu, “Intrusion Detection for Cybersecurity of Smart Meters,” IEEE Trans. Smart Grid, vol. 12, no. 1, pp. 612–622, 2021. [CrossRef]
  117. Z. El May, H. K. Ben Ayed, and D. MacHfar, “State of the art on privacy risk estimation related to android applications,” 2019 15th Int. Wirel. Commun. Mob. Comput. Conf. IWCMC 2019, pp. 889–894, 2019. [CrossRef]
  118. https://ecah.amsterdam/smart-energy-hub/.
  119. https://www.energylabnordhavn.com/.
Preprints 118087 g001
Figure 1. A schematic diagram of traditional and future integrated energy systems [7].
Figure 1. A schematic diagram of traditional and future integrated energy systems [7].
Preprints 118087 g001
Preprints 118087 g002
Figure 2. Article layout.
Figure 2. Article layout.
Preprints 118087 g002
Preprints 118087 g003
Figure 3. Historical development presentation of the SEH concept using the VOSviewer platform.
Figure 3. Historical development presentation of the SEH concept using the VOSviewer platform.
Preprints 118087 g003
Preprints 118087 g004
Figure 4. The power transformation modeling through an EH [7].
Figure 4. The power transformation modeling through an EH [7].
Preprints 118087 g004
Preprints 118087 g005
Figure 5. Various configurations of EHs structures [7].
Figure 5. Various configurations of EHs structures [7].
Preprints 118087 g005
Preprints 118087 g006
Figure 6. Key characteristics of IoT devices.
Figure 6. Key characteristics of IoT devices.
Preprints 118087 g006
Preprints 118087 g007
Figure 7. Categories of IOE.
Figure 7. Categories of IOE.
Preprints 118087 g007
Preprints 118087 g008
Figure 8. Four-layered architecture for the IoT-enabled SHE.
Figure 8. Four-layered architecture for the IoT-enabled SHE.
Preprints 118087 g008
Preprints 118087 g009
Figure 9. Cloud computing features.
Figure 9. Cloud computing features.
Preprints 118087 g009
Preprints 118087 g010
Figure 10. Edge computing classifications.
Figure 10. Edge computing classifications.
Preprints 118087 g010
Preprints 118087 g011
Figure 11. Examples of IoT integration in SEH [44].
Figure 11. Examples of IoT integration in SEH [44].
Preprints 118087 g011
Preprints 118087 g012
Figure 12. General paradigm of cybersecurity in SEHs.
Figure 12. General paradigm of cybersecurity in SEHs.
Preprints 118087 g012
Preprints 118087 g013
Figure 13. Different cyberattack approaches in IoT-enabled SEHs.
Figure 13. Different cyberattack approaches in IoT-enabled SEHs.
Preprints 118087 g013
Table 1. Advantages of SEH over traditional EH.
Table 1. Advantages of SEH over traditional EH.
Features Traditional EH SEH
Communication One-way communication Two-way communication with interaction
Topology Radial Different network topology
Energy generation Centralized Distributed generation, provides support during peak hours when load demand increase
Operation and maintenance Manual monitoring, periodic equipment maintenance Real-time monitoring, prognostic, and event-driven maintenance
Customer participation Limited interaction or none Extensive interaction
Metering Electro-mechanical Advanced metering arrangement that drives the facility to track and regulate energy consumption
Power quality control Less use of sensors and less power quality Contains numerous modules, for example, sensors, smart meters, and technologies on the distribution grid that aid in managing the parameters, such as voltage and power factor, to improve the power quality
Reliability Prone to failure and cascading outages Pro-active, real-time, and islanding
Renewable power source integration Optimized for non- renewable resources Offers essential insights and enables automation for renewable power resources to supply electricity to grids while their management is being optimized
Energy restoration Manual equipment checks and time-based maintenance Self-healing; SEH can anticipate, identify, and respond to faults and outages
Operational cost and wastage at peak hour High at peak hours Low at peak hour due to distributed generation and control over the power consumption
Table 2. Wireline communication technology is commonly utilized in SEH systems.
Table 2. Wireline communication technology is commonly utilized in SEH systems.
Technology Date Rate Coverage Application Advantages Disadvantages Network Type
Broadband PLC (Power Line Carrier) Up to 300 Mbps Up to 1500 m SCADA and backbone communication in t h e power generation sector Existing infrastructure, standardized and high-reliability Noisy channel environment and disturbance Neighborhood area network (NAN), Family Action Network (FAN), and wide area networks (WAN)
Narrowband PLC 10–500 Kbps Up to 3 km SCADA and backbone communication in the power generation sector Existing infrastructure, standardized and high- reliability Noisy channel environment and disturbance NAN, FAN, and WAN
Ethernet Up to 100 Gbps Up to 100 m In-home communication and backbone communication Good for short distances Coverage limitations Premise Network, NAN, FAN, and WAN
HomePlug 4, 5, and 10 Mbps Up to 200 m In-home communication and smart appliances Low cost and energy Coverage limitations and disturbance Premise Network
Fiber Optic Up to 100 Gbps Up to 100 km SCADA and backbone communication in the power generation sector High bandwidth, high data rate and not susceptible to electromagnetic interference Costly WAN
Table 3. Frequently employed wireless communication technologies in the field of SEH.
Table 3. Frequently employed wireless communication technologies in the field of SEH.
Technology Data rate Coverage Application Advantages Disadvantages Network Type
WiMAX 75 Mbps Up to 50 km In-home communication and smart meter reading Low cost and low energy Not widespread, coverage, highly reduced if loss in line of sight NAN, FAN, and WAN
Z-Wave 9–40 Kbps Up to 30 m Wireless mesh network Mesh capability, simplicity, mobility, low cost, and energy Low data rate, short range and poor interference Premise Network
ZigBee 20–250 Kbps Up to 100 m In-home communication, energy monitoring, smart appliances, and home automation Mesh capability, simplicity, mobility, low cost, and energy Low data rate, short range and poor interference Premise Network, NAN, and FAN
Wi-Fi1. 2 Mbps–7 Gbps Up to 100 m In-home communication, smart appliances. Home automation and SCADA Good for short distances Security Premise Network,
3G Up to 42 Mbps 70 km SCADA and smart meter reading Already-existing network, high security, low cost, and large coverage Network shared with customers may result in congestion NAN, FAN, And WAN
4G/LTE Up to979 Mbps Up to 16 km SCADA and smart meter reading Already-existing network, high security, low cost, and large coverage Network shared with customers may result in congestion NAN, FAN, and WAN
LTE-M 7 Mbps Up to 10 km Smart meter reading low cost, and energy and scalability Low data rate NAN and FAN
NB-IoT 159 Kbps Up to 10 km Smart meter reading low cost, and energy and scalability Low data rate NAN and FAN
5G Up to 20 Gbps Up to 500 m SCADA, remote control, and smart meter Low energy, low latency, high data rate, and scalability - NAN, FAN, and WAN
Satellite 50 Mbps - Backup, remote location communication Good when no other alternative is feasible High cost WAN
Table 4. Fog-based SCADA system architecture.
Table 4. Fog-based SCADA system architecture.
Preprints 118087 i001
Table 5. Key attributes of IoT-enabled smart meters.
Table 5. Key attributes of IoT-enabled smart meters.
Preprints 118087 i002
Table 6. Comparison of 5G-based IoT-enabled SEH for the DRPs.
Table 6. Comparison of 5G-based IoT-enabled SEH for the DRPs.
Items Advantages Challenges
Massive links among flows for more accurate and fairer DRP control
  • More precise and accurate DR control thanks to the terminal and built-in controllers.
  • Monitoring user’s consumption in shorter time intervals.
  • Necessity to set accurate financial compensations for users.
  • Users participating in DR programs need to be controlled for their electricity intake.
Fast transfer speed and low latency
  • Removing instability and oscillations during the frequency regulation process.
  • Considering RES in the frequency regulation services in the maintenance of system balance.
  • Costs of adapting faster communication methods.
  • Dealing with the issue of power deviation, system frequency, and area control
Robust security and data privacy
  • Enabling data transfer security and diversified services.
  • Generating network function sets for tailor-made customer services (network slices)
  • Implementing a 5G network slices individual structures
High system stability and reliability
  • Optimized power consumption by the system.
  • Lower system failure rate.
  • Logistical problems due to linking a large number of appliances.
  • Costs of installing the 5G terminal controllers
Energy saving and low power consumption
  • Higher energy transfer capability.
  • Compliance with the sustainable development goals and priorities.
  • Low number of real-life practical testing and applications.
  • Few implementations around the world of testing and applications
Table 7. Outline of attacks associated with EH operation.
Table 7. Outline of attacks associated with EH operation.
Preprints 118087 i003
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Prerpints.org logo

Preprints.org is a free preprint server supported by MDPI in Basel, Switzerland.

facebook logotwitter logolinkedin logo
MDPI Initiatives
Important Links
Subscribe

Disclaimer

Terms of Use

Privacy Policy

© 2024 MDPI (Basel, Switzerland) unless otherwise stated