Communication Concept in Smart Grid Using Internet of Things

Abstract

The use of distributed energy sources in combination with energy storage is gaining widespread attention due to ensuring the continuity of power supply. Artificial intelligence is used to control such a system effectively. This manuscript sheds light on how the communication challenges of the smart grid. Here, the radical and exciting conception based on Internet of Things (IoT) for sharing data information is presented.

1. Introduction

This research on smart grid communication systems based on wireless techniques has a short tradition. As this field is maturing, amounts of methods using algorithms are becoming more and more understandable. A challenging problem in this domain is delivering the required energy supplies to connected loads simultaneously using the available power from renewable energy sources. This problem has attracted more attention in the field of artificial intelligence.

One of the most innovative ways of tackling the issue of communication in smart grid systems is the application of Internet of Things modules. One approach to solve this problem involves using a wide range of frequencies to send data to platforms. This technique enables communication and results from the basic functionality of automatic control. To illuminate the uncharted areas in smart grid systems, systems in this field are indicated with a detailed discussion of the processes occurring in the operation of microgrids. In addition, the functionalities of artificial intelligence in metering data collection were defined.

The critical elements in the tested structure necessary for the system’s proper operation were also indicated. The result of the research is the methodology of controlling the communication system using the Internet of Things. In addition, an invented method aimed at sharing and analyzing the flowing data allows for an efficient energy supply to the receivers.

The chapter begins with an introduction, providing an overview of the topic and highlighting the importance of networking and communications solutions in the context of the Internet of Things (IoT) within the smart grid. Next, the chapter conducts a comprehensive literature review, exploring the existing body of knowledge on networking and communications solutions for IoT in the smart grid. It showcases the practical implementation of networking and communications solutions in a real-world scenario, offering valuable insights and examples from this case study. It delves into the role of AI in optimizing grid operations, improving efficiency, and enabling intelligent decision-making. This section examines the role of Information and Communication Technology (ICT) systems in supporting networking and communications solutions for IoT in the smart grid. It addresses the challenges and opportunities associated with these communication systems, emphasizing their impact on grid connectivity and efficiency.

This section provides a comprehensive overview of the IoT system concept within the smart grid context. It examines factors such as security, interoperability, scalability, and reliability, offering insights into potential mitigation strategies. The discussion section serves as a platform for the authors to analyze and interpret the findings. The chapter concludes by summarizing the key points discussed and highlighting the main takeaways.

2. Literature review

An ever-increasing body of literature shows that smart grids are being studied more and more. Also, in recent years, there has been considerable interest in implementing artificial intelligence into standard systems, e.g., medical decision support systems [1].

Ref. [2] reviewed a relatively new area of research connected with the application of Internet of Things modules. The idea is that monitoring and notifying the occurrence of a fault can be done by the developed system that operates on solar and wind energy sources.

Initial studies on hybrid microgrids supported by artificial intelligence primarily focused on the import and export of solar power, as shown in [3]. Syncing information in the proposed database system is up to be a provider of reports with simultaneous notification. This paper aims to create a visualization data system that makes analysis.

Ref. [4] Projected estimation method in minimization of power losses in distribution and transmission infrastructures. The invented idea uses the assumptions of a smart grid, which combines the possibilities of using Internet of Things modules. Another aspect of microgrids to consider is a storage system due to the insurance of renewable energy sources.

Studies by Minai et al. [5] have led to a more profound understanding of Internet of Things possibilities. The battery charges consider using artificial intelligence to transfer the surplus energy. It can be used to power demand side power management.

The problem of maintaining security in smart grid systems was discussed by Barman et al. [6]. The author employed an AI methodology that prescribes IoT modules’ usage to detect power theft. In addition, the proposed system enables producers and consumers to view uploaded information taken from artificial intelligence-based devices.

Different approaches to managing protection systems aim to identify transmission line faults in the field of smart grids Pradyumna, [7]. The whole process can be done using an IoT-based two-way connection between producer and consumer by periodically monitoring and checking.

Parameters Farid et al. [8] studies is an area of research that emerged from the need for data analysis. The consistent use of IoT can also create a platform for collecting exported data in energy management systems. The architecture of IoT systems was presented, and the services offered to users were distinguished.

3. Smart grid case

A Smart Grid is considered to be an electricity-delivery network based on digital technology. It can supply electrical power to loads with the usage of two-way communication. The system can monitor the energy assessment unit’s status and power delivery through the smart grid’s delivery networks [9]. It can assist in calculating the electrical energy demand by monitoring and identifying degradation to the appliances, which can also help reduce maintenance costs. Furthermore, it can transmit and receive information through the smart grid so that people can know the status of their instruments in different time zones. This helps the healthy management of the power supply chain so that the system works properly.

The smart grid will enable citizens to participate actively in energy provisioning. Additionally, it will accommodate all generation and storage options, as well as facilitate new energy product and service markets, optimize existing asset utilization, offer efficient power quality for digital businesses and smart devices, keep pace with changing prices, and operate resiliently to attack and natural disaster,

Frequency stability and control are among the most critical problems in smart grid design and performance. Control loops try to keep the electricity grid parameters close to their reference value [10]. Each has its regulation component, ensuring stability (low-frequency or high-frequency control). With proper operation, there are critical issues in power system projects and processes. Most of the balance on the demand-supply side is achieved by controlling the output of distributed generating units.

Another crucial parameter to control is voltage. This is one of the leading smart grid operational challenges due to the high use of renewable and distributed sources. Alternative energy sources depending on weather conditions, such as wind speed and insolation, significantly change the voltage profile in the network [11]. As a result, combined generation and load profiles can lead to high system losses due to undervoltage and overvoltage.

Considering [the Smart Grid Reference Architecture working group report], the infrastructure of the studied system comprises five recurrent levels that reflect various goals and procedures as well as roles, data modeling, communication protocols, and elements. The compatibility categories were introduced in the Reference Architecture workgroup review and represented by five levels, as shown in Figure 1. Each layer contains at least two elements referring to smart grid operations.

Figure 1.

Smart grid layers due to the Canelec concept [12].

The representation proposed by CENELEC [8] includes domains in the context of the smart grid architecture, showing the generation, transmission, and distribution of energy from power sources to loads. At the same time, zones represent the hierarchical structure, including components of each domain, as Figure 2 presents.

Figure 2.

Smart grid structure with connections [12].

Generating refers to producing vast quantities of electric power from fossil, hydro, and nuclear power plants, local photovoltaic power systems, and offshore wind farms. Transmission is understood as a representation of the structure and group responsible for long-distance power transportation. Finally, the distributing estate process and entity responsible for supplying power to consumers.

Power sources may be distributed electrical resources are small-scale power generation systems directly linked to the standard electric grid (generally in the range of 3 to 10.000 kW).

Both end users and producers of electricity are housed on customer premises. The property includes a commercial, residential, and industrial structure. Additionally, energy is hosted, including power panels, batteries, and stores for electric vehicles and microturbines.

4. Artificial intelligence in smart system

The Smart Grid Architecture is an electrical grid model that aims to understand how electricity is connected to the energy network. In addition, the model seeks to portray expansion, the installation of the electric grid, and the transition to new technologies and methods for the smart grid.

Artificial intelligence is used to control such a system effectively. This article sheds light on the communication challenges of the smart grid [13]. Here, the radical and exciting conception based on IoT for sharing data information is presented.

The Smart Grid Architecture is a model of the electrical grid that aims to provide an understanding of how electricity is connected to the energy network. In addition, the model aims to portray the installation of the electric grid and the transition to new technologies and methods for the smart grid.

Database systems ‘migrate’ across remarkably various information structures at multiple places and with diverse ownerships because services delivered by smart grid (i.e., general application case) stretch over many areas/regions. As a result, it is advised that the data sets should be classified and tagged.

The diversity and richness of the Smart Grid have reflected as well as the many cell (area/region) specificities, which highlight how challenging it is to handle the integrity of the Smart Grid in its entirety. All relevant organizational and technological elements must be considered for each cell. Additionally, when a use case spans multiple cells, issues are made even more complicated.

According to the Ministry of Power in India, the term Smart Grid covers an appropriately automated power grid in which communication employing IT systems plays an important role—controlling the power flow by monitoring it in the area of the electricity generation zone and the location of its final consumption, taking into account the devices powered by energy. In smart grid systems, load limiting is also used to adjust the generated energy constantly in real time.

Smart grids can be achieved by implementing efficient transmission and distribution systems, system operations, consumer integration, and integration with renewable sources [4]. In addition, smart grid solutions help to monitor, measure, and control power flows in real-time, which can help to identify losses and thus take appropriate technical and management actions to stop losses.

With the use of smart communication systems, it is possible to obtain economic benefits resulting from the reduction of losses during peak load management and in systems performing the transmission and distribution of electricity, as depicted in the Figure 3.

Figure 3.

Smart grid levels with primary operations [12].

In addition, sharing information on energy generation, demand, and supply can enable improved service quality, more appropriate asset management, and increased reliability of renewable energy integration. In the longer term, the use of artificial intelligence in power systems may create opportunities for the creation of self-healing networks.

The ‘Internet of Things’ refers to the network of connected anything. The term is intended to relate to Internet-based protocols, local vocabularies, standards, and application logic [14]. The changed networks then adopt the outlook of the Internet of Everything items throughout to conduct information sensing, exchanging, and communicating to realize smart registrations, locations, maps, and administrations.

Monitoring equipment performance and avoiding unnecessary maintenance using AI replaced the work of many people. Instead of employing routine human inspections, connected sensor technologies can offer a more thorough understanding of the technology that supports and is used in the smart grid [15]. Furthermore, to guarantee there is no waste, the equipment of IoT can be monitored, among other things, for voltage, frequency, and power.

5. ICT systems

Current communication channels must be used, and technological advances should be incorporated to some level depending on the importance and intensity of the operations. They may be used to decrease the expense of establishing such a smart information exchange.

Over the ensuing years, technological advances can increase power systems’ connectivity, intelligence, efficiency, reliability, and sustainability as the technical transition between analog and digital progress.

Technologies for information and communication (ICT), current detectors, big data in machine learning, and IoT are among the techniques to help improve efficiency and reduce carbon emissions of energy infrastructure [16].

The ICT infrastructure is instrumental so that consumers can benefit from new ways to engage in the energy transition and better services based on digital innovations, more efficient energy use, and energy savings.

It is crucial to ensure that the ICT sector operates effectively and sustainably in terms of energy use and consumption. A future-proof architecture that includes shared protocols and reliable servers for future and current generations is required to create digital capabilities.

Appropriate protection, stability, security and moral principles must be maintained in the face of problems brought on by digitalization and the widespread processing of data, especially concerning cyber security concerns.

Various principles for energy efficiency are being developed as a consequence of the growing interaction between individuals and their electronics, what is known as the Internet of Things (IoT). In addition, it can positively affect the power industry, such as enhancing the oversight of energy flows in operating electric smart grids.

A revolutionary role for IoT [17] in power generation, improved productivity, accessibility, and dependability, can be seen in enhancing transparency in the transfer of energy subsidies. Furthermore, IoT may significantly contribute to energy conservation and savings by detecting and monitoring.

This AI-based technique could also enhance bandwidth utilization through systems that rely upon productivity growth brought on by more industrial automation, networks, and equipment. In addition, by allocating loads based on availability, data systems can promote behavioral modifications and lower emissions.

It may expand e-government projects, offering a communications infrastructure to boost the quality of the electric grid and structure reliability, mechanization, and extraordinary interconnection in transit systems, and collecting evidence of energy markers to evaluate advancement with recognition of highlighted issues.

Giving independent users more influence over systems of distributed generation like alternative energy sources, trying to raise customer perception of sustainability and green energy demand, and speeding up the creation of brand-new electricity systems that AI modules may operate.

To handle the fluctuating and decentralized character of renewable sources, IoT-enabling technologies are crucial for expanding the distributed energy sector. It can assist with improving utility performance and expense and integrating an approaching generation of sustainable energy sources. Moreover, further usage can be economically advantageous when coupled with demand-side control initiatives.

Without IoT-enabled proper monitoring, regulation, and supervision, it could be hard to penetrate the market for renewable energy sources further because they are irregular (air velocity for wind, irradiation for sun, and water flow are all unpredictable). Furthermore, to reduce the costs of evaluation, management, and maintenance, IoT enables the application of extensive data analysis [18] and convergence of consumption, delivery, and energy generation.

6. Internet of things-based communication

Services that are hard to accomplish with the use of wires, such as long-range communications, are made possible by wireless operations. The phrase is frequently utilized in telecommunication services to describe major platforms (such as transmitters and receivers, remote controllers, Internet technology, and connectivity) [19] that transmit information wirelessly using one or more types of energy, such as radio frequency (RF), infrared light, laser beam, light waves, sound energy, etc. This way, information is transmitted over billions of kilometers for radio communications and short distances (only a few meters using a television remote control).

Several IoT use cases include applications that require ultra-reliability, real-time connectivity, extremely low latency, extremely high availability, and assured in-time supply. For such applications, the unit-approving concept for spectrum access might be appropriate to control the level of service, avoid undesirable delays, and reduce interference.

Smart grids can gather information from their surroundings and operations and respond in real-time thanks to sensors. As they provide them with the signals needed to collect essential data, sensors are undoubtedly the foundation of smart systems. Moreover, to meet various application goals, several sensors can be implemented.

By identifying high-energy devices and taking immediate action to limit their consumption, smart devices, and electric connectors also enable more accurate energy consumption monitoring across a facility. In addition, IoT sensors can provide adequate energy management solutions when used to their maximum capacity, which is a challenge for system managers as energy prices rise and sustainability goals climb.

The significance of coordinating spectrum utilization across all services is undeniable, as is the growing challenge in achieving this shared objective as spectrum demand rises. For successful communications, spectrum allocation and usage must be coordinated. According to the author in [5], the multi-stakeholder character of spectrum use relates to requests on a finite resource necessary for services to assure, among other things, socioeconomic development, worldwide coverage, and life safety. Moreover, innovative services and solutions require an adequate spectrum in suitable frequency ranges.

It is essential to consider that technology, depending on the scope, will be beneficial in achieving the UN Sustainable Development Goals, reducing the consequences of climate change, and moving toward sustainable human development worldwide. As unpredictable weather phenomena occur widely, monitoring and controlling the environment, weather forecasting, and related services are more crucial than ever. These services also assist in the security of individuals and properties, improving continuous improvement and reducing global warming.

The energy industry (utility services and network operators) highlighted the need for technological advances (LTE-450, LTE-M, and NB-IoT) to function in the 450 MHz frequency range. Also, it is indicated in [20] that the tendency for ‘smart meter’ application in the electrical sector is viewed as ‘critical communication’. Therefore, it will be necessary to significantly enhance data rates and spectrum access to implement techniques based on radio spectrum to smart grids.

Relying on the GSM/EDGE and UMTS/HSPA specifications, LTE, which is Long-Term Evolution, is a communication protocol for portable devices and data centers. It enhances the capacity and performance over those specifications by leveraging a radio broadcasting configuration and network infrastructure upgrades.

The low-powered wireless technology known as NB, or NarrowBand, was created from scratch for large-area machine-to-machine (M2M) connectivity. The wireless connections standard was designed to enable the most stable, power-efficient, and trustworthy sensing interaction.

One prominent LPWAN technology is LoRa (Long Range), which utilizes a low-power, wide-area modulation technique to enable long-range communication with low power consumption. LoRaWAN is the corresponding network protocol that enables secure and efficient communication between LoRa devices and gateways. LoRaWAN has gained popularity for its ability to provide long-range coverage, deep penetration, and low power consumption, making it suitable for applications such as smart cities, agriculture, asset tracking, and environmental monitoring.

Radio Spectrum Policy Group from the Directorate-General for Communications Networks, Content and Technology EC unit [21] proposes a roadmap to access the IoT spectrum in Europe, reflecting different uses and scenarios. Enhanced spectrum harmonization and intervention must be undertaken with caution, especially as it is not cost-effective and needs to take into account the other national circumstances of each country involved. The roadmap recommends focusing on specific types of spectrum bands, which can lead to the more cost-effective use of those bands at the national level.

Today nearly every object on the planet is connected to the Internet to receive and send data. Another related information is that most such devices now have connectivity to advanced wireless communication protocols. For example, we are now able to launch data using protocols, including some data via radio frequency, near-field technologies, Bluetooth, Wi-Fi and radio frequency identification (RFID).

Historically, communication technologies began to develop due to the discovery of electromagnetic waves and the study of their potential. This enabled the use of radio frequencies since 1901, when Guglielmo Marconi transmitted the first intercontinental signal wirelessly, linking North America to Europe [22]. However, the rules of the radio signal and the impact of the atmosphere on radio communications were still a great deal to be discovered. As a result, Marconi played a significant role in radio studies and advancements over the following 30 years.

With the development of technology, a rocket was built that launched the first spacecraft in 1962 [23]. After that, it became a telecommunications satellite that enabled the transmission of communication signals actively to distant places on earth. Radio technology has continued to be developed into a form that allows tracking tags to be attached to objects and automatically identify them [24]. The Pacific NW Bell telecommunications company created a type of wireless phone in the same year that connected the device to a radio transmitter, which attached customers to operators.

In 1983, an object communicating using RFID (Radio Frequency Identification) technology was registered in the Patent Office. This technology is based on offering distinctive identification and backend integration that enables various applications. Multiple parts make up each RIFD electronic system: a scanning transmitter, a receiver, and a reader.

An RFID transponder or analyzer is used when the scanning transmitter and receiver are integrated. Fixed readers and portable readers are the two different categories of RFID transponders, which are network-connected gadgets that can be carried about or set to a surface. It sends information that turns on the tags using electromagnetic radiation. After being turned on, the label returns a signal to the transmitter, which is converted into information.

In the meantime, mobile phones began to be developed, and one of the technology providers, Ericsson, developed Bluetooth radio technology in 1994 [25]. The BluetoothSpecial Interest Group was then formed, which focused on creating an open specification for small-area wireless data transmission for short-range communications, including between computers.

WLAN (Wireless Local Area Network) was introduced to connect many devices simultaneously in 1997 [26]. Based on the IEEE 802.11 standard, the Wi-Fi protocol was defined, allowing computers to connect wirelessly in a locally limited area. However, it described technology that increased our data rates and gave us more excellent channels but did not address efficiency. As a result, the Wi-Fi traffic congestion prevents efficient use of the medium despite the more excellent data rates and 40/80/160 MHz channels used by 802.11n/ac devices.

The continuation of the development of RFID was a particular category named Near-Field Communication (NFC) that allows communication close, invented by NXP Semiconductors and Sony in 2002 and introduced in 2004 [27]. When used by devices close to one another, NFC allows contact or contactless information transmission within a range of one to four centimeters and up to 10 units. While technologies like Wi-Fi and Bluetooth concentrate on radio broadcasts, NFC uses electromagnetic radio fields.

5G is a cutting-edge technology that represents the latest advancement in mobile communication systems. It is designed to provide faster data transfer speeds, lower latency, and increased network capacity compared to its predecessors. While 5G is commonly associated with improved mobile connectivity for smartphones and tablets, its potential extends far beyond that.

One of the significant advantages of 5G is its ability to support a wide range of applications, including IoT. With 5G, the IoT can reach new heights, enabling what is known as massive IoT [28]. This means that 5G networks can support a massive number of connected devices simultaneously, allowing for seamless communication and data exchange between them.

Figure 4 shows a timeline indicating the emergence of protocols supporting Internet of Things communication.

Figure 4.

Protocols supporting the internet of things modules with year implementation.

7. Internet of things system concept in smart grid

We advised starting with a particular issue, learning how the collected information may assist a Smart Grid system in making choices, and then tackling one other potential area when developing the IoT system for Smart Grid.

The first step in creating a smart system solution is to identify areas of improvement, catalog the current infrastructure, and develop the additional equipment needed to get started.

It is a beneficial idea to begin by posing some questions. To project an efficient IoT-based communication system for the smart grid, we chose several areas concerned. We selected several areas, including establishing measurements, recognizing the issue or opportunity, starting solid evidence, choosing patterns, and making an itemized list of the current systems and information.

There are a few established standard ways for setting up a Smart Gird. In almost any location, it can emerge. Despite the variations, several standard procedures have to be taken into account.

It is essential to consider maintenance while you develop a smart grid solution, set up new equipment, or introduce new communication technologies [29]. You might not be able to resolve problems if it was involved in the creation of apps for smart systems.

You might add many devices to your Smart Grid system to build a network protocol for your IoT devices, increasing the area covered and exposure. However, you could significantly lower the surface area and susceptibility if the connected technology does not connect with your core network directly but rather with a gateway that reduces the attackable surface.

When planning cost estimates, it is vital to remember that while a strategy to manage Smart Grid system space might be implemented in months, a solution to increase security might take much longer. To be sure the IoT-based communication system is safe, time should be anticipated to create specialized hardware.

Once a course of action has been chosen, gather the aid of existing staff and outside collaborators to develop a verification that will allow you to validate the solution. Again, trying out technology in your context, gathering data, and assessing performance may determine goals.

Including determining areas of concern and potential fields to develop, making a list of the current system and data, establishing configuration parameters, choosing layouts and launching a proof-of-concept test [30]. Figure 5 shows the five fields listed above with crucial questions in each area.

Figure 5.

Steps toward implementation of IoT-based communication system.

8. IoT communication issues

From defining IoT goals and using cases that are appropriate for the smart grid, choosing the required IoT elements that are appropriate for the used model, building, and prototyping with optional integration and IoT applications with some other cutting-edge innovations and applying necessary security measurements, there are still some challenges during IoT implementation.

Tools used for IoT analysis assist in evaluating the information gathered by IoT sensors to enable more informed decision-making. Although there are unique obstacles to IoT, general data analysis problems equally apply to IoT implementation.

Adding more sensors to your system raises the likelihood of cyberattacks, although the IoT initiatives offer multiple economic prospects. More IoT-connected devices are susceptible to cybersecurity breaches, according to research [2]. As a result, the main obstacle to the adoption of IoT protection.

IoT platform consists of various instruments, scanners, and gadgets, and every supplier tries to set the mainstream technology. Therefore, interoperability of IoT modules with current systems is essential for effective deployment. Integrated cloud solutions, a dearth of defined M2M communications, and differences in technology and data processing between IoT systems are some inconsistencies.

Although some IoT systems operate on AC power, such structure contains equipment employed in harsh environments and relies solely on their battery for power. Businesses may keep records of whether an IoT device’s battery has to be charged or updated. Generate a responsible IoT system by discovering gadgets that create or preserve energy while not being used. Replacement can be difficult, particularly if a device is positioned in a challenging-to-access location.

When IoT technologies are deployed, the dataset expands rapidly. As a result, owners require powerful information processing technology, large-sized storage, and increasing data transfer to collect IoT data and execute analyses.

Unstructured data collected by IoT devices is challenging to analyze. For example, if the surroundings or components around the detectors are unstable, the information gathered may even have abnormalities. Such data quality problems must be found to enhance decision-making.

Artificial intelligence capabilities, including IoT, require high-quality and readily accessible data. Therefore, an operator needs to utilize the appropriate data sets and have a reliable supply of pertinent information that is fresh, available, well-governed, and protected to offer the most effective and up-to-date AI algorithms.

Large-scale data accessibility is required for most artificial intelligence technology solutions to develop strategies. However, even though producing enormous amounts of data opens up more business prospects, doing so also raises storage and safety concerns.

Needful information must be safeguarded from stealing or tampering, while artificial intelligence largely depends on knowledge for forecasts and recommendations.

Because AI systems are sophisticated, installing and training them takes time before they become suitable. Therefore, one of the main benefits of employing AI techniques is the implementation time delay.

Maintaining confidentiality, safety, and privacy in the communication system is necessary. Another challenge concerning IoT technology is using resources and spectrum, where the evaluation of dynamic architecture and network functions is needed [31].

A platform for communications requires wireless energy and data transfer integration to improve power generation and sharing. Creating remote access strategies with coding and sequencing may also be a rigid demand, as well as managing resources and interference.

Challenges during IoT-based communication system implementation can be sorted into six categories. The areas of issues are shown in Figure 5.

9. Discussion

The process of their validation based on a worldwide ISO standard is a crucial one that is currently at the beginning stage but is predicted to contribute considerably to the ongoing advancement of intelligent energy structures in the area of communications, as Figure 6 illustrates. The development of sustainable power architecture must be built on recognized, interconnected solutions that guarantee the level of services rendered, security with electricity, and information assurance [32]. By taking assessment metrics into account and documenting the communications patterns, this procedure can provide the essential basis for ongoing advancements.

Figure 6.

Main fields of IoT-based communication system implementation issues.

For management services or specifications to be widely used and, correspondingly, for the entire agreement to be reached in the design, maintenance, and support of current energy regulations, it is essential to defeat competitive monopolistic efforts. To guarantee the continued deployment of smart grid technology, concerted efforts must be made to fix the problems and flaws discovered in the current norms and create brand-new ones [7]. This specification will offer a collection of rules, instructions, and methods to make sure that all essential components needed for an intelligent system to succeed are noticed.

The numerous options in the present rules and regulations that can define the layout, scheduling, sequence, management, and restoration of faults during information transmission make an overriding factor that poses an energy infrastructure challenge for designers [19]. This is to simplify the reality that existing standards of regulation cross over one another, causing major misunderstandings and operational inadequacies.

10. Protocol example for smart grid

First, it is important to identify the specific requirements of the smart grid system. This includes understanding the communication needs, data collection requirements, security considerations, interoperability requirements, and any specific constraints or limitations [33].

Next, key use cases and scenarios that the protocol needs to support should be defined. This involves identifying interactions between different components of the smart grid system, such as smart meters, sensors, controllers, and the control center for demand response.

The communication architecture for the smart grid system needs to be determined. This includes defining the layered architecture, components, and interfaces involved. Consideration should be given to the different communication technologies and protocols that may be used, such as LPWAN, SCADA protocols, or IoT protocols like MQTT or CoAP.

Designing the message formats is another crucial step. This involves specifying the data fields, encoding formats, and metadata for the messages exchanged between the smart grid components.

Based on the identified requirements, use cases, architecture, and message formats, the communication protocol for the smart grid system can be developed. Factors such as reliability, security, scalability, and interoperability should be considered during the design process. This may involve defining message exchange patterns, error-handling mechanisms, and synchronization protocols.

Once the protocol is designed, it needs to be implemented and tested. Software libraries should be developed to implement the protocol, and interactions between different components of the smart grid system should be tested through simulations. This ensures that the protocol meets the identified requirements and use cases.

Gathering feedback from stakeholders and experts is essential for refining the protocol. Their input can help identify areas for improvement and make necessary adjustments, updates, or enhancements to the protocol.

Formally documenting the protocol specifications and considering standardization is important for broader adoption and interoperability in the industry.

Lastly, it is crucial to continuously monitor the evolving needs and advancements in the smart grid domain. Staying updated with new technologies, security measures, and industry standards ensures that the protocol example remains relevant and effective over time.

By following these steps, a protocol example can be developed for a smart grid system that addresses the specific processes and requirements, enabling efficient and secure communication between different components and facilitating effective management and control of the grid.

To deploy a communication infrastructure with LPWAN in the smart grid, several steps need to be followed and they are shown in Figure 7.

Figure 7.

Communication infrastructure with LPWAN deployment.

First, the coverage area for the LPWAN network in the smart grid must be determined. This involves identifying areas that require connectivity and strategically deploying LPWAN gateways to provide comprehensive coverage.

Once the coverage area is established, the LPWAN gateways need to be installed and configured. These gateways will connect to the LPWAN network server, which manages the gateways and handles communication with IoT devices. Security protocols, encryption, and authentication mechanisms should be implemented to ensure secure communication and protect the network from unauthorized access.

Next, IoT devices specific to the smart grid, such as smart meters, sensors, and controllers, should be identified. These devices should be equipped with LPWAN communication modules compatible with the chosen LPWAN technology. They are then deployed across the smart grid infrastructure, considering factors like coverage, data collection requirements, and reliable connectivity.

The LPWAN gateways need to be configured to efficiently transmit data from the IoT devices to the LPWAN network server. This ensures a reliable flow of information throughout the network. Additionally, connectivity should be established between the LPWAN network server and cloud/edge computing resources. Data ingestion mechanisms are set up to transfer collected data to the cloud/edge infrastructure for further analysis and storage. The cloud/edge infrastructure should be configured to process, store, and analyze the data, providing valuable insights for decision-making.

To facilitate communication between IoT devices, gateways, and the LPWAN network server, the chosen LPWAN protocol (e.g., LoRaWAN or NB-IoT) should be implemented. This ensures efficient and secure communication. Compatibility and interoperability with other existing communication protocols and systems in the smart grid environment, such as SCADA or distribution management systems, should also be ensured.

Robust security measures, including encryption, authentication, access control, and intrusion detection systems, should be implemented to safeguard the communication infrastructure and protect transmitted data from potential threats. Regular updates and monitoring of security protocols are necessary to address emerging threats and vulnerabilities.

Integration mechanisms should be established to enable seamless data exchange and interoperability between the LPWAN communication infrastructure and existing systems and technologies in the smart grid. This ensures smooth coordination and data sharing between LPWAN-based IoT devices and other systems like SCADA or grid management platforms.

Finally, scalability and flexibility should be considered when designing the communication infrastructure. It should be able to accommodate the addition of new IoT devices and gateways as the smart grid evolves and grows. The infrastructure should also be flexible enough to adapt to future advancements in LPWAN technology and emerging requirements, allowing for the integration of new technologies seamlessly.

By following these steps, a robust and efficient communication infrastructure with LPWAN can be deployed in the smart grid, enabling effective data collection, analysis, and decision-making processes.

11. Conclusion

This article proposes innovative communication protocols for the unique problems of smart grid applications that must be considered while developing and implementing architectural designs. Due to the variability of structures and the changing nature of their area of operations, complexities must constantly be reduced, development works must be completed more quickly, and additional capacity must be added.

The supply of interconnected technical solutions that guarantee the accessibility of the electrical system parts and decrease the danger of different systems dropping in value is necessary for creating smart grids. Furthermore, to improve quality with strict policies, effective integration requires a straightforward, unambiguous approach to delivering end-to-end network connectivity rooted in proactive and compatible standards.