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Article

Communication Network Standards for Smart Grid Infrastructures

by
Konstantinos Demertzis
1,2,*,
Konstantinos Tsiknas
3,
Dimitrios Taketzis
4,
Dimitrios N. Skoutas
5,
Charalabos Skianis
5,
Lazaros Iliadis
2 and
Kyriakos E. Zoiros
3
1
Department of Physics, Faculty of Sciences, Kavala Campus, International Hellenic University, 65404 St. Loukas, Greece
2
Faculty of Mathematics Programming and General Courses, School of Civil Engineering, Democritus University of Thrace, Kimmeria, 67100 Xanthi, Greece
3
Department of Electrical and Computer Engineering, Democritus University of Thrace, Vas. Sofias 12, 67100 Xanthi, Greece
4
Hellenic National Defense General Staff, Stratopedo Papagou, Mesogeion 227-231, 15561 Athens, Greece
5
Department of Information and Communication Systems Engineering, University of the Aegean, Samos, 83200 Karlovassi, Greece
*
Author to whom correspondence should be addressed.
Network 2021, 1(2), 132-145; https://0-doi-org.brum.beds.ac.uk/10.3390/network1020009
Submission received: 31 May 2021 / Revised: 27 July 2021 / Accepted: 28 July 2021 / Published: 3 August 2021
Browse Figures
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Abstract

:
Upgrading the existing energy infrastructure to a smart grid necessarily goes through the provision of integrated technological solutions that ensure the interoperability of business processes and reduce the risk of devaluation of systems already in use. Considering the heterogeneity of the current infrastructures, and in order to keep pace with the dynamics of their operating environment, we should aim to the reduction of their architectural complexity and the addition of new and more efficient technologies and procedures. Furthermore, the integrated management of the overall ecosystem requires a collaborative integration strategy which should ensure the end-to-end interconnection under specific quality standards together with the establishment of strict security policies. In this respect, every design detail can be critical to the success or failure of a costly and ambitious project, such as that of smart energy networks. This work presents and classifies the communication network standards that have been established for smart grids and should be taken into account in the process of planning and implementing new infrastructures.

1. Introduction

Electricity networks are a point of friction in the current era which is characterized by the waste of natural resources, the destruction of the climate and economic austerity, as they can be a starting point for the beginning of a new era. Therefore, it is extremely important to support the transition from the outdated infrastructure of the existing electricity transmission networks to a new smart model of production, management and distribution of electricity [1].
Smart grids use digital technology, artificial intelligence, and advanced communication techniques to monitor, control, and manage electricity to meet the varying needs of their customers. In particular, smart grids are able to coordinate the needs and capabilities of all the market entities (i.e., producers, network operators, consumers), so that all parts of the system operate optimally, thus minimizing the financial and environmental costs and maximizing, at the same time, the stability and reliability of the provided services [2].
In this regard, the design and implementation of a secure and cost-effective electricity supply network requires the integration of an efficient, reliable, and interoperable communication system [3,4,5].
Based on the semantic approach of smart grids, standardization organizations guided by the object-mapping processes have adopted a holistic conceptual model of architecture, which classifies energy networks into seven sub-sectors. Specifically, these sectors are the generation (bulk and non-bulk), distribution, transmission, operation, service providers, customer, and markets. This conceptual approach provides the architectural background for describing and analyzing the interoperability of current standards, as well as for developing new ones. A conceptual representation of smart energy grid architecture based on distinct operating areas is shown in Figure 1.
Within this architecture, the individual domains interact with each other, respectively, through electrical or communication flow, and by using several available communication architectures, including wired, wireless, or power line communication infrastructure networks, the corresponding interfaces, and the appropriate protocols. A brief description of the interconnection modes is shown in Figure 2.
The five basic principles of designing an integrated and technically sound communication system for the application in smart grid networks are [2,6,7]:
  • Interoperability: The architecture of smart grids and their components, both in terms of hardware and software, and refers to their ability to interact directly with each other. This allows easy and efficient exchange of information without disturbing the end user.
  • Interconnectivity: The ability to communicate through all available means of participants in the energy ecosystem (stations, substations, machines, devices, sensors, applications, and people).
  • Classified access to information: The provision of easy and instant access to useful information, to and from all points of the energy process.
  • System monitoring: The cyber-physical systems that support all processes, collecting and visualizing information in almost real-time.
  • Decentralized decision-making: The cyber-physical systems that usually take optimal decisions autonomously. A hierarchically higher level usually intervenes only in cases of conflicting objectives.
Taking into account the above design principles, we can conclude that tasks such as data analysis, remote preventive/predictive maintenance, and on-demand use of equipment are essential functions for smart grids [5,8,9]. The work aims to clearly identify all the issues regarding the communication standards that have been established for smart grid networks in order offer interoperability, privacy, and confidentiality among their functional components.
The study is organized as follows: Section 2 gives a detailed description of the network standards that support communication in smart grid infrastructures. Section 3 presents a discussion on our study, and finally, the last section draws the conclusions and outlines future research directions.

2. Network Standards That Support Communication in Smart Grid Infrastructures

Given the constantly evolving field of telecommunications, standardization organizations provide standards that clearly define the communication parameters between all the key components in a smart grid network [2,7,10]. The various smart grid applications and the specific features of the employed telecommunication technologies are presented in detail below.

2.1. Smart Transmission Systems

The objective of power transmission systems is to transmit power from one point to another in a reliable, safe, and environmentally friendly way [2,3,11]. Typical transmission systems include the flexible AC transmission systems (FACTS) [3,12] and the high-voltage direct current [1,3,6,13]. FACTS technology is used for the reliable transfer of large amounts of energy. However, in cases where technical or economic expediencies require the transmission of energy over long distances as well as the interconnection of asynchronous electrical networks, the use of HVDC technology is mandatory. Thus, the main requirement that must be met is the complete integration of the advanced technology equipment of the FACTS and HVDC transmission systems towards the optimization of the load flow and stability of the overall network.
The sub-stations that support the monitoring and coordination processes of FACTS and HVDC technologies typically use serial connections, as defined by the IEC 60870-5-101 & 104 and DNP3 Secure, at low bit rates of 64 kbps. Even when Ethernet technology is used, transmission rates remain at extremely low levels, as no interface is designed to utilize higher bandwidth, which in turn refrains them from sending large amounts of data. In addition, the IEC 60870-5 [14] standard cannot fully cooperate with the range of requirements of IP network technology, which leads to operational incompatibilities. This type of communication is only possible and is covered by IEC 61850, Ed. 1.0–2.009-12 42/136 and later, as the data exchange in these standards is purely based on TCP/IP and provides interfaces for HVDC and FACTS [7,12,13].

2.2. Blackout Prevention Management

Energy transmission networks use mechanisms that process large data sets which, with the help of appropriate algorithms, can make real-time decisions about managing the load flow in the grid. These decisions are then compared with the operational limit values of each individual network equipment to timely inform the operator for overloads, limit value violations or general malfunctions [15]. In addition, the analysis of the load flow provides the mathematical background required by optimization procedures so that appropriate corrective actions can be taken with high accuracy [16,17].
The need for distributed production disperses the energy production centers and, consequently, the exchange of information has to be performed over large geographical areas. Therefore, there is a need for the incorporation of specialized, bandwidth-demanding measurement systems, such as power quality control systems, remote monitoring technologies, and system integrity protection schemes (SIPS) [18], which minimize the possibility of an extended power outage in case of an emergency.
A basic condition for meeting the above requirements is the deployment of a telecommunication protocol that can support broadband communications services. In addition, it is also necessary to have an extensive network of sensors, such as the phasor measurement units (PMU) [19,20,21,22], which can be implemented in various topologies and use different data and protocol formats [23,24]. However, IEC 61850 [14] is the only internationally recognized standard used to exchange high-, medium-, and low-voltage metering data with support for broadband TCP/IP connections. In addition, IEC 61850 complies with IEEE C37.118, which allows the integration of synchrophasors [20].

2.3. Advanced Distribution Management

The energy distribution network is the most advanced part of a smart grid as it is supported by information technology on issues such as security and optimization. These networks consist of high-voltage networks, which feed medium- and low-voltage subnetworks through power stations and substations, which subsequently supply electricity to the end consumers [25,26]. The management system includes all the functions required for the distribution network to operate efficiently [27,28] and its main responsibility is the monitoring of the energy demands as well as the control of the energy exchange in the distribution system.
Given the wide coverage area of the distribution networks and the diversification of the employed telecom equipment, the communication specifications for these networks are extremely complex [7,29]. As an example, the control mechanism requires standards that provide operational support to technologies and automation systems including remote control surveillance, geographic information system (GIS), forecasting demand, and fault management etc. [30,31,32,33].
These requirements are met by a set of available broadband services that are IEC 61968 compliant [34,35], while this standard also provides compatibility in terms of network optimization, maintenance, and expansion. In addition, the harmonization of IEC 61968 with IEC 61850-7-420 and the adoption of an open architecture improves network scalability, whereas the integration of intelligent measurement models (advanced metering infrastructure, AMI), home networking (home area network, HAN), and their interfaces allows for demand forecasting and modeling of load behavior at the end-user level [36,37].

2.4. Distribution Automation

The electricity distribution system in countries outside Europe includes distribution sub-stations which supply energy through long-distance lines (250 Km or more). As a result, in these systems voltage losses and power outages are very serious problems, which may lead to social problems and significant revenue losses for utilities [38]. To overcome these problems, over-the-air distribution lines are supported by Sectionalizers & Reclosers [39], which can be used to reshape power in the event of a disturbance. Furthermore, specialized equipment, such as voltage regulators, power regulators, and fault indicators, are utilized to ensure optimal operation and assist troubleshooting [27,35].
The introduction of microprocessor-based intelligent electronic devices (IEDs), together with cost-effective communication technologies, upgrades and automates the processes of energy distribution and rapid debugging and isolation of malicious material [40,41,42]. Therefore, based on the effective automation of the distribution process, utilities have the opportunity to create new business models of highly reliable power supply for both critical and industrial applications as well as residential consumers.
Conversely, the structure of the power distribution network in Europe is based on a completely different approach. The backbone of this structure is a very dense grid of a large number of distribution substations while the energy transmission lines typically have a length of 5 to 20 km and the average number of served customers per single distribution feeder is usually less than 1000. Additionally, the connection of the loads to the grid is performed with precise programming and measurements, which lead to highly symmetrical loads. Furthermore, the distribution of substations are fully automated and include microprocessor, protection relays [43], actuators, etc., so that they can be remotely controlled in real time [1,16,32].
Low-voltage transformer stations operate manually, as there is no incentive to automate distribution feeders because, in the event of disruptions, the number of affected customers is low, as is the amount of revenue loss. Nevertheless, the increasing integration of diffuse generation systems, such as low-voltage photovoltaic systems or medium-voltage wind turbines, creates voltage quality problems and makes it difficult or impossible to protect them with conventional non-automatic surge protectors [7,24,38].
For the automation of the distribution systems, the remote control and the supervision of the secondary substations and transformers, is considered vital. Therefore, the exchange of information between them and the management system must be based on common operating protocols, which will provide, among other things, cybersecurity. This directly suggests that they must also support all communication technologies, given the different geographical conditions and infrastructures encountered. Furthermore, the implementation of protection systems should meet the individual specifications of distributed generation systems and, more generally, the automation of the European distribution system and consider the developments in the technological specifications of micro-networks [14,23,44].
IEC 61850-7-4 covers a very large set of use cases for various types of distribution automation and supports the most prevalent information data models. Moreover, for the interconnection of distributed power sources and their remote monitoring, standardizations for the development of a crosslinking profile between IEC 61580-7-420 and IEC 61968, respectively, have been proposed. Finally, IEC 61970 provides a set of guidelines and techniques for active energy distribution and efficient operation of the energy management systems [34,45,46,47].

2.5. Smart Substation Automation

The technological development of large-scale integrated circuits, which led to the current availability of advanced, fast, and powerful microprocessors, has resulted in the development of automated digital substations which, by utilizing IEDs, are able to perform functions, such as local and remote monitoring, equipment supervision, etc. Furthermore, the use of digital technology improved the interoperability and communication between devices of different suppliers and technological generations [48,49]. The specific technology, which is also referred to as the process bus, contributed to the interoperability between heterogeneous systems and the configuration of functions that are based on open architecture to make the system viable. Regarding the communication between devices, their absolute synchronization is required to perform the exchange of large data sets with small latency and precision, in addition to the use of open communication standards such as Ethernet, TCP/IP, and XML to achieve interoperability.
The above goals can be accomplished by standardizing the synchronization process of IEC 61850-9-2 functions as well as wideband technologies that support quality of service (QoS), such as 5G telecommunication networks [50,51]. Concluding, IEC 61850 is the only international standard for substation automation that is based on open architecture and incorporates specifications for sampling and timing synchronization based on IEEE 1588 in local and in wide area networks [14,49].

2.6. Distributed Energy Resources

The decentralized production of electricity is paralleled with the so-called virtual power plants, which are a collection of small and very small decentralized power plants, monitored and controlled by a high-quality management system. Successful operation of a virtual power plant requires an efficient and reliable telecommunication network through which the management system monitors, manages, and optimizes the operation of decentralized units [28,48].
In large virtual power stations, control systems based on protocols, such as IEC 61850 and IEC 61400, can be implemented. Given the increasing number of virtual power stations, it is expected that the communication channels and protocols will play a crucial role and the conventional telemetry technique will be gradually replaced by more advanced communication techniques based on TCP/IP protocols. Transmission techniques via the power lines (Power Line Communication–PLC) could also be utilized [44,48,52,53].
The IEC 61850-7-420 standard is compatible with the majority of modern equipment and dispersed energy production systems. However, there is no complete mapping of the IEC 61850- 8-x communication protocols, as well as the respective protocols developed for wind energy systems in IEC 61400-25 [54,55]. Furthermore, no system configuration language (SCL) has been developed for the communication between the dispersed energy sources in IEC 61850-6-x, while a major drawback is the fact that the electrical connection points (ECPs) are often of specific configuration and are compatible only with specific systems/regions [7,14].

2.7. Advanced Metering Infrastructure

In the smart grid infrastructure, the system that assumes the special role of interconnecting the distribution network with the systems of smart metering, building automation, industrial automation [56], e-mobility, and distributed energy resources is the advanced metering infrastructure (AMI). It includes data collection and analysis systems as well as energy audit systems, and provides interactive communication between customers, suppliers, utilities, and service providers [9,41]. Among others, AMI includes specifications for network monitoring, power quality monitoring, fraud detection, load leveling, recording of capacity utilization, load/source-shedding, and remote switching procedures.
All available wired or wireless communication systems can be used for the AMI integration (markets, transmission, bulk generation, non-bulk generation, distribution, customer, service provider, building, industry, e-mobility, and foundational support systems) [53,54]. The main standards regarding AMI are IEC/TR 62051, IEC 61968 to 9, and AEIC Guidelines v. 3.0, as well as a vast array of parallel and even conflicting standards with many differences in functionality developed in different countries/geographic areas (e.g., China: GB/Z 20965, USA: ANSI/ASHRAE 135-2008/ISO 16484-5 BACnet), without the clear definition of distinct subsets of common semantics. This makes it difficult to define a common set of cross-cutting requirements between standards to facilitate the exchange of classified information [38].
It should be mentioned that there is a process underway in order to extend IEC 61850 to include the ((DLMS—Device Language Message Specification)/(COSEM—Companion Specification for Energy Metering objects), thus promoting the coexistence of smart application networks. The extension will include a set of interoperability characteristics of processes such as energy measurement, load control, pricing, exchange of information, modeling data management events, etc. [7].

2.8. Smart Metering

Smart metering systems allow consumers to play an active role in the operation of electricity markets, as they are the gateway to access the new grid. In particular, they utilize applications which allow interfacing with home automation systems, remote access to energy billing data, collection of additional information on grid operation, power quality and downtime, as well as information on consumption and consumption-based pricing, firmware updates etc. [4,9,41].
Smart metering is based on the IEC 62056–x and IEC 61334-x standards which support the most well-known wired and wireless communication technologies, including IPv6 [7]. It is based on open and flexible specifications, and efforts are made towards the creation of a user-friendly management environment [57,58] as well as to ensure the protection of user privacy and secure data transmission (cryptography integration, blockchain, etc.). Standardization and compliance of equipment and software with specifications of DLMS/COSEM technology is also supported [59].

2.9. Demand Response/Load Management

The demand response and load management mechanisms are related to the rational distribution of electrical loads on the network, taking into account the requirements of the users [58,60]. Therefore, these two mechanisms are directly related to the smart metering system, the automation of home energy management, and especially to the distributed energy production.
Regarding distributed energy production, it should be emphasized that it is a rather complicated process in comparison to the easily regulated production of electricity from measurable natural resources (fossil fuels, nuclear fuel, etc.). The energy production from renewable sources (solar, wind, hydroelectric, etc.) is not entirely predictable as it includes several uncertainty factors. Given also that the worldwide share of the regulated power is constantly decreasing, the challenges in the management system create a new specialized area of study and research. In any case, the management mechanisms that will emerge from the research in this area will benefit from a highly reliable and high-speed telecommunication system.
In conclusion, an optimal load management process requires the modeling of energy data which must be detailed and continuously available including load profile information, production information, etc. [7,61]. These requirements are met by IEC 61968 and IEC 61850-7-420, which support all TCP/IP-based wired and wireless communication technologies including IPv6. A Distributed Energy Management System and a Home and Building Electronic Systems/Building Automation and Control System (HBES/BACS), respectively, are supported by a wide set of standards (ISO 16484 series, ISO/IEC 14543-3, EN 13321 series, EN 50090 series, EN 50428, EN 50491 series, China: GB/Z 20965, USA: ANSI/ASHRAE 135) which require upgrades/modifications in order to enable their interoperability through appropriate interfaces [62].

2.10. Smart Home and Building Automation

According to ISO 16484-2 and ISO 16484-3, HBES/BACS refers to the equipment required for automatic control, monitoring, manual intervention, and management of optimization services, including outdoor installations and other equipment [10,63]. The main benefit from the development of smart grids exclusively concerns the distribution and the forecasting of energy loads. A main objective of HBES/BACS technologies is the reduction of the consumed energy through the use of energy optimization functions as well as the cost reduction that can be achieved by appropriate load distribution based on graduated pricing (cost reduction per kWh) and, of course, the conventional power limitations on a case-by-case basis [61,64,65].
In addition, in the context of smart grids and the development of smarter billing methods, the ability to handle additional billing information is a prerequisite. Finally, HBES/BACS systems can handle or integrate in their operation, alternative sources (for instance wind turbines), as well as energy storage systems (for instance electric cars). This leads to a semantic and syntactic compatibility requirement between HBES/BACS systems and AMI technology [10,64].
The operation of HBES/BACS integrates all TCP/IP-based wired and wireless communication technologies through the establishment of the following standards: ISO 16484 series, ISO/IEC 14543-3, EN 13321 series, EN 13757 series, EN 50090 series, EN 50428, IEEE P1701 to IEEE P1705, EN 50491 series, ISO/IEC 15045, ISO/IEC 15067-3, ISO/IEC 18012, China: GB/Z 20965, USA: ANSI/ASHRAE 135 together with other protocols such as OpenHAN, HomePlug AV & C&C, Z-wave, ITU G.9960, etc. [7]. It should be noted that the standardization of common semantics, data models, and cooperation methods between AMI and HBES/BACS is pending.

2.11. Electric Storage

The electric grid operates under the assumption that the energy is produced and consumed at the same time. This means that the transmission and distribution systems are designed to be able to meet the maximum and not the average power flow, resulting in the underutilization of their components. Energy storage can enhance the reliability of the grid, allowing a more efficient use of baseload generation, while facilitating greater penetration of renewable energy sources. Furthermore, it can be implemented on a large, medium and small scale, while it can be distinguished between real electrical storage, (i.e., storage of electricity that can be introduced directly into the system), and storage in an alternative forms in buffers, (e.g., hydrogen, thermal, etc.) [7,66,67].
In electrical power systems, various forms of energy storage can be utilized, with the most common being the use of batteries (for instance lead-acid, lithium-ion, sodium, etc.). Furthermore, in a broader sense, energy storage can be carried out by storing water in hydroelectric power plants or by storing compressed air, or by producing and storing hydrogen, etc. Distributed energy storage can aid towards the efficient handling of load fluctuations, acting as a manageable means of loading and unloading power from the grid, when necessary. Furthermore, distributed energy resources can be connected directly to the distribution network or integrated into HBES/BACS.
A key prerequisite for the various forms of storage is safety in relation to the specifications of the equipment and the way it is handled. Moreover, in order to be successfully integrated and operate in a smart grid environment, including HBES/BACS [62], the latter should be informed on their electrical potential as well as their pricing policy. In addition, effective maintenance planning and scheduling is considered necessary. Robustness, cyclical self-discharge consistency, start-up time, lifespan, and power efficiency are also critical factors for their efficient operation [52,68]. In addition, an essential requirement concerns the communication with the energy resources incorporated in the network, while for the various forms of energy storage, appropriate communication protocols and data modeling for information are required, such as energy storage type, charging status, load history, availability, etc.
The standards that support these requirements are IEC 61850-7-410 and IEEE P2030.2 [67], while there are not any standards for storage devices other than hydroelectric (IEC 61850-7-410) [49]. Moreover, no appropriate specifications have been yet developed regarding the amount and type of data to be exchanged between smart grid and energy storage systems. Finally, specifications for charging storage devices as well as their periodic inspection should also be developed [6,7].

2.12. E–Mobility

E-mobility includes the use of fully electric or hybrid vehicles and is one of the main perspectives of smart grids both on its own (e.g., a vehicle-to-vehicle energy exchange scenario, based on cloud technology, can be found at [69]) as well as a method of energy storage, given that the full range of possibilities of this technology can be achieved only after smart grid architecture is fully implemented [7,70].
The specifications of the vehicle’s batteries should be standardized and meet the minimum requirements of charging cycle and power stability, which are dictated by the specifications of the overall network to function as an integral part of it. The safety and electromagnetic compatibility (EMC) requirements must also be met in full [71,72].
While two-way communication is not required for electrοmobility, its integration into a smart grid requires information exchange between the smart grid and electric vehicles. This communication requires the use of appropriate communication protocols and data modeling within a framework that will allow the semantic understanding of the exchanged information.
Although there is a lot of standardization activity as shown in Table 1, the data models and protocols within ISO/IEC 15118-1, -2, and -3 are not precisely identified. The same holds true for the IEC 61851 set of standards regarding the choice of plug and socket (1/3-phase host carrier, 400 V, 63 A) [7,66].

2.13. Condition Monitoring

The electricity grid faces several challenges given the ever-increasing demand, the requirements for continuous improvement of its availability, and the provision of quality value-added services in the most efficient way. Key objectives in overcoming these challenges are the extension of the average life cycle of the equipment and the minimization of its maintenance costs. These objectives are based on smart grid functions such as real-time monitoring of the network, real-time infrastructure quality control, forecasting and early diagnosis of faults, predictive maintenance, etc. Specifically, a condition monitoring function provides all the technical information required for maintaining availability and, at the same time, for maximizing efficiency in the energy supply chain, thus helping to optimize the network by preventing unplanned downtime or hardware failures. The condition monitoring function also includes specialized processes such as monitoring of transformers (refrigeration, lubrication, contact opening speed, switch operating time, etc.), insulators, ground, wiring lines, ARRESTER mechanisms, etc. [52,73,74].
Regarding telecommunications, the deployment of a system that meets the measurement and control procedures requirements is essential, as well as the description of uniform data models for all subsystems of the smart network. Although there is a lot of standardization activity in this field, the basic communication standards are IEC 61970 (EMS) & IEC 61968 and IEC 61850, which are supported by most wired and wireless communications technologies [34,35].

2.14. Renewable Energy Generation

Given the energy crisis, the depletion of natural resources, the destruction of the environment and the growing electricity demand the production of energy from renewable sources seems to be the only feasible solution to the upcoming energy crisis [38,75,76]. However, as such a solution is not based on quantifiable natural resources, it does not provide a solid basis for planning and leads to performance uncertainty, which in turn creates many and diverse challenges [77,78].
Smart Grids, in terms of their interconnection with renewable energy sources, are facing various operational issues per sector of operation such as:
  • Wind energy (control, certification, wind-generator design requirements, measurement, and evaluation of generated energy, etc.).
  • Solar energy (testing, certification, interconnection, protection of photovoltaic systems, measurement, and evaluation of produced energy, etc.).
  • Marine power (design requirements for marine energy systems, wave performance evaluation, energy converters, etc.).
  • Fuel cells (safety of fuel cell power generation systems, fuel efficiency and testing, etc.).
  • Pumped storage (hydraulic turbine tests, storage pumps, etc.).
Overall, given the tremendous development of the sector and its standardization process, we can conclude that smart grids include technical standards that enable the interconnection of different scale systems as well the procedures of testing, maintenance, and management of these systems as shown in Table 2. Although wired communications are adequately supported, it is recognized that most telecommunication solutions are provided through wireless systems [7,38].

3. Discussion

Sustainable energy infrastructure design must be based on interoperable, standardized technologies, which ensure the quality of services provided, energy, information, security, and privacy [41]. A key condition is overcoming competitive monopoly efforts for the prevalence of business solutions or standards and, respectively, the full consensus in the establishment, development, and support of existing energy standards. Coordinated efforts should be made to address the gaps and problems identified in existing standards and develop new ones, which will ensure the further adoption of smart grid technologies [7].
An important fact that confuses energy infrastructure designers when it comes to communications is the multiple choices in existing standards and rules that can determine the form, timing, order, control, and correction of errors during information transmission [79]. The fact that the established standards overlap themselves creates serious confusion which lead to functional gaps.
A significant process which is currently at an initial level but is estimated to contribute greatly to the further development of intelligent energy infrastructure in terms of communications is the process of their certification based on an international ISO standard. This standard will provide a set of policies, guidelines, and documented procedures to ensure that no key elements required by an intelligent network to be successful are omitted. This process will consider evaluation criteria and provide documentation of communication standards mapping, thus providing the essential framework for further developments.

4. Conclusions

The development of smart grids necessarily goes through the provision of integrated technological solutions that ensure the interoperability of the components of the electrical system and reduce the risk of devaluation of various technologies. The heterogeneity of infrastructures and the dynamics of their operating environment requires the continuous reduction of complexity, the faster processing of expansion works, and the addition of new ones. Integrated management requires a clear, and unequivocal way of providing end-to-end communication services based on active and interoperable standards, to ensure quality based on strict policies.
This paper presents the institutionalized and active standards of communication related to the specific issues of smart grid applications, which are necessary and should be taken seriously in the process of architectural design and implementation of energy upgrades of the existing infrastructure.
Future extensions concern the validation of active standards which are constantly changing and rearranging, as well as the inclusion of new standards that have recently been certified. Finally, an important development concerns the recording of the general recommendations by the standardization bodies by sector of operation of the smart networks, as well as the corresponding gaps that may have been identified and concern further development and evaluation procedures.

Author Contributions

Conceptualization, K.D. and C.S.; Investigation, K.D., K.T., D.T. and D.N.S.; Methodology, K.D. and C.S.; Supervision, D.N.S., C.S., L.I. and K.E.Z.; Writing—original draft, K.D. and K.T.; Writing—review & editing, K.D., K.T., D.T., D.N.S., C.S., L.I. and K.E.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Halder, T. A smart grid. In Proceedings of the 2014 6th IEEE Power India International Conference (PIICON), Delhi, India, 5–7 December 2014; pp. 1–6. [Google Scholar] [CrossRef]
  2. Annaswamy, A. IEEE Vision for Smart Grid Control: 2030 and Beyond Roadmap; IEEE: Piscataway, NJ, USA, 2013; pp. 1–12. [Google Scholar] [CrossRef]
  3. Chen, K.-C.; Yeh, P.-C.; Hsieh, H.-Y.; Chang, S.-C. Communication infrastructure of smart grid. In Proceedings of the 2010 4th International Symposium on Communications, Control and Signal Processing (ISCCSP), Limassol, Cyprus, 3–5 March 2010; pp. 1–5. [Google Scholar] [CrossRef]
  4. Zhou, X.; Ma, Y.; Gao, Z.; Wang, H. Summary of smart metering and smart grid communication. In Proceedings of the 2017 IEEE International Conference on Mechatronics and Automation (ICMA), Takamatsu, Japan, 6–9 August 2017; pp. 300–304. [Google Scholar] [CrossRef]
  5. Jin, X.; Zhang, Y.; Wang, X. Strategy and coordinated development of strong and smart grid. In Proceedings of the IEEE PES Innovative Smart Grid Technologies, Tianjin, China, 21–24 May 2012; pp. 1–4. [Google Scholar] [CrossRef]
  6. International Energy Agency. Technology Roadmap: Smart Grids; OECD Publishing: Paris, France, 2011. [Google Scholar] [CrossRef]
  7. Gopstein, A.; Nguyen, C.; O’Fallon, C.; Hastings, N.; Wollman, D. NIST Framework and Roadmap for Smart Grid Interoperability Standards, Release 4.0. February 2021. Available online: https://www.nist.gov/publications/nist-framework-and-roadmap-smart-grid-interoperability-standards-release-40 (accessed on 30 May 2021).
  8. Banafa, A. 2 The Industrial Internet of Things (IIoT): Challenges, Requirements and Benefits. In Secure and Smart Internet of Things (IoT): Using Blockchain and AI; River Publishers: Gistrup, Denmark, 2018; pp. 7–12. Available online: https://0-ieeexplore-ieee-org.brum.beds.ac.uk/document/9226906 (accessed on 19 January 2021).
  9. Bansal, P.; Singh, A. Smart metering in smart grid framework: A review. In Proceedings of the 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), Waknaghat, India, 22–24 December 2016; pp. 174–176. [Google Scholar] [CrossRef]
  10. Albataineh, H.; Nijim, M.; Bollampall, D. The Design of a Novel Smart Home Control System using Smart Grid Based on Edge and Cloud Computing. In Proceedings of the 2020 IEEE 8th International Conference on Smart Energy Grid Engineering (SEGE), Oshawa, ON, Canada, 12–14 August 2020; pp. 88–91. [Google Scholar] [CrossRef]
  11. Colistra, J. The Evolving Architecture of Smart Cities. In Proceedings of the 2018 IEEE International Smart Cities Conference (ISC2), Kansas City, MO, USA, 16–19 September 2018; pp. 1–8. [Google Scholar] [CrossRef]
  12. Eremia, M.; Liu, C.-C.; Edris, A.-A. FACTS Technologies. In Advanced Solutions in Power Systems: HVDC, FACTS, and Artificial Intelligence; IEEE: Piscataway, NJ, USA, 2016; pp. 269–270. [Google Scholar] [CrossRef]
  13. Barnes, M.; Van Hertem, D.; Teeuwsen, S.P.; Callavik, M. HVDC Systems in Smart Grids. Proc. IEEE 2017, 105, 2082–2098. [Google Scholar] [CrossRef]
  14. Brunner, C. IEC 61850 for power system communication. In Proceedings of the 2008 IEEE/PES Transmission and Distribution Conference and Exposition, Chicago, IL, USA, 21–24 April 2008; pp. 1–6. [Google Scholar] [CrossRef]
  15. Hou, Y.; Mei, S.; Zhou, H.; Zhong, J. Blackout prevention: Managing complexity with technology. In Proceedings of the 2008 IEEE Power and Energy Society General Meeting—Conversion and Delivery of Electrical Energy in the 21st Century, Pittsburgh, PA, USA, 20–24 July 2008; pp. 1–6. [Google Scholar] [CrossRef]
  16. Makarov, Y.; Reshetov, V.; Stroev, A.; Voropai, I. Blackout Prevention in the United States, Europe, and Russia. Proc. IEEE 2005, 93, 1942–1955. [Google Scholar] [CrossRef]
  17. Pimjaipong, W.; Junrussameevilai, T.; Maneerat, N. Blackout Prevention Plan—The Stability, Reliability and Security Enhancement in Thailand Power Grid. In Proceedings of the 2005 IEEE/PES Transmission Distribution Conference Exposition: Asia and Pacific, Dalian, China, 18 August 2005; pp. 1–6. [Google Scholar] [CrossRef]
  18. IEEE Approved Draft Guide for Engineering, Implementation, and Management of System Integrity Protection Schemes; IEEE PC37250D130 November 2019; IEEE: Piscataway, NJ, USA, 2020; pp. 1–68.
  19. Almasabi, S.; Mitra, J. An overview of synchrophasors and their applications in smart grids. In Proceedings of the 2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI), Kolkata, India, 21–23 October 2016; pp. 179–183. [Google Scholar] [CrossRef]
  20. Barchi, G.; Macii, D.; Petri, D. Phasor measurement units for smart grids: Estimation algorithms and performance issues. In Proceedings of the AEIT Annual Conference 2013, Palermo, Italy, 3–5 October 2013; pp. 1–6. [Google Scholar] [CrossRef]
  21. Phadke, A.G. Synchronized phasor measurements-a historical overview. In Proceedings of the IEEE/PES Transmission and Distribution Conference and Exhibition, Yokohama, Japan, 6–10 October 2002; Volume 1, pp. 476–479. [Google Scholar] [CrossRef]
  22. Sharma, R.B.; Dhole, G.M.; Tasare, M.B. Design of single phase Phasor Measurment Unit prototype. In Proceedings of the 2016 International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India, 12–13 August 2016; pp. 1–5. [Google Scholar] [CrossRef]
  23. Amaripadath, D.; Roche, R.; Joseph-Auguste, L.; Istrate, D.; Fortune, D.; Braun, J.; Gao, F. Power quality disturbances on smart grids: Overview and grid measurement configurations. In Proceedings of the 2017 52nd International Universities Power Engineering Conference (UPEC), Heraklion, Greece, 28–31 August 2017; pp. 1–6. [Google Scholar] [CrossRef] [Green Version]
  24. Zhao, M.; Wang, Z.; Xue, Y. An Overview on Application Analysis of Power Electronic Technology in Smart Grid. In Proceedings of the 2018 Chinese Control and Decision Conference (CCDC), Shenyang, China, 9–11 June 2018; pp. 5186–5189. [Google Scholar] [CrossRef]
  25. Ding, Y.; Li, X.; Li, C.; Teng, F.; Zheng, Y. Boundary Device Management Tool for Distribution Network Model Resource Center in Advanced Distribution Management System. In Proceedings of the 2019 7th International Conference on Smart Grid (icSmartGrid), Newcastle, NSW, Australia, 9–11 December 2019; pp. 113–117. [Google Scholar] [CrossRef]
  26. Smith, H.L. DA/DSM directions. An overview of distribution automation and demand-side management with implications of future trends. IEEE Comput. Appl. Power 1994, 7, 23–25. [Google Scholar] [CrossRef]
  27. Williams, B.; Ao, S. Advanced Distribution Management can bridge the chasm on the road to grid modernization. In Proceedings of the 2012 China International Conference on Electricity Distribution, Shanghai, China, 10–14 September 2012; pp. 1–4. [Google Scholar] [CrossRef]
  28. Vukobratović, M.; Hercog, M.; Varga, I.; Vuković, D.; Klaić, Z.; Vranješ, D. Survey of Methods for Advanced Distribution Management Systems. In Proceedings of the 2020 International Conference on Smart Systems and Technologies (SST), Osijek, Croatia, 14–16 October 2020; pp. 253–256. [Google Scholar] [CrossRef]
  29. Gebremichael, T.; Ledwaba, L.; Eldefrawy, M.H.; Hancke, G.P.; Pereira, N.; Gidlund, M.; Akerberg, J. Security and Privacy in the Industrial Internet of Things: Current Standards and Future Challenges. IEEE Access 2020, 8, 152351–152366. [Google Scholar] [CrossRef]
  30. Fan, L.; Li, J.; Pan, Y.; Wang, S.; Yan, C.; Yao, D. Research and Application of Smart Grid Early Warning Decision Platform Based on Big Data Analysis. In Proceedings of the 2019 4th International Conference on Intelligent Green Building and Smart Grid (IGBSG), Hubei, China, 6–9 September 2019; pp. 645–648. [Google Scholar] [CrossRef]
  31. Hou, L.; Zhang, Y.; Yu, Y.; Shi, Y.; Liang, K. Overview of Data Mining and Visual Analytics towards Big Data in Smart Grid. In Proceedings of the 2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI), Beijing, China, 20–21 October 2016; pp. 453–456. [Google Scholar] [CrossRef]
  32. Lee, C.; Shin, S. Fault Tolerance for Software-Defined Networking in Smart Grid. In Proceedings of the 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), Shanghai, China, 15–17 January 2018; pp. 705–708. [Google Scholar] [CrossRef]
  33. Demertzis, K.; Iliadis, L.S.; Anezakis, V.-D. An innovative soft computing system for smart energy grids cybersecurity. Adv. Build. Energy Res. 2018, 12, 3–24. [Google Scholar] [CrossRef]
  34. Ling, L.; Hongyong, Y.; Xia, C. Model Differences between IEC 61970/61968 and IEC 61850. In Proceedings of the 2013 Third International Conference on Intelligent System Design and Engineering Applications, Hong Kong, China, 16–18 January 2013; pp. 938–941. [Google Scholar] [CrossRef]
  35. Lv, G.; Zhao, J.; Su, J.; Zhang, D. Research on IEC 61968 Standard Oriented Function Framework of Adapter in Smart Distribution Grid. In Proceedings of the 2013 Fourth International Conference on Digital Manufacturing Automation, Shinan, China, 29–30 June 2013; pp. 1061–1065. [Google Scholar] [CrossRef]
  36. Othman, H.; Aji, Y.; Fakhreddin, F.; Al-Ali, A. Controller Area Networks: Evolution and Applications. In Proceedings of the 2006 2nd International Conference on Information Communication Technologies, Damascus, Syria, 24–28 April 2006. [Google Scholar]
  37. Sharan, S. Home networks—Getting there. In Proceedings of the 2002 IEEE 4th International Workshop on Networked Appliances (Cat. No.02EX525), Gaithersburg, MD, USA, 15–16 January 2002. [Google Scholar]
  38. Gopstein, A.; Goldstein, A.; Anand, D.; Boynton, P. Summary Report on NIST Smart Grid Testbeds and Collaborations Workshops, March 2021. Available online: https://www.nist.gov/publications/summary-report-nist-smart-grid-testbeds-and-collaborations-workshops (accessed on 30 May 2021).
  39. IEEE Draft Standard Requirements for Overhead, Pad-Mounted, Dry-Vault, and Submersible Automatic Line Sectionalizers for Alternating Current Systems Up to 38 kV; IEEE PC3763D5 November 2012; IEEE: Piscataway, NJ, USA, 2012; pp. 1–48.
  40. Kazičková, T.; Buhnova, B. ICT architecture for the Smart Grid: Concept overview. In Proceedings of the 2016 Smart Cities Symposium Prague (SCSP), Prague, Czech Republic, 26–27 May 2016; pp. 1–4. [Google Scholar] [CrossRef]
  41. Kumar, P.; Lin, Y.; Bai, G.; Paverd, A.; Dong, J.S.; Martin, A. Smart Grid Metering Networks: A Survey on Security, Privacy and Open Research Issues. IEEE Commun. Surv. Tutor. 2019, 21, 2886–2927. [Google Scholar] [CrossRef] [Green Version]
  42. Demertzis, K.; Iliadis, L.; Tziritas, N.; Kikiras, P. Anomaly detection via blockchained deep learning smart contracts in industry 4.0. Neural Comput. Appl. 2020, 32, 17361–17378. [Google Scholar] [CrossRef]
  43. Nomikos, N.; Nieto, A.; Makris, P.; Skoutas, D.N.; Vouyioukas, D.; Rizomiliotis, P.; Lopez, J.; Skianis, C. Relay selection for secure 5G green communications. Telecommun. Syst. 2015, 59, 169–187. [Google Scholar] [CrossRef]
  44. Molzahn, D.K.; Dörfler, F.; Sandberg, H.; Low, S.H.; Chakrabarti, S.; Baldick, R.; Lavaei, J. A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems. IEEE Trans. Smart Grid 2017, 8, 2941–2962. [Google Scholar] [CrossRef]
  45. Shenming, Z.; Jianguo, Y.; Zhihong, Y.; Mei, C.; Qiang, S. Implementation of standard IEC 61970 in EMS systems. In Proceedings of the 2004 International Conference on Power System Technology, PowerCon 2004, Singapore, 21–24 November 2004; pp. 114–118. [Google Scholar] [CrossRef]
  46. Yaqin, Y.; Zhongxi, W.; Chunming, H. A Novel implementation of IEC 61970 CIS based on Ice middleware. In Proceedings of the 2005 IEEE/PES Transmission Distribution Conference Exposition: Asia and Pacific, Dalian, China, 18 August 2005; pp. 1–4. [Google Scholar] [CrossRef]
  47. Zhou, Y.; Liu, Y.; Sun, J. IEC 61970 CIM/CIS in power dispatching automation system standard conformance test technology research. In Proceedings of the CICED 2010 Proceedings, Nanjing, China, 13–16 September 2010; pp. 1–4. [Google Scholar]
  48. Gill, H.M. Smart Grid distribution automation for public power. In Proceedings of the IEEE PES T&D 2010, New Orleans, LA, USA, 19–22 April 2010; pp. 1–4. [Google Scholar] [CrossRef]
  49. Bosisio, A.; Berizzi, A.; Morotti, A.; Pegoiani, A.; Greco, B.; Iannarelli, G. IEC 61850-based smart automation system logic to improve reliability indices in distribution networks. In Proceedings of the 2019 IEEE 8th International Conference on Advanced Power System Automation and Protection (APAP), Florence, Italy, 18–20 October 2019; pp. 1219–1222. [Google Scholar] [CrossRef]
  50. Tatsis, V.I.; Karavolos, M.; Skoutas, D.N.; Nomikos, N.; Vouyioukas, D.; Skianis, C. Energy-aware clustering of CoMP-DPS transmission points. Comput. Commun. 2019, 135, 28–39. [Google Scholar] [CrossRef]
  51. Skoutas, D.N.S.; Nomikos, N.N.; Vouyioukas, D.V.; Skianis, C.S.; Antonopoulos, A.A. Hybrid resource sharing for QoS preservation in virtual wireless networks. In Cloud and Fog Computing in 5G Mobile Networks: Emerging Advances and Applications; Institution of Engineering and Technology: London, UK, 2017; pp. 303–324. [Google Scholar] [CrossRef]
  52. Wei-Chun, G.; Huan-Huan, L.; Hong-Hao, Z.; Qiang, G.; Gui-Ping, Z.; Yi-Ling, M.; Li-Na, F. Research on communication technology of power monitoring system based on medium voltage power line carrier and low power wide area network. In Proceedings of the 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), Beijing, China, 26–28 November 2017; pp. 1–4. [Google Scholar] [CrossRef]
  53. IEEE Draft Standard for Low Frequency (Less Than 500 kHz) Narrow Band Power Line Communications for Smart Grid Applications; IEEE P19012D00800 May 2013; IEEE: Piscataway, NJ, USA, 2013; pp. 1–336.
  54. Huang, L.; Duan, B.; Lin, Y.; Tian, S. Design of IEC 61400-25 gateway for RIU replacement. In Proceedings of the 2009 International Conference on Sustainable Power Generation and Supply, Nanjing, China, 6–7 April 2009; pp. 1–5. [Google Scholar] [CrossRef]
  55. Lee, J.-H.; Seo, M.-J.; Kim, G.-S.; Lee, H.-H. IEC 61400-25 interface using MMS and web service for remote supervisory control at wind power plants. In Proceedings of the 2008 International Conference on Control, Automation and Systems, Seoul, Korea, 14–17 October 2008; pp. 2719–2723. [Google Scholar] [CrossRef]
  56. Tsiknas, K.; Taketzis, D.; Demertzis, K.; Skianis, C. Cyber Threats to Industrial IoT: A Survey on Attacks and Countermeasures. IoT 2021, 2, 163–188. [Google Scholar] [CrossRef]
  57. Sajjad, M.I.A.; Napoli, R.; Chicco, G.; Martirano, L. A conceptual framework for the business model of smart grids. In Proceedings of the 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC), Florence, Italy, 7–10 June 2016; pp. 1–5. [Google Scholar] [CrossRef]
  58. Elma, O.; Selamoğullari, U.S. An overview of demand response applications under smart grid concept. In Proceedings of the 2017 4th International Conference on Electrical and Electronic Engineering (ICEEE), Ankara, Turkey, 8–10 April 2017; pp. 104–107. [Google Scholar] [CrossRef]
  59. Kheaksong, A.; Lee, W. Packet transfer of DLMS/COSEM standards for smart grid. In Proceedings of the The 20th Asia-Pacific Conference on Communication (APCC2014), Pattaya, Thailand, 1–3 October 2014; pp. 391–396. [Google Scholar] [CrossRef]
  60. Dong, M.; Tian, S.; Zhu, W.; Jia, B.; Li, B.; Qi, B. Research and development of automated demand response standard system. In Proceedings of the 2017 2nd International Conference on Power and Renewable Energy (ICPRE), Chengdu, China, 20–23 September 2017; pp. 608–611. [Google Scholar] [CrossRef]
  61. Deotare, P.; Dole, L. Overview of automation of Smart Grid network. In Proceedings of the 2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO), Coimbatore, India, 9–10 January 2015; pp. 1–3. [Google Scholar] [CrossRef]
  62. Martirano, L.; Mitolo, M. Building Automation and Control Systems (BACS): A Review. In Proceedings of the 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I CPS Europe), Madrid, Spain, 9–12 June 2020; pp. 1–8. [Google Scholar] [CrossRef]
  63. Hasan, M.; Biswas, P.; Bilash, T.I.; Dipto, A.Z. Smart Home Systems: Overview and Comparative Analysis. In Proceedings of the 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), Kolkata, India, 22–23 November 2018; pp. 264–268. [Google Scholar] [CrossRef]
  64. Paul, C.; Ganesh, A.; Sunitha, C. An overview of IoT based smart homes. In Proceedings of the 2018 2nd International Conference on Inventive Systems and Control (ICISC), Coimbatore, India, 19–20 January 2018; pp. 43–46. [Google Scholar] [CrossRef]
  65. Vasicek, D.; Jalowiczor, J.; Sevcik, L.; Voznak, M. IoT Smart Home Concept. In Proceedings of the 2018 26th Telecommunications Forum (TELFOR), Belgrade, Serbia, 20–21 November 2018; pp. 1–4. [Google Scholar] [CrossRef]
  66. Nezevak, V.L.; Shatokhin, A.P. Control of Hybrid Electric Energy Storage Unit Parameters in Electric Traction System. In Proceedings of the 2019 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon), Vladivostok, Russia, 1–4 October 2019; pp. 1–4. [Google Scholar] [CrossRef]
  67. IEEE Standard Test Procedures for Electric Energy Storage Equipment and Systems for Electric Power Systems Applications; IEEE Std 20303-2016; IEEE: Piscataway, NJ, USA, 2016; pp. 1–72. [CrossRef]
  68. Demertzis, K.; Iliadis, L. A Hybrid Network Anomaly and Intrusion Detection Approach Based on Evolving Spiking Neural Network Classification. In E-Democracy, Security, Privacy and Trust in a Digital World; Sideridis, A.B., Kardasiadou, Z., Yialouris, C.P., Zorkadis, V., Eds.; Springer International Publishing: Cham, Switzerland, 2014; Volume 441, pp. 11–23. [Google Scholar] [CrossRef]
  69. Zhang, K.; Mao, Y.; Leng, S.; Maharjan, S.; Zhang, Y.; Vinel, A.; Jonsson, M. Incentive-Driven Energy Trading in the Smart Grid. IEEE Access 2016, 4, 1243–1257. [Google Scholar] [CrossRef]
  70. Brenna, M.; Foiadelli, F.; Longo, M.; Zaninelli, D. e-Mobility Forecast for the Transnational e-Corridor Planning. IEEE Trans. Intell. Transp. Syst. 2016, 17, 680–689. [Google Scholar] [CrossRef]
  71. Kippke, M.A.; Arboleya, P.; El Sayed, I. Communication Infrastructure for E-Mobility Charging Stations V2G Applications. In Proceedings of the 2020 8th International Conference on Power Electronics Systems and Applications (PESA), Hong Kong, China, 7–10 December 2020; pp. 1–3. [Google Scholar] [CrossRef]
  72. Kurfirt, M.; Kaspirek, M.; Hlavnicka, J. E-mobility Impact on Supply in Distribution Grid. In Proceedings of the 2019 20th International Scientific Conference on Electric Power Engineering (EPE), Kouty nad Desnou, Czech Republic, 15–17 May 2019; pp. 1–4. [Google Scholar] [CrossRef]
  73. Khan, F.; Siddiqui, M.A.B.; Rehman, A.U.; Khan, J.; Asad, M.T.S.A.; Asad, A. IoT Based Power Monitoring System for Smart Grid Applications. In Proceedings of the 2020 International Conference on Engineering and Emerging Technologies (ICEET), Lahore, Pakistan, 22–23 February 2020; pp. 1–5. [Google Scholar] [CrossRef]
  74. Demertzis, K.; Kikiras, P.; Tziritas, N.; Sanchez, S.L.; Iliadis, L. The Next Generation Cognitive Security Operations Center: Network Flow Forensics Using Cybersecurity Intelligence. Big Data Cogn. Comput. 2018, 2, 35. [Google Scholar] [CrossRef] [Green Version]
  75. Dawood, K. An overview of renewable energy and challenges of integrating renewable energy in a smart grid system in Turkey. In Proceedings of the 2020 International Conference on Electrical Engineering (ICEE), Istanbul, Turkey, 25–27 September 2020; pp. 1–6. [Google Scholar] [CrossRef]
  76. Diakov, V.; Brinkman, G.; Denholm, P.; Jenkin, T.; Margolis, R. Renewable generation effect on net regional energy interchange. In Proceedings of the 2015 IEEE Power Energy Society General Meeting, Denver, CO, USA, 26–30 July 2015; pp. 1–4. [Google Scholar] [CrossRef]
  77. Molotov, P.; Vaskov, A.; Tyagunov, M. Modeling Processes in Microgrids with Renewable Energy Sources. In Proceedings of the 2018 International Ural Conference on Green Energy (UralCon), Chelyabinsk, Russia, 4–6 October 2018; pp. 203–208. [Google Scholar] [CrossRef]
  78. Wang, H. Microgrid generation planning considering renewable energy target. In Proceedings of the 2016 IEEE International Conference on Power and Energy (PECon), Melaka, Malaysia, 28–29 November 2016; pp. 356–360. [Google Scholar] [CrossRef]
  79. Angelis, N.; Archontos, N.; Vouyioukas, D.; Nomikos, N.; Skianis, C. An integrated NAN architecture for smart energy grid. In Proceedings of the 2018 IEEE International Energy Conference (ENERGYCON), Limassol, Cyprus, 3–7 June 2018; pp. 1–6. [Google Scholar] [CrossRef]
Network 01 00009 g001 550
Figure 1. Smart Grid Domains.
Figure 1. Smart Grid Domains.
Network 01 00009 g001
Network 01 00009 g002 550
Figure 2. Communications paths between Smart Energy Grid domains.
Figure 2. Communications paths between Smart Energy Grid domains.
Network 01 00009 g002
Table
Table 1. E-mobility Standards.
Table 1. E-mobility Standards.
Product and Safety StandardsSmart Grid StandardsPhysical InterconnectionCommunicationMarket InformationGeneral Standards
IEC 61982-1,2,3,4,5
IEC 62576
IEC/NWIP 62619		IEC 60364-5-53
IEC 60364-5-55
IEC 60364-7-712
IEC 60364-7-722
IEC/NP 60364-7-760
IEEE P2030.1		IEC 60309 Ed. 4.1
IEC 60309-1 Ed 4.1
IEC 60309-2 Ed 4.1
IEC 62196 Ed, 1,0
IEC 62196-1		IEC 61850
IEC 61968
IEC 61851-31
IEC 61851-32
ISO/IEC 15118
ISO/IEC 15118-1,2,3		IEC/TR 62325
IEC/TR 62325-501		ISO/CD 12405
ISO 6469-1,2,3
SAE J1772
SAE J2836/1-3
SAE J2847/1-3
USA–SAE J1771
USA–SAE J2836
Table
Table 2. Renewable Energy Generation Standards.
Table 2. Renewable Energy Generation Standards.
Wind PowerSolar VoltaicFuel CellsPumped StoageDistributed GenerationNuclear GenerationConventional Power
IEC 61400 series
ISO 81400-4		IEC-60904 series
IEC 61194
IEC 61724
IEC 61730 series
IEC 61730-1
IEC 61730-2
IEC/TS 61836
IEC 62446
IEC/TS 62257
IEC 61727		IEC 62282-x
IEC 62282-1
IEC 62282-2
IEC 62282-3-1
IEC 62282-3-2
IEC 62282-3-3
IEC 62282-5-1
IEC 62282-6-200
IEC 62282-6-300		IEC 60193
IEC 60041		IEC 62257-1,2,3,4,5,6,7
IEC 62257-7-3
IEC 62257-8-1
IEC 62257-9-1,2,3,4,5,6
IEEE 1547
IEEE 1547.3
MAIN Guide NO3B		NERC/NUC-001-1IEC 60308
IEC 61850-7-410
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Demertzis, K.; Tsiknas, K.; Taketzis, D.; Skoutas, D.N.; Skianis, C.; Iliadis, L.; Zoiros, K.E. Communication Network Standards for Smart Grid Infrastructures. Network 2021, 1, 132-145. https://0-doi-org.brum.beds.ac.uk/10.3390/network1020009

AMA Style

Demertzis K, Tsiknas K, Taketzis D, Skoutas DN, Skianis C, Iliadis L, Zoiros KE. Communication Network Standards for Smart Grid Infrastructures. Network. 2021; 1(2):132-145. https://0-doi-org.brum.beds.ac.uk/10.3390/network1020009

Chicago/Turabian Style

Demertzis, Konstantinos, Konstantinos Tsiknas, Dimitrios Taketzis, Dimitrios N. Skoutas, Charalabos Skianis, Lazaros Iliadis, and Kyriakos E. Zoiros. 2021. "Communication Network Standards for Smart Grid Infrastructures" Network 1, no. 2: 132-145. https://0-doi-org.brum.beds.ac.uk/10.3390/network1020009

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