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1 March 2025

Co-Simulation of Interconnection Between Smart Power Grid and Smart Cities Platform via Massive Machine-Type Communication

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1
Electrical Engineering Department, Universidade de São Paulo, Sao Paulo 01000-000, Brazil
2
Instituto Federal de São Paulo Electrical Engineering Department, Sao Paulo 01000-000, Brazil
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Author to whom correspondence should be addressed.
Sensors2025, 25(5), 1517;https://doi.org/10.3390/s25051517
This article belongs to the Section Communications
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Abstract

With the advent of Industry 5.0, the electrical sector has been endowed with intelligent devices that are propelling high penetration of distributed energy microgeneration, VPP, smart buildings, and smart plants and imposing new challenges on the sector. This new environment requires a smarter network, including transforming the simple electricity customer into a “smart customer” who values the quality of energy and its rational use. The SPG (smart power grid) is the perfect solution for meeting these needs. It is crucial to understand energy use to guarantee quality of service and meet data security requirements. The use of simulations to map the behavior of complex infrastructures is the best strategy because it overcomes the limitations of traditional analytical solutions. This article presents the ICT laboratory structure developed within the Department of Electrical Engineering of the Polytechnic School of the Universidade de São Paulo (USP). It is based on an architecture that utilizes LTE/EPC wireless technology (4G, 5G, and B5G) to enable machine-to-machine communication (mMTC) between SPG elements using edge computing (MEC) resources and those of smart city platforms. We evaluate this proposal through simulations using data from real and emulated equipment and co-simulations shared by SPG laboratories at POLI-USP. Finally, we present the preliminary results of integration of the power laboratory, network simulation (ns-3), and a smart city platform (InterSCity) for validation and testing of the architecture.

1. Introduction

We are currently witnessing the advent of Industry 5.0, which was estimated to enable approximately 50 billion devices to be connected via the Internet of Things (IoT) by the end of 2023 []. There are more than 1.8 M2M (machine-to-machine) connections for every person on the globe, which is a significant figure []. As a result of this revolution, the electrical power sector was also equipped with intelligent technology, which led to a significant increase in distributed and non-programmable electrical power microgeneration, smart buildings, smart homes, and smart meters (SM) []. This, in turn, has created new challenges for the operation of energy systems. This new environment calls for a smarter network, including the transformation of the traditional energy electricity customer into a “smart client” and, in many cases, into a “smart prosumer.” This has, in turn, led to the emergence of new challenges for the operation of power systems.
It becomes clear there is a need for a smarter grid []. InterSCity is a platform for smart cities that was developed by USP within a multidisciplinary project that uses digital technologies to make all city services more efficient and reliable, including the supply of electricity []. The implementation of the electric energy vertical on smart city platforms through applications (load prediction and monitoring, among others) in SPG requires (1) data analysis algorithms to evaluate the data generated by intelligent devices and meters and (2) performance evaluation of the various possible architectures to interconnect all SPG elements at the link and physical layer level. On the other hand, to connect these systems, a communication backbone is needed. Smart grids present many different communication challenges, and 5G cellular network technology helps address these challenges because it was designed to meet the heterogeneity of this demand []. Recent technologies, like MEC, NFV, and SDN, help 4G and 5G cellular networks [,].
In this sense, an architecture is proposed (Figure 1) based on MEC (multiaccess edge computing) and LTE (long-term evolution) technologies that allow mMTC (massive machine-type communications) between SPG components, legacy software resources, and smart city platforms. Our architecture was evaluated through simulations and emulations using data from real and simulated devices (Paravirtualization, HIL, and SIL) shared by the SPG laboratories at POLI-USP (NAPREI—Research Support Center at SPG, L-SISPOT—Power Systems Laboratory, and L-PROT—Electrical Grid Protection Laboratory [,,,,,]), in São Paulo, Brazil. The use of data generated by simulators and emulators available in these laboratories opens a range of options for performance and interoperability tests of the various possible solution architectures in the interconnection of applications and these network elements that meet the demands of “prosumer” customers [,]. These options are intended to explore communication (mMTC, MEC, B5G—beyond 5G) between real equipment (IEDs—intelligent electronics devices, SMs, etc.) that make up the network of devices installed in the laboratories.
Figure 1. Our proposed integration architecture that uses data generated by POLI laboratories (POLI Labs: L-PROT, Lab-SISPOT, and NAPREI), the USP campus PS (power substation), and the smart cities platform (InterSCity) through ns-3 simulation, which implements mMTC via MEC for applications that manage SPG functionalities.
The rest of this paper is divided as follows: Section 2 describes the objectives of this proposal. Section 3 describes the methodology used in developing the work. A survey of related works is presented in Section 4, and a description summary of the systems studied, that is, SPG and the interaction between the NAPREI laboratory and the architectural blocks and LTE, mMTC, and the InterSCity platform will be covered in Section 5. Section 6 presents the results of utilizing the laboratory to test and validate the architecture’s functionalities, and Section 7 presents the conclusions and future work.

2. Goals

This work presents a new system architecture that guarantees reliability and latency for SPG using a co-simulation platform that exploits a paravirtualized simulation approach to evaluate the performance of the integrated SPG with legacy information systems and simulated data networks (LTE and mMTC). This approach allows access and management of the devices necessary to enable the connection of smart microgrids, smart homes, smart buildings, smart factories, etc., in the context of smart cities.

3. Proposed Methodology

The steps that guided the methodology in this work were as follows:
  • Definition and execution of a series of benchmarks to evaluate the best experimental environment to perform simulations and emulations of LTE network communications in an SPG environment;
  • Survey of architecture proposals previously found in the literature and evaluation of them in terms of refined monitoring of the electrical grid and services for SPG systems;
  • Adaptation of the best architecture from the previous item so that it can support massive machine-to-machine communication expected in SPG environments;
  • Modification of the previous version of the InterSCity platform to support SPG applications with QoS guarantees.

5. Description of Co-Simulated Systems

This section describes the implementation of A co-simulation of an SPG environment in the context of smart cities. The individual systems are described below.

5.1. Smart Power Grids (SPG) and NAPREI Laboratory

According to Kagan [], SPG must integrate automation, intelligent measurement and actuation systems, and distributed energy resources. Currently, the SEP (electrical power system) needs to provide functionalities to meet specific objectives and, therefore, requires ICT (information and communication technology) infrastructure. To manage such intelligent and complex electrical networks, SPG applications use advanced control strategies []. JRC’s “Smart Grid Laboratories Inventory 2020” report [] states that the infrastructure necessary for research in the sector is of vital importance in validating prototypes and their solutions. The interoperability of new computer systems introduced and the performance evaluation of SPG are also proven by this infrastructure. In this sense, NAPREI developed an SPG emulator to study and test a range of functionalities of IEDs and SMs that need to interact with legacy distribution management systems, that is, SCADA, MDM, and DMS systems, among others. In ref. [], Rosa presents in detail the SPG emulator and states that it proposes an original and innovative form of systemic testability of SPG functionalities in a controlled environment, as it includes IEDs, a measurement island, telecommunications infrastructure, and legacy systems of information, which are integrated into a diversity of simulations in complex and non-simplified electrical energy distribution networks. The block diagram of NAPREI’s SPG laboratory is presented in Figure 2, in schematic form, covering the research objects. The REI emulator (I) was developed for systemic testing of SPG functionalities involving equipment (hardware) and computer systems (software) in the laboratory environment. The measuring island (II) consists of load/generation emulators (Emuladores de cargas e geração), smart meters (Medidores Inteligentes), and communication infrastructure, and is capable of representing any consumption/generation situation of an electrical energy consumer belonging to an electrical distribution network. The telecommunications infrastructure (III) provides the interconnection of the whole system. The use of physical intelligent electronic devices (IEDs) (IV) is a fact of great importance in REIs, as they are increasingly making use of new systems spread across distribution networks and the IT systems (V).
Figure 2. Block diagram of the NAPREI’s SPG laboratory with emphasis on elements (I, II, III, IV, and V) of each research block. Source: [].

5.2. Communication Systems

As can be seen in the central part of Figure 2, the telecommunications infrastructure (III) provides all the interconnection of the emulator (I), the measuring island (II), the IEDs (IV) to the center, and the IT systems (V). In our work, this functionality is carried out by connecting via LTE, as presented in the next section. The ability to interconnect a heterogeneous network (HetNet) is one of the advantages of using 5G that uses LTE, as it allows a connection with a macro-cell (Macro BS), that is, through a radio base station (RBS ou eNB, and in Portuguese ERB—Estação Rádio Base), to a connection through a Femtocell. The purpose of this study is to develop a communication architecture that serves as an IoT interface for NAPREI laboratory simulations by providing an energy control system perspective on the implementation of global services through SPG systems, such as residential load prediction and NILM (non-intrusive load monitoring). Thus, this architecture is intended to be the basis for future implementation of the energy vertical for smart city platforms.
The electricity sector is one of the most challenging “test cases” for 5G cellular mobile networks, as it has a huge number of different requirements that need to be addressed, such as managing smart metering and low latency in fault locations. There are still several issues related to automation, security, resilience, scalability, and portability of 5G network management. As one can see in Figure 2, typically, the literature just describes the needs of a communication system. Rossi et al. [] presented several challenges of smart networks in the interconnection between the physical infrastructure with ICTs (information and communication technologies). One of today’s challenges dealing with the explosion in wireless traffic is the deployment of many small cells giving rise to networks. MEC is the most frequently indicated technology to support smart city needs [], as its multi-access allows the connectivity of a wide variety of devices, including wired interfaces and Wi-Fi (GPRS/UMTS/LTE), simultaneously. Kagan [] presented telecommunications in different communication protocols, that is, a heterogeneous network, so the proposal of this subproject is precisely to use 4G LTE to support 5G and thus carry out all communications in all protocols. The cloud communication technologies in Figure 2 are the focus of our proposal. Data transfer latency provoked by the security system (blockchain) is a very important theme in a system that integrates several heterogeneous components, like SPG [].

5.3. Smart Cities Platform—InterSCity

The JRC report [], which discusses smart grid labs, highlights that the theme of “smart cities” and its nature of connecting different layers of technology demand complete and holistic solutions, which adds value to the smart grid sector. The report highlights the increase in investments in smart grid labs and that there is no forecast of a reduction in the number of active labs in this area. The InterSCity platform is designed to simplify the management and integration of smart city services by offering a suite of high-level, web-based micro-services, as described in Figure 3 []. These services afford the necessary tools to handle the IoT technologies, enabling the finding of city services and devices, storing and processing data, and intermediating action commands. The platform supports a range of smart city applications across various domains, known as verticals, with a specific example being electrical energy vertical as the REIs in Figure 2. By mediating data transfer between city applications and services for citizens, InterSCity abstracts the complexities involved in city-scale data management and the specific communication protocols required by the underlying IoT devices. This abstraction layer ensures that users can focus on developing and deploying applications without needing to worry about the intricate details of the city’s infrastructure.
Figure 3. InterSCity is a web-based microservice. IoT gateway for SPG is emphasized in the red ellipse.

6. Results

Rodriques and Almeida [] presented, in Portuguese, the preliminary results of the LTE/EPC cellular simulation for integration of laboratories for validation and testing of the architecture. However, in this article, it was decided to evaluate the available systems separately for data network and electrical network simulators, as presented in the following subsections.

6.1. LTE/MEC Data Network Simulation

In their article [], Trajano et al. proposed and evaluated an architecture based on MEC (mobile edge computing), which is efficient in meeting reliability and latency demands. This considers the distributed applications for SPG via mobile cellular networks utilizing LTE/EPC (4G and B5G). The authors used ns-3 to demonstrate the proposal’s ability to handle a realistic number of SMs, supporting many SPG deployment use cases. MEC is a technology standardized by the ETSI (European Telecommunications Standards Institute) []. In our data network simulation, there is a similar proposal, as shown in Figure 4.
Figure 4. MEC applications in simulation eNB’s.
To revalidate the simulation in this article, mainly determining whether the simulated network can support many SPG devices, the LTE network was set up using the parameters listed in the ITU-R M.2135-1 report []. In the experiments, the topologies extracted from deployments are used with real LTE/EPC from neighborhoods in the cities of São Paulo, Brazil, which is one of the largest cities in Latin America and has 11.4 million inhabitants with 7528.26 hab/km2 population density, according to the last census []. Figure 5 shows the actual locations of the base stations (ERBs) of a cellular mobile telephone operator on a map implemented from Google Maps, in São Paulo. The Cerqueira César neighborhood used in the simulation, located between the neighborhoods of Jardins da Bandeira, Sumaré and Bixiga. This region includes a big shopping center (Shopping Pátio Higienópolis) and two famous museums: football (Football Museum) and art (MASP—São Paulo Art Museum).
Figure 5. A Google Maps® view of the Cerqueira César neighborhood (São Paulo—SP) utilized in the simulation. The BSs are marked by blue pin icons with white flags, while the red pin denotes the central area of interest for the experiments.
The experiments were conducted using ns-3 version 3.28, leveraging its built-in LTE/EPC module, which provides robust support for LTE-based simulations. The setup involved a machine equipped with an Intel Core i5 8th Generation processor, 12 cores, and 4 GB of RAM. From the central limit theorem (95% confidence interval is used to calculate the error), the margin repeats all simulations 30 times. Each process for the different scenarios took about 5 h to complete. Smart devices and meters had their positions randomly generated within a circle with a radius of 1 km from the center of the chosen region (InCor Hospital) and represented in ns-3 with fixed positions using model-defined ConstantPositionMobilityModel. The coordinates for the LTE BS were sourced from those installed in the city (Figure 4) in alignment with the macro urban cell scenario. Figure 6 illustrates the average perceived delay, measured in milliseconds, in communication between devices and the MEC servers. The aggregation factor (AF) (percentage of SMs connected via concentrators) is a crucial parameter to consider for designing a robust intelligent network deployment, as demonstrated in Figure 6.
Figure 6. Average message delay (in milliseconds) sent from multiple smart meters to MEC server in simulation of 0%, 25%, and 50% aggregation.
In Figure 6, as observed in the scenario with 10,000 SMs without any aggregation device, the delay of 90 ms for application requirements is deemed high for certain SPG use cases. According to IEC, this value is not accepted for services proposed for communication in SPG [,]. However, with aggregation, the delay decreased. For an AF of 25%, the delay dropped to 60 ms, and for an AF of 50%, the delay dropped to 35 ms, which are considered acceptable [,].
As can be seen in Figure 5, there is a relatively large region in the upper part of the circle surrounding our area of interest, including the center of the circle and the Football Museum, which is serviced by eNBs very far apart (ERB 1 to 5 and 11). Therefore, after a simple cellular service optimization technique, a relocation was proposed for the eNB identified as ERB 3. In Figure 7, the ERB 3 actual position is marked with the red “X”, and the new proposal position is denoted with the yellow pin inside the red circle.
Figure 7. Relocation of ERB 3 within the region of interest.
Figure 8 shows how effective the relocation was; the average delay perceived in milliseconds reduced to about 65 ms without aggregation. For an AF of 25%, the delay dropped to 43 ms, and for an AF of 50%, the delay dropped to 25 ms. These values are acceptable in the services proposed for SPG.
Figure 8. Average message delay after the relocation of ERB 3.

6.2. Integration of the InterSCity Platform Between SPG and Other Smart City Verticals

In ref. [], del Esporte et al. focused on the architecture of InterSCity, highlighting its flexibility, extensibility, and scalability. They presented experimental results that demonstrate how well the platform can scale, likely in various urban or IoT-based scenarios.
In ref. [], Viana et al. provided an experience report on the software engineering practices involved in developing applications using the InterSCity platform. This likely includes insights into the development of the lifecycle, challenges faced, and how these practices impacted the creation of smart city solutions. In our experiments, we show how the platform can serve several competing verticals without presenting any scalability problems. During three periods (2023–2024), 17 computer engineering undergrad students’ teams used InterSCity and developed their projects, creating software documentation in GitHub repositories [,,,,,,,,,,,,,]. Those projects were deployed in only one instance of the platform, at the Intelligent Distributed Systems Laboratory (LSDi—Laboratório de Sistemas Distribuídos Inteligentes) of the Universidade Federal do Maranhão (UFMA). Several projects of different verticals of service provision for smart cities were implemented. As a study of NILM, the Hedwig project [], which is developed by students from the POLI-USP postgraduate program, integrates grouped data with the InterSCity API [,]. After collecting real data from September 22nd to October 21st, 2020, in a connected home testbed in the city of Santo André (10 PIR motion sensors, 10 lamp status sensors, and 8 non-current sensors, invasive—total consumption—and 4 sectors—stored in modules installed in the connected home), pre-processing (one-hot encoding) and data grouping were carried out with unsupervised learning algorithms. The k-means algorithm performs the grouping of movement events and lighting status of the connected home.
In terms of scalability [] and security [], other experiments with the InterSCity platform using a blockchain-based management model were conducted. These experiments have proven that the InterSCity platform perfectly meets the requirements demanded for SPG.

7. Conclusions and Future Works

This work presents massive machine-to-machine communication applications for SPG on a smart cities platform (InterSCity), which uses data generated by the POLI-USP Laboratories (NAPREI, L-SISPOT, and L-PROT) to implement mMTC architecture via MEC for the InterSCity platform as applications that manage SPG functionalities. The most significant contribution of this work is filling the gap found in network integration of electrical and telecommunications infrastructure necessary for its management and operation. The use of co-simulation demonstrates the effectiveness of the proposed solution without the need to physically implement any equipment in the field, which would be expensive and traumatic in the operation of a real electrical network. The use of NAPREI simulators and emulators opened a range of options for performance and interoperability testing of the various possible solution architectures in the interconnection of applications and smart microgrids, smart buildings, smart electronic devices (IDEs), and smart meters. Of these possibilities, we highlight the generation of data for the future development of testing applications in big data to access the levels of real off-shore equipment (IDEs, smart meters, etc.), emulations/simulations of specific configurations for scalability testing, heterogeneity, data management, privacy, and security, among other challenges that are intended to be implemented in a vertical of smart electric grids on the InterSCity platform, including the implementation of global energy management. It is proposed, as one of the most important future works of the proposal, to replicate this prediction methodology in the NAPREI network. The project’s proximity to NAPREI is a very promising partnership to add to the expertise of SPG laboratories and the InterSCity platform. The various advances achieved at NAPREI demonstrate the laboratory’s capacity and that the development of global management is one of the possibilities envisioned in a partnership. Paravirtualization is the hybrid of co-simulation and virtualization. This technology is very interesting because it has all the elements developed so far in research and can then be integrated into the physical infrastructure of the NAPREI laboratory, that is, all its equipment and ICT equipment (NAPREI software) and the data network simulator paravirtualization modules simulated in ns-3. Integration of the physical infrastructure of the NAPREI laboratory with two other laboratories at POLI-USP, namely, L-PROT (Protection Laboratory), which includes a real-time digital simulator (RTDS), and the Power Systems Laboratory, which includes four mini generation systems with digital interface, was implemented with NI’s LabView2024 software [].

Author Contributions

Conceptualization, C.F.M.A., N.K. and L.H.N.R.; methodology, L.H.L.R. and M.L.d.S.; software, L.H.N.R., L.H.L.R. and M.L.d.S.; validation, L.H.L.R., C.F.M.A. and M.L.d.S.; formal analysis, M.L.d.S.; investigation, L.H.N.R., L.H.L.R. and C.F.M.A.; resources, L.H.N.R., L.H.L.R. and C.F.M.A.; data curation, N.K.; writing—original draft preparation, L.H.N.R.; writing—review and editing, L.H.L.R. and C.F.M.A.; visualization, M.L.d.S.; supervision, N.K. and C.F.M.A.; project administration, N.K. and C.F.M.A.; funding acquisition, C.F.M.A. and N.K. All authors have read and agreed to the published version of the manuscript.

Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001.

Data Availability Statement

The original contributions presented in this study are included in this article; further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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