Smart Electrical Networks Based on Renewable Sources

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1		Wilian Paul Arévalo Cordero	--	2928	2024-01-17 12:53:34	\|
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This entry is adapted from the peer-reviewed paper 10.3390/su16020698

The advent of the Fourth Industrial Revolution facilitates real-time data exchange among a diverse array of intelligent meters for various purposes, enabling the integration of smart grids with Industry 4.0. This integration leads to more efficient and sustainable industrial processes, improved energy management, and cost savings.

electrical-distribution network renewable energy sources Industry 4.0 efficiency cybersecurity

1. Introduction

Smart grids are advanced electrical systems that integrate information and communication technology to enhance the efficiency, reliability, and sustainability of electrical-energy distribution. These networks are typically organized into multiple layers to manage various aspects of the infrastructure. Each layer plays a crucial role in the operation and enhancement of the smart grid. In Figure 1, each of these layers is described, providing a general overview of how they are organized within a smart electrical grid. This structure is divided into six layers: data management, electrical infrastructure, automation and control, consumer interaction, communication, and security.

Figure 1. General diagram of the six layers of the smart electrical grid.

2. Data-Management Layer

This layer is responsible for processing and managing big data across various domains, including smart homes ^[1], smart grids ^[2], smart cities ^[3], and healthcare. In the context of smart homes, a layered architecture for big data processing and management is proposed ^[1]. This architecture consists of multiple layers, including a data-management and -visualization layer, which is tasked with handling the collection, storage, and visualization of data ^[4]. Furthermore, an integrated framework for big data management and analysis is required to efficiently enable real-time data control and management, as demonstrated in the research study ^[5].

In Figure 2, it is observed that the communication layer exchanges information passively across all layers through a bidirectional flow of energy and data. This flow extends from energy generation, through the transmission and distribution network, to the energy consumer, utilizing tools such as big data for data management, blockchain for energy trading among prosumers, and data usage ^[6]^[7]. The significance of efficient big data analytics in the smart grid is crucial as it can prevent economic, social, and technical losses under unfavorable conditions ^[8].

Figure 2. Bidirectional flow of information data in smart grid.

3. Electrical-Infrastructure Layer

This layer encompasses power-generation plants, such as power stations, wind farms, and PV plants. When utilizing RESs, their unpredictable nature, such as wind speed, must be considered. For instance, wind farms, as a source of renewable energy, have been subject to numerous studies in different contexts. In Europe, there has been an analysis of the impact of wind turbines on the electrical grid and the regulatory framework for wind farms ^[9]. Furthermore, the importance of evolving infrastructure for connecting offshore wind farms to the centralized onshore grid has been emphasized ^[10].

On the other hand, in Brazil, challenges have been highlighted in locating regions with reliable winds for wind farm implementation due to the scarcity of valid data ensuring park efficiency ^[11]. Concerning the establishment of wind farms, the significance of various stages, including determining the installation site, assessing local wind potential, identifying wind power, and predicting wind power generation, has been underscored ^[12]. Moreover, research has delved into the economic feasibility of wind energy hybrid systems linked to the power grid, emphasizing the critical role of economic factors in the development of wind farms ^[13]. In Table 1, wind farms play a significant role in specific geographical areas, exhibiting variations in both their geographical location and design. For example, the Alta Wind Energy Center, situated in California, is an expansive onshore facility comprising over 600 turbines. This facility stands among the largest globally, possessing a total capacity of 1548 MW. In contrast, Sheringham Shoal, located in the North Sea, is an offshore wind farm featuring 88 turbines with a capacity of 317 MW. The distinction lies in the park type, with Alta Wind Energy Center being onshore, while Sheringham Shoal operates offshore. Environmental assessments have been conducted by the parks to understand and minimize their impact. The Alta Wind Energy Center significantly contributes to California’s grid, while Sheringham Shoal has been pivotal in renewable energy generation in the UK. The turbine technology also differs, with onshore turbines in Alta Wind Energy Center and offshore turbines in Sheringham Shoal. The parks are operational, with inaugurations between 2010 and 2015 for the Alta Wind Energy Center and in 2012 for Sheringham Shoal.

Table 1. Wind farms’ comparison.

Aspect	Alta Wind Energy Center, USA	Sheringham Shoal, UK
Location capacity type	California, USA 1548 MW onshore	North Sea, off Norfolk, UK 317 MW offshore
Number of turbines Operator	Over 600 turbines (multiple phases) NRG Energy	88 turbines equinor (formerly Statoil)
Inauguration Grid Connection	Phases between 2010 and 2015	2012
Inauguration Grid Connection	Connects to California’s grid	Connects to the UK’s national grid
Environmental-impact studies	Conducted	Conducted
Technology	Land-based turbines	Offshore wind turbines
Significance	One of the largest onshore wind farms	Significant contribution to UK’s offshore wind capacity
Economic viability Studies	No specific information available	No specific information available
Infrastructure	Transmission infrastructure for grid connection	Submarine connections and transformation stations

These wind farms represent significant advancements in renewable energy generation, contributing uniquely to their respective regions: Alta Wind Energy Center on land extension and Sheringham Shoal in marine expansion. In Figure 3, two blocks are depicted. The first block is the energy-transmission block, responsible for transporting electricity over long distances through high-voltage lines. The second block enables distribution, delivering electricity to end users through substations and distribution lines ^[14]. This layer is interconnected through various layers such as the communication layer, the consumer-interaction layer, the automation and control layer, and the data-management layer to prevent energy losses, maintain a self-healing network, and analyze data to detect cybersecurity threats against electrical systems that could have physical impacts on the electrical layer ^[15].

Figure 3. Flow diagram of electrical distribution.

4. Automation and Control Layer

4.1. Power-System Optimization and Supply–Demand Analytics

To optimize smart grid power systems, predictive models are needed for demand forecasting and supply and demand analysis, and optimization techniques have been widely used to address the challenges of integrating uncertain renewable energy sources. Power-system deregulation and random-load fluctuations, methods used to predict the volatility of financial markets, can also be used to predict the volatility of environmental and load demands highlighting the importance of predictive modeling in this context ^[16].

Demand management and economic-performance optimization require the application of machine-learning algorithms to identify consumers with similar lifestyles based on their daily energy consumption. Forecasting, fault diagnosis, maintenance, operation planning, sizing, and risk management require the deployment of Bayesian networks for probabilistic modeling of uncertain components of a power system ^[17].

To schedule smart meter devices and optimize demand-response approaches, it is necessary to develop incentive-based demand-response strategies, together with reliable load-forecasting techniques based on classical statistics or AI and machine learning. These strategies are critical to increasing the technical and economic efficiency of the smart grid, making them an essential part of power-system optimization.

4.2. Power-System Automation in Electrical Networks

Automation in the smart grid is a crucial aspect of modern-electrical-network infrastructure. It involves the integration of advanced technology such as automation systems, communication protocols, and control functions to enhance the efficiency, reliability, and security of the network ^[16]. One key component of automation in the smart grid is the implementation of control functions. Systems-control engineering plays a fundamental role in designing and optimizing control strategies for smart grid applications ^[17]. These control functions enable the network to dynamically respond to changes in electricity demand and supply, ensuring efficient and reliable operation.

To maximize the utilization of local resources and enhance the efficiency of energy-management systems, microgrids are employed ^[18]. A study explores different types of microgrid control systems via IoT, SCADA monitoring, and cloud computing ^[19]. Microgrids are not the only case of automation and control. In the context of battery-energy-storage systems, control-based smoothing approaches have been proposed to optimize state of charge and prevent overcharging or discharging ^[20].

Automation in the smart grid, within the context of Industry 4.0, has become a topic of great relevance. The Spanish Domotics Association defines intelligent automation as the use of technology to efficiently manage energy usage, provide security and comfort, and enable communication between the user and the system ^[21]. This intelligent automation has extended to manufacturing industries, involving the interconnection of sensitive parts of businesses to improve adaptability through artificial intelligence (AI) processes ^[22].

The integration of cyber–physical systems in industrial automation has been a topic of interest, with the application of cyber–physical production systems (CPPSs) enabling access to innovative functionalities based on network connectivity and Internet access ^[23]. The importance of infrastructure evolution for connecting offshore wind farms to the centralized onshore grid has also been emphasized, highlighting the relevance of automation in electricity generation from renewable sources ^[10].

In the realm of industrial automation, the impact of new technology such as intelligent big data and cloud computing has been assessed ^[24]. These technological advances have contributed to the efficiency, flexibility, and adaptability of production systems, underscoring the importance of intelligent automation in the context of Industry 4.0. Automation in the smart grid, under the Industry 4.0 framework, spans from intelligent home automation to the integration of cyber–physical systems in the industry, transforming how energy resources are managed and production processes are optimized. Table 2 demonstrates that the automation of electrical networks not only improves operational efficiency but also establishes the foundation for a more sustainable and resilient network capable of effectively addressing future energy challenges.

Table 2. Power grid automation technology.

Technology	Description	Benefits
Advanced Metering Infrastructure (AMI)	Utilizes smart meters for real-time-consumption monitoring	- Remote reading - Early detection of problems - Implementation of dynamic tariffs
Supervisory Control and Data Acquisition (SCADA)	Monitor and control equipment in real time	- Improves visibility and control - Facilitates real-time decision making
Substation Automation	Uses automated systems and devices	- Improved network reliability - Reduced response time to failures - More efficient operations
Smart Grids	Integrates technology to optimize network	- Improved energy efficiency - Integration of renewable sources - Improved demand response
Energy-Storage Systems with Automatic Control	Uses batteries to store and release power	- Improved grid stability - Efficient management of intermittent renewable energy
Predictive Analytics and Maintenance	Uses algorithms to anticipate failures and proactive maintenance	- Reduced downtime - Prolongation of equipment service life
Advanced Protection Systems	Use advanced protection devices	- Improved safety and reliability - Fast and accurate fault detection and isolation
IoT Integration	Connects devices and sensors on the network	- Advanced monitoring and control - Decision making based on real-time information

A rapidly growing field of research and development is the integration of IoT devices into the electrical network, with applications ranging from enhancing power quality to optimizing renewable energy generation and network operational efficiency. The incorporation of IoT (Internet of Things) devices into the electrical grid stands as an exceptionally pertinent subject in contemporary times. The transformative impact of IoT devices, capable of gathering and transmitting real-time data, has revolutionized the monitoring, control, and management of the electrical network. The deployment of IoT devices within the electrical network facilitates a more intricate monitoring of aspects such as energy demand, distributed generation, service quality, and operational efficiency. The academic literature has addressed the integration of IoT devices into the electrical network from various perspectives. For instance, it has been demonstrated that a multilevel inverter D-STATCOM can reduce total harmonic distortion in electrical-distribution systems with nonlinear loads ^[25]. This type of device exemplifies the application of advanced technology to improve power quality in the electrical network.

Furthermore, a detailed analysis of the PV and wind energy supply system has been conducted, considering its dependence on the electrical grid. This study emphasizes the importance of understanding how renewable energy systems, integrated with IoT devices, interact with the existing electrical network ^[26]. On the other hand, a pre-feasibility study of a hybrid wind and H₂ energy system connected to the electrical grid has been carried out. This type of research demonstrates how integrating IoT devices into renewable energy generation can have significant economic implications, highlighting the importance of economic considerations in the development of IoT-connected energy systems ^[13].

These methodologies employ fuzzy control mechanisms and low-pass filters to regulate the charging or discharging power, guaranteeing the battery’s operation within secure thresholds. The control administration involving supercapacitors within smart grids represents a developing field of investigation with the goal of enhancing the efficiency and functionality of supercapacitors within the realm of smart grid applications. Supercapacitors, identified as energy-storage devices, possess the capability to store and release substantial amounts of energy rapidly. They exhibit characteristics such as high power density, swift charging and discharging capabilities, and extended life cycles ^[27]^[28]. One potential application of supercapacitors in smart grids is as backup power sources or uninterruptible power systems. Supercapacitors can provide additional power during peak demand periods or in case of outages, ensuring a stable and reliable power supply ^[29]. This can contribute to improving the overall resilience and reliability of the smart grid system.

5. Consumer-Interaction Layer

Consumer-interaction layer is characterized by the integration of hardware and computational intelligence to monitor, control, and manage energy generation, distribution, storage, and consumption through smart meters ^[30]. To understand the role of smart meters in energy management, it is crucial to consider the existing academic literature on the subject. For example, a comprehensive review has been conducted on the data analytics of smart meters, highlighting applications, methodologies, and associated challenges ^[31]. This study provides an overview of the various applications and methodological approaches used in smart meter data analytics, underscoring the importance of this technology in energy management. Moreover, the availability of datasets such as the UK-DALE dataset, providing electricity-demand data at the appliance and whole-house levels from five UK households, has allowed significant advances in understanding household energy consumption ^[32]. This type of data is essential for comprehending energy-consumption behavior and optimizing its management through smart meters.

In Table 3, a considerable number of providers in the market is evident, and the mentioned companies serve as illustrative examples. The selection of a smart meter implementer relies on the specific requirements of the electrical grid and the energy efficiency objectives of the entity. Additionally, the research underscores the significance of technological advancements in managing electric energy generated by off-grid PV systems, emphasizing the imperative need for intelligent measurement systems for efficient energy management ^[33]. Moreover, a study has been conducted on the data intelligence of smart meters for future energy systems, highlighting the importance of computational intelligence in energy management ^[34]. However, user acceptance and participation in the smart grid depend on their perception of its reliability ^[35], as well as the functions and interactions of users with other stakeholders. The market and technological advancements must be assessed for a comprehensive understanding of the development of smart grids, particularly in the residential sector ^[36].

Table 3. Smart-meter-development companies.

Technology	Description	Benefits
Itron	Leading in technology and services for energy management.	Advanced meters, communication, and data management.
Landis + Gyr	Global provider of solutions for intelligent energy management.	Advanced meters, communication systems, and analytics.
Schneider Electric	Offers comprehensive solutions for energy efficiency.	Smart meters, energy-management systems.
Siemens	Global-technology-solutions provider.	Smart meters, energy-management platforms.
Kamstrup	Specialized in intelligent metering solutions.	Smart meters for electricity, water, and heating.
Elster (Honeywell)	Provides advanced metering solutions.	Smart meters and communication systems.
Sensus (Xylem)	Offers advanced metering solutions.	Smart meters and communication systems.
Holley Technology	Specializes in smart metering solutions.	Smart meters and energy-management systems.
Kromschroeder (IDEX)	Provides intelligent metering solutions.	Smart meters, including gas meters.

6. Communication Layer

The communication layer facilitates bidirectional communication between the utility company and energy consumers through the transmission of data among devices, sensors, and systems across the entire network ^[37]. This ensures the security and reliability of communication in smart grid environments. In the study ^[38] it was determined that sharing dynamic states among power system generators is crucial for wide-area monitoring, decentralized control, and the protection of the future smart grid. However, this sharing process is subject to cyber–physical contingency conditions, limiting its precision. Figure 4 incorporates a cyber layer with the electrical system to guarantee the precise exchange of dynamic states of generators. The proposed framework utilizes a weighted-average consensus mechanism within a blockchain environment ^[38].

Figure 4. Description of the physical layer and digital-layer diagram.

In a recent study ^[39], a transmission/reception system is proposed for data communication in a smart grid, using optimal orthogonal frequency division multiplexing (OFDM). The performance of a transmission channel with high-, medium-, and low-voltage power lines is considered Class A noise, composed of Gaussian noise and impulsive noise. Digital data is transmitted through the transmitter, passes through the Class A noise channel, and is received at the receiver, where OFDM performance estimation is conducted. The bit error rate (BER) is obtained by comparing transmitted and received bits and calculating the number of incorrectly received bits. This is completed for each signal-to-noise-ratio (SNR) value, starting at zero decibels and increasing by two decibels. The tabulated values are then used to create BER versus SNR graphs ^[39].

7. Security Layer

It is one of the most important aspects of smart grid systems due to their increased vulnerability to cyber threats ^[40]. The functionality of a smart grid heavily relies on cyber communication. To address the cybersecurity issues of smart grid systems, research has been conducted focusing on various aspects, such as intrusion-detection systems (IDSs), which play an essential role in protecting smart grid systems ^[41]. A common cyberattack on smart grids is the injection of false data, which distorts the measurements collected by the system operator ^[42]. Proactive identification of security threats is achieved through the development of formal security-verification techniques that uncover cybersecurity and physical-security threats in different components of smart grid systems.

The smart grid is described as a dynamic and interactive infrastructure that integrates millions of energy devices and sensors, enabling rapid transmission, monitoring, and bidirectional energy management through advanced communication systems and smart meters. Overall, the architecture of a smart grid is a multifaceted and hierarchical system that requires careful consideration of control, communication, security, and flexibility to ensure its efficient and reliable operation.

References

Mokhtari, G.; Anvari-Moghaddam, A.; Zhang, Q. A New Layered Architecture for Future Big Data-Driven Smart Homes. IEEE Access 2019, 7, 19002–19012.
Ananthavijayan, R.; Shanmugam, P.K.; Padmanaban, S.; Holm-Nielsen, J.B.; Blaabjerg, F.; Fedak, V. Software Architectures for Smart Grid System—A Bibliographical Survey. Energies 2019, 12, 1183.
Li, C.; Dai, Z.; Liu, X.; Sun, W. Evaluation System: Evaluation of Smart City Shareable Framework and Its Applications in China. Sustainability 2020, 12, 2957.
Wang, L.; Jiang, R.; Chen, X.; Xie, J.; Liu, X.; Tian, L.; Wang, M.; Wang, L.; Jiang, R.; Chen, X.; et al. Design and Application of Digital Twin Platform Based Smart Weihe River Basin. In Proceedings of the International Conference on Smart Transportation and City Engineering (STCE 2022), Chongqing, China, 16–18 December 2022.
Bashir, M.R.; Gill, A.Q.; Beydoun, G. A Reference Architecture for IoT-Enabled Smart Buildings. SN Comput. Sci. 2022, 3, 493.
Botero, M.C.B.; Velasco, O.G.D. Data Management Architecture a Need in Smart Grids Domains. In Proceedings of the 2018 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE), Kajang, Malaysia, 29 May–1 June 2018.
Kim, S.M.; Lee, T.; Kim, S.; Park, L.W.; Park, S. Security Issues on Smart Grid and Blockchain-Based Secure Smart Energy Management System. In Proceedings of the International Conference on Power Science and Engineering (ICPSE) Web Conferences, Dublin, Ireland, 2–4 December 2019.
Ponnusamy, V.K.; Kasinathan, P.; Elavarasan, R.M.; Ramanathan, V.; Anandan, R.K.; Subramaniam, U.; Ghosh, A.; Hossain, E. A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid. Sustainability 2021, 13, 13322.
Lázaro, E.C.; Millán, A.R.; Peral, P.R. Analysis of Cogeneration in the Present Energy Framework. Fuel Process. Technol. 2006, 87, 163–168.
Herrera, D.B.; Julio; Normey-Rico, E.; Galván, E.; Carrasco, J.M. Sistema de Controle Distribuído Para Uma Rede de Turbinas Eólicas Offshore Conectado Por Um Link HVDC Baseado Em Retificador de Diodo. In Proceedings of the Simpósio Brasileiro de Automação Inteligente—SBAI, Virtual, 20 October 2021.
Stüker, E.; Schuster, C.H.; Schuster, J.J.; Santos, D.C.; Medeiros, L.E.; Costa, F.D.; Demarco, G.; Puhales, F.S. Comparação Entre os Dados de Vento Das Reanálises Meteorológicas ERA-Interim e CFSR Com os Dados das Estações Automáticas Do INMET No Rio Grande Do Sul. Ciência Nat. 2016, 38, 284–290.
Bellini, D.; de Oliveira, E.C.; Lagioia, U.C.T.; da Silva, A.C.B.; Melo, J.L. Energia eólica: Desenvolvimento de geração de energia sustentável. Rev. Ibero-Am. Ciências Ambient. 2017, 8, 205–223.
Remesal, A.V.; Millan, A.R.; Lazaro, E.C.; Peral, P.R. Pre-Feasibility Study of Hybrid Wind Power-H2 System Connected to Electrical Grid. IEEE Lat. Am. Trans. 2011, 9, 800–807.
Manis, P.; Bloodworth, A.G. Climate Change and Extreme Wind Effects on Transmission Towers. Struct. Build. 2017, 170, 81–97.
Cameron, C.; Patsios, C.; Taylor, P.C.; Pourmirza, Z. Using Self-Organizing Architectures to Mitigate the Impacts of Denial-of-Service Attacks on Voltage Control Schemes. IEEE Trans. Smart Grid 2019, 10, 3010–3019.
Andrén, F.P.; Strasser, T.I.; Resch, J.; Schuiki, B.; Schöndorfer, S.; Panholzer, G.; Brandauer, C. Towards Automated Engineering and Validation of Cyber-Physical Energy Systems. Energy Inform. 2019, 2, 21.
Palaniappan, R.; Molodchyk, O.; Shariati-Sarcheshmeh, M.; Asmah, M.W.; Liu, J.; Schlichtherle, T.; Richter, F.; Kwofie, E.A.; Festner, D.R.; Blanco, G.; et al. Experimental Verification of Smart Grid Control Functions on International Grids Using a Real-Time Simulator. IET Gener. Transm. Distrib. 2022, 16, 2747–2760.
Fazal, S.; Enamul Haque, M.; Taufiqul Arif, M.; Gargoom, A.; Oo, A.M.T. Grid Integration Impacts and Control Strategies for Renewable Based Microgrid. Sustain. Energy Technol. Assess. 2023, 56, 103069.
Albarakati, A.J.; Boujoudar, Y.; Azeroual, M.; Eliysaouy, L.; Kotb, H.; Aljarbouh, A.; Khalid Alkahtani, H.; Mostafa, S.M.; Tassaddiq, A.; Pupkov, A. Microgrid Energy Management and Monitoring Systems: A Comprehensive Review. Front. Energy Res. 2022, 10, 1097858.
Arévalo, P.; Benavides, D.; Tostado-Véliz, M.; Aguado, J.A.; Jurado, F. Smart Monitoring Method for Photovoltaic Systems and Failure Control Based on Power Smoothing Techniques. Renew. Energy 2023, 205, 366–383.
Pǎtru, I.I.; Carabaş, M.; Bǎrbulescu, M.; Gheorghe, L. Smart Home IoT System. In Proceedings of the Networking in Education and Research: RoEduNet International Conference Edition (RoEduNet), Bucharest, Romania, 7–9 September 2016.
Ríos-Ramírez, L.C.; Pérez-Domínguez, L.; Pérez-Olguin, I.J.C. Tendencias Actuales de La Industria 4.0. Reflex. Contab. UFPS 2019, 2, 8–22.
Murillo, F.M.; Díaz, D.J. Arquitectura Inteligente CPPS Integrada En El Uso de La Norma IEC-61499, Con Bloque de Funciones Altamente Adaptables En La Industria 4.0. In Proceedings of the KnE Engineering, Panama City, Panama, 11–13 October 2017.
García, M.V.; Irisarri, E.; Pérez, F. Integración Vertical En Plantas Industriales Utilizando OPC UA e IEC-61499. Enfoque UTE 2017, 8, 287–299.
Callacando, M.; Pavón, W.; Ortíz, L. Multilevel Inverter D-STATCOM for Reducing Total Harmonic Distortion in a Non-Linear Loads Electrical Distribution System. Rev. Técnica Energía 2022, 19, 85–91.
Mikati, M.; Santos, M.; Armenta, C. Modelado y Simulación de Un Sistema Conjunto de Energía Solar y Eólica Para Analizar Su Dependencia de La Red Eléctrica. Rev. Iberoam. Automática Informática Ind. RIAI 2012, 9, 267–281.
Cano, A.; Arévalo, P.; Benavides, D.; Jurado, F. Comparative Analysis of HESS (Battery/Supercapacitor) for Power Smoothing of PV/HKT, Simulation and Experimental Analysis. J. Power Sources 2022, 549, 232137.
Na, J.; Zheng, D.; Kim, J.; Gao, M.; Azhar, A.; Lin, J.; Yamauchi, Y.; Na, J.; Yamauchi, Y.; Zheng, D.; et al. Material Nanoarchitectonics of Functional Polymers and Inorganic Nanomaterials for Smart Supercapacitors. Small 2022, 18, 2102397.
Benavides, D.; Arévalo, P.; Aguado, J.A.; Jurado, F. Experimental Validation of a Novel Power Smoothing Method for On-Grid Photovoltaic Systems Using Supercapacitors. Int. J. Electr. Power Energy Syst. 2023, 149, 109050.
Jihen, E.K.; Lotfi, B.; Mohamed Najeh, L. Part of Modelling and Optimal Management of Smart Grids. Int. J. Artif. Intell. Emerg. Technol. 2022, 5, 1–9.
Wang, Y.; Chen, Q.; Hong, T.; Kang, C. Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges. IEEE Trans. Smart Grid 2019, 10, 3125–3148.
Kelly, J.; Knottenbelt, W. The UK-DALE Dataset, Domestic Appliance-Level Electricity Demand and Whole-House Demand from Five UK Homes. Sci. Data 2015, 2, 150007.
México, M.; Elizabeth, B.; Pisco, C.; Superior, I.; Ciudad, T.; Quevedo -Ecuador, V.; Cesar, P.; Ocapana, C.; Andres, C.; Andino, M.; et al. Diseño de Un Medidor Para La Gestión de Energía Eléctrica Generada Por Un Sistema Fotovoltaico off Grid. Cienc. Lat. Rev. Cient. Multidisc. 2023, 7, 2789–2801.
Alahakoon, D.; Yu, X. Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey. IEEE Trans. Industr. Inform. 2016, 12, 425–436.
Mashal, I.; Khashan, O.A.; Hijjawi, M.; Alshinwan, M. The Determinants of Reliable Smart Grid from Experts’ Perspective. Energy Inform. 2023, 6, 10.
Van Mierlo, B. Users Empowered in Smart Grid Development? Assumptions and Up-To-Date Knowledge. Appl. Sci. 2019, 9, 815.
Hussain, H.M.; Javaid, N.; Iqbal, S.; Ul Hasan, Q.; Aurangzeb, K.; Alhussein, M. An Efficient Demand Side Management System with a New Optimized Home Energy Management Controller in Smart Grid. Energies 2018, 11, 190.
Abdelsalam, H.A.; Eldosouky, A.; ElGebaly, A.E.; Khalaf, M.; Zaki Diab, A.A.; Rangarajan, S.S.; Alghamdi, S.; Albalawi, H. A Cyber-Layer Based on Weighted Average Consensus in Blockchain Environment for Accurate Sharing of Power Systems’ Dynamic States. Int. J. Electr. Power Energy Syst. 2024, 155, 109558.
Fernández, R.W.; Rodríguez, R.A.; Fernández, R.W.; Rodríguez, R.A. OFDM Óptimo Para La Comunicación Bidireccional En Las Redes Eléctricas Inteligentes. Ingeniare 2018, 26, 43–53.
Sakhnini, J.; Karimipour, H.; Dehghantanha, A.; Parizi, R.M.; Srivastava, G. Security Aspects of Internet of Things Aided Smart Grids: A Bibliometric Survey. Internet Things 2021, 14, 100111.
Khan, S.; Kifayat, K.; Kashif Bashir, A.; Gurtov, A.; Hassan, M. Intelligent Intrusion Detection System in Smart Grid Using Computational Intelligence and Machine Learning. Trans. Emerg. Telecommun. Technol. 2021, 32, e4062.
Bi, S.; Zhang, Y.J.A. Graph-Based Cyber Security Analysis of State Estimation in Smart Power Grid. IEEE Commun. Mag. 2017, 55, 176–183.

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