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With the ongoing trends in the energy sector such as vehicular electrification and renewable energy, the Smart Grid (SG) is clearly playing a more and more important role in the electric power system industry. One essential feature of the SG is the information flow over high-speed, reliable, and secure data communication networks in order to manage the complex power systems effectively and intelligently. SGs utilize bidirectional communication to function whereas traditional power grids mainly only use one-way communication. The communication requirements and suitable techniques differ depending on the specific environment and scenario. In this paper, we provide a comprehensive and up-to-date survey on the communication technologies used in the SG, including the communication requirements, physical layer technologies, network architectures, and research challenges. This survey aims to help the readers identify the potential research problems in the continued research on the topic of SG communications.

Given the various aspects of climate change and the growing demand for energy, energy efficiency and environmental protection have become major concerns worldwide. If not taken care of, energy demand will become unmanageable due to technological growth in cities and nations. The solution to the global energy crisis could be an advanced two-way digital power flow system that is capable of self-healing, interoperability, and predicting conditions under various uncertainties and is equipped with cyber protections against malicious attacks. The smart grid enables the integration of renewable energy sources such as solar, wind, and energy storage into the grid. Therefore, the perception of the smart grid and the weight given to it by researchers and policymakers are of utmost importance. In this paper, the studies of many researchers on smart grids are examined in detail. Based on the literature review, various principles of smart grids, the development of smart grids, functionality of smart grids, technologies of smart grids with their characteristics, communication of smart grids, problems in the implementation of smart grids, and possible future studies proposed by various researchers have been presented.

INTRODUCTION: In an era marked by rising energy demand and environmental concerns, integrating smart grid technologies is a crucial solution for promoting industrial expansion through efficient electricity control. OBJECTIVES: Here, we examine the revolutionary capabilities of smart grids to enhance economic growth by leveraging an innovative hybrid optimisation method, Trevally Optimisation (TrevOpt), at its core. The study's main focus is to show how Turnbridge's new approach can efficiently manage energy distribution and use within industrial ecosystems by leveraging TrevOpt's computational power, which combines the benefits of evolutionary algorithms and heuristics. METHODS: The heart of this paper is a discussion of how the use of smart grid technologies can catalyse industrial development. Operation eff was on another level in this experiment, enabling the optimisation of production parameters using the TrevOpt model. Integrating real-time analytics into the TrevOpt framework allows proactive management of energy resources through dynamic tuning, thereby reducing waste and enhancing system reliability. RESULTS: This highlights the potential of these technologies to inspire industrial competitiveness, drive investment in sustainability, and open new areas in energy-intensive industries to spur economic growth. The simulation presented at the end of the numerical section outlines the concrete benefits of using the TrevOpt method, including a 20% reduction in energy consumption, a 15% reduction in operational costs, and a 25% increase in overall system reliability. CONCLUSION: This study therefore provides a solid foundation that enables industries to leverage smart grid developments as a cornerstone for transforming their businesses while also protecting the environments in which people live.

The smart-grid era is characterized by a progressive penetration of distributed energy resources into the power systems. To ensure the safe operation of the system, it is necessary to evaluate the interactions that those devices and their associated control algorithms have between themselves and the pre-existing network. In this regard, Hardware-in-the-Loop (HIL) testing approaches are a necessary step before integrating new devices into the actual network. However, HIL is a device-oriented testing approach with some limitations, particularly considering the possible impact that the device under test may have in the power system. This paper proposes the Power System Hardware-in-the-Loop (PSHIL) concept, which widens the focus from a device- to a system-oriented testing approach. Under this perspective, it is possible to evaluate holistically the impact of a given technology over the power system, considering all of its power and control components. This paper describes in detail the PSHIL architecture and its main hardware and software components. Three application examples, using the infrastructure available in the electrical engineering laboratory of the University of Sevilla, are included, remarking the new possibilities and benefits of using PSHIL with respect to previous approaches.

Buildings contribute significantly to global energy consumption, positioning them as pivotal to achieving global sustainability and climate goals. Although renewable energy technologies hold significant transformative potential, their integration into building systems is hindered by fragmented technological, economic, policy, and social dimensions. This systematic review addresses the existing lack of holistic synthesis by examining peer-reviewed studies published from 2019 to the second quarter (Q2) of 2025. Methodologically, the study adheres to the PRISMA 2020 framework to ensure transparency and replicability and employs bibliometric analysis to map thematic clusters across disciplines. Five major themes emerged from the synthesis: photovoltaic integration, retrofitting strategies, governance frameworks, smart grid applications, and stakeholder acceptance. Analysis highlights notable regional disparities, with Western Europe and East Asia demonstrating higher integration rates due to robust policy structures and established financial incentives. Conversely, regions like Sub-Saharan Africa and Latin America continue to experience significant barriers linked to financing constraints, regulatory fragmentation, and infrastructural limitations. Community-led microgrid initiatives in Kenya and Brazil emerge as exemplars of successful context-sensitive, low-cost renewable integration, emphasizing the value of inclusive governance and localized solutions. The review reconceptualizes buildings as active socio-technical nodes within decentralized energy networks rather than passive energy infrastructures. For renewable energy integration to progress effectively, coordinated systemic efforts spanning technical innovation, adaptive governance, and behavior-sensitive policy design are imperative. These insights carry substantial implications, offering actionable guidance for policymakers, engineers, and urban planners seeking equitable, contextually appropriate, and scalable renewable energy transitions within the built environment.

This study focused on energy saving in energy hub system using smart grid technologies and management of the energy demand. The two-layer energy management is proposed for implementing energy saving. In first layer, energy demand such as electrical, thermal and natural gas are optimized subject to optimal level of the demand at day-ahead. Then, optimized energy demand is applied in second layer to reduction energy generation costs. The optimization of the proposed approach is done by shuffled frog leaping algorithm (SFLA), and results at several case studies to confirmation of the proposed approach are investigated.

Advances in distributed generation and increased contribution of renewable energy source (RES) require development of smart grid technologies. Smart metering systems, as a part of smart grid technologies, in cooperation with modern buildings equipped with building management system allows for improvement of energy efficiency. It is possible to partially cover the power demand of a building from the local RESs. However, in order to ensure maximum added value, energy management system (EMS) is essential. This article presents the project and practical implementation of an EMS implemented in smart-meter. The designed system is based on an original algorithm using fuzzy logic. The rule base was created in FCL language and the implementation was carried out in C++ with the object-oriented programming (OOP). For the efficiency rating indicator, peak-to-average ratio (PAR) was selected. This ratio depending on the daily load profile decreased within a range from 15 to 54%, and the average value was 30%. The proposed energy management algorithm helps to reduce energy consumption at peak demand by 34%, with the total reduction of energy consumption during the day of 7%. The described solution demonstrates a potential for real implementation and was tested in hardware.

This Editorial provides an introduction to the Special Issue “Methods and Concepts for Designing and Validating Smart Grid Systems”. Furthermore, it also provides an overview of the corresponding papers that where recently published in MDPI’s Energies journal. The Special Issue took place in 2018 and accepted a total of 19 papers from 19 different countries.

The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in processing the data; thus, the applications of artificial intelligence (AI) techniques in the smart grid are becoming more apparent. This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in the smart grid and power systems. It also provides further research challenges for applying AI technologies to realize truly smart grid systems. Finally, this survey presents opportunities of applying AI to smart grid problems. The paper concludes that the applications of AI techniques can enhance and improve the reliability and resilience of smart grid systems.

Summary This paper presents a robust evaluation framework for the application of smart grid technologies in generation expansion planning with enforced uncertainties. Considering the availability of smart grid technologies, mainly including load shifting, cost reduction, and environmental benefits, a deterministic optimization model underlying a robust investment plan is designed as the original generation planning problem that minimizes both cost and emission and subjects to the constraints of the basic plan. The proposed robust optimization methodology is used to explore the robust counterparts of the original planning problem with the uncertainties from the forecast load demand and the availability of new technologies. Moreover, the theoretical issue that the different types of uncertainties lead to different tractable robust measures is addressed by the application of the proposed approach in this paper. The optimal generation planning solution obtained from the presented approach can show the impact of smart grid technologies on investment scheduling under the worst‐case uncertainties. The evaluation framework can identify a robustness decision for all realizations of the plan uncertainties, in which simulation results will demonstrate the benefits of the proposed models in case studies. Copyright © 2015 John Wiley & Sons, Ltd.