Energies

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Open AccessEditorial

Smart Grid and Energy Storage: Innovations, Challenges, and Applications

by Adel Merabet

Energies 2026, 19(5), 1251; https://doi.org/10.3390/en19051251 - 2 Mar 2026

Viewed by 587

Abstract

Smart grids are complex systems that require efficient solutions integrating demand response, distributed generation, and energy storage to achieve optimal performance [...] Full article

(This article belongs to the Special Issue Smart Grid and Energy Storage)

Research

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15 pages, 3014 KB

Open AccessArticle

Probabilistic Visualisation Approach Using Polar Histograms to Examine the Influence of Networked Distributed Generation

by Yasmin Nigar Abdul Rasheed, Ashish P. Agalgaonkar and Kashem Muttaqi

Energies 2026, 19(3), 799; https://doi.org/10.3390/en19030799 - 3 Feb 2026

Viewed by 422

Abstract

The variability of renewable energy sources, coupled with the decentralised configuration of distributed generation (DG), significantly complicates grid management, necessitating sophisticated visual analytics to enhance power system performance and energy distribution. This paper presents a probabilistic visualisation technique based on polar histograms to [...] Read more.

The variability of renewable energy sources, coupled with the decentralised configuration of distributed generation (DG), significantly complicates grid management, necessitating sophisticated visual analytics to enhance power system performance and energy distribution. This paper presents a probabilistic visualisation technique based on polar histograms to identify the dynamic influence zones of DG units by analysing line current flows. The proposed framework explicitly accounts for the probabilistic representation of reverse power flows, which provides an overall view of DG impacts on distribution networks. Quasi-dynamic simulations are conducted on a 33-bus distribution system using DIgSILENT PowerFactory 2020, MATLAB R2020, and Python 3.8. The results demonstrate that the polar histogram approach provides intuitive insights into DG influence, revealing zones of grid-dominated, DG-dominated, and shared interactions. These findings act as a potential practical tool for voltage management, demand balancing, and secure integration of renewable DG units into modern power grids. Full article

(This article belongs to the Special Issue Smart Grid and Energy Storage)

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36 pages, 5355 KB

Open AccessArticle

Smart Grids and Sustainability in the Age of PMSG-Dominated Renewable Energy Generation

by Plamen Stanchev and Nikolay Hinov

Energies 2026, 19(3), 772; https://doi.org/10.3390/en19030772 - 2 Feb 2026

Cited by 1 | Viewed by 645

Abstract

This study investigates the physical and cyber-physical resilience of smart grids with a high share of renewable energy sources (RESs) dominated by permanent magnet synchronous generators (PMSGs). The originality of this work lies in the development and unified evaluation of five integrated control [...] Read more.

This study investigates the physical and cyber-physical resilience of smart grids with a high share of renewable energy sources (RESs) dominated by permanent magnet synchronous generators (PMSGs). The originality of this work lies in the development and unified evaluation of five integrated control strategies, the PLL with grid following, VSG with grid shaping, VSG+BESS, VSG+STATCOM, and VSG+BESS+STATCOM, implemented within a coherent simulation framework based on Python. Unlike previous works that analyze these methods in isolation, this study provides a comprehensive quantitative comparison of their dynamic characteristics, including frequency root mean square deviation, maximum deviation, and composite resilience index (RI). To extend the analysis beyond static conditions, a multi-generator (multi-PMSG) scenario with heterogeneous inertia constants and variable load profiles is introduced. This dynamic model allows the evaluation of natural inertia diversity and the effects of inter-generator coupling compared to the synthetic inertia emulation provided by VSG-based control. The combined VSG+BESS+STATCOM configuration achieves the highest synthetic resilience, improving frequency and voltage stability by up to 15%, while the multi-PMSG system demonstrates comparable or even higher RI values due to its inherent mechanical inertia and decentralized response behavior. In addition, a cyber-physical scenario is included to evaluate the effect of communication delays and false data injection (FDI) on VSG frequency control. The results show that a communication delay of 50 ms reduces RI by approximately 0.2%, confirming that even minor cyber disturbances can affect synchronization and transient recovery. However, hybrid control architectures with local energy buffering (BESS) show superior resilience under such conditions. The main technical contribution of this work is the establishment of an integrated analytical and simulation framework that enables the joint assessment of synthetic, natural, and cyber-physical resilience in converter-dominated smart grids. This framework provides a unified basis for the analysis of dynamic stability, hybrid control interaction, and the impact of cyber uncertainty, thereby supporting the design of low-inertia, resilient, and secure next-generation power systems. Full article

(This article belongs to the Special Issue Smart Grid and Energy Storage)

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17 pages, 2914 KB

Open AccessArticle

Solar Photovoltaic Model Parameter Identification with Improved Metaheuristic Algorithm Based on Balanced Search Strategies

by Sujoy Barua, Sukanta Paul and Adel Merabet

Energies 2026, 19(2), 315; https://doi.org/10.3390/en19020315 - 8 Jan 2026

Viewed by 958

Abstract

Accurate identification of solar photovoltaic model parameters is crucial for reliably representing electrical behavior, improving maximum power point tracking, and enhancing overall system performance. Owing to the nonlinear and multimodal nature of the single-diode model, analytical closed-form solutions are difficult to obtain, which [...] Read more.

Accurate identification of solar photovoltaic model parameters is crucial for reliably representing electrical behavior, improving maximum power point tracking, and enhancing overall system performance. Owing to the nonlinear and multimodal nature of the single-diode model, analytical closed-form solutions are difficult to obtain, which necessitates the use of advanced optimization techniques. Metaheuristic methods are particularly suitable for this task due to their strong global search capability, independence from gradient information, and adaptability to complex solution landscapes. In this study, a hybrid metaheuristic approach called the Jackal Arithmetic Algorithm is evaluated by integrating the Arithmetic Optimization Algorithm with the Golden Jackal Optimization method. The optimization framework combines arithmetic-based operators to enhance global exploration with adaptive predatory-inspired strategies to strengthen local exploitation, enabling a smooth transition between exploration and exploitation and resulting in improved convergence stability. Simulation results confirm that the Jackal Arithmetic Algorithm provides highly accurate parameter estimation for the single-diode photovoltaic model, achieving a minimum root mean square error of 0.00078 with a population size of 70, outperforming all compared algorithms. Overall, the combined method offers a robust and effective solution for photovoltaic modeling, with direct benefits for system design, control, and real-time monitoring. Full article

(This article belongs to the Special Issue Smart Grid and Energy Storage)

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17 pages, 1039 KB

Open AccessArticle

An Adaptive Multi-Layer Heuristic Framework for Real-Time Energy Optimization in Smart Grids

by Atef Gharbi, Mohamed Ayari, Nasser Albalawi, Ahmad Alshammari, Nadhir Ben Halima and Zeineb Klai

Energies 2026, 19(2), 307; https://doi.org/10.3390/en19020307 - 7 Jan 2026

Viewed by 627

Abstract

Smart grids face significant challenges in coordinating demand-side management (DSM), dynamic pricing, data aggregation, and network feasibility in real time. To address this, we propose H-EMOS-Lite, an adaptive, multi-layer heuristic framework that integrates these components into a unified, real-time optimization loop. Evaluated on [...] Read more.

Smart grids face significant challenges in coordinating demand-side management (DSM), dynamic pricing, data aggregation, and network feasibility in real time. To address this, we propose H-EMOS-Lite, an adaptive, multi-layer heuristic framework that integrates these components into a unified, real-time optimization loop. Evaluated on fully reproducible generated demand, price, and grid datasets based on realistic residential energy systems, H-EMOS-Lite achieves a 2.1% reduction in peak load and completes a full 24 h (96-interval) optimization for 100 households in under 0.25 s, demonstrating its suitability for near-real-time residential energy systems. The framework outperforms three baselines—Independent DSM, Sequential Optimization, and Particle Swarm Optimization (PSO)—by effectively balancing energy cost, peak load reduction, and temporal smoothness of the aggregate load profile, while avoiding abrupt, unsynchronized load shifts that induce secondary peaks—common in uncoordinated approaches. By embedding physical feasibility and cross-layer feedback directly into the optimization loop, H-EMOS-Lite enables scalable, interpretable, and deployable coordination for smart distribution systems. Full article

(This article belongs to the Special Issue Smart Grid and Energy Storage)

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24 pages, 4042 KB

Open AccessFeature PaperArticle

Towards Net Zero in Poland: A Novel Approach to Power Grid Balance with Centralized Hydrogen Production Units

by Dariusz Bradło, Witold Żukowski, Jan Porzuczek, Małgorzata Olek and Gabriela Berkowicz-Płatek

Energies 2025, 18(7), 1576; https://doi.org/10.3390/en18071576 - 21 Mar 2025

Cited by 1 | Viewed by 1743

Abstract

The net zero emissions policy represents a crucial component of the global initiative to address climate change. The European Union has set a target of achieving net zero greenhouse gas emissions by 2050. This study assesses Poland’s feasibility of achieving net zero emissions. [...] Read more.

The net zero emissions policy represents a crucial component of the global initiative to address climate change. The European Union has set a target of achieving net zero greenhouse gas emissions by 2050. This study assesses Poland’s feasibility of achieving net zero emissions. Currently, Poland relies on fossil fuels for approximately 71% of its electricity generation, with electricity accounting for only approximately 16% of the country’s total final energy consumption. Accordingly, the transition to net zero carbon emissions will necessitate significant modifications to the energy system, particularly in the industrial, transport, and heating sectors. As this is a long-term process, this article demonstrates how the development of renewable energy sources will progressively necessitate the utilisation of electrolysers in line with the ongoing industrial transformation. A new framework for the energy system up to 2060 is presented, with transition phases in 2030, 2040, and 2050. This study demonstrates that it is feasible to attain a sustainable, zero-emission, and stable energy system despite reliance on uncontrolled and weather-dependent energy sources. Preparing the electricity grid to transmit almost three times the current amount represents a significant challenge. The resulting simulation capacities, comprising 64 GW of onshore wind, 33 GW of offshore wind, 136 GW of photovoltaic, 10 GW of nuclear, and 22 GW of electrolysers, enable a positive net energy balance to be achieved under the weather conditions observed between 2015 and 2023. To guarantee system stability, electrolysers must operate within a centralised framework, functioning as centrally controlled dispatchable load units. Full article

(This article belongs to the Special Issue Smart Grid and Energy Storage)

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17 pages, 2433 KB

Open AccessArticle

A Win-Win Coordinated Scheduling Strategy Between Flexible Load Resource Operators and Smart Grid in 5G Era

by Nan Zhang, Di Liu, Tianbao Liu, Xueyan Zhang, Jing Guo, Fusheng Lan, Qingyao Li, Weiyi Lu and Xiaolong Yang

Energies 2025, 18(6), 1510; https://doi.org/10.3390/en18061510 - 19 Mar 2025

Cited by 2 | Viewed by 966

Abstract

With the rapid expansion of 5G base stations, the increasing energy consumption and fluctuations in power grid loads pose significant challenges to both network operators and grid stability. This paper proposes a coordinated scheduling strategy designed to address these pressing issues by leveraging [...] Read more.

With the rapid expansion of 5G base stations, the increasing energy consumption and fluctuations in power grid loads pose significant challenges to both network operators and grid stability. This paper proposes a coordinated scheduling strategy designed to address these pressing issues by leveraging the flexible load management capabilities of 5G base stations and their potential for inter-regional power demand response within the smart grid framework. This study begins by quantifying the dispatch potential of 5G base stations through a detailed analysis of their load dynamics, particularly under tidal fluctuations, which are critical for understanding the temporal variability of energy consumption. Building on this foundation, dormancy and load transfer strategies are introduced to model the scheduling potential for regional energy storage, enabling more efficient utilization of available resources. To further enhance the optimization of energy distribution, a many-to-many proportional energy-sharing algorithm is developed, which facilitates the aggregation of scheduling capacities across multiple regions. Finally, a comprehensive multi-objective, two-layer collaborative dispatching strategy is proposed, aiming to mitigate grid load volatility and reduce electricity procurement costs for 5G operators. Extensive simulation results demonstrate the effectiveness of this strategy, showing a significant reduction in grid load variance by 37.88% and a notable decrease in operational electricity costs for 5G base stations from CNY 4616.0 to 3024.1. These outcomes highlight the potential of the proposed approach to achieve a win-win scenario, benefiting both base station operators and the smart grid by enhancing energy efficiency and grid stability. Full article

(This article belongs to the Special Issue Smart Grid and Energy Storage)

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19 pages, 13304 KB

Open AccessArticle

A Deep-Neural-Network-Based Surrogate Model for DC/AC Converter Topology Selection Using Multi-Domain Simulations

by Gabriel Avila Saccol, Van-Hai Bui and Wencong Su

Energies 2024, 17(24), 6467; https://doi.org/10.3390/en17246467 - 22 Dec 2024

Cited by 4 | Viewed by 2371

Abstract

The selection of optimal DC/AC power converter topologies for specific applications is often a time-consuming and complex task, which can lead to suboptimal choices. This paper proposes an AI-assisted methodology to identify the most efficient DC/AC converter based on a set of input [...] Read more.

The selection of optimal DC/AC power converter topologies for specific applications is often a time-consuming and complex task, which can lead to suboptimal choices. This paper proposes an AI-assisted methodology to identify the most efficient DC/AC converter based on a set of input design parameters. Separate deep-neural-network-based surrogate models are developed for each considered topology, trained by a large dataset of simulation results obtained from MATLAB/Simulink and PSIM, so that the efficiency of each converter can be determined without performing additional simulations. The proposed methodology allows for quick and accurate efficiency estimation, significantly reducing the analysis time for topology selection. A case study for the two-level converter is also presented, demonstrating that additional parameters, such as the semiconductors junction temperature and output current distortion, can also be predicted using a similar methodology. Results are presented to demonstrate the feasibility of the proposed method. Full article

(This article belongs to the Special Issue Smart Grid and Energy Storage)

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17 pages, 5133 KB

Open AccessArticle

Controller Hardware in the Loop Platform for Evaluating Current-Sharing and Hot-Swap in Microgrids

by Juan Martínez-Nolasco, Víctor Sámano-Ortega, Heriberto Rodriguez-Estrada, Mauro Santoyo-Mora, Elias Rodriguez-Segura and José Zavala-Villalpando

Energies 2024, 17(15), 3803; https://doi.org/10.3390/en17153803 - 2 Aug 2024

Cited by 1 | Viewed by 2299

Abstract

Microgrids have increased in popularity thanks to both the integration of renewable energy resources and their energy distribution capability for remote locations. Moreover, the microgrids, mainly using multiple generators connected in parallel, acquire additional advantages by using both Hot-Swap and Current-Sharing techniques. This [...] Read more.

Microgrids have increased in popularity thanks to both the integration of renewable energy resources and their energy distribution capability for remote locations. Moreover, the microgrids, mainly using multiple generators connected in parallel, acquire additional advantages by using both Hot-Swap and Current-Sharing techniques. This paper presents the development of a Hardware in the Loop platform to test Current-Sharing algorithms. It is reinforced that the use of a real-time simulation based on Hardware in the Loop is a viable and cost-effective alternative in the validation of controllers. The platform was developed in a graphical programming environment (LabVIEW 2015) and implemented with NI MyRIO 1900 (National Instruments Corp., Austin, TX, USA) development boards for easier reproducibility. The entire code project is openly available and provided in this paper. A system of photovoltaic energy generators was used to evaluate the performance of the HIL platform. As a result, the platform was able to reproduce a similar behavior to the photovoltaic generator, presenting average mean errors of 0.4 V and 0.2 A in its voltage and current, respectively. Additionally, the platform showed its capability to test Current-Sharing algorithms in the occurrence of Hot-Swap events. This work contributes with a validation tool for energy management systems applied to microgrids. Full article

(This article belongs to the Special Issue Smart Grid and Energy Storage)

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21 pages, 10921 KB

Open AccessArticle

ANN for Temperature and Irradiation Prediction and Maximum Power Point Tracking Using MRP-SMC

by Mokhtar Jlidi, Oscar Barambones, Faiçal Hamidi and Mohamed Aoun

Energies 2024, 17(12), 2802; https://doi.org/10.3390/en17122802 - 7 Jun 2024

Cited by 8 | Viewed by 2240

Abstract

Currently, artificial intelligence (AI) is emerging as a dominant force in various technologies, owing to its unparalleled efficiency. Among the plethora of AI techniques available, neural networks (NNs) have garnered significant attention due to their adeptness in addressing diverse challenges, particularly for prediction [...] Read more.

Currently, artificial intelligence (AI) is emerging as a dominant force in various technologies, owing to its unparalleled efficiency. Among the plethora of AI techniques available, neural networks (NNs) have garnered significant attention due to their adeptness in addressing diverse challenges, particularly for prediction tasks. This study offers a comprehensive review of predominant AI-based approaches to photovoltaic (PV) energy forecasting, with a particular emphasis on artificial neural networks (ANNs). We introduce a revolutionary methodology that amalgamates the predictive capabilities of ANN with the precision control afforded by the minimum-risk problem and sliding mode control (MRP-SMC), thereby revolutionizing the PV panel performance enhancement. Building upon this methodology, our hybrid approach utilizes the ANN as a proficient weather forecaster, accurately predicting the temperature and solar radiation levels impacting the panels. These forecasts serve as guiding principles for the MRP-SMC algorithm, enabling the proactive determination of the Maximum Power Point (MPP). Unlike conventional methods that grapple with weather unpredictability, the MRP-SMC algorithm transforms stochastic optimization challenges into controllable deterministic risk problems. Our method regulates the boost converter’s work cycle dynamically. This dynamic adaptation, guided by environmental predictions from ANNs, unlocks the full potential of PV panels, maximizing energy recovery efficiency. To train the model, we utilized a large dataset comprising 60,538 temperature and solar radiation readings from the Department of Systems Engineering and Automation at the Faculty of Engineering in Vitoria (University of the Basque Country). Our approach demonstrates a high regression coefficient (R = 0.99) and low mean square error (MSE = 0.0044), underscoring its exceptional ability to predict real energy values. In essence, this study proposes a potent fusion of artificial intelligence and control mechanisms that unleash the untapped potential of photovoltaic panels. By utilizing forecasts to guide the converter, we are paving the way for a future where solar energy shines brighter than ever. Full article

(This article belongs to the Special Issue Smart Grid and Energy Storage)

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24 pages, 655 KB

Open AccessArticle

Prosumer Impact on Cellular Power Systems

by Jens Maiwald and Tino Schütte

Energies 2024, 17(9), 2195; https://doi.org/10.3390/en17092195 - 3 May 2024

Cited by 1 | Viewed by 1710

Abstract

This paper explores the impact of an increasing number of prosumers in electricity supply systems and investigates how market mechanisms can mitigate the negative effects. The Regional Energy Market Model simulates a supply system based on cellular structures, employing agent-based modeling to capture [...] Read more.

This paper explores the impact of an increasing number of prosumers in electricity supply systems and investigates how market mechanisms can mitigate the negative effects. The Regional Energy Market Model simulates a supply system based on cellular structures, employing agent-based modeling to capture individual behaviors and simulate real market dynamics. This study includes various supply scenarios, such as a solely photovoltaic scenario and a technically diversified scenario with biogas-fueled combined heat and power units. For each scenario, fixed and flexible pricing scenarios are simulated to analyze their effects. The findings reveal that systems heavily reliant on photovoltaics experience negative effects at certain points due to seasonal limitations, while technically diversified supply scenarios demonstrate fewer drawbacks. Flexible pricing systems stimulate demand in a manner beneficial to the system, creating regional added value, and contributing to the balance between generation and consumption, depending on the supply scenario. However, the study underscores that economic incentives alone are insufficient for balancing generation and consumption. The results highlight the importance of exploring opportunities through the interplay of economic incentive mechanisms and technical possibilities. Full article

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33 pages, 2783 KB

Open AccessArticle

Growing Importance of Micro-Meteorology in the New Power System: Review, Analysis and Case Study

by Huijun Zhang, Mingjie Zhang, Ran Yi, Yaxin Liu, Qiuzi Han Wen and Xin Meng

Energies 2024, 17(6), 1365; https://doi.org/10.3390/en17061365 - 12 Mar 2024

Cited by 6 | Viewed by 2776

Abstract

With the increasing penetration of renewable energy resources, their variable, intermittent and unpredictable characteristics bring new challenges to the power system. These challenges require micro-meteorological data and techniques to provide more support for the power systems, including planning, dispatching, operation, and so on. [...] Read more.

With the increasing penetration of renewable energy resources, their variable, intermittent and unpredictable characteristics bring new challenges to the power system. These challenges require micro-meteorological data and techniques to provide more support for the power systems, including planning, dispatching, operation, and so on. This paper aims to provide readers with insights into the effects of micro-meteorology on power systems, as well as the actual improvement brought by micro-meteorology in some power system scenarios. This paper provides a review including the relevant micro-meteorological techniques such as observation, assimilation and numerical techniques, as well as artificial intelligence, presenting a relatively complete overview of the most recent and relevant micro-meteorology-related literature associated with power systems. The impact of micro-meteorology on power systems is analyzed in six different forms of power generation and three typical scenarios of different stages in the power system, as well as integrated energy systems and disaster prevention and reduction. Finally, a case study in China is provided. This case takes wind power prediction as an example in a power system to compare the performance when applying micro-meteorological data or not. The experimental results demonstrated that using the micro-meteorological reanalysis dataset with high spatial--temporal resolution for wind power prediction performed better, verifying the improvement of micro-meteorology to the power system to some extent. Full article

(This article belongs to the Special Issue Smart Grid and Energy Storage)

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18 pages, 3126 KB

Open AccessArticle

Research on Edge-Computing-Based High Concurrency and Availability “Cloud, Edge, and End Collaboration” Substation Operation Support System and Applications

by Yun Long, Yude Bao and Linjun Zeng

Energies 2024, 17(1), 194; https://doi.org/10.3390/en17010194 - 29 Dec 2023

Cited by 13 | Viewed by 3643

Abstract

With the continuous promotion of digital transformation in the field of power transformation, the diversification of application scenarios, and the scale of pilot construction, the real-time, concurrency, and security requirements for data fusion and application support of the power monitoring system, management information [...] Read more.

With the continuous promotion of digital transformation in the field of power transformation, the diversification of application scenarios, and the scale of pilot construction, the real-time, concurrency, and security requirements for data fusion and application support of the power monitoring system, management information system, and other business platforms are getting higher and higher, and this paper puts forward a high concurrency and availability “cloud-side-end collaboration” based on edge computing. This paper proposes a high concurrency and availability “cloud, edge and end collaboration” architecture based on edge computing for substation operation support systems. First, this paper summarizes the development status of domestic substation operation support systems and analyzes the advantages and disadvantages of various technical architectures. Then, a “cloud-side-end cooperative” substation operation support system architecture with “high real-time, high concurrency, high security and high stability” is proposed, which focuses on remote inspection, remote operation, and remote safety control of substation businesses from the perspective of engineering applications. It realizes transparent monitoring of equipment operation, unified management of operation data, and integration of production command and decision-making; solves the problems of dispersed coexistence of multiple systems for dispatching, monitoring, analysis, management, and other businesses, switching between multiple systems, and insufficient real-time and stability of the system; and controls the risks of the grid, reduces the potential safety hazards, and solves the contradiction between the continuous growth of the grid equipment and the shortage of production personnel. The results of engineering application examples show that the proposed architecture compared with the existing system architecture has greater advantages and can meet the requirements of large-scale access to the substation, with feasible popularization and application. Full article

(This article belongs to the Special Issue Smart Grid and Energy Storage)

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Review

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77 pages, 8596 KB

Open AccessReview

Smart Grid Systems: Addressing Privacy Threats, Security Vulnerabilities, and Demand–Supply Balance (A Review)

by Iqra Nazir, Nermish Mushtaq and Waqas Amin

Energies 2025, 18(19), 5076; https://doi.org/10.3390/en18195076 - 24 Sep 2025

Cited by 7 | Viewed by 4080

Abstract

The smart grid (SG) plays a seminal role in the modern energy landscape by integrating digital technologies, the Internet of Things (IoT), and Advanced Metering Infrastructure (AMI) to enable bidirectional energy flow, real-time monitoring, and enhanced operational efficiency. However, these advancements also introduce [...] Read more.

The smart grid (SG) plays a seminal role in the modern energy landscape by integrating digital technologies, the Internet of Things (IoT), and Advanced Metering Infrastructure (AMI) to enable bidirectional energy flow, real-time monitoring, and enhanced operational efficiency. However, these advancements also introduce critical challenges related to data privacy, cybersecurity, and operational balance. This review critically evaluates SG systems, beginning with an analysis of data privacy vulnerabilities, including Man-in-the-Middle (MITM), Denial-of-Service (DoS), and replay attacks, as well as insider threats, exemplified by incidents such as the 2023 Hydro-Québec cyberattack and the 2024 blackout in Spain. The review further details the SG architecture and its key components, including smart meters (SMs), control centers (CCs), aggregators, smart appliances, and renewable energy sources (RESs), while emphasizing essential security requirements such as confidentiality, integrity, availability, secure storage, and scalability. Various privacy preservation techniques are discussed, including cryptographic tools like Homomorphic Encryption, Zero-Knowledge Proofs, and Secure Multiparty Computation, anonymization and aggregation methods such as differential privacy and k-Anonymity, as well as blockchain-based approaches and machine learning solutions. Additionally, the review examines pricing models and their resolution strategies, Demand–Supply Balance Programs (DSBPs) utilizing optimization, game-theoretic, and AI-based approaches, and energy storage systems (ESSs) encompassing lead–acid, lithium-ion, sodium-sulfur, and sodium-ion batteries, highlighting their respective advantages and limitations. By synthesizing these findings, the review identifies existing research gaps and provides guidance for future studies aimed at advancing secure, efficient, and sustainable smart grid implementations. Full article

(This article belongs to the Special Issue Smart Grid and Energy Storage)

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77 pages, 2936 KB

Open AccessReview

Enhancing Smart Grid Security and Efficiency: AI, Energy Routing, and T&D Innovations (A Review)

by Hassam Ishfaq, Sania Kanwal, Sadeed Anwar, Mubarak Abdussalam and Waqas Amin

Energies 2025, 18(17), 4747; https://doi.org/10.3390/en18174747 - 5 Sep 2025

Cited by 8 | Viewed by 5499

Abstract

This paper presents an in-depth review of cybersecurity challenges and advanced solutions in modern power-generation systems, with particular emphasis on smart grids. It examines vulnerabilities in devices such as smart meters (SMs), Phasor Measurement Units (PMUs), and Remote Terminal Units (RTUs) to cyberattacks, [...] Read more.

This paper presents an in-depth review of cybersecurity challenges and advanced solutions in modern power-generation systems, with particular emphasis on smart grids. It examines vulnerabilities in devices such as smart meters (SMs), Phasor Measurement Units (PMUs), and Remote Terminal Units (RTUs) to cyberattacks, including False Data Injection Attacks (FDIAs), Denial of Service (DoS), and Replay Attacks (RAs). The study evaluates cutting-edge detection and mitigation techniques, such as Cluster Partition, Fuzzy Broad Learning System (CP-BLS), multimodal deep learning, and autoencoder models, achieving detection accuracies of (up to 99.99%) for FDIA identification. It explores critical aspects of power generation, including resource assessment, environmental and climatic factors, policy and regulatory frameworks, grid and storage integration, and geopolitical and social dimensions. The paper also addresses the transmission and distribution (T&D) system, emphasizing the role of smart-grid technologies and advanced energy-routing strategies that leverage Artificial Neural Networks (ANNs), Generative Adversarial Networks (GANs), and game-theoretic approaches to optimize energy flows and enhance grid stability. Future research directions include high-resolution forecasting, adaptive optimization, and the integration of quantum–AI methods to improve scalability, reliability, and resilience. Full article

(This article belongs to the Special Issue Smart Grid and Energy Storage)

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