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Digital transformation is a phenomenon introduced by the transformative power of digital technologies, and it has become a key driver for the energy sector, with advancements in technology leading to significant changes in the way energy is produced, transmitted, and consumed. The impact of digital transformation on the energy sector is profound, with benefits such as improved efficiency, cost reduction, and enhanced customer experience. This article provides a review of the impact of digital transformation on the energy sector, highlighting key trends and emerging technologies that are transforming the sector. The article begins by defining the concept of digital transformation, describing its scope, and explaining two conceptual frameworks to provide a deep understanding of the concept. This article then explores the benefits of digital transformation, examines its impact, and identifies its enablers and barriers. Each source examined was analyzed to extract qualitative results and assess its contribution to the researched topic. This paper also acknowledges the challenges posed by digital transformation, including concerns about cybersecurity, data privacy, and workforce displacement. Finally, we discuss the potential developments that are expected in the future of digital transformation in the power sector and conclude that digital transformation has the potential to significantly improve the energy sector’s efficiency, sustainability, and resiliency.

The operational efficiency of remote environmental wireless sensor networks (EWSNs) has improved tremendously with the advent of Internet of Things (IoT) technologies over the past few years. EWSNs require elaborate device composition and advanced control to attain long-term operation with minimal maintenance. This article is focused on power supplies that provide energy to run the wireless sensor nodes in environmental applications. In this context, EWSNs have two distinct features that set them apart from monitoring systems in other application domains. They are often deployed in remote areas, preventing the use of mains power and precluding regular visits to exchange batteries. At the same time, their surroundings usually provide opportunities to harvest ambient energy and use it to (partially) power the sensor nodes. This review provides a comprehensive account of energy harvesting sources, energy storage devices, and corresponding topologies of energy harvesting systems, focusing on studies published within the last 10 years. Current trends and future directions in these areas are also covered.

Wildfires increasingly threaten the reliable operation of electric power systems due to climate-driven factors and expanding infrastructure. However, existing research remains fragmented, limiting the development of integrated resilience strategies. The objective of this study is to systematically review the literature on power system resilience under wildfire events, focusing on modeling approaches, operational strategies, and learning-based methods. This review was conducted in accordance with PRISMA 2020 guidelines. A structured search was performed in the Scopus database (May 2025; updated January 2026). Studies published between 2016 and 2025 were screened in two stages using predefined eligibility criteria. Studies addressing power system operation under wildfire disturbances with optimization or learning-based methods were included, whereas purely ecological studies were excluded. Thirty studies were included. Data extraction and qualitative thematic synthesis were conducted across four analytical layers. Risk of bias was not formally assessed, and no meta-analysis was performed. Results show increasing research activity and a shift toward stochastic and data-driven methods. Optimization remains dominant, while reinforcement learning is emerging. Hybrid approaches that integrate optimization and learning-based methods are emerging as particularly promising solutions. However, the evidence is limited by methodological heterogeneity and lack of standardized validation.

Microwave planar sensors employ conventional passive complementary split ring resonators (CSRR) as their sensitive region. In this work, a novel planar reflective sensor is introduced that deploys CSRRs as the front-end sensing element at fres=6 GHz with an extra loss-compensating negative resistance that restores the dissipated power in the sensor that is used in dielectric material characterization. It is shown that the S11 notch of −15 dB can be improved down to −40 dB without loss of sensitivity. An application of this design is shown in discriminating different states of vanadium redox solutions with highly lossy conditions of fully charged V5+ and fully discharged V4+ electrolytes.

The integration of renewable energy sources into modern power systems requires advanced control strategies to maintain stability, reliability, and efficiency. This paper presents a comprehensive review of the application of artificial intelligence techniques, including machine learning, deep learning, and reinforcement learning, in improving droop control for renewable energy integration. These artificial intelligence-based methods address key challenges such as frequency and voltage regulation, power sharing, and grid compliance under conditions of high renewable penetration. Machine learning approaches, such as support vector machines, are used to optimize droop parameters for dynamic grid conditions, while deep learning models, including recurrent neural networks, capture complex system dynamics to enhance the stability of distributed energy systems. Reinforcement learning algorithms enable adaptive, autonomous control, improving multi-objective optimization within microgrids. In addition, emerging directions such as transfer learning and real-time data analytics are explored for their potential to enhance scalability and resilience. Overall, this review synthesizes recent advances to demonstrate the growing impact of artificial intelligence in droop control and outlines future pathways toward more intelligent and sustainable power systems.

Distribution network reconfiguration (DNR) is used by utilities to enhance power system performance in various ways, such as reducing line losses. Conventional DNR algorithms rely on accurate values of network parameters and lack scalability and optimality. To tackle these issues, a new data-driven algorithm based on reinforcement learning is developed for DNR in this paper. The proposed algorithm comprises two main parts. The first part, named action-space sampling, aims at analyzing the network structure, finding all feasible reconfiguration actions, and reducing the size of the action space to only the most optimal actions. In the second part, deep Q-learning (DQN) and dueling DQN methods are used to train an agent to take the best switching actions according to the switch states and loads of the system. The results show that both DQN and dueling DQN are effective in reducing system losses through grid reconfiguration. The proposed methods have faster execution time compared to the conventional methods and are more scalable.

This article presents a novel methodology for analyzing the resilience of an active distribution system (ADS) integrated with an urban gas network (UGN). It demonstrates that the secure adoption of gas turbines with optimal capacity and allocation can enhance the resilience of the ADS during high-impact, low-probability (HILP) events. A two-level tri-layer resilience problem is formulated to minimize load shedding as the resilience index during post-event outages. The challenge of unpredictability is addressed by an adaptive distributionally robust optimization strategy based on multi-cut Benders decomposition. The uncertainties of HILP events are modeled by different moment-based probability distributions. In this regard, considering the nature of each uncertain variable, a different probabilistic method is utilized. For instance, to account for the influence of power generated from renewable energy sources on the decision-making process, a diurnal version of the long-term short-term memory network is developed to forecast day-ahead weather. In comparison with standard LSTM, the proposed approach reduces the mean absolute error and root mean squared error by approximately 47% and 71% for wind speed, as well as 76% and 77% for solar irradiance network. Finally, the optimal operating framework for improving power grid resilience is validated using the IEEE 33-bus ADS and 7-node UGN.

Microwave sensors are principally sensitive to effective permittivity, and hence not selective to a specific material under test (MUT). In this work, a highly compact microwave planar sensor based on zeroth-order resonance is designed to operate at three distant frequencies of 3.5, 4.3, and 5 GHz, with the size of only λg−min/8 per resonator. This resonator is deployed to characterize liquid mixtures with one desired MUT (here water) combined with an interfering material (e.g., methanol, ethanol, or acetone) with various concentrations (0%:10%:100%). To achieve a sensor with selectivity to water, a convolutional neural network (CNN) is used to recognize different concentrations of water regardless of the host medium. To obtain a high accuracy of this classification, Style-GAN is utilized to generate a reliable sensor response for concentrations between water and the host medium (methanol, ethanol, and acetone). A high accuracy of 90.7% is achieved using CNN for selectively discriminating water concentrations.

This study presents a comprehensive comparative analysis of the operational strategies for multi-microgrid systems that integrate battery energy storage systems and electric vehicles. The analyzed strategies include individual operation, community-based operation, a cooperative game-theoretic method, and the alternating direction method of multipliers for multi-microgrid systems. The operation of multi-microgrid systems that incorporate electric vehicles presents challenges related to coordination, privacy, and fairness. Mathematical models for each strategy are developed and evaluated using annual simulations with real-world data. Individual operation offers simplicity but incurs higher costs due to the absence of power sharing among microgrids and limited optimization of battery usage. However, individual optimization reduces the multi-microgrid system cost by 47.5% when compared to the base case with no solar PV or BESS and without optimization. Community-based operation enables power sharing, reducing the net cost of the multi-microgrid system by approximately 7%, as compared to individual operation, but requires full data transparency, raising privacy concerns. Game theory ensures fair benefit allocation, allowing some microgrids to achieve cost reductions of up to 13% through enhanced cooperation and shared use of energy storage assets. The alternating direction method of multipliers achieves a reduction in the electricity costs of each microgrid by 6–7%. It balances privacy and performance without extensive data sharing while effectively utilizing energy storage. The findings highlight the trade-offs between cost efficiency, fairness, privacy, and computational efficiency, offering insights into optimizing multi-microgrid operations that incorporate advanced energy storage solutions.

In recent years, machine learning methods have found numerous applications in power systems for load forecasting, voltage control, power quality monitoring, anomaly detection, etc. Distributed learning is a subfield of machine learning and a descendant of the multi-agent systems field. Distributed learning is a collaboratively decentralized machine learning algorithm designed to handle large data sizes, solve complex learning problems, and increase privacy. Moreover, it can reduce the risk of a single point of failure compared to fully centralized approaches and lower the bandwidth and central storage requirements. This paper introduces three existing distributed learning frameworks and reviews the applications that have been proposed for them in power systems so far. It summarizes the methods, benefits, and challenges of distributed learning frameworks in power systems and identifies the gaps in the literature for future studies.