Cybersecurity challenges in power networks with distributed energy resources: A comprehensive survey

Fractional order dynamics and optimal control of risk contagion in power networks

<p>Any country's economic progress is heavily reliant on its power infrastructure, network, and availability, as energy has become an essential component of daily living in today's globe. Electricity's distinctive quality is that it cannot be stored in huge quantities, which explains why global demand for home and commercial electricity has grown at an astonishing rate. On the other hand, electricity costs have varied in recent years, and there is insufficient electricity output to meet global and local demand. The solution is a series of case studies designed to forecast future residential and commercial electricity demand so that power producers, transformers, distributors, and suppliers may efficiently plan and encourage energy savings for consumers. However, load prognosticasting has been one of the most difficult issues confronting the energy business since the inception of electricity. This study covers a new one–dimensional approach algorithm that is essential for the creation of a short–term load prognosticasting module for distribution system design and operation. It has numerous operations, including energy purchase, generation, and infrastructure construction. We have numerous time series forecasting methods of which autoregressive integrated moving average (ARIMA) outperforms the others. The auto–regressive integrated moving average model, or ARIMA, outperforms all other techniques for load forecasting.</p>

Tri-level collaborative optimization strategy for coupled power and transportation networks considering energy scheduling of fast charging stations

Improvement of combined LFC-AVR system performance with fuzzy PID controller in single and multi-area power networks

Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networks

Hybrid multi-agent deep reinforcement learning for multi-type mobile resources dispatching under transportation and power network recovery

Design of an intelligent AI-based multi-layer optimization framework for grid-tied solar PV-fuel cell hybrid energy systems.

The sixth generation (6G) of wireless communication systems is envisioned to be inherently AI-native, integrating intelligence into every network layer to support unprecedented capabilities, including terabit-per-second data rates, submillisecond latency, and pervasive sensing . This ambition re- quires managing extreme complexity introduced by revolutionary technologies such as Terahertz (THz) communication, Ultra- Massive MIMO (UM-MIMO), and Reconfigurable Intelligent Surfaces (RIS) . Machine Learning (ML) is recognized as the computational backbone for this transformation, enabling adaptive, self-optimizing, and context-aware wireless environments that fundamentally redefine how networks operate . This paper presents a systematic review, mapping ML across three progressive integration paradigms: AI for Network (AI4NET), Network for AI (NET4AI), and AI as a Service (AIaaS). We detail ML’s pivotal role in enhancing the physical layer through deterministic Wireless Environment Control (WEC) and robust channel estimation using generative models . Furthermore, we elaborate on distributed intelligence architectures, such as Federated Learning (FL) and Split Learning (SL), which are essential for balancing high computational demands with data privacy and resource constraints in the emerging Computing Power Network (CPN) . Finally, we argue that the core viability of 6G depends on embedding trustworthiness into its architecture, emphasizing the mandatory roles of Explainable AI (XAI) for operational accountability and Distributed Ledger Technology (DLT) for immutable data provenance .

This paper studies the discernibility of topological variations of networked systems, in which the nodes are heterogeneous higher-dimensional linear time invariant (LTI) dynamical systems, and the network topology is weighted and directed. The influence of the heterogeneity of nodes on the discernibility of topological variations of networked systems is discussed. It is found that the topological variation of heterogeneous networked systems can be indiscernible even if the topological variations are discernible for homogeneous networked systems with identical node dynamics, and vice versa. A sufficient and necessary condition is derived for the discernibility of networked systems with heterogeneous dynamics by observing system states. For two typical cases (link variation and node disconnection), sufficient and/or necessary discernibility conditions are specified. Furthermore, the discernibility of topological variations by observing output trajectories is also investigated. The new conditions derived are more widely applicable. The effectiveness of the results is clarified by some examples, especially an application in a real power network.

The rapid evolution of Generative Artificial Intelligence (GenAI) is reshaping the energy sector, enabling new levels of adaptability, efficiency, and user-centric interaction. This review systematically maps and critically evaluates the chosen literature across buildings, grids, and urban systems. Through major scientific databases and for the span of five years, from 2021 to 2025, the review aims to identify key application domains, synergies, and research gaps. The analysis on recent advancements illustrates how GenAI enhances energy forecasting, demand–response strategies, anomaly detection, and cyber-resilience in power networks, while also supporting predictive modeling and optimal control in distributed renewable integration. Within smart buildings, GenAI empowers autonomous agents and AI copilots to balance comfort with energy efficiency through adaptive environmental control and user preference modeling. At the grid level, generative models improve renewable generation forecasting, grid stability, and decision support for operators. A further emerging application lies in the generation of synthetic energy data, which supports model training, scenario simulation, and robust decision-making in data-scarce environments. In the broader context of smart cities, GenAI-driven digital twins, multi-agent systems, and conversational interfaces facilitate sustainable planning and energy-aware citizen engagement. A central theme across these applications is the alignment of technological solutions with human needs and sustainability objectives. Key challenges remain in uncertainty quantification, trustworthy deployment, and data governance, underscoring the need for secure, adaptive, and human-centered GenAI systems to drive the next generation of intelligent energy management. This review provides a comprehensive analysis to promote a better understanding of generative models as they are being applied in a variety of scenarios in the energy domain.

NTT developed a method using lunar regolith for transmitting power using electric field waves. This method achieves high transmission efficiency over a wide area. It will be possible to supply power to various locations, including the moon.To construct a power network for a lunar base, it is not realistic to transport and lay down numerous power cables, as the transportation costs would be too high. Wireless power supply to devices such as rovers is expected. Wireless technology uses microwaves to transmit energy over long distances, but the energy diffuses. In contrast, wired power transmission using cables has a high transmission efficiency.The proposed technology does not require laying new cables. It can use the already existing regolith as a transmission path, so it can deliver power to the receiver with high efficiency. This new technology combines the advantages of both wireless and wired technologies.We're developing a sustainable energy system at the NTT Space Environment and Energy Laboratories. We're researching and developing innovative energy transmission technology to power generation and transmission.

Underground power lines are everywhere these days, and honestly, finding faults in buried cables is way tougher than dealing with over head wires. That’s why we need better ways to spot problems fast. Here, I’m introducing a system that uses a microcontroller specifically, an Arduino to pinpoint where a cable has failed, based on how resistance changes along its length. The Arduino pulls in resistance readings from sensors, runs them through its built-in ADC, and figures out roughly where the fault is. You get the results right away on an LCD screen. Plus, with things like GSM alerts and a buzzer, you can get notified whether you’re on-site or checking in from somewhere else. Forget the old manual tracing methods. This setup finds the faulty section quickly without shutting down the rest of the line, so there’s less down time and less hassle for repairs. It’s affordable, flexible, and works for both power and communication networks. Basically, it’s a step toward smarter, tougher systems for the future.

To address the impact of source-load uncertainty on voltage security and power flow distribution, this study proposes an active distribution network (ADN) active–reactive power coordinated optimal dispatch strategy that incorporates the grid balance degree (GBD). First, we analyzed GBD by defining it across three key dimensions: power flow, voltage, and network structure. We combined GBD with economic indicators to establish a pre-assessment indicators system to determine grid operational status. Second, to address source-load uncertainty, a dispatch model is established, incorporating a load factor penalty term into the optimal objective. Nonlinear terms in the model are linearized for efficient solution using the Gurobi solver. Finally, GBD is adjusted through measures such as load factor penalty threshold, voltage constraint upper/lower limits, and network restructuring. The optimal dispatch strategy is selected by comparing pre-assessment indicators of operational states across different dispatch schemes. Case studies demonstrate that compared to the baseline dispatch scheme, the proposed strategy achieves a 17.4% reduction in heavy load rates, a 5.7% decrease in power flow entropy, a 5.2% reduction in voltage peak-to-valley difference rates, and a 3% decrease in network losses. Although operating costs slightly increase by 0.56%, the operation reliability of the distribution network and power quality are further optimized. This fully demonstrates the gain value of GBD optimization and provides a reference for the optimization of ADN dispatch to achieve high operational reliability.

Identifying effective coalitions of agents for task execution within large multiagent settings is a challenging endeavor. The problem is exacerbated by the presence of coalitional value uncertainty, which is due to uncertainty regarding the values of synergies among the different collaborating agent types. Intuitively, in such environments, a hypergraph can be used to concisely represent coalition–task pairs in the form of hyperedges, along with their associated rewards. Therefore, this paper proposes harnessing the power of Hypergraph Neural Networks (HGNNs) that fit generic hypergraph-structured historical representations of coalitional task executions to learn the unknown values of coalitional configurations undertaking the tasks. However, the fitted model by itself cannot be used to provide suggestions on which coalitions to form; it can only be queried for the values of given coalition–task configurations. To actually provide coalitional suggestions, this work relies on informed search approaches that incorporate the output of the HGNN as an indicator of the quality of the proposed coalition configurations. The resulting approach is illustrated, via simulation results, to be able to effectively capture the uncertain values of multiagent synergies and thus suggest highly rewarding coalitional configurations. Specifically, the proposed novel hybrid approach can outperform competing baseline approaches and achieve close to 80% performance of the theoretical maximum in this setting.

The rapid global transition toward renewable energy sources has introduced new challenges in maintaining the stability, reliability, and efficiency of modern power grids. The intermittency of solar, wind, and other renewables disrupts traditional deterministic grid models, demanding advanced mathematical and computational frameworks to predict, optimize, and stabilize system performance. This study presents a hybrid modelling approach that integrates mathematical optimization techniques such as linear programming, stochastic modelling, and differential equation systems with computational algorithms, including machine learning and numerical simulations, to enhance grid adaptability. The proposed framework evaluates renewable energy integration through multi-objective optimization, focusing on minimizing power imbalance, forecasting demand-supply variations, and improving real-time decision-making. Using simulation data, the results reveal a significant improvement in system stability (by 18%) and reduction in energy curtailment (by 12%) compared to conventional deterministic models. These findings underscore the potential of mathematically driven computational tools in achieving sustainable energy transitions. The paper contributes a robust model for policy makers and energy engineers to design intelligent, data-driven power networks that efficiently integrate renewable sources while ensuring reliability, cost-effectiveness, and environmental sustainability.

With the accelerating digital transformation of modern society, numerous data center (DC) agents are connected to the distribution power networks (DPNs) via microgrid and engaging in fierce market competition. To address the asymmetric operational risks faced by each data center agent, particularly those arising from market volatility and equipment failures, a novel cooperative game-theoretic approach is proposed in this paper. Firstly, a cooperative operation framework for the microgrid-integrated data centers (MDCs) system is established from two dimensions: joint task allocation across MDCs on the computing side and energy sharing among MDCs on the power side. Moreover, an optimal operating model for MDCs is established, which integrates the task allocation model that takes into account the task processing capacity of MDCs. Then, a cooperative operation model for the MDCs system based on Nash game theory is developed, and a joint solution framework for task allocation and the cooperative operation model is designed. Finally, the proposed cooperative game-theoretic approach is validated in a test system. The results show that the proposed approach ensures the reliable operation of the DPN while avoiding asymmetric operation risks among MDCs. It enhances the stability and security of distributed data processing. Furthermore, the Nash game-theoretic model achieves a symmetric distribution of profits and risks across MDCs, eliminating individual biases and maximizing the overall benefits of the cooperative alliance.

This study investigates the impact of a Static Var Compensator (SVC) on voltage stability enhancement and loss reduction in the N'Djamena distribution network, Chad. Using the NEPLAN software environment, a detailed model of the 32-node network was developed and power flow analyses were conducted using the Newton-Raphson method under both nominal (100%) and stressed (120%) loading conditions. The SVC, equipped with a Proportional-Integral (PI) control scheme and an operating range of -50 to +50 Mvar, was implemented for dynamic reactive power compensation. Simulation results demonstrate substantial performance improvements: voltage levels at critical nodes (NT30, N1T100, N2T100, N15LA) increased by up to 20.19 percentage points under maximum load conditions, while active power losses were reduced by up to 1.586 MW and reactive power losses by up to 3.677 Mvar. The findings quantitatively confirm that SVC implementation significantly enhances voltage stability margins and reduces technical losses in weak distribution networks. This research provides a validated framework for SVC integration in similar developing power systems and offers practical insights for utility planners seeking to improve grid performance while accommodating future renewable energy integration.

In this study, distributed generators (DGs) based on renewable energy sources (RESs), besides capacitor banks are optimally allocated in power distribution networks with a proposed multi-objective optimization approach. The proposed approach is used to maximize the hosting capacity (HC) of RES DGs besides decreasing energy loss and voltage deviation in power networks. Uncertainties of load demand and RESs are considered. To facilitate the optimization processes, reduction criterion is utilized for reducing the numerous numbers of uncertain data. The proposed approach is applied to practical and standard power networks for many cases under the uncertain scenarios. Comparative study with other algorithms is performed and robustness of proposed approach is verified in long-term dynamic environment. Also, impacts of changing parameters values on performance are investigated. Additionally, Wilcoxon statistical tests are applied with the proposed approach. Also, comparative study is carried out between weighted sum and Pareto front techniques. Results reveal efficacy of the proposed approach with distribution power networks.

Dare to Speak 2 is an innovative board game designed to help mitigate speaking anxiety in the workplace. Evolving from its original version, Dare to Speak 1, introduced in 2013, which focused on English language learning, the game has expanded into multiple languages, including Bahasa Melayu (Minda Kritis), Mandarin (Fun with Mandarin), and Arabic. Dare to Speak 2 incorporates gamified learning principles by combining the concepts of Snakes and Ladders with Monopoly, offering learners a fun and engaging gamification platform to expose and practice workplace communication. The project uniquely incorporates authentic film dialogues with workplace themes to expose learners to different forms of phrases in offers, acceptances, and declines, helping them manage speaking anxiety, and at the same time, are perceived as polite in real professional scenarios. With existing recognition awards (Silver 2013 and 2015; Gold 2018) and prior registered intellectual property under MyIPO and UiTM’s RIBU, the project is well-positioned for wider adoption. These recognitions validate the product’s innovation and effectiveness, strengthening its credibility for potential investors, collaborators, and licensing opportunities. Future collaborations with education technology companies, Dewan Bahasa and Pustaka (DBP), and industry partners may further support the sustainable commercialization, ensuring that Dare to Speak 2 continues to evolve and meet the dynamic needs of workplace communication training.