Aug 2026· IEEE Energy Sustainability Magazine· Vol 2, pp. 65-74· 0 citations· 5 references
Abstract
As renewable energy becomes the dominant generation resource in modern power systems, system stability and safety pose critical challenges due to increased uncertainty, reduced inertia, and escalating system complexity. Artificial intelligence (AI) provides unprecedented capabilities for real-time control, predictive optimization, and adaptive decision making, yet its adoption in safety-critical power system applications is hampered by concerns over robustness, interpretability, and the absence of formal guarantees. This article outlines how stability- and safety-guaranteed AI approaches can bridge this gap to enable reliable integration of renewables. In this article, we review emerging methods that embed physics, control theory, and optimization constraints in AI models for power systems, discuss advances in certifiable robustness and Lyapunov-based learning, and chart the path for deployment in renewable-dominated grids. The article concludes with policy, regulatory, and research recommendations for ensuring that AI not only accelerates the clean energy transition but also preserves resilience and trust in critical infrastructure.
The increased interdependency among the renewable energy source, energy storage systems, and computer-based control systems has transformed the power grid of today into a high-tech, adaptive network, but now increasingly susceptible to stochastic fluctuation and cascading uncertainties. This paper presents a next-generation Uncertainty Quantification and MultiObjective Optimization (UQ-MOO) approach to improving Integrated Renewable Energy Systems' resilience, adaptability, and efficiency. The study provides an answer to a relevant question: How can uncertainty, instead of being an obstacle, be utilized intentionally to enhance the adaptive intelligence and resilience of renewable power grids? The envisioned framework combines state-of-the-art probabilistic analysis, such as Bayesian inference, Monte Carlo simulation, and Polynomial Chaos Expansion, with multi-objective evolutionary optimization and reinforcement learning-based decision intelligence to support real-time, data-driven operation management. When brought to hybrid solar–wind–hydrogen microgrid systems, the approach registered a 36% system reliability improvement, 29% gain in energy efficiency, and 41% loss reduction in volatility-tolerant performance over traditional optimization algorithms. Aside from computational gain, the result sets a new standard in renewable energy research: uncertainty is no longer addressed as a constraint but as a dynamic facilitator of resilience and intelligent adaptation. Finally, this book is leading the way in smart, self-adjusting energy systems, with uncertainty science, optimization theory, and green system engineering coming together to create an unbreakable net-zero energy future.
Peace Chinonyerem Ike, Adjoa Okezie Okuma, Odunmorayo David Adedokun et al.· International Journal of Eng...· 0 citations
Renewable energy integration causes intermittency, nonlinear dynamics, and grid-code restrictions in modern power systems. Although hybrid renewable energy systems combining wind, photovoltaic, and fuel cell sources increase energy availability, conventional control approaches often fail to maintain voltage stability, power quality, and rapid fault recovery under varying operating conditions. High renewable penetration and noisy conditions worsen these concerns. Existing methods typically address voltage regulation, transient stability, fault resilience, and power sharing independently using fixed or offline-tuned controllers, limiting adaptability during grid disturbances. To overcome these challenges, this study proposes an AI-enhanced STATCOM-controlled hybrid renewable energy system with learning-based control, predictive stability assessment, and multi-agent coordination. Adaptive reactive power support, noise-resilient fault detection, renewable source power sharing, predictive voltage regulation, and physiologically inspired transient stability prediction using hierarchical reinforcement learning. The simulation results maintain system voltage deviation within ±2%, harmonic distortion below 2%, fault detection within 7 ms, and transient stability prediction accuracy above 98% across varied operating conditions. Voltage recovery, overshoot suppression, and resource utilization efficiency improve above benchmark techniques. Thus, findings demonstrate that AI- enhanced STATCOM works as cognitive grid-interfacing agents rather than passive compensators for improving stability, power quality, and operational resilience in various deployment settings.
Bhishan Wadhai, N. Dhote, M. Kolhe· International Journal of App...· 0 citations
Load Frequency Control (LFC) plays a vital role in keeping the frequency stable and maintaining the tie line power in interconnected power systems. With the addition of a large number of Renewable Energy Resources (RES), reduced system inertia, communication delay and cybersecurity issues, the conventional controllers fail to deliver optimal performance. This research presents a systematic and comprehensive review of various LFC methodologies from classical to intelligent control frameworks. This work provides a structured analysis based on quantitative and qualitative performance parameters, which include settling time, overshoot, robustness, scalability and computational complexity. An in-depth classification of various control techniques from conventional controllers to advanced controllers, which include model predictive control, robust control, AI-based controllers and optimisation methods, is presented. Further, the impact of emerging technologies, which include HVDC links, energy storage systems and decentralised architectures, on load frequency control is examined. The main challenges include cyber threats, high computational burden and lack of real-time validation. The study highlights that intelligent controllers and optimisation-based controllers offer superior performance, but the practical application is limited by complexity and stability concerns. Finally, future research is focused on real-time execution of LFC strategies for next-generation smart grids.
Unknown authors· International Journal of Ele...· 0 citations
The rapid expansion of renewable energy systems has intensified the need for advanced Battery
Energy Storage Systems (BESS) capable of supporting grid stability, operational efficiency, and
resilient infrastructure development. This paper proposes an AI-driven integrated framework for
the construction, operation, and grid optimization of BESS, addressing limitations in existing
fragmented approaches that treat design, control, and grid interaction as isolated processes. The
proposed next-generation architecture introduces a multi-layered system that unifies
construction design, digital monitoring, artificial intelligence optimization, and grid integration
into a cohesive framework. The construction layer emphasizes modular design principles and
advanced thermal safety systems to enhance scalability, reliability, and lifecycle performance.
The digital layer incorporates real-time monitoring and digital twin models, enabling continuous
system representation, predictive simulation, and performance tracking. The AI layer leverages
machine learning algorithms for predictive dispatch, fault detection, and adaptive control,
ensuring efficient energy utilization and proactive system maintenance. The grid layer focuses on
frequency regulation and voltage stabilization, enabling seamless integration with renewable
energy sources and enhancing overall grid resilience. A key contribution of this study is the
development of a holistic BESS architecture that integrates AI into construction-informed design,
allowing operational insights to influence structural and system configurations. This
bidirectional interaction between design and operation improves system optimization and
reduces long-term operational risks. Furthermore, the framework establishes a foundation for
smart grid resilience by enabling real-time decision-making, automated control, and adaptive
response to grid disturbances. The proposed model advances the field by providing a unified
approach to BESS deployment, offering practical implications for energy providers,
infrastructure developers, and policymakers seeking to enhance sustainability and reliability in
modern power systems.
H. Shittu, Oghenemaero Oteri, Mujeeb A. Shittu et al.· International Journal of Eng...· 0 citations
Over the last couple of decades, a transition has evolved from the traditional centralized power grid to a distributed grid of microgrids (GoMGs) dominated by power electronics-based generation. The primary objective of this evolution is to achieve a sustainable, resilient grid while ensuring clean, reliable, and self-adaptive energy access across various operating conditions. However, this new energy paradigm, with high penetration of renewable energy, poses amplified challenges in controlling and securing GoMGs to maintain resiliency, reliability, sustainability, and operational stability. To address these issues and ensure a smarter, cybersecure, data-driven, and sustainable MG, many researchers at the intersection of power electronics, power systems, and artificial intelligence (AI) are exploring ways to develop and implement efficient and reliable AI-based techniques. This article sheds light on the multidimensional perspectives of sustainability and resiliency in GoMGs, focusing on security, stability, accessibility, and scalability in relation to the current state of technological maturity. Building on this vision, a futuristic roadmap is presented to enhance sustainability and resiliency using cutting-edge AI applications, enabling pre-event strategies, such as prediction, optimization, and adaptation, as well as post-event mitigation and restoration techniques.
M. Shadmand, Uzair Asif, Debotrinya Sur et al.· IEEE Energy Sustainability M...· 0 citations
: Complex networks in cyber–physical, transportation, and information infrastructures operate under topology variations, unmeasured disturbances, and limited actuation. This paper proposes robust disturbance-aware data-driven decision control (R-D3C), which couples a sliding-window graph-regularized estimator, disturbance-envelope adaptation, sparse intervention allocation, receding-horizon optimization, and a robust safety projection. The theory directly bounds the dynamic prediction regret of the implemented sliding-window estimator. A checkable sufficient condition for safety-filter feasibility is coupled with an explicit slack-and-backup fallback when the strict projection is infeasible. Practical input-to-state stability and sparse-allocation risk reduction are established. The nominal comparison uses 30 paired runs with standard deviations and significance tests, while the separate deployment-impairment benchmark uses 20 runs; evaluation also includes the IEEE 39-bus topology and timing tests up to 1000 nodes. R-D3C achieves the lowest constraint-violation rate; its residual error is statistically comparable with adaptive MPC while preserving an explicit safety layer.
Unknown authors· Computers, Materials & C...· 0 citations
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