This paper holistically assesses the current literature by examining how cross-vector flexibility is observed, coordinated, and verified in low-carbon energy systems based on sector coupling. To this end, the interplay between cross-vector flexibility, digital twins and artificial intelligence (AI)-enabled observability, predictive control approaches, local coordination mechanisms, and flexibility markets is addressed within a common analytical framework. This review shows that cross-vector flexibility is more than an energy conversion technology problem; it is also a coordination problem. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) framework, a systematic Scopus search and screening process yielded an analytical core of 107 studies. Within this core, only 19 studies (17.8%) progress beyond synthetic simulation: thirteen use real or case-based data, six provide co-simulation, hardware-in-the-loop or closed-loop field evidence, and none verifies delivered flexibility against a baseline. Observability is addressed as a primary concern in only seven of the 107 studies, making it the least represented layer of the coordination stack. In addition, 88 studies (82.2% of the core) represent no socio-technical conditions. The value of flexibility depends not only on the capacity to shift, store, or convert energy, but also on the ability to monitor system states, ensure coordination between different actors and energy carriers, verify the service provided, and create mechanisms by which flexibility can be procured and evaluated as a service. When these layers are considered together, the observability and validation deficiencies spread upwards throughout the coordination stack, which limits the applicability of advanced control approaches and market designs in real systems. Therefore, greater emphasis should be placed on observability infrastructures that support validation, transparent interface definitions between coordination layers, validation processes that extend beyond simulation, and assessment frameworks that explicitly consider behavioral and institutional realities. Developing these elements is critical to transforming cross-vector flexibility from a concept primarily assessed in simulation environments into a verifiable, transferable, and operationally deployable service in decarbonized multi-energy systems.
This paper introduces a novel 2D fractional sine-cosine (2D-FSC) hyperchaotic map and its application in a robust image encryption scheme. Through comprehensive dynamic analysis including bifurcation diagrams, Lyapunov exponents, butterfly effect, Shannon entropy, and phase space visualization, it is demonstrated the map's superior chaotic characteristics compared to conventional 1D chaotic systems. The proposed encryption framework employs a three-stage diffusion-permutation-confusion architecture enhanced by two key innovations: (1) a genetic algorithm optimizing diffusion and confusion processes to maximize entropy (7.998 +/- 0.002) and minimize correlation coefficients (|cc| < 0.004) of the encrypted image, and (2) a novel chaos-based pixel fusion technique for efficient bit-level permutation. Hardware implementation on a Cyclone IV FPGA (6057 logic elements, 34.29 MHz max frequency) validates the practical feasibility of the chaotic map. Security analysis confirms resistance against brute-force (key space > 2(750)), statistical (chi(2) < 184.2), differential (NPCR 99.61 +/- 0.04 %, UACI 33.46 +/- 0.09 %), and noise/clipping attacks. The algorithm processes 256x 256 images in 0.32 s which is 28-42 % faster than comparable schemes, while maintaining consistent performance across grayscale, color, and edge-case images. These advancements establish a new benchmark for chaos-based cryptosystems in applications requiring both high security and computational efficiency.
The expansion of green hydrogen requires technologies that are both manufacturable at a GW-to-TW power scale and adaptable for decentralized, renewable-driven energy systems. Recent advances in proton exchange membrane, alkaline, and solid oxide electrolysis reveal persistent bottlenecks in catalysts, membranes, porous transport layers, bipolar plates, sealing, and high-temperature ceramics. Emerging fabrication strategies, including roll-to-roll coating, spatial atomic layer deposition, digital-twin-based quality assurance, automated stack assembly, and circular material recovery, enable high-yield, low-variance production compatible with multi-GW power plants. At the same time, these developments support decentralized hydrogen systems that demand compact, dynamically operated, and material-efficient electrolyzers integrated with local renewable generation. The analysis underscores the need to jointly optimize material durability, manufacturing precision, and system-level controllability to ensure reliable and cost-effective hydrogen supply. This paper outlines a convergent approach that connects critical-material reduction, high-throughput manufacturing, a digitalized balance of plant, and circularity with distributed energy architectures and large-scale industrial deployment.
Local energy systems (LESs) are becoming larger and more heterogeneous as distributed energy resources, electrified loads, and active prosumers proliferate, increasing the need for reliable coordination of operation, markets, and community governance. This Perspective synthesizes recent literature to map how artificial intelligence (AI) supports forecasting and situational awareness, optimization, and real-time control of distributed assets, and community-oriented markets and engagement, while arguing that adoption is limited by system-level credibility rather than model accuracy alone. The analysis highlights interlocking deployment barriers, such as governance-integrated explainability, distributional equity, privacy and data governance, robustness under non-stationarity, and the computational footprint of AI. Building on this diagnosis, the paper proposes principles-as-constraints for sustainable, trustworthy LES AI and a deployment-oriented validation and reporting framework. It recommends evaluating LES AI with deployment-ready evidence, including stress testing under shift and rare events, calibrated uncertainty, constraint-violation and safe-fallback behavior, distributional impact metrics, audit-ready documentation, edge feasibility, and transparent energy/carbon accounting. Progress should be judged by measurable system benefits delivered under verifiable safeguards.
Conventional fault-tolerant control (FTC) schemes typically assume the exponent of the faulty input to be 1, overlooking its impact on actuator power. In this article, we propose a novel FTC strategy that extends the exponent to any positive odd integer, thus capturing higher-order fault effects. In addition, by integrating a Gaussian function to modify the constraint boundaries, a novel suction-cup-type prescribed performance function is proposed. Unlike existing prescribed performance functions, this design uses a suction cup module to regulate output overshoot without requiring asymmetric design. This design is globally effective, eliminating the initial feasibility conditions. Simulation results validate the effectiveness of the proposed scheme.
