Search Results (13,238)

This review provides a comprehensive analysis of advanced control strategies and operational optimization of energy systems, focusing on heat pumps, with an emphasis on their role in enhancing energy efficiency and operational flexibility. The study concentrates on methods supported by artificial intelligence algorithms, highlighting Model Predictive Control (MPC), Reinforcement Learning (RL), and hybrid approaches that combine the advantages of both. These methods aim to optimize both the operation of heat pumps and their interaction with thermal energy storage (TES) systems, renewable energy sources, and power grids, thereby enhancing the flexibility and adaptability of the systems under real operating conditions. Through a systematic analysis of the existing literature, 95 studies published after 2019 were examined to identify research trends, key challenges such as computational requirements and algorithm interpretability, and future opportunities. Furthermore, significant benefits of applying advanced control compared to conventional practices were highlighted, such as reduced operational costs and lower CO₂ emissions, emphasizing the importance of heat pumps in the energy transition. Thus, the analysis highlights the need for digital solutions, robust and adaptive control frameworks, and holistic techno-economic evaluation methods in order to fully exploit the potential of heat pumps and accelerate the transition to sustainable and flexible energy systems. Full article

Given the urgent demand for flexible peak-shaving in power systems and underutilized distributed photovoltaic (PV) regulation potential, this paper proposes a distributed PV peak-shaving control strategy based on the temporal coupling self-organizing map (TC-SOM) neural network and a bi-level model. First, the SOM algorithm is improved for efficient feature extraction and accurate clustering of distributed PV data, realizing rational PV cluster division. On this basis, a bi-level peak-shaving model for distributed PV is constructed, forming a hierarchical peak-shaving mechanism from node demand to PV clusters to individual PVs to ensure inter- and intra-cluster coordination. This hierarchical structure embodies symmetric response logic, enabling balanced interaction between upper-layer node demand guidance and lower-layer PV execution, as well as inter-cluster coordination. Simulations on the IEEE-33 node system confirm its effectiveness: it significantly smooths the load curve, reduces peak–valley differences, and optimizes the flexible utilization of distributed PV through coordinated control, aggregation management, and curtailment regulation, providing strong support for precise PV cluster regulation and stable operation of high-proportion PV-integrated power grids. Full article

Traditional electricity market bidding typically focuses on unilateral structures, where independent energy storage units and flexible loads act merely as price takers. This reduces bidding motivation and weakens the balancing capability of regional power systems, thereby limiting the large-scale utilization of renewable energy. To address these challenges and support sustainable power system operation, this paper proposes a double-sided auction market strategy for heterogeneous multi-resource (HMR) participation based on multi-agent reinforcement learning (MARL). The framework explicitly considers the heterogeneous bidding and quantity reporting behaviors of renewable generation, flexible demand, and energy storage. An improved incentive mechanism is introduced to enhance real-time system power balance, thereby enabling higher renewable energy integration and reducing curtailment. To efficiently solve the market-clearing problem, an improved Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) algorithm is employed, along with a temporal-difference (TD) error-based prioritized experience replay mechanism to strengthen exploration. Case studies validate the effectiveness of the proposed approach in guiding heterogeneous resources toward cooperative bidding behaviors, improving market efficiency, and reinforcing the sustainable and resilient operation of future power systems. Full article

Biodegradable starch-based films often suffer from insufficient mechanical strength, which limits their practical applications. To enhance film performance, this study optimized the preparation of composite thermoplastic starch (CTPS) films composed of corn starch, sorbitol, whey protein isolate (WPI), and gallic acid (GA). The optimized formulation—0.074 g/mL corn starch, 47.5% sorbitol, 5.6% WPI, and 2.0 mg/mL GA—yielded films with a tensile strength of 3.11 ± 0.31 MPa and an elongation at break of 43.35 ± 0.69%, achieving a desirable balance between flexibility and strength. Mechanistic investigations using in situ Fourier-transform infrared spectroscopy (FTIR), low-field nuclear magnetic resonance (LF-NMR), confocal laser scanning microscopy (CLSM), and molecular docking revealed a ternary interaction system among starch, WPI, and GA. These components primarily interacted through hydrogen bonding and van der Waals forces. Such non-covalent interactions enhanced the short-range molecular ordering of the starch matrix, stabilized the secondary structure of WPI, and facilitated water redistribution during film formation. The resulting interaction network among starch, proteins, and polyphenols significantly improved the mechanical properties and antioxidant capacity of the CTPS films. Full article

The celebrated question of whether $P=NP$ continues to define the boundary between the feasible and the intractable in computer science. In this paper, we revisit the problem from two complementary angles: Time-Relative Description Complexity and automated discovery, adopting an epistemic rather than ontological perspective. Even if polynomial-time algorithms for NP-complete problems do exist, their minimal descriptions may have very high Kolmogorov complexity. This creates what we call an epistemic barrier, making such algorithms effectively undiscoverable by unaided human reasoning. A series of structural results—relativization, Natural Proofs, and the Probabilistically Checkable Proofs (PCPs) theorem—already indicate that classical proof techniques are unlikely to resolve the question, which motivates a more pragmatic shift in emphasis. We therefore ask a different, more practical question: what can systematic computational search achieve within these limits? We propose a certificate-first workflow for algorithmic discovery, in which candidate algorithms are considered scientifically credible only when accompanied by machine-checkable evidence. Examples include Deletion/Resolution Asymmetric Tautology (DRAT)/Flexible RAT (FRAT) proof logs for SAT, Linear Programming (LP)/Semidefinite Programming (SDP) dual bounds for optimization, and other forms of independently verifiable certificates. Within this framework, high-capacity search and learning systems can explore algorithmic spaces far beyond manual (human) design, yet still produce artifacts that are auditable and reproducible. Empirical motivation comes from large language models and other scalable learning systems, where increasing capacity often yields new emergent behaviors even though internal representations remain opaque. This paper is best described as a position and expository essay that synthesizes insights from complexity theory, Kolmogorov complexity, and automated algorithm discovery, using Time-Relative Description Complexity as an organising lens and outlining a pragmatic research direction grounded in verifiable computation. We argue for a shift in emphasis from the elusive search for polynomial-time solutions to the constructive pursuit of high-performance heuristics and approximation methods grounded in verifiable evidence. The overarching message is that capacity plus certification offers a principled path toward better algorithms and clearer scientific limits without presuming a final resolution of $P\stackrel{?}{=}NP$. Full article

Europe’s energy transition beyond 2025 faces a resilience gap as reconfigured pipeline flows, stricter methane rules, and rising variable renewables increase the need for seasonal flexibility and system adequacy. This study examines how Ukraine’s gas transmission network and underground gas storage—among the largest in Europe—can serve as a “seasonal battery” for the EU. We integrate a policy and market review with quantitative scenarios for 2026–2030. Methods include security-of-supply indicators (the rule that the system must keep operating even if its largest single infrastructure element fails, peak-day coverage, and winter adequacy), estimates of market-accessible storage volumes and withdrawal rates for European market participants, and a techno-economic screening of hydrogen-readiness comparing repurposing with new-build options. Methane intensity constraints and compliance with monitoring, reporting, and verification and leak detection and repair requirements are applied. The results indicate that reallocating part of Europe’s seasonal balancing to Ukrainian underground gas storage can enhance resilience to extreme winter demand and liquefied natural gas price shocks, reduce price volatility and the curtailment of variable renewables, and enable phased, cost-effective hydrogen corridors via repurposable pipelines and compressors. We outline a policy roadmap specifying transparent access rules, interoperable gas quality and methane standards, and risk mitigation instruments needed to operationalise cross-border storage and hydrogen-ready investments without carbon lock-in. Full article

As the world shifts toward a low-carbon future, green hydrogen has emerged as a critical pillar of the energy transition. It is produced using renewable energy to power water electrolysis, and it is a clean and flexible alternative to hydrogen made from fossil fuels. However it is still hard to roll out on a large scale because of technological limits, high costs, and the need for infrastructure. This review critically analyzes current electrolysis methods, including established systems like alkaline and PEM electrolyzers, as well as newly developed concepts such as AEMWE and SOWE. It discusses how they can be used in renewable energy systems, important techno-economic and durability problems, system modeling, and grid interaction. This work clarifies both the technological potential and the practical limitations of green-hydrogen electrolyzer systems while highlighting key directions for future research and implementation. Full article

Flexible microscale neural interfaces are advancing current strategies for recording and modulating electrical activity in the brain and spinal cord. The aim of this review is to colligate recent progress in thin-film micro-electrocorticography (μECoG) systems and establish a framework for their translation toward spinal bioelectronic implants. We first outline substrate and electrode material design, ranging from polymeric and hydrogel-based materials to nanostructured conductive materials that enable high-fidelity recording on mechanically compliant platforms. We then summarize structural design rules for μECoG arrays, including electrode size, pitch, and channel scaling, and relate these to data-driven μECoG applications in brain–computer interfaces and closed-loop neuromodulation. Bidirectional μECoG architectures for simultaneous stimulation and recording are examined, with emphasis on safe charge injection, electrochemical and thermal limits, and state-of-the-art hardware and algorithmic strategies for stimulation-artifact suppression. Building upon these cortical technologies, we briefly describe adaptation to spinal interfaces, where anatomical constraints demand optimized mechanical properties. Finally, we discuss the convergence of flexible bioelectronics, wireless power and telemetry, and embedded AI decoding as a path toward autonomous, clinically translatable μECoG and spinal neuroprosthetic systems. Ultimately, by synthesizing these multidisciplinary advances, this review provides a strategic roadmap for overcoming current translational barriers and realizing the full clinical potential of soft bioelectronics. Full article

The paper introduces a real-time digital-twin controller that manages evacuation routes while operating GEEM for emergency energy management during building fires. The system consists of three interconnected parts which include (i) a physics-based hazard surrogate for short-term smoke and temperature field prediction from sensor data (ii), a router system that manages path updates for individual users and controls exposure and network congestion (iii), and an energy management system that regulates the exchange between PV power and battery storage and diesel fuel and grid electricity to preserve vital life-safety operations while reducing both power usage and environmental carbon output. The system operates through independent modules that function autonomously to preserve operational stability when sensors face delays or communication failures, and it meets Industry 5.0 requirements through its implementation of auditable policy controls for hazard penalties, fairness weight, and battery reserve floor settings. We evaluate the controller in co-simulation across multiple building layouts and feeder constraints. The proposed method achieves superior performance to existing AI/RL baselines because it reduces near-worst-case egress time (\(T_{95}\) and worst-case exposure) and decreases both event energy \(E_{\mathrm{event}}\) and CO₂-equivalent \(CO_{\mathrm{2event}}\) while upholding all capacity, exposure cap, and grid import limit constraints. A high-VRE, tight-feeder stress test shows how reserve management, flexible-load shedding, and PV curtailment can achieve trade-offs between unserved critical load \(U_{\mathrm{energy}}\) and emissions. The team delivers implementation details together with reporting templates to assist researchers in reaching reproducibility goals. The research shows that emergency energy systems, which integrate evacuation systems, achieve better safety results and environmental advantages that enable smart-city integration through digital thread operations throughout design, commissioning, and operational stages. Full article

Transmission tower guy wires are critical flexible tension members ensuring the stability and safe operation of overhead power transmission networks. However, these components are vulnerable to external impacts from falling rocks, ice masses, and other natural hazards, which can cause excessive deformation, anchorage loosening, and catastrophic failure. Current design standards primarily consider static loads, lacking comprehensive models for predicting dynamic impact responses. This study presents a theoretical model for predicting the peak impact response of guy wires by modeling the impact process as a point mass impacting a nonlinear spring system. Using an energy-based elastic potential method combined with cable theory, analytical solutions for axial force, displacement, and peak impact force are derived. Newton–Cotes numerical integration solves the implicit function to obtain closed-form solutions for efficient prediction. Validated through finite element simulations, deviations of peak displacement, peak impact force, and peak axial force between theoretical and numerical results are within ±4%, ±18%, and ±4%, respectively. Using the validated model, parametric studies show that increasing the inclination angle from 15° to 55° slightly reduces peak displacement by 2–4%, impact force by 1–13%, and axial force by 1–10%. Higher prestress (100–300 MPa) decreases displacement and impact force but increases axial force. Longer lengths (15–55 m) cause linear displacement growth and nonlinear force reduction. Impacts near anchorage points help control displacement risks, and impact velocity generally has a more significant influence on response characteristics than impactor mass. This model provides a scientific basis for impact-resistant design of power grid infrastructure and guidance for optimizing de-icing strategies, enhancing transmission system safety and reliability. Full article

Urban metro systems face increasing pressure to reconcile passenger service quality with infrastructure maintenance demands during late-night operations. This study proposes a coordinated optimization framework that integrates train timetabling with a flexible and selective skip-stop strategy. A mixed-integer programming model is formulated to jointly maximize passenger Origin–Destination (OD) accessibility and extend available maintenance windows. To solve the high-dimensional and computationally intensive model efficiently, a hybrid GWO-CNN algorithm is designed, where a Convolutional Neural Network (CNN)-based surrogate model replaces the time-consuming fitness evaluation process in the Grey Wolf Optimizer (GWO). A real-world case study on the Beijing metro network demonstrates that the proposed method increases OD accessibility by 23.60% and extends maintenance window by 8310 s. Compared to the conventional GWO, the GWO-CNN algorithm achieves superior solution quality with a 98.4% reduction in computation time. Sensitivity analyses further reveal the trade-offs between skip-stop rates, objective weight settings, and optimization outcomes, offering practical insights for metro operators in tailoring late-night scheduling strategies to both passenger demand and maintenance priorities. Full article

This research investigates a methane-fueled open gas system, enhanced by Prof. Szewalski’s idea of venting exhaust gases at various turbine stages. It assesses the impact of hydrogen co-combustion, which can range from 0% to 100%, on system parameters. The novel approach increased the gas turbine’s electrical efficiency to 41.25%. Two additional heat exchangers raised the inlet fluid temperature, affecting the exhaust gases entering the turbine. The highest exhaust gas temperature reached was 1491.08 °C. A higher hydrogen ratio significantly lowered CO₂ emissions. The study’s originality lies in its innovative technology combination, allowing flexible combustion adjustments to meet energy demands and fuel availability. The gas turbine model provides a detailed analysis of cooling air at each expander stage, enhancing understanding of efficiency factors. Integration with Power-to-Fuel technology facilitates the creation of energy systems that efficiently store and use renewable energy. This contributes to sustainable energy technology development, crucial for achieving climate goals and reducing emissions. Full article

This paper proposes a comprehensive framework for control of an extended Morphing Aerial System (MAS) designed to achieve both mission flexibility and fault tolerance. The proposed quadrotor features a morphing configuration that integrates a two-dimensional planar folding structure with a tilt mechanism. This morphing capability offers structural simplicity and operational versatility, which enables stable flight in various established modes. The control strategy utilizes feedback linearization and a Linear Quadratic Regulator (LQR), adapted to the system’s nonlinear dynamics and capable of controlling the MAS across various configurations (X, H, and O modes). An Extended Kalman Filter (EKF) is also incorporated for state estimation. To ensure fault resilience, we introduce the Y-mode configuration and a corresponding Fault-Tolerant Control (FTC) architecture. Numerical simulations demonstrate that while a nominal controller fails immediately upon motor failure, the proposed FTC method successfully recovers flight stability, converging to the reference trajectory within 6.9 s. Furthermore, robustness analysis confirms that the system maintains operational integrity for fault detection latencies up to 0.40 s, demonstrating its feasibility under realistic sensing constraints. Full article

With the continuous development of the robotics industry, using a single wireless system to charge different types of robots has become a critical issue that urgently needs to be addressed. To solve this problem, in the present work, we propose a single-switch inverter module wireless charging system based on parallel module number frequency modulation to achieve the expected variable voltage output by adjusting the operating frequency and the number of parallel modules, thereby enhancing the interoperability between devices. To meet the charging requirements of lithium batteries, which require constant current (CC) first and constant voltage (CV) thereafter, we first discuss how to implement CC and CV charging modes, then demonstrate that the proposed system can provide the required CC and CV output under various load conditions. Subsequently, a simplified equivalent circuit model to achieve this wireless charging system is proposed and an exact expression for its equivalent input voltage source is provided. Subsequently, based on the analysis of the amplitude–frequency characteristics of voltage gain under the CV mode, we propose the relevant method and program to realize this variable output system, and specifically build a prototype system based on a three-module parallel configuration. Experimental results show that the present prototype system can indeed provide the constant current (CC) and constant voltage (CV) outputs required for lithium battery charging, and the expected variable voltage output achieved by frequency modulation (FM) is verified. Its maximum efficiency can approach 91.3%. Compared with other wireless charging systems with single-switch inverters, this prototype experimental system possesses significant advantages in completing the full charging process of lithium batteries, maintaining stable voltage output during the constant voltage phase, and enabling flexible multi-voltage output. Full article

Extensive goat farming is the dominant livestock system in the Mediterranean region, where woody rangelands represent essential forage resources for goats. Understanding how goats move and select vegetation within these heterogeneous landscapes–and how these patterns are shaped by herding decisions-is critical for improving grazing management. This study investigated the spatio-temporal movement behavior of a goat flock in a complex woody rangeland using GPS tracking combined with GIS-based vegetation and land morphology mapping. The influence of seasonal changes in forage availability and the shepherd’s management on movement trajectories and vegetation selection was specifically examined over two consecutive years. Goat movement paths, activity ranges, and speed differed among seasons and years, reflecting changes in resource distribution, physiological stage, and herding decisions. Dense oak woodland and moderate shrubland were consistently the most selected vegetation types, confirming goats’ preference for woody species. The shepherd’s management—particularly decisions on grazing duration, route planning, and provision or withdrawal of supplementary feed—strongly affected movement characteristics and habitat use. Flexibility in adjusting grazing strategies under shifting economic conditions played a crucial role in shaping spatial behavior. The combined use of GPS devices, GIS software, vegetation maps, and direct observation proved to be an effective approach for assessing movement behavior, forage selection and grazing pressure. Such integration of technological and classical methods provides valuable insights into diet composition and resource use and offers strong potential for future applications in precision livestock management. Real-time monitoring and decision support tools based on this approach could help farmers optimize grazing strategies, improve forage utilization, and support sustainable rangeland management. Full article