Search Results (237)

Power generation and transmission systems face increasing challenges in coordinating maintenance planning under economic pressure and carbon emission constraints. This study proposes an optimization framework that integrates preventive maintenance scheduling with operational dispatch decisions, aiming to achieve both cost efficiency and emission reduction. A bi-layer scenario-based mixed-integer optimization model is formulated, where the upper layer determines annual preventive maintenance windows, and the lower layer performs hourly economic dispatch considering renewable generation and demand uncertainty. To manage the exposure to extreme carbon outcomes, a Conditional Value-at-Risk (CVaR) constraint is embedded, jointly controlling economic and environmental risks. A parallel cut-generation decomposition algorithm is developed to ensure computational scalability for large-scale systems. Numerical experiments on six-bus and IEEE 118-bus systems demonstrate that the proposed model reduces total carbon emissions by up to 32.1%, while maintaining cost efficiency and system reliability. The scenario analyses further show that adjusting maintenance schedules according to seasonal carbon intensity effectively balances operation and emission targets. The results confirm that the proposed optimization framework provides a practical and scalable approach for achieving low-carbon, reliable, and economically efficient power system maintenance planning. Full article

Directional flying ad hoc networks (FANETs) equipped with phased array antennas are pivotal for applications demanding high-capacity, low-latency communications. While directional beamforming extends the communication range, it necessitates the intricate joint optimization of the beam direction, power, and time-slot scheduling under hardware constraints. Existing resource allocation schemes predominantly follow two paradigms: (i) conventional physical-layer multiple access (CPMA) approaches, which enforce strict orthogonality within each beam and thus limit spatial efficiency; and (ii) advanced physical-layer techniques like rate-splitting multiple access (RSMA), which have been applied to terrestrial and omnidirectional UAV networks but not systematically integrated with the beam-based scheduling constraints of directional FANETs. Consequently, jointly optimizing the beam direction, intra-beam rate-splitting-based node pairing, transmit power, and time-slot scheduling remains largely unexplored. To bridge this gap, this paper introduces an intra-beam rate-splitting-based resource allocation (IBRSRA) framework for directional FANETs. This paper formulates an optimization problem that jointly designs the beam direction, constrained rate-splitting (CRS)-based node pairing, power control, modulation and coding scheme (MCS) selection, and time-slot scheduling, aiming to minimize the total number of time slots required for data transmission. The resulting mixed-integer nonlinear programming (MINLP) problem is solved via a computationally efficient two-stage algorithm, combining greedy scheduling with successive convex approximation (SCA) for non-convex optimization. Simulation results demonstrate that the proposed IBRSRA algorithm substantially enhances spectral efficiency and reduces latency. Specifically, for a network with 16 nodes, IBRSRA reduces the required number of transmission time slots by more than 42% compared to the best-performing baseline scheme. This confirms the significant practical benefit of integrating CRS into the resource allocation design of directional FANETs. Full article

Multimodal Large Language Models (MLLMs) offer powerful capabilities for processing and generating text, image, and audio data, enabling real-time intelligence in diverse applications. Deploying MLLM services at the edge can reduce transmission latency and enhance responsiveness, but it also introduces significant challenges due to the high computational demands of these models and the heterogeneity of edge devices. In this paper, we propose DistMLLM, a profit-oriented framework that enables efficient MLLM service deployment in heterogeneous edge environments. DistMLLM disaggregates multimodal tasks into encoding and inference stages, assigning them to different devices based on capability. To optimize task allocation under uncertain device conditions and competing provider interests, it employs a multi-agent bandit algorithm that jointly learns and schedules encoder and inference tasks. Extensive simulations demonstrate that DistMLLM consistently achieves higher long-term profit and lower regret than strong baselines, offering a scalable and adaptive solution for edge-based MLLM services. Full article

Unmanned Aerial Vehicle (UAV)-aided two-way relaying systems have attracted widespread attention due to their ability to improve communication efficiency, reduce deployment costs, and enhance reliability. However, most existing systems employ the Time-Division Multiple Access (TDMA) protocol, which suffers from rigid resource allocation and fails to efficiently manage antenna resources within a time slot for multiple users. Furthermore, the reliance on simple Line-of-Sight (LoS) channel models in many studies is often inaccurate, leading to significant performance degradation. To address these issues, this paper investigates a UAV-assisted two-way relaying system based on the Probabilistic Line-of-Sight (PrLoS) model. We propose a novel two-way transmission protocol, termed the Dynamic Dual-Antenna Time-Slot Allocation Protocol (DDATSAP), to facilitate flexible antenna resource allocation for multiple user pairs. To maximize the minimum average message rate for ground users, we jointly optimize the Resource Scheduling Factor (RSF), transmit power, and UAV trajectory. Since the formulated problem is non-convex and challenging to solve directly, we propose an efficient iterative algorithm based on Successive Convex Approximation (SCA) and Block Coordinate Descent (BCD) techniques. Numerical simulation results demonstrate that the proposed scheme exhibits superior performance compared to benchmark systems. Full article

To address the pressing challenges of low new energy utilization, high power system operating costs, and compromised power supply reliability in regional grids, we propose a multi-time-scale source–storage–load coordinated scheduling strategy that explicitly accounts for the characteristic distribution of grid-connected energy storage stations, including their state-of-charge constraints, round-trip efficiency profiles, and location-specific operational dynamics. A day-ahead scheduling framework is developed by integrating the multi-time-scale behavioral patterns of diverse load-side demand response resources with the dynamic operational characteristics of energy storage stations. By embedding intra-day rolling optimization and real-time corrective adjustments, we mitigate prediction errors and adapt to unforeseen system disturbances, ensuring enhanced operational accuracy. The objective function minimizes a weighted sum of system operation costs encompassing generation, transmission, and auxiliary services; wind power curtailment penalties for unused renewables; and load shedding penalties from unmet demand, balancing economic efficiency with supply quality. A mixed-integer programming model formalizes these tradeoffs, solved via MATLAB 2020b coupled CPLEX to guarantee optimality. Simulation results demonstrate that the strategy significantly cuts wind power curtailment, reduces system costs, and elevates new energy consumption—outperforming conventional single-time-scale methods in harmonizing renewable integration with grid reliability. This work offers a practical solution for enhancing grid flexibility in high-renewable penetration scenarios. Full article

Driven by the dual-carbon strategy, achieving low-carbon economic operations through coordinated optimization of multi-energy flows in integrated energy systems (IES) has emerged as a critical research focus. This paper proposes a low-carbon optimized scheduling strategy for IES based on frequency-domain modeling and multi-agent collaborative game theory, presenting a dual-dimensional innovative methodology for electricity–heat–gas integrated energy systems. At the physical modeling level, the study overcomes the limitations of conventional steady-state models and finite difference methods by pioneering a frequency-domain analytical approach for day-ahead scheduling. Through Fourier transform, the partial differential equations (PDEs) governing thermal and gas network dynamics are converted into linear complex algebraic equations, significantly reducing solution complexity while preserving modeling accuracy and enhancing computational efficiency. In operational optimization, a multi-agent cooperative mechanism is established by partitioning system operators into a tripartite alliance comprising power-to-gas (P2G) facilities, carbon capture units, and energy storage systems. A collaborative optimization model incorporating dynamic energy transmission characteristics is developed, with innovative application of Shapley value method to quantify agent contributions and allocate collaborative surplus. Simulation results demonstrate that the proposed strategy maintains dynamic constraint accuracy in gas–thermal networks while achieving notable improvements: significant reduction in total operational costs, enhanced wind power accommodation rates, and decreased carbon emission intensity. This research provides novel insights that help to resolve the modeling accuracy–computational efficiency dilemma in multi-energy coupled systems, concurrently establishing an equitable and economically viable benefit distribution mechanism for multi-agent collaboration. The findings offer substantial theoretical significance for advancing the low-carbon transition of modern power systems. Full article

Hydrogen energy is developing rapidly, and the hydrogen-based integrated energy system (HIES) offers improved economic performance, flexibility, and environmental benefits compared with conventional power systems. However, the increasing frequency of natural disasters caused by climate change introduces significant vulnerabilities that threaten system security. Preventive scheduling provides a proactive and economical means to enhance system resilience against such uncertainties. This paper proposes a preventive scheduling model for HIES based on adaptive robust optimization (ARO) to address the uncertain impacts of natural disasters on transmission lines, pipelines, and roads. The model incorporates the operational constraints and interdependencies among multiple energy subsystems and integrates flexible scheduling strategies such as power-to-hydrogen-and-heat (P2HH) and hydrogen transportation (HT). A hybrid algorithm is developed to efficiently solve the large-scale ARO problem with numerous integer variables. Case studies performed on two test systems demonstrate that the proposed preventive scheduling model effectively reduces operational costs and load curtailments. Simulation results show that coordinating P2HH and HT reduces power, heat, hydrogen, and gas load curtailments by 14.35%, 43.39%, 49.97%, and 40.32%, respectively, as well as operational costs by 14.60%. Moreover, the proposed hybrid algorithm enhances computational efficiency, reducing solution time by 21% with only a 2% deviation from the solution obtained by the conventional C&CG–AOP algorithm. Full article

Network slicing has emerged as a pivotal technology in addressing the diverse customization requirements of the Industrial Internet of Things (IIoT) within 5G networks, enabling the deployment of multiple logical networks over shared infrastructure. Efficient resource management in this context is essential to ensure energy efficiency and meet the stringent real-time demands of IIoT applications. This study focuses on the scheduling problem of minimizing average transmission power while maintaining Age of Information (AoI) tolerance constraints within 5G wireless network slicing. To tackle this challenge, an improved Dueling Double Deep Q-Network (D3QN) is leveraged to devise intelligent slicing schemes that dynamically allocate resources, ensuring optimal performance in time-varying wireless environments. The proposed improved D3QN approach introduces a novel heuristic-based exploration strategy that restricts action choices to the most effective options, significantly; reducing ineffective learning steps. The simulation results show that the method not only speeds up convergence considerably but also achieves lower transmit power while preserving strict AoI reliability constraints and slice isolation. Full article

Underground mine emergencies destroy communication infrastructure when situational awareness is most critical. Current systems rely on centralized network infrastructure, which fails during emergencies when miners are trapped and require rescue coordination. This paper proposes an energy-harvesting LoRa mesh network that addresses self-powered operation, interference management, and adaptive physical layer optimization under severe underground propagation conditions. A dual-antenna architecture separates RF energy harvesting (860 MHz) from LoRa communication (915 MHz), enabling continuous operation with supercapacitor storage. The core contribution is a decentralized scheduler that derives optimal timing offsets by modeling concurrent transmissions as a Poisson collision process, exploiting LoRa’s capture effect while maintaining network coherence. A SINR-aware physical layer adapts spreading factor, bandwidth, and coding rate with hysteresis, controls recomputing timing parameters after each change. Experimental validation in Missouri S&T’s operational mine demonstrates far-field wireless power transfer (WPT) reaching 35 m. Simulations across 2000 independent trials show a 2.2× throughput improvement over ALOHA (49% vs. 22% delivery ratio at 10 nodes/hop), 64% collision reduction, and 67% energy efficiency gains, demonstrating resilient emergency communications for underground environments. Full article

The operation of modern power networks is increasingly exposed to overlapping climate extremes and volatile system conditions, making it essential to adopt scheduling approaches that are resilient as well as economical. In this study, a two-stage stochastic formulation is advanced, where indicators of system adaptability are embedded directly into the optimization process. The objective integrates standard operating expenses—generation, reserve allocation, imports, responsive demand, and fuel resources—with a Conditional Value-at-Risk component that reflects exposure to rare but damaging contingencies, such as extreme heat, severe cold, drought-related hydro scarcity, solar output suppression from wildfire smoke, and supply chain interruptions. Key adaptability dimensions, including storage cycling depth, activation speed of demand response, and resource ramping behavior, are modeled through nonlinear operational constraints. A stylized test system of 30 interconnected areas with a 46 GW demand peak is employed, with more than 2000 climate-informed scenarios compressed to 240 using distribution-preserving reduction techniques. The results indicate that incorporating risk-sensitive policies reduces expected unserved demand by more than 80% during compound disruptions, while the increase in cost remains within 12–15% of baseline planning. Pronounced spatiotemporal differences emerge: evening reserve margins fall below 6% without adaptability provisions, yet risk-adjusted scheduling sustains 10–12% margins. Transmission utilization curves further show that CVaR-based dispatch prevents extreme flows, though modest renewable curtailment arises in outer zones. Moreover, adaptability provisions promote shallower storage cycles, maintain an emergency reserve of 2–3 GWh, and accelerate the mobilization of demand-side response by over 25 min in high-stress cases. These findings confirm that combining stochastic uncertainty modeling with explicit adaptability metrics yields measurable gains in reliability, providing a structured direction for resilient system design under escalating multi-hazard risks. Full article

Edge computing networks represent an emerging technological paradigm that enhances real-time responsiveness for mobile devices by reallocating computational resources from central servers to the network’s edge. This shift enables more efficient computing services for mobile devices. However, deploying computing services on inappropriate edge nodes can result in imbalanced resource utilization within edge computing networks, ultimately compromising service efficiency. Consequently, effectively leveraging the resources of edge computing devices while minimizing the energy consumption of terminal devices has become a critical issue in resource scheduling for edge computing. To tackle these challenges, this paper proposes a resource scheduling algorithm for edge computing networks based on multi-objective optimization. This approach utilizes the entropy weight method to assess both dynamic and static metrics of edge computing nodes, integrating them into a unified computing power metric for each node. This integration facilitates a better alignment between computing power and service demands. By modeling the resource scheduling problem in edge computing networks as a multi-objective Markov decision process (MOMDP), this study employs multi-objective reinforcement learning (MORL) and the proximal policy optimization (PPO) algorithm to concurrently optimize task transmission latency and energy consumption in dynamic environments. Finally, simulation experiments demonstrate that the proposed algorithm outperforms state-of-the-art scheduling algorithms in terms of latency, energy consumption, and overall reward. Additionally, it achieves an optimal hypervolume and Pareto front, effectively balancing the trade-off between task transmission latency and energy consumption in multi-objective optimization scenarios. Full article

Wireless sensor networks (WSNs) are an advanced solution for data collection in Internet of Things (IoT) applications and remote and harsh environments. These networks rely on a collection of distributed sensors equipped with wireless communication capabilities to collect low-cost and small-scale data. WSNs face numerous challenges, including network congestion, slow speeds, high energy consumption, and a short network lifetime due to their need for a constant and stable power supply. Therefore, improving the energy efficiency of sensor nodes through solar energy harvesting (SEH) would be the best option for charging batteries to avoid excessive energy consumption and battery replacement. In this context, modern wireless communication technologies, such as Wi-Fi and Li-Fi, emerge as promising solutions. Wi-Fi provides internet connectivity via radio frequencies (RF), making it suitable for use in open environments. Li-Fi, on the other hand, relies on data transmission via light, offering higher speeds and better energy efficiency, making it ideal for indoor applications requiring fast and reliable data transmission. This paper aims to integrate Wi-Fi and Li-Fi technologies into the SEH-WSN architecture to improve performance and efficiency when used in all applications. To achieve reliable, efficient, and high-speed bidirectional communication for multiple devices, the paper utilizes a Markov model, sleep scheduling, and smart switching algorithms to reduce power consumption, increase signal-to-noise ratio (SNR) and throughput, and reduce bit error rate (BER) and latency by controlling the technology and power supply used appropriately for the mode, sleep, and active states of nodes. Full article

Calibration of power equipment has become an essential task in modern power systems. This paper proposes a distributed remote calibration prototype based on a cloud–edge–end architecture by integrating intelligent sensing, Internet of Things (IoT) communication, and edge computing technologies. The prototype employs a high-precision frequency-to-voltage conversion module leveraging satellite signals to address traceability and value transmission challenges in remote calibration, thereby ensuring reliability and stability throughout the process. Additionally, an environmental monitoring module tracks parameters such as temperature, humidity, and electromagnetic interference. Combined with video surveillance and optical character recognition (OCR), this enables intelligent, end-to-end recording and automated data extraction during calibration. Furthermore, a cloud-edge task scheduling algorithm is implemented to offload computational tasks to edge nodes, maximizing resource utilization within the cloud–edge collaborative system and enhancing service quality. The proposed prototype extends existing cloud–edge collaboration frameworks by incorporating calibration instruments and sensing devices into the network, thereby improving the intelligence and accuracy of remote calibration across multiple layers. Furthermore, this approach facilitates synchronized communication and calibration operations across symmetrically deployed remote facilities and reference devices, providing solid technical support to ensure that measurement equipment meets the required precision and performance criteria. Full article

Unmanned aerial vehicle (UAV) clusters are increasingly deployed in power system applications, such as transmission line inspection, fault diagnosis, and post-disaster emergency communication restoration. Nonetheless, limitations of range and battery capacity have rendered the assurance of uninterrupted task operation a critical concern. Efficient cooperation and energy replenishment solutions are crucial for effective UAV cluster scheduling to resolve this issue. This study proposes an innovative scheduling method that integrates UAV path planning with laser-based remote charging technology. Initially, a scheduling model incorporating both energy consumption and task completion time is established. Subsequently, an integrated laser-powered UAV model is proposed, unifying charging operations with mission execution processes. Furthermore, a distributed robust optimization (DRO) framework is proposed to handle spatiotemporal uncertainties, particularly those caused by weather conditions. Finally, the proposed scheduling method is applied to a disaster recovery scenario of a power system. Simulation results demonstrate that the proposed strategy significantly outperforms traditional scheduling methods without remote charging by achieving higher task completion rates and improved energy efficiency. These findings substantiate the effectiveness and engineering feasibility of the proposed method in enhancing UAV cluster operational capabilities under stringent energy constraints. Full article

Space-Air-Ground Integrated Network (SAGIN), which is considered a network architecture with great development potential, exhibits significant cross-domain collaboration characteristics at present. However, most of the existing works ignore the matching and adaptability of differential tasks and heterogeneous resources, resulting in significantly inefficient task execution and undesirable network performance. As a consequence, we formulate a category attribute-oriented resource allocation and task offloading optimization problem with the aim of minimizing the overall scheduling cost. We first introduce a task–resource matching matrix to facilitate optimal task offloading policies with computation resources. In addition, virtual queues are constructed to take the impacts of randomized task arrival into account. To solve the optimization objective which jointly considers bandwidth allocation, transmission power control and task offloading decision effectively, we proposed a deep reinforcement learning (DRL) algorithm framework considering type matching. Simulation experiments demonstrate the effectiveness of our proposed algorithm as well as superior performance compared to others. Full article