Search Results (134)

This paper presents a deep reinforcement learning-based demand response (DR) optimization framework for active distribution networks under uncertainty and user heterogeneity. The proposed model utilizes a Double Deep Q-Network (Double DQN) to learn adaptive, multi-period DR strategies across residential, commercial, and electric vehicle (EV) participants in a 24 h rolling horizon. By incorporating a structured state representation—including forecasted load, photovoltaic (PV) output, dynamic pricing, historical DR actions, and voltage states—the agent autonomously learns control policies that minimize total operational costs while maintaining grid feasibility and voltage stability. The physical system is modeled via detailed constraints, including power flow balance, voltage magnitude bounds, PV curtailment caps, deferrable load recovery windows, and user-specific availability envelopes. A case study based on a modified IEEE 33-bus distribution network with embedded PV and DR nodes demonstrates the framework’s effectiveness. Simulation results show that the proposed method achieves significant cost savings (up to 35% over baseline), enhances PV absorption, reduces load variance by 42%, and maintains voltage profiles within safe operational thresholds. Training curves confirm smooth Q-value convergence and stable policy performance, while spatiotemporal visualizations reveal interpretable DR behavior aligned with both economic and physical system constraints. This work contributes a scalable, model-free approach for intelligent DR coordination in smart grids, integrating learning-based control with physical grid realism. The modular design allows for future extension to multi-agent systems, storage coordination, and market-integrated DR scheduling. The results position Double DQN as a promising architecture for operational decision-making in AI-enabled distribution networks. Full article

This paper presents the design and implementation of an Intelligent Home Energy Management System in a smart home. The system is based on an economically decentralized hybrid concept that includes photovoltaic technology, a proton exchange membrane fuel cell, and a hydrogen refueling station, which together provide a reliable, secure, and clean power supply for smart homes. The proposed design enables power transfer between Vehicle-to-Home (V2H) and Home-to-Vehicle (H2V) systems, allowing electric vehicles to function as mobile energy storage devices at the grid level, facilitating a more adaptable and autonomous network. Our approach employs Double Deep Q-networks for adaptive control and forecasting. A Multi-Agent System coordinates actions between home appliances, energy storage systems, electric vehicles, and hydrogen power devices to ensure effective and cost-saving energy distribution for users of the smart grid. The design validation is carried out through MATLAB/Simulink-based simulations using meteorological data from Tunis. Ultimately, the V2H/H2V system enhances the utilization, reliability, and cost-effectiveness of residential energy systems compared with other management systems and conventional networks. Full article

With the large-scale access to a large number of distributed electric and thermal flexible resources and multiple loads on the user side, the energy management of the integrated energy system (IES) has become an effective way for the efficient and low-carbon economic operation of energy systems. In order to explore a new mode of IES energy management with the participation of energy service providers (ESPs) and user clusters (UCs), this paper puts forward an energy management method for electric–thermal microgrids, considering the optimization of user energy consumption characteristics. Firstly, an energy management framework with multi-agent participation of ESP and user cluster is proposed, and a user energy preference model is established considering the user’s electricity and heat consumption preferences. Secondly, considering the operation benefit of ESP and user cluster, based on the reinforcement learning (RL) framework, an energy management model between ESPs and users is established, and a distributed solution algorithm combining Q-learning and quadratic programming is proposed. Finally, the IESs with different user scales and energy units are taken as the test system, and the optimal energy management strategy of the system, considering the user’s energy preference, is analyzed. The simulation results demonstrate that the energy management model proposed enhances the economic efficiency of IES operations and reduces emissions. In a test system with two UCs, the optimized system achieves a 5.05% reduction in carbon emissions. The RL-based distributed solution algorithm efficiently solves the energy management model for systems with varying UC scales, requiring only 6.55 s for systems with two UCs and 13.26 s for systems with six UCs. Full article

As electric vehicles (EVs) gain widespread adoption in industrial environments supported by Industrial Internet of Things (IIoT) smart grids technology, coordinated charging of multiple EVs has become vital for maintaining grid stability. In response to the scalability challenges faced by traditional algorithms in multi-device environments and the limitations of discrete action spaces in continuous control scenarios, this paper proposes a dynamic charging scheduling algorithm for EVs based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG). The algorithm combines real-time electricity prices, battery status monitoring, and distributed sensor data to dynamically optimize charging and discharging strategies of multiple EVs in continuous action spaces. The goal is to reduce charging costs and balance grid load through coordinated multi-agent learning. Experimental results show that, compared with baseline methods, the proposed MADDPG algorithm achieves a 41.12% cost reduction over a 30-day evaluation period. Additionally, it effectively adapts to price fluctuations and user demand changes through Vehicle-to-Grid technology, optimizing charging time allocation and enhancing grid stability. Full article

Low-Earth-orbit (LEO) satellite networks face significant vulnerabilities to malicious jamming and co-channel interference, compounded by dynamic topologies, resource constraints, and complex electromagnetic environments. Traditional anti-jamming approaches lack adaptability, centralized intelligent methods incur high overhead, and distributed intelligent methods fail to achieve global optimization. To address these limitations, this paper proposed a value decomposition network (VDN)-based multi-agent deep reinforcement learning (DRL) anti-jamming spectrum access approach with a centralized training and distributed execution architecture. Following offline centralized ground-based training, the model was deployed distributedly on satellites for real-time spectrum-access decision-making. The simulation results demonstrate that the proposed method effectively balances training costs with anti-jamming performance. The method achieved near-optimal user satisfaction (approximately 97%) with minimal link overhead, confirming its effectiveness for resource-constrained LEO satellite networks. Full article

This paper aims to investigate a Nash equilibrium (NE)-seeking approach for the aggregative game problem of second-order multi-agent systems (MAS) with uncontrollable malicious players, which may cause the decisions of global players to become uncontrollable, thereby hindering the ability of normal players to reach the NE. To mitigate the influence of malicious players on the system, a malicious player detection and disconnection (MPDD) algorithm is proposed, based on the fixed-time convergence method. Subsequently, a predefined-time distributed NE-seeking algorithm is presented, utilizing a time-varying, time-based generator (TBG) and state-feedback scheme, ensuring that all normal players complete the game problem within the predefined time. The convergence properties of the algorithms are analyzed using Lyapunov stability theory. Theoretically, the aggregative game problem with malicious players can be solved using the proposed algorithms within any user-defined time. Finally, a numerical simulation of electricity market bidding verifies the effectiveness of the proposed algorithm. Full article

This paper presents a distributed optimization algorithm for time-varying objective functions utilizing a prescribed-time convergent multi-agent system within undirected communication networks. Departing from conventional time-invariant optimization paradigms with static optimal solutions, our approach specifically addresses the challenge of tracking dynamic optimal trajectories in evolving environments. A novel continuous-time distributed optimization algorithm is developed based on prescribed-time consensus, guaranteeing the consensus attainment among agents within a user-defined timeframe while asymptotically converging to the time-dependent optimal solution. The proposed methodology enables explicit predetermination of convergence duration, representing a significant advancement over existing asymptotic convergence methods. Moreover, two simulation examples on the rendezvous problem and multi-robots control are presented to validate the theoretical results, exhibiting precise time-controlled convergence characteristics and effective tracking performance for time-varying optimization targets. Full article

This review paper examines the integration of artificial intelligence (AI) in wireless communication, focusing on cognitive radio (CR), spectrum sensing, and dynamic spectrum access (DSA). As the demand for spectrum continues to rise with the expansion of mobile users and connected devices, cognitive radio networks (CRNs), leveraging AI-driven spectrum sensing and dynamic access, provide a promising solution to improve spectrum utilization. The paper reviews various deep learning (DL)-based spectrum-sensing methods, highlighting their advantages and challenges. It also explores the use of multi-agent reinforcement learning (MARL) for distributed DSA networks, where agents autonomously optimize power allocation (PA) to minimize interference and enhance quality of service. Additionally, the paper discusses the role of machine learning (ML) in predicting spectrum requirements, which is crucial for efficient frequency management in the fifth generation (5G) networks and beyond. Case studies show how ML can help self-optimize networks, reducing energy consumption while improving performance. The review also introduces the potential of generative AI (GenAI) for demand-planning and network optimization, enhancing spectrum efficiency and energy conservation in wireless networks (WNs). Finally, the paper highlights future research directions, including improving AI-driven network resilience, refining predictive models, and addressing ethical considerations. Overall, AI is poised to transform wireless communication, offering innovative solutions for spectrum management (SM), security, and network performance. Full article

Given the problems of uneven distribution, strong time variability of ground service demands, and low utilization rate of on-board resources in Low-Earth-Orbit (LEO) satellite communication systems, how to efficiently utilize limited beam resources to flexibly and dynamically serve ground users has become a research hotspot. This paper studies the dynamic resource allocation and interference suppression strategies for beam hopping satellite communication systems. Specifically, in the full-frequency-reuse scenario, we adopt spatial isolation techniques to avoid co-channel interference between beams and construct a multi-objective optimization problem by introducing weight coefficients, aiming to maximize user satisfaction and minimize transmission delay simultaneously. We model this optimization problem as a Markov decision process and apply a value decomposition network (VDN) algorithm based on cooperative multi-agent reinforcement learning (MARL-VDN) to reduce computational complexity. In this algorithm framework, each beam acts as an agent, making independent decisions on hopping patterns and power allocation strategies, while achieving multi-agent cooperative optimization through sharing global states and joint reward mechanisms. Simulation results show that the applied algorithm can effectively enhance user satisfaction, reduce delay, and maintain high resource utilization in dynamic service demand scenarios. Additionally, the offline-trained MARL-VDN model can be deployed on LEO satellites in a distributed mode to achieve real-time on-board resource allocation on demand. Full article

This study introduces a novel approach to optimal Service Level Agreement (SLA) tier selection in online services by incorporating client-side obsolescence factors into effective SLA planning. We analyze a comprehensive dataset of 600 million records collected over four years, focusing on the lifecycle patterns of browsers published into the iPhone and Samsung ecosystems. Using Gaussian Process Regression with a Matérn kernel and exponential decay models, we model browser version adoption and decline rates, accounting for data sparsity and noise. Our methodology includes a centroid-based filtering technique and a quadratic decay term to mitigate bot-related anomalies. Results indicate distinct browser delivery refresh cycles for both ecosystems, with iPhone browsers showing peaks at 22 and 42 days, while Samsung devices exhibit peaks at 44 and 70 days. We quantify the support duration required to achieve various SLA tiers as follows: for 99.9% coverage, iPhone and Samsung browsers require 254 and 255 days of support, respectively; for 99.99%, 360 and 556 days; and for 99.999%, 471 and 672 days. These findings enable more accurate and effective SLA calculations, facilitating cost-efficient service planning considering the full service delivery and consumption pipeline. Our approach provides a data-driven framework for balancing aggressive upgrade requirements against generous legacy support, optimizing both security and performance within given cost boundaries. Full article

The rapid electrification of transportation, driven by stringent decarbonization targets and supportive policies, poses significant challenges for distribution system operators (DSOs). When numerous electric vehicles (EVs) charge concurrently, local transformers risk overloading—a problem that current tariff-based strategies do not adequately address. This paper introduces an aggregator-based coordination mechanism that shifts EV charging from congested to underutilized periods using a rule-based scheduling algorithm. Unlike conventional methods that depend on complex real-time pricing signals or optimization-heavy solutions, the aggregator approach uses a simple yet effective “laxity” measure to prioritize charging flexibility. To assess technical and economic viability, a multi-agent simulation was developed to replicate residential user behavior and DSO constraints under the use of a 400 kVA low-voltage transformer. The results indicate that overloads are completely eliminated with minimal inconvenience to users, whose increased charging costs are offset by the aggregator at an annual total of under DKK 6000—significantly lower than the cost of infrastructure reinforcement. This study contributes by (i) quantifying the compensation needed to prevent large-scale overloads, (ii) presenting a replicable, computationally feasible, rule-based aggregator model for DSOs, and (iii) comparing aggregator solutions to costly transformer upgrades, underscoring the aggregator’s role as a viable tool for future distribution systems. Full article

In this paper, we investigate the classical Axelrod model of cultural dissemination under an adaptive network framework. Unlike the original model, we place agents on a complex network, where they cut connections with any agent that does not share at least one cultural trait. This rewiring process alters the network topology, and key parameters—such as the number of traits, the neighborhood search range, and the degree-based preferential attachment exponent—also influence the distribution of cultural traits. Unlike conventional Axelrod models, our approach introduces a dynamic network structure where the rewiring mechanism allows agents to actively modify their social connections based on cultural similarity. This adaptation leads to network fragmentation or consolidation depending on the interaction among model parameters, offering a framework to study cultural homogeneity and diversity. The results show that, while long-range reconnections can promote more homogeneous clusters in certain conditions, variations in the local search radius and preferential attachment can lead to rich and sometimes counterintuitive dynamics. Extensive simulations demonstrate that this adaptive mechanism can either increase or decrease cultural diversity, depending on the interplay of network structure and cultural dissemination parameters. These findings have practical implications for understanding opinion dynamics and cultural polarization in social networks, particularly in digital environments where rewiring mechanisms are analogous to recommendation systems or user-driven connection adjustments. Full article

Task delegation in multi-agent systems (MASs) is crucial for ensuring efficient collaboration among agents with different capabilities and skills. Traditional delegation models rely on social mechanisms such as trust and reputation to evaluate potential partners. While these approaches are effective in selecting competent agents, they often lack transparency, making it difficult for users to understand and trust the decision-making process. To address this limitation, we propose a novel task delegation model that integrates explainability through argumentation-based reasoning. Our approach employs the quantitative argumentation with votes framework (QuAD-V), a voting-based argumentation system that enables agents to justify their partner selection. We evaluate our model in a scenario involving the distribution of petroleum products via pipelines, where agents represent bases capable of temporarily storing a quantity of product. The connections between agents represent transportation routes, allowing the product to be sent from an origin to a destination base. The results demonstrate the effectiveness of our model in optimizing delegation decisions while maintaining clear, understandable explanations for agents’ decisions. Full article

Efficient parking distribution is crucial for urban traffic management; nevertheless, variable demand and spatial disparities raise considerable obstacles. Current research emphasizes local optimization but neglects the fundamental challenges of real-time parking allocation, resulting in inefficiencies within intricate metropolitan settings. This research delineates two key issues: (1) A dynamic imbalance between supply and demand, characterized by considerable fluctuations in parking demand over time and across different locations, rendering static allocation solutions inefficient; (2) spatial resource optimization, aimed at maximizing the efficiency of limited parking spots to improve overall system performance and user satisfaction. We present a Multi-Agent Reinforcement Learning (MARL) framework that incorporates adaptive optimization and intelligent collaboration for dynamic parking allocation to tackle these difficulties. A reinforcement learning-driven temporal decision mechanism modifies parking assignments according to real-time data, whilst a Graph Neural Network (GNN)-based spatial model elucidates inter-parking relationships to enhance allocation efficiency. Experiments utilizing actual parking data from Melbourne illustrate that Multi-Agent Reinforcement Learning (MARL) substantially surpasses conventional methods (FIFO, SIRO) in managing demand variability and optimizing resource distribution. A thorough quantitative investigation confirms the strength and flexibility of the suggested method in various urban contexts. Full article

Background/Objectives: Inhaler devices have been developed for the effective delivery of inhaled medications used in the treatment of pulmonary diseases. However, differing operating procedures across the devices can lead to user errors and reduce treatment efficacy, especially when patients use multiple devices simultaneously. To address this, we developed a novel dry powder inhaler (DPI), combining fluticasone propionate (FP), salmeterol xinafoate (SX), and tiotropium bromide (TB) into a single device designed for bioequivalent delivery compared to existing commercial products in an animal model. Methods: The micronized FP/SX/TB-loaded capsule was prepared by sieving, blending, and filling capsules. Capsule suitability of the drugs was investigated from the comparison of the stability of drugs within various capsule formulations to that of commercial products. The particle size of the drugs was adjusted using spiral air jet milling, and the ratio of lactose hydrate carriers was optimized by comparing the aerodynamic particle size distribution (APSD) with that of commercial products. To investigate the bioequivalence of micronized FP/SX/TB-loaded DPI to commercial products, the dissolution profile of FP/SX/TB particles and pharmacokinetics in rats were evaluated and compared to commercial products. Results: Capsules with hydroxypropyl methylcellulose (HPMC) without a gelling agent showed superior stability of the drugs compared to commercial products. The deposition pattern was influenced by the particle size of the drugs, and fine particle mass exhibited a significant correlation with the amount of fine carrier. Micronized FP/SX/TB-loaded DPI gave a similar APSD and dissolution profile compared to the commercial products and showed dose uniformity by the DPI device. Furthermore, micronized FP/SX/TB-loaded DPI exhibited bioequivalence to commercial products, as evidenced by no significant differences in pharmacokinetic parameters following intratracheal administration in rats. Conclusions: A novel triple-combination DPI containing FP/SX/TB was successfully developed, demonstrating comparable pharmacological performance to commercial products. Optimized FP/SX/TB-loaded DPI with HPMC capsule achieved bioequivalence in rat studies, suggesting its potential for improved patient compliance and therapeutic outcomes. This novel single-device DPI offers a promising alternative for triple therapy in pulmonary diseases. Full article