Search Results (328)

Driven by energy transition strategies, distributed resources are being extensively integrated into the distribution network (DN). However, sufficient coordination among these resources remains challenging due to their diverse ownership structures. To address this, a hybrid game-theoretic economic scheduling method for the distribution network based on grid–storage–load interaction is proposed. A two-layer game framework, “distribution network–shared energy storage–microgrid alliance (MGA)”, is established to enable coordinated utilization of flexible resources across the grid, storage, and load sides. The upper-layer distribution network determines time-of-use electricity prices to guide the energy strategies of storage and microgrid alliance. The lower-layer agents engage in a two-stage interaction: Stage 1, multiple microgrids (MGs) form an alliance to lease shared energy storage to smooth net-load profiles. The shared energy storage operator (SESO) then utilizes its surplus capacity to assist the distribution network in peak shaving, thereby maximizing its own revenue. Stage 2, the alliance facilitates mutual power support and implements demand response (DR), reducing its energy costs and assisting the system in peak shaving and valley filling. Case analysis demonstrates that, compared to baseline without coordination, the proposed method reduces the distribution network’s electricity procurement cost by 11.28% and lowers the system’s net load peak-to-valley difference rate by 56.53%. Full article

This research simulates Aswan City’s urban transportation dynamics utilizing the Multi-Agent Transport Simulation (MATSim) framework. As a fast-expanding urban center, Aswan has many transportation difficulties that require extensive modeling toward sustainable mobility solutions. MATSim, recognized for its agent-based methodology, offers a detailed portrayal and analysis of individual travel behaviors and their interactions within the metropolitan transportation system. This study compiled and combined many databases, including demographic data, road infrastructure, public transit plans, and travel demand trends. These data are altered to produce a realistic digital clone of Aswan’s transportation system. Simulated scenarios analyze the consequences of several actions, such as increased public transit scheduling, traffic flow management, and the adoption of alternative transport modes, on minimizing congestion and boosting accessibility. Pilot findings show that MATSim effectively captures the distinct features of Aswan’s transportation network and offers practical insights for decision-makers. The results identified some opportunities to improve mobility and promote sustainable urban growth in developing cities. This study emphasized the importance of agent-based simulations in designing future transportation systems and urban infrastructure. Full article

Generative Agent (GA) has emerged as a promising framework for simulating human-like behaviors. However, it is required for GA to generate a schedule that consistently reflects the agent’s E-I trait particularly in the extroversion–introversion (E-I) category to improve the realism of GA. We propose an E-I evaluation and rescheduling method that adjusts the agent’s schedule. Specifically, our method takes as input a one-hour schedule segmented into five-minute tasks and a corresponding E-I trait classified into seven degrees ranging from extremely high extroversion to extremely high introversion. Using the Evaluator powered by GPT-4o mini, each task is assessed for the alignment with the E-I traits. Each task that fails to meet a threshold is regenerated using few-shot prompting based on a collected successful schedule. This process is repeated until all tasks are aligned with the corresponding traits. Finally, the evaluator accesses the overall E-I consistency of the schedule that contains the tasks. Therefore, it is possible for the proposed method to enable E-I-consistent schedule generation in GA without retraining any models. In experiments, the proposed framework improved E-I alignment from an average of 14.7% to that of 78.4% with only 1.38 iterations on average, demonstrating both practical effectiveness and computational efficiency. Full article

Aiming at multi-agent interest demands and environmental benefits, a distributionally robust game-theoretic optimization algorithm based on a green certificate–carbon trading mechanism is proposed for uncertain microgrids. At first, correlated wind–solar scenarios are generated using Kernel Density Estimation and copula theory and the probability distribution ambiguity set is constructed combining 1-norm and $\infty$-norm metrics. Subsequently, with gas turbines, renewable energy power producers, and an energy storage unit as game participants, a two-stage distributionally robust game-theoretic optimization scheduling model is established for microgrids considering wind and solar correlation. The algorithm is constructed by integrating a non-cooperative dynamic game with complete information and distributionally robust optimization. It minimizes a linear objective subject to linear matrix inequality (LMI) constraints and adopts the column and constraint generation (C&CG) algorithm to determine the optimal output for each device within the microgrid to enhance its overall system performance. This method ultimately yields a scheduling solution that achieves both equilibrium among multiple stakeholders’ interests and robustness. The simulation result verifies the effectiveness of the proposed method. Full article

High variability in multi-product manufacturing environments and rapidly changing customer demands make decentralized coordination of work-in-process (WIP) and production resources increasingly important. However, the intrinsic rigidity of conventional centralized and monolithic manufacturing execution systems (MESs) renders them unsuitable for such highly dynamic environments. To address this limitation, this study proposes an agent-based distributed, decentralized MES architecture. The manufacturing execution process is realized through collaboration among constituent agents based on an employment network (EmNet). Specifically, three types of agents are introduced: WIPAgents (representing WIPs), PAgents (representing processing resources), and MHAgents (representing material-handling resources). Collaboration among agents (e.g., collaborator discovery, partner selection, and data sharing/exchange) is facilitated by a data-space-based collaboration platform which was introduced in our prior work. To validate the proposed architecture, we built a digital-twin-based simulation testbed and conducted simulation experiments. The experimental results confirm the validity and operational feasibility of the proposed architecture. Full article

Existing metaheuristic approaches often struggle to maintain an effective exploration–exploitation balance and are prone to premature convergence when addressing highly conflicting time–cost–safety–risk trade-off problems (TCSRTPs) under complex construction project constraints, which can adversely affect project productivity, safety, and the provision of decent jobs in the construction sector. To overcome these limitations, this study introduces a hybrid metaheuristic called the Q-Learning Inspired Mountain Gazelle Optimizer (QL-MGO) for solving multi-objective TCSRTPs in construction project management, supporting the delivery of resilient infrastructure and resilient building projects. QL-MGO enhances the original MGO by integrating Q-learning with an opposition-based learning strategy to improve the balance between exploration and exploitation while reducing computational effort and enhancing resource efficiency in construction scheduling. Each gazelle functions as an adaptive agent that learns effective search behaviors through a state–action–reward structure, thereby strengthening convergence stability and preserving solution diversity. A dynamic switching mechanism represents the core innovation of the proposed approach, enabling Q-learning to determine when opposition-based learning should be applied based on the performance history of the search process. The performance of QL-MGO is evaluated using 18- and 37-activity construction scheduling problems and compared with NDSII-MGO, NDSII-Jaya, NDSII-TLBO, the multi-objective genetic algorithm (MOGA), and NDSII-Rao-2. The results demonstrate that QL-MGO consistently generates superior Pareto fronts. For the 18-activity project, QL-MGO achieves the highest hypervolume (HV) value of 0.945 with a spread of 0.821, outperforming NDSII-Rao-2, MOGA, and NDSII-MGO. Similar results are observed for the 37-activity project, where QL-MGO attains the highest HV of 0.899 with a spread of 0.674, exceeding the performance of NDSII-Jaya, NDSII-TLBO, and NDSII-MGO. Overall, the integration of Q-learning significantly enhances the search capability of MGO, resulting in faster convergence, improved solution diversity, and more reliable multi-objective trade-off solutions. QL-MGO therefore serves as an effective and computationally efficient decision-support tool for construction scheduling that promotes safer, more reliable, and resource-efficient project delivery. Full article

Rapid integration of renewable energy sources poses a serious problem to the functionality of microgrids since they are characterized by underlying uncertainties and variability. This paper proposes a multi-stage approach to energy management to overcome these issues in a virtual power plant that combines heterogeneous microgrids. The solution is based on multi-agent deep reinforcement learning to coordinate internal energy pricing, microgrid scheduling, and virtual power plant-level energy storage system management. The proposed model autonomously learns the optimal dynamic pricing strategies based on load and generation dynamics, which is efficient in dealing with operational uncertainties and maintaining microgrid privacy due to its decentralized structure. The efficiency of the proposed solution is tested on comparative simulations based on real-world data, which prove the superiority of the framework to the traditional operation modes, which are isolated microgrids and the energy sharing scenarios. The findings prove that the suggested solution has a dual beneficial impact on both virtual power plant operators and involved microgrids, as it leads to profit enhancement and, at the same time, system stability. This process facilitates the successful balancing of conflicting interests among the stakeholders at a time when the operation is low-carbon. The study offers an overall solution to dealing with complicated multi-microgrids and brings substantial changes in the integration of renewable energy, as well as the distributed management of energy resources. The framework is a scalable model that can be used in the future perspective of power systems with high-renewable penetration to address both economic and operational issues of the contemporary energy grids. Full article

In the digital era, rapid urban growth and the demand for sustainable mobility are placing increasing pressure on transport systems, where congestion, energy consumption, and schedule variability complicate intermodal journey planning. This work proposes an AI-enhanced transfer-graph framework that models each transport mode as an independent subnetwork connected through explicit transfer arcs. This modular structure captures modal interactions while reducing graph complexity, enabling algorithms to operate more efficiently in time-dependent contexts. A Deep Q-Network (DQN) agent is further introduced as an exploratory alternative to exact and meta-heuristic methods for learning adaptive routing strategies. Exact (Dijkstra) and meta-heuristic (ACO, DFS, GA) algorithms were evaluated on synthetic networks reflecting Casablanca’s intermodal structure, achieving coherent routing with favorable computation and memory performance. The results demonstrate the potential of combining transfer-graph decomposition with learning-based components to support scalable intermodal routing. Full article

We study UAV-assisted 5G uplink connectivity for disaster response, in which a UAV (unmanned aerial vehicle) acts as an aerial base station to restore service to ground users. We formulate a joint control problem coupling UAV kinematics (bounded acceleration and velocity), per-subchannel uplink power allocation, and uplink non-orthogonal multiple access (UL-NOMA) scheduling with adaptive successive interference cancellation (SIC) under a minimum user-rate constraint. The wireless channel follows 3GPP urban macro (UMa) with probabilistic line of sight/non-line of sight (LoS/NLoS), realistic receiver noise levels and noise figure, and user equipment (UE) transmit-power limits. We propose a bounded-action proximal policy optimization with generalized advantage estimation (PPO-GAE) agent that parameterizes acceleration and power with squashed distributions and enforces feasibility by design. Across four user distributions (clustered, uniform, ring, and edge-heavy) and multiple rate thresholds, our method increases the fraction of users meeting the target rate by 8.2–10.1 percentage points compared to strong baselines (OFDMA with heuristic placement, PSO-based placement/power, and PPO without NOMA) while reducing median UE transmit power by $64.6\%$. The results are averaged over at least five random seeds, with $95\%$ confidence intervals. Ablations isolate the gains from NOMA, adaptive SIC order, and bounded-action parameterization. We discuss robustness to imperfect SIC and CSI errors and release code/configurations to support reproducibility. Full article

Although emerging technologies are increasingly adopted in teaching and learning, their potential to enhance educational administration remains underexplored. In particular, few studies examine how conversational agents, virtual reality (VR), and robotic process automation (RPA) can jointly streamline administrative workflows in multilingual and multicultural university environments. This study addresses this gap by presenting an integrated solution deployed on the website of an engineering faculty where programs are delivered in foreign languages. The proposed system combines a multilingual chatbot, a VR-based administrative guide and virtual tour, and RPA modules supporting certificate generation, password resets, and exam scheduling. Through an A/B usability test, usage analytics, and qualitative feedback, we evaluate the effectiveness of these technologies in improving access to information, reducing response time, and lowering administrative workload. Results show that this triad significantly enhances efficiency and student experience, particularly for international students requiring continuous support. The paper contributes a replicable model for leveraging emerging technologies in educational administration and offers insights for institutions seeking scalable and student-centered digital transformation. Full article

Sixth-generation (6G) wireless systems aim to integrate terrestrial, aerial, and satellite networks to support large-scale remote sensing and service delivery. In such non-terrestrial networks (NTNs), channels change quickly and the multi-tier architecture is heterogeneous, which makes real-time channel state acquisition and cooperative resource scheduling difficult. This paper proposes an FMA-MADDPG framework that combines a channel prediction module with a constraint-based multi-agent deep deterministic policy gradient scheme. The Fusion of Mamba and Attention (FMA) predictor uses a Mamba state-space backbone and a multi-head self-attention block to learn both long-term channel evolution and short-term fluctuations, and forecasts future CSI. The predicted channel information is added to the agents’ observations so that scheduling decisions can take expected channel variations into account. A constraint-based reward is also designed, with explicit performance thresholds and anti-idle penalties, to encourage fairness, avoid free-riding, and promote cooperation among heterogeneous agents. In a representative NTN uplink scenario, the proposed method achieves higher total reward, efficiency, load balance, and cooperation than several DRL baselines, with relative gains around 10–20% on key metrics. These results indicate that prediction-aware cooperative reinforcement learning is a useful approach for resource optimization in future 6G NTN systems. Full article

To address the state estimation and detection problem in the presence of noisy sensor observations, probing costs, and communication noise, we in this paper propose a soft actor-critic (SAC) deep reinforcement learning (DRL) framework for dynamically scheduling sensors and sequentially probing the state of a stochastic system. Moreover, considering Byzantine attacks, we design a generative adversarial network (GAN)-based framework to identify the Byzantine sensors. The GAN-based Byzantine detector and SAC-DRL-based agent are developed to operate in coordination to detect the state of the system reliably and fast while incurring small sensing cost. To evaluate the proposed framework, we measure the performance in terms of detection accuracy, stopping time, and the total probing cost needed for detection. Via simulation results, we analyze the performances and demonstrate that soft actor–critic algorithms are flexible and effective in action selection in imperfectly known environments due to the maximum entropy strategy and they can achieve stable performance levels in challenging test cases (e.g., involving jamming attacks, imperfectly known noise power levels, and high sensing cost scenarios). We also provide comparisons between the performances of the proposed soft actor–critic and conventional actor–critic algorithms as well as fixed scheduling strategies. Finally, we analyze the impact of Byzantine attacks and identify the reliability and accuracy improvements achieved by the GAN-based approach when combined with the SAC-DRL-based decision-making agent. Full article

Grid-forming power converters play a foundational role in modern microgrids and inverter-dominated distribution systems by establishing voltage and frequency references during islanded or low-inertia operation. However, when subjected to nonlinear or impulsive impact-type loads, these converters often suffer from severe harmonic distortion and transient current overshoot, leading to waveform degradation and protection-triggered failures. While virtual impedance control has been widely adopted to mitigate these issues, conventional implementations rely on fixed or rule-based tuning heuristics that lack adaptivity and robustness under dynamic, uncertain conditions. This paper proposes a novel reinforcement learning-based framework for real-time virtual impedance scheduling in grid-forming converters, enabling simultaneous optimization of harmonic suppression and impact load resilience. The core of the methodology is a Soft Actor-Critic (SAC) agent that continuously adjusts the converter’s virtual impedance tensor—comprising dynamically tunable resistive, inductive, and capacitive elements—based on real-time observations of voltage harmonics, current derivatives, and historical impedance states. A physics-informed simulation environment is constructed, including nonlinear load models with dominant low-order harmonics and stochastic impact events emulating asynchronous motor startups. The system dynamics are modeled through a high-order nonlinear framework with embedded constraints on impedance smoothness, stability margins, and THD compliance. Extensive training and evaluation demonstrate that the learned impedance policy effectively reduces output voltage total harmonic distortion from over 8% to below 3.5%, while simultaneously limiting current overshoot during impact events by more than 60% compared to baseline methods. The learned controller adapts continuously without requiring explicit load classification or mode switching, and achieves strong generalization across unseen operating conditions. Pareto analysis further reveals the multi-objective trade-offs learned by the agent between waveform quality and transient mitigation. Full article

The proliferation of terrestrial and space-based communication systems introduces significant radio frequency interference (RFI), which severely compromises data acquisition for radio telescopes, necessitating robust and dynamic scheduling solutions. This study addresses this challenge by implementing a Deep Recurrent Reinforcement Learning (DRL) framework for the control and dynamic scheduling of the X-Y pedestal-mounted KMITL radio telescope, explicitly trained for RFI avoidance. The methodology involved developing a custom simulation environment with a domain-specific Convolutional Neural Network (CNN) feature extractor and a Long Short-Term Memory (LSTM) network to model temporal dynamics and long-horizon planning. Comparative evaluation demonstrated that the recurrent DRL agent achieved a mean effective survey coverage of 475 deg²/h, representing a 72.7% superiority over the non-recurrent baseline, and maintained exceptional stability with only 1.0% degradation in median coverage during real-world deployment. The DRL framework offers a highly reliable and adaptive solution for telescope scheduling that is capable of maintaining survey efficiency while proactively managing dynamic RFI sources. Full article

Growing EV adoption is reshaping how Distributed Energy Resources (DERs) interact with the grid, playing a pivotal role in global decarbonization efforts and the transition towards a sustainable energy future. This study built a Virtual Power Plant (VPP) operation framework centered on EV behavioral dynamics, connecting individual driving and charging behaviors with the physical and economic layers of energy management. The EV behavioral dynamic model quantifies the stochastic travel, parking, and charging behaviors of individual EVs through an Agent-Based Trip and Charging Chain (AB-TCC) simulation, producing a Behavioral Flexibility Trace (BFT) that represents time-resolved EV availability and flexibility. The Forecasting Model employs a Bi-directional Long Short-Term Memory (Bi-LSTM) network trained on historical meteorological data to predict short-term renewable generation and represent physical variability. The two-stage optimization model integrates behavioral and physical information with market price signals to coordinate day-ahead scheduling and real-time operation, minimizing procurement costs and mitigating imbalance penalties. Simulation results indicate that the proposed framework yielded an approximately 15% increase in revenue over 7 days through EV-based flexibility utilization. These findings demonstrate that the proposed approach effectively leverages EV flexibility to manage renewable generation variability, thereby enhancing both the profitability and operational reliability of VPPs in local distribution systems. This facilitates greater penetration of intermittent renewable energy sources, accelerating the transition to a low-carbon energy system. Full article