Communication optimization algorithms in the complex and changing environments are an important research topic in the information field. optimization of the multi-path transmission control protocol model in 5G network...
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When we deploy sensors, we face severe problems to achieve resilience and robust coverage. It happens more in stochastic and dynamic environments. In this paper, we propose a hybrid framework which is a combination of...
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Heterogeneous network is a promising solution to meet higher network capacity requirements and faster transmission rates. Under the spectrum-sharing strategy, the interference problem among user devices is a major fac...
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ISBN:
(纸本)9798350388732;9798350388725
Heterogeneous network is a promising solution to meet higher network capacity requirements and faster transmission rates. Under the spectrum-sharing strategy, the interference problem among user devices is a major factor affecting the network's performance. Power control methods employing deep reinforcement learning are extensively utilized for interference management purposes. A previous study formulated a multi-agent power control algorithm utilizing deep reinforcement learning to tackle the sum-rate optimization problem within a fixed base station deployment scenario in heterogeneous networks. Specifically, the aforementioned research exclusively examined a specific scenario wherein the femto base stations are situated within the central coverage area of the macro base station. Inspired by the research, this paper considers the variation of femto base stations' location and explores the impact of base station distribution on the sum-rate using deep Q learning algorithm. Simulation results show that the average sum-rate enhances with the increase of the sum distance between the macro base station and the femto base stations.
Since wireless sensor network coverage belongs to the category of combinatorial optimization, to improve its coverage in the case of a limited number of nodes, we introduced and improved the PGSO hybrid algorithm. In ...
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With the growing popularity of electric vehicles (EVs) and advances in vehicle-to-grid (V2G) technology, large-scale EV aggregations (EVAs) have become a critical and integral part of the electricity system. The judic...
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ISBN:
(纸本)9798331532932;9798331532925
With the growing popularity of electric vehicles (EVs) and advances in vehicle-to-grid (V2G) technology, large-scale EV aggregations (EVAs) have become a critical and integral part of the electricity system. The judicious use of EVAs can improve grid's operational efficiency and economic benefits. However, effectively capturing the different idle energy storage characteristics of EVAs across regions and seamlessly integrating them into power system operations remains a challenge. In this paper, a methodology based on dynamic programming and multi-level optimization is proposed and further investigated to form a multi-level coordinated control system for vehicle, garage, and grid. The proposed method considers the characteristics of user needs and solution objectives within the vehicle-garage-grid system. It aims to reduce user costs, increase the economic benefits of garages, and improve the operational efficiency of the power system. Case studies based on real data show that the proposed multi-level coordinated control system (MCCS) significantly improves both the economic benefits and operational efficiency of the power system.
Electric vehicles (EVs) play a crucial role in the global transition to low-carbon and clean energy. Large amounts of uncontrolled EVs charging can cause short-term overloading problems with distribution transformers ...
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ISBN:
(纸本)9798350373707;9798350373691
Electric vehicles (EVs) play a crucial role in the global transition to low-carbon and clean energy. Large amounts of uncontrolled EVs charging can cause short-term overloading problems with distribution transformers in some areas. EVs as a mobile energy storage component with Vehicle-to-Grid (V2G) capability can provide a solution to alleviate the overloading and aging problems of distribution transformers. This paper comprehensively considers the charging and discharging losses of EVs and the peak load of distribution transformers, with the constraint of EVs' own charging and discharging demands. The rolling-horizon optimization is employed to solve the model, which can adapt to dynamic changes in load conditions and provide rapid solutions. In this paper, the effectiveness of the proposed method is verified and the loads exceeding the capacity of the transformer are successfully shifted to other times and the charging needs of the users participating in the V2G program are met.
The dynamic characteristics of the power system are becoming more and more complex, and the difficulty of operation control is increasing. Preventive control is the main means of power system transient stability contr...
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The dynamic characteristics of the power system are becoming more and more complex, and the difficulty of operation control is increasing. Preventive control is the main means of power system transient stability control. This paper proposes a stacking ensemble learning-driven power system transient stability preventive controloptimization method. Firstly, a transient stability assessment model based on Stacking Ensemble Deep Belief Nets (SEDBN) network is established in this research. The performance of weak classifiers is improved by SEDBN's multi-layer ensemble structure, and the created transient stability estimator can extract diverse features and has better robustness and generalization abilities. Secondly, the trained transient stability estimator is integrated into the Aptenodytes Forsteri optimization (AFO) algorithm as a "transient stability constraint discriminator". Finally, with the goal of minimizing the cost of preventive control, an optimization algorithm for the preventive control of power system transient stability driven by SEDBN is established. Simulation results on IEEE 39-bus systems show that the proposed method can achieve highly efficient control solutions. (c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under theCCBYlicense (http://***/licenses/by/4.0/).
This paper presents a novel microgrid model for EV charging stations, primarily powered by renewable energy sources such as solar photovoltaics (PV) and wind. The proposed model employs an advanced control algorithm t...
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An innovative three-dimensional cooperative guidance law for striking stationary target, based on proximal policy optimization (PPO), is introduced. This novel guidance law directly correlates engagement state informa...
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ISBN:
(纸本)9798350373707;9798350373691
An innovative three-dimensional cooperative guidance law for striking stationary target, based on proximal policy optimization (PPO), is introduced. This novel guidance law directly correlates engagement state information with the navigation ratio of proportional navigation guidance (PNG). Initially, the cooperative homing guidance problem is transformed into a Markov decision process, and the reward function incorporates considerations for the zero-effort-miss (ZEM) and the consensus error of time-to-go. Subsequently, the cooperative homing guidance problem is transposed into the framework of reinforcement learning (RL). Ultimately, the effectiveness of the cooperative homing guidance solution is validated through numerical simulations, encompassing agent model training and Monte Carlo cases.
Based on the Internet of Things (IoT) the heterogeneous wireless sensor networks (HWSN) have gained popularity and are crucial for developing a variety of human-centered applications. The researchers have proposed a n...
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