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8th International Conference on Power and Renewable Energy, ICPRE 2023

作者： Xu, Tingting Fang, Xiaohan Fan, Yuan Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Hefei230601 China

ISBN: (纸本)9798350328813

Multi-microgrid (MMG) systems offer notable advantages in terms of power sharing and mutual support when compared to individual microgrids (MGs). This paper presents a based on distributed event-triggered consensus strategy for optimizing the economic dispatch of multiple microgrids. The incorporation of the event-triggered mechanism into the multi-microgrid framework significantly reduces unnecessary communication among microgrids in contrast to the conventional time-triggered approach. For the multi-microgrid setup, a two-layer control strategy consisting of inter-microgrid and intra-microgrid layers is employed to achieve coherence by regulating the micro-incremental rates of distributed generation (DG) units. Leaderless event-triggered consistency is utilized for inter-microgrid coordination, while leader event-triggered consistency is implemented for intra-microgrid coordination, ultimately realizing the economic objective of minimizing the operational costs of the multi-microgrid system. Simulation results validate the efficacy of introducing the event-triggered mechanism and its impact on the economic dispatch of the multi-microgrid system. © 2023 IEEE.

关键词： Consensus Event-triggered Micro-incremental rates Multi-microgrid

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Optimal Dispatch Strategy of Electric Vehicles under Coupled Transportation Network and Power Grid 8

Optimal Dispatch Strategy of Electric Vehicles under Coupled...

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8th International Conference on Power and Renewable Energy, ICPRE 2023

作者： Luo, Keming Fang, Xiaohan Fan, Yuan Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Hefei230601 China

ISBN: (纸本)9798350328813

This paper addresses the complex nature of electric vehicles (EVs) as both transportation means and mobile loads, and proposes an optimal dispatching scheduling strategy for EVs in the coupled transportation network and power grid. Firstly, a dynamic traffic network model is established by utilizing a vast amount of real-world traffic data, including Origin-Destination (OD) information, battery states, and departure times. A graph theory-based analysis is then employed to determine the optimal paths for EVs in the traffic network. Secondly, the charging behavior of EVs is integrated with the power grid, and charge-discharge scheduling is conducted for charging stations to optimize the power grid load curve while meeting the charging demands of EVs. Additionally, adaptive electricity prices are formulated based on the power grid's load generation through a dynamic pricing mechanism, further optimizing the charging strategy for EVs. Lastly, the impact of EV charging on the power grid load is analyzed by contrasting ordered and unordered charging and discharging modes. The research results demonstrate that the proposed optimal charging scheduling strategy for EVs in traffic-aware power grids can significantly reduce peak-valley differences in power grid load, enhance the economic and operational stability of the power grid, and effectively meet the charging demands of EV users. These findings provide valuable insights and promising potential for practical applications and widespread adoption. © 2023 IEEE.

关键词： Charging Dispatch Coupled Transportation Network and Power Grid Dynamic Pricing Mechanism Electric Vehicles (EVs)

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Stackelberg Game Model for Optimal Dispatching of Electricity Consumption for Community Microgrid Considering Demand Response Mechanism 8

Stackelberg Game Model for Optimal Dispatching of Electricit...

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8th International Conference on Power and Renewable Energy, ICPRE 2023

作者： Tan, Di Fang, Xiaohan Fan, Yuan Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Hefei230601 China

ISBN: (纸本)9798350328813

Community microgrid is a kind of new power system that acts as an intermediary between community consumers and the grid, playing an important role in realising distributed autonomy and improving the power supply level. To achieve optimal power dispatch in community microgrid, this paper establishes a Stackelberg game model with the community microgrid operator (CMO) as the leader and the consumers aggregator as the follower, and combines the internal pricing method with the demand response mechanism. The paper uses a combination of genetic algorithm and solver to solve the Stackelberg game model to maximise the interests of all parties. Simulation results show that the model can secure the interests of each game subject, balance power supply and demand, and promote peak shaving and valley filling of electrical loads. © 2023 IEEE.

关键词： Community microgrid Demand response mechanism Genetic algorithm Internal prices Stackelberg game model

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An Optimization Method Based on Deep Learning for Electromagnetic Inverse Scattering Problems

An Optimization Method Based on Deep Learning for Electromag...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： Hu, Wangchao Xie, Guoda Dai, Jinpeng Hou, Guilin Li, Xiang Huang, Zhixiang Anhui University The Key Laboratory of Intelligent Computing and Signal Processing Hefei China Anhui Province Key Laboratory of Target Recognition and Feature Extraction Education of Anhui Province Luan237000 China Ministry of Education The Key Laboratory of Intelligent Computing and Signal Processing Hefei China Anhui Province Anhui University Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China Anhui University Hefei China

ISBN: (纸本)9798350355581

In this paper, a deep learning optimization method combining U-net model and cycle generative adversarial network (Cycle-GAN) is proposed to efficiently solve the electromagnetic inverse scattering (EMIS) problems. Firstly, a U-net model is used to obtain a rough preliminary reconstruction. Through down-sampling and up-sampling processes, the basic features of the object are extracted and reconstructed from the measured scattered field, providing a coarse dielectric parameter distribution. Then, a Cycle-GAN model further refines the output of the U-net. The Cycle-GAN model, through its adversarial training mechanism involving a generator and a discriminator, enhances the details of the preliminary reconstruction, resulting in a finely detailed dielectric parameter distribution his method improves the problem of rough results obtained by using U-net method alone, and can effectively solve and optimize EMIS problems. © 2024 IEEE.

关键词： Generative adversarial networks

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Optimization Strategies for Hydrogen Mixing Scheduling in Natural Gas Networks in Integrated Energy Systems 8

Optimization Strategies for Hydrogen Mixing Scheduling in Na...

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8th International Conference on Power and Renewable Energy, ICPRE 2023

作者： Zhang, Zixun Fang, Xiaohan Fan, Yuan Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Hefei230601 China

ISBN: (纸本)9798350328813

Developing a clean and efficient Integrated Energy System (IES) is critical to optimizing the use of renewable energy sources and harnessing new forms of energy to achieve the country's 2030 carbon peak and 2060 carbon neutrality targets. Existing natural gas pipelines in China facilitate the transport of hydrogen blended into the natural gas network and offer the possibility of storing hydrogen blended into natural gas pipelines. This paper proposes an optimal integrated energy dispatch strategy based on electricity-gas-hydrogen-heat-cooling demand, taking into account carbon emissions and economic efficiency, where renewable energy is no longer connected to the grid or stored in batteries, but converted to hydrogen mixed into the natural gas grid for transport and storage, with the economic objective of minimizing the total IES operating cost, the wind and light abandonment cost and the carbon emission cost. The CPLEX solver is used to solve the problem, and the feasibility of the proposed model is verified by simulation results, which promotes the consumption of renewable and clean energy. © 2023 IEEE.

关键词： hybrid hydrogen transportation hydrogen energy Integrated energy systems natural gas pipeline network

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An Empirical Study on Neural Networks Pruning: Trimming for Reducing Memory or Workload 5

An Empirical Study on Neural Networks Pruning: Trimming for ...

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5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Xiao, Kaiwen Cai, Xiaodong Xu, Ke School of Artificial Intelligence Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China

ISBN: (纸本)9798350393521

Most of existing studies on neural network pruning only consider memory-based pruning strategies. However pruning for computational workload is often more important in hardware deployments due to a greater focus on model computation reductions. In addition, most pruning schemes restore model accuracy during pruning at the expense of adding hyperparameters, extending training time and training complexity. This work proposes a statistical-based globally soft iterative pruning scheme. With little extra calculation, an extremely sparse model can be obtained without additional hyperparameters and extended training time. Moreover, this work proposes the concept of computational intensity to balance model memory and computational workload during pruning. Focusing on memory orientated pruning, we can achieve 303×, 100× and 25× parameter compression on LeNet-5 (MNIST), VGG (CIFAR-10) and AlexNet (ImageNet) models, respectively. In particular, combined with cluster quantization, the LeNet-5 model parameters can be compressed to 3232×. Focusing on workload orientated pruning, we can reduce the computation by 7}.6× on the AlexNet model, without accuracy loss, significantly higher than prior work. In addition, in order to verify the versatility of the pruning method, we also migrate the pruning task to the object detection and implement 10× parameter compression and 2.8× computation compression for YOLOv2 with reduced mAP within 1%. © 2023 IEEE.

关键词： Iterative methods

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Real-time pricing method through market auction based on multi-agent reinforcement learning 8

Real-time pricing method through market auction based on mul...

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8th International Conference on Power and Renewable Energy, ICPRE 2023

作者： Hong, Peng He, Shuping Fang, Xiaohan Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Hefei230601 China

ISBN: (纸本)9798350328813

With the development of distributed energy systems, pricing different energy sources in microgrids has become a significant challenge. To solve this problem, this paper proposes an real-time pricing method through market auction based on multi-agent reinforcement learning. The energy supply side provides different energy production and quotations, while consumers on the energy demand side freely quote and purchase the required energy in the energy market based on their own needs. The final transaction price of different energy sources is determined by the quotes of the buyer and seller who have reached the transaction. The simulation results indicate that the proposed pricing method have a good performance. © 2023 IEEE.

关键词： Market auction Multi-agent reinforcement learning Real-time pricing

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APFA:Ameliorated Pathfinder Algorithm for Engineering Applications

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Journal of Bionic Engineering 2024年第3期21卷 1592-1616页

作者： keyu Zhong Fen Xiao Xieping Gao Key Laboratory of Computing and Stochastic Mathematics of Ministry of Education Hunan Normal UniversityChangsha410081China School of Mathematics and Statistics Hunan Normal UniversityChangsha410081China School of Computer Science Xiangtan UniversityXiangtan411105China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal UniversityChangsha410081China

Pathfinder algorithm(PFA)is a swarm intelligent optimization algorithm inspired by the collective activity behavior of swarm animals,imitating the leader in the population to guide followers in finding the best food *** algorithm has the characteristics of a simple structure and high ***,PFA faces challenges such as insufficient population diversity and susceptibility to local optima due to its inability to effectively balance the exploration and exploitation *** paper proposes an Ameliorated Pathfinder Algorithm called APFA to solve complex engineering optimization ***,a guidance mechanism based on multiple elite individuals is presented to enhance the global search capability of the ***,to improve the exploration efficiency of the algorithm,the Logistic chaos mapping is introduced to help the algorithm find more high-quality potential solutions while avoiding the worst ***,a comprehensive following strategy is designed to avoid the algorithm falling into local optima and further improve the convergence *** three strategies achieve an effective balance between exploration and exploitation overall,thus improving the optimization performance of the *** performance evaluation,APFA is validated by the CEC2022 benchmark test set and five engineering optimization problems,and compared with the state-of-the-art metaheuristic *** numerical experimental results demonstrated the superiority of APFA.

关键词： Pathfinder algorithm Swarm intelligent Metaheuristic Engineering problems

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An Efficient Multi-objective Approach Based on Golden Jackal Search for Dynamic Economic Emission Dispatch

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Journal of Bionic Engineering 2024年第3期21卷 1541-1566页

Dynamic Economic Emission Dispatch(DEED)aims to optimize control over fuel cost and pollution emission,two conflicting objectives,by scheduling the output power of various units at specific *** many methods well-performed on the DEED problem,most of them fail to achieve expected results in practice due to a lack of effective trade-off mechanisms between the convergence and diversity of non-dominated optimal dispatching *** address this issue,a new multi-objective solver called Multi-Objective Golden Jackal Optimization(MOGJO)algorithm is proposed to cope with the DEED *** proposed algorithm first stores non-dominated optimal solutions found so far into an ***,it chooses the best dispatching solution from the archive as the leader through a selection mechanism designed based on elite selection strategy and Euclidean distance index *** mechanism can guide the algorithm to search for better dispatching solutions in the direction of reducing fuel costs and pollutant ***,the basic golden jackal optimization algorithm has the drawback of insufficient search,which hinders its ability to effectively discover more Pareto *** this end,a non-linear control parameter based on the cosine function is introduced to enhance global exploration of the dispatching space,thus improving the efficiency of finding the optimal dispatching *** proposed MOGJO is evaluated on the latest CEC benchmark test functions,and its superiority over the state-of-the-art multi-objective optimizers is highlighted by performance ***,empirical results on 5-unit,10-unit,IEEE 30-bus,and 30-unit systems show that the MOGJO can provide competitive compromise scheduling solutions compared to published DEED ***,in the analysis of the Pareto dominance relationship and the Euclidean distance index,the optimal dispatching solutions provided by MOGJO are the closest to the ideal solutions for minimizing fuel costs

关键词： Dynamic economic emission dispatch Multi-objective optimization Golden jackal Euclidean distance index

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An ultra-wideband high-isolation dual-port MIMO antenna design

An ultra-wideband high-isolation dual-port MIMO antenna desi...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： Cao, Shuangqing Li, Minquan Zi, Zhenfa Li, Zhonghui Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei230601 China Hefei Normal University School of Physics and Materials Engineering Hefei230601 China

ISBN: (纸本)9798350355581

In this paper, an antenna for ultra-wideband multiple-input and multiple-output (UWB-MIMO) is designed. The design comprises two hexagonal monopole antennas. In order to achieve ultra-wideband characteristics and mitigate the coupling, a stepped microstrip line structure for impedance matching and T-shaped defective ground structure are employed, which optimizes the broadband characteristics and achieves high isolation characteristics. The antenna achieves a wide impedance bandwidth of 3.5 GHz to 13.9 GHz (relative bandwidth of 119%). Furthermore, the isolation between the two feed ports of the antenna exceeds 18 dB, the envelope correlation coefficient (ECC) is less than 0.012, and it exhibits favorable radiation characteristics. © 2024 IEEE.

关键词： Bandwidth

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