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Low Carbon-Economic Dispatch of Integrated Energy Systems Considering CHP-CCS-P2GH2 Joint Operation Under Carbon-Energy Market

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Low Carbon-Economic Dispatch of Integrated Energy Systems Co...

IEEE Student Conference on Electric Machines and Systems

作者： Haimin Xie Hui Liu Jinshuo Su Ni Wang School of Electrical Engineering Guangxi University Nanning China Department of Electrical and Computer Engineering State Key Laboratory of Internet of Things for Smart City University of Macau Macao China

To fully exploit the carbon reduction capacity of the integrated energy system (IES), a low-carbon economic dispatching model of the IES considering combined heating and power and carbon capture system (CHP-CCS) and a refined power-to-gas (P2G) with two-staged heating recycling (CHP-CCS-P2GH 2 ) under the coupling of carbon and energy market is proposed. Firstly, a flexible joint operation model of CHP-CCS and a refined two-staged P2G model are constructed. Then, the carbon trading mechanism is introduced to construct a stepped carbon trading cost model to restrict emissions. Numerical results show that the proposed model has advantages in reduce operation cost and carbon emissions, compared with existing works.

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Implicit Neural Compression of Point Clouds

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arXiv 2024年

作者： Ruan, Hongning Shao, Yulin Yang, Qianqian Zhao, Liang Zhang, Zhaoyang Niyato, Dusit Department of Information Science and Electronic Engineering Zhejiang University China State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau China School of Computer Science and Engineering Nanyang Technological University Singapore

Point clouds have gained prominence in numerous applications due to their ability to accurately depict 3D objects and scenes. However, compressing unstructured, high-precision point cloud data effectively remains a significant challenge. In this paper, we propose NeRC3, a novel point cloud compression framework leveraging implicit neural representations to handle both geometry and attributes. Our approach employs two coordinate-based neural networks to implicitly represent a voxelized point cloud: the first determines the occupancy status of a voxel, while the second predicts the attributes of occupied voxels. By feeding voxel coordinates into these networks, the receiver can efficiently reconstructs the original point cloud’s geometry and attributes. The neural network parameters are quantized and compressed alongside auxiliary information required for reconstruction. Additionally, we extend our method to dynamic point cloud compression with techniques to reduce temporal redundancy, including a 4D spatial-temporal representation termed 4D-NeRC3. Experimental results validate the effectiveness of our approach: for static point clouds, NeRC3 outperforms octree-based methods in the latest G-PCC standard. For dynamic point clouds, 4D-NeRC3 demonstrates superior geometry compression compared to state-of-the-art G-PCC and V-PCC standards and achieves competitive results for joint geometry and attribute compression. Copyright © 2024, The Authors. All rights reserved.

关键词： Spatio-temporal data

Stackelberg Game Based Optimal Multi-energy Management for Commercial Building Operators 4

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Stackelberg Game Based Optimal Multi-energy Management for C...

4th International Conference on Energy, electrical and Power engineering, CEEPE 2021

作者： Zhao, Yinbo Gao, Hongjun Li, Haibo Zhang, Hongcai Lao, Keng Weng Sichuan University College of Electrical Engineering Chengdu China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering China

ISBN: (纸本)9781665433631

Park integrated energy system is an important direction of future energy system development. In this context, taking the commercial park as the object, a multi-energy management optimization decision model for commercial building operators is proposed. This paper constructs a master-slave game(Stackelberg game) decision-making model between commercial park operator(as master) and commercial building operators(as slaves) considering the electricity substitution and integrated demand response. By optimizing the management of internal energy consumption, building operators can use their responsive resources as bargaining chips to urge park operator to reduce energy prices in the game, so as to improve their own interests while promoting wind power consumption in the park. Finally, a case analysis is given to verify the effectiveness of the proposed model in promoting local wind power consumption and improving the benefits of both the master and the slaves. © 2021 IEEE.

关键词： Electric power utilization

Frequency-Constrained Dispatching Method for an Integrated Electricity-Heat Microgrid with Synergic Primary Frequency Regulation Resources

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Frequency-Constrained Dispatching Method for an Integrated E...

IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)

作者： Hui Li Hongcai Zhang Ji Zhang Chi Kong Wong State Key Laboratory of Internet of Things for Smart City University of Macau Macao China Department of Electrical and Computer Engineering University of Macau Macao China

This paper proposes a frequency-constrained dispatching method for integrated electricity-heat microgrids (IEHMs). It employs synergic primary frequency regulation (PFR) resources to arrest the frequency drops. First, the frequency security problem in IEHMs is described. Combined heat and power (CHP) unit and battery storage coordinate to PFR provision. The frequency nadir in the dynamic frequency evolution is modeled as second-order cone constraints. The total PFR provision is restricted considering quasi-steady-state frequency. Second, a frequency-constrained dispatching model for IEHMs is presented based on the frequency security constraints. PFR reserve capacities of CHP unit and battery storage are determined to secure frequency security and reduce the PFR reserve costs. Wind power uncertainty is described by a distributionally robust chance constraint. Finally, the effectiveness of the proposed method on the frequency security and economy is validated in case studies. The impact of heat load on PFR in IEHMs is also revealed by numerical tests.

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Process-and-Forward: Deep Joint Source-Channel Coding Over Cooperative Relay Networks

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arXiv 2024年

作者： Bian, Chenghong Shao, Yulin Wu, Haotian Ozfatura, Emre Gündüz, Deniz The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau China

We introduce deep joint source-channel coding (DeepJSCC) schemes for image transmission over cooperative relay channels. The relay either amplifies-and-forwards its received signal, called DeepJSCC-AF, or leverages neural networks to extract relevant features from its received signal, called DeepJSCC-PF (Process-and-Forward). We consider both half- and full-duplex relays, and propose a novel transformer-based model at the relay. For a half-duplex relay, it is shown that the proposed scheme learns to generate correlated signals at the relay and source to obtain beamforming gains. In the full-duplex case, we introduce a novel block-based transmission strategy, in which the source transmits in blocks, and the relay updates its knowledge about the input signal after each block and generates its own signal. To enhance practicality, a single transformer-based model is used at the relay at each block, together with an adaptive transmission module, which allows the model to seamlessly adapt to different channel qualities and the transmission powers. Simulation results demonstrate the superior performance of DeepJSCC-PF compared to the state-of-the-art BPG image compression algorithm operating at the maximum achievable rate of conventional decode-and-forward and compress-and-forward protocols, in both half- and full-duplex relay scenarios over AWGN and Rayleigh fading channels. © 2024, CC0.

关键词： Rayleigh fading

Variable-Length Feedback Codes via Deep Learning

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arXiv 2024年

作者： Lai, Wenwei Shao, Yulin Ding, Yu Gündüz, Deniz State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau China Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom

Variable-length feedback coding has the potential to significantly enhance communication reliability in finite block length scenarios by adapting coding strategies based on real-time receiver feedback. Designing such codes, however, is challenging. While deep learning (DL) has been employed to design sophisticated feedback codes, existing DL-aided feedback codes are predominantly fixed-length and suffer performance degradation in the high code rate regime, limiting their adaptability and efficiency. This paper introduces deep variable-length feedback (DeepVLF) code, a novel DL-aided variable-length feedback coding scheme. By segmenting messages into multiple bit groups and employing a threshold-based decoding mechanism for independent decoding of each bit group across successive communication rounds, DeepVLF outperforms existing DL-based feedback codes and establishes a new benchmark in feedback channel coding. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel coding

A Deep Joint Source-Channel Coding Scheme for Hybrid Mobile Multi-hop Networks

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arXiv 2024年

作者： Bian, Chenghong Shao, Yulin Gündüz, Deniz The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau China

Efficient data transmission across mobile multi-hop networks that connect edge devices to core servers presents significant challenges, particularly due to the variability in link qualities between wireless and wired segments. This variability necessitates a robust transmission scheme that transcends the limitations of existing deep joint source-channel coding (DeepJSCC) strategies, which often struggle at the intersection of analog and digital methods. Addressing this need, this paper introduces a novel hybrid DeepJSCC framework, h-DJSCC, tailored for effective image transmission from edge devices through a network architecture that includes initial wireless transmission followed by multiple wired hops. Our approach harnesses the strengths of DeepJSCC for the initial, variable-quality wireless link to avoid the cliff effect inherent in purely digital schemes. For the subsequent wired hops, which feature more stable and high-capacity connections, we implement digital compression and forwarding techniques to prevent noise accumulation. This dual-mode strategy is adaptable even in scenarios with limited knowledge of the image distribution, enhancing the framework’s robustness and utility. Extensive numerical simulations demonstrate that our hybrid solution outperforms traditional fully digital approaches by effectively managing transitions between different network segments and optimizing for variable signal-to-noise ratios (SNRs). We also introduce a fully adaptive h-DJSCC architecture with both SNR-adaptive (SA) and rate-adaptive (RA) modules capable of adjusting to different network conditions and achieving diverse rate-distortion objectives, thereby reducing the memory requirements on network nodes. © 2024, CC0.

关键词： Channel coding

Point Cloud Compression with Implicit Neural Representations: A Unified Framework

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Point Cloud Compression with Implicit Neural Representations...

IEEE International Conference on Communications in China (ICCC)

作者： Hongning Ruan Yulin Shao Qianqian Yang Liang Zhao Dusit Niyato Department of Information Science and Electronic Engineering Zhejiang University Department of Electrical and Computer Engineering State Key Laboratory of Internet of Things for Smart City University of Macau S.A.R Macau School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Point clouds have become increasingly vital across various applications thanks to their ability to realistically depict 3D objects and scenes. Nevertheless, effectively compressing unstructured, high-precision point cloud data remains a significant challenge. In this paper, we present a pioneering point cloud compression framework capable of handling both geometry and attribute components. Unlike traditional approaches and existing learning-based methods, our framework utilizes two coordinatebased neural networks to implicitly represent a voxelized point cloud. The first network generates the occupancy status of a voxel, while the second network determines the attributes of an occupied voxel. To tackle an immense number of voxels within the volumetric space, we partition the space into smaller cubes and focus solely on voxels within non-empty cubes. By feeding the coordinates of these voxels into the respective networks, we reconstruct the geometry and attribute components of the original point cloud. The neural network parameters are further quantized and compressed. Experimental results underscore the superior performance of our proposed method compared to the octree-based approach employed in the latest G-PCC standards. Moreover, our method exhibits high universality when contrasted with existing learning-based techniques.

关键词： Point cloud compression Geometry Learning systems Three-dimensional displays Quantization (signal) Neural networks Rate-distortion

Using Deep Reinforcement Learning in Optimal Energy Management for Residential House Aggregators with Uncertain User Behaviors

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Using Deep Reinforcement Learning in Optimal Energy Manageme...

IEEE Student Conference on Electric Machines and Systems

作者： Yujun Lin Linfang Yan Hongxun Hui Yin Chen Xia Chen Jinyu Wen State Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China State Grid (Suzhou) City & Energy Research Institute Suzhou China Department of Electrical and Computer Engineering State Key Laboratory of Internet of Things for Smart City University of Macau Macau China Department of Electronic and Electrical Engineering University of Strathclyde Glasgow U.K.

ISBN: (数字)9798350366945

ISBN: (纸本)9798350366952

In this study, the home energy management problem, which can be regarded as a high-dimensional optimization problem, for numerous residential houses, is addressed. The concept of the aggregator is utilized to reduce the state and action space and to handle the high dimensionality. A two-stage deep reinforcement learning (DRL)-based approach is proposed for the aggregators to track the schedule from a superior grid and guarantee the operation constraints. In the first stage, a DRL control agent is set to learn the optimal scheduling strategy interacting with the environment based on the soft-actor-critic framework and generate the aggregate control actions. In the second stage, the aggregate control actions are disaggregated to individual appliances considering the users' behaviors. The uncertainty of an electric vehicle's charging demand is quantitatively expressed based on the driver's experience. An aggregate anxiety concept is introduced to characterize the driver's anxiety on the electric vehicle's range and uncertain events. Finally, simulations are conducted to verify the effectiveness of the proposed approach under dynamic user behaviors, and comparisons show the superiority of the proposed approach over other benchmark methods.

关键词： Schedules Uncertainty Aggregates Anxiety disorders Optimal scheduling Deep reinforcement learning Electric vehicle charging Temperature control Vehicle dynamics Resistance heating