For the security transmission and information controllability and confidentiality when trading with virtual power plants and distributed nodes, ensuring the security of data communication between users and virtual pow...
For the security transmission and information controllability and confidentiality when trading with virtual power plants and distributed nodes, ensuring the security of data communication between users and virtual power plants, as well as the difficulty of reliably correlating off-chain data and on-chain information and ensuring the credibility of data sources, this article proposes a virtual power plant management system and device based on blockchain and secure multi-party computation. Firstly, a hardware/software integrated terminal based on blockchain is developed to achieve terminal security certification and management, data collection and trusted computing, ensuring the authenticity and reliability of source data being uploaded to the ***, using a secure multi-party computation protocol consisting of “secret sharing + homomorphic encryption + Pedersen commitment” to ensure that all parties participating in aggregated transactions in the virtual power plant maintain data privacy and security when data is uploaded onto the blockchain. In order to encourage protocol participants to remain loyal, an incentive mechanism is designed. Based on the secure multi-party computation encryption algorithm, this effectively addresses information security and data reliability issues that may arise when virtual power plant participants engage in aggregated transactions, ensuring the safe and stable transmission of transaction data.
The global electricity landscape is undergoing a profound transformation, with an increasing demand for resilient and sustainable energy infrastructure. In this context, microgrids (MGs) have emerged as a promising so...
The global electricity landscape is undergoing a profound transformation, with an increasing demand for resilient and sustainable energy infrastructure. In this context, microgrids (MGs) have emerged as a promising solution, offering localized, decentralized energy generation and distribution. This research paper proposes a distributed energy management system for grid-connected hybrid AC-DC MGs, interconnected through a DC link. The work proposes a three-layer cloud fog-enabled energy management system of networked MGs which aims to minimize the energy cost by facilitating optimal energy utilization within each MG as well as among the connected MGs. The paper presents a fog-enabled comprehensive mathematical model of networked MGs to ensure fast data transmission and real-time decision-making within the system. K-mean clustering is used to segregate the load into three categories residential, commercial, and industrial each of which is primarily supplied by an individual MG. Python 3.10.12 programming has been employed for simulating the model, ensuring a realistic and adaptable approach to assess the suggested energy management system's efficacy and performance within the context of networked MGs. Simulation results demonstrate that the proposed model of networked MGs integrating fog computing and MILP optimization, enhances optimal energy allocation and utilization within and among MGs along with minimizing the operating cost of networked MGs effectively.
The modern power system advances to an inverter dominant system, due to the high penetration of Renewable Energy Source (RES) based distributed Generation (DG). The effective operation of the AC micro-grid can be achi...
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ISBN:
(纸本)9781665486644
The modern power system advances to an inverter dominant system, due to the high penetration of Renewable Energy Source (RES) based distributed Generation (DG). The effective operation of the AC micro-grid can be achieved by employing an efficient Cascaded $H -$bridge Multilevel Inverter (CHBMLI) integrated with RES across the Point of Common Coupling (PCC). In this paper, the grid-connected 3-phase, 7-level CHBMLI topology with Photo Voltaic-Battery Energy Storage System (PV-BESS) is presented. The bidirectional DC-DC Battery system is connected to solar PV for maintain the constant DC input voltage across CHBMLI bridges irrespective of irradiation ‘G' $(W/m^{2})$. To increase the utilization and the reliability of PV-Battery power effectively at the load end, it is integrated with the AC grid. In this proposed work, the performance analysis of voltage-current controller for the bidirectional and DQ-control technique under dynamic conditions of solar-PV, DG-MLI power, and load demand are examined. The advantage of the proposed PV-battery CHBMLI control is that whole capacity of the PV-battery system is utilized by enhancing reactive power support along with active power when solar insolation is low. The mathematical modelling of the system is presented and design of 3-kVA system is carried out. The simulations in MATLAB/ Simulink environment verified the design of the controller and effectiveness of the proposed system under dynamic conditions.
With the continuous development of science and technology, more and more energy has poured into human life and production activities. The energy supply chain is responsible for managing the entire life cycle of energy...
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With the continuous development of science and technology, more and more energy has poured into human life and production activities. The energy supply chain is responsible for managing the entire life cycle of energy, but there are still some problems in the current energy supply chain. On the one hand, energy data lacks an effective sharing mechanism, and on the other hand, the privacy of user data cannot be guaranteed. In addition, different entities in the current energy supply chain system manage their data, and there will be data barriers, so it is difficult to effectively supervise and track energy data. Based on the consortium blockchain, this paper proposes an energy management system to provide a credible data collaboration environment for each entity in the energy supply chain. Data encryption ensures the privacy of data, better realizes the sharing of energy data, and breaks the data barriers between participants. The system uses smart contracts to set a unified data operation specification, and at the same time designs an energy management mechanism that is friendly to regulators, so as to better realize the supervision of the whole process of energy supply. Experiments show that the proposed system is practical.
In order to effectively manage power demand, optimize energy usage, and integrate distributed renewable energy production, home energy management systems, or HEMSs, are essential. The primary aim is to optimize energy...
In order to effectively manage power demand, optimize energy usage, and integrate distributed renewable energy production, home energy management systems, or HEMSs, are essential. The primary aim is to optimize energy efficiency while maintaining customer comfort. Numerous factors, including as energy prices, meteorological conditions, load the profiles, and customer comfort levels, influence how HEMSs function. They greatly reduce the amount of power used in both personal and business smart networks as they become more and more common. This study performs a comprehensive analysis of the body of research on HEMS, including key ideas, configurations, and the supporting technologies that support their operation. Giving a thorough review of the status of HEMS innovation and its uses today is the aim. The study also discusses popular communication methods used in demand management applications and analyzes current advancements in HEMS computing. The study that follows provides readers with a comprehensive overview of current and upcoming developments in HEMS technology and solutions. Presenting a polished and original synthesis of the current material while preserving its integrity and excluding any needless elaboration is the aim.
The Go language (Go/Golang) has been attracting increasing attention from the industry over recent years due to its strong concurrency support and ease of deployment. This programming language encourages developers to...
The Go language (Go/Golang) has been attracting increasing attention from the industry over recent years due to its strong concurrency support and ease of deployment. This programming language encourages developers to use channel-based concurrency, which simplifies the development of concurrent programs. Unfortunately, it also introduces new concurrency problems that differ from those caused by the mechanism of shared memory concurrency. However, there are only few works that aim to detect such Go-specific concurrency issues. Even state-of-the-art testing tools will miss critical concurrent bugs that require fine-grained and effective interleaving exploration. This paper presents GoPie, a novel testing approach for detecting Go concurrency bugs through primitive-constrained interleaving exploration. GoPie utilizes execution histories to identify new interleavings instead of relying on exhaustive exploration or random scheduling. To evaluate its performance, we applied GoPie to existing benchmarks and large-scale open-source projects. Results show that GoPie can effectively explore concurrent interleavings and detect significantly more bugs in the benchmark. Furthermore, it uncovered 11 unique previously unknown concurrent bugs, and 9 of which have been confirmed.
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
This paper presents signal space constellations that minimize symbol-error probability in an uncoded communications system. We develop a modified projected gradient optimization method to solve for these constellation...
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This paper presents signal space constellations that minimize symbol-error probability in an uncoded communications system. We develop a modified projected gradient optimization method to solve for these constellations that scales efficiently, enabling the optimization of very large constellations. The optimized constellation shapes show significant improvements in symbol-error probability over a wide range of channel conditions compared with uniformly-spaced constellations. At high SNR, small deviations from uniform spacing can make substantial improvements in system performance. These results have promising applications to low-complexity systems with real-time data transmission requirements.
The emergence of the Internet of Things (IoT) has led to a remarkable increase in the volume of data generated at the network edge. In order to support real-time smart IoT applications, massive amounts of data generat...
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ISBN:
(纸本)9781450388160
The emergence of the Internet of Things (IoT) has led to a remarkable increase in the volume of data generated at the network edge. In order to support real-time smart IoT applications, massive amounts of data generated from edge devices need to be processed using methods such as deep neural networks (DNNs) with low latency. To improve application performance and minimize resource cost, enterprises have begun to adopt Edge computing, a computation paradigm that advocates processing input data locally at the network edge. However, as edge nodes are often resource-constrained, running data-intensive DNN inference tasks on each individual edge node often incurs high latency, which seriously limits the practicality and effectiveness of this model. In this paper, we study the problem of distributed execution of inference tasks on edge clusters for Convolutional Neural Networks (CNNs), one of the most prominent models of DNN. Unlike previous work, we present Fully Decomposable Spatial Partition (FDSP), which naturally supports resource heterogeneity and dynamicity in edge computing environments. We then present a compression technique that further reduces network communication overhead. Our system, called ADCNN, provides up to 2.8x speed up compared to state-of-the-art approaches, while achieving a competitive inference accuracy.
Large scale DNN training tasks are exceedingly compute-intensive and time-consuming, which are usually executed on highly-parallel platforms. Data and model parallelization is a common way to speed up the training pro...
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ISBN:
(纸本)9781728165974
Large scale DNN training tasks are exceedingly compute-intensive and time-consuming, which are usually executed on highly-parallel platforms. Data and model parallelization is a common way to speed up the training progress across devices. However, they tend to achieve sub-optimal performance due to the communication overheads and unbalanced load among servers. Recent emerging pipelining solutions mitigate the above issues, incorporating the advantages of data and model parallelism. In this paper, we make a step further towards optimizing the execution of pipelining. We introduce PipePar, a pipeline-parallel DNN training method that provides optimized execution strategies of layer-stacked DNNs. PipePar considers the entire tensor partition space of pipelining and explores potential hybrid parallel configurations of each stage in the pipeline. Additionally, we notice the network heterogeneity between different GPU servers and it is inevitable to transfer tensors with different bandwidths and latency. So, taking into account both computation and communication capacity of different GPU servers, PipePar is intended to find a elastic load distribution strategy at different levels. We evaluate PipePar with a set of real-world DNNs on 4 GPU servers. Our experimental results show that PipePar is able to find an efficient strategy that are up to 2.16x faster than state-of-the-art hybrid parallelization approaches.
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