Erratum: Chen, B., et al. Risk Assessment for the Power Grid Dispatching Process Considering the Impact of Cyber Systems. Energies 2019, 12, 1084

Chen, Haoying; Zhang, Yiyi; Zhao, Junhui; Manla, Emad

doi:10.3390/en12112172

Cited by 2 publications

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“…Chen et al put forward a collaborative dispatching model of wind, water, and wind VPP based on the classic scene set and transformed the random optimization problem into a classic scene [9]. Saad et al compare several collaborative optimization algorithms of the VPP and simulate them with MATLAB [10].…”

Section: Related Workmentioning

confidence: 99%

Distributed Scheduling Strategy of Virtual Power Plant Using the Particle Swarm Optimization Neural Network under Blockchain Background

Chen

2022

Computational Intelligence and Neuroscience

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Large-scale and widely dispersed distributed energy resource (DER) can be gathered by a virtual power plant (VPP) in a given area, and its parameters can be combined into a single external operation profile. Each distributed energy source in the VPP has a complete backup of the critical information for the entire network because it is a node of blockchain. The distribution network can be accessed by DER freely and adaptable under the scientific management of the VPP, and it can offer the system high-reliability, high-quality, and high-security power services. An energy blockchain network model based on particle swarm optimization (PSO) to optimise the neural network is proposed in this paper as a solution to the issues with the current VPP models. This will enable distributed dispatching of the VPP and reasonable load distribution among units. According to the simulation results, this algorithm’s error is minimal and its accuracy can reach 94.98 percent. This model can more accurately capture demand-side real-time information, which benefits VPP’s stable scheduling with a welcoming environment and transparent information. It also enhances the system’s data security and storage security. This system can successfully address the issues of subject-to-subject mistrust and high information interaction costs in the VPP.

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Section: Related Workmentioning

confidence: 99%

Distributed Scheduling Strategy of Virtual Power Plant Using the Particle Swarm Optimization Neural Network under Blockchain Background

Chen

2022

Computational Intelligence and Neuroscience

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How does digital village construction influences carbon emission? The case of China

2022

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Taking 30 provinces in China from 2011 to 2020 as a research sample, this paper empirically tests the impact of digital village construction on carbon emissions. This study found that there is an "inverted U" curve relationship between digital rural construction and rural carbon emissions. Agricultural planting structure and agricultural technology efficiency are important ways for digital village construction to reduce agricultural carbon emissions. The study also found that the higher the level of economic development, the stronger the carbon emission reduction effect of digital village construction. In addition, there are also significant differences in the carbon emission reduction effect of digital village construction in regions with different environmental regulation intensities. Finally, in terms of the relationship between digital economic activities and carbon emission reduction, the research conclusions of this paper have important implications.

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Erratum: Chen, B., et al. Risk Assessment for the Power Grid Dispatching Process Considering the Impact of Cyber Systems. Energies 2019, 12, 1084

Abstract: The authors wish to make the following correction to this paper [...]

Cited by 2 publications

References 1 publication

Distributed Scheduling Strategy of Virtual Power Plant Using the Particle Swarm Optimization Neural Network under Blockchain Background

Distributed Scheduling Strategy of Virtual Power Plant Using the Particle Swarm Optimization Neural Network under Blockchain Background

How does digital village construction influences carbon emission? The case of China

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