Microgrids provide efficient means of incorporating renewable energy resources (RES) into the power network. the deployment of an energy management system into a microgrid is essential for achieving efficient utilizat...
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ISBN:
(纸本)9798350349009;9798350349016
Microgrids provide efficient means of incorporating renewable energy resources (RES) into the power network. the deployment of an energy management system into a microgrid is essential for achieving efficient utilization of resources and ensuring stable grid operation at a favorable cost. However, the inherent intermittent nature of consumer loads and RES introduces uncertainty, posing significant challenges for system design. this paper proposes a generation scheduling approach that can optimally manage energy resources in a microgrid in the presence of load and generation uncertainties. First, a data-driven machine learning algorithm is employed to forecast PV and wind generation as well as electrical power demand from weather data and actual dataset. Next, optimal unit commitment based on energy prices to minimize system costs is conducted using Mixed Integer Linear Programming (MILP). this approach provides optimal generation scheduling among PV and wind turbine generation systems as well as the required power from the utility grid. Simulation results for different case studies is carried out in order to demonstrate the performance of the proposed method for hourly RES and load profile forecast. Furthermore, results indicate that optimal generation scheduling can be effective in minimizing the operating cost under the worst-case of RES and load uncertainty.
the transition away from carbon-based energy sources poses several challenges for the operation of electricity distribution systems. Increasing shares of distributed energy resources (e.g. renewable energy generators,...
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ISBN:
(纸本)9781728162515
the transition away from carbon-based energy sources poses several challenges for the operation of electricity distribution systems. Increasing shares of distributed energy resources (e.g. renewable energy generators, electric vehicles) and internet-connected sensing and control devices (e.g. smart heating and cooling) require new tools to support accurate, data-driven decision making. Modelling the effect of such growing complexity in the electrical grid is possible in principle using state-of-the-art power-power flow m odels. I n p ractice, t he detailed information needed for these physical simulations may be unknown or prohibitively expensive to obtain. Hence, data-driven approaches to power systems modelling, including feedforward neural networks and auto-encoders, have been studied to leverage the increasing availability of sensor data, but have seen limited practical adoption due to lack of transparency and inefficiencies o n l arge-scale p roblems. O ur w ork a ddresses this gap by proposing a data- and knowledge-driven probabilistic graphical model for energy systems based on the framework of graph neural networks (GNNs). the model can explicitly factor in domain knowledge, in the form of grid topology or physics constraints, thus resulting in sparser architectures and much smaller parameters dimensionality when compared with traditional machine-learning models with similar accuracy. Results obtained from a real-world smart-grid demonstration project show how the GNN was used to inform grid congestion predictions and market bidding services for a distribution system operator participating in an energy flexibility market.
In this paper, for photomask-free lithography machines, table motion control, as an important module of lithography equipment, is equipped with wafers to complete the exposure motion. It can be seen that the table mot...
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the proceedings contain 284 papers. the topics discussed include: collision avoidance with artificial potential function for two-wheeled mobile robot tracking desired trajectory;key frame extraction of assembly proces...
ISBN:
(纸本)9781538670569
the proceedings contain 284 papers. the topics discussed include: collision avoidance with artificial potential function for two-wheeled mobile robot tracking desired trajectory;key frame extraction of assembly process based on deep learning;mobile robot localization using wireless sensor based landmark and particle filter;recovery method for missing sensor data in multi-sensor based walking recognition system;a semi-supervised learning method using deep conv-deconv network and robust-KSH for image retrieval;an open source framework based unmanned all-terrain vehicle(U-ATV) for wild patrol and surveillance;parameter self-turning fuzzy PID controller design for atomic force microscopy in Z-axis;distributed time-varying formation and optimization for uncertain Euler-Lagrange systems;and structure design and kinematics analysis of omni-directional mobile platform.
this study addresses the problem of data-driven modeling and tracking control for autonomous vehicles with unknown parameters. We use Koopman theory and deep neural networks to approximate vehicle dynamics, learning a...
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In this paper, a deep policy learning method based on Koopman operator networks is proposed for reinforcement learning tasks. Specifically, a deep neural network based on the Koopman operator is designed for nonlinear...
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In recent years, China has been actively constructing new-type power systems to optimize the nation-wide energy structure and improve energy efficiency. As a result, ultra-high voltage(UHV) transmission has become inc...
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ISBN:
(纸本)9798350349009;9798350349016
In recent years, China has been actively constructing new-type power systems to optimize the nation-wide energy structure and improve energy efficiency. As a result, ultra-high voltage(UHV) transmission has become increasingly popular, and the proportion of renewable energy in power systems has gradually increased. these phenomena have resulted in unprecedentedly complicated power grids with hybrid AC/DC interconnection structures, increased system uncertainty, and difficulty in maintaining a balance between power generation and load. In this context, system transient frequency stability is greatly challenged. To address this challenge, this paper proposes a method to model and analyze frequency stability characteristics of receiving-end power grids based on interpretable deep learning. First, the PSASP simulation software is employed to set up a series of representative operating conditions and transient faults to obtain sample data. Next, based on the simulated sample data, a residual convolutional neural network (ResNet) is utilized to predict the stability of frequency after a fault occurs in the power grid under the influence of AC-DC hybrid structure. Finally, the network prediction results are analyzed using the shapley additive explanations method (SHAP). the proposed method is verified with a realistic receiving-end system, which verifies its effectiveness and provides important guidance for follow-up frequency stability control measures.
Machine learning techniques and Python programming can be transformed to solve the shortcomings of traditional systemsthat rely a lot on historical data for credit risk assessments. An essential component of financia...
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this research reflects on the role of Machine learning (ML) in making small retail shop owners in India profitable, since they come up with inventory management problems, pricing issues and ways of engaging their cust...
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the transformative Internet-of-Vehicles (IoV) paradigm comes inadvertently with challenges which involve security vulnerabilities and privacy breaches. In this context, denial-of-service (DoS) attacks may perniciously...
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ISBN:
(纸本)9781665406949
the transformative Internet-of-Vehicles (IoV) paradigm comes inadvertently with challenges which involve security vulnerabilities and privacy breaches. In this context, denial-of-service (DoS) attacks may perniciously affect the normal operation of IoV systems by causing extensive periods of network unavailability where legitimate vehicles are prevented from accessing vehicular services. In this paper, we offer an in-depth vulnerability assessment of 5G-enabled IoV systems when DoS attack variants are launched at multiple network domains. We further evaluate the resilience of an IoV-tailored authentication mechanism against DoS attacks under various configurations. A data-driven detection scheme is also proposed to address DoS variants in the radio access network, which take the form of false data injection attacks on the exchanged vehicular information. Our performance assessment withthe aid of an open-source dataset reveals that the proposed scheme is able to accurately detect DoS traffic originated from malicious vehicles.
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