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Machine Learning for Cyber-Physical Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 6379

Special Issue Editors


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Guest Editor
School of Automation, Central South University, Changsha 410083, China
Interests: smart grid; time-delay systems; deep learning; advanced control algorithm
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Multienergy microgrids play an important role in improving the comprehensive utilization rate of multienergy on the user side. With the wide interconnection of source–storage–load equipment at the multienergy microgrid level through wired/wireless communication networks, multienergy microgrids have gradually evolved into highly coupled cyber–physical systems, and traditional operation as well as control methods are difficult to apply. This Special Issue of Energies aims at addressing the top challenges in the development of energy systems, including electric power systems, heating and cooling systems, and gas transportation systems. Special attention will be given to the mathematical modeling and control of cyber–physical systems.

Original submissions focusing on theoretical and practical issues related to the theory and applications of machine learning, including novel optimization and operations research methods and their applications, design techniques and methodologies, reliability analyses, and practical implementation aspects, are welcome.

The Special Issue will include, but is not limited to, the following:

  • Mathematical modeling and control of cyber–physical systems;
  • Energy systems flexibility, efficiency, and power quality;
  • Coupling interaction mechanisms and the fusion interaction architecture of multiple-information flow/energy flow;
  • Situation perception method of the source–storage stability;
  • Distributed energy storage cooperative control and frequency–voltage flexible control;
  • Data-driven energy management strategies and unit commitment problem solvers.

Prof. Dr. Denis N. Sidorov
Prof. Dr. Fang Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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Research

23 pages, 7032 KiB  
Article
Control Strategy for Building Air Conditioning Cluster Loads Participating in Demand Response Based on Cyber-Physical System
by Xiaoling Yuan, Hao Cao, Zheng Chen, Jieyan Xu and Haoming Liu
Energies 2024, 17(6), 1291; https://doi.org/10.3390/en17061291 - 7 Mar 2024
Viewed by 798
Abstract
In recent years, with rising urbanization and ongoing adjustments in industrial structures, there has been a growing dependence on public buildings. The load of public buildings gradually becomes the main component of the peak load in summer, among which the load of air [...] Read more.
In recent years, with rising urbanization and ongoing adjustments in industrial structures, there has been a growing dependence on public buildings. The load of public buildings gradually becomes the main component of the peak load in summer, among which the load of air conditioning is particularly prominent. To clarify the key problems and solutions to these challenges, this study proposes a multi-objective optimization control strategy for building air conditioning cluster participation in demand response based on Cyber-Physical System (CPS) architecture. In a three-layer typical CPS architecture, the unit level of the CPS achieves dynamic information perception of air conditioning clusters through smart energy terminals. An air conditioning load model based on the multiple parameter types of air conditioning compressors is presented. Then, the system level of the CPS fuses multiple pieces of information through smart energy gateways, analyzing the potential for air conditioning clusters when they participate in demand response. The system of system level (SoS level) of the CPS deploys a multi-objective optimization control strategy which includes the uncertainty of the initial states of air conditioning clusters within the intelligent building energy management system. The optimal model takes into account the differences in the environmental conditions of each individual air conditioning unit within the cluster and sets different operating modes for each unit to achieve load reduction while maintaining temperatures within a comfortable range for the human body. The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm based on Pareto frontiers is applied to solve this optimization control strategy and to optimize the operational parameters of the air conditioning clusters. A comparative analysis is conducted with single-objective optimization results obtained using the traditional Particle Swarm Optimization (PSO) algorithm. The case study results indicate that the proposed multi-objective optimization control strategy can effectively improve the thermal comfort of the human body towards the controlled temperatures of air conditioning clusters while meeting the accuracy of demand response. In the solution phase, the highest temperature within the air conditioning clusters is 24 °C and the lowest temperature is 23 °C. Adopting the proposed multi-objective optimization control strategy, the highest temperature is 26 °C and the lowest temperature is 23.5 °C within the clusters and the accuracy of demand response is up to 92%. Compared to the traditional PSO algorithm, the MOPSO algorithm has advantages in convergence and optimization precision for solving the proposed multi-objective optimization control strategy. Full article
(This article belongs to the Special Issue Machine Learning for Cyber-Physical Energy Systems)
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25 pages, 22529 KiB  
Article
Study on Nonlinear Dynamic Characteristics of RV Reducer Transmission System
by Zhenhua Han, Hao Wang, Rirong Li, Wentao Shan, Yunda Zhao, Huachao Xu, Qifeng Tan, Chang Liu and Youwu Du
Energies 2024, 17(5), 1178; https://doi.org/10.3390/en17051178 - 1 Mar 2024
Viewed by 1036
Abstract
Rotate vector (RV) reducers have widely been used in high-performance precision drives for industrial robots. However, the current nonlinear dynamic studies on RV reducers are not extensive and require a deeper focus. To bridge this gap, a translational–torsional nonlinear dynamic model for an [...] Read more.
Rotate vector (RV) reducers have widely been used in high-performance precision drives for industrial robots. However, the current nonlinear dynamic studies on RV reducers are not extensive and require a deeper focus. To bridge this gap, a translational–torsional nonlinear dynamic model for an RV reducer transmission system is proposed. The gear backlash, time-varying mesh stiffness, and comprehensive meshing errors are taken into account in the model. The dimensionless vibration differential equations of the system were derived and solved numerically. By means of bifurcation diagrams, phase trajectories, Poincaré sections, and the power spectrum, the motion state of the system was studied with the bifurcation parameters’ variation, including excitation frequency and meshing damping. The results demonstrate that this system presents enriched nonlinear dynamic characteristics under different parameter combinations. The motion state of the system is more susceptible to change at lower frequencies. Increasing the meshing damping coefficient proves effective in suppressing the occurrence of chaos and reducing vibration amplitudes, significantly enhancing the stability of the transmission system. Full article
(This article belongs to the Special Issue Machine Learning for Cyber-Physical Energy Systems)
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27 pages, 5086 KiB  
Article
Multi-Objective Optimization Design of Cycloid-Pin Gears Based on RV Reducer Precision Transmission Performance
by Yunda Zhao, Zhenhua Han, Qifeng Tan, Wentao Shan, Rirong Li, Hao Wang and Youwu Du
Energies 2024, 17(3), 654; https://doi.org/10.3390/en17030654 - 30 Jan 2024
Viewed by 839
Abstract
This paper aims to realize multi-objective optimization of cycloid-pin gears to improve the positioning accuracy and load-carrying capacity of the rotary vector (RV) reducer, via the consideration of backlash, transmission error, and torsional stiffness. Initially, the analytical models of the RV transmission backlash [...] Read more.
This paper aims to realize multi-objective optimization of cycloid-pin gears to improve the positioning accuracy and load-carrying capacity of the rotary vector (RV) reducer, via the consideration of backlash, transmission error, and torsional stiffness. Initially, the analytical models of the RV transmission backlash and transmission error are developed by using both purely geometrical and equivalent model methods individually. Based on the generalized Hooke’s law, a torsion angle model is established to characterize the torsional stiffness of the system, utilizing methods such as Hertzian contact theory and bearing stiffness models. Subsequently, employing the Monte Carlo method, extremum method, and quality loss function, mapping objective functions for dimensional accuracy (tolerance) and transmission performance (backlash, transmission error, and torsional stiffness) are constructed. The geometry dimensions, dimensional accuracy, and modification of the cycloid-pin gear are considered as design variables to create a multi-objective optimization model. The improved Parallel Adaptive Genetic Algorithm using Deferential Evolution (PAGA-DE) is used for multi-objective solutions. Through example calculations, the impact of cycloid-pin gear parameters on transmission performance before and after optimization is determined. The reliability of backlash after optimization within 1.5′ reaches 99.99%, showing an increase of 8.24%. The reliability of transmission error within 1′ reaches 98.52%, demonstrating an increase of 1.35%. The torsional angle is reduced by 8.9% before optimization. The results indicate that the proposed multi-objective optimization design method for cycloid-pin gears can achieve the goal of improving the transmission performance of the RV reducer. Full article
(This article belongs to the Special Issue Machine Learning for Cyber-Physical Energy Systems)
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11 pages, 2446 KiB  
Article
Modeling Unpredictable Behavior of Energy Facilities to Ensure Reliable Operation in a Cyber-Physical System
by Ivan Postnikov, Ekaterina Samarkina, Andrey Penkovskii, Vladimir Kornev and Denis Sidorov
Energies 2023, 16(19), 6960; https://doi.org/10.3390/en16196960 - 5 Oct 2023
Cited by 1 | Viewed by 766
Abstract
This research focuses on exploring various techniques and models for simulating the random behavior of energy facilities or systems. These simulations are essential in identifying the likelihood of component failures within the studied facilities. By assessing the potential consequences of emergency scenarios, this [...] Read more.
This research focuses on exploring various techniques and models for simulating the random behavior of energy facilities or systems. These simulations are essential in identifying the likelihood of component failures within the studied facilities. By assessing the potential consequences of emergency scenarios, this analysis serves as a fundamental aspect of synthesizing and analyzing reliability in the cyber-physical system. Ultimately, the study aims to enhance the management and control of reliability and safety for these facilities. In this study, a unified heating source is considered as an energy facility (as part of district heating systems), for example, a combined heat and power plant. However, the developed methods and models have sufficient universality for their adaptation to other energy facilities without significant changes. The research methodology is based on the use of Markov random processes and laws of the probability theory. The basic model of the energy facilities is formulated for the conditions of the simplest events flow with appropriate assumptions and constraints, in particular, ordinary events and independence of events (failures and restorations). To take into account the non-ordinary events (failures) and dependences between some failures, corresponding modifications of the basic model are proposed. A computational experiment was carried out using the developed models, and graphical interpretations of the results are presented. The obtained results allow us to formulate some preliminary conclusions about the range of influence of the simulated factors on the reliability analysis of studied facilities and to outline conditions and areas of their admissible application. Full article
(This article belongs to the Special Issue Machine Learning for Cyber-Physical Energy Systems)
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12 pages, 4293 KiB  
Article
Multi-Scroll Attractor and Multi-Stable Dynamics of a Three-Dimensional Jerk System
by Fudong Li and Jingru Zeng
Energies 2023, 16(5), 2494; https://doi.org/10.3390/en16052494 - 6 Mar 2023
Cited by 6 | Viewed by 1508
Abstract
A multi-scroll attractor reflects the structural diversity of the dynamic system, and multi-stability behavior reflects its state diversity. Multi-scroll and multi-stability chaotic systems can produce complex random sequences, which have important application values in the field of data security. However, current works on [...] Read more.
A multi-scroll attractor reflects the structural diversity of the dynamic system, and multi-stability behavior reflects its state diversity. Multi-scroll and multi-stability chaotic systems can produce complex random sequences, which have important application values in the field of data security. However, current works on multi-scroll–multi-steady behavior have been carried out separately, rather than simultaneously. This paper considers a three-dimensional Jerk system with a sinusoidal nonlinear term. The basic dynamic behaviors, such as the stability of equilibrium points, bifurcation of parameters and initial values, phase diagrams, and basins of attraction, were analyzed. It was found that the system has infinite equilibrium points. Moreover, the system not only generates complex dynamics, such as single-scroll, double-scroll, and multi-scroll but also realizes the self-reproduction of these dynamic characteristics by controlling the initial value of the system. Therefore, by expanding the equilibrium point, the effective controls of the system’s structural diversity and state diversity are realized at the same time, having important theoretical significance and application value. Full article
(This article belongs to the Special Issue Machine Learning for Cyber-Physical Energy Systems)
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