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Recent Advances in Artificial Intelligence and Computational Methods in Energy Storage Systems and Other Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D: Energy Storage and Application".

Deadline for manuscript submissions: 28 May 2024 | Viewed by 4335

Special Issue Editors

Prof. Dr. Kai Wang

E-Mail Website
Guest Editor
College of Electrical Engineering, Qingdao University, Qingdao 266071, China
Interests: life prediction of new energy storage devices; energy storage device; storage of new energy; distributed microgrid
Special Issues, Collections and Topics in MDPI journals
Dr. Xiufeng Liu

E-Mail Website
Guest Editor
Department of Technology, Management and Economics, Technical University of Denmark, Lyngby, Denmark
Interests: building monitoring; smart meter data; data analytics; data management (in particular, data warehousing); big data; data mining; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yongcheng Zhang

E-Mail Website
Guest Editor
College of Physics, Qingdao University, Qingdao 266071, China
Interests: energy conversion; energy storage; high-performance piezoelectric ceramics; transparent electro-optical ceramics; optical communication; transparent piezoelectric ceramics; photoacoustic imaging
Dr. Licheng Wang

E-Mail Website
Guest Editor
College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
Interests: power system operation and control; renewable energy integration into distribution systems; distributed algorithms; deep reinforcement learning and its application in networked systems
Special Issues, Collections and Topics in MDPI journals
Dr. Saleem Riaz

E-Mail Website
Guest Editor
School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: iterative learning control; robust control; nonlinear dynamical control systems; optimal control theory; power Electronics; motor control; robotics control theory; power converters; renewable energy
Dr. Jinyan Song

E-Mail Website
Guest Editor
School of Information Engineering, Dalian Ocean University, Dalian 116023, China
Interests: energy storage componets; state assessment and life prediction of new energy storage devices; storage and conversion of new energy; echelon utilization of energy storage devices; application of new energy devices in smart ocean

Special Issue Information

Dear Colleagues,

In today's era of rapid economic development, the depletion of oil resources and environmental problems are becoming more and more serious. The need for sustainable development is becoming increasingly prominent. Therefore, clean and environmentally friendly new energy storage devices have attracted much attention. However, ensuring the safe and reliable operation of energy storage systems composed of new energy storage devices is an urgent problem that needs to be solved. In recent years, artificial intelligence and computational methods have been widely used for modeling and predictive analysis of various thermal/electrical energy systems based on energy storage with high accuracy. By employing these methods, the performance of the system can be predicted and the influencing factors and their relative importance can be found. Furthermore, models developed through computational methods and artificial intelligence can be used to optimize energy storage systems. This Special Issue aims to provide the latest advances in artificial intelligence and computational methods for predicting and optimizing energy storage devices and systems; additionally, it will be a platform that is fully within the scope of this journal where researchers can come together to discuss the latest research and to develop new ideas and research directions. This Special Issue focuses on the application of artificial intelligence and computational methods for predicting and optimizing energy storage devices and systems, which are essential for ensuring sustainable energy management in today’s era of rapid economic development. Energy storage devices and systems can store excess energy from renewable sources and release it when needed, but they pose significant challenges related to their safe and reliable operation. Artificial intelligence and computational methods can help us model and analyze various thermal/electrical energy systems based on energy storage with high accuracy and optimize their performance under various operating conditions. This Special Issue aims to provide the latest advances, discuss the current research challenges and future directions, and encourage continued research and innovation in this important area. We invite researchers from academia and industry to submit original research articles, review articles, case studies, or any other types of submissions that are relevant to this theme.

Potential topics include, but are not limited to:

  • Applications of data-driven methods in technical, economic, and environmental modeling of thermal/electrical energy storage systems;
  • Development of computational and data-driven methods for applying to thermal/electrical energy storage systems;
  • Performance comparison of various optimization or data-driven approaches in modeling of thermal/electrical energy storage systems;
  • Recently developed optimization approaches based on computational methods and artificial intelligence that are applicable in thermal/electrical energy storage systems;
  • Thermophysical modeling of materials applicable in different thermal/electrical energy storage systems;
  • Artificial intelligence-assisted or data-driven diagnostics for energy storage systems;
  • Utilization of artificial intelligence for predicting the performance and output of energy storage systems;
  • Life cycle assessment;
  • Artificial intelligence for renewable energies;
  • Renewable energy, wind, solar, fuel cells, and distributed generation within microgrids;
  • Computational intelligence and optimization;
  • Life cycle assessment, pricing, policies, and energy planning;
  • Artificial intelligence for industrial process optimization;
  • Optimization of industrial applications and energy systems;
  • Modeling, simulation, and data management;
  • Filtering algorithm and application of energy storage systems.

Prof. Dr. Kai Wang
Dr. Xiufeng Liu
Prof. Dr. Yongcheng Zhang
Dr. Licheng Wang
Dr. Saleem Riaz
Dr. Jinyan Song
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.

Keywords

  • energy storage system
  • electrical energy system
  • batteries
  • supercapacitor
  • optimization approaches artificial intelligence
  • machine learning
  • data-driven methods
  • renewable energy systems
  • state estimation

Published Papers (6 papers)

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Research

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16 pages, 5948 KiB  
Article
Economic Dispatch between Distribution Grids and Virtual Power Plants under Voltage Security Constraints
by Tiankai Yang, Jixiang Wang, Yongliang Liang, Chuan Xiang and Chao Wang
Energies 2024, 17(1), 117; https://doi.org/10.3390/en17010117 - 25 Dec 2023
Viewed by 633
Abstract
Due to the high penetration of virtual power plants (VPPs), the bi-directional power flow between VPPs and active distribution grids makes the grid operation complex. Without congestion management, the operation schedule only considers the economic benefits, and power flow constraints might be violated. [...] Read more.
Due to the high penetration of virtual power plants (VPPs), the bi-directional power flow between VPPs and active distribution grids makes the grid operation complex. Without congestion management, the operation schedule only considers the economic benefits, and power flow constraints might be violated. Hence, it is necessary to conduct power interaction within the operation constraints. This paper proposes a coordinated economic dispatch method under voltage security constraints. The linear expressions were derived by simplifying the AC power flow equations to reduce the computation complicity. Then, optimal economic dispatch models with voltage security constraints were established for the active distribution grid and VPPs, respectively. Meanwhile, the transacted power and clearing price were set as the communication variables, and a coordinated strategy was proposed for the overall optimal goal. The modified IEEE 33-node and PG&E-node distribution grids were utilized for the simulations, and the results affirmed the validity of the proposed method. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Computational Methods in Energy Storage Systems and Other Systems)
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17 pages, 3693 KiB  
Article
Risk Assessment of Power Supply Security Considering Optimal Load Shedding in Extreme Precipitation Scenarios
by Gang Zhou, Jianxun Shi, Bingjing Chen, Zhongyi Qi and Licheng Wang
Energies 2023, 16(18), 6660; https://doi.org/10.3390/en16186660 - 17 Sep 2023
Viewed by 834
Abstract
Extreme rainfall may induce flooding failures of electricity facilities, which poses power systems in a risk of power supply interruption. To quantitatively estimate the risk of power system operation under extreme rainfall, a multi-scenario stochastic risk assessment method was proposed. First, a scenario [...] Read more.
Extreme rainfall may induce flooding failures of electricity facilities, which poses power systems in a risk of power supply interruption. To quantitatively estimate the risk of power system operation under extreme rainfall, a multi-scenario stochastic risk assessment method was proposed. First, a scenario generation scheme considering waterlogged faults of power facilities was constructed based on the storm water management model (SWMM) and the extreme learning machine method. These scenarios were merged with several typical scenario sets for further processing. The outage of power facilities will induce power flow transfer which may consequently lead to transmission lines’ thermal limit violation. Semi-invariant and Gram–Charlier level expansion methods were adopted to analytically depict the probability density function and cumulative probability function of each line’s power flow. The optimal solution was performed by a particle swarm algorithm to obtain proper load curtailment at each bus, and consequently, the violation probability of line thermal violations can be controlled within an allowable range. The volume of load curtailment as well as their importance were considered to quantitatively assess the risk of power supply security under extreme precipitation scenarios. The effectiveness of the proposed method was verified in case studies based on the Southeast Australia Power System. Simulation results indicated that the risk of load shedding in extreme precipitation scenarios can be quantitatively estimated, and the overload probability of lines can be controlled within the allowable range through the proposed optimal load shedding scheme. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Computational Methods in Energy Storage Systems and Other Systems)
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16 pages, 5417 KiB  
Article
Experimental Validation of Iterative Learning Control for DC/DC Power Converters
by Bingqiang Li, Saleem Riaz and Yiyun Zhao
Energies 2023, 16(18), 6555; https://doi.org/10.3390/en16186555 - 12 Sep 2023
Cited by 2 | Viewed by 713
Abstract
In order to solve the problem that the parameters of traditional proportional–integral (PI) control are not easy to adjust, an iterative learning control (ILC) technique for a DC/DC power converter is proposed in this paper. Firstly, we have developed a system which is [...] Read more.
In order to solve the problem that the parameters of traditional proportional–integral (PI) control are not easy to adjust, an iterative learning control (ILC) technique for a DC/DC power converter is proposed in this paper. Firstly, we have developed a system which is composed of two different states of DC/DC converter in order to obtain its equivalent linear time-varying system, and then the open-loop PD-type ILC law has been used to control it. Secondly, an experimental setup is arranged to verify and compare the simulated results. The experimental results show that, as compared with the traditional PI control, the proposed strategy is easy to implement and optimal with regard to debugging parameters, and it can achieve zero steady-state tracking errors without overshooting. Finally, the experimental results have also proven that our proposed scheme of iterative learning control for a DC/DC power converter is robust as compared to traditional PI control. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Computational Methods in Energy Storage Systems and Other Systems)
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14 pages, 5016 KiB  
Article
Virtual Synchronous Generator (VSG) Control Strategy Based on Improved Damping and Angular Frequency Deviation Feedforward
by Sue Wang and Yuxin Xie
Energies 2023, 16(15), 5635; https://doi.org/10.3390/en16155635 - 26 Jul 2023
Cited by 2 | Viewed by 1467
Abstract
The output active power of a grid-connected inverter controlled by a traditional virtual synchronous generator (VSG) has the problems of oscillation and steady-state errors. A VSG control strategy based on improved damping and angular frequency deviation feedforward is proposed. This strategy reduces the [...] Read more.
The output active power of a grid-connected inverter controlled by a traditional virtual synchronous generator (VSG) has the problems of oscillation and steady-state errors. A VSG control strategy based on improved damping and angular frequency deviation feedforward is proposed. This strategy reduces the steady-state error of active power by adding a transient damping link to a traditional VSG damping feedback channel. At the same time, the angular frequency deviation feedforward compensation is used to improve the response speed of the VSG to the active power instruction and reduce the active power overshoot in the dynamic process. First, the VSG active power closed-loop small-signal model is established. The effects of inertia and damping on the dynamic and steady-state performance of the VSG are analyzed by the root locus method. The effect of the proposed control strategy on the system is analyzed by using a closed-loop zero-pole diagram. This strategy improves the precision of active power control the dynamic performance of the system effectively. Finally, the effectiveness and superiority of the proposed control strategy are verified by Matlab/Simulink simulation and semi-physical simulation platform RT-LAB. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Computational Methods in Energy Storage Systems and Other Systems)
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18 pages, 7906 KiB  
Article
Extended Recursive Three-Step Filter for Linear Discrete-Time Systems with Dual-Unknown Inputs
by Shigui Dong, Na Wang, Xueyan Wang and Zihao Lu
Energies 2023, 16(15), 5603; https://doi.org/10.3390/en16155603 - 25 Jul 2023
Viewed by 768
Abstract
This paper proposes two new extended recursive three-step filters for linear discrete systems with dual-unknown inputs, which can simultaneously estimate unknown input and state. Extended recursive three-step filter 1 (ERTSF1) introduces an innovation for obtaining the estimates of the unknown input in the [...] Read more.
This paper proposes two new extended recursive three-step filters for linear discrete systems with dual-unknown inputs, which can simultaneously estimate unknown input and state. Extended recursive three-step filter 1 (ERTSF1) introduces an innovation for obtaining the estimates of the unknown input in the measurement equation, then derives the estimates of the unknown input in the state equation. After that, it uses the already obtained estimates of the dual-unknown inputs to correct the one-step prediction of the state, and finally, it obtains the minimum-variance unbiased estimate of the system state. Extended recursive three-step filter 2 (ERTSF2) establishes a unified innovation feedback model, then applies linear minimum-variance unbiased estimation to obtain the estimates of the system state and the dual-unknown inputs to refine a more concise recursive filter. Numerical Simulation Ex-ample demonstrates the effectiveness and superiority of the two filters in this paper compared with the traditional method. The battery state of charge estimation results demonstrate the effectiveness of ERTSF2 in practical applications. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Computational Methods in Energy Storage Systems and Other Systems)
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Review

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17 pages, 4022 KiB  
Review
Study on Lifetime Decline Prediction of Lithium-Ion Capacitors
by Shuhui Cui, Saleem Riaz and Kai Wang
Energies 2023, 16(22), 7557; https://doi.org/10.3390/en16227557 - 13 Nov 2023
Cited by 3 | Viewed by 867
Abstract
With their high-energy density, high-power density, long life, and low self-discharge, lithium-ion capacitors are a novel form of electrochemical energy storage devices which are extensively utilized in electric vehicles, energy storage systems, and portable electronic gadgets. Li-ion capacitor aging mechanisms and life prediction [...] Read more.
With their high-energy density, high-power density, long life, and low self-discharge, lithium-ion capacitors are a novel form of electrochemical energy storage devices which are extensively utilized in electric vehicles, energy storage systems, and portable electronic gadgets. Li-ion capacitor aging mechanisms and life prediction techniques, however, continue to be active research areas. This paper examines the aging process for Li-ion batteries, covering the alterations in cell composition, the effect of the electrode charge state, temperature effects, and electrolyte deterioration. Additionally, this research offers approaches for predicting the lifespan of lithium-ion batteries, including those based on physical models, machine learning, and artificial intelligence. In this work, cycle life testing techniques are also discussed, including accelerated aging experiments for lithium-ion capacitors. The paper concludes by discussing future directions for the creation of aging mechanisms and lithium-ion capacitor life prediction techniques. Full article
(This article belongs to the Special Issue Recent Advances in Artificial Intelligence and Computational Methods in Energy Storage Systems and Other Systems)
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