Complexity

Learning and Adaptation for Optimization and Control of Complex Renewable Energy Systems


Publishing date
01 Jan 2021
Status
Closed
Submission deadline
21 Aug 2020

Lead Editor

1Kunming University of Science and Technology, Kunming, China

2Universitat Politècnica de Catalunya (UPC), Barcelona, Spain

3Qingdao University, Qingdao, China

4Zhejiang University of Technology, Hangzhou, China

5University of Warwick, Coventry, Ireland

This issue is now closed for submissions.
More articles will be published in the near future.

Learning and Adaptation for Optimization and Control of Complex Renewable Energy Systems

This issue is now closed for submissions.
More articles will be published in the near future.

Description

To achieve sustainable development, renewable energies including solar, wind, nuclear, and fuel cells have become emerging choices in many applications. However, the guarantee of stable energy generation rate and safe system operation is not easy, because of their intermittent characteristics and the spatial complexity of renewable energy generation and transmission plants.

In general, accurate mathematical models for renewable energy systems are difficult to derive due to the existence of unavoidable parameter uncertainties, nonsmooth dynamics, and external disturbances. In this respect, developing efficient yet applicable learning and adaptation methods for modeling, optimization, and control of complex renewable energy systems could provide a new way to improve the system efficacy and efficiency. This has attracted significant attention worldwide.

The aim of this Special Issue is to collect the latest research results on the relevant topics of learning and adaptation for modelling, optimization, and control to promote the awareness of the related research methodologies of complex renewable energy systems. Authors are invited to present new modelling, optimization and control algorithms, hardware configuration, software architectures, experiments, and applications, which can bring new information about relevant theories and techniques of complex energy systems. All papers related to the theoretical methods and their application for optimization and control of complex energy systems are welcome. In particular, we encourage authors to submit their original research and review articles with either theoretical and methodological development or practical focus, such as simulation models, algorithms, experiments, and applications about advanced control and optimization techniques for complex energy systems.

Potential topics include but are not limited to the following:

  • Modelling, simulation and validation for complex renewable energy systems
  • Design and dynamic analysis for renewable energy systems with multiple energy storage components, generators, and motors
  • Modelling and compensation of nonsmooth dynamics in renewable energy generation systems
  • Bio-inspired optimization and optimal control for renewable energy systems with generators, storage, and motors
  • Artificial intelligence methods for learning, adaptation, and optimization
  • Data-driven modeling and control for renewable energy systems
  • Deep learning and integrative learning-based optimization and control designs
  • Adaptive parameter estimation for modeling of renewable energy systems
  • Learning and adaptation approaches for renewable energy generation, storage, and distribution
  • Adaptive dynamic programming for renewable energy generation and transmission
  • Intelligent control technique (e.g., neural network and fuzzy logic control) for renewable systems
  • Adaptive observer design and estimation for complex energy systems
  • Iterative learning for optimization and control with applications to renewable energy systems

Articles

  • Special Issue
  • - Volume 2020
  • - Article ID 8828794
  • - Research Article

An Asynchronous Data Transmission Method in Heterogeneous Wireless Networks

Changhao Wang | Shining Li | ... | Qianwu Chen
  • Special Issue
  • - Volume 2020
  • - Article ID 8874813
  • - Research Article

A New Variable Exponential Power Reaching Law of Complementary Terminal Sliding Mode Control

Feng Xu | Na An | ... | Shubo Yang
  • Special Issue
  • - Volume 2020
  • - Article ID 8814455
  • - Research Article

Leader-Following Bounded Consensus and Multiconsensus of Multiagent Systems

Chao Long | Chunxi Yang | ... | Shichang Han
  • Special Issue
  • - Volume 2020
  • - Article ID 6046729
  • - Research Article

Multimodel Anomaly Identification and Control in Wet Limestone-Gypsum Flue Gas Desulphurization System

Xiaoli Li | Quanbo Liu | ... | Yang Li
  • Special Issue
  • - Volume 2020
  • - Article ID 8879906
  • - Research Article

Robust Stochastic Dynamic Optimal Power Flow Model of Electricity-Gas Integrated Energy System considering Wind Power Uncertainty

Zhengfeng Qin | Xiaoqing Bai | Xiangyang Su
  • Special Issue
  • - Volume 2020
  • - Article ID 8816334
  • - Research Article

Voltage Stability Analysis of Front-End Speed Controlled Wind Turbine Integrated into Regional Power Grid Based on Bifurcation Theory

Kaide Ren | Hongwei Li | ... | Haiying Dong
  • Special Issue
  • - Volume 2020
  • - Article ID 8879045
  • - Research Article

Adaptive Robust SMC-Based AGC Auxiliary Service Control for ESS-Integrated PV/Wind Station

Xiao-Ling Su | Zheng-Kui Zhao | ... | Yong-Qing Guo
  • Special Issue
  • - Volume 2020
  • - Article ID 8842297
  • - Research Article

Vehicle Detection Based on Multifeature Extraction and Recognition Adopting RBF Neural Network on ADAS System

Xuewen Chen | Huaqing Chen | Huan Xu
  • Special Issue
  • - Volume 2020
  • - Article ID 3871595
  • - Erratum

Erratum to “Algorithms for U-Model-Based Dynamic Inversion (UM-Dynamic Inversion) for Continuous Time Control Systems”

Ruobing Li | Quanmin Zhu | ... | Weicun Zhang
  • Special Issue
  • - Volume 2020
  • - Article ID 8854618
  • - Research Article

Online Parameter Identification of the Lithium-Ion Battery with Refined Instrumental Variable Estimation

An Wen | Jinhao Meng | ... | Qian Xiao
Complexity
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Acceptance rate43%
Submission to final decision64 days
Acceptance to publication35 days
CiteScore3.200
Impact Factor2.462
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