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 2021
  • - Article ID 8834234
  • - Research Article

The Active Frequency Control Strategy of the Wind Power Based on Model Predictive Control

Ya-ling Chen | Yin-peng Liu | Xiao-fei Sun
  • Special Issue
  • - Volume 2021
  • - Article ID 8886945
  • - Research Article

Iterative Learning Consensus Control for Nonlinear Partial Difference Multiagent Systems with Time Delay

Cun Wang | Xisheng Dai | ... | Zupeng Zhou
  • Special Issue
  • - Volume 2021
  • - Article ID 8874226
  • - Research Article

Kinematic Calibration of Industrial Robots Based on Distance Information Using a Hybrid Identification Method

Guanbin Gao | Yuan Li | ... | Shichang Han
  • Special Issue
  • - Volume 2021
  • - Article ID 8885821
  • - Research Article

Feature Tracking for Target Identification in Acoustic Image Sequences

Jue Gao | Ya Gu | Peiyi Zhu
  • Special Issue
  • - Volume 2021
  • - Article ID 8868617
  • - Research Article

Multiobjective Optimization of Large-Scale EVs Charging Path Planning and Charging Pricing Strategy for Charging Station

Weicheng Hou | Qingsong Luo | ... | Gangquan Si
  • Special Issue
  • - Volume 2021
  • - Article ID 8895496
  • - Research Article

A Novel Pigeon-Inspired Optimized RBF Model for Parallel Battery Branch Forecasting

Yanhui Zhang | Shili Lin | ... | Wei Feng
  • Special Issue
  • - Volume 2021
  • - Article ID 8878686
  • - Research Article

Modified Whale Optimization Algorithm for Solar Cell and PV Module Parameter Identification

Xiaojia Ye | Wei Liu | ... | Hailong Huang
  • Special Issue
  • - Volume 2021
  • - Article ID 3041205
  • - Corrigendum

Corrigendum to “Controller Design Based on Echo State Network with Delay Output for Nonlinear System”

Xianshuang Yao | Siyuan Fan | ... | Shengxian Cao
  • Special Issue
  • - Volume 2021
  • - Article ID 8816250
  • - Research Article

State of Charge Estimation of Composite Energy Storage Systems with Supercapacitors and Lithium Batteries

Kai Wang | Chunli Liu | ... | Liwei Li
  • Special Issue
  • - Volume 2021
  • - Article ID 8870659
  • - Research Article

Output Feedback Recursive Dynamic Surface Control with Antiwindup Compensation

Guofa Sun | Hui Du | ... | Hanbo Yu
Complexity
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Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.