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 8643029
  • - Research Article

Controller Design Based on Echo State Network with Delay Output for Nonlinear System

Xianshuang Yao | Siyuan Fan | ... | Shengxian Cao
  • Special Issue
  • - Volume 2020
  • - Article ID 8828453
  • - Research Article

Neural Network-Based Nonlinear Fixed-Time Adaptive Practical Tracking Control for Quadrotor Unmanned Aerial Vehicles

Jianhua Zhang | Yang Li | Wenbo Fei
  • Special Issue
  • - Volume 2020
  • - Article ID 9673764
  • - Research Article

An Improved Multisensor Self-Adaptive Weighted Fusion Algorithm Based on Discrete Kalman Filtering

Shifen Shao | Kaisheng Zhang
  • Special Issue
  • - Volume 2020
  • - Article ID 9673724
  • - Research Article

Identification and Classification of Atmospheric Particles Based on SEM Images Using Convolutional Neural Network with Attention Mechanism

Changchang Yin | Xuezhen Cheng | ... | Meng Zhao
  • Special Issue
  • - Volume 2020
  • - Article ID 6814263
  • - Research Article

Two Improved Methods of Generating Adversarial Examples against Faster R-CNNs for Tram Environment Perception Systems

Shize Huang | Xiaowen Liu | ... | Lingyu Yang
  • Special Issue
  • - Volume 2020
  • - Article ID 3796849
  • - Research Article

A New Nonlinear Active Disturbance Rejection Control for the Cable Car System to Restrain the Vibration

Xinyan Hu | Lina Li
  • Special Issue
  • - Volume 2020
  • - Article ID 9250937
  • - Research Article

Integrated Machine Learning and Enhanced Statistical Approach-Based Wind Power Forecasting in Australian Tasmania Wind Farm

Fang Yao | Wei Liu | ... | Li Song
  • Special Issue
  • - Volume 2020
  • - Article ID 8520835
  • - Research Article

An Exponential Varying-Parameter Neural Network for Repetitive Tracking of Mobile Manipulators

Ying Kong | Qingqing Tang | ... | Ruiyang Zhang
  • Special Issue
  • - Volume 2020
  • - Article ID 4681767
  • - Research Article

Performance Analysis of a Wind Turbine Pitch Neurocontroller with Unsupervised Learning

J. Enrique Sierra-García | Matilde Santos
  • Special Issue
  • - Volume 2020
  • - Article ID 4346803
  • - Research Article

Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization

Xue-Bo Jin | Hong-Xing Wang | ... | Jian-Lei Kong
Complexity
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 Journal metrics
Acceptance rate43%
Submission to final decision64 days
Acceptance to publication35 days
CiteScore3.200
Impact Factor2.462
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