Special Issue "Artificial Intelligence in Renewable Energy and Storage Systems"

Submission Deadline: 28 October 2022
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Guest Editors
Prof. Kai Wang, Qingdao University, China
Dr. Xiufeng Liu, Technical University of Denmark, Denmark
Prof. Licheng Wang, Zhejiang University of Technology, China
Dr. Yang Zhang, Strategic Research Institute, State Power Investment Corporation, China


Energy demand worldwide grows every year. Thus, there is a great interest in reducing energy consumption (both domestic and industrial) and in optimizing energy supply systems. The amount of data available from industrial systems or domestic buildings can be used to prevent faults or to optimize production in energy systems. An additional important goal is to use these data to optimize maintenance and control strategies with the goal of reducing energy consumption in industrial applications or in domestic buildings.


The increasing penetration of stochastic and uncertain inverter-based distributed energy resources (DERs), such as wind and solar photovoltaic (PV), has a considerable influence on power system dynamics, causing reliability and resilience concerns. This requires innovations in power system modelling, operation, and control to deal with these emerging challenges. In addition, coordinated control among different devices typically relies on communication systems. Communication-control coupled systems bring both opportunities and challenges to the future development of DER-rich power systems.


Artificial intelligence systems can make use of the available data to address the challenges discussed above. Accordingly, this Special Issue will focus on the artificial intelligence in renewable energy and storage systems (e.g., wind, solar, supercapacitor and fuel cells). We invite papers on innovative technical developments, case studies, and theoretical papers from different disciplines, which are relevant to renewable energy and storage systems. Original research and review articles are both welcome.


Potential topics include but are not limited to the following:


●    Energy storage technologies and systems

●    Plug-in hybrid electric vehicle (PHEV) systems, Compressed natural gas (CNG) vehicles, clean Energy

●    Power electronic converters and drives

●    Demand monitoring and energy efficient systems

●    Modelling of communication-control coupled systems

●    Frequency regulation in low inertia systems with high wind penetration

●    Grid modelling, simulation, and data management

●    Energy efficiency, conservation, and savings

●    Big data for industrial and energy systems

●    Grid protection, reliability, energy / power quality, and maintenance

●    Smart metering, measurement, instrumentation, and control

●    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

●    Artificial intelligence for renewable energies

distributed energy resources, renewable energy, electric vehicle, storage systems, data driven, energy management

Published Papers
  • Research on Volt/Var Control of Distribution Networks Based on PPO Algorithm
  • Abstract In this paper, a model free volt/var control (VVC) algorithm is developed by using deep reinforcement learning (DRL). We transform the VVC problem of distribution networks into the network framework of PPO algorithm, in order to avoid directly solving a large-scale nonlinear optimization problem. We select photovoltaic inverters as agents to adjust system voltage in a distribution network, taking the reactive power output of inverters as action variables. An appropriate reward function is designed to guide the interaction between photovoltaic inverters and the distribution network environment. OPENDSS is used to output system node voltage and network loss. This method realizes… More
  •   Views:89       Downloads:27        Download PDF

  • A Fractional Order Fast Repetitive Control Paradigm of Vienna Rectifier for Power Quality Improvement
  • Abstract Due to attractive features, including high efficiency, low device stress, and ability to boost voltage, a Vienna rectifier is commonly employed as a battery charger in an electric vehicle (EV). However, the 6k ± 1 harmonics in the acside current of the Vienna rectifier deteriorate the THD of the ac current, thus lowering the power factor. Therefore, the current closed-loop for suppressing 6k ± 1 harmonics is essential to meet the desired total harmonic distortion (THD). Fast repetitive control (FRC) is generally adopted; however, the deviation of power grid frequency causes delay link in the six frequency fast repetitive control… More
  •   Views:421       Downloads:148        Download PDF

  • A Fault Risk Warning Method of Integrated Energy Systems Based on RelieF-Softmax Algorithm
  • Abstract The integrated energy systems, usually including electric energy, natural gas and thermal energy, play a pivotal role in the energy Internet project, which could improve the accommodation of renewable energy through multi-energy complementary ways. Focusing on the regional integrated energy system composed of electrical microgrid and natural gas network, a fault risk warning method based on the improved RelieF-softmax method is proposed in this paper. The raw data-set was first clustered by the K-maxmin method to improve the preference of the random sampling process in the RelieF algorithm, and thereby achieved a hierarchical and non-repeated sampling. Then, the improved RelieF… More
  •   Views:19       Downloads:7        Download PDF

  • Research on Distributed Cooperative Control Strategy of Microgrid Hybrid Energy Storage Based on Adaptive Event Triggering
  • Abstract Distributed collaborative control strategies for microgrids often use periodic time to trigger communication, which is likely to enhance the burden of communication and increase the frequency of controller updates, leading to greater waste of communication resources. In response to this problem, a distributed cooperative control strategy triggered by an adaptive event is proposed. By introducing an adaptive event triggering mechanism in the distributed controller, the triggering parameters are dynamically adjusted so that the distributed controller can communicate only at a certain time, the communication pressure is reduced, and the DC bus voltage deviation is effectively reduced, at the same time,… More
  •   Views:867       Downloads:337        Download PDF

  • State Estimation of Regional Power Systems with Source-Load Two-Terminal Uncertainties
  • Abstract

    The development and utilization of large-scale distributed power generation and the increase of impact loads represented by electric locomotives and new energy electric vehicles have brought great challenges to the stable operation of the regional power grid. To improve the prediction accuracy of power systems with source-load two-terminal uncertainties, an adaptive cubature Kalman filter algorithm based on improved initial noise covariance matrix Q0 is proposed in this paper. In the algorithm, the Q0 is used to offset the modeling error, and solves the problem of large voltage amplitude and phase fluctuation of the source-load two-terminal uncertain systems. Verification of the… More

  •   Views:159       Downloads:102        Download PDF

  • Low Carbon Economic Dispatch of Integrated Energy System Considering Power Supply Reliability and Integrated Demand Response
  • Abstract Integrated energy system optimization scheduling can improve energy efficiency and low carbon economy. This paper studies an electric-gas-heat integrated energy system, including the carbon capture system, energy coupling equipment, and renewable energy. An energy scheduling strategy based on deep reinforcement learning is proposed to minimize operation cost, carbon emission and enhance the power supply reliability. Firstly, the low-carbon mathematical model of combined thermal and power unit, carbon capture system and power to gas unit (CCP) is established. Subsequently, we establish a low carbon multi-objective optimization model considering system operation cost, carbon emissions cost, integrated demand response, wind and photovoltaic curtailment,… More
  •   Views:766       Downloads:304       Cited by:2        Download PDF

  • Optimal Scheduling for Flexible Regional Integrated Energy System with Soft Open Point
  • Abstract The Regional Integrated Energy System (RIES) has brought new modes of development, utilization, conversion, storage of energy. The introduction of Soft Open Point (SOP) and the application of Power to Gas (P2G) technology will greatly deepen the coupling of the electricity-gas integrated energy system, improve the flexibility and safety of the operation of the power system, and bring a deal of benefits to the power system. On this background, an optimal dispatch model of RIES combined cold, heat, gas and electricity with SOP is proposed. Firstly, RIES architecture with SOP and P2G is designed and its mathematical model also is… More
  •   Views:661       Downloads:417        Download PDF

  • Fractal Dimension Analysis Based Aging State Assessment of Insulating Paper with Surface Microscopic Images
  • Abstract The insulating paper of the transformer is affected by many factors during the operation, meanwhile, the surface texture of the paper is easy to change. To explore the relationship between the aging state and surface texture change of insulating paper, firstly, the thermal aging experiment of insulating paper is carried out, and the insulating paper samples with different aging times are obtained. After then, the images of the aged insulating paper samples are collected and pre-processed. The pre-processing effect is verified by constructing and calculating the gray surface of the sample. Secondly, the texture features of the insulating paper image… More
  •   Views:635       Downloads:397        Download PDF