Skip to main content

Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

New-Generation Artificial Intelligence Techniques Applications on Smart Distribution Network Planning and Dispatching

With large-scale renewable energy and more power electronic devices being widely integrated into distribution networks, electric power market reform is gradually emphasizing on power distribution system while the distribution network will transform into a complex active distribution network characterized by strong stochastic and flexible interconnection, which will make distribution network planning and dispatching extremely thorny. Particularly, the practical application of traditional distribution network planning and dispatching techniques mainly exists several drawbacks, as follows:

(a)Spontaneous power demand response can result in severe changes in original load characteristic curve, which makes the traditional load forecasting methods based on time series basically fail;
(b)With the ever-increasing information-physical coupling in distribution network, the planning strategy of separating the primary side and secondary side can no longer satisfy the engineering requirements;
(c)The coordination planning and regulation of power electronics based distribution network has not been fully investigated/developed;
(d)The new development trend of energy conservation and emission reduction, such as large-scale renewable energy utilization and electricity substitution, are not adequately considered in traditional distribution network planning and dispatching.

With the rapid advancement of computer science, artificial intelligence (AI) techniques have attracted extensive applications. It is noteworthy that the new-generation AI technique has its own distinctive advantages to remedy the aforementioned drawbacks in terms of big data analysis, data mining, and independent decision-making. Generally speaking, distribution network planning and dispatching can be considered as a series of complex engineering calculation and non-linear optimization problems, which can be well solved by AI techniques. Compared with traditional planning and dispatching strategies, AI techniques can significantly improve many uncertain factors to enhance data analysis performance based on their strong optimization ability. Besides, AI techniques can also significantly enhance the efficiency of data processing. Thus, the reliability, economy and scalability of distribution network planning, as well as the safe and stable operation of power dispatching can be effectively guaranteed and improved with AI techniques.

Hence, in order to further promote the AI applications on smart distribution network planning and dispatching, a special issue entitled “New-Generation Artificial Intelligence Technique Applications on Smart Distribution Network Planning and Dispatching” is proposed for the international journal of Protection and Control of Modern Power Systems. Topics include, but are not limited to, the following research topics and technologies:

  1. Load characteristics analysis based on data mining and machine learning
  2. Accurate load forecasting technologies based on AI algorithms
  3. Primary side and secondary side coordinated planning and intelligent decision methods for distribution networks
  4. Power electronic based distribution network planning and intelligent dispatching technologies
  5. Influence of new energy storage and power generation technologies on distribution network planning and dispatching related to AI algorithms
  6. New software and engineering application technologies of distribution network coordinated planning

Submission Guidelines

This special issue solicits any original work that is not under consideration for publication in other journals and magazines. Authors should refer to https://www.pcmp.springeropen.com or https://www.pcmp.info for information about content and formatting of submissions.

Special issue Guest Editors-in-Chief

Prof. Tao Yu, South China University of Technology, Guangzhou, China

taoyu1@scut.edu.cn

Co-Editors:

Associate Prof. Bo Yang, Kunming University of Science and Technology, Kunming, China
yangbo_ac@outlook.com

Associate Prof. Xiaoshun Zhang, Shantou University, Shantou, China
xszhang1990@sina.cn

Advertisement