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Protection and Control of Modern Power Systems is committed to strictly follow the Ethics & Diclosures here. 

Statistical machine learning model for capacitor planning considering uncertainties in photovoltaic power

Featured Article

It is obvious that renewable energy is becoming more and more important in the modern energy system because of its environmental friendliness. In recent years, photovoltaic (PV) have developed rapidly. Renewable energy integration and flexible demand response make smart grid operation scenarios complex and changeable, which bring challenges to network planning. If every possible scenario is considered, the solution to the planning can become extremely time-consuming and difficult. This paper introduces statistical machine learning (SML) techniques to carry out multi-scenario based probabilistic power flow calculations and describes their application to the stochastic planning of distribution networks. The proposed SML includes linear regression, probability distribution, Markov chain, is oprobabilistic transformation, maximum likelihood estimator, stochastic response surface and center point method. Based on the above SML model, capricious weather, photovoltaic power generation, thermal load, power flow and uncertainty programming are simulated. Taking a 33-bus distribution system as an example, this paper compares the stochastic planning model based on SML with the traditional models published in the literature. The results verify that the proposed model greatly improves planning performance while meeting accuracy requirements.  Read more.

Open thematic series

Data-driven Analytics in Power System Modeling, Analysis and Control

Modeling, Operation, and Planning of Multi-Energy Microgrid

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

Article Collections

Power System Protection and Control Containing Renewable Energy Power Generation 

Edited by : Xinzhou Dong, Aiqin Zhang

Energy Storage across Multiple Energy Systems

Edited by: Jinyu Wen, Jiakun Fang, Jialin Li, Gangu Yan, Jinghua Li

Integrating Centralized and Distributed Renewables in Future Power Systems

Edited by: Chongqing Kang, Ning Zhang

Power System Protection and Control

Edited by: Zhiqian Bo, Jinmei Shi, Xinli Jiang

Forecasting and Scheduling Method of Wind and Solar Power Generations

Advances in Energy Management of User and Building-level Integrated Energy Systems

Special Issue on “Energy System Planning and Operation Under the Coupled Carbon Trading Market and Power Market”

Aims and scope

Protection and Control of Modern Power Systems is an international academic journal co-published by Power System Protection and Control Press and Springer. The journal is devoted to presenting new theories, technologies and top-level academic achievements in the field of protection and control in modern power systems. It strives to accelerate the development of the field by serving as a bridge between Chinese and global researchers in the field. In doing so, Protection and Control of Modern Power Systems makes an important contribution to the power industry.

The journal has an international authorship and a broad scope, including contemporary topics such as:

  • power system relay protection
  • power system analysis and control
  • power system planning
  • internet of energy
  • alternative energy generation
  • smart substations
  • intelligent power transmission and utilization techniques
  • interactions between large-scale electric vehicles and power grids
  • microgrid techniques
  • application of power electronics in power systems
  • electric power automation and remote control techniques
  • power system communication
  • power quality
  • electricity market

Affiliated with

Annual Journal Metrics

The journal is indexed by

  • Science Citation Index Expanded
  • Engineering Compendex
  • DOAJ
  • INIS Atomindex
  • Google Scholar
  • EBSCO Discovery Service
  • OCLC WorldCat Discovery Service
  • ProQuest-ExLibris Summon
  • ProQuest-ExLibris Primo
  • Institute of Scientific and Technical Information of China
  • Naver
  • Chinese Academy of Sciences (CAS) - GoOA
  • WTI Frankfurt eG
  • CNKI