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Distribution Network Planning Method Considering Flexible Load Interaction in High Proportion New Energy Power System

Published: 10 April 2023 Publication History

Abstract

The flexible load with the function of ' virtual energy storage ' can change the original load distribution in time and space and reduce the peak-valley difference. However, the existing planning has not carried out in-depth research on it. Firstly, by constructing a flexible load response model, a contract strategy is proposed to preferentially transfer the flexible load during the peak period to the peak period of photovoltaic output, thereby improving the utilization rate of new energy. Secondly, based on the understanding and prediction of power grid situation, the expansion scheme of distribution network is solved and evaluated based on embedded power flow particle swarm optimization algorithm. The modified IEEE-RTS 24 bus system is simulated. The results show that from the economic point of view, flexible load can effectively reduce the operation cost of power grid and improve the consumption capacity of new energy.

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ICITEE '22: Proceedings of the 5th International Conference on Information Technologies and Electrical Engineering
November 2022
739 pages
ISBN:9781450396806
DOI:10.1145/3582935
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 April 2023

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Author Tags

  1. Distribution network planning
  2. Flexible load
  3. Operation situation
  4. demand response
  5. particle swarm

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