A STUDY OF MID-TO LONG-TERM DISTRIBUTION PLANNING BASED ON PV INSTALLATION FORECASTING

Jintae Cho, Hongjoo Kim, Hosung Ryu, Jiwon Lee, Juyong Kim, Yongju Son, Sungyum Choi

Research output: Contribution to journalConference articlepeer-review

Abstract

Recently, distributed generations connected to the power distribution system is rapidly increasing due to issue of carbon neutral. For this reason, uncertainty and complexity are increasing in distribution system planning. However, it is difficult to plan the distribution system using the existing method with the distributed generations such as PVs are rapidly connected in Korea. Due to the continuous demand for PV connection, the construction of distribution lines and power substations is required, and it is difficult to plan the size and timing of them. Therefore, the capacity of distribution system should be evaluated through mid- to long-term load and distributed generation forecasting. This paper proposed the PV installation forecasting method using machine learning. It is described in the optimization algorithm that plans the substations and feeders for distribution system so that the cost of the distribution system is minimized by applying it to KEPCO's distribution planning system. In addition, this paper presented the development of KEPCO's power distribution planning system using PV installation forecasting scenario and the result of testing on a KEPCO's branch office.

Original languageEnglish
Pages (from-to)2731-2735
Number of pages5
JournalIET Conference Proceedings
Volume2023
Issue number6
DOIs
Publication statusPublished - 2023
Event27th International Conference on Electricity Distribution, CIRED 2023 - Rome, Italy
Duration: 2023 Jun 122023 Jun 15

Bibliographical note

Publisher Copyright:
© The Institution of Engineering and Technology 2023.

ASJC Scopus subject areas

  • General Engineering

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