Clustering Method for Load Demand to Shorten the Time of Annual Simulation

Yuya Tanigawa (), Narayanan Krishnan, Eitaro Oomine, Atushi Yona, Hiroshi Takahashi and Tomonobu Senjyu
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Yuya Tanigawa: Fuculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho 903-0213, Nakagami, Okinawa, Japan
Narayanan Krishnan: Department of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur 613401, Tamil Nadu, India
Eitaro Oomine: Central Research Institute of Electric Power Industry, 2-6-1 Nagasaka, Yokosuka-City 240-0196, Kanagawa, Japan
Atushi Yona: Fuculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho 903-0213, Nakagami, Okinawa, Japan
Hiroshi Takahashi: Fuji Elctric Co., Ltd., Tokyo 141-0032, Japan
Tomonobu Senjyu: Fuculty of Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho 903-0213, Nakagami, Okinawa, Japan

Energies, 2023, vol. 16, issue 5, 1-22

Abstract: UC (unit commitment) for grid operation has been attracting increasing attention due to the growing interest in global warming. Compared to other methods, MILP, which is one of the calculation methods for UC, has the disadvantage of a long calculation time, although it is more accurate in considering constraints and in finding solutions. However, RLCs (representative load curves) require a more accurate clustering method to select representative dates because the calculation results vary greatly depending on the clustering method. DBSCAN, one of the clustering methods, has the feature that the clustering accuracy varies depending on two parameters. Therefore, this paper proposes two algorithms to automatically determine the two parameters of DBSCAN to perform RLCs using DBSCAN. In addition, since DBSCAN has the feature of being able to represent different data as two-dimensional elements, a survey of the data to be used as clustering was conducted. As a result, the proposed algorithms enabled a more accurate clustering than the conventional method. It was also proved that clustering including temperature and load demand as clustering classification factors enables clustering with higher accuracy. The simulation with shorter time was also possible for the system including storage batteries as a demand response.

Keywords: unit commitment; clustering; DBSCAN; RLC (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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