Scenario generation with clustering for optimal allocation of renewable DG

Ikki Tanaka, Hiromitsu Ohmori

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Active introduction of renewable distributed generation (DG) to the distribution system has become one of the challenging research in recent years. In the introduction plan of the renewable energy in Japan, long-term goal for high penetration of DG towards 2030 is shown. However, specific implementation plan such as the allocation, capacity, and timing has not been decided. In this paper, for decision-making of introducing DG, a novel scenario generation method with K-means is proposed. This method generates electric load, wind, and PV-production scenarios representing uncertainty, which are used as input for stochastic programming. Then, the problem is formulated as two-stage multiperiod mixed-integer linear programming (MILP) model. In this model, investment variables are decided in the first stage and operation and maintenance variables that depends on scenarios are solved in the second stage. This model minimize the total distributed system cost that is affected by demand growth. Constraints include power flow constraints, substation and feeders capacities, investment constraints, voltage, and current limits. In numerical simulation, several scenarios are generated using historical weather and demand data of Japan and the model is tested on 34-bus system.

Original languageEnglish
Title of host publication2016 IEEE Innovative Smart Grid Technologies - Asia, ISGT-Asia 2016
PublisherIEEE Computer Society
Pages966-971
Number of pages6
ISBN (Electronic)9781509043033
DOIs
Publication statusPublished - 2016 Dec 22
Event2016 IEEE Innovative Smart Grid Technologies - Asia, ISGT-Asia 2016 - Melbourne, Australia
Duration: 2016 Nov 282016 Dec 1

Other

Other2016 IEEE Innovative Smart Grid Technologies - Asia, ISGT-Asia 2016
CountryAustralia
CityMelbourne
Period16/11/2816/12/1

Fingerprint

Distributed power generation
Electric loads
Stochastic programming
Linear programming
Decision making
Computer simulation
Electric potential
Costs

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

Cite this

Tanaka, I., & Ohmori, H. (2016). Scenario generation with clustering for optimal allocation of renewable DG. In 2016 IEEE Innovative Smart Grid Technologies - Asia, ISGT-Asia 2016 (pp. 966-971). [7796516] IEEE Computer Society. https://doi.org/10.1109/ISGT-Asia.2016.7796516

Scenario generation with clustering for optimal allocation of renewable DG. / Tanaka, Ikki; Ohmori, Hiromitsu.

2016 IEEE Innovative Smart Grid Technologies - Asia, ISGT-Asia 2016. IEEE Computer Society, 2016. p. 966-971 7796516.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Tanaka, I & Ohmori, H 2016, Scenario generation with clustering for optimal allocation of renewable DG. in 2016 IEEE Innovative Smart Grid Technologies - Asia, ISGT-Asia 2016., 7796516, IEEE Computer Society, pp. 966-971, 2016 IEEE Innovative Smart Grid Technologies - Asia, ISGT-Asia 2016, Melbourne, Australia, 16/11/28. https://doi.org/10.1109/ISGT-Asia.2016.7796516
Tanaka I, Ohmori H. Scenario generation with clustering for optimal allocation of renewable DG. In 2016 IEEE Innovative Smart Grid Technologies - Asia, ISGT-Asia 2016. IEEE Computer Society. 2016. p. 966-971. 7796516 https://doi.org/10.1109/ISGT-Asia.2016.7796516
Tanaka, Ikki ; Ohmori, Hiromitsu. / Scenario generation with clustering for optimal allocation of renewable DG. 2016 IEEE Innovative Smart Grid Technologies - Asia, ISGT-Asia 2016. IEEE Computer Society, 2016. pp. 966-971
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