Flexible generation mix under multi objectives and uncertainties

R. Tanabe; K. Yasuda; R. Yokoyama; H. Sasaki

doi:10.1109/59.260824

Flexible generation mix under multi objectives and uncertainties

R. Tanabe, K. Yasuda, R. Yokoyama, H. Sasaki

Research output: Contribution to journal › Article › peer-review

26 Citations (Scopus)

Abstract

This paper presents criteria for evaluating flexibility of a generation mix and develops an efficient computational algorithm for determining a flexible generation mix considering uncertainties in the power system expansion planning. The flexible generation mix problem is formulated as multiobjective optimization problem and solved by the dynamic programming technique in which each type of generation plants is selected as a stage and generation capacity is selected as a state. The proposed method can treat easily not only the uncertainties by also many constraints of generation planning such as integer solutions of generation capacities, reliability constraints, and so on. The effectiveness and feasibility of the proposed method are demonstrated on a typical power system model.

Original language	English
Pages (from-to)	581-586
Number of pages	6
Journal	IEEE Transactions on Power Systems
Volume	8
Issue number	2
DOIs	https://doi.org/10.1109/59.260824
Publication status	Published - 1993 May

ASJC Scopus subject areas

Electrical and Electronic Engineering

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10.1109/59.260824

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AB - This paper presents criteria for evaluating flexibility of a generation mix and develops an efficient computational algorithm for determining a flexible generation mix considering uncertainties in the power system expansion planning. The flexible generation mix problem is formulated as multiobjective optimization problem and solved by the dynamic programming technique in which each type of generation plants is selected as a stage and generation capacity is selected as a state. The proposed method can treat easily not only the uncertainties by also many constraints of generation planning such as integer solutions of generation capacities, reliability constraints, and so on. The effectiveness and feasibility of the proposed method are demonstrated on a typical power system model.

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Flexible generation mix under multi objectives and uncertainties

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