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Unit Commitment Problem in the Deregulated Market

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Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12482))

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Abstract

In Japan, the electric power market has been fully deregulated since April 2016, and many Independent Power Producers have entered the electric power market. Companies participating in the market conduct transactions between market participants to maximize their profits. When companies consider maximization of their profit, it is necessary to optimize operation of generators in consideration of market transactions. However, it is not easy to consider trading in the market because the market contains many complex and uncertain factors. Even now, the number of participating companies is increasing, and research on operation of generators in consideration of market transactions is an important field. In the power market, there are various markets such as a day-ahead market and an adjustment market, and various transactions are performed between market participants. In this study, we discuss the day-ahead market trading. In the day-ahead market, power prices and demands vary greatly depending on the trends in power sell and purchase bidding. It is necessary for business operators to set operational schedules that takes into account fluctuations in power prices and demand. We consider an optimization model of generator operation considering market transactions and apply stochastic programming to solve the problem. In addition, we show that scheduling based on the stochastic programming method is better than conventional deterministic planning.

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References

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Correspondence to R. Mikami or T. Shiina .

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Mikami, R., Fukuba, T., Shiina, T., Tokoro, K. (2020). Unit Commitment Problem in the Deregulated Market. In: Huynh, VN., Entani, T., Jeenanunta, C., Inuiguchi, M., Yenradee, P. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2020. Lecture Notes in Computer Science(), vol 12482. Springer, Cham. https://doi.org/10.1007/978-3-030-62509-2_9

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  • DOI: https://doi.org/10.1007/978-3-030-62509-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62508-5

  • Online ISBN: 978-3-030-62509-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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