TY - GEN
T1 - Unit Commitment Problem in the Deregulated Market
AU - Mikami, R.
AU - Fukuba, T.
AU - Shiina, T.
AU - Tokoro, K.
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Optimization
KW - Stochastic programming
UR - http://www.scopus.com/inward/record.url?scp=85096601964&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85096601964&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-62509-2_9
DO - 10.1007/978-3-030-62509-2_9
M3 - Conference contribution
AN - SCOPUS:85096601964
SN - 9783030625085
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 96
EP - 107
BT - Integrated Uncertainty in Knowledge Modelling and Decision Making - 8th International Symposium, IUKM 2020, Proceedings
A2 - Huynh, Van-Nam
A2 - Entani, Tomoe
A2 - Jeenanunta, Chawalit
A2 - Inuiguchi, Masahiro
A2 - Yenradee, Pisal
PB - Springer Science and Business Media Deutschland GmbH
T2 - 8th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2020
Y2 - 11 November 2020 through 13 November 2020
ER -