Algorithmic Bidding for Virtual Trading in Electricity Markets
We consider the problem of optimal bidding for virtual trading in two-settlement electricity markets. A virtual trader aims to arbitrage on differences between day-ahead and real-time market prices that are random and unknown to market participants. An online learning algorithm is proposed to maximi...
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| Veröffentlicht in: | IEEE transactions on power systems 2019-01, Vol.34 (1), p.535-543 |
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| container_title | IEEE transactions on power systems |
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| creator | Baltaoglu, Sevi Tong, Lang Zhao, Qing |
| description | We consider the problem of optimal bidding for virtual trading in two-settlement electricity markets. A virtual trader aims to arbitrage on differences between day-ahead and real-time market prices that are random and unknown to market participants. An online learning algorithm is proposed to maximize the cumulative payoff over a finite number of trading sessions by allocating the trader's budget among his bids for K options in each session. It is shown that the expected payoff of the proposed algorithm converges, with an almost optimal convergence rate, to the expected payoff of the global optimal corresponding to the case when the underlying price distribution is known. The proposed algorithm is also generalized for trading strategies with a risk measure. By using both cumulative payoff and Sharpe ratio as performance metrics, evaluations were performed based on the historical data spanning ten year period of NYISO and PJM markets. It was shown that the proposed strategy outperforms standard benchmarks and the S&P 500 index over the same period. |
| doi_str_mv | 10.1109/TPWRS.2018.2862246 |
| format | Article |
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A virtual trader aims to arbitrage on differences between day-ahead and real-time market prices that are random and unknown to market participants. An online learning algorithm is proposed to maximize the cumulative payoff over a finite number of trading sessions by allocating the trader's budget among his bids for <inline-formula><tex-math notation="LaTeX">K</tex-math></inline-formula> options in each session. It is shown that the expected payoff of the proposed algorithm converges, with an almost optimal convergence rate, to the expected payoff of the global optimal corresponding to the case when the underlying price distribution is known. The proposed algorithm is also generalized for trading strategies with a risk measure. By using both cumulative payoff and Sharpe ratio as performance metrics, evaluations were performed based on the historical data spanning ten year period of NYISO and PJM markets. It was shown that the proposed strategy outperforms standard benchmarks and the S&P 500 index over the same period.</description><identifier>ISSN: 0885-8950</identifier><identifier>EISSN: 1558-0679</identifier><identifier>DOI: 10.1109/TPWRS.2018.2862246</identifier><identifier>CODEN: ITPSEG</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>algorithmic bidding ; Algorithms ; Benchmark testing ; Convergence ; Distance learning ; Electricity ; Electricity markets ; Electricity supply industry ; Hidden Markov models ; ISO ; Machine learning ; Markets ; online machine learning ; Performance measurement ; Pricing ; Real-time systems ; Stock market indexes ; virtual transactions</subject><ispartof>IEEE transactions on power systems, 2019-01, Vol.34 (1), p.535-543</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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A virtual trader aims to arbitrage on differences between day-ahead and real-time market prices that are random and unknown to market participants. An online learning algorithm is proposed to maximize the cumulative payoff over a finite number of trading sessions by allocating the trader's budget among his bids for <inline-formula><tex-math notation="LaTeX">K</tex-math></inline-formula> options in each session. It is shown that the expected payoff of the proposed algorithm converges, with an almost optimal convergence rate, to the expected payoff of the global optimal corresponding to the case when the underlying price distribution is known. The proposed algorithm is also generalized for trading strategies with a risk measure. By using both cumulative payoff and Sharpe ratio as performance metrics, evaluations were performed based on the historical data spanning ten year period of NYISO and PJM markets. It was shown that the proposed strategy outperforms standard benchmarks and the S&P 500 index over the same period.</description><subject>algorithmic bidding</subject><subject>Algorithms</subject><subject>Benchmark testing</subject><subject>Convergence</subject><subject>Distance learning</subject><subject>Electricity</subject><subject>Electricity markets</subject><subject>Electricity supply industry</subject><subject>Hidden Markov models</subject><subject>ISO</subject><subject>Machine learning</subject><subject>Markets</subject><subject>online machine learning</subject><subject>Performance measurement</subject><subject>Pricing</subject><subject>Real-time systems</subject><subject>Stock market indexes</subject><subject>virtual transactions</subject><issn>0885-8950</issn><issn>1558-0679</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kMtKAzEUhoMoWKsvoJsB11NPrk02Qi31AhVFqy5Dmklq6rRTk3TRt3d6wdWB__zfOfAhdImhhzGom8nr19t7jwCWPSIFIUwcoQ7mXJYg-uoYdUBKXkrF4RSdpTQHANEuOuh2UM-aGPL3ItjiLlRVWM4K38TiM8S8NnUxiWaXhWUxqp3NMdiQN8WziT8up3N04k2d3MVhdtHH_WgyfCzHLw9Pw8G4tJSqXCpFsQDLuKVTb7ikRlA2hX4lHfcECOOgTEWxc5IbLyqDgTjrlTTKA0hOu-h6f3cVm9-1S1nPm3Vcti81wVxRyYRQbYvsWzY2KUXn9SqGhYkbjUFvPemdJ731pA-eWuhqDwXn3D8gGWFAgf4BeZRjeQ</recordid><startdate>201901</startdate><enddate>201901</enddate><creator>Baltaoglu, Sevi</creator><creator>Tong, Lang</creator><creator>Zhao, Qing</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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A virtual trader aims to arbitrage on differences between day-ahead and real-time market prices that are random and unknown to market participants. An online learning algorithm is proposed to maximize the cumulative payoff over a finite number of trading sessions by allocating the trader's budget among his bids for <inline-formula><tex-math notation="LaTeX">K</tex-math></inline-formula> options in each session. It is shown that the expected payoff of the proposed algorithm converges, with an almost optimal convergence rate, to the expected payoff of the global optimal corresponding to the case when the underlying price distribution is known. The proposed algorithm is also generalized for trading strategies with a risk measure. By using both cumulative payoff and Sharpe ratio as performance metrics, evaluations were performed based on the historical data spanning ten year period of NYISO and PJM markets. 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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | algorithmic bidding Algorithms Benchmark testing Convergence Distance learning Electricity Electricity markets Electricity supply industry Hidden Markov models ISO Machine learning Markets online machine learning Performance measurement Pricing Real-time systems Stock market indexes virtual transactions |
| title | Algorithmic Bidding for Virtual Trading in Electricity Markets |
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