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EnglishThis paper proposes a data-based web platform to maximize the profit of a player participating in the Italian spot electricity markets. To ensure the scalability and portability of the platform, the micro-services approach are chosen with four stand-alone services communicating among one another. In the platform, a web crawler module retrieves data from the official and open sources, and a machine learning-based module is created to forecast the bidding strategies of all the market players for the next day across different spot markets. Based on the forecasted strategies, simplified market-clearing mechanisms are adopted to obtain the initial market information. Then by focusing on a single player, his/her strategy is optimized to maximize the daily profit over multiple spot markets with different payment schemes. The client can access the system through a simple, user-friendly web application. The simulation of the platform shows that around 92% of the market players can increase their profits. An example of a randomly selected player shows that the platform suggests a strategy leading to a 127.6% increase in its profit compared with its real bids in the markets.
A Data-based Platform for Supporting Profit-driven Strategy in the Italian Spot Electricity Markets / Huang, T.; Gioacchini, L.; Guaiana, F.; Valente, B.; Pio Domiziani, G.. - (2020), pp. 1-6. ((Intervento presentato al convegno 55th International Universities Power Engineering Conference, UPEC 2020 tenutosi a ita nel 2020.
| Titolo: | A Data-based Platform for Supporting Profit-driven Strategy in the Italian Spot Electricity Markets |
| Autori: | |
| Data di pubblicazione: | 2020 |
| Abstract: | This paper proposes a data-based web platform to maximize the profit of a player participating in... the Italian spot electricity markets. To ensure the scalability and portability of the platform, the micro-services approach are chosen with four stand-alone services communicating among one another. In the platform, a web crawler module retrieves data from the official and open sources, and a machine learning-based module is created to forecast the bidding strategies of all the market players for the next day across different spot markets. Based on the forecasted strategies, simplified market-clearing mechanisms are adopted to obtain the initial market information. Then by focusing on a single player, his/her strategy is optimized to maximize the daily profit over multiple spot markets with different payment schemes. The client can access the system through a simple, user-friendly web application. The simulation of the platform shows that around 92% of the market players can increase their profits. An example of a randomly selected player shows that the platform suggests a strategy leading to a 127.6% increase in its profit compared with its real bids in the markets. |
| ISBN: | 978-1-7281-1078-3 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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http://hdl.handle.net/11583/2859299
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