The balancing market as a significant part of the spot market addresses fair transaction settlements to eliminate the system imbalances in real time. However, traditional penalty mechanisms that have been also adopted for renewable generators may incur unintended consequences for such intermittent producers and even drive them out of the market. Therefore, here a flexible penalty mechanism (FPM) is adopted instead of the traditional policies to decrease the undesired impacts of traditional penalty mechanism (TPM) on the WPP's revenue. Aligning with the FPM, making a contract between the WPP and the insurance provider in which the system operator (SO) is in charge of system balance is proposed as a remedy instrument to control the risks of wind volatilities. The proposed framework is formulated as a bi-level trilateral problem, in which in the upper level, the WPP maximizes its expected profit and in the lower level, the SO determines the market clearing prices (MCP) and maximizes the social welfare. Due to the importance of forecasting wind power generation, three deep learning algorithms are also used. Simulation results show that by applying FPM, the WPP's profit improves depending on the contract it signed with the insurance provider while the SO preserves social welfare.

Implementing a Flexible Penalizing Mechanism for Wind Power Producers in the Regulating Market

Parente M.;Siano P.
2023-01-01

Abstract

The balancing market as a significant part of the spot market addresses fair transaction settlements to eliminate the system imbalances in real time. However, traditional penalty mechanisms that have been also adopted for renewable generators may incur unintended consequences for such intermittent producers and even drive them out of the market. Therefore, here a flexible penalty mechanism (FPM) is adopted instead of the traditional policies to decrease the undesired impacts of traditional penalty mechanism (TPM) on the WPP's revenue. Aligning with the FPM, making a contract between the WPP and the insurance provider in which the system operator (SO) is in charge of system balance is proposed as a remedy instrument to control the risks of wind volatilities. The proposed framework is formulated as a bi-level trilateral problem, in which in the upper level, the WPP maximizes its expected profit and in the lower level, the SO determines the market clearing prices (MCP) and maximizes the social welfare. Due to the importance of forecasting wind power generation, three deep learning algorithms are also used. Simulation results show that by applying FPM, the WPP's profit improves depending on the contract it signed with the insurance provider while the SO preserves social welfare.
2023
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4853066
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