In the last decade, several applications and studies have demonstrated that, thanks to their fast response time, Battery Energy Storage Systems (BESSs) are a promising technology to incorporate operational flexibility in grids and plants, opening to the possibility of providing a set of different grid services. In this context, this paper defines an optimization model to encode UK market rules and output the optimal set of offers on day-ahead and intraday markets, dynamic frequency response services and imbalance settlement, with a specific focus on a BESS. The proposed mathematical model includes BESS technical constraints and market temporal sequencing, also modelling the State Of Energy Management. Limitations on the number of cycles per day and optimization of the battery use are implemented. A Mixed Integer Linear Programming problem is defined to reduce the computational burden and to integrate the model in the Energy Management System (EMS) developed by the University of Genoa. Specific tests have been performed, allowing to compare scenarios in which ancillary services are enabled or not. According to the results, Dynamic Frequency Response services are the most profitable market. The integration in the EMS of the constraints related to the encoded market sections allows to exploit this tool to evaluate the profitability of investments involving a BESS over long time horizon.
A Tool to Optimize the Participation of BESS to the UK Ancillary Services Market
Virginia Casella;Alice La Fata;
2024-01-01
Abstract
In the last decade, several applications and studies have demonstrated that, thanks to their fast response time, Battery Energy Storage Systems (BESSs) are a promising technology to incorporate operational flexibility in grids and plants, opening to the possibility of providing a set of different grid services. In this context, this paper defines an optimization model to encode UK market rules and output the optimal set of offers on day-ahead and intraday markets, dynamic frequency response services and imbalance settlement, with a specific focus on a BESS. The proposed mathematical model includes BESS technical constraints and market temporal sequencing, also modelling the State Of Energy Management. Limitations on the number of cycles per day and optimization of the battery use are implemented. A Mixed Integer Linear Programming problem is defined to reduce the computational burden and to integrate the model in the Energy Management System (EMS) developed by the University of Genoa. Specific tests have been performed, allowing to compare scenarios in which ancillary services are enabled or not. According to the results, Dynamic Frequency Response services are the most profitable market. The integration in the EMS of the constraints related to the encoded market sections allows to exploit this tool to evaluate the profitability of investments involving a BESS over long time horizon.
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