Berg, Wendy van den (2024) Optimizing the bidding strategy of agents owning a battery in the day-ahead energy market under price uncertainty. Research Project, Industrial Engineering and Management.
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Abstract
The deregulation of electricity markets has led to an increased demand for optimizing market bidding strategies. Consequently, this research focuses on the optimization of bidding strategies for an agent owning a Battery Energy Storage System (BESS), participating in the day-ahead Local Electricity Market (LEM). It investigates both stochastic and robust optimization domains to navigate the uncertainty inherent in LEM day-ahead prices. Consequently, this research develops two stochastic optimization (SO) models and one robust optimization (RO) model aimed at maximizing the agent’s bidding schedule’s profitability. In addition, the models are extended to account for the risk attitude of the agent. In the stochastic optimization models, risk management is implemented through the utilization of the Conditional Value at Risk (CVaR) measure. Robust optimization adjusts the size of the uncertainty set using the Budget of Uncertainty, thereby adapting the risk attitude of the agent. Evaluation of the developed models is conducted through a series of case studies, evaluating the pre-clearance and post-clearance performance metrics. The incorporation of post-clearance performance evaluation in addition to pre-clearance performance assessment represents a novel approach utilized in this research. SO model 2 outperforms the other models in terms of expected profit and bid clearance performance, indicating its effectiveness in the LEM day-ahead market.
Item Type: | Thesis (Research Project) |
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Supervisor name: | Monshizadeh Naini, N. and Cherukuri, A.K. |
Degree programme: | Industrial Engineering and Management |
Thesis type: | Research Project |
Language: | English |
Date Deposited: | 13 Mar 2024 12:17 |
Last Modified: | 15 Apr 2024 11:20 |
URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/32045 |
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