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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.12305 |
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| _version_ | 1866918158410973184 |
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| author | Aung, Thiha Ludkovski, Mike |
| author_facet | Aung, Thiha Ludkovski, Mike |
| contents | We develop a mathematical model for intraday dispatch of co-located wind-battery energy assets. Focusing on the primary objective of firming grid-side actual production vis-a-vis the preset day-ahead hourly generation targets, we conduct a comprehensive study of the resulting stochastic control problem across different firming formulations and wind generation dynamics. Among others, we provide a closed-form solution in the special case of a quadratic objective and linear dynamics, as well as design a novel adaptation of a Gaussian Process-based Regression Monte Carlo algorithm for our setting. Extensions studied include an asymmetric loss function for peak shaving, capturing the cost of battery cycling, and the role of battery duration. In the applied portion of our work, we calibrate our model to a collection of 140+ wind-battery assets in Texas, benchmarking the economic benefits of firming based on outputs of a realistic unit commitment and economic dispatch solver. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_12305 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Intraday Battery Dispatch for Hybrid Renewable Energy Assets Aung, Thiha Ludkovski, Mike Optimization and Control Computational Finance Mathematical Finance We develop a mathematical model for intraday dispatch of co-located wind-battery energy assets. Focusing on the primary objective of firming grid-side actual production vis-a-vis the preset day-ahead hourly generation targets, we conduct a comprehensive study of the resulting stochastic control problem across different firming formulations and wind generation dynamics. Among others, we provide a closed-form solution in the special case of a quadratic objective and linear dynamics, as well as design a novel adaptation of a Gaussian Process-based Regression Monte Carlo algorithm for our setting. Extensions studied include an asymmetric loss function for peak shaving, capturing the cost of battery cycling, and the role of battery duration. In the applied portion of our work, we calibrate our model to a collection of 140+ wind-battery assets in Texas, benchmarking the economic benefits of firming based on outputs of a realistic unit commitment and economic dispatch solver. |
| title | Intraday Battery Dispatch for Hybrid Renewable Energy Assets |
| topic | Optimization and Control Computational Finance Mathematical Finance |
| url | https://arxiv.org/abs/2503.12305 |