Learning-assisted Stochastic Capacity Expansion Planning: A Bayesian Optimization Approach
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910529906278400 |
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| author | Brenner, Aron Khorramfar, Rahman Mallapragada, Dharik Amin, Saurabh |
| author_facet | Brenner, Aron Khorramfar, Rahman Mallapragada, Dharik Amin, Saurabh |
| contents | Solving large-scale capacity expansion problems (CEPs) is central to cost-effective decarbonization of regional-scale energy systems. To ensure the intended outcomes of CEPs, modeling uncertainty due to weather-dependent variable renewable energy (VRE) supply and energy demand becomes crucially important. However, the resulting stochastic optimization models are often less computationally tractable than their deterministic counterparts. Here, we propose a learning-assisted approximate solution method to tractably solve two-stage stochastic CEPs. Our method identifies low-cost planning decisions by constructing and solving a sequence of tractable temporally aggregated surrogate problems. We adopt a Bayesian optimization approach to searching the space of time series aggregation hyperparameters and compute approximate solutions that minimize costs on a validation set of supply-demand projections. Importantly, we evaluate solved planning outcomes on a held-out set of test projections. We apply our approach to generation and transmission expansion planning for a joint power-gas system spanning New England. We show that our approach yields an estimated cost savings of up to 3.8% in comparison to benchmark time series aggregation approaches. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_10451 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning-assisted Stochastic Capacity Expansion Planning: A Bayesian Optimization Approach Brenner, Aron Khorramfar, Rahman Mallapragada, Dharik Amin, Saurabh Systems and Control Machine Learning Solving large-scale capacity expansion problems (CEPs) is central to cost-effective decarbonization of regional-scale energy systems. To ensure the intended outcomes of CEPs, modeling uncertainty due to weather-dependent variable renewable energy (VRE) supply and energy demand becomes crucially important. However, the resulting stochastic optimization models are often less computationally tractable than their deterministic counterparts. Here, we propose a learning-assisted approximate solution method to tractably solve two-stage stochastic CEPs. Our method identifies low-cost planning decisions by constructing and solving a sequence of tractable temporally aggregated surrogate problems. We adopt a Bayesian optimization approach to searching the space of time series aggregation hyperparameters and compute approximate solutions that minimize costs on a validation set of supply-demand projections. Importantly, we evaluate solved planning outcomes on a held-out set of test projections. We apply our approach to generation and transmission expansion planning for a joint power-gas system spanning New England. We show that our approach yields an estimated cost savings of up to 3.8% in comparison to benchmark time series aggregation approaches. |
| title | Learning-assisted Stochastic Capacity Expansion Planning: A Bayesian Optimization Approach |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2401.10451 |