Learning-assisted Stochastic Capacity Expansion Planning: A Bayesian Optimization Approach

• URL: http://arxiv.org/abs/2401.10451v4
• Date: Wed, 17 Jul 2024 16:43:25 GMT
• Title: Learning-assisted Stochastic Capacity Expansion Planning: A Bayesian Optimization Approach
• Authors: Aron Brenner, Rahman Khorramfar, Dharik Mallapragada, Saurabh Amin,
• Abstract summary: Large-scale capacity expansion problems (CEPs) are central to costeffective decarbonization of regional energy systems. Here, we propose a learning-assisted approximate solution method to tractably solve two-stage CEPs. We show that our approach yields an estimated cost savings of up to 3.8% in comparison to series aggregation approaches.
• Score: 3.124884279860061
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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