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

arXiv: https://arxiv.org/abs/2401.10451 · HTML: https://mlsanity.com/paper/2401.10451/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 19 Jan 2024.

## Builds on (most cited first, 9 of 9)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon](https://mlsanity.com/paper/1811.06128/) (2018)
- [Bayesian Optimization of Risk Measures](https://mlsanity.com/paper/2007.05554/) (2020)
- [Extreme events in time series aggregation: A case study for optimal residential energy supply systems](https://mlsanity.com/paper/2002.03059/) (2020)
- [A modeler's guide to handle complexity in energy systems optimization](https://mlsanity.com/paper/2009.07216/) (2020)
- [Electric-Gas Infrastructure Planning for Deep Decarbonization of Energy Systems](https://mlsanity.com/paper/2212.13655/) (2022)
- [Learning Active Constraints to Efficiently Solve Linear Bilevel Problems: Application to the Generator Strategic Bidding Problem](https://mlsanity.com/paper/2010.06344/) (2020)
- [Cost-effective Planning of Decarbonized Power-Gas Infrastructure to Meet the Challenges of Heating Electrification](https://mlsanity.com/paper/2308.16814/) (2023)
- [Learning Spatio-Temporal Aggregations for Large-Scale Capacity Expansion Problems](https://mlsanity.com/paper/2303.08996/) (2023)

## Built on by (most cited first, 1 of 1)

- [Tractable Probabilistic Models for Investment Planning](https://mlsanity.com/paper/2511.13888/) (2025)

## Abstract (arXiv)

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.