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arxiv: 2604.10795 · v1 · submitted 2026-04-12 · 📡 eess.SY · cs.SY

Optimization Under Uncertainty for Energy Infrastructure Planning: A Synthesis of Methods, Tools, and Open Challenges

Rahman Khorramfar , Aron Brenner , Lara Booth , Ana Rivera , Ruaridh Macdonald , Priya Donti , Saurabh Amin This is my paper

Pith reviewed 2026-05-10 15:33 UTC · model grok-4.3

classification 📡 eess.SY cs.SY
keywords energy infrastructure planningoptimization under uncertaintystochastic programmingrobust optimizationdistributionally robust optimizationgeneration and transmission expansionmachine learning integrationresearch gaps
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The pith

A survey categorizes optimization under uncertainty methods for energy infrastructure planning and traces gaps in fidelity, uncertainty handling, and solutions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper synthesizes recent advances in generation and transmission expansion planning when facing uncertainties from electrification, decarbonization, and extreme weather. It focuses on stochastic programming, robust optimization, and distributionally robust optimization as core approaches. The literature is organized along three axes of modeling fidelity, uncertainty characterization, and solution methods to spot dominant features and open research gaps. The work further examines how machine learning tools such as surrogate modeling, learned uncertainty sets, probabilistic forecasting, and synthetic scenarios can be embedded into these planning models. A sympathetic reader would care because clearer identification of gaps can guide more reliable long-term investment decisions for complex energy systems.

Core claim

The paper surveys recent advances at the intersection of generation and transmission expansion and optimization under uncertainty, with a focus on stochastic programming, robust optimization, and distributionally robust optimization. It categorizes modeling needs along the axes of modeling fidelity, uncertainty characterization, and solution methods to identify dominant modeling features and trace research gaps. It further examines emerging directions at the interface of optimization and machine learning, including surrogate modeling, learning uncertainty sets, probabilistic forecasting, and synthetic scenarios, and discusses how these tools can be embedded within infrastructure planning.

What carries the argument

The three-axis categorization framework of modeling fidelity, uncertainty characterization, and solution methods, which organizes reviewed methods to reveal dominant features and research gaps.

If this is right

  • Dominant features in current models can be used as benchmarks for evaluating new planning approaches.
  • Gaps in handling multi-carrier interdependencies and extreme events point to priorities for method development.
  • Embedding machine learning tools like surrogate modeling and synthetic scenarios can address computational and data challenges in existing frameworks.
  • Distributionally robust optimization offers a pathway to combine benefits of stochastic and robust methods for uncertain energy systems.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Filling the identified gaps would allow energy planners to produce investment strategies that remain stable under rapid electrification and climate-driven weather extremes.
  • The categorization could serve as a template for synthesizing optimization methods in related infrastructure domains such as water networks or transportation systems.
  • Empirical testing of new hybrid optimization-ML models against the dominant features listed could quantify progress on the traced gaps.
  • Stronger uncertainty characterization for extreme events may require closer ties to climate science data sources not fully covered in current energy planning literature.

Load-bearing premise

The reviewed literature and the chosen categorization axes comprehensively capture the dominant features and gaps in the field without significant omissions or biases in paper selection.

What would settle it

A new comprehensive review that identifies major bodies of literature on energy infrastructure planning under uncertainty using substantially different methods or axes, or that finds the selected papers omit key recent work, would falsify the synthesis.

Figures

Figures reproduced from arXiv: 2604.10795 by Ana Rivera, Aron Brenner, Lara Booth, Priya Donti, Rahman Khorramfar, Ruaridh Macdonald, Saurabh Amin.

Figure 1
Figure 1. Figure 1: Conditional co-occurrence of modeling features in the surveyed literature. Each cell reports the [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Propagation of tail event-induced disruption to other systems. In the left part, in pale green, the [PITH_FULL_IMAGE:figures/full_fig_p020_2.png] view at source ↗
read the original abstract

Energy infrastructure planning under uncertainty has become increasingly complex as electrification, interdependence between energy carriers, decarbonization, and extreme weather events reshape long-term investment decisions. This paper surveys recent advances at the intersection of generation and transmission expansion, and optimization under uncertainty, with a focus on stochastic programming, robust optimization, and distributionally robust optimization. We then categorize modeling needs along the axes of modeling fidelity, uncertainty characterization, and solution methods to identify dominant modeling features and trace research gaps. We further examine emerging directions at the interface of optimization and machine learning, including surrogate modeling, learning uncertainty sets, probabilistic forecasting, and synthetic scenarios, and discuss how these tools can be embedded within infrastructure planning models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The paper surveys recent advances at the intersection of generation and transmission expansion planning and optimization under uncertainty, with emphasis on stochastic programming, robust optimization, and distributionally robust optimization. It categorizes modeling needs along the axes of modeling fidelity, uncertainty characterization, and solution methods in order to identify dominant features and trace research gaps. The manuscript further examines emerging interfaces with machine learning, including surrogate modeling, learning uncertainty sets, probabilistic forecasting, and synthetic scenarios, and discusses their potential embedding within infrastructure planning models.

Significance. If the review is balanced and the categorization captures the literature without major omissions, the synthesis would be useful as a reference that consolidates methods for handling uncertainties arising from decarbonization, electrification, and extreme weather in long-term energy planning. The explicit discussion of open challenges and ML-integration pathways provides forward-looking value for the field.

minor comments (3)
  1. The abstract states that the categorization identifies 'dominant modeling features,' yet the manuscript provides only qualitative descriptions without summary statistics (e.g., counts or percentages of papers falling into each category) that would substantiate dominance claims.
  2. In the section reviewing distributionally robust optimization, the discussion of ambiguity-set construction would benefit from explicit comparison tables showing computational trade-offs versus stochastic programming on benchmark expansion-planning instances.
  3. The manuscript cites a number of recent works on surrogate modeling but does not indicate the publication-year cutoff used for the overall literature search, which affects reproducibility of the claimed coverage.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive assessment of our survey and for the accurate summary of its scope, contributions, and potential utility to the field. We are pleased that the synthesis of stochastic, robust, and distributionally robust optimization methods, the categorization framework, and the discussion of machine-learning interfaces are viewed as forward-looking and reference-worthy. No specific major comments were provided in the report.

Circularity Check

0 steps flagged

No significant circularity: survey of external literature

full rationale

The paper is a survey synthesizing advances in stochastic programming, robust optimization, and distributionally robust optimization for generation and transmission expansion planning. It categorizes modeling needs along axes of fidelity, uncertainty characterization, and solution methods, then discusses interfaces with machine learning. No original derivations, predictions, or equations are presented that reduce to the paper's own inputs by construction. Central claims rest on reviewed external works rather than self-referential steps, self-citations, or fitted parameters renamed as predictions. This matches the default expectation for non-circular survey papers.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

As a review paper, there are no new free parameters, invented entities, or novel axioms introduced; the work relies on standard assumptions in optimization literature and the completeness of the reviewed body of work.

axioms (1)
  • domain assumption The selected literature represents the state-of-the-art in optimization under uncertainty for energy infrastructure.
    Invoked implicitly in the survey's categorization and gap identification.

pith-pipeline@v0.9.0 · 5431 in / 1363 out tokens · 91036 ms · 2026-05-10T15:33:46.463136+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

8 extracted references · 8 canonical work pages · 2 internal anchors

  1. [1]

    Babbush, R

    (2026). ML OPF Wiki.https://energy.hosting.acm.org/wiki/index.php/ML_OPF_wiki. ACM SIGEnergy Wiki on Machine Learning for Optimal Power Flow. Abada, I., Ehrenmann, A., and Smeers, Y. (2017). Modeling gas markets with endogenous long-term contracts. Operations research, 65(4):856–877. Abbas, A., Ambainis, A., Augustino, B., Bärtschi, A., Buhrman, H., Coffr...

    work page arXiv 2026

  2. [2]

    Bonaldo, L., Chakrabarti, S., Cheng, F., Ding, Y., Jenkins, J., Luo, Q., Macdonald, R., Mallapragada, D., Manocha, A., Mantegna, G., Morris, J., Patankar, N., Pecci, F., Schwartz, A., Schwartz, J., Schivley, G., Sepulveda, N., and Xu, Q. (2026). Genx. 25 Borozan, S., Giannelos, S., Falugi, P., Moreira, A., and Strbac, G. (2024). Machine learning-enhanced ...

    work page arXiv 2026

  3. [3]

    Chen, B., Liu, T., Liu, X., He, C., Nan, L., Wu, L., and Su, X. (2022a). Distributionally robust coordinated expan- sion planning for generation, transmission, and demand side resources considering the benefits of concentrating solar power plants.IEEE Transactions on Power Systems, 38(2):1205–1218. Chen, B., Liu, T., Liu, X., He, C., Nan, L., Wu, L., Su, ...

    work page internal anchor Pith review arXiv 2025

  4. [4]

    Hansen, J., Sato, M., and Ruedy, R. (2012). Perception of climate change.Proceedings of the National Academy of Sciences, 109(37):E2415–E2423. Hauser, P. (2021). Does ‘more’equal ‘better’?–analyzing the impact of diversification strategies on infrastructure in the european gas market.Energy Policy, 153:112232. Hdidouan, D. and Staffell, I. (2017). The imp...

    work page 2012

  5. [5]

    Regional energy deployment system (ReEDS) model documentation: Version

    Ho, J., Becker, J., Brown, M., Brown, P., Chernyakhovskiy, I., Cohen, S., Cole, W., Corcoran, S., Eurek, K., Frazier, W., Gagnon, P., Gates, N., Greer, D., Jadun, P., Khanal, S., Machen, S., Macmillan, M., Mai, T., Mowers, M., Murphy, C., Rose, A., Schleifer, A., Sergi, B., Steinberg, D., Sun, Y., andZhou, E.(2021). Regional energy deployment system (ReED...

    work page 2021

  6. [6]

    Technical Report NREL/TP-6A20- 78195, National Renewable Energy Laboratory, Golden, CO. Hong, L. J., Huang, Z., and Lam, H. (2021). Learning-based robust optimization: Procedures and statistical guarantees.Management Science, 67(6):3447–3467. Howells, M., Rogner, H., Strachan, N., Heaps, C., Huntington, H., Kypreos, S., Hughes, A., Silveira, S., DeCarolis...

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  7. [7]

    Fsnet: Feasibility-seeking neural network for constrained optimization with guarantees.arXiv preprint arXiv:2506.00362,

    Moreira, A., Pozo, D., Street, A., andSauma, E.(2016). Reliablerenewablegenerationandtransmissionexpansion planning: co-optimizing system’s resources for meeting renewable targets.IEEE transactions on power systems, 32(4):3246–3257. Moreira, A., Pozo, D., Street, A., Sauma, E., and Strbac, G. (2021). Climate-aware generation and transmission expansion pla...

    work page arXiv 2016

  8. [8]

    Zhang, D., Zhang, Y., and Sen, S. (2025a). An adaptive stochastic dual progressive hedging algorithm for stochastic programming.https://arxiv.org/pdf/2407.20944. Zhang, H., Grossmann, I. E., McKinnon, K., Knudsen, B. R., Nava, R. G., and Tomasgard, A. (2025b). Integrated investment, retrofit and abandonment energy system planning with multi-timescale unce...

    work page arXiv 2024