J.A. la Poutré

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Dive into the research topics where J.A. la Poutré is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

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39 Conference contribution
12 Article
8 Conference article
4 Chapter
More
- 2 Paper
- 1 Report

Topology Reduction for Determining Worst-Case Attacks in Radially Operated Distribution Networks
Cremers, S., Dijk, M. V. & Poutré, H. L., 2025, Proceedings of the 2025 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe). IEEE, 5 p.
Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review
Incentives for Accurate Energy Predictions: How to Reduce Epistemic Uncertainty
Saur, R., la Poutré, J. A. & Yorke-Smith, N., 2024, Proceedings of the e-Energy '24. p. 192 - 202
Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

Open Access
File
Fair Pricing for Time-Flexible Smart Energy Markets
Saur, R., Poutré, H. L. & Yorke-Smith, N., 2023, In: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS. 2023-May, p. 2703-2705 3 p.
Research output: Contribution to journal › Conference article › Scientific › peer-review

Open Access
File
56 Downloads (Pure)
Fairness vsWelfare: A Hybrid Congestion Aftermarket
Hekkelman, B. & La Poutré, H., 2022, e-Energy 2022 - Proceedings of the 2022 13th ACM International Conference on Future Energy Systems. Association for Computing Machinery (ACM), p. 93-104 12 p. (e-Energy 2022 - Proceedings of the 2022 13th ACM International Conference on Future Energy Systems).
Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

Open Access
File
2 Link opens in a new tab Citations (Scopus)

66 Downloads (Pure)
AI as an accelerator of the energy transitition: Opportunities for a carbon-free energy system
Agterberg, P., Bijl, M., Hurink, J. L., la Poutré, J. A., van de Vreede, G., de Weerdt, M. M. & Wilbrink, T., 2021, 28 p.
Research output: Book/Report › Report › Professional

File

136 Downloads (Pure)

Code supporting the publication: Towards optimal demand-side bidding in parallel auctions for time-shiftable electrical loads
Yorke-Smith, N. (Creator), Saur, R. (Creator) & la Poutré, J. A. (Creator), TU Delft - 4TU.ResearchData, 18 Mar 2026
DOI: 10.4121/A6DB0891-31EE-491F-B5C1-CF5A0FECA4F3
Dataset/Software: Dataset
Data and code supporting the publication: Incentives for accurate energy predictions: How to reduce epistemic uncertainty
Yorke-Smith, N. (Creator), Saur, R. (Creator) & la Poutré, J. A. (Creator), TU Delft - 4TU.ResearchData, 18 Mar 2026
DOI: 10.4121/1C4EFCD7-1ACA-498D-9337-091C8AE49DB2
Dataset/Software: Dataset

J.A. la Poutré

Fingerprint

Collaborations and top research areas from the last five years

Dive into details

Topology Reduction for Determining Worst-Case Attacks in Radially Operated Distribution Networks

Incentives for Accurate Energy Predictions: How to Reduce Epistemic Uncertainty

Fair Pricing for Time-Flexible Smart Energy Markets

Fairness vsWelfare: A Hybrid Congestion Aftermarket

AI as an accelerator of the energy transitition: Opportunities for a carbon-free energy system

Code supporting the publication: Towards optimal demand-side bidding in parallel auctions for time-shiftable electrical loads

Data and code supporting the publication: Incentives for accurate energy predictions: How to reduce epistemic uncertainty

J.A. la Poutré

Fingerprint

Collaborations and top research areas from the last five years

Research output

Topology Reduction for Determining Worst-Case Attacks in Radially Operated Distribution Networks

Incentives for Accurate Energy Predictions: How to Reduce Epistemic Uncertainty

Fair Pricing for Time-Flexible Smart Energy Markets

Fairness vsWelfare: A Hybrid Congestion Aftermarket

AI as an accelerator of the energy transitition: Opportunities for a carbon-free energy system

Datasets

Code supporting the publication: Towards optimal demand-side bidding in parallel auctions for time-shiftable electrical loads

Data and code supporting the publication: Incentives for accurate energy predictions: How to reduce epistemic uncertainty