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Robust Unit Commitment in District Heating Networks: Chance-Constrained and CVaR Optimization Under Demand Uncertainty — arXiv:2608.10899

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.10899
License (as archived): CC0-1.0
Archived: 2026-10-19T06:26:36.775Z
SHA-256 of the archived text: 8444fd73e65f39772d1a49ad737a29e0e704a47354e08543eea7dbdabacb7275
Stable URL of this page: https://metraipolis.com/documento/dc_75aead91ea97

How to cite

Robust Unit Commitment in District Heating Networks: Chance-Constrained and CVaR Optimization Under Demand Uncertainty — arXiv:2608.10899 — arXiv (en), https://arxiv.org/abs/2608.10899 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_75aead91ea97 (Metraipolis archive dc_75aead91ea97, sha256 8444fd73e65f39772d1a49ad737a29e0e704a47354e08543eea7dbdabacb7275)

BibTeX

@misc{dc75aead91ea97,
  title = {Robust Unit Commitment in District Heating Networks: Chance-Constrained and CVaR Optimization Under Demand Uncertainty — arXiv:2608.10899},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.10899},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_75aead91ea97; sha256 8444fd73e65f39772d1a49ad737a29e0e704a47354e08543eea7dbdabacb7275},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.10899. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

While district heating networks are a key component of the energy transition, their operational planning is challenging due to substantial uncertainty in heat demand. We address this volatility without compromising system reliability. By adapting chance-constrained programming (CC) and conditional value-at-risk (CVaR) optimization to the mixed-integer unit-commitment problem for district-heating networks, we can compute optimal unit-commitment schedules under demand uncertainty. We generate heat demand time series using a Bayesian model, which explicitly quantifies forecasting uncertainty and yields predictive distributions as input to the network optimization. To handle the inherent uncertainty in heat demand, we apply both robust optimization approaches: chance-constrained programming limits the probability of unmet demand, while CVaR optimization penalizes severe shortfalls in the tail of the distribution. We evaluate the approaches both on real-world data from the Berlin district heating network and on a benchmark set of synthetic, realistically parameterized instances of varying sizes. The results compare the two uncertainty-handling methods with respect to solution quality, risk exposure, and computational effort.

Tema degli esploratori: matematica e informatica teorica (retro-catalogo arXiv: annunci del 2026-08-12 in math.OC).
Categorie arXiv: Optimization and Control (math.OC).
Pagina del lavoro: https://arxiv.org/abs/2608.10899 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T06:19:30.098Z dalla pagina https://arxiv.org/catchup/math.OC/2026-08-12?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).