A stochastic program for biomass contract selection under demand uncertainty

Daniela Guericke*, Henrik Madsen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


Due to the carbon neutral goals in many countries, a shift from traditional fuels to biomass is currently taking place in the energy sector. In this publication, we are looking at the long-term biomass contracting decisions for combined heat and power plants and power producers. A major share of biomass contracts are long-term contracts with runtimes of around 1 year, so the actual biomass demand is still uncertain when the contracts are negotiated. The operators can select different types of contracts ranging from fixed contracts with fixed amounts and deliveries to more flexible contracts and call options that allow for some flexibility in terms of amount and delivery times. We propose a stochastic program to optimize the contract selection including amounts and deliveries taking the biomass storage and uncertain demand into account. We present results of a case study from industry and show how the model utilizes the contracts for flexibility to adapt to different demand scenarios. Furthermore, the model is used for investigating the tradeoff between storage restrictions and fulfilling the demand in all scenarios. We show why it is important to model this problem as a stochastic program and why considering an expected demand is not enough.

Original languageEnglish
JournalEnergy Systems
Number of pages19
Publication statusPublished - 2021

Bibliographical note

Funding Information:
The authors thank Ørsted A/S for their valuable input and comments to this work. The work of D. Guericke and H. Madsen is funded by Innovation Fund Denmark through the CITIES research center (no. 1035-00027B).

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.


  • Biomass contracts
  • Biomass supply planning
  • Flexibility
  • Stochastic programming
  • Value of stochastic solution


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