CO2 emissions trading planning in combined heat and power production via multi-period stochastic optimization

Aiying Rong*, Risto Lahdelma

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

72 Citations (Scopus)


The EU emissions trading scheme (ETS) taking effect in 2005 covers CO2 emissions from specific large-scale industrial activities and combustion installations. A large number of existing and potential future combined heat and power (CHP) installations are subject to ETS and targeted for emissions reduction. CHP production is an important technology for efficient and clean provision of energy because of its superior carbon efficiency. The proper planning of emissions trading can help its potential into full play, making it become a true "winning technology" under ETS. Fuel mix or fuel switch will be the reasonable choices for fossil fuel based CHP producers to achieve their emissions targets at the lowest possible cost. In this paper we formulate CO2 emissions trading planning of a CHP producer as a multi-period stochastic optimization problem and propose a stochastic simulation and coordination approach for considering the risk attitude of the producer, penalty for excessive emissions, and the confidence interval for emission estimates. In test runs with a realistic CHP production model, the proposed solution approach demonstrates good trading efficiency in terms of profit-to-turnover ratio. Considering the confidence interval for emission estimates can help the producer to reduce the transaction costs in emissions trading. Comparisons between fuel switch and fuel mix strategies show that fuel mix can provide good tradeoff between profit-making and emissions reduction.

Original languageEnglish
Pages (from-to)1874-1895
Number of pages22
JournalEuropean Journal of Operational Research
Issue number3
Publication statusPublished - 1 Feb 2007
MoE publication typeA1 Journal article-refereed


  • CO emissions trading
  • Combined heat and power (CHP) production
  • Energy
  • Stochastic optimization


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