A risk-averse cooperative framework for neighboring energy hubs under joint carbon, heat and electricity trading market with P2G and renewables

Liwen Zhang, Ting Hao Liu, Chenzhou Xu, Jun Zhang, Yujie Gao, Xin Cheng Li, Hao Tian Wang, Xin Li, Mahdi Mir

Research output: Contribution to journalArticlepeer-review

Abstract

The concept of neighboring energy hubs (NEHBs), which enable internal energy sharing and trading, is regarded as a viable strategy for advancing low-carbon energy transitions. This study proposes an optimization framework that integrates diverse technologies to coordinate electricity, heat, and carbon trading activities among multiple NEHBs. The framework accounts for the heterogeneity of NEHBs, facilitating the exchange of various energy forms and carbon emissions while incorporating renewable energy sources, electric vehicles, power-to-gas systems, and demand response programs. By enabling energy hubs to share surplus energy and carbon credits with neighboring hubs, the framework aims to maximize sustainability within an environmentally conscious operational model. To address uncertainties associated with stochastic renewable generation, unpredictable behavior of electric vehicles owners and energy demands, a probabilistic risk assessment algorithm is developed. This algorithm evaluates the expectation of downside risks of the scenario-based stochastic model, ensuring robust and reliable system performance. The results indicate that a nearly complete elimination of carbon emissions can be achieved with only a 4.86 % increase in operational costs. The achievement of substantial carbon reduction, even under the worst uncertainty scenarios, highlights the effectiveness of integrating carbon and energy trading with sustainable generation and carbon recycling technologies.

Original languageEnglish
Article number123241
JournalRenewable Energy
Volume250
DOIs
StatePublished - Sep 2025

Keywords

  • Carbon recycling
  • Down-side risk constraint
  • Electricity and heat trading market
  • Joint carbon
  • Power-to-gas facilities
  • Renewable resources
  • Stochastic programming

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