Day-ahead interval optimization for CCHP system considering uncertainty of wind power and PV

Linjun Zeng, Jiazhu Xu*, Min Wu, Jiachang Tang, Qiuwei Wu, Qike Wang, Guoqi Fan

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review


To address the uncertainty of renewable energy and different load demands under different seasons in combined cooling, heating, and power (CCHP) system, a day-ahead interval scheduling optimization method is proposed for the CCHP system taking into account the uncertainty of wind power and photovoltaic (PV). Based on the interval theory, the uncertainty of wind power and PV are expressed by intervals. The total operating cost of carbon oxide emissions treatment cost and energy purchase cost is minimized as an objective function. A day-ahead interval optimization model of the CCHP system including wind power, PV, and energy storage is established considering different load demands in four seasons. The established nonlinear integer programming model is converted into the mixed-integer linear programming (MILP) model. The model is solved by interval linear programming. The CCHP system of a park is studied and compared with robust optimization. The simulation results verify the effectiveness of the method in this paper to achieve effective cooperative operation of the economy and environmental protection.

Original languageEnglish
Article number107895
JournalInternational Journal of Electrical Power and Energy Systems
Number of pages9
Publication statusPublished - 2021


  • CCHP
  • Day-ahead optimization
  • Interval optimization
  • MILP
  • Uncertainty


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