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A Linear Chance-Constrained Mixed-Integer Programming Model for Optimizing Regional Electric Power Systems under Carbon Constraints

Y. Xu1, C. Z. Huang2 *, J. P. Chen1, B. Luo1, and Y. Y. Liu1

  1. Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan S4S0A2, Canada
  2. Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G2R3, Canada

*Corresponding author. Tel.: 1-778-858-0870. E-mail address: (C. Z. Huang).


In view of increasing population size and energy consumption, greenhouse gas (GHG) emissions are increasing and are one of the main causes of climate change. The regional electric power system is one of the main sources of carbon emissions, so there is an urgent need to optimize the regional electric power system to meet the Paris Agreement's long-term temperature goal. Therefore, this study provided a linear chance-constrained mixed-integer programming (LCMI) model with the objective of maximizing the total system profit and applying it to the regional electric power system. Chance-constrained programming and mixed-integer programming were integrated into the LCMI model to address input uncertainties. Including five commonly used power generation technologies, namely coal-fired, natural gas-fired, hydropower, wind power, and solar power, the model can provide the optimal electricity generation schemes and capacity expansion plans for different technologies at the regional level to meet the end-user’s needs while meeting the carbon dioxide emission targets under different risk levels. The outcomes of the research will offer decision-makers a framework for optimizing conventional regional electric power systems for their long-term sustainability in environmental and economic development.

Keywords: chance-constrained programming, uncertainty, system planning, capacity expansion, renewable energy

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