A multi‐period optimization model for power sector with CO2 emission considerations. (5th October 2021) - Hakeem, Luqman; Khan, Muhammad Anas; Muhammad, Rizwan; Elkamel, Ali; Muhammad, Zaman
Summary: A multi‐period mixed‐integer linear programming model was developed to minimize the cumulative cost associated with electricity generation and reduce the carbon dioxide emissions. Fuel balancing and carbon capture and sequestration were investigated for carbon dioxide mitigation. The developed model was implemented on the power sector of Pakistan for a period of 11 years starting from 2020. All the existing and new planned power plants were included, and capacity expansion was only considered where required. Low, normal, and high gross domestic product growth rate‐based electricity demands were considered, keeping summer and winter demands separately. Fuel balancing can mitigate 38.5% average carbon dioxide emissions for both summer and winter, whereas carbon capture and sequestration can mitigate 26.2% and 2.2% average carbon dioxide emissions for summer and winter, respectively. Carbon capture and sequestration can mitigate 23.4% average carbon dioxide emissions (for normal gross domestic product summer) compared to 16.5% mitigation in fuel balancing (for normal gross domestic product summer and winter). Carbon dioxide emissions mitigation was negligible in carbon dioxide case (ie, 1.1%) for normal gross domestic product winter. For high gross domestic product case, fuel balancing could not mitigate carbon dioxide emissions, and carbon capture and sequestration was the only option for carbon dioxide mitigation, which resulted in 23.4% and 6.3% mitigation for summer and winter, respectively. Abstract : The main novelties of this research work include the incorporation of low, normal, and high GDP‐based energy demands for summer and winter separately, fuel balancing, and carbon capture and sequestration as mitigation techniques, capacity expansion in addition to the already planned power plants, variable transmission and distribution losses, and retirement plan for the existing power plants in the optimization model. The complex problem of energy planning was solved using economic and operation‐related data specific to the case study chosen for realistic results.