A Stochastic Inexact Robust Model for Regional Energy System Management and Emission Reduction Potential Analysis—A Case Study of Zibo City, China

Yulei Xie, Linrui Wang, Guohe Huang, Dehong Xia and Ling Ji
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Yulei Xie: School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
Linrui Wang: The Vehicle Pollution Prevention and Control Center of Jinan, Jinan Environmental Protection Bureau, Jinan 250099, Shandong, China
Guohe Huang: Environmental Systems Engineering Program, Faculty of Engineering, University of Regina, Regina, SK S4S 0A2, Canada
Dehong Xia: School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
Ling Ji: School of Economics and Management, Beijing University of Technology, Beijing 100124, China

Energies, 2018, vol. 11, issue 8, 1-24

Abstract: In this study, in order to improve regional energy system adjustment, a multistage stochastic inexact robust programming (MSIRP) is proposed for electric-power generation planning and structure adjustment management under uncertainty. Scenario-based inexact multistage stochastic programming and stochastic robust optimization were integrated into general programming to reflect uncertainties that were expressed as interval values and probability distributions in the objective function and constraints. An MSIRP-based energy system optimization model is proposed for electric-power structure management of Zibo City in Shandong Province, China. Three power demand scenarios associated with electric-power structure adjustment, imported electricity, and emission reduction were designed to obtain multiple decision schemes for supporting regional sustainable energy system development. The power generation schemes, imported electricity, and emissions of CO 2 and air pollutants were analyzed. The results indicated that the model can effectively not only provide a more stable energy supply strategies and electric-power structure adjustment schemes, but also improve the balanced development between conventional and new clear power generation technologies under uncertainty.

Keywords: scenario-based multistage stochastic programming; energy system management model; stochastic robust optimization; electric-power structure adjustment; energy conservation and emissions reduction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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