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

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 stoc...

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Bibliographic Details
Published in:Energies (Basel) 2018-08, Vol.11 (8), p.2108
Main Authors: Xie, Yulei, Wang, Linrui, Huang, Guohe, Xia, Dehong, Ji, Ling
Format: Article
Language:English
Subjects:
Air pollution
Carbon dioxide
Case studies
electric-power structure adjustment
Electricity
Electricity consumption
Emission analysis
Emissions control
Energy
energy conservation and emissions reduction
Energy management
energy system management model
Multistage
Objective function
Optimization
Pollutants
Regional development
scenario-based multistage stochastic programming
Stochastic programming
stochastic robust optimization
Sustainable development
Uncertainty
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Summary: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 CO2 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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en11082108