Multistage stochastic inexact chance-constraint programming for an integrated biomass-municipal solid waste power supply management under uncertainty
In this study, a multistage stochastic inexact chance-constraint programming (MSICCP) model is developed for power supply management under uncertainties. In the MSICCP model, methods of multistage stochastic programming (MSP), interval-parameter programming (IPP), and chance-constraint programming (...
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| Published in: | Renewable & sustainable energy reviews Vol. 41; pp. 1244 - 1254 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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01.01.2015
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| ISSN: | 1364-0321, 1879-0690 |
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| Abstract | In this study, a multistage stochastic inexact chance-constraint programming (MSICCP) model is developed for power supply management under uncertainties. In the MSICCP model, methods of multistage stochastic programming (MSP), interval-parameter programming (IPP), and chance-constraint programming (CCP) are introduced into a general optimization framework, such that the developed model can tackle uncertainties described in terms of interval values and probability distribution s over a multistage context. Moreover, it can reflect dynamic and randomness of energy resources during the planning horizon. The developed method has been applied to a case of managing the process of power supply in an integrated biomass-municipal solid waste power plant. Useful solutions for the power supply management have been generated. Interval solutions associated with different risk levels of constraint violation have been obtained. The generated solutions can provide desired energy resource allocation with a minimized system cost, maximized system reliability and a maximized energy security. Tradeoffs between system costs and constraint-violation risks can also be tackled. Higher costs will increase system stability, while a desire for lower system costs will run into a risk of potential instability of the management system. They are helpful for supporting (a) adjustment or justification of allocation patterns of energy resources, and (b) analysis of interactions among economic cost, environmental requirement, and power supply security.
•Multistage stochastic inexact chance-constraint programming is developed.•Uncertainties are expressed as probability density functions and interval numbers.•Solutions associated with different risk levels have been obtained.•Tradeoffs between system costs and constraint-violation risks could be addressed. |
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| AbstractList | In this study, a multistage stochastic inexact chance-constraint programming (MSICCP) model is developed for power supply management under uncertainties. In the MSICCP model, methods of multistage stochastic programming (MSP), interval-parameter programming (IPP), and chance-constraint programming (CCP) are introduced into a general optimization framework, such that the developed model can tackle uncertainties described in terms of interval values and probability distribution s over a multistage context. Moreover, it can reflect dynamic and randomness of energy resources during the planning horizon. The developed method has been applied to a case of managing the process of power supply in an integrated biomass-municipal solid waste power plant. Useful solutions for the power supply management have been generated. Interval solutions associated with different risk levels of constraint violation have been obtained. The generated solutions can provide desired energy resource allocation with a minimized system cost, maximized system reliability and a maximized energy security. Tradeoffs between system costs and constraint-violation risks can also be tackled. Higher costs will increase system stability, while a desire for lower system costs will run into a risk of potential instability of the management system. They are helpful for supporting (a) adjustment or justification of allocation patterns of energy resources, and (b) analysis of interactions among economic cost, environmental requirement, and power supply security.
•Multistage stochastic inexact chance-constraint programming is developed.•Uncertainties are expressed as probability density functions and interval numbers.•Solutions associated with different risk levels have been obtained.•Tradeoffs between system costs and constraint-violation risks could be addressed. |
| Author | Xu, Y. Huang, G.H. Li, W. Xie, Y.L. Wu, C.B. |
| Author_xml | – sequence: 1 givenname: C.B. surname: Wu fullname: Wu, C.B. organization: MOE Key Laboratory of Regional Energy and Environmental Systems Optimization, S-C Resources and Environmental Research Academy, North China Electric Power University, Beijing 102206, China – sequence: 2 givenname: G.H. surname: Huang fullname: Huang, G.H. email: huang@iseis.org organization: MOE Key Laboratory of Regional Energy and Environmental Systems Optimization, S-C Resources and Environmental Research Academy, North China Electric Power University, Beijing 102206, China – sequence: 3 givenname: W. surname: Li fullname: Li, W. organization: MOE Key Laboratory of Regional Energy and Environmental Systems Optimization, S-C Resources and Environmental Research Academy, North China Electric Power University, Beijing 102206, China – sequence: 4 givenname: Y.L. surname: Xie fullname: Xie, Y.L. organization: MOE Key Laboratory of Regional Energy and Environmental Systems Optimization, S-C Resources and Environmental Research Academy, North China Electric Power University, Beijing 102206, China – sequence: 5 givenname: Y. surname: Xu fullname: Xu, Y. organization: Chinese Academy for Environmental Planning, Beijing 100012, China |
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| Keywords | Uncertainty Multistage stochastic programming Power supply management Chance-constraint programming |
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| Title | Multistage stochastic inexact chance-constraint programming for an integrated biomass-municipal solid waste power supply management under uncertainty |
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