Multistage stochastic inexact chance-constraint programming for an integrated biomass-municipal solid waste power supply management under uncertainty - Wu, C.B.; Huang, G.H.; Li, W.; Xie, Y.L.; Xu, Y. (Renewable & sustainable energy reviews, 2015)
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.