An Interval-Parameter Based Two-Stage Stochastic Programming for Regional Electric Power Allocation

Author(s):  
Jingqi Dai ◽  
Xiaoping Li
Author(s):  
Xianrui Liao ◽  
Chong Meng ◽  
Zhixing Ren ◽  
Wenjin Zhao

The optimization of ecological water supplement scheme in Momoge National Nature Reserve (MNNR), using an interval-parameter two-stage stochastic programming model (IPTSP), still experiences problems with fuzzy uncertainties and the wide scope of the obtained optimization schemes. These two limitations pose a high risk of system failure causing high decision risk for decision-makers and render it difficult to further undertake optimization schemes respectively. Therefore, an interval-parameter fuzzy two-stage stochastic programming (IPFTSP) model derived from an IPTSP model was constructed to address the random variable, the interval uncertainties and the fuzzy uncertainties in the water management system in the present study, to reduce decision risk and narrow down the scope of the optimization schemes. The constructed IPFTSP model was subsequently applied to the optimization of the ecological water supplement scheme of MNNR under different scenarios, to maximize the recovered habitat area and the carrying capacity for rare migratory water birds. As per the results of the IPFTSP model, the recovered habitat areas for rare migratory birds under low, medium and high flood flow scenarios were (14.06, 17.88) × 103, (14.92, 18.96) × 103 and (15.83, 19.43) × 103 ha, respectively, and the target value was (14.60, 18.47) × 103 ha with a fuzzy membership of (0.01, 0.83). Fuzzy membership reflects the possibility level that the model solutions satisfy the target value and the corresponding decision risk. We further observed that the habitat area recovered by the optimization schemes of the IPFTSP model was significantly increased compared to the recommended scheme, and the increases observed were (5.22%, 33.78%), (11.62%, 41.88%) and (18.44%, 45.39%). In addition, the interval widths of the recovered habitat areas in the IPFTSP model were reduced by 17.15%, 17.98% and 23.86%, in comparison to those from the IPTSP model. It was revealed that the IPFTSP model, besides generating the optimal decision schemes under different scenarios for decision-makers to select and providing decision space to adjust the decision schemes, also shortened the decision range, thereby reducing the decision risk and the difficulty of undertaking decision schemes. In addition, the fuzzy membership obtained from the IPFTSP model, reflecting the relationship among the possibility level, the target value, and the decision risk, assists the decision-makers in planning the ecological water supplement scheme with a preference for target value and decision risk.


2009 ◽  
Vol 36 (4) ◽  
pp. 592-606 ◽  
Author(s):  
Y.P. Li ◽  
G.H. Huang

In this study, an interval-parameter robust optimization (IPRO) method is developed through incorporating techniques of interval-parameter programming and robust optimization within a two-stage stochastic programming framework. The IPRO improves upon the two-stage stochastic programming methods by allowing uncertainties presented as both intervals and random variables to be handled in the optimization system. Moreover, in the modeling formulation, penalties are exercised with the recourse against any infeasibility, and robustness measures are introduced to examine the variability of the second-stage costs that are above the expected level. The IPRO is generally suitable for risk-aversive planners under high-variability conditions. The developed method is applied to a case of long-term waste management under uncertainty. Interval solutions under different robustness levels have been generated. They cannot only be used for analyzing various policy scenarios that are related to different levels of economic penalties when the pre-regulated waste allocation allowances are violated, but also help decision makers to analyze the interrelationships between the penalties and their variabilities.


2016 ◽  
Vol 30 (7) ◽  
pp. 2227-2243 ◽  
Author(s):  
Qiang Fu ◽  
Ke Zhao ◽  
Dong Liu ◽  
Qiuxiang Jiang ◽  
Tianxiao Li ◽  
...  

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