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针对缺水地区的水资源供需不平衡问题，假设水源地可供水量为随机变量，将供水风险定义为未来实际供水量小于规划供水量的可能性，同时以经济收益最大化和环境污染最小化为目标函数，构建了基于供水风险的多目标讨价还价模型(RMOBM)。最后，将RMOBM模型应用于以岳城水库为水源的邯郸市受水区农业配水案例中，并且与确定性多目标讨价还价模型(DMOBM)做比较。结果表明：(1)通过RMOBM模型得到的方案更加灵活多样化，决策者可以根据不同的风险等级选择相应的配水方案；(2)供水目标水位等级越高，规划供水量越大，未来实际供水量越难以达到规划供水量，即供水风险越高，经济收益越大，环境污染越严重；(3)低水位情景下，RMOBM模型的配水方案与DMOBM模型配水方案相比，平水年配水差值最大，丰水年次之，枯水年最小；中等水位和高水位情景下，枯水年差值最大，丰水年次之，平水年最小。
Abstract：A multi-objective bargaining model based on water supply risk(RMOBM) is established by taking the maximization of economic benefits and the minimization of environmental pollution as the objective functions. Then the water supply risk is defined as the possibility that the actual future water supply is less than the planned water supply aiming at the unbalanced supply and demand of water resources in the water shortage area. And the water supply of the water source is assumed as a random variable. Finally, the RMOBM model is applied to the case of agricultural water distribution in Handan, which takes Yuecheng Reservoir as the water source, and is compared with the deterministic multi-objective bargaining model(DMOBM). The results show that:(1) the schemes are more flexible and diversified for the RMOBM model, and the decision makers can choose the corresponding water distribution schemes according to different risk levels;(2) as the target water level and the planned water supply increase, it is more difficult for the actual future water supply to reach the planned water supply, that is, the water supply risk is higher, the economic benefits are greater and the environmental pollution is more serious;(3) in the low water level scenario, the difference is the largest in normal year between RMOBM model and DMOBM model, followed by wet year, and the smallest in dry year; under the scenarios of medium and high water levels, the difference of dry year is the largest, followed by wet year, and the smallest in normal year.
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基本信息:
DOI：10.13928/j.cnki.wrahe.2022.09.010
中图分类号:TV213.4
引用信息:
[1]安静,庞树江,王小胜,等.基于供水风险的灌溉水资源多目标优化配置模型[J],2022,53(09):100-111.DOI:10.13928/j.cnki.wrahe.2022.09.010.
基金信息:
国家自然科学基金(61873084)