摘要
针对灌区水资源调度系统中的不确定性和复杂性,该文以红兴隆灌区为研究区域,构建区间两阶段模糊随机规划模型,并将其应用到灌区地表水和地下优化配置中,模型以灌区多水源联合调度系统收益最大为目标函数,引入区间数、模糊数、随机变量表示系统中的不确定性,对地表水和地下水在各作物之间配水目标进行优化。通过计算得到不同水源向不同作物配水的最优配水目标值及最优配置水量,模型不仅可以充分考虑到不确定性因素对系统收益的影响,而且可以将经济效益与处罚风险进行权衡。以2006年红兴隆灌区作物种植情况及灌溉情况为例进行研究分析,得到系统最大收益值在1 355.144×106～2 371.792×106元之间,该优化结果以区间形式给出,可以为决策者提供更为宽裕的决策空间,从而获得最为科学的决策方案。
Rapid population growth and economy development has led to increasing reliance on water resources. It is even aggravated for agricultural irrigation systems where more water is necessary to support the increasing population. In this study, an interval-parameter two-stage Fuzzy-stochastic optimization model was developed for dispatching the underground and surface water systems for different crops in Hong Xinglong irrigation of China under the conditions of uncertainty and complexity. In the model, the maximal system benefit was regarded as the objective function and 3 methods of probability density function, discrete intervals and fuzzy sets were introduced into the two-stage linear programming framework to resolve uncertain issues. The model allocated a predefined water to crops in the first stage, according to benefit and punishment for water shortage condition to adjust the water supply in the second stage, making the system reach the balance of systems benefit and the risk of punishment, the process of water allocation for multiple corps was simulated, meanwhile, the allocation of water from various sources was optimized. Because inflows water was of obvious probability characteristics in irrigation area, the model took into account of the random of inflow, and assumed that the probability of occurrence for high, middle and low levels were 0.2, 0.6 and 0.2. Since the quantity of stream flows, water requirement of crop and available water supply were uncertain, and uncertainties might also exist in system benefits and costs, the uncertain parameters of above-mentioned were described by interval variables. The available water in the irrigation area was represented by fuzzy sets based on credibility theory. The different probabilities, discrete interval number and fuzzy sets together were used to build the irrigation multi-water resource, multi-crop water distribution model. The model was solved by the method of linear programming, the optimal distribution scheme of water was achieved and the maximum benefit was 1 355.144×106-2 371.792×106 RMB. It could reflect not only uncertainties in water resources system, but also provide an effective linkage between conflicting economic benefits and the associated penalties attributed to the violation of the predefined water distribution target. Meanwhile, the results were presented in the forms of interval number, proving a more broad decision space for decision makers. Moreover, the results indicated that farmer planted a large number of high-yield and high water consumption of crops such as rice and corn in irrigation area and single planting structure would lead to the risk of the decrease of crop production in dry year, the model was valuable for supporting the adjustment or justification of the existing irrigation patterns and identify a desired water allocation plan for agricultural irrigation under uncertainty. Compared with the other traditional two-stage model, this model had advantages： 1） it considered uncertain factors as much as possible, made the model more close to actual condition; 2） The model effectively relieved groundwater pressure of water supply by utilizing surface water and groundwater; 3） The model results would suggest managers reducing planting area of high water consumption crops; 4） Water resources management by system benefit would stimulate employee enthusiasm; and 5） The model data was relatively easy to access.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2016年第12期107-114,共8页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然基金项目(51509036)
关键词
灌溉
水资源
不确定分析
区间两阶段模糊随机规划
多水源
优化配置
irrigation
water resources
uncertainty analysis
interval-parameter two-stage Fuzzy-stochastic programming
multi-water resource
optimal allocation
