张 帆, 蔡 壮, 杨明皓. 基于随机机会约束规划的农村风/水/光发电容量配置[J]. 农业工程学报, 2010, 26(3): 267-271.
    引用本文: 张 帆, 蔡 壮, 杨明皓. 基于随机机会约束规划的农村风/水/光发电容量配置[J]. 农业工程学报, 2010, 26(3): 267-271.
    Capacity allocation of rural hybrid generating system based on stochastic chance constrained programming[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2010, 26(3): 267-271.
    Citation: Capacity allocation of rural hybrid generating system based on stochastic chance constrained programming[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2010, 26(3): 267-271.

    基于随机机会约束规划的农村风/水/光发电容量配置

    Capacity allocation of rural hybrid generating system based on stochastic chance constrained programming

    • 摘要: 开发利用农村丰富的风、水、光资源是缓解农村能源供需矛盾的有效途径,因地制宜多能源互补发电系统的优化配置方法是亟待解决的实际问题。该文采用逆变换法对农村风速和微水流量进行随机抽样,建立了风力和水力发电量的随机模型,在此基础上提出了基于随机机会约束规划的农村风水光发电系统优化配置数学模型。该模型以投资和年费用最小、供电可靠性和资源利用率尽可能高为目标,满足发用电功率平衡约束和机组发电功率符合资源条件约束。用蒙特卡罗模拟—遗传算法求解该模型可以得到在各种置信水平下满足目标和约束的若干优化配置方案以及各方案的评价指标,为用户提供决策支持。

       

      Abstract: It is proved to be an efficient way to improve energy supply in rural area by developing wind, hydro and solar energy. But how to optimize configuration properly is still an immediate problem to be solved. The generating unit models for wind and water power are statistic random models based on inverse transformation from random sampling of wind speed and water flow, and what’s more, the optimum disposition model of rural hybrid generating system based on random chance constrained programming is proposed. The disposition model are to minimize investment capital and operational costs per year, and to maximize the utilization of resources and the reliability of power supply, which is also subjected to power balance and constraints of local resources. A Monte Carto-GA approach for solving the generation disposition problem is given, and the solution is a set of optimum plans listed by an annual rate of resource utilization, the loss of load probability and the loss of power supply probability. An application case study is given to prove the feasibility of the model.

       

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