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Determination of the Cascade Reservoir Operation for Optimal Firm-Energy Using Genetic Algorithms

机译:用遗传算法确定最优能源的梯级水库调度

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- Indonesia today face a new paradigm in water management where aim to apply integrated water resources management has become unavoidable task in purpose of achieving greater level of effectiveness and efficiency. On of most interesting case study is the case of Citarum river, one of the most potential river for water supply in West Java, Indonesia. Alongside the river, Saguling, Cirata and Djuanda Reservoirs had been constructed in series/cascade. Saguling and Cirata reservoirs are particularly operated for hydroelectric power and Djuanda is multipurpose reservoir mainly operated for irrigation and contribute domestic water supply for Jakarta (capital city of Indonesia). Basically all reservoirs are relying on same resources, therefore this condition has considered addressing management and operational problem. Therefore, an approach toward new management and operation system are urgently required in order to achieve the effective and efficient output and to avoid conflicts of water used. This study aims to obtain energy production from Citarum Cascade Reservoir System using Genetic Algorithms optimization with the objective function to maximize firm-energy. Firm-energy is the minimum energy requirements must be available in a certain time period. Then, the result obtained by using the energy produced by GA is compared to the conventional searching technique of Non-Linier Programming (NLP). The GA derived operating curves reveal the higher energy and firm-energy than NLP model. Key w ords: Genetic Algorithms, Citarum Cascade Reservoir, Firm-Energy, Electrical Energy.
机译:-印度尼西亚今天面临着水资源管理的新范式,为了实现更高水平的效益和效率,旨在应用综合水资源管理已成为不可避免的任务。最有趣的案例研究是Citarum河,这是印度尼西亚西爪哇省供水潜力最大的河流之一。沿河,萨古林,西拉塔和朱安达水库已按系列/梯级建造。 Saguling和Cirata水库主要用于水力发电,Djuanda是多功能水库,主要用于灌溉,并为雅加达(印度尼西亚的首都)提供生活用水。基本上所有水库都依赖于相同的资源,因此这种情况已考虑解决管理和运营问题。因此,迫切需要一种新的管理和运营系统的方法,以实现有效和高效的产出并避免用水冲突。这项研究的目的是使用目标函数使企业能源最大化的遗传算法优化,从Citarum梯级水库系统获得能源产量。固定能量是一定时间内必须具备的最低能量要求。然后,将使用遗传算法产生的能量获得的结果与传统的非线性规划(NLP)搜索技术进行比较。 GA得出的工作曲线显示出比NLP模型更高的能量和坚定能量。关键指标:遗传算法,Citarum级联水库,公司能源,电能。

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