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Real-time energy optimization of irrigation scheduling by parallel multi-objective genetic algorithms

机译：平行多目标遗传算法灌溉调度的实时能量优化

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The present work is motivated by the need to reduce the energy costs arising from the pressure demands of drip and sprinkling irrigation, compounded by the increase in the energy price in recent years. Researchers have demonstrated that proper operation of the irrigation network reduces associated pumping costs. The main challenge was to obtain the optimal operation parameters on near real-time due to the fact that the high complexity of the optimization problem requires a great computational effort. The classic approach to the problem imposes a strict fulfilment of minimum pressures as a restriction. This study, however, presents a new methodology for the reordering of irrigation scheduling, incorporating the constraint of daily volume requests for each hydrant. The methodology is capable of minimizing the cost of energy while maximizing pressures at the critical hydrants. Cost reductions of about 6-7% were reached for scenarios without pressure deficit for the case study. Greater computational efficiency was achieved by posing the problem from a multi-objective approach, on the one hand, and by establishing the parallel evaluation of the objective function, on the other. The speed-up obtained by combining a reduction in the number of function evaluations thanks to the faster convergence of the multi-objective approach and the reduction of the computational time due to the parallelization of the algorithm achieved results about 10 times faster. This improvement allowed the tool to be implemented for the daily optimization of irrigation requests.
机译：目前的工作是有必要降低滴水和喷洒灌溉的压力需求所产生的能源成本，近年来能源价格的增加。研究人员已经证明，灌溉网络的适当运行降低了相关的泵送成本。主要挑战是在近实时获得最佳运行参数，因为优化问题的高复杂性需要巨大的计算工作。该问题的经典方法强烈满足最小压力作为限制。然而，本研究提出了重新排序灌溉调度的新方法，包括每次消防栓的日常量请求的约束。该方法能够最小化能量成本，同时最大限度地提高关键消防栓的压力。对于案例研究的情况，达到了大约6-7％的成本减少约6-7％。通过一方面从多目标方法构成问题，并通过建立对象函数的并行评估来实现更大的计算效率。通过组合函数评估数量的减少而获得的速度，得益于多目标方法的更快的收敛性和由于算法的并行化导致的计算时间的减少而速度快大约10倍。这种改进允许该工具用于灌溉请求的日常优化。

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