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

•The methodology minimizes the cost of energy while maximizes service pressures.•Cost reductions of about 6–7% were reached for scenarios without pressure deficit.•Computation time was reduced by almost 80%.•The optimizer was implemented as a series of web services by API-REST technology. The presen...

Full description

Saved in:
Bibliographic Details
Published in:Agricultural water management Vol. 227; p. 105857
Main Authors: Alonso Campos, J.C., Jiménez-Bello, M.A., Martínez Alzamora, F.
Format: Journal Article
Language:English
Published: Elsevier B.V 20.01.2020
Subjects:
algorithms
case studies
Computational efficiency
cost effectiveness
Cost minimization
energy
energy costs
irrigation scheduling
irrigation systems
Online optimization
Pressure maximization
system optimization
Online optimization
Cost minimization
Computational efficiency
Pressure maximization
ISSN:0378-3774, 1873-2283
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•The methodology minimizes the cost of energy while maximizes service pressures.•Cost reductions of about 6–7% were reached for scenarios without pressure deficit.•Computation time was reduced by almost 80%.•The optimizer was implemented as a series of web services by API-REST technology. 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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0378-3774
1873-2283
DOI:10.1016/j.agwat.2019.105857