PATs selection towards sustainability in irrigation networks: Simulated annealing as a water management tool

Abstract Irrigation networks involve many water and energy aspects, in which the sustainability plays a paramount role. Nowadays, water tariff in irrigation networks represents a high percentage within of farmers' costs, partially due to the low hydraulic and energy efficiency. The installation of pumps working as turbines enables to reduce the pressure and it makes possible the energy generation. In this research, a new maximization methodology to recover energy, considering the feasibility of the installation, was proposed to allocate PATs within networks. Simulated annealing techniques were used with different objective functions as well as different number of machines. Once the maximum energy lines were defined, real machines were selected through the discharge and the head number, considering the available net head in each allocation. Furthermore, the use of WaterGEMS® software enabled to simulate the flow, the pressure and the efficiency variation in the installed machine over time. The combined use of WaterGEMS® and the simulated annealing in the proposed methodology is a powerful water management tool towards the search of the sustainability in irrigation networks. To illustrate the proposed methodology, a case study was presented, obtaining a recovered energy of 26.51 MW h/year (10% of the provided energy in the network).

[1]  Masayuki Ishii,et al.  Split-Pipe Design of Water Distribution Network Using Simulated Annealing , 2007 .

[2]  Helena M. Ramos,et al.  Modeling Irrigation Networks for the Quantification of Potential Energy Recovering: A Case Study , 2016 .

[3]  José Maria Tarjuelo,et al.  Energy efficiency of pressurised irrigation networks managed on-demand and under a rotation schedule. , 2010 .

[4]  Fran Estrada Tarragó Micro-hydro solutions in Alqueva Multipurpose Project (AMP) towards water-energy-environmental efficiency improvements , 2014 .

[5]  Mac McKee,et al.  Estimation of Surface Soil Moisture in Irrigated Lands by Assimilation of Landsat Vegetation Indices, Surface Energy Balance Products, and Relevance Vector Machines , 2016 .

[6]  Nikolaos Theodosiou,et al.  Exploring the potential of energy recovery using micro hydropower systems in water supply systems , 2014 .

[7]  Piero P. Bonissone,et al.  On heuristics as a fundamental constituent of soft computing , 2008, Fuzzy Sets Syst..

[8]  Kathryn A. Dowsland,et al.  Simulated Annealing , 1989, Encyclopedia of GIS.

[9]  Stefano Malavasi,et al.  A Control Valve for Energy Harvesting , 2014 .

[10]  Sadiq M. Sait,et al.  Evolutionary algorithms, simulated annealing and tabu search: a comparative study , 2001 .

[11]  Scott Kirkpatrick,et al.  Optimization by Simmulated Annealing , 1983, Sci..

[12]  Helena M. Ramos,et al.  Pumps as turbines: an unconventional solution to energy production , 1999 .

[13]  Hongxing Yang,et al.  A novel vertical axis water turbine for power generation from water pipelines , 2013 .

[14]  Marius Sinclair,et al.  Comparison of the performance of modern heuristics for combinatorial optimization on real data , 1993, Comput. Oper. Res..

[15]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[16]  Rob A. Rutenbar,et al.  Simulated annealing algorithms: an overview , 1989, IEEE Circuits and Devices Magazine.

[17]  Stefano Malavasi,et al.  GreenValve: hydrodynamics and applications of the control valve for energy harvesting , 2016 .

[18]  Armando Carravetta,et al.  Energy Recovery in Water Systems by PATs: A Comparisons among the Different Installation Schemes , 2014 .

[19]  Armando Carravetta,et al.  Energy Production in Water Distribution Networks: A PAT Design Strategy , 2012, Water Resources Management.

[20]  Hamid Reza Baghaee,et al.  Optimal Sizing of a Stand-alone Wind/Photovoltaic Generation Unit using Particle Swarm Optimization , 2009, Simul..

[21]  Heidar Ali Talebi,et al.  Reliability/cost-based multi-objective Pareto optimal design of stand-alone wind/PV/FC generation microgrid system , 2016 .

[22]  Gevork B. Gharehpetian,et al.  Multi-objective optimal power management and sizing of a reliable wind/PV microgrid with hydrogen energy storage using MOPSO , 2017, J. Intell. Fuzzy Syst..

[23]  A. Offermans,et al.  Socially Robust River Management: Role of Perspective Dependent Acceptability Thresholds , 2016 .

[24]  Enrique Cabrera,et al.  Towards an Energy Labelling of Pressurized Water Networks , 2014 .

[25]  Mariano Arriaga,et al.  Pump as turbine – A pico-hydro alternative in Lao People's Democratic Republic , 2010 .

[26]  Anton Schleiss,et al.  Experimental characterization of a five blade tubular propeller turbine for pipe inline installation , 2016 .

[27]  Sheng-Feng Kuo,et al.  COMPARATIVE STUDY OF OPTIMIZATION TECHNIQUES FOR IRRIGATION PROJECT PLANNING 1 , 2003 .

[28]  Fernando Martínez Alzamora,et al.  Random Scenarios Generation with Minimum Energy Consumption Model for Sectoring Optimization in Pressurized Irrigation Networks Using a Simulated Annealing Approach , 2012 .

[29]  Mateus Ricardo Nogueira Vilanova,et al.  Energy and hydraulic efficiency in conventional water supply systems , 2014 .

[30]  Jeffrey G. Arnold,et al.  Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations , 2007 .

[31]  Nicola Fontana,et al.  Optimal Location of PRVs and Turbines in Water Distribution Systems , 2014 .

[32]  M. Pérez-Sánchez,et al.  Energy Recovery in Existing Water Networks: Towards Greater Sustainability , 2017 .

[33]  Armando Carravetta,et al.  Pump as Turbine (PAT) Design in Water Distribution Network by System Effectiveness , 2013 .

[34]  Santo Marcello Zimbone,et al.  A simple method to evaluate the technical and economic feasibility of micro hydro power plants in existing irrigation systems , 2016 .

[35]  Basheer M. Khumawala,et al.  An empirical comparison of tabu search, simulated annealing, and genetic algorithms for facilities location problems , 1997 .

[36]  A. Ruiz-Canales,et al.  Comparative analysis of energy efficiency in water users associations. , 2010 .

[37]  Kong Fanyu,et al.  Theoretical, numerical and experimental prediction of pump as turbine performance , 2012 .

[38]  Juan Antonio Rodríguez-Díaz,et al.  The role of energy audits in irrigated areas. The case of ‘Fuente Palmera’ irrigation district (Spain) , 2010 .

[39]  Maria da Conceição Cunha,et al.  Tabu search algorithms for water network optimization , 2004, Eur. J. Oper. Res..

[40]  Helena M. Ramos,et al.  Simulated Annealing in Optimization of Energy Production in a Water Supply Network , 2016, Water Resources Management.

[41]  Armando Carravetta,et al.  PAT Design Strategy for Energy Recovery in Water Distribution Networks by Electrical Regulation , 2013 .

[42]  M. A. Jiménez-Bello,et al.  Methodology to improve water and energy use by proper irrigation scheduling in pressurised networks , 2015 .

[43]  Paul Coughlan,et al.  Optimization of Water Distribution Networks for Combined Hydropower Energy Recovery and Leakage Reduction , 2016 .

[44]  Helena M. Ramos,et al.  New design solutions for low-power energy production in water pipe systems , 2009 .

[45]  Helena M. Ramos,et al.  Clean power in water supply systems as a sustainable solution: from planning to practical implementation. , 2010 .

[46]  Shahram Derakhshan,et al.  Experimental study of characteristic curves of centrifugal pumps working as turbines in different specific speeds , 2008 .

[47]  Helena M. Ramos,et al.  Pressure Control for Leakage Minimisation in Water Distribution Systems Management , 2006 .

[48]  Enrique Cabrera,et al.  Energy audit of irrigation networks , 2013 .

[49]  Consolación Gil,et al.  Application of Several Meta-Heuristic Techniques to the Optimization of Real Looped Water Distribution Networks , 2008 .

[50]  Hamid Reza Baghaee,et al.  Security/cost-based optimal allocation of multi-type FACTS devices using multi-objective particle swarm optimization , 2012, Simul..

[51]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.