Trinity College Dublin The University of Dublin Pump-as-turbines for hydropower energy recovery from on-demand irrigation networks: Flow fluctuation characterisation, energy potential extrapolation, and real-scale implementation. Miguel Crespo Chacón Thesis submitted for the fulfilment of the requirements for the Degree of Doctorate of Philosophy in Engineering to the University of Dublin, Trinity College. August 2020 Supervisor: Prof. Aonghus McNabola Co-Supervisors: Prof. Juan Antonio Rodríguez Díaz & Prof. Jorge García Morillo Department of Civil, Structural & Environmental Engineering, University of Dublin, Trinity College, Dublin i Declaration I declare that this thesis has not been submitted as an exercise for a degree at this or any other university and it is entirely my own work. I agree to deposit this thesis in the University’s open access institutional repository or allow the library to do so on my behalf, subject to Irish Copyright Legislation and Trinity College Library conditions of use and acknowledgement. Miguel Crespo Chacón August 2020 Summary More water efficient irrigation techniques have been studied and developed during the last decades, and are becoming of significant importance in arid and semi-arid regions, as these are leading to more energy intensive irrigation infrastructures. This thesis presents hydropower energy recovery as a potential measure to improve the energy efficiency in on-demand irrigation networks. Findings in four main elements of work are developed, presented and discussed, related to: i) flow fluctuations prediction, ii) feasibility assessment, iii) energy potential extrapolation and iv) real-scale implementation. On the first element, a new methodology to predict the in-pipe flow variations in on-demand networks along an irrigation season was developed. As fluctuations in the flow rate provokes considerable effects on turbine efficiency for hydropower energy recovery, this characterisation is largely important to quantify in detail the hydropower potential. Furthermore, the theoretical performance of pump-as-turbines was considered based on the theoretical best efficiency point, selecting the device returning the minimum payback period. Pumps-as-turbines are conventional pumps working in reverse mode as turbines. Using them for energy recovery has been shown to be cost-effective at sites with small power output capacity rather than conventional turbines. Their cost-effectiveness lies in the fact that pumps are mass produced and many models exist of differing sizes. This results in considerably cheaper machinery covering a wide range of flow and head combinations. However, anticipating their performance is a well-known challenge. Secondly, the methodology feasibility was evaluated comparing the results predicted in nine points of an on-demand irrigation network with actual data recorded for the 2015 irrigation season. Several statistical parameters and efficiency criteria were used to compare the results coming from simulations and from the application of actual flow observations in a real network, in high resolution, and over a 1-year period. The validation of this methodology will allow its application in different irrigation networks to quantify the existing potential and study how PATs could improve their energy efficiency. The overall result of the methodology comparing actual records and predicted data was satisfactory. In the case of the flow, it presented a good fit between the predicted and the actual values, with a MAE and RMSE of 0.0026 and 0.0068 on the occurrence probability. Values for R2 and efficiency criteria of 0.804 and 0.576 respectively, were obtained. Therefore, the results showed a feasible average accuracy for flow prediction, which allowed a more accurate estimation of the hydropower potential. iii Once the method was developed and validated, a large-scale energy recovery assessment was carried out, which could provide an approximation of the potential benefits associated with hydropower in on-demand irrigation networks. Linear regression models and artificial neural networks were used to estimate the energy recovery potential in an irrigated surface of about 164,000 ha. Three proxy variables were used, including: irrigated surface, theoretical crop irrigation requirements and slope. Using the results provided by artificial neural networks, the economic, environmental and energetic impacts were quantified in the area analysed. A reduction in energy consumption in the agriculture sector of this magnitude could have significant impacts on food production and climate change. This was the largest scale assessment of hydropower potential conducted in irrigation networks to date with the next nearest being an assessment of 4,000 ha. Finally, an experimental hydropower plant using a pump-as-turbine was designed and constructed in an actual on-demand irrigation network to supply energy to a local farm in Southern Spain. A 4 kW pump-as-turbine was installed in a by-pass, recovering around 20 m of head and turbining 30 l s-1, connected to a bank of batteries that worked as backup for periods where no electricity generation was possible. The pilot plant was design using the methodologies developed in the earlier parts of the thesis. The plant supplied the energy demanded at the farm during the entire irrigation season, eliminating a diesel generator previously used to fulfil the energy demand. Significant benefits were achieved, exceeding €2,000 of economic savings and more than 8 t eCO2. Lastly, an analysis of two pump-as-turbine regulation schemes (hydraulic and electric), and the global efficiency of the plant were carried out. The results obtained in this research could lead to a more efficient plant designs and a better understanding of PAT performance working under actual conditions in irrigation networks. Thereby improving the plant power and global efficiency, and sustainability of energy sources applied to the agriculture sector. Acknowledgments There are so many people I would like to thank for their constant support and help during these three years along my PhD journey. Firstly, I would like to thank Aonghus McNabola, Juan Antonio Rodrigruez Diaz and Jorge Garcia Morillo, my PhD supervisors for their patience, advice and guidance, as well as for their commitment and trust. More than a significant part of the work shown along this thesis is belongs to you. I am also very grateful to the REDAWN project team. I have had the opportunity of working within an excellent project, full of great professionals and experts, and this can be reflected in the three marvellous experimental plants built within this project. I hope this is just the starting point for the technology fostered in the project to become more and more used, reaching the target of a less energy dependent water industry. I would like to thank to the red brick allstars office. Ana de Almeida, Nilki Aluthge Dona, Irene Fernandez Garcia, Djordje Mitrovic, Himanshu Nagpal, Daniele Novara, Jan Spriet, and Szu- Hsin Wu for all the moments, lunches and dinner together. I am delighted of sharing this time with you. I am really grateful to Djordje and Jan for all those family moments in our little abode in Dublin. I am very grateful to my chosen family, “Las Llagas”. Without your support, I would have felt very far from my beloved Puente Genil. You all friends have been a very important pillar. Your calls, pictures, videos and welcoming every time I stepped into Puente Genil charged my batteries when I really needed it. Last but not less, I want especially thank to my family. Mum, Dad, Nano, Kiko, Mavi, my little guys (Javi, Lola, Sofía and Ángela) and quite a lot to Cristina, for guiding and supporting me from the distance. Your love and support have been the main help during these three years. No words could compensate all your support. I just can say: Thank you! v List of Publications Journal Publications: 1. M. Crespo Chacón, J. A. Rodríguez Díaz, J. García-Morillo, and A. McNabola (2019). “Pump-as-turbine selection methodology for energy recovery in irrigation networks: Minimising the payback period,” Water (Switzerland), MDPI, vol. 11, no. 1, pp 1-20. 2. M. Crespo Chacón, J. A. Rodríguez Díaz, J. Garcia-Morillo, and A. Mcnabola (2020). “Hydropower energy recovery in irrigation networks: validation of a methodology for flow prediction and pump as turbine selection,” Renewable Energy, 147, 1728-1738. 3. M. Crespo Chacón, J. A. Rodríguez Díaz, J. Garcia-Morillo, and A. Mcnabola (2020). “Estimating regional potential for micro-hydropower energy recovery in irrigation networks on a large geographical scale,” Renewable Energy, 155, 396-406. https://doi.org/10.1016/j.renene.2020.03.143. 4. M. Crespo Chacón, J. A. Rodríguez Díaz, J. Garcia-Morillo, and A. Mcnabola. “Evaluation of the design and long-term performance of a micro hydropower plant in a pressurised irrigation network: real world application at farm-level in Southern Spain”, Renewable Energy (Under review). Conference Publications: 1. M. Crespo Chacón, J. A. Rodríguez Díaz, J. Garcia-Morillo, J. Gallagher, P. Coughlan and A. McNabola (2018). “Potential Energy Recovery Using Micro-Hydropower Technology in Irrigation Networks: Real-World Case Studies in the South of Spain.” Proceedings, 679. 3rd International EWaS Conference: “Insights on the Water-Energy- Food Nexus”, Lefkada Island, Greece 27-30 June 2018. 2. M. Crespo Chacón, J. A. Rodríguez Díaz, J. Garcia-Morillo, and A. Mcnabola (2019). “Pump-as-turbines for energy recovery: An attractive