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Least Economic Cost Regional Water Supply Planning – Optimising Infrastructure Investments and Demand Management for South East England’s 17.6 Million People

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  • Silvia Padula
  • Julien Harou
  • Lazaros Papageorgiou
  • Yiming Ji
  • Mohammad Ahmad
  • Nigel Hepworth

Abstract

This paper presents a deterministic capacity expansion optimisation model designed for large regional or national water supply systems. The annual model selects, sizes and schedules new options to meet predicted demands at minimum cost over a multi-year time horizon. Options include: supply-side schemes, demand management (water conservation) measures and bulk transfers. The problem is formulated as a mixed integer linear programming (MILP) optimisation model. Capital, operating, carbon, social and environmental costs of proposed discrete schemes are considered. User-defined annual water saving profiles for demand management schemes are allowed. Multiple water demand scenarios are considered simultaneously to ensure the supply–demand balance is preserved across high demand conditions and that variable costs are accurately assessed. A wide range of supplementary constraints are formulated to consider the interdependencies between schemes (pre-requisite, mutual exclusivity, etc.). A two-step optimisation scheme is introduced to prevent the infeasibilities that inevitably appear in real applications. The model was developed for and used by the ‘Water Resources in the South East’ stakeholder group to select which of the 316 available supply schemes (including imports) and 511 demand management options (considering 272 interdependencies) are to be activated to serve the inhabitants of South East of England. Selected schemes are scheduled and sized over a 25 year planning horizon. The model shows demand management options can play a significant role in the region’s water supply and should be considered alongside new supplies and regional transfers. Considering demand management schemes reduced overall total discounted economic costs by 10 % and removed two large reservoirs from the least-cost plan. This case-study optimisation model was built using a generalised data management software platform and solved using a mixed integer linear programme. Copyright The Author(s) 2013

Suggested Citation

  • Silvia Padula & Julien Harou & Lazaros Papageorgiou & Yiming Ji & Mohammad Ahmad & Nigel Hepworth, 2013. "Least Economic Cost Regional Water Supply Planning – Optimising Infrastructure Investments and Demand Management for South East England’s 17.6 Million People," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(15), pages 5017-5044, December.
    • Handle: RePEc:spr:waterr:v:27:y:2013:i:15:p:5017-5044
    DOI: 10.1007/s11269-013-0437-6
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    References listed on IDEAS

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    1. Frederick Chou & Hao-Chih Lee & William Yeh, 2013. "Effectiveness and Efficiency of Scheduling Regional Water Resources Projects," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(3), pages 665-693, February.
    2. B. Luo & I. Maqsood & G. Huang, 2007. "Planning water resources systems with interval stochastic dynamic programming," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(6), pages 997-1014, June.
    3. Hanan Luss, 1982. "Operations Research and Capacity Expansion Problems: A Survey," Operations Research, INFORMS, vol. 30(5), pages 907-947, October.
    4. Richard Bellman, 1957. "On a Dynamic Programming Approach to the Caterer Problem--I," Management Science, INFORMS, vol. 3(3), pages 270-278, April.
    5. P. Lany & F. Choudhury & N. Hepworth & K. Akande, 2013. "Applying Optimisation and Uncertainty Analysis to Help Develop an Integrated Water Resources Plan for South East England," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(4), pages 1111-1122, March.
    6. -, 2009. "The economics of climate change," Sede Subregional de la CEPAL para el Caribe (Estudios e Investigaciones) 38679, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
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    2. Md Haque & Ataur Rahman & Dharma Hagare & Golam Kibria, 2014. "Probabilistic Water Demand Forecasting Using Projected Climatic Data for Blue Mountains Water Supply System in Australia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(7), pages 1959-1971, May.
    3. Corentin Girard & Jean-Daniel Rinaudo & Manuel Pulido-Velazquez, 2015. "Index-Based Cost-Effectiveness Analysis vs. Least-Cost River Basin Optimization Model: Comparison in the Selection of a Programme of Measures at the River Basin Scale," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(11), pages 4129-4155, September.
    4. Swati Sirsant & M. Janga Reddy, 2021. "Optimal Design of Pipe Networks Accounting for Future Demands and Phased Expansion using Integrated Dynamic Programming and Differential Evolution Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(4), pages 1231-1250, March.
    5. Livia Rasche & Uwe A. Schneider & Martha Bolívar Lobato & Ruth Sos Del Diego & Tobias Stacke, 2018. "Benefits of Coordinated Water Resource System Planning in the Cauca-Magdalena River Basin," Water Economics and Policy (WEP), World Scientific Publishing Co. Pte. Ltd., vol. 4(01), pages 1-27, January.

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