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Optimisation of Supply Demand Balance Model under Uncertainty

This research aims to develop a better understanding of supply-demand balance problems for water resources management under uncertainty. It focuses on developing a SDB model based on new risk-based methods and optimizing the model under uncertainty, while investigating the impact of uncertainty on decision-making. The research also aims to establish how decisions are determined by various strategies, policies, and risk approaches.

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Optimisation of Supply Demand Balance Model under Uncertainty

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  1. Optimisation of Supply Demand Balance Model under Uncertainty Damian Staszek Supervisors: Professor Dragan Savic & Professor Guangtao Fu University of Exeter ds463@exeter.ac.uk

  2. Introduction Every five years Water Only Companies (WOC) and Water and Sewage Companies (WaSC) publish their Water Resources Management Plans (WRMP) and Business Plans. Ofwat Key Themes for PR19 www.wisecdt.org

  3. Uncertainty • Uncertainties affecting WSS: • due to imperfect knowledge about socioeconomic drivers, • due to climate variability and climate change, • as a result of changing system dynamics, • related to regulatory framework or legal aspects. Source: Thames Water: DWRMP April 2013 www.wisecdt.org

  4. The research questions: • What are optimal or near optimal solutions for Supply Demand Balance Problem under various sources of uncertainty? • What is an impact of the uncertainty and adopted risk approach on decision making? www.wisecdt.org

  5. Aim and objectives • Aim: To develop a better understanding of supply demand balance problems for water resources management under uncertainty. • Objectives: • To develop an SDB model based on the input generated by new risk-based methods. • To optimise the model under uncertainty. • To investigate the impact of uncertainty on decision-making. • To establish how decisions are determined by various adopted strategies, policies and risk. www.wisecdt.org

  6. Objective 1: To develop an SDB model • Bristol Water Models : • HYSIM • Mass Balance Model • MISER • SDB Optimiser www.wisecdt.org

  7. All data are per year • Water volume in Ml/d • Horizon: 25 years www.wisecdt.org

  8. Options (Schemes) • Schemes categories: • Demand • Distribution • Production • Resources • Drought Schemes • Data collected: • Yield per year • Costs per year • Project timing, years to build Artist Impression of Cheddar 2 Reservoir www.wisecdt.org

  9. www.wisecdt.org

  10. Objective 2: To optimise the model under uncertainty • Various investment scenarios are considered • Model runs in R environment • Genetic algorithm is used to provide optimal solutions www.wisecdt.org

  11. Integration between Mass Balance Model / MISER and SDB Optimiser is required • Uncertainty needs to be captured at data-gathering and sub-modelling level www.wisecdt.org

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