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In the last decade, many communities in Australia have faced prolonged drought periods. Water authorities have responded with various demand and supply measures ranging from water conservation and water restrictions to seawater desalination. Most of the above measures are associated with some environmental, economic and/or social impacts. These measures can also affect the key objectives of water utilities such as providing a reliable supply of low cost, high quality water while maintaining the environmental health. Some of the above objectives tend to be mutually conflicting, highlighting that decisions associated with key operations and system planning must be based on an assessment of such multiple objectives. For many water authorities, the multi-objective decisions on key operations and system planning need to be made considering uncertainties in future streamflow conditions. For water supply systems that have the capacity to store large volumes of water, this may require the consideration of potential streamflow conditions over many future years. In addition, the decisions may also involve trading-off short-term operations such as greater use of higher-cost water sources with longer-term solutions to supply-demand balance. The latter may include a range of demand management or supply augmentation measures. The complexity of making the above decisions calls for a multi-objective optimisation approach that considers key operation decisions and supply-demand balance decisions over temporal and spatial scales ranging from long-term strategic decisions down to more detailed annual system operation planning. Despite this clear need, optimisation tools are not widely available or used in the water industry. In contrast, simulation modelling tools, such as REALM, have been widely used by many water authorities in Australia to assist with system planning and operations. REALM simulates the water allocation within a system in accordance with user-defined operating rules, but does not optimise the system operations over time. The integration of new supplies and the need to consider multiple objectives in water system planning and operations prompted Melbourne Water to engage water system optimisation specialists Optimatics to develop a decision support system, Optimizer WSS, for water supply system optimisation. Optimizer WSS employs a Multi-Objective Genetic Algorithm (MOGA) optimisation which in turn uses Melbourne Water's existing REALM simulation model to efficiently assess the consequences of millions of combinations of alternative decisions on key operations and the long-term supply-demand balance for a range of potential future hydrological scenarios. The optimisation identifies superior solutions termed 'Pareto-Optimal solutions' considering the range of water supply objectives. The Pareto-Optimal solutions enable Melbourne Water and its stakeholders to select the most suitable solution by optimally balancing one or more water supply objectives against the others, through a Multi-Criteria Decision Analysis (MCDA) considering an array of additional assessment criteria as well. The complex issue of streamflow uncertainty is addressed through a streamflow-scenario based optimisation followed by testing of optimal solutions for their robustness under different streamflow scenarios. The whole process is built on a high level of stakeholder engagement. Initial tests of Optimizer WSS comprising MOGA, MCDA and Solution Presentation components have shown the potential of the tool, as part of a suite of tools, to support decisions on water system planning and operations. The paper presents the Optimizer WSS modelling process, the multi-objective model formulation used for testing, and the initial test results under a hypothetical streamflow and demand scenario.

Multi-Objective Optimisation using Optimizer WSS to support operation and planning decisions of Melbourne water supply system