This paper attempts to set the basic methodological framework for an integrated action plan (in terms of successive steps) to be developed that will guarantee the reliable calculation of the Full Water Cost (FWC), as defined by the WFD 2000/60/EC. Towards this goal, the crucial role of the water losses occurring in a water distribution system is demonstrated. This will help an effective and socially just water pricing policy to be developed. The cost components (direct -DC; environmental -EC; and resource -RC) comprising the FWC are analysed, introducing approaches for their reliable calculation. Regarding the DC, the marginal capacity cost and the necessary preconditions for its integration to the final water price along with its contribution towards effective water demand reduction are analysed. Regarding the EC, its dynamic character and the ways it interacts with the DC are presented. The role of the stakeholders in setting those price levels is also checked. Crucial parameters are analysed for a socially just water cost allocation to domestic users. The role of the water utility is examined, considering its responsibility in water losses. The basic policies (market-based vs. conventional) used to achieve conservative water use are evaluated. In addition, the role of the State is criticized.

Dividing a water distribution network (WDN) into district metered areas (DMAs) is the first vital step towards pressure management and real losses reduction. However, other factors of water quality such as the water age must be taken into account while forming DMAs. The current study uses genetic algorithm (GA) optimization methods to achieve the desired WDN segmentation conditions in terms of: (a) reducing the operating pressure, thus reducing the system's real losses; and (b) reducing the water age, thus improving the feeling of water freshness and preventing growth of disinfection byproducts. Techniques based on GA are a proven way to provide a very good solution to optimization problems. The solution is obtained using an objective function and setting boundary constraints. The formation of the objective functions is tested through Matlab's optimization toolbox. The logic of the objective functions' formulation for both the operating pressure and the water age optimization is recorded and analyzed. The method's application utilized a sample network model assisted by EPANET and Bentley's WaterGEMS software tools. The morphology of the DMAs is presented for each scenario, as well as the results of the network's segmentation according to the operating pressure and the water age.

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