Optimal Rehabilitation Procedure for Intermittent Water Supply Systems

Water demand continuously increases in urban zones, and water scarcity is frequently associated with a lack or a reduction of water availability at water sources and maintenance problems, such as leakage and pipe aging. These facts inevitably lead to challenging water distribution system (WDS) management. In this scenario, intermittent operation emerges as an alternative to system operation. This is not the most desirable solution from a social perspective because many consumers cannot be supplied as desired for days. To tackle problems like this, many works have investigated how to help decision makers improve water system efficiency during the last decades. Nevertheless, few works have considered combining several structural interventions, such as pipe replacement, installation of new pump stations, fixing leaks, and installing and controlling pump stations and valves. One reason is that alternatives for recovering the hydraulic capacity in decision-making processes are computationally burdensome, mathematically complex, and, sometimes, even physically incompatible. Considering the problem stated by the Battle of Intermittent Water Supply, this work proposes a methodology for optimal operation and recovery of a WDS. The Battle problem is presented in two stages organized during different years: Year 0 and the following 5 years. For Year 0, only operational optimization is allowed. Consequently, optimal operation of pumps and valves is proposed for this initial year to maximize the number of nodes being supplied. For the rest of the years, because implementing structural changes is allowed within a defined budget, the proposal suggests applying a search space reduction process based on a cost-benefit trade-off and the hydraulic relevance of each structural alternative evaluated individually in terms of the nine indicators proposed in the Battle statement. Those alternatives that better improve the indicators are then considered in a multiobjective optimization setting. For every year, a set of structural changes is selected, followed by related changes in the operational setup. The alternatives are selected year by year and evaluated considering the past selected alternatives to assess the effects during the five evaluation years. This is done in a dynamic programming process, ensuring that a near optimal is achieved by the end of the last, fifth, year.