Optimizing system capacity expansion schedules for groundwater supply

Groundwater plays a vital role in regional water resources management. In conventional design, a full-scale network system is installed initially to use groundwater resources. However, the system capacity may exceed water demand in the early stages because water demand generally increases with time. Therefore, this work proposes a novel optimal capacity expansion model capable of determining an optimal schedule to expand system capacity according to increasing water demand. The proposed algorithm hybridizes a genetic algorithm (GA) and constrained differential dynamic programming (CDDP). The chromosomes of the GA represent a possible design alternative, a groundwater network with capacity that expands with time. The CDDP algorithm is then used to compute the optimal pumping policy associated with the chromosome. Simulation results indicate that the capacity expansion model saves more total present value cost than conventional designs for the same annual interest rate and water demand. Results of this study demonstrate promise for the proposed model in facilitating a cost-effective groundwater network design with capacity expansion for regional groundwater supply.