Optimizing capacity-expansion planning of groundwater supply system between cost and subsidence

This work solves an optimal capacity-expansion planning problem with land subsidence constraints using a hybrid algorithm that combines the genetic algorithm (GA) and constrained differential dynamic programming (CDDP). The main structure of the hybrid algorithm is the GA, in which each chromosome represents a possible network design and its expansion schedule. The present fixed cost of each chromosome is computed easily using the GA, and CDDP is then used to solve the optimal pumping rates and compute the optimal present operating costs associated with the chromosome. Simulation results indicate that the well network has lower total present cost with the capacity-expansion system instead of installing the full system capacity at the beginning. However, the well network designed by the capacity-expansion model without considering land subsidence may induce more local land subsidence than that determined by the conventional model demonstrating the necessity of considering land subsidence constraints in the system design. This work also investigates other important issues related to the optimal design of the system-installing schedule. The proposed model is highly promising for facilitating a cost-efficient well system design for regional groundwater supply and environmental conservation.