A Benchmark Study for Minimizing Energy Cost of Constant and Variable Speed Pump Operation

Energy saving for pumping water system represents an important cost saving potential. To minimize the energy cost, a solution methodology is developed by applying fast messy genetic algorithm (fmGA), one of most competent genetic algorithms, to the optimization of pump operation while meeting a set of system operating criteria. A benchmark case study has been conducted and compared with the results obtained by using conventional nonlinear optimization techniques and simulated annealing. The study shows that the fmGA approach brings the great potential benefit of energy cost saving for both constant and variable speed pumps. The approach is more effective at searching for the optimal pump schedule and able to locate less expensive pump operating policy, which may result in more than 10% energy cost more than the conventional pump operation strategy. The paper illustrates that using variable speed pump further minimizes the energy cost in most of cases. It demonstrates that the integration of optimization-based pump scheduling provides engineers and decision-makers a powerful tool for cost-effective management of pump operations.