ML-based energy management of water pumping systems for the application of peak shaving in small-scale islands

This study introduces an energy management method that smooths electricity consumption and shaves peaks by scheduling the operating hours of water pumping stations in a smart fashion. Machine learning models are first used to accurately forecast the electricity consumed and produced by renewable energy sources on an hourly level. Then, the forecasts are exploited by an algorithm that optimally allocates the operating hours of the pumps with the objective to minimize predicted peaks. Constraints related with the operation of the pumps are also considered. The performance of the proposed method is evaluated considering the case of a Greek remote island, Tilos. The island involves an energy management system that facilitates the monitoring and control of local water pumping stations that support residential water supply and irrigation. Results indicate that smart scheduling of water pumps in a small-scale island environment can reduce the daily and weekly deviation of electricity consumption by more than 15% at no monetary cost. It is also concluded that the potential gains of the proposed approach are strongly connected with the amount of load that can be shifted each day, the accuracy of the forecasts used, and the amount of electricity produced by renewable energy sources.

Automated summary

This study introduces an energy management method that smooths electricity consumption and shaves peaks by smart scheduling of water pumping stations. Machine learning models are used to accurately forecast electricity consumption and production of renewable energy sources, and an algorithm is used to optimize the allocation of operating hours of the pumps to minimize predicted peaks. An evaluation on a Greek remote island shows that the method can reduce the deviation of electricity consumption and provides some conclusions.