Real-Time Scheduling of Pumps in Water Distribution Systems Based on Exploration-Enhanced Deep Reinforcement Learning

Abstract

Effective ways to optimise real-time pump scheduling to maximise energy efficiency are being sought to meet the challenges in the energy market. However, the considerable number of evaluations of popular optimisation methods based on metaheuristics cause significant delays for real-time pump scheduling, and the simplification of traditional deterministic methods may introduce bias towards the optimal solutions. To address these limitations, an exploration-enhanced deep reinforcement learning (DRL) framework is proposed to address real-time pump scheduling problems in water distribution systems. The experimental results indicate that E-PPO can learn suboptimal scheduling policies for various demand distributions and can control the application time to 0.42 s by transferring the online computation-intensive optimisation task offline. Furthermore, a form of penalty of the tank level was found that can reduce energy costs by up to 11.14% without sacrificing the water level in the long term. Following the DRL framework, the proposed method makes it possible to schedule pumps in a more agile way as a timely response to changing water demand while still controlling the energy cost and level of tanks.