A genetic algorithm approach to optimization of power peaks in an automated warehouse

Abstract

The simultaneous operation of the automated storage and retrieval machines (ASRs) in an automated warehouse can increase the likelihood that high power demand peaks turn unstable the electric system. Furthermore, high power peaks mean the need for more electrical power contracted, which in turns leads to more fixed operation cost and inefficient use of the electrical installations. In this context, we present a genetic algorithm approach to implement demandside management (DSM) in an automated warehouse. It has been based on real data from ASRs and models of prognosis of load profile of ASRs. We took into account two main goals: minimize instantaneous power demand and keeping the performance of the system store and retrieval times.