Coordination of heterogeneous production equipment under an external signal for sustainable energy. (1st March 2022) - Scholz, Sebastian; Meisel, Frank
Abstract: Increasing yet volatile generation of electricity from renewable energy sources constitutes challenges to prevent grid bottlenecks and to ensure grid stability. Similarly, energy-intensive industrial companies have to take care of their internal power load management to prevent energy infrastructure overloads. We bring these two fields together by (1.) optimizing production scheduling and equipments' charging decisions such that the internal load management of a company is respected while (2.) supporting grid stability through increased local consumption in periods of peak generation of renewable energy. For this, the considered company receives an external signal about the availability of (excessive) renewable energy. We present two optimization models that incorporate this signal when making production and charging decisions for heterogeneous types of equipment. The main objectives consider minimization of total tardiness, maximization of energy consumption in periods of excessive renewable energy generation and minimization of peak loads. Total job completion time forms a subordinate objective. We further propose a production coordination platform (PCP) that orchestrates the decision making of both optimization models. Computational experiments consider a manufacturing system that consists of two heterogeneous production equipment types with two machines and two support devices and demonstrate that the PCP is competitive compared to an integrated optimization model. It is shown that a hierarchical order of the diverse objectives is properly reflected in the decision making such that a company can flexibly adapt its internal load management to the current situation of renewable energy generation. Sensitivity analyzes of control parameters reveal how the performance of the PCP reacts to different degrees of information availability. We finally show that the signal-driven PCP can achieve a substantial reduction of production-related CO 2 emissions. Highlights: Two optimization models that take care of production equipment heterogeneity. A production coordination platform to orchestrate the decision making. Various objectives that account for high ecological performance and service quality. Integrate an external signal to account for excessive renewable energy generation.