Optimal Industrial Clusters: Flex4Fact Summer Research Report

Abstract

We study the integration of digitization, smart scheduling, local renewable energy production, and variable energy prices in different industrial com panies to make an optimization entity for industrial cluster energy use. We discuss two energy optimization approaches: a centralized choice for all energy consumption and local-level trading of energy surplus and develop a graph-based package to implement these mechanisms given mixed-integer programming models for each of the firms. Testing our modeling package with data from the EU project Flex4Fact, we show that clustering decreases aggregate costs due to the lack of sell-back penalties, and the relative be nefit among firms depends on internal prices.