EnICO – Energy efficient Industry Cluster Optimization

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Limited resources, rise in energy costs and continuous change in government policies, e.g. exit from nuclear and fossil fuel energy, exert pressure on the economical division of industrial companies. In addition, the call for higher sustainability, the reduction in environmental pollution and efficiency in resource consumption aim for the improved handling of energy resources. In this context, the focus of national support programs pursue the increasing efficiency of end-use energy consumption, especially at the costumer level. In contrast, the potentials of energetic interdependencies between industrial companies within a local cluster (e.g. industrial parks) do not get on closer examination so far.Consequently, the primary objective of the research project comprise of both modelling and optimization of energetic and substantial cooperation between productive companies within a cluster. Following the resource efficiency concept, the optimization of industrial clusters has two main objectives – maximization of cluster internal resource utilization as well as minimization of external resource demand. Therefore, the development of an optimization tool (EnICO generator) is planned which will support the digital mapping, self-optimization with the goal of energy efficiency and in the long-term, the planning of ideal eco-industrial parks. The optimization problem is discrete-continuous and a dynamic combination of simulation and optimization is necessary. One aim is to identify the interrelations, which may have positive effects on energy resource demand within the cluster. The second aim is to reach an energetic-optimal cooperation behaviour, which increases the energy efficiency. This is based on the generation of solutions for solving the set of problems, which comprises time-discrete, dynamic and sequence-relevant allocation of restricted resources. For this, the identification as well as parametrization of relevant correlations, the modelling of optimization objective criteria and the definition of control algorithm are essential. The overall project objective leads to a high complexity of the optimization model. One of the innovative and advantageous factors of the proposed project will be the mapped level of abstraction, which is essential to model and optimize the efficiency of energetic interrelations between individual enterprises. On the other hand, intelligent cluster optimization is ensured by implementation of a multi agent system (MAS). This MAS systematically combines “optimization knowledge” to rule sets, based on the approach of Machine Learning. As long-term objective, it should be possible to generate a specific optimal model. For this, solely the developed set of rules automatically generate appropriate interrelations into a base model of an industrial cluster.