In addition to renewable energy sources, the transition of the German energy system will increasingly involve the use of decentralized combined heat and power plants (CHP). In order to use this promising technology cost-optimally, modeling approaches must be developed that enable optimization of the systems. Mixed integer linear programming (MILP) is a powerful tool for solving mathematical optimization problems. However, to reduce the computing time the model formulation requires compelling simplifications in relation to reality. The aim of this paper is to present a modeling approach for a combined heat and power plant that depicts dynamic power changes more accurately than existing approaches. Power gradients are mapped by differentiating between the control signal of the CHP unit and the actually generated power output for thermal and electrical power. Finally, the accuracy of the modeling approach is examined in a field test and evaluated according to the accuracy achieved.
Energy optimization of factory operations has gained increasing importance over recent years since it is understood as one way to counteract climate change. At the same time, the number of research teams working on energy-optimized factory operations has also increased. While many tools are useful in this area, our team has recognized the importance of a comprehensive framework to combine functionality for optimization, simulation, and communication with devices in the factory. Therefore, we developed a framework that provides a standardized interface to research energy-optimized factory operations with a rolling horizon approach. The optimization part of the framework is based on the OpenAI gym environment. The framework also provides connectors for multiple communication protocols including Open Platform Communication Unified Architecture and Modbus via Transmission Control Protocol. These facilities can be utilized to implement rolling horizon optimizations for factory systems easily and directly control devices in the factory with the optimization results. In this article, we present the framework and show some examples to prove the effectiveness of our approach.
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