Boosting operational optimization of multi-energy systems by artificial neural nets

Computers & Chemical Engineering, vol. 173, pp. 108208

Abstract

The operation of multi-energy systems has to be optimized repeatedly, e.g., to react to changing energy prices. Thus, operational optimization problems need to be solved in a reliably short time. Reliably short computations are challenging for optimizing multi-energy systems due to complex time-coupling constraints. These time-coupling constraints reflect effects such as component start costs or energy storage. However, time-coupling constraints increase the computational effort. Here, we present a decomposition method to solve the operational optimization using artificial neural nets efficiently. The method decomposes the operational optimization into single-time-step optimizations. The single-time-step optimizations incorporate predictions from artificial neural networks trained on long-term operational optimizations. In two case studies, the method provides high-quality solutions for all operational optimization problems in less than 2 min. The method is significantly faster up to a factor of 375 than directly solving the operational optimization problem while practically retaining the quality of the solution.

Authors 4

1. Andreas Kämper Aachen Institute of Technical Thermodynamics

   RWTH Aachen University · ETH Zurich

   Affiliation as printed

   Energy & Process Systems Engineering, Department of Mechanical and Process Engineering, ETH Zurich, 8092 Zurich, Switzerland

   Institute of Technical Thermodynamics, RWTH Aachen University, 52062 Aachen, Germany

2. Roman Delorme Aachen Institute of Technical Thermodynamics

   RWTH Aachen University

   Affiliation as printed

   Institute of Technical Thermodynamics, RWTH Aachen University, 52062 Aachen, Germany

3. Ludger Leenders

   ETH Zurich

   Affiliation as printed

   Energy & Process Systems Engineering, Department of Mechanical and Process Engineering, ETH Zurich, 8092 Zurich, Switzerland

4. André Bardow corresponding

   ETH Zurich

   Affiliation as printed

   Energy & Process Systems Engineering, Department of Mechanical and Process Engineering, ETH Zurich, 8092 Zurich, Switzerland