Journal
COMPUTERS & CHEMICAL ENGINEERING
Volume 173, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compchemeng.2023.108208
Keywords
Mixed-integer linear programming; Decomposition algorithm; Machine learning; Supervised learning; Long short-term memory
Ask authors/readers for more resources
The operation of multi-energy systems requires repeated optimization to respond to changing energy prices. However, solving operational optimization problems in a reliably short time is challenging due to complex time-coupling constraints. This study presents a decomposition method that efficiently solves the operational optimization by utilizing artificial neural networks.
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
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available