期刊
COMPUTERS & CHEMICAL ENGINEERING
卷 173, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compchemeng.2023.108208
关键词
Mixed-integer linear programming; Decomposition algorithm; Machine learning; Supervised learning; Long short-term memory
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.
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