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

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.

Automated summary

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.