Due to large fluctuations in energy demand, high level of uncertainty in the power grid, and increased penetration of intermittent renewable energy sources, the careful consideration of operational constraints has become crucial for the optimal design of sustainable energy systems. This often results in large-scale mixed-integer optimization problems as one has to integrate long-term design or capacity expansion decisions and short-term operational decisions.
In this talk, we present two instances in which we have successfully applied a number of approaches that help mitigate the computational intensity of such multiscale optimization problems. In the first case, we consider the design of integrated process networks for renewables-based fuels and power production. When solving this problem, we rely on efficient surrogate models and a simplified time representation based on the use of presentative weeks. In the second case, we address the capacity planning problem for an industrial air separation plant that interacts with the electricity market. Here, we have developed a column generation algorithm that effectively decomposes the large-scale problem into several smaller subproblems that can be solved in parallel.
Speaker: Ai Zhang, Univ. of Minnesota
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Stanford, CA 94305
Website: Click to Visit