Surrogate Framework for Energy System Modeling

Energy system modeling (ESM) through Mixed-integer Linear Programming (MILP) is the dominant tool of choice for national level grid expansion or congestion studies. Such models, while being precise, are computationally intensive and produce many datasets which are eventually aggregated to produce one net demand timeseries. This paper introduces a surrogate modeling framework through LSTM and Transformer architecture, which approximates the behavior of MILP based ESM, significantly reducing the computation demand with a reasonable compromise in accuracy. The framework also uses many data augmentation techniques to capture seasonal, weekly and daily patterns in the datasets. The best performing surrogates are able to predict the annual aggregated demand timeseries and peak demand with under 1% error. Once trained, the surrogates can evaluate each new demand scenario in under 5 seconds. Additionally, we employ transfer learning techniques on a pre-trained surrogate to accurately learn the behavior of a new grid with only two weeks data per scenario instead of the whole year. The proposed framework offers a scalable and computationally efficient approach to grid planning, making it suitable for national level grid studies where many distribution grids must be evaluated. The transfer learning aspects further highlight their practicality as pre-trained surrogates on benchmark grids can be re-used for new grids without needing extensive new training data.