A Model-Adaptive Clustering Method for Low-Carbon Energy System Optimization

TL;DR

This paper introduces an adaptive clustering method that reduces computational complexity in low-carbon energy system optimization by selecting representative time periods, improving accuracy over traditional methods.

Contribution

The paper presents a novel adaptive clustering approach based on optimization model decision variables, enhancing efficiency and accuracy in multi-time-scale energy system modeling.

Findings

Significantly reduces approximation error compared to traditional clustering.

Effectively handles multi-time-scale uncertainty in renewable energy integration.

Applicable across various energy system optimization models.

Abstract

Intermittent renewable energy resources like wind and solar pose great uncertainty of multiple time scales, from minutes to years, on the design and operation of power systems. Energy system optimization models have been developed to find the least-cost solution to matching the uncertainty with flexibility resources. However, input data that capture such multi-time-scale uncertainty are characterized with a long time horizon and bring great difficulty to solving the optimization model. Here we propose an adaptive clustering method based on the decision variables of optimization model to alleviate the computational complexity, in which the energy system is optimized over selected representative time periods instead of the full time horizon. The proposed clustering method is adaptive to various energy system optimization models or settings, because it extracts features from the optimization models. Results show that the proposed clustering method can significantly lower the error in approximating the solution with the full time horizon, compared to traditional clustering methods.