Optimization-based expansion planning for power and hydrogen systems with feedback from a unit commitment model

This paper presents a novel long-term model for the joint expansion planning of power and hydrogen systems with short-term operational considerations. We propose linear reserve constraints with adjustable parameters within the planning model to address the reliability requirements from significant levels of variable renewable generation. These adjustable parameters are calibrated using an iterative training methodology that connects the long-term planning model with a short-term unit commitment model. A crucial aspect of our methodology is the iterative improvement of investment decisions through the feedback obtained by evaluating the operational performance of the investment decisions in different scenarios computed under the unit commitment model. Computational experiments in a case study of a large-scale Chilean power-hydrogen system show the effects of the proposed methodology on capacity expansion recommendations, where a correct balance between variable and flexible technologies is obtained. Moreover, when simulating the operation of the system for a full year, we observe that the proposed methodology generates an investment plan that achieves a lower total cost compared to other methodologies commonly used in the literature. Additional experiments in 100% renewable scenarios show how conventional methodologies reaches levels of not-supplied-energy between 2%-7%, while the proposed methodology achieves less than 1% in the same metric.