(572i) Circular Economy Indicators in the Optimal Planning of Energy Systems

The Circular Economy seeks to maximize resource efficiency. In the context of the transition towards renewable energies, the focus is usually mostly on the operational phase of energy systems. However, the Circular Economy advocates a more holistic perspective, extending optimization to the design stage. In this sense, innovative Circular Economy indicators play a fundamental role in planning electricity generation due to the massive electrification of economic activities, offering the option of using fewer material resources in the system infrastructure to reduce the depletion of non-renewable resources. Therefore, when planning a power supply system, it is essential to assess technical, environmental, and economic aspects to guarantee the system's proper functioning and an infrastructure design with the minimum impact. This work presents a mathematical programming approach that aims to introduce rigorous metrics on the requirements of non-renewable resources used during the process and energy self-sufficiency since they have not been studied as one of the aspects of the Circular Economy within the energy sector. In addition, the model determines the optimal selection of the required infrastructure and necessary capacities in energy production to satisfy the energy demand required in a specific period. Together, the quantification of materials is normalized using the Abiotic Depletion Potential as a comparison of the depletion of non-renewable resources. The proposed Mixed Integer Nonlinear Programming (MINLP) multi-objective model is solved using the goal programming technique, allowing to determine the compensations between the total cost, CO_2eq emissions (related to the production of energy in the different energy generating technologies) and Circular Economy indicators. A case study for power generation in Mexico and green hydrogen production is presented and the results show that by implementing Circular Economy indicators as an addition to the economic and environmental functions, "achievement in performance" for the different objectives ranges from 84.00% and 89.96%. In addition, the optimization shows a 76.17% decrease in the total cost and 48.73% in CO_2eq emissions concerning the respective worst case (Nadir point). This work contributes to the growing body of literature on the integration of Circular Economy metrics, specifically in optimizing the energy sector.