(434g) Infrastructure Planning for a Sustainable Energy Transition in Urban Areas: A Case of Study on University of Wisconsin-Madison Campus

Energy transition is a significant structural change to the global energy sector, driving transformation from fossil-based to zero-carbon systems, aiming to reduce greenhouse gas emissions and limit climate change [1]. One of the main challenges is integrating renewable energy sources while dealing with the increasing energy demand, managing multiple energy carriers including electricity, heat and cooling, and holistically considering those sectors [2,3]. In the past, urban areas were regarded as energy consumers only, and power systems, gas systems, and heating and cooling systems were designed individually and operated separately. Nowadays, it is necessary to complement multi-energy systems, combine different energy processes, coordinate design, installation, operation, and closure phases, and consider trade-offs between multiple criteria including cost, efficiencies, emissions and land use. Integrated urban energy system design is a promising approach to holistically study different energy carriers while considering multiple design criteria [3-6].

In this work, we propose a formulation of a multi-objective mixed-integer linear optimization problem for the design and infrastructure planning of energy systems transition from a fossil-based to a zero-carbon system, accoupling short and long-term decisions [6,7]. The model aims to determine the optimal configuration and capacity of energy generation and storage units, and facilities' installation and decommission decisions while fulfilling the electricity, heating, and cooling demands. It includes a detailed formulation for energy sources and storing systems and considers the intermittency and availability of resources, production and emission targets, and policy choices [5]. The proposed model is applied to a case study considering the energy transition on the University of Wisconsin-Madison campus. The outputs of this model include a science informed and optimized roadmap toward carbon neutrality, along with the evaluation of different policy scenarios.

References

[1] Vergara-Zambrano, J., Kracht, W., & Díaz-Alvarado, F. A. (2022). Integration of renewable energy into the copper mining industry: A multi-objective approach. Journal of Cleaner Production, 372(February), 133419. https://doi.org/10.1016/j.jclepro.2022.133419

[2] Chen, Z., Avraamidou, S., Liu, P., Li, Z., Ni, W., & Pistikopoulos, E. N. (2021). Optimal design of integrated urban energy systems under uncertainty and sustainability requirements. Computers and Chemical Engineering, 155, 107502. https://doi.org/10.1016/j.compchemeng.2021.107502

[3] Fodstad, M., Crespo del Granado, P., Hellemo, L., Knudsen, B. R., Pisciella, P., Silvast, A., ... Straus, J. (2022). Next frontiers in energy system modelling: A review on challenges and the state of the art. Renewable and Sustainable Energy Reviews, 160(December 2021). https://doi.org/10.1016/j.rser.2022.112246

[4] Tso, W. W., Demirhan, C. D., Heuberger, C. F., Powell, J. B., & Pistikopoulos, E. N. (2020). A hierarchical clustering decomposition algorithm for optimizing renewable power systems with storage. Applied Energy, 270(May), 115190. https://doi.org/10.1016/j.apenergy.2020.115190

[5] Kakodkar, R., He, G., Demirhan, C. D., Arbabzadeh, M., Baratsas, S. G., Avraamidou, S., ... Pistikopoulos, E. N. (2022). A review of analytical and optimization methodologies for transitions in multi-scale energy systems. Renewable and Sustainable Energy Reviews, 160(December 2021). https://doi.org/10.1016/j.rser.2022.112277

[6] Tian, X., Zhou, Y., Morris, B., & You, F. (2022). Sustainable design of Cornell University campus energy systems toward climate neutrality and 100% renewables. Renewable and Sustainable Energy Reviews, 161(January), 112383. https://doi.org/10.1016/j.rser.2022.112383

[7] Lara, C. L., Mallapragada, D. S., Papageorgiou, D. J., Venkatesh, A., & Grossmann, I. E. (2018). Deterministic electric power infrastructure planning: Mixed-integer programming model and nested decomposition algorithm. European Journal of Operational Research, 271(3), 1037–1054. https://doi.org/10.1016/j.ejor.2018.05.039