@article{ZHAO2026, 
author = {Yebo ZHAO and Senjia JIN and Peiyuan LI and Liqun YANG and Liang TANG and Jialing XU and Zuming LIU},
title = {An integrated urban energy system optimization method based on segmented linearized modeling},
year = {2026},
journal = {Journal of Northwest University (Natural Science Edition)},
volume = {56},
number = {1},
pages = {83-95},
keywords = {renewable energy, integrated urban energy systems, segmented linearized model, mixed integer linear programming, multi-objective optimization},
url = {https://www.sciopen.com/article/10.16152/j.cnki.xdxbzr.2026-01-008},
doi = {10.16152/j.cnki.xdxbzr.2026-01-008},
abstract = {This study presents a flexible optimization framework for urban integrated energy systems, aimed at meeting the energy demands of cities, reducing carbon emissions, and enhancing economic performance. Firstly, based on the principle of cascading energy utilization, an urban integrated energy system incorporating renewable energy sources is constructed. A novel, more practically meaningful high-precision inter-zone modeling method is proposed, considering the impact of equipment capacity on both performance and economic parameters. Secondly, from the perspective of system feasibility, a flexible constraint on the construction area factor for the user-end is introduced, and the influence of area constraints on system performance is explored. Finally, a new mixed-integer linear programming (MILP) model framework is developed, targeting the minimization of annual total costs and carbon emissions, integrating system equipment selection, capacity configuration, and scheduling. The results demonstrate that, compared to the baseline scenario with unsegmented equipment modeling, the annual total cost is reduced by 59%, and greenhouse gas emissions decrease by 29%. Furthermore, adopting a multi-objective optimization approach to balance system performance results in a 49% reduction in annual total costs and a 74% decrease in greenhouse gas emissions. By introducing the piecewise high-precision modeling method and user-oriented flexible constraints, this study innovatively optimizes the design and scheduling strategies of urban integrated energy systems, which provides an effective solution for achieving sustainable urban energy management.}
}