Urban Integrated Energy System Planning Integrating Temporal Characteristics and Two-Layer Optimization
DOI:
https://doi.org/10.13052/spee1048-5236.4531Keywords:
Energy system, temporal characteristics, two-layer optimization, Voronoi diagram, NSGA-II algorithm, demand-side responseAbstract
To address the problems of insufficient consideration of source-load temporal characteristics, weak coordination between site and equipment configuration, and low efficiency of multi-objective optimization in the planning of urban smart local energy systems, this paper constructs a system planning method that integrates temporal characteristic analysis and two-layer multi-objective optimization. By constructing a source-load temporal coupling model, the dynamic characteristics of combined heat and power loads and renewable energy outputs such as wind, solar, hydrogen, and storage are accurately characterized. The temporal model provides dynamic data input for the lower-layer equipment configuration optimization. A two-layer optimization framework based on Voronoi diagrams and genetic algorithms is designed. The upper layer uses Voronoi diagrams to partition the space and select station sites. The lower layer employs an improved NSGA-II algorithm to optimize equipment selection and capacity configuration. The two layers interact iteratively: the upper layer provides zoning and load information to the lower layer, and the lower layer feeds back configuration costs to update the upper-layer planning. The framework achieves collaborative site selection and zoned energy supply for multiple energy stations. A fuzzy membership decision mechanism is introduced to enhance the engineering applicability of the Pareto solution set. Experimental results show that, for economics, the total annual cost of the system is reduced by approximately 12% compared to the traditional step-by-step optimization method. In terms of environmental protection, carbon emissions are reduced by approximately 15%. In terms of robustness, even considering demand-side response and load fluctuations of up to 20%, the system can still maintain an energy supply reliability of over 88%. The core innovation lies in the deep integration of high-resolution temporal dynamics with spatial-device collaborative optimization through a closed-loop iterative mechanism, which significantly improves planning accuracy, efficiency, and system adaptability. This method provides a theoretical and technical support for the low-carbon and efficient layout of urban smart local energy systems.
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