Paper
10 October 2023 Research on optimal scheduling strategy of regional integrated energy system based on improved genetic algorithm
Jiangnan Li, Renli Cheng, Shouquan Tang
Author Affiliations +
Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127992P (2023) https://doi.org/10.1117/12.3005801
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia
Abstract
In order to solve the problems of increasing energy demand, environmental pollution and low efficiency of energy conversion and utilization, a research on optimal scheduling strategy of regional integrated energy system based on an improved genetic algorithm is proposed. First, the optimal dispatch model of the regional integrated energy system is established, and the objective function is decomposed and iteratively solved by the column constraint generation algorithm. Integer coding is used for pipe diameter coding, and simulated annealing penalty function is used to transform constraints. The global optimization capability of the improved genetic algorithm and the local search capability of simulated annealing are complementary and integrated to realize the optimal scheduling of the regional comprehensive energy system. The experimental results show that the operating cost is 55416 CNY without considering the changing working conditions of the equipment; the operating cost when considering the changing working conditions of the equipment is 49388 CNY, which verifies that the established optimal scheduling model is efficient and feasible.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Jiangnan Li, Renli Cheng, and Shouquan Tang "Research on optimal scheduling strategy of regional integrated energy system based on improved genetic algorithm", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127992P (10 October 2023); https://doi.org/10.1117/12.3005801
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KEYWORDS
Solar energy

System integration

Genetic algorithms

Mathematical optimization

Algorithms

Batteries

Turbines

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