考虑综合需求响应的改进WSO算法的低碳经济调度

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Jiaqi Li
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引用次数: 0

摘要

针对区域一体化能源系统的低碳经济调度问题,提出了综合需求响应与碳捕集与封存技术相结合的低碳经济调度模型,并采用改进的大白鲨优化算法进行求解。实验结果表明,该方法可以显著降低系统调度成本。与传统调度方法相比,总调度成本分别降低22.81%和17.77%。同时,提高了风能和太阳能的利用率,风电弃风率和太阳能弃风率分别降至0%。改进的大白鲨优化算法在求解过程中具有更快的收敛速度和更高的求解精度。与传统的大白鲨优化算法和鲸鱼优化算法相比,其求解时间成本分别降低了66.45%和45.74%。本研究为实现区域一体化能源系统的低碳经济运行提供了新的战略思路,为促进能源结构转型和可持续发展做出了重要贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RIES Low-Carbon Economic Dispatch With Improved WSO Algorithm Considering Integrated Demand Response

Aiming at the low carbon economic dispatch problem of the regional integrated energy system, a low carbon economic dispatch model combining integrated demand response and carbon capture and storage technology is proposed, and an improved great white shark optimization algorithm is used for solving. Experimental results show that this method can significantly reduce the system dispatch cost. Compared with traditional methods, the total dispatch cost is reduced by 22.81% and 17.77%, respectively. Meanwhile, the utilization rates of wind energy and solar energy are improved, and the curtailment rates of wind power and solar power are reduced to 0% respectively. In addition, the improved great white shark optimization algorithm exhibits a faster convergence speed and higher solution accuracy during the solving process. Its solution time cost is reduced by 66.45% and 45.74% compared with the traditional great white shark optimization algorithm and the whale optimization algorithm respectively. This research provides a new strategy for achieving the low-carbon economic operation of the regional integrated energy system and makes an important contribution to promoting the transformation of the energy structure and sustainable development.

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来源期刊
Concurrency and Computation-Practice & Experience
Concurrency and Computation-Practice & Experience 工程技术-计算机:理论方法
CiteScore
5.00
自引率
10.00%
发文量
664
审稿时长
9.6 months
期刊介绍: Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.
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