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A Self-Organized Optimal Scheduling Approach for Integrated Energy Systems Using Bottom-Up Modelling

Journal of Building Engineering(2024)SCI 2区

Zhejiang Univ

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Abstract
In recent years, decentralized methods have received growing attentions in the field of optimal scheduling of integrated energy systems (IESs) due to their advantages in independent operation and privacy protection. However, the development of decentralized scheduling strategies is usually challenging and time-consuming in practice. To address this problem, this paper proposes a generic self-organized multi-agent-based decentralized scheduling framework for IESs. Four types of base agent models were defined firstly to characterize the individual benefits of various energy entities in IESs. They could construct customized decentralized scheduling models easily. A topology-based fully-decentralized coordination approach was further presented to resolve the conflicts of individual benefits among agents. The coordination rules could be generated in a selforganized manner according to the topological structure of target IESs. Finally, an improved fullydecentralized alternating direction method of multipliers was developed to achieve the fullydecentralized optimization. Simulation results indicate that the generated strategy can reach satisfactory optimization accuracy and computational efficiency. The relative error of optimal results with the benchmark strategy is lower than 0.02 %, and the computational time is reduced by 46.7 % compared with conventional decentralized strategies. This study provides a generic and cost-effective solution to generate customized decentralized scheduling strategies for various types of IESs.
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Key words
Optimal scheduling,Decentralized scheduling,Integrated energy system,Multi-agent system,Distributed optimization,Economic dispatch
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要点】:本文提出了一种基于自组织多代理模型的分散式调度框架,用于集成能源系统的优化调度,有效解决了独立操作和隐私保护问题,同时提高了调度策略的生成效率。

方法】:文章首先定义了四种基础代理模型,以表征IES中不同能源实体的个体利益,然后提出了一种基于拓扑结构的完全分散式协调方法,以解决代理之间个体利益的冲突。

实验】:通过模拟实验验证了所提策略,使用的数据集为自定义IES拓扑结构,实验结果表明,该策略在优化准确性和计算效率上均优于传统分散式策略,相对误差低于0.02%,计算时间减少了46.7%。