基于改进灰狼算法的微网多主体主从博弈策略
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TM734

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国家电网有限公司总部管理科技项目(5400-202199541A-0-5-ZN)


Research on multi-agent master-slave game strategy of microgrid based on improved grey wolf algorithm
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    摘要:

    为平衡包含电、热两种能源形式的微网系统内各参与者间的利益关系,本文通过改进灰狼算法提出了一种微网能量管理模型。首先,在充分分析微网结构及其各主体功能的基础上,为综合考虑源-网-荷的决策能力,将主从博弈方法应用于产能商、微网运营商、负荷聚合商之间的互动,建立一主多从的微网能量管理数学模型;其次,针对博弈上层模型高维、非线性的特点,文章在传统灰狼算法基础上,利用Tent映射对种群进行初始化、采用非线性收敛因子平衡种群搜索能力、利用莱维飞行策略降低陷入局部最优的风险。在模型求解时,博弈上层采用改进灰狼算法,下层采用二次规划方法,二者结合以探讨使各主体利益最大的策略;最后,通过算例进行验证,结果表明:文中算法更加高效,所提模型在提高参与者收益,平滑用户负荷分布方面更加优越。

    Abstract:

    In order to balance the interests of various participants in a microgrid system containing two forms of energy, electricity and heat, a microgrid energy management model based on the improved Grey Wolf algorithm is proposed. Firstly, the microgrid structure and the functions of various entities within the microgrid are analyzed.In order to comprehensively consider the decision-making ability of source-grid-load, the master-slave game method is applied to the interaction among energy producer, microgrid operators, and load aggregators; Secondly, to address the characteristics of high dimensionality and nonlinearity in the upper-layer model, tent mapping is used to initialize the population, a nonlinear convergence factor is employed to balance the population search capability and the Levy flight strategy is utilized to reduce the risk of falling into local optimum.In the process of model solving, the improved gray wolf algorithm is used for the upper-level , and quadratic programming methods are used for the lower-level. The combination is explored to discover the best strategy that maximize the interests of each entity; Finally, the efficiency of the algorithm and the superiority of the proposed model in improving the participants' revenue and smoothing the load distribution are verified by an arithmetic example.

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陈晓梅,周博,蔡烨. 基于改进灰狼算法的微网多主体主从博弈策略[J]. 科学技术与工程, 2024, 24(18): 7701-7709.
Chen Xiaomei, Zhou Bo, Cai Ye. Research on multi-agent master-slave game strategy of microgrid based on improved grey wolf algorithm[J]. Science Technology and Engineering,2024,24(18):7701-7709.

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  • 收稿日期:2023-05-21
  • 最后修改日期:2024-03-28
  • 录用日期:2023-11-14
  • 在线发布日期: 2024-07-05
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