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Multi-objective optimization of smart community integrated energy considering the utility of decision makers based on the Lévy flight improved chicken swarm algorithm
Sustainable Cities and Society ( IF 11.7 ) Pub Date : 2021-06-05 , DOI: 10.1016/j.scs.2021.103075
Jianwei Gao , Fangjie Gao , Zeyang Ma , Ningbo Huang , Yu Yang

A community integrated energy system can play a key role in alleviating urban energy shortages and environmental degradation, but the presence of multiple participants and links makes the operation of such a system more complicated. How to choose the optimal energy use strategy among different decision makers is a problem that urgently needs to be solved today. Therefore, first, this paper proposes a comprehensive energy multi-objective scheduling model based on the established smart community energy management framework, which considers the utility of decision makers. From the perspective of risk, the model divides decision makers into adventurous, intermediate and conservative types in order to study the impacts of different decision makers on energy use strategies. Second, to prevent the phenomenon of the solution of the intelligent algorithm falling into the local optimum, this paper innovatively uses the Lévy flight to optimize the learning step length of the chicken swarm algorithm to quickly solve the proposed mixed integer nonlinear programming model. Finally, the model is tested through multi-scenario simulation. The results show that decision makers have an important influence on energy use strategies, and different decision makers have different energy use strategies when utility is maximized. In addition, compared with the chicken swarm algorithm, the improved algorithm increases the utility of adventurous, intermediate and conservative decision makers in type-I cases by 0.54%, 1.2%, and 1.5%, respectively; and in type-II cases by 4.5%, 4.8%, and 3.6%, respectively.



中文翻译:

基于Lévy飞行改进鸡群算法的考虑决策者效用的智慧社区综合能源多目标优化

社区综合能源系统可以在缓解城市能源短缺和环境恶化方面发挥关键作用,但多参与者和多环节的存在使得这种系统的运行更加复杂。如何在不同决策者之间选择最优的能源使用策略是当今急需解决的问题。因此,本文首先基于已建立的智慧社区能源管理框架,考虑决策者的效用,提出综合能源多目标调度模型。该模型从风险的角度将决策者分为冒险型、中间型和保守型,以研究不同决策者对能源使用策略的影响。第二,为防止智能算法求解陷入局部最优的现象,本文创新性地利用Lévy飞行优化鸡群算法的学习步长,快速求解所提出的混合整数非线性规划模型。最后,通过多场景仿真对模型进行测试。结果表明,决策者对能源使用策略有重要影响,当效用最大化时,不同的决策者有不同的能源使用策略。此外,与鸡群算法相比,改进算法在I类案例中分别增加了冒险、中间和保守决策者的效用0.54%、1.2%和1.5%;在 II 型病例中分别为 4.5%、4.8% 和 3.6%。本文创新性地利用Lévy飞行优化鸡群算法的学习步长,快速求解所提出的混合整数非线性规划模型。最后,通过多场景仿真对模型进行测试。结果表明,决策者对能源使用策略有重要影响,当效用最大化时,不同的决策者有不同的能源使用策略。此外,与鸡群算法相比,改进算法在I类案例中分别增加了冒险、中间和保守决策者的效用0.54%、1.2%和1.5%;在 II 型病例中分别为 4.5%、4.8% 和 3.6%。本文创新性地利用Lévy飞行优化鸡群算法的学习步长,快速求解所提出的混合整数非线性规划模型。最后,通过多场景仿真对模型进行测试。结果表明,决策者对能源使用策略有重要影响,当效用最大化时,不同的决策者有不同的能源使用策略。此外,与鸡群算法相比,改进算法在I类案例中分别增加了冒险、中间和保守决策者的效用0.54%、1.2%和1.5%;在 II 型病例中分别为 4.5%、4.8% 和 3.6%。

更新日期:2021-06-05
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