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SoS-based multiobjective distribution system expansion planning

机译：基于SoS的多目标配电系统扩展规划

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This paper coordinates the reconfiguration of distribution systems with the expansion problem while the potential of demand response (DR) programs and distributed generation (DG) units are modeled in the active distribution expansion planning. The concept of system of systems (SoS) is proposed to model the expansion of DGs that are owned by private investors. SoS is an efficient system consisting of some autonomous and heterogeneous systems with distinct objective functions. According to the concept of SoS, a decision-making paradigm is developed to determine the location, size, and time of DG investment made by a commercial agent, as well as the price of generated power. From the distribution company (DISCO) viewpoint, the proposed model is a multi-objective (MO) optimization problem. The first objective function is the net present value of the total investment and operation costs related to the network. The second objective function is a reliability index, i.e. the expected energy not-supplied (EENS). The uncertainty of load growth in future years is handled by using a scenario-based approach. The introduced problem is solved by using multi-objective particle swarm optimization (MOPSO) algorithm empowered with an innovative three-layer procedure that is provided to better manage the space of the decision variables. The first layer is based on PSO particles while the second and third layers are based on a sensitivity analysis. Finally, a standard 33-bus distribution system is utilized to obtain the simulation results that show the performance and advantages of the proposed method. (C) 2016 Elsevier B.V. All rights reserved.
机译：本文协调具有扩展问题的配电系统的重新配置，同时在主动配电扩展计划中对需求响应（DR）程序和分布式发电（DG）单元的潜力进行建模。提出了系统系统（SoS）的概念，以对私人投资者拥有的DG的扩张进行建模。 SoS是一个高效的系统，由一些具有不同目标功能的自治系统和异构系统组成。根据SoS的概念，制定了决策范例，以确定商业代理商进行DG投资的位置，规模和时间，以及发电价格。从分销公司（DISCO）的角度来看，所提出的模型是一个多目标（MO）优化问题。第一个目标函数是与网络相关的总投资和运营成本的净现值。第二个目标函数是可靠性指标，即未提供的预期能量（EENS）。未来几年负载增长的不确定性通过使用基于方案的方法来处理。通过使用多目标粒子群优化（MOPSO）算法解决了引入的问题，该算法具有创新的三层过程，可以更好地管理决策变量的空间。第一层基于PSO颗粒，而第二层和第三层基于敏感性分析。最后，利用标准的33总线配电系统获得仿真结果，该仿真结果表明了该方法的性能和优点。 （C）2016 Elsevier B.V.保留所有权利。

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