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Investment Decisions in Distribution Networks Under Uncertainty With Distributed Generation—Part I: Model Formulation

机译:不确定分布式发电条件下的配电网投资决策-第一部分:模型制定

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摘要

Investment in distributed generation (DG) is an attractive distribution planning option for adding flexibility to an expansion plan, mainly by deferring network reinforcements. In this first part of a two-part paper, a risk-based optimization approach is proposed to model a multistage distribution expansion planning problem that takes DG into account as a flexible option to temporarily defer large network investments. Five features of the installation of DG related to location, size, type, operation and timing are all optimized. The evolutionary particle swarm optimization (EPSO) method is applied to solve this mixed integer nonlinear problem. A return-per-risk index is proposed to assess expansion investments. This index achieves an efficient synergy between the expected return and the risk of investments by performing Monte Carlo simulations. In turn, the flexibility of network investment deferral is assessed through a real option valuation. Finally, in order to quantify the investment deferral benefit, the expected return from a traditional expansion plan (without DG) is compared to the return obtained from a flexible expansion plan (with DG). In the companion paper, the proposed approach is tested on a typical Latin American distribution network; implementation aspects and analysis of numerical results are presented.
机译:<?Pub Dtl?>分布式发电(DG)投资是一种有吸引力的配电计划选择,主要通过推迟网络加固来增加扩展计划的灵活性。在由两部分组成的本文的第一部分中,提出了一种基于风险的优化方法来对多阶段配电扩展规划问题进行建模,该问题将DG作为临时推迟大型网络投资的灵活选择。 DG安装的五个与位置,大小,类型,操作和时间有关的功能均已优化。应用进化粒子群算法(EPSO)解决了该混合整数非线性问题。建议采用每风险收益指数来评估扩张投资。通过执行蒙特卡洛模拟,该指数在预期收益和投资风险之间实现了有效的协同作用。反过来,通过实物期权评估来评估网络投资延期的灵活性。最后,为了量化投资递延收益,将传统扩展计划(不包含DG)的预期收益与灵活扩展计划(包含DG)获得的收益进行比较。在随附的论文中,该建议的方法在典型的拉丁美洲分销网络上进行了测试;介绍了实现方面和数值结果分析。

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