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Smart distribution grid multistage expansion planning under load forecasting uncertainty

机译:负荷预测不确定性下的智能配电网多阶段扩展规划

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摘要

The optimal distribution system planning (ODSP) is a complicated problem with multi-objective function and multi-constraints. The complexity of the problem is increased in smart distribution grids with uncertainties in both load and generation. In this study, problem of optimal smart distribution grids multistage expansion planning is presented in which reinforcement or installation time, capacity and location of MV substation and DER are taken into consideration. The binary global search optimization algorithm is proposed to solve the ODSP problem. The proposed cost function considers the capital investment, operation and the levelized energy cost (LEC) of each energy source. Loss characteristic matrix has been used for locating of MV substation and DER. The aspect of modeling under load growth uncertainty and multistage planning and multiple objective functions which are related to ODSP problem are considered in an integrated model. The multistage planning procedure is proposed to consider the pseudo-dynamic behavior of planning and continuing growth of demand. Load uncertainty is represented by point estimated approach and the results are compared with the Monte Carlo simulation (MCS). Presented methodology has been tested from base to long-term period on distribution network. The obtained results confirm the ability and validity of the presented method.
机译:最优配电系统规划(ODSP)是一个具有多目标功能和多个约束的复杂问题。在负荷和发电都不确定的智能配电网中,问题的复杂性增加了。在这项研究中,提出了优化智能配电网多阶段扩展计划的问题,其中考虑了中压变电站和DER的加固或安装时间,容量和位置。提出了二进制全局搜索优化算法来解决ODSP问题。拟议的成本函数考虑了每个能源的资本投资,运营和平均能源成本(LEC)。损耗特征矩阵已用于中压变电站和DER的定位。在集成模型中考虑了在负载增长不确定性和多阶段计划以及与ODSP问题相关的多目标函数下进行建模的方面。提出了多阶段计划程序,以考虑计划的拟动态行为和需求的持续增长。负荷不确定性由点估计方法表示,并将结果与​​蒙特卡洛模拟(MCS)进行比较。所提出的方法已在分销网络上从基础到长期进行了测试。获得的结果证实了所提出方法的能力和有效性。

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