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Semi-decentralized and fully decentralized multiarea economic dispatch considering participation of local private aggregators using meta-heuristic method

机译:考虑使用Meta-heuristic方法,半分散和完全分散的多ALEA经济派遣参与当地私人聚合器的参与

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This paper presents a semi-decentralized (SD) and a fully decentralized (FD) multiarea economic dispatch (MAED) model based on meta-heuristic optimization (MO) for optimal operation of transmission system operators (TSOs) and private aggregators (PAs). The existing MO-based MAED studies are limited to either using centralized models or not considering multiple autonomous participants in their decision-making framework. The proposed models allow to determine the effective integration of autonomous PAs in the transmission systems (TS) and their co-operation with the TSOs. The objective of both models is to minimize the total operation cost of the system by effectively coordinating the TSOs and PAs operations. The TSOs and PAs evaluate their operational uncertainties and determine the power reserves considering the best and worst-case scenarios of the uncertain variables, thus enabling the resulting models to be solved in three stages using a robust Real-Coded Elitism Genetic Algorithm (RCEGA). To preserve the ownership of TSOs and PAs, the RCEGA efficiently utilizes separate population sets to solve the operations of the areas in parallel in a two-layer operation approach, allowing the TSOs and PAs to achieve optimal operations, independently. Case studies are performed on a modified Nigerian 330 kV 39-bus transmission systems having three TSOs each with three PAs to demonstrate the effectiveness of the proposed models.
机译:本文介绍了一个半分散的(SD)和一个完全分散的(FD)经济调度(MAED)模型,基于元启发式优化(MO),以实现传输系统运营商(TSOS)和私有聚合器(PAS)的最佳运行。现有的基于MO的MAED研究仅限于使用集中模型,或者在其决策框架中考虑多个自主参与者。所提出的模型允许确定自主PAS在传输系统(TS)中的有效集成及其与TSOS的合作。两种模型的目的是通过有效协调TSOS和PAS操作来最小化系统的总运营成本。 TSOS和PAS评估其运行不确定性,并确定考虑不确定变量的最佳和最差情况的电源储备,从而使用稳健的实际编码的精油遗传算法(RCEGA)实现所得模型。为了保留TSOS和PAS的所有权,RCEGA有效利用单独的人口集来解决双层操作方法并行地区的区域,允许TSOS和PA独立地实现最佳操作。在改进的尼日利亚330 kV 39-母线传输系统上进行案例研究,每个TSO有三个PAS,用于展示所提出的模型的有效性。

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