In recent years, the definition of the distributed energy management frameworks has become the core of research due to its distinguished advantages including the less time-consuming, more accurate and secure than the ...
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In recent years, the definition of the distributed energy management frameworks has become the core of research due to its distinguished advantages including the less time-consuming, more accurate and secure than the centralized frameworks. In this sense, this work proposes an effective energy management framework for a distributed networked microgrid (DNM) based on the primal-dual method of multipliers (PDMM). The networked microgrid is comprised of a wind turbine (WT), battery unit and photovoltaic (PV) system which supply the demands of the networked microgrid. The power negotiation process among the networked microgrid' segments will continue until obtaining an adequate solution to achieve the best performance of the proposed microgrid. A Reinforcement Learning (RL) approach is proposed that can increase the accuracy of modeling uncertainty of the parameters of the DNM compared to other manners. To make the secured communication platform, a secured energy transaction structure based on the directed acyclic graph (DAG) approach, is proposed as one of the other aims of this paper. The results indicate the effectiveness of the proposed model and its applicability for the networked microgrid.
A complex system may be controlled and optimized in a more efficient and manageable fashion by treating it as a distributed Multi-Agent System (MAS). But the major challenge in such an approach is to make the agents w...
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ISBN:
(纸本)9781424481262
A complex system may be controlled and optimized in a more efficient and manageable fashion by treating it as a distributed Multi-Agent System (MAS). But the major challenge in such an approach is to make the agents work in a coordinated way to optimize the system objective via optimizing their individual local goals. This paper describes the modified Probability Collectives (PC) as an evolutionary and distributedapproach to achieve the system objective. The approach is validated solving a combinatorial optimization problem such as the Single Depot Multiple Traveling Salesmen Problem (MTSP). Moreover, as constraint handling in evolutionary systems has remained a challenge for years, an effort towards developing a generalized technique incorporating constraints into the PC approach is also attempted. It is validated by solving the practical problem of a spring design. The optimum results are obtained at a reasonable computational cost.
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