Semnan University Press Journal of Modeling in Engineering 2008-4854 17 57 2019 06 22 Modelling of Distributed Energy Resources Management in Microgird using Distributed Algorithm Modelling of Distributed Energy Resources Management in Microgird using Distributed Algorithm 241 252 3923 10.22075/jme.2019.13739.1345 FA Ghasem Mirbabaee Aliabad University Masoud Radmehr Aliabad Alireza Zakariazadeh University of Science and Technology of Mazandaran 0000-0001-6400-0999 Journal Article 2018 01 28 The smart energy management system as a powerful tool is implemented to manage both demands and generation units. The energy management problem in a Microgrid is usually formulated as a nonlinear optimization problem. According to nonlinear and discreet nature of the problem, solving it by a centralized method requires high computational capabilities. In this paper, two distributed energy management system called Alternating Direction Method of multiplier Predictor (ADMM) and Corrector Proximal Multiplier (PCPM) have been investigated in order to jointly schedule the central controller as well as local controllers. The algorithms consider optimal power flow equations within the distributed energy management problem. The proposed distributed algorithms have been investigated on a typical MG and the efficiency of the algorithm has been evidenced through case studies. Simulation results show that the proposed method decreases the operational cost of MG. Also, the results evidenced that the ADMM has been converged faster and provided a lower operation cost if compared to the PCPM. The smart energy management system as a powerful tool is implemented to manage both demands and generation units. The energy management problem in a Microgrid is usually formulated as a nonlinear optimization problem. According to nonlinear and discreet nature of the problem, solving it by a centralized method requires high computational capabilities. In this paper, two distributed energy management system called Alternating Direction Method of multiplier Predictor (ADMM) and Corrector Proximal Multiplier (PCPM) have been investigated in order to jointly schedule the central controller as well as local controllers. The algorithms consider optimal power flow equations within the distributed energy management problem. The proposed distributed algorithms have been investigated on a typical MG and the efficiency of the algorithm has been evidenced through case studies. Simulation results show that the proposed method decreases the operational cost of MG. Also, the results evidenced that the ADMM has been converged faster and provided a lower operation cost if compared to the PCPM. https://modelling.semnan.ac.ir/article_3923_9ddc295eee46ce9730b65c7a9d89a0ca.pdf