Stochastic-based scheduling of the microgrid operation including wind turbines, photovoltaic cells, energy storages and responsive loads

Microgrids with different technologies in distributed generations (DGs), different control facilities and power electronic interfaces require proper management and operation strategies. In these strategies, in order to reach the optimum scheduling, the stochastic nature of some decision variables sh...

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Bibliographic Details
Published in:	IET generation, transmission & distribution Vol. 9; no. 12; pp. 1498 - 1509
Main Authors:	Talari, Saber, Yazdaninejad, Mohsen, Haghifam, Mahmoud-Reza
Format:	Journal Article
Language:	English
Published:	The Institution of Engineering and Technology 04.09.2015
Subjects:	Computer simulation demand response demand side management DG dispatch Distributed generation distributed power generation economic efficiency Energy storage integer programming linear programming linear two‐stage stochastic model load curtailment Mathematical models microgrid operation mixed‐integer linear programming Monte Carlo methods Monte Carlo simulation photovoltaic cell photovoltaic power systems power electronic interface power generation dispatch power generation economics power generation scheduling renewable energy source RES availability Scheduling solar cells stochastic processes stochastic scheduling stochastic variable computation module Stochasticity Strategy Uncertainty wind turbine wind turbines integer programming wind turbine linear programming distributed power generation linear two-stage stochastic model power generation scheduling RES availability power generation economics Monte Carlo methods DG dispatch energy storage stochastic variable computation module stochastic processes load curtailment photovoltaic power systems photovoltaic cell microgrid operation stochastic scheduling economic efficiency power generation dispatch distributed generation mixed-integer linear programming wind turbines demand side management solar cells power electronic interface Monte Carlo simulation renewable energy source demand response
ISSN:	1751-8687, 1751-8695
Online Access:	Get full text
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