Open AccessStochastic‐based scheduling of the microgrid operation including wind turbines, photovoltaic cells, energy storages and responsive loads
Author(s) -
Talari Saber,
Yazdaninejad Mohsen,
Haghifam MahmoudReza
Publication year - 2015
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2014.0040
Subject(s) - microgrid , dispatchable generation , photovoltaic system , scheduling (production processes) , demand response , computer science , mathematical optimization , stochastic programming , renewable energy , monte carlo method , energy storage , wind power , linear programming , reliability engineering , distributed generation , power (physics) , engineering , electricity , mathematics , electrical engineering , statistics , physics , quantum mechanics
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 should be considered. Subsequently, it will lead to a decrease in the forced load curtailment and an increase in the economic efficiency from the perspective of both the maingrid and the microgrid owner. In this study, the availability of dispatchable DGs, energy storage, renewable energy sources (RESs) and the maingrid as well as the power generation of RESs and load are studied through their uncertainty natures. For dealing with these uncertainties, stochastic variables computation module is designed which generates several scenarios by Monte Carlo simulation at each hour. The microgrid operation is optimised in uncertainty environment through a linear two‐stage stochastic model. The stochastic scheduling model which is solved by mixed‐integer linear programming is compared with a deterministic model through three different cases in presence of demand response on a sample microgrid. The results explicitly show benefits of the proposed stochastic model since it provides accuracy in scheduling and decreases the operation cost.