Multi objective stochastic microgrid scheduling incorporating dynamic voltage restorer

•Stochastic microgrid scheduling is addressed incorporating wind and solar units.•Energy storage systems and different loads are included in the programming.•Dynamic Voltage Restorer (DVR) is utilized in the problem.•Problem is expressed as a stochastic mixed integer, linear programming.•Problem is...

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Vydáno v:International journal of electrical power & energy systems Ročník 93; s. 316 - 327
Hlavní autoři: Jirdehi, Mehdi Ahmadi, Tabar, Vahid Sohrabi, Hemmati, Reza, Siano, Pierluigi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.12.2017
Témata:
Augmented Epsilon-constraint method
Dynamic voltage restorer
Microgrid programming
Multi objective programming
Stochastic programming
Multi objective programming
Augmented Epsilon-constraint method
Dynamic voltage restorer
Microgrid programming
Stochastic programming
ISSN:0142-0615, 1879-3517
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Shrnutí:•Stochastic microgrid scheduling is addressed incorporating wind and solar units.•Energy storage systems and different loads are included in the programming.•Dynamic Voltage Restorer (DVR) is utilized in the problem.•Problem is expressed as a stochastic mixed integer, linear programming.•Problem is solved by using the augmented Epsilon-constraint method in GAMS/CPLEX. This paper focus on optimal scheduling of microgrid including thermal and electrical loads, renewable energy sources (solar and wind), combined heat and power (CHP), conventional energy sources (boiler and micro turbine), energy storage systems (thermal and electrical storages), and series flexible alternating current transmission system (FACTS) devices. Dynamic Voltage Restorer (DVR) is included in the line between the main network and the microgrid in order to achieve a higher power transfer to the upstream grid. In the proposed method, wind speed, solar radiation, and loads are modelled as uncertain parameters based on a stochastic approach. The problem is modelled as a linear, mixed integer, constrained, and multi objective optimization one aiming at minimizing cost and pollution at the same time. Also, a sensitivity analysis is proposed for studying the sensitive parameters in microgrid management. The proposed multi objective and stochastic problem is solved by using the augmented Epsilon-constraint method. All results and calculations are obtained by using GAMS24.1.3/CPLEX12.5.1. Finally, in order to confirm the results of the proposed method, final results are compared to the genetic algorithm method. Simulation results demonstrate the viability and effectiveness of the proposed scheduling method for microgrid.
ISSN:0142-0615
1879-3517
DOI:10.1016/j.ijepes.2017.06.010