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Stochastic and scheduling and energy management of a microgrid is implemented using the 2 m + 1 two-point estimation method (PEM) considering the mobile energy storage system (MESS) and demand response (DR) considering renewable generation and load demand uncertainties.
A three-dimensional multi-objective framework for stochastic scheduling and energy management of the energy microgrid is defined to minimize the operation and emission costs as well as maximize the hosting capacity
The DR and MESS are applied in microgrid scheduling and energy management to overcome the uncertainty of renewable energy sources and increase the hosting capacity.
A multi-objective enhanced grey wolf optimizer (MOEGWO) is proposed based on a Logistic chaotic mapping technique for circumventing local optima and achieving more precise solutions.
The structure of the sections in this paper is as follows. Formulation of the problem, which includes the MESS model, the goal functions, and the constraints are presented. Then, the multi-objective optimization framework used by the problem-solver based on the MOEGWO algorithm is outlined. In this study, 2 m + 1 PEM approach to model the uncertainties is presented. Finally, the outcomes and findings summaries are given.
In this study, a stochastic and multi-objective optimization model for distribution microgrid scheduling IS proposed considering the DR and dynamic MESS based on the two-point estimation method (2 m + 1 PEM) and MOEGWO with objective of maximizing the generation hosting of ERs, minimization of operational costs as well as pollution emission cost minimization. In the following, the modeling of PV and WT is presented along with the energy storage.
A microgrid includes distributed generation and renewable energy sources, energy storage, and load demand that can operate independently of or connected to the main power grid. The microgrid studied in this research is a 33-bus distribution network type and includes PV, WT, MT, FC, and dynamic MESS.
Wind energy model: The WT''s production power is calculated using manufacturer data and wind speed information. The generated electricity of a wind turbine is defined by the following formula2,4,5.
where PWT is wind turbine output power, vW wind speed, vcutin low cut-off speed, vcutout cut-out wind speed in m/s, PWT,max is upper turbine output power in kW and Pfurl is output power at high cut-out wind speed.
To model the uncertainty of wind power, the most appropriate distribution is the Weibull PDF, therefore, in this study, the Weibull PDF is used for the wind speed, and it should be evaluated by considering the appropriate parameters of the wind speed change curve. This function can be shown as follows4:
PV energy model: The production power of the PV panel is calculated based on the manufacturer''s data and radiation and temperature data. The output power of a PV panel is defined as follows2,6.
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