Energy management system for microgrids using weighted salp swarm algorithm and hybrid forecasting approach
Author(s)
Tayab, UB
Lu, J
Yang, F
AlGarni, TS
Kashif, M
Year published
2021
Metadata
Show full item recordAbstract
The concept of a microgrid (MG) has been introduced to integrate the conventional generators, different renewable energy resources and energy storage systems (ESS) to meet the specific load demand. However, the intermittent nature of renewable energy resources produces a variable output, which drives an imbalance between power generation and demand in MG. The ESS is utilized to makes a balance between power generation and demand. When several renewable energy resources and ESS are available in MG as energy resources, then an energy management system (EMS) is required that can handle the stochastic nature of renewable energy ...
View more >The concept of a microgrid (MG) has been introduced to integrate the conventional generators, different renewable energy resources and energy storage systems (ESS) to meet the specific load demand. However, the intermittent nature of renewable energy resources produces a variable output, which drives an imbalance between power generation and demand in MG. The ESS is utilized to makes a balance between power generation and demand. When several renewable energy resources and ESS are available in MG as energy resources, then an energy management system (EMS) is required that can handle the stochastic nature of renewable energy resources, schedule the power of renewable energy resources and ESS for managing the power flow among MG resources and main grid while ensuing cost-effective operation. Therefore, this paper proposed an optimum EMS that aims to minimize the overall operating cost of grid-connected MG along with the short-term forecasting of PV power and load demand. The proposed EMS consists of four modules: forecasting, scheduling, data acquisition (DAQ), and human–machine interface (HMI) modules. An improved hybrid forecasting approach that combines a 3-level stationary wavelet transform (SWT) and grey wolf optimization-based least-square support vector machine (GWO-LSSVM) is proposed in the forecasting module to achieve day-ahead forecasting of PV power and load demand. In the scheduling module, the weighted salp swarm algorithm-based scheduling is applied to achieve the optimum power flow of grid-connected MG. Then, the DAQ and HMI module is used to monitor, analyze, and modified the input variables of the forecasting and scheduling module. The MATLAB/Simulink environment is then used to simulate the proposed EMS for grid-connected MG. Finally, numerical results demonstrate the efficiency of the proposed EMS for grid-connected MG with commercial load demand over the existing competitive approaches.
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View more >The concept of a microgrid (MG) has been introduced to integrate the conventional generators, different renewable energy resources and energy storage systems (ESS) to meet the specific load demand. However, the intermittent nature of renewable energy resources produces a variable output, which drives an imbalance between power generation and demand in MG. The ESS is utilized to makes a balance between power generation and demand. When several renewable energy resources and ESS are available in MG as energy resources, then an energy management system (EMS) is required that can handle the stochastic nature of renewable energy resources, schedule the power of renewable energy resources and ESS for managing the power flow among MG resources and main grid while ensuing cost-effective operation. Therefore, this paper proposed an optimum EMS that aims to minimize the overall operating cost of grid-connected MG along with the short-term forecasting of PV power and load demand. The proposed EMS consists of four modules: forecasting, scheduling, data acquisition (DAQ), and human–machine interface (HMI) modules. An improved hybrid forecasting approach that combines a 3-level stationary wavelet transform (SWT) and grey wolf optimization-based least-square support vector machine (GWO-LSSVM) is proposed in the forecasting module to achieve day-ahead forecasting of PV power and load demand. In the scheduling module, the weighted salp swarm algorithm-based scheduling is applied to achieve the optimum power flow of grid-connected MG. Then, the DAQ and HMI module is used to monitor, analyze, and modified the input variables of the forecasting and scheduling module. The MATLAB/Simulink environment is then used to simulate the proposed EMS for grid-connected MG. Finally, numerical results demonstrate the efficiency of the proposed EMS for grid-connected MG with commercial load demand over the existing competitive approaches.
View less >
Journal Title
Renewable Energy
Volume
180
Funder(s)
ARC
Grant identifier(s)
LP190101251
DP160103567
Subject
Electrical engineering
Mechanical engineering
Other engineering
Engineering