Microgrid Energy Management System for Residential Microgrid Using an Ensemble Forecasting Strategy and Grey Wolf Optimization

Usman Bashir Tayab, Junwei Lu, Seyedfoad Taghizadeh, Ahmed Sayed M. Metwally and Muhammad Kashif
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Usman Bashir Tayab: School of Engineering and Built Environment, Griffith University, Gold Coast, QLD 4215, Australia
Junwei Lu: School of Engineering and Built Environment, Griffith University, Gold Coast, QLD 4215, Australia
Seyedfoad Taghizadeh: School of Engineering, Macquarie University, Macquarie Park, NSW 2019, Australia
Ahmed Sayed M. Metwally: Department of Mathematics, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
Muhammad Kashif: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China

Energies, 2021, vol. 14, issue 24, 1-19

Abstract: Microgrid (MG) is a small-scale grid that consists of multiple distributed energy resources and load demand. The microgrid energy management system (M-EMS) is the decision-making centre of the MG. An M-EMS is composed of four modules which are known as forecasting, scheduling, data acquisition, and human-machine interface. However, the forecasting and scheduling modules are considered the major modules from among the four of them. Therefore, this paper proposed an advanced microgrid energy management system (M-EMS) for grid-connected residential microgrid (MG) based on an ensemble forecasting strategy and grey wolf optimization (GWO) based scheduling strategy. In the forecasting module of M-EMS, the ensemble forecasting strategy is proposed to perform the short-term forecasting of PV power and load demand. The GWO based scheduling strategy has been proposed in scheduling module of M-EMS to minimize the operating cost of grid-connected residential MG. A small-scale experiment is conducted using Raspberry Pi 3 B+ via the python programming language to validate the effectiveness of the proposed M-EMS and real-time historical data of PV power, load demand, and weather is adopted as inputs. The performance of the proposed forecasting strategy is compared with ensemble forecasting strategy-1, particle swarm optimization based artificial neural network, and back-propagation neural network. The experimental results highlight that the proposed forecasting strategy outperforms the other strategies and achieved the lowest average value of normalized root mean square error of day-ahead prediction of PV power and load demand for the chosen day. Similarly, the performance of GWO based scheduling strategy of M-EMS is analyzed and compared for three different scenarios. Finally, the experimental results prove the outstanding performance of the proposed scheduling strategy.

Keywords: energy management system; grey wolf optimization; forecasting; microgrid; particle swarm optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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