## 7 Conclusions  
The study summarized the recently published literature that focuses on methods to reduce power consumption and costs. Furthermore, the recent literature discussed using cloud computing to store EMSs and managing them intelligently. Furthermore, it discussed how a well-maintained system of power mixing (power used to charge the batteries) can lead to better environmental results by reducing the carbon footprint. Furthermore, it discussed the recent literature that used cloud computing to store EMSs and manage them intelligently. As a result of this extensive literature review, the researcher proposed a final chart of the model that will consider three aspects: battery management, RE, and cloud computing, which will be the main contribution of the future study conducted by the researcher.  
Drafted the original manuscript, conceptualization, literature analysis, A.H.A.A.-J.; Conceptualization and methodology, Y.L.A.M.; Investigation and supervision and validation R.S. and Z.A.A.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Malaysian Fundamental Research Grant Scheme (FRGS) under the code PP-FTSM-2021 and TAP-K01149.  
Not applicable.  
The authors gratefully acknowledge the financial support of the Laboratory of Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Malaysia and University of Fallujah, Iraq.  
Figure 9: The framework system model for energy sustainability.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Ethics and Permission to Participate:** This manuscript has not been previously released and is not now under consideration by any journal for publication.  
**Abbreviations**  
In this review, the following abbreviations are used:  
**Abbreviations**  
**The Details**  
VPPs  
Virtual Power Plants  
DC  
Direct Current  
CDE  
Carbon Dioxide Emissions  
RE  
Renewable Energy  
USDOE  
United States Department of Energy  
SG  
Smart Grid  
SGs  
Smart Grid  
SES  
Smart Energy Systems  
AI  
Artificial Intelligence  
DR  
Demand Response  
PS  
Power Supply  
DER  
Distributed Energy Resource  
MT  
Microgrid Trading  
DDoS  
Distributed Denial of Service  
CPU's  
Central Processing Units  
SPM  
Static Power Management  
DPM  
Dynamic Power Management  
DPC  
Dynamic Power Consumption  
C  
Coulomb  
A  
Amperes  
V  
Volts  
W
Watts  
WH  
Watt-Hours  
GA  
Genetic Algorithm  
PSO  
Particle Swarm Optimization  
FL  
Fuzzy Logic  
MOA  
Metaheuristic Optimization Algorithms  
SoC  
State of Charge  
KW  
kilowatt  
GPR  
Gaussian Process Regression  
GIESBs  
Grid-Integrated Energy Storage Batteries  
PVs  
Photo Voltic's  
WTs  
Wind Turbines  
EBMS  
Electric Bus Management System  
ANNs  
Artificial Neural Networks  
EMS  
Energy Management System  
HMG  
Hybrid Micro-Grid  
MOPSO  
Multi-Objective Particle Swarm Optimization  
PMP  
Pontryagin's Minimum Principle  
MEET  
Maximum Efficiency Tracking  
FAFC  
First Access First Charge  
MDP  
Markov Decision Process  
HEMS  
Home energy management system  
SHEMS  
Smart Home Energy Management System  
BMS  
Battery Management system  
MWs  
Mega Watts