## 2 Literature Review  
In a multi-energy microgrid, numerous unknowns exist regarding the interactions between renewable energy sources, power demands, and electricity transaction costs. A two-stage, mixed-integer, deterministic, linear programming model of the problem has been developed, and it can be addressed by linearizing constraints and generating and reducing uncertain scenarios. The suggested approach is then tested on a microgrid that uses an IEEE 33 bus distribution network to control energy from various sources [34]. As smart grids replace conventional electrical grids, one of the significant problems that have developed is maintaining the system's safety. However, if the design and supporting infrastructure are created from the initial concept with security in mind, this problem can be solved. Therefore, implementing cyber security is a crucial and additional step. The National Institute of Standards and Technology initially recognized confidentiality, integrity, and availability as the three principles of smart grid security [35]. However, the authors highlighted the importance of accountability for smart grid security in Secrets that are frequently compromised when unauthorized people access private data. On the other hand, integrity guarantees that data is sent without being changed or deleted. However, accessibility is a critical feature that ensures users access to the system's data in the context of smart grids. People cannot obtain information since it is not available [36]. Accountability assures that the system can be tracked and must be verified by a person, a device, or a government organization, which is essential for the security of the smart grid. Additionally, the recorded data can be used as proof in the event \begin{table}
\begin{tabular}{l l l l} \hline
**Abbreviations** & **Full Form** & **Abbreviations** & **Full Form** \\ \hline P.M.Us & power monitoring units & N.LS & National Institute of Standards \\ A.I & Artificial Intelligence & A.M.I & advanced metering infrastructure \\ W.A.M.R & wireless asset management relay & IoE & Internet of Energy \\ S.G & Smart grid & E.I & Energy Internet \\ S.S & Smart system & IoT & Internet of Things \\ S.H & Smart House & D.O.E & Department of Energy \\ E.V & Electric Vehicle & EISA & Energy Independence and Security Act \\ I.G & Intelligent grid & NASPI & North American Synchro Phasor Initiative \\ N.E.TL & National Energy & NERC & North American Electric \\  & Technology Laboratory & Reliability Corporation & \\ L.A.N & Local area network & EEGI & European Electric Grids Initiative \\ H.A.N & home-area network & ISGTF & Indian Smart grid Task Force \\ S.G.M.M & Smart grid Maturity Model & CPRI & Central Power Research Institute’s \\ \hline \end{tabular}
\end{table} 
Table 2: List of abbreviations. Figure 4: Structure of the paper. of an attack to establish the actions taken by each user, including administrators, and to guarantee the accuracy of the data collected from each device [36]. Consequently, adopting the following four rules confidentiality, integrity, availability, and accountability, is the best way to safeguard smart grid systems. Smart grid networks are vulnerable to numerous attacks due to insufficient communication. AI is widely used in the field of Cybersecurity. The digitization of manufacturing processes is usually correlated with machine learning, natural language processing, and robot-assisted process automation [37]. However, Cybersecurity has long used techniques of a similar nature. Consider the filtering system as an example of how machine learning might be helpful. It has been used since the early 2000s [38]. It is clear that methods have changed through time, and current algorithms can draw much more complex judgments. The digital security of smart grids has significantly improved due to recent AI developments. These improvements enhance the defenses against various threats. The five most common uses of machine learning are security (detection of fraud and viruses), privacy, business, and IT.