# Energy Intelligence: A Systematic Review of Artificial Intelligence for Energy Management
## 4 Artificial Intelligence and Machine Learning in EMS of Power Systems
The administration of ESS, which is essential for balancing supply and demand, is referred to as storage, particularly in the context of intermittent RES. Desalination emphasizes the incorporation of desalination processes, potentially utilizing surplus power for water purification. Price signals are the process of utilizing market price signals to make sound decisions regarding the allocation of energy resources as well as the optimization of costs and efficiency. The footprint manifests endeavor to reduce environmental and carbon footprints by employing efficient dispatch strategies. Based on the information provided in Figures 11 and 12, AI/ML models have a similar initial process (according to Figure 13) and a difference in their application that requires dynamics or they are data-driven, which are manifested in Figure 14.  
Figure 12: Applications of RL in EMS of power systems. data are the actual observations that are acquired from experiments or real-world applications, and they serve as a solid foundation for the training of data-driven models. Techniques such as continuity and smoothness are indispensable for the refinement of model outputs during the post-processing stage. Smoothness is concerned with the elimination of noise and irregularities, which leads to more interpretable and reliable results, while continuity is achieved by ensuring consistency in the predictions. Methods such as transfer learning are the primary focus of initialization. This method enhances efficiency and performance, particularly when dealing with limited data by utilizing pre-trained models from related tasks. Transfer learning is instrumental in the adaptation of existing knowledge to new problems, thereby reducing the necessity for extensive data. Dynamics awareness is a component of optimizer design that presents the significance of comprehending the fundamental dynamics of the system being modeled. Models can more accurately predict and perform better by incorporating dynamic principles into the optimizer design, which allows them to better encapsulate the behavior of complex systems. Invariances and symmetries are integrated into the design of architecture. Symmetries assist in the reduction in complexity and the enhancement of generalization, while invariances guarantee that the model's predictions remain consistent under specific transformations. The design of model architectures that are both efficient and robust is guided by these principles. Finally, the loss function encompasses conservation laws and ordinary differential equations, partial differential equations, and stochastic differential equations (ODEs/PDEs/SDEs). The loss function can enforce physical constraints and conservation principles by incorporating these mathematical formulations, resulting in models that exhibit realistic behavior and adhere to known laws of nature. In conventional power systems, the rotating bulk of synchronous generators serves as inertia. When the demand for electricity rises, these generators operate at a higher speed, while when it decreases, they operate at a lower speed. The frequency of the electrical grid is stabilized by this inertia. Furthermore, traditional systems can be dampened by regulating the generator's speed and dissipating energy when it operates at a non-synchronous speed. Inverters are the primary means by which RES technologies are typically connected to the grid. These inverters convert the direct current (DC) electricity produced by renewable sources into alternating current (AC) electricity. Inverters are unable to have the same level of stability to the power grid as synchronous generators due to their lack of inertia and damping properties.