# Energy Intelligence: A Systematic Review of Artificial Intelligence for Energy Management
## 4 Artificial Intelligence and Machine Learning in EMS of Power Systems
### Energy Storage System (ESS)  
The integration of ESS into RES-dominant power systems has been identified as a viable measure for reducing the operational risks associated with the inherent uncertainty of RESs [231, 232]. These systems can ensure the system's stability by providing virtual inertia and damping, acting as power supplies during high-demand periods, and storing surplus energy during off-peak periods. Traditional control principles such as the bang-bang control principle necessitate precise system models and are unable to effectively address the cacophony and disturbances in the power system that RE introduces. The real-time responsiveness of controllers for ESS can be improved through the use of AI-based techniques. As an example, adaptive fuzzy neural networks [233; 234] have been implemented to regulate the operation of ESS by serving as a virtual synchronous generator within the power system. To compensate for the low inertia and inadequate damping capabilities of renewable power systems, neural networks are employed to adaptively modify the parameters of the virtual synchronous generator, including the charging and discharging power of the system. Additionally, RL-based different strategies [235; 236; 237] have been employed to develop and operate power system controllers, which encompass various forms of ESS. The establishment of response models is facilitated by the precise prediction of voltage and frequency fluctuations in renewable power systems by DL algorithms. These models can be further improved through the use of techniques such as MPC. Nevertheless, the scale of DL algorithms' networks may result in latency issues when applied to real-time control tasks as they necessitate a significant amount of labeled training data and computational resources. Conversely, DRL-based algorithms offer adaptable and flexible control for ESS. DRL algorithms are capable of managing the inherent variability and uncertainty in renewable power systems by learning control strategies through interaction with the environment. DRL is well-suited for real-time control tasks due to its adaptability, despite the potential challenges associated with computational demands and training time. Additionally, Refs. [238; 239; 240; 241; 242; 243] considered the chance-constrained models in EMS and control of power systems, as well as their integration with IoT, ESS, and hubs. Finally, the presented different fundamental models are compared based on their robustness, dynamics requirements, cloud-based control abilities, and computational complexity in Table 4 and Figure 16.  
Figure 16: Correlation of the models’ requirements with their criteria in the EMS of power systems, as \(3=\texttt{OOD},2=\texttt{OCO},\) and \(1=\texttt{OOD}.\)  
LSTM and BiLSTM are more robust than ANN and Neuro-Fuzzy in terms of robustness, as they are capable of effectively managing long-term dependencies and sequential data. However, they may be less robust in chaotic conditions. DTs are inferior due to their propensity to overfit, while RF is intermediate, benefiting from its ensemble approach. Continuous learning and adaptation are the hallmarks of RL, which is distinguished by its robustness. LSTM, BiLSTM, and RL are superior in terms of dynamics as they are capable of effectively managing complex temporal and dynamic environments. In contrast, RF and DT are inferior as they struggle to handle dynamic data. LSTM and BiLSTM are effective with sequential data, while ANN and Neuro-Fuzzy excel with diverse data types in data handling. RF is capable of effectively managing structured data, whereas DT and RL are less well-suited for direct data processing. In terms of cloud-based control, ANN is the most effective, followed by LSTM, BiLSTM, and Neuro-Fuzzy, which are also deployable but have variable resource requirements. The efficiency issues of RF and DT render them less suited for cloud applications.