# Energy Intelligence: A Systematic Review of Artificial Intelligence for Energy Management
## 4 Artificial Intelligence and Machine Learning in EMS of Power Systems
### Generation  
A challenge to the operational stability of power generators is introduced by the inherent uncertainty of RES. The power output is subject to substantial and unpredictable fluctuations due to the intermittent and variable nature of resources such as solar and wind. In the event of a sudden decrease in RES output [190], grid administrators may be required to promptly implement control measures to bring additional generators online and maintain a consistent power output. The nonlinear characteristics and noise introduced by RES generators frequently present a challenge for traditional linear control principles, such as proportional-integral-differential control (PID) [191; 192], leading to unsatisfactory dynamic performance. AI-based control mechanisms have garnered attention for power system control [193], particularly for systems with high integration of RES. One method of guaranteeing the stability of power systems is to directly regulate the output frequency of RE generators. For example, the excitation current in the field winding  
Figure 15: AI control concepts for RES. can be adjusted to manage frequency deviations in the power output of wind turbines and regulate rotor speed. The magnetic field surrounding the rotor is fortified by increasing the excitation current, which in turn increases the rotor's speed to align with the grid frequency [194]. Nevertheless, a secondary frequency decline in the power system [195] may result from the recovery of rotor speed when the excitation current is reduced. To regulate the frequency of wind power, intelligent algorithms [196], dynamic (multi-agent proximal policy optimization), as well as data-driven [197], and actor-critic RL [195; 198] are implemented with an emphasis on the mechanical and electrical characteristics of wind turbines and their coupling relationships. Attention mechanisms monitor defects and noise in the power system, while RL-based algorithms (such as multi-agent proximal policy optimization and actor-critic) make decisions and send control signals. The electrical characteristics of wind turbine blades, towers, and drivetrains are influenced by their properties, which indirectly affect the output frequency. This phenomenon is challenging to simulate using conventional control methods. Nevertheless, AI-based controllers can identify and integrate these relationships to precisely regulate wind power frequency by processing complex high-dimensional state spaces and extracting information [199]. Inverters [200; 201] regulate the output power frequency, as photovoltaic solar cells do not have rotors, as opposed to wind turbines. The transient frequency of inverters is influenced by a variety of factors, including solar irradiance, which presents a challenge for conventional controllers due to its uncertainty. To account for these factors when making decisions and transmitting control signals, AI-based controllers can integrate them into the state space. AI methods are particularly adept at extracting information from intricate, high-dimensional data and analyzing the complex relationships between output frequency and other variables. As a result, control signals for solar photovoltaic devices have been generated using AI-based controllers, including PSO, random forest regression, attention-enabled multi-agent DRL [202], and ANN [203], to modulate their output frequency. Voltage regulation is nearly as essential as frequency regulation in the pursuit of power system stability [204; 205]. The low inertia of power electronic inverters that are employed to connect REs to the grid can result in voltage fluctuations as a result of the uncertainty associated with RESs. RNNs [206], RL [207], and DRL [208] are among the AI-based techniques that can generate optimal control strategies and implement real-time voltage regulation, some based on dynamics, and some based on the pre-trained frameworks, to mitigate the adverse effects of this uncertainty.