# Energy Intelligence: A Systematic Review of Artificial Intelligence for Energy Management
## 4 Artificial Intelligence and Machine Learning in EMS of Power Systems
### Transmission and Distribution  
Power delivery encompasses two critical phases, namely, transmission and distribution. Distribution is the process of converting high-voltage power to lower voltages that are appropriate for end-users such as homes and businesses, while transmission involves the bulk movement of power over long distances at high voltages. The primary cause of voltage and frequency fluctuations in transmission and distribution is the imbalance between electricity generation and consumption. Voltage reductions occur when demand exceeds supply, which occurs when RES output decreases from its expected amount. In contrast, voltage surges may result from low demand and a high-RES penetration output. The rotation speed of generators is influenced by the balance between generation and consumption, resulting in frequency variations. To regulate these fluctuations and preserve the stability of the power grid, a variety of devices are implemented, including capacitor banks, on-load tap converters, and tap-changing transformers. To guarantee the stability of RES-dominant power systems [209], voltage and frequency must be maintained throughout the power transmission and distribution. Linear control principles are employed in conventional control methods to ensure system stability. Nevertheless, the complexity of power transmission and distribution systems in renewable power systems is a result of the variability of RESs and the necessity of constructing new transmission lines to connect remote RESs to the grid. Accurately establishing response functions for voltage and frequency regulation in these systems is a difficult task. As a result, neural networks, including attention-based models [210] and their alternatives [211], are employed to approximate the relationship between the output voltage and frequency and the condition of the RES-based power system, including wind velocity, solar irradiance, and ESS integration. Attention mechanisms have also been employed to monitor the transient states of voltage and frequency in transmission and distribution systems and to adapt response models [212; 213]. Accurate system models are developed through the utilization of high-quality historical data in these methodologies. Nevertheless, the acquisition of specific information from the power transmission and distribution system can be difficult, particularly in low-probability extreme scenarios involving RESs or newly constructed RES farms where there may not be enough historical data to train neural networks. To circumvent the necessity for precise system models, model-free control strategies like DRL-based algorithms have been implemented to regulate voltage and frequency. These strategies are capable of adapting to system variations and uncertainties by learning control techniques from environmental interactions and experiences. These individuals are well-suited to managing intricate systems due to their capacity to learn response models efficiently. In single-agent DRL [214; 215], a single AI-based control device is trained on historical data to generate control signals (as the masters) for transmission and distribution devices (as the slaves). In contrast, multi-agent DRL assigns agents with individual reward functions and coordinates them to obtain improved control results [216; 217; 218; 219]. The utilization of multiple agents enhances the scalability, representation of system dynamics, and coordination of distributed decision-making for voltage control [220]. Nevertheless, the applicability of this multi-agent approach may be diminished as a result of the potential for privacy concerns to arise during data transmission and the resulting increase in communication overhead and computation time. The introduction of distributed DRL has been made to minimize communication overhead by training agents locally without transmitting data to a centralized server. However, privacy concerns persist.