# Energy Intelligence: A Systematic Review of Artificial Intelligence for Energy Management
## 4 Artificial Intelligence and Machine Learning in EMS of Power Systems
### Electricity Demand  
The traditional method of regulating voltage and frequency in power systems is to modulate generators to maintain system stability. Nevertheless, the availability of conventional power facilities that are capable of participating in frequency and voltage control will decrease as the proportion of RESs increases. As a result, the stability of RES power systems are becoming more dependent on flexible load demands, which are able to be altered or shifted in response to changes in the power system.  
Conventional control methods are insufficient for the preservation of power system stability due to the complexity and diversity of flexible load demands [223]. Rather, AI techniques are implemented to regulate a variety of flexible burdens. For example, various AI techniques, including DRL [224, 225], and neural networks alternatives [226], have been suggested as approaches for managing the charging of EVs. These AI-based control mechanisms strategically manage charging to accomplish load-demand balancing by analyzing historical and real-time data to capture charging demand and grid load trends. Furthermore, AI control mechanisms have the capacity to learn and optimize autonomously, thereby refining their control strategies over time. This improves the power system's response capabilities and enhances comprehension of it.  
Consequently, to balance power supply/demand, AI techniques such as multi-agent methods [227], different types of RL [228, 229, 230] are also implemented to manage other flexible load demands, including electric water heaters and HVAC systems. These control mechanisms, which are AI-based, engage with a variety of RES power systems and adaptable loads to continuously optimize control strategies.  
Demand-side control, which involves requesting that consumers modify their electricity consumption, has not been extensively implemented due to consumers' potential reluctance to engage in demand regulation. To resolve this issue, it is imperative to conduct additional research on incentive mechanisms to enhance consumer interest in participating in initiatives related to this dynamic.  
Numerous AI-based algorithms exist for managing flexible loads in renewable power systems, each with its own individual advantages and disadvantages. Heuristic algorithms are particularly effective in problems with substantial search spaces; however, they may be inefficient for real-time load requirements. DL-based control algorithms are capable of effectively managing complex tasks and large-scale data, rendering them appropriate for flexible load control. Conversely, they may encounter challenges in terms of inadequate data accessibility or inadequate explanatory capabilities. DRL algorithms combine the advantages of DL and RL, providing improved adaptability and flexibility for flexible load demand control. Real-time learning and decision-making are enabled by these algorithms, which acquire optimal decision-making strategies through interaction with the environment without the necessity of extensive labeled training data. Consequently, dynamically changing demands can be accommodated by DRL techniques, which can also manage real-time flexible load control duties. However, the training and optimization of DRL-based algorithms can be resource-intensive and time-consuming, and they may experience stability or convergence issues when managing complex problems.