## 6 State of Art in Control and Automation in Distribution Networks  
The control and automation technologies in ADNs are very important. They enhance grid stability, enable real-time decision-making, and optimize energy distribution. Control algorithms have evolved significantly, enabling more precise and adaptive control strategies [94]. The latest developments in control algorithms include model predictive control (MPC), distributed control, and machine learning-based approaches. The distributed control system is an essential component of an ADN, enabling efficient, reliable, and resilient grid operation in the face of increasing complexity and the integration of renewable energy sources [14]. It empowers utilities and operators to manage the dynamic nature of ADNs effectively and adapt to evolving energy landscapes, ultimately contributing to a more sustainable and efficient energy distribution system. Supervisory Control and Data Acquisition (SCADA) systems are the backbone of control and automation in ADNs. The control and automation technologies are indispensable for managing the complexities of ADNs [21]. Active distribution networks (ADNs) enable grid operators to adapt to the changing energy landscape, integrate renewable resources, optimize grid operation, enhance grid resilience, and respond to grid events in real-time, ultimately contributing to a more reliable, efficient, and sustainable energy distribution system. This section critically evaluates the enabling technologies that facilitate control and automation in ADNs, with a primary focus on control and automation technologies themselves. The main operational goal of the distribution network is to deliver electricity of an adequate level of quality securely and reliably while ensuring economical, efficient, and environmentally sustainable operation. To facilitate the realization of this goal, the distribution network operator (DNO) makes use of a distribution management system (DMS). A DMS is essentially a set of functionalities (or applications) that facilitate the monitoring, control, and automation of distribution network operations. It acts as a decision support system, utilizing which the DNO can have a real-time overview of the state of the network and initiate automatic or manual actions meant to maintain reliable and efficient system operation or mitigate anomalous network conditions. Reliability and quality of supply are ensured primarily by minimizingthe occurrence of power outages, minimizing the outage time, and maintaining the system frequency and voltage magnitude at nominal levels throughout the network [95]. A distribution management system (DMS) can also be seen as an approach that unites primarily disparate facilities to operate the distribution system in an effective, efficient, and organized way. It has several functionalities to assist operators in managing and optimizing distribution networks through suitable decision-making. A DMS has two types of application functions, which are the (i) real-time application functions and (ii) analytical application functions [96]. DMS is also required to perform a variety of on- and offline analyses to implement such functionalities as fault location, isolation service restoration, and network reconfiguration. A Supervisory Control and Data Acquisition (SCADA) system is an integral component of a DMS, which enables network monitoring, automation, and control. The architecture and key functionalities of DMS are seen in Figure 5. Among the key functionalities of the DMS, the following can be identified, some of which are also depicted below. Distribution system power flow (DSPF): The voltage phasors are computed on the nodes given the information of the grid characteristics and power dispatched, representation supply, and loads. Real-time DSPF employs forward/backward sweep for radial network and current injections for meshed distribution network [97; 98].