# Demand-Response Control in Smart Grids
## 4 Comparison between Centralized and Distributed Control for Demand Response
### Comparison between Centralized and Distributed Control  
The demand-response control in smart grids can be divided into two categories: centralized and distributed control. We present two well-known solutions for centralized and distributed control. In DR based on centralized solutions, all the customer agents interact with the power utility. The centralized architecture is based on centric communication and is suitable for a small network due to its simplicity. In centralized control, global information is needed to make decisions for the whole system; that is, all agents are required to exchange all data with central control. Therefore, centralized control is considered simple to implement and easy to maintain in the case of single-point failure. However, it suffers from different drawbacks such as computational burden, scaling difficult, and a high needed level of connectivity. From a reliability perspective, a single point of failure leads to an unstable system, which is one of the main challenges in a centralized solution. From a scalability perspective, the maximum number of customers is restricted by the power utility's capacity. Additionally, when demand response is implemented as an iterative process, communication latency between the power utility and customers might be significant. When the power utility is secured by firewalls or intrusion detection systems, the latency increases even further. From the standpoint of system security, because customers are typically situated outside the power utility's physical security perimeter, cyber attackers may be able to exploit them when communication takes place via IP-enabled networks. All customers in a DR-based centralized solution must know the IP addresses of the specified power-utility side. As a result, from a security standpoint, using the IP address for demand response exposes the system to cyberattacks like distributed denial of service (DDoS) from hacked customers. Coordination of distributed customers in a centralized solution is difficult due to the high communication load and computational complexity. The difficulty is in determining how to ensure privacy and algorithm convergence. Moreover, the centralized approach needs private consumer details, which is difficult to ensure in the case of information asymmetry. Using distributed algorithms has the following advantages: they are easy to extend (high extensibility), have low calculation cost (parallel computing), have no single point of failure, are convenient for large-scale systems, are unaffected by system topology changes, and are convenient for plugin features in smart-grid clients. Furthermore, anonymity is maintained (a small amount of data is requested from customers). Under distributed approaches, a large-scale model can be divided into several sub-problems. These more tractable sub-problems are then treated in parallel and independently, resulting in a significant increase in overall efficiency. Furthermore, the distributed approaches are scalable, meaning that the number of iterations does not increase as the number of consumers grows. The distributed architecture ensures a secure, scalable, efficient, and reliable algorithm. However, the distributed control suffers from the following drawbacks: it needs synchronization, it can be tedious for local customers to achieve consensus, the convergence rates may be influenced by the topology of the communication system, and it requires a bidirectional communications infrastructure. The distributed approach may suffer from islanding, which occurs if a microgrid with local loads or distributed generation/storage becomes isolated from the main power grid.