*2.1. Centralized Control*

Centralized control approaches use a single central controller (CC), which is characterized by a high-performance computing unit and a secure communication infrastructure in order to manage different entities of the system (e.g., RESs, storage systems, TEG). Each entity uses a local controller (LC) in order to communicate and directly interact with the CC. Moreover, using recent communication and computing technologies (e.g., IoT, Big-Data), the CC is able to monitor, collect, and analyze real-time data. This allows all entities to collaborate with the central EM controller while ensuring a flexible MG operation in both grid-connected and standalone mode (Figure 2). The CC collects data, such as RES energy production, energy consumption pattern, the energy price from market operators, and weather conditions, and then executes the optimal and efficient system's control.

**Figure 2.** Centralized control structure.

Numerous research works have developed and deployed centralized EM strategies. For instance, the authors of [22] proposed a centralized controller in order to optimize the operation of MG by maximizing the production of distributed RESs generators while establishing back-and-forth energy transfer with the main utility grid. The efficiency of the proposed solution on MG system was investigated by considering a typical case network operating under various market policies and spot market prices. Moreover, the authors of [19] developed a centralized EM system for a standalone MG system based on the model predictive control method in order to reduce the computational loads. In fact, the studied problem was solved iteratively by nonlinear programming (NLP) and mixed integer linear programming (MILP) techniques. Other centralized control strategies are summarized in Table 2. However, despite the ease of implementing the centralized strategies, they have shown their limits, especially when dealing with large-scale hybrid systems [23].

#### *2.2. Decentralized Control*

Unlike centralized strategies, in decentralized control, each entity is considered autonomous using a LC. This means that groups of entities are controlled separately by a leader. In literature, the terms 'decentralized' and 'distributed controls' are often used in place of each other [24,25]. The distributed control can be considered as a decentralized

control in which LCs use local measurements, such as frequency and voltage values, to elect the leader entity. They are also allowed to share information with neighbors. For a distributed control, LCs do not only use local measurements but also are able to send and receive required information to other LCs [26]. In decentralized control approaches, limited local connections are required and the control decisions are made based only on local measurements (Figure 3). It does not require a high-performance computing unit and a high-level connectivity [27].

**Figure 3.** Decentralized control structure.

As depicted in Figure 3, each LC operates individually on managed energy sources, storage systems, and loads without central control. The control decisions are determined locally based on local measurements, which are shared among controllers using peer-topeer communication.

However, monitoring, processing, and data visualization is considered critical in order to coordinate various distributed controllers and achieve a global operation goal. This process is standardized by the norm IEC-61968 for a single-building energy management system and by IEC-61850 for interoperability between building MG systems [28,29]. Depending on the communication network availability, the decentralized control can be classified into three operation modes: (i) Fully dependent, in which the distributed controllers generate local control decision while communicating information with each other via a CC; (ii) partially independent, in which LCs communicate with each other and share information with the CC in order to generate central decisions; and (iii) fully independent, in which the distributed controllers communicate directly with each other and independently from the CC [30]. However, despite the flexibility of these operational modes, the decentralized control structure presents low performance compared to centralized control [25,31–33]. This is due to the low response time and the incomplete information about the total MG system installation.
