Microgrid Energy Management System (MEMS)

In order to execute the duties described so far, a microgrid utilizes a microgrid management system. This system ensures that different components of the microgrid are managed to serve towards a certain objective [97]. It typically comprises of three hierarchical levels of control as shown in Figure 2.7.

Microgrid Central Controller (MGCC) acts as an interface between the microgrid and the outside world. It communicates with Distribution Network Operator (DNO) and Market Operator (MO) and optimizes microgrid operation through Local Controllers (LCs). It ensures that in a network where more than one microgrid exists, microgrids work in harmony to sustain a reliable and safe operation. LCs are responsible to control components of a microgrid such as distributed generators, storage devices, loads or protection equipment. MGCC manages LCs and updates their operation modes and points in parallel with the events occurring in the network and/or the microgrid. Once the updates are received, LCs mostly behave autonomously until a new instruction is received from MGCC [98]. Based on the decision making scheme, the control systems (also known as supervisory systems [8]) are categorized as centralized and decentralized microgrid control [99].

Figure 2.7. Control Levels of the Microgrid [100]

Centralized control strives to maximize the local production according to market prices. For this to occur, there is a two-way communication between the MGCC and each LC [8]. Starting from this point there is a new concept called Virtual Power Plants (VPPs) where distinct entities inside a microgrid are controlled by a central unit to act as a power plant [101].

Conversely, in decentralized control, the larger part of the decision making is in the hands of LCs. They act in a smart fashion and communicate with each other to increase the revenue and the performance of the microgrid. The most popular way to design this intelligent system, which is composed of smaller less intelligent components, is by using multi-agent systems [100]. Multi-agent systems are already proposed for the protection [102], stabilization [103], restoration [104]of large power systems.

There are on-going research projects being carried out to investigate alternative control systems. A control system based on „Game Theory‟ is proposed in [105]. In this approach,

power electronics based inverters are treated as variable impedance loads and every generator in the microgrid is taken as a player. The stabilization and the control of the system may be achieved through real-time or turn-based games. For different scenarios, different games such as “Maximum Power Game” or “Microgrid Stability Game” can be played. Depending on the

level of communication, games might be cooperative and non-cooperative.

In an another study, the “Ant Colony Optimization” algorithm is proposed to solve constraint

satisfaction problem for microgrids [106]. Ant colony optimization is a nature-inspired artificial intelligence technique where several ant colonies cooperate to solve a problem [107]. In microgrid applications, distributed generators, storage devices and loads are represented by a variable. The domain of each variable represents the quantized operating points at which the generator or load could be commanded to operate [106]. By stipulating constrains, ants walk on the construction graphs and each variable will be assigned an operating point. The touring of the colonies continue until the solution is attained for each and every variable.

By implementing a central control unit in microgrids, different objectives can be realized. Minimizing system disturbances, maximizing efficiency, meeting power demands from local resources, stabilizing the system are to name a few. This formulation outlines the difficulty of the control task. With competing objectives and highly variable parameters, a real-time power management system which is both robust and flexible is needed.

Microgrid Energy Management Systems (MEMS) which are aimed at controlling the microgrid in a holistic sense are fairly new in the literature. Although the very concept of MEMS is proposed some time ago [5], real design and implementation of MEMS are crucial to get more knowledge and experience in the field. Conceptual designs look appealing but implementations or simulations on models shall be carried out to see the real side of the picture[14].

Microgrids have dynamic structures which change more often than the conventional large networks. This dynamic behavior of microgrids is a major protection challenge since the conventional selectivity methods assume a fixed network structure and a predetermined relay hierarchy [108]. Whenever restructuring occurs, the selective levels assigned prior to that become erroneous. For a proper operation, the selective levels of relays should follow the changing conditions of the network. Whenever a structure variation takes place in a microgrid, new relay hierarchy should be extracted and corresponding settings including time delays should be determined before updating these settings within the relays via communication lines [109, 110]. It is essential that this process is performed automatically so that the processing time is minimal and there is no interruption in power delivery. This requires an algorithm which will determine the current structure of the system and yield the relay hierarchy at all branches of the network.

In order to ensure protection under these conditions, a centralized management is required to monitor and communicate with the microgrid components and assign suitable operation parameters. It is almost impossible to know the parameters of a microgrid beforehand. Therefore, a centralized microgrid management system is required for monitoring the grid components, their operating points, new deployments and connection changes occurring in the microgrid. Then, necessary adjustments can be made by the MEMS and through communication lines related protective devices can be programmed accordingly.