1.1 Introduction
In recent years, approaches towards energy transition and sustainable development have been ever-increasing due to the need for mitigating climate change issues and the efficient utilization of existed energy resources. With this regard, state-of-the-art technologies and infrastructures along with active operation and control of different energy resources would become crucial. Amongst all energy resources, microgrids (MGs) are believed to be one of the highly potent resources to deal with the issues of electrical systems. In other words, active operation and control of MGs in which there exist different kinds of demands and energy resources (e.g., energy storages, micro-generation units, etc.) would be beneficial not only for MG stakeholders in terms of cost-benefit efficiency but also for power system operators in terms of MGsā contribution to grid's flexibility [1, 2, 3, 4, 5].
In order to unlock the active utilization of MGs, cutting-edge technologies along with efficient infrastructure are a necessity. These technologies together in communication with the MGsā energy resources are known as energy management systems (EMSs). EMSs are intelligent automated systems that contribute to, for instance, lowering/shifting energy consumption in critical moments along with a reduction in the MGsā costs. Although the utilization of EMS might consider other objectives such as CO2 emission reduction or self-sufficiency, they mostly employ optimization techniques either as single-objective or multi-objective approaches. EMSs can also enable either the bidirectional energy exchange with the network in grid-connected mode, or stand-alone operation of MGs in islanded-mode [6, 7, 8, 9, 10].
In this chapter, the focus of the study is on the MGs equipped with an EMS. There have been introduced several approaches to the energy management of MGs. However, in most of them, economic aspects, i.e., cost reduction, are the top priority desire of the problem from the MG stakeholdersā point of view. This could be done in different ways. On the one hand, reducing the total costs of the MGs by maximum utilization of self-production facilities (PV panels, wind turbines, etc.) as well as changing the energy consumption over time from peak hours to off-peak hours during the day. On the other hand, exploiting MGsā flexibility so as to help the upstream grid in critical moments for monetary profits in return. Accordingly, the authors first present an introduction to flexible energy resources (FERs) in MG along with their characteristics in Section 1.2. Afterward, the MGEM modeling approaches are widely presented in Section 1.3. In this section, first, the different kinds of management method deployed in the MGs are illustrated. Then, various objectives for energy management in MGs will be introduced. Regarding this section, we introduce a number of approaches based on well-known optimization algorithms considering different MG-related as well as grid-related constraints. Microgridsā constraints are related to the physics and limitations of the MG's resources whilst the constraints of the grid are related to the limitation of energy exchange with the upstream grid (e.g., congestion management, emission reduction, and/or energy loss reduction). Moreover, the application of the MGEM system in MGs with FERs such as energy storages, electric vehicles (EVs), and thermostatically controllable loads (TCLs) which exchange energy and flexibility with the grid will be discussed as well which is followed by the flexibility services that MGs could provide to the different levels of power system. Finally, this chapter will be summarized and con...
