Microgrids are a collection of loads and local generations that are also normally connected to the legacy grid. However, the grid connection may be simply for reliability reason, if the local generations and loads match. The grid may just serve as a trading link depending on the mutual need. Considering its many other benefits, microgrid market is growing more rapidly; nowadays, more than 1,840 projects that represent a power capacity of almost 19,280 MW are under development worldwide. According to a research report by the market research and strategy consulting firm, Global Market Insights, Inc, the microgrid market will exceed $19 billion by 2024 [1]. This chapter presents an overview, technical challenges, and future important issues of microgrid developments.

This chapter focusses on the effective usage of transactive energy (TE) and the importance of developing an economical TE-management (TEM) process. TE is a concept that can play a vital role in improving the efficiency and reliability of a power system. This notion is promising for the energy industry in providing an intelligent and interactive future. This concept initiates various requirements for power distribution and transmission that works efficiently and is totally reliable. This leads to the exploration of requirements engineering (RE) approaches which can play a vital role in the development of TE and management process. This chapter explains the usage of RE models in relation to micro-grid and smart grid development. The wide-ranging development of smart grid systems demands supplementary software models so that its full potential can be explored and utilised. It only makes sense that consolidation of extensive usage of distributed energy and renewable energy sources is important in relation to the future of smart grid to bring about an economical and reliable functioning of a power system. An innovative approach in the form of TE towards the future smart grid is highly beneficial for the power-system operations. This novel approach has been extensively researched in recent years around the world. Within this chapter, we are outlining a goal-oriented RE (GORE) approach to structure TEM system. The main objective of this chapter is to perform reasoning and impact of nonfunctional requirements (NFRs) on the TEM. This reasoning will help decision makers in getting the desired outcomes from an efficient and reliable power system.

Microgrids (MGs) are referred to as isolated and self-sufficient electricity supply systems that well suit remote areas. These systems are generally composed of nondispatchable and dispatchable energy resources to reduce the electricity production cost. Emergencies such as overloading, faults and shortfalls can result in difficulty for the smooth operation of MGs. The main aim of this study is to discuss the operation of MGs by presenting a power transaction management scheme. It focuses on the scenario when MGs are provisionally coupled to resolve the emergency situation and termed clustered MGs. For this purpose, power transaction is taken as an instance of purchasing or selling of electricity amongst healthy and problem MGs. The key objective of a suitable power transaction technique should then be regulating the power amongst the provisionally coupled MGs by adjusting the suitable power generation from all available dispatchable sources. An optimization problem is formulated for achieving this purpose, and its main purpose is to minimize the costs and technical impacts while focusing on the above-considered parameters. Genetic algorithm which is a heuristic optimization technique is used to solve the formulated optimization problem, and the performance of the suitable power transaction strategy is evaluated by several numerical analyses.

A microgrid (MG) is a distinct energy system consisting of distributed energy resources (DERs) and loads having the ability to operate in parallel with, or independently from, the main power grid. MGs, which were initially introduced to ensure smooth operation and control of DERs in distribution networks, offer unprecedented economic and reliability benefits to electricity consumers with minimal carbon emission. These benefits, however, must be analysed and compared with the capital investment cost of the MG to ensure a complete return on investment and to justify the MG deployment. The biggest obstacle for the widespread and rapid deployment of MGs is the high capital investment cost of MGs. A true assessment of MGs economic benefits is a challenging task due to the significant uncertainties involved in the assessment. These uncertainties may include the intermittency of the renewable generation, the varying states of charge (SoC) of battery energy storage system (BESS), the uncertain demands, the varying market price, the probability of the MG islanding, the level of developer's risk-aversion and the unpredictably of the user preferences in the smart load management system. Moreover, some of the assessment metrics, such as the measure of reliability improvements are difficult to comprehend for consumers when represented in terms of the supply availability. Thus, efficient and optimum planning models are required to ensure the economic feasibility of MG deployments and to justify the investments based on cost-to-profit analysis under uncertain conditions. This chapter demonstrates a detailed model for the optimum sizing of MG components under the uncertainties involved in the system. The proposed model is validated with the simulation of several case studies conducted on a system depicting a similar MG in a medium-voltage (MV)-distribution system derived from electricity network of a power utility in New South Wales, Australia. The results from the case studies demonstrate the efficacy of the proposed model for the optimum sizing of the MG components to justify the MG deployment.

The recent advancements in the Internet of Things (IoT) and telecommunication infrastructure have significantly increased the reliability and effectiveness of communication protocols in microgrid environment. Nowadays, the equipment in a smart microgrid not only exchange information with one another much faster but also control, monitor and diagnose faults much faster and more reliably. Despite the benefits of new technologies, they are tailored to satisfy specific requirements, and therefore, they can be only used for certain applications. The microgrid communication model consists of a three-layer architecture, where the energy management system (EMS) sits in the top layer and controls the overall operations of the island of microgrids. The middle layer includes the local controllers (LCs) that regulate operations within the local grid. The bottom layer includes IoT devices, such as smart meters, fault recorders and protective relays, which continuously capture and transmit the stream of sensed data. Such hierarchical architecture introduces specific computation and latency requirements for each layer of microgrid communication. To meet these requirements, each layer must use different communication equipment and protocols. This chapter provides an insight into communication requirements, system architecture, standards, protocols and tools used in microgrid communications. The chapter concludes with a case study, where wireless technology is utilised for reliable and optimal operations in a microgrid.

Due to low inertia, stochastic nature of renewable energy sources (RESs) and sudden load changes, nearly all the modern microgrids are associated with the dynamic frequency stability issues. Thus it restricts the maximum number of the renewable energy systems that can be penetrated to the microgrid. In order to increase the penetration of low-inertia sources to the microgrid, the frequency stability issues need to be addressed. The frequency stability issues of a typical microgrid are addressed by the addition of extra inertial support from the power sources using power converters and appropriate control loop. In this chapter, the readers are introduced with the basic concepts of the synthetic inertia support for the dynamic frequency stability issues of a hydro-photovoltaic (PV) microgrid. The details of the frequency-power-response-based synthetic inertial support and current control loops are elaborated using a simulation example. The deviation in system's frequency is usually compensated by sourcing or absorbing the active power by the inertial support loop, so by utilizing the concept of inertial loop, the maximum number of RESs integration can be enhanced.

This chapter describes different control techniques applied in microgrids (MGs) with responsive distributed energy resources (DER) for intentional and automatic islanding, deployment of a local black start strategy and including the reconnection to the upstream distribution network. The coordination of MG local resources, achieved through an appropriated network of controllers and communication system, enables some cells of the distribution system, with sufficient autonomy, to operate interconnected to the upstream network or autonomously in some circumstances. In this case, the potentialities of DER can be truly realized if the islanded operation is allowed and bottom-up black start functionalities are implemented. In particular, black start will be addressed for medium voltage (MV) MGs, made of several low voltage (LV) MGs, leading to the concept of a multi-MG (MMG). The chapter will then describe the black start sequence that can be implemented in such a grid considering also the contribution from electric vehicle storage.

Power electronics is the key enabling technology for modern power systems. Power converters are increasingly used in a wide range of applications from generation to consumption levels. Due to the significant importance of power electronics in modern power systems, this book chapter presents the possible structure of the future power systems employing the microgrid (MG) technologies. Different MG topologies and converter structures are introduced. Moreover, the control and operation principles of power electronic converters in MGs are discussed. Two case studies are provided in order to show the importance of power electronics in operation and control of modern power systems. Finally, the chapter is summarized with highlighting challenges encountering modern power electronics-based power systems.