The Economics of Microgrids E-Book

Amin Khodaei

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 425

Veröffentlichungsjahr: 2023

Beschreibung

THE ECONOMICS OF MICROGRIDS An incisive and practical exploration of the engineering economics of microgrids In The Economics of Microgrids, a pair of distinguished researchers delivers an expert discussion of the microeconomic perspectives on microgrids in the context of low-carbon, sustainable energy delivery. In the book, readers will explore an engineering economics framework on the investment decisions and capital expenditure analyses required for an assessment of microgrid projects. The authors also examine economic concepts and models for minimizing microgrid operation costs, including the cost of local generation resources and energy purchases from main grids to supply local loads. The book presents economic models for the expansion of microgrids under load and market price uncertainties, as well as discussions of the economics of resilience in microgrids for optimal operation during outages and power disturbances. Readers will also find: * A thorough introduction to the engineering and economics of microgrids * Comprehensive explorations of microgrid planning under uncertainty * Practical discussions of microgrid expansion planning, operations management, and renewable energy integration * Fulsome treatments of asset management and resilience economics in microgrids Perfect for senior undergraduate and graduate students as well as researchers studying power system design, The Economics of Microgrids will also benefit professionals working in the power system industry and government regulators and policymakers with an interest in microgrid technologies and infrastructure.

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Diomidis Spinellis

James Duncan

Hai Li

Adam Drobot

Amin Moeness

Brian Johnson

Tom Robertazzi

Desineni Subbaram Naidu

Ahmet Murat Tekalp

The Economics of Microgrids

Amin Khodaei

Department of Electrical and Computer Engineering

University of Denver

Denver, CO, USA

Ali Arabnya

Department of Electrical and Computer Engineering

University of Denver

Denver, CO, USA

Copyright © 2024 by The Institute of Electrical and Electronics Engineers, Inc.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey. All rights reserved.Published simultaneously in Canada.

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Library of Congress Cataloging‐in‐Publication DataNames: Khodaei, Amin, author. | Arabnya, Ali, author.Title: The economics of microgrids / Amin Khodaei, Ali Arabnya.Description: Hoboken, New Jersey : Wiley, [2024] | Includes bibliographical references and index.Identifiers: LCCN 2023028414 (print) | LCCN 2023028415 (ebook) | ISBN 9781394162451 (hardback) | ISBN 9781394162468 (adobe pdf) | ISBN 9781394162475 (epub)Subjects: LCSH: Microgrids (Smart power grids)–Costs. | Microgrids (Smart power grids)--Economic aspects. | Energy transition.Classification: LCC TK3105 .K54 2024 (print) | LCC TK3105 (ebook) | DDC 621.31–dc23/eng/20230825LC record available at https://lccn.loc.gov/2023028414LC ebook record available at https://lccn.loc.gov/2023028415

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About the Authors

Amin Khodaei, PhD, is a Professor in the Electrical and Computer Engineering department at the University of Denver. His research is focused on the climate crisis, the grid of the future, and advanced technologies to modernize the grid, including artificial intelligence and quantum computing. He has authored/co‐authored over 200 peer‐reviewed technical papers and has advised over 40 graduate students and postdoctoral associates over the past 10 years. As an active member of the IEEE, he has served as the technical chair of the 2016 and 2018 IEEE PES T&D Conferences and the technical chair of the 2022 IEEE PES General Meeting. He is a Senior Member of IEEE’s Power & Energy Society and holds a PhD degree in Electrical Engineering from the Illinois Institute of Technology.

Ali Arabnya, PhD, (also known as Ali Arab) is a Research Professor of Electrical and Computer Engineering at the University of Denver. Previously, he was a consultant climate economist with The World Bank in Washington, DC. Prior to that, he served as Data and Analytics Manager with the Risk and Capital Management practice of Protiviti, a global management consulting firm in New York City. He works at the interface of engineering, finance, and policy in addressing climate change mitigation and adaptation in the power and energy sector. His international professional experience includes North America, the Middle East, and Southeast Asia regions. He is a Senior Member of IEEE’s Power and Energy Society and holds a PhD degree in Industrial Engineering from the University of Houston.

Acknowledgments

Several chapters of this book were written based on valuable contributions made by the following co‐authors:

Mohammad Shahidehpour, PhD

Sina Parhizi, PhD

Alireza Majzoobi, PhD

Hossein Lotfi, PhD

Mohsen Mahoor, PhD

Acronyms

AC

Alternative current

ANFIS

Adaptive network‐based fuzzy inference system

BCR

Benefit‐cost ratio

CAGR

Compound annual growth rate

CAIDI

Customer average interruption duration index

CAIFI

Customer average interruption frequency index

CapEx

Capital expenditure

CBM

Condition‐based maintenance

DC

Direct current

DES

Distributed energy system

DER

Distributed energy resource

DG

Distributed generation

DMO

Distribution market operator

DNO

Distribution network operator

DSO

Distribution system operator

DSPP

Distributed system platform provider

EDC

Electric distribution company

EMS

Energy management system

ENS

Energy not supplied

ESS

Energy storage system

EV

Electric vehicle

GENCO

Generation company

GHG

Greenhouse gas

HMI

Human–machine interface

IDSO

Independent distribution system operator

ISO

Independent system operator

LC

Local controllers

LDC

Load duration curve

LMP

Locational marginal price

MAE

Mean absolute error

MARR

Minimum acceptable rate of return

MAS

Multi‐agent system

MIP

Mixed‐integer programming

MILP

Mixed‐integer linear program

MLP

Multi‐layer perceptron

MMC

Microgrid master controller

NPV

Net present value

O&M

Operations and maintenance

PCC

Point of common coupling

PDC

Price duration curve

PV

Photovoltaics

RBF

Radial basis function

RPS

Renewable portfolio standard

RUL

Remaining useful life

SAIDI

System average interruption duration index

SAIFI

System average interruption frequency index

SCUC

Security‐constrained unit commitment

SOC

State of charge

TRANSCO

Transmission company

T&D

Transmission and distribution

UC

Unit commitment

VOLL

Value of lost load

VVC

Volt‐VAR control

1Fundamentals of Microgrids

1.1 Introduction to Microgrids

The earliest idea of the microgrid dates back to 1882, when Thomas Edison built Pearl Street Station, the world's first commercial central power plant, in the Financial District of Manhattan in New York City. Edison's company installed fifty microgrids in four years. At that time, centrally controlled and operated utility grids were not yet formed. With the utility grid subsequently utilizing large, centralized power plants, which benefited from the economies of scale, and significantly increased transmission connections for reliability purposes, the electric grid turned into a monopolistic market structure by connecting isolated microgrids, leading these microgrids to be faded away. After more than a century, however, this concept has been revisited, and modern microgrids have gained significant traction, which is driven in part by the need for higher reliability and power quality, higher resilience against disruptive events through grid decentralization, advancements in power electronics, distributed generation (DG), energy storage technologies, and the rise of prosumers – i.e. the electricity customers that both consume and produce electric power [1].

While the concept is still evolving, the modern microgrid is defined as “a group of interconnected loads and distributed energy resources (DERs) with clearly defined electrical boundaries that acts as a single controllable entity with respect to the grid and can connect and disconnect from the grid to enable it to operate in both grid‐connected or island [sic] modes,” according to the U.S. Department of Energy [2]. Based on this definition, DER installations can be considered as a microgrid if it comprised three distinct characteristics, as follows: (i) they have electrical boundaries that are clearly defined; (ii) there exists a master controller to control and operate DERs and loads as a single controllable entity; and (iii) the installed generation capacity exceeds the peak critical load, thus it can be disconnected from the utility grid (the islanded mode) and seamlessly supply local critical loads. These characteristics further characterize microgrids as small‐scale power systems that self‐supply and have islanding capability, which can generate, distribute, and regulate the flow of electricity to local customers.

Microgrids are more than just backup generation units. Backup generation units have existed for quite some time to provide a temporary supply of electricity to local loads when the supply of electricity from the utility grid is interrupted. Microgrids, however, provide a wider range of functions and are significantly more flexible than backup generation units. The main components of microgrids include loads, DERs, master controllers, smart switches, protective devices, as well as communication, control, and automation systems. Microgrid loads are commonly categorized into two types: fixed and flexible (also known as adjustable or responsive) loads. Fixed loads cannot be altered and must be satisfied under normal operating conditions, while flexible loads are responsive to controlling signals. Flexible loads can be curtailed (curtailable loads) or deferred (shiftable loads) in response to economic incentives or islanding requirements. DERs consist of DG units and distributed energy storage systems (ESS), which can be installed at electric utility facilities and/or electricity consumers' premises. Microgrid DGs are either dispatchable or non‐dispatchable. Dispatchable units can be controlled by the microgrid master controller and are subject to technical constraints depending on the type of unit, such as capacity limits, ramping limits, minimum on/off time limits, and fuel and emission limits. Non‐dispatchable units, on the contrary, cannot be controlled by the microgrid master controller since the input source is uncontrollable. Non‐dispatchable units are mainly renewable DGs (typically solar and wind), which produce volatile (generation is fluctuating in different time scales) and intermittent (generation is not always available) output power. These characteristics negatively impact the non‐dispatchable unit generations and increase the forecast error; therefore, these units are generally stabilized with ESS. The primary application of ESS is to coordinate with DGs to ensure the microgrid's generation adequacy. They can also be used for energy arbitrage, where the stored energy at low‐price hours is generated back to the microgrid when the market price is high. The ESS also plays a major role in microgrid islanding applications. Smart switches and protective devices manage the connection between DERs and loads in the microgrid by connecting/disconnecting distribution lines. When there is a fault in a part of the microgrid, smart switches and protective devices disconnect the faulty area and reroute the power, preventing the fault from propagating in the microgrid. The switch at the point of common coupling (PCC) performs microgrid islanding by disconnecting the microgrid from the main grid. The microgrid scheduling in grid‐connected and islanded modes is performed by the microgrid master controller based on economic and security considerations. The master controller determines the microgrid's interaction with the utility grid, the decision to switch between interconnected and islanded modes, and optimal operations of local resources. Communications, control, and automation systems are also used to implement these control actions and to ensure constant, effective, and reliable interaction among microgrid components.

Microgrids offer significant benefits to the customers and the utility grid at system level, as follows: (i) improved reliability by introducing self‐healing at the local distribution network; (ii) higher power quality by managing local loads; (iii) reduction in carbon emission by the diversification of energy sources; (iv) economical operations by reducing transmission and distribution (T&D) costs and utilization of less costly renewable DGs; and (v) offering energy efficiency by responding to real‐time market prices. The islanding capability is the most salient feature of a microgrid, which is enabled by using switches at the PCC and allows the microgrid to be disconnected from the utility grid in case of upstream disturbances or voltage fluctuations. During utility grid disturbances, the microgrid is transferred from the grid‐connected to the islanded mode, and a reliable and uninterrupted supply of consumer loads is offered by local DERs. The islanded microgrid would be resynchronized with the utility grid once the disturbance is eliminated [3, 4].

Microgrids, as enablers of DG integration, improved grid performance, and the green energy economy, have been deployed on a large scale over the past decade and are expected to continue their growth for the foreseeable future. The installed microgrid capacity in 2012 was estimated at 1.1 GW, while as of May 2021, there had been over 460 operational microgrids in the United States that provided a total of 3.1 gigawatts of reliable electricity. The global microgrid market size is estimated to grow from USD 24.6 billion in 2021 to USD 42.3 billion by 2026 at a compound annual growth rate (CAGR) of 11.4% during this period. The expected growth is primarily driven by the decarbonization trend, demand for higher grid resilience, and economies of electrification in rural and remote areas [5].

1.2 Distributed Energy Resources for Microgrids

DERs are small‐scale energy resources, which can be placed at utility facilities or at customers' premises to provide a local supply of electricity. DERs can fundamentally change both energy mix and structure of the current energy generation systems – in which the electricity is predominantly generated at large‐scale, non‐renewable, centralized power plants and transmitted over long distances through “less‐than‐efficient,” high‐voltage transmission lines to reach the electricity demand load areas. DER technologies can further provide power to remote locations where required T&D facilities are not available or are costly to build. Moreover, DERs offer a low construction and deployment time compared to large generators and T&D facilities. A comprehensive review of DERs and current practices in microgrids as well as the interaction problems arising from integration of various DERs in a microgrid can be found in [6, 7], respectively. As discussed in detail in [8], DERs include a variety of technologies. Two widely used DERs include renewable DGs and ESS. There has been an increasing traction for the utilization of renewable DGs, such as wind and solar energy resources, in recent years. That is primarily due to the regulatory mandates, reduced cost of renewable generation, and policy incentives for decarbonization and reduction of greenhouse gas (GHG) emissions to address climate change. Renewable DGs are primarily dependent on meteorological factors, resulting in high levels of variability and uncertainty in their power generation share. That is one of the challenges that need to be overcome in order to enable broader integration of renewable energy resources. Sunlight is the origin of most renewable DGs either directly (e.g. solar energy), or indirectly (e.g. wind, hydroelectric, and biomass energy resources). Sunlight is directly converted to solar energy using solar panels. Wind and hydroelectric power are the results of differential heating of the earth's surface. Biomass energy is the sunlight energy stored in plants. There are also some other types of energy not driven by the sun, such as geothermal energy, whose origin is the internal heat in the Earth, as well as ocean energy, which comes from tides and winds. Renewable DGs offer several benefits including sustainability, being emission‐free, and benefiting from almost ubiquitous primary sources of energy [9, 10]. As detailed in [11], there are numerous policies and regulations rolled out in a number of states within the United States to support investments in renewable DGs. Some of the examples include renewable portfolio standards, public benefit funds for renewable energy, output‐based environmental regulations, interconnection standards, net metering, feed‐in tariffs, property‐assessed clean energy, and other financial incentives. Based on renewable portfolio standards, all electricity providers should provide a specific amount of electric power using renewable DGs. Public benefit funds are obtained by levying small taxes on electricity rates. Output‐based environmental regulations (such as cap‐and‐trade programs) ordain emission limits in order to encourage electric producers to increase efficiency and control air pollution. Interconnection standards are technical requirements, which should be met by electricity providers that want to connect renewable DGs to the grid. These standards determine how electric utilities in a jurisdiction would treat renewable DGs. Net metering rules are used to compensate for the power generation by prosumers. For instance, if the local power generated by a customer is more than their load, the surplus power is sold back to the utility grid, and on the other hand, if the generated power is not sufficient to supply loads, they use electricity from the utility grid. This procedure requires accurate metering of the electricity demand from consumers. Feed‐in tariff is a policy incentive to encourage renewable energy development, which requires electric utilities to make long‐term payments for the power fed into the grid by renewable energy developers. The payments may comprise both electricity sales and payments for renewable energy certificates. Feed‐in tariff policies can economically incentivize a rapid development of renewable DGs. Implementing feed‐in tariffs has been a successful experience to meet economic development and renewable energy targets around the world. Based on property‐assessed clean energy policies, the cost of renewable energy installations or increasing energy efficiency is refunded to residential properties instead of individual borrowers. As a result, property owners would be encouraged to invest in renewable energy deployment on their premises.

The variable nature of renewable DGs in microgrids necessitates the presence of an energy source to compensate for their fluctuations. The islanding events in microgrids and the need for a power supply to ensure seamless transfer to the islanded mode also make the case for integration of ESS in microgrids. ESS enhances flexibility in power generation, delivery, and consumption. It provides utility grids with several benefits and large cost savings. Large‐scale ESS increases the efficiency of utility grids, which means lower operations costs, reduced emissions, and increased reliability. Considering the increasing penetration of renewable DGs and their intermittent nature, the application of ESS has significantly increased in recent years. For example, wind farms or solar photovoltaics (PVs) generate power when the wind is blowing or the sun is shining. Accordingly, the employment of ESS allows the utility grid to store energy when it is more than the amount required to meet the demand and supply loads in peak hours. Therefore, this technology enables variable generation resources to continue their power generation even in the absence of wind and sunlight, which means providing electric utilities with continuous and reliable power. Storing energy from various resources to economically serve shifting loads based on electricity prices and to serve non‐shiftable loads during peak hours is one of the several applications of ESS. The deployment of ESSs can improve power quality via frequency regulation, benefit electric producers by allowing them to generate power when it is most efficient and least expensive, provide critical loads with a continuous source of power, and help customers during emergencies such as power outages due to climate shocks and natural disasters, equipment failures, or malicious cyber‐physical attacks. As discussed in [12], benefits can be in the form of either avoided costs or additional revenue received by the operator. Based on this concept, if an ESS is used such that there is no need for generation equipment, the economic benefit of this ESS includes but is not limited to the avoided cost of generation. For the ESS owner, benefits from additional revenue can be realized from selling surplus energy and other services. For an electricity end‐user deploying ESS for reducing electricity bills, the economic benefits can be realized from lower cost of energy [13, 14]. In [15] microgrids were classified based on their value proposition into three types, as follows: (i) reliability; (ii) energy arbitrage; and (iii) power quality. It was shown that as energy source of inverter‐based microgrids responds slowly, ESS is only necessary for inverter‐based microgrids – with a most critical load designed to have a power quality higher than the utility grid – and is optional for other types of microgrids. Reference [16] demonstrates that ESSs deployed in microgrids can perform the tasks of active power balancing and voltage regulation at the same time. In the grid‐connected mode, ESS may ensure load leveling and reduce the power exchange with the network, which makes the system's operation more efficient and flexible. In addition, ESS may enhance DER penetration and contribute to better quality of energy delivery to customers.

A wide range of DERs are not suitable to be directly connected to the microgrid network. Therefore, power electronic interfaces are required to enhance/enable their integration [6]. Examples are PV cells and ESS, which generate DC power, or wind turbines that need improvements in generated power quality and frequency. Although power electronic devices would enhance integration and controllability of these resources, they can also bring new challenges regarding control and protection. In an islanded microgrid, rotating generators can serve the role of a voltage source and manage the grid frequency, but in their absence, the power electronic converters are needed to behave as voltage sources. In the grid‐connected mode, the converters function as current sources feeding the microgrid. In addition to enabling an efficient connection, power electronics devices are capable of providing additional benefits to microgrids. Power electronic interfaces can improve the power quality of customers by improving harmonics and providing extremely fast switching times for sensitive loads. Power electronics can also provide benefits to the connected utility grid by providing reactive power control and voltage regulation at the distributed energy system connection point. A useful feature of a power electronic interface is the ability to reduce or eliminate fault current contributions from distributed energy systems, thereby allowing negligible impacts on protection coordination. Finally, power electronic interfaces provide flexibility in operations with various other DERs and can potentially reduce overall interconnection costs through standardization and modularity [17].

1.3 The Role of Microgrid in Power Systems

Microgrids can play an important role in power grids through improved reliability, increased resilience, emission reduction, reduced costs of recurring system upgrades, enhanced energy efficiency and power quality, lowered energy costs, and financial gains through energy arbitrage [18, 19]. In this section, we focus on the most important roles that microgrids can play in power systems, as follows.

1.3.1 Reliability

One of the most important benefits of microgrids is that they improve power supply reliability. Electric utilities constantly monitor customers' reliability levels and perform required system upgrades to improve supply availability and to reach or maintain desired performance. Consumer reliability is typically evaluated in terms of system and customer average interruption frequency and/or duration indices (such as System Average Interruption Frequency Index “SAIFI,” System Average Interruption Duration Index “SAIDI,” Customer Average Interruption Frequency Index “CAIFI,” and Customer Average Interruption Duration Index “CAIDI”). Outage causes such as storms or equipment failure, can impact reliability levels by increasing the average frequency and duration of interruptions; however, when a microgrid is deployed, these metrics can be significantly improved. This is due to the intrinsic intelligence (control and automation systems) of microgrids and the utilization of DERs that allow islanded operation from the utility grid. In particular, since the generation in community microgrids is located in close proximity to consumer loads, it is less prone to be exposed to and affected by grid disturbances and infrastructure issues. Additional flexibility to provide service under these conditions is provided by the ability to adjust loads (e.g. demand response) via building and/or microgrid master controllers. Improved reliability can be translated into economic benefits for consumers and utility due to a reduction in interruption costs and the amount of energy not supplied (ENS). The magnitude of these economic benefits is dependent upon load criticality, the value of lost load (VOLL), and also the availability of other alternatives such as backup generation or automatic load transfer. Microgrid studies associated with reliability can be considered from two perspectives: evaluation and improvement. For example, in the context of microgrid reliability evaluation, studies in [20–26] consider reliability assessment of islanded microgrids with renewable DGs. In the context of microgrid reliability improvement, methods for increasing reliability have been proposed through coupled microgrids [27], adding renewable DGs [28], autonomous customer‐driven microgrids [29], efficient operation of DGs [30], and vehicle‐to‐grid integration [31], among others.

1.3.2 Resiliency

Resiliency refers to the capability of power systems to withstand low‐probability‐high‐impact events by minimizing possible power outages and quickly returning to normal operating state [32]. These events include extreme weather events and natural disasters, such as hurricanes, tornadoes, earthquakes, snowstorms, and floods, as well as manmade disasters such as cyberattacks and malicious physical attacks, among others. Recent climate shocks to the power grid infrastructure in the United States and the potential significant social disruptions have spawned a great deal of debates in the power and energy industry about the value and application of microgrids. If the power system is impacted by these events and critical components are severely damaged (e.g. generating facilities and T&D infrastructure), service may be disrupted for days, weeks, or even a longer period. The impact of these events on consumers can be minimized by decentralizing the grid by deployment of microgrids, which enable the local supply of loads even when the supply of power from the utility grid is not available. Examples of research work in this area include [33–38], among others.

1.3.3 Power Quality

Consumers' need for higher power quality has significantly increased during past decades due to the growing application of voltage‐sensitive loads, including a large number and variety of electronic loads and LEDs. Utilities are always seeking efficient ways of improving power quality issues by addressing prevailing concerns stemming from harmonics and voltage. Microgrids provide a quick and efficient answer for addressing power quality needs by enabling local control of frequency, voltage, load, and the rapid response from ESS. For example, power quality improvement through microgrids has been investigated in [39–46].

1.4 Microgrid Technologies

1.4.1 Microgrid Power Management and Control

It is common to use a hierarchical control structure to control microgrids. The hierarchical structure typically consists of three broad layers, as follows: (i) primary control that stabilizes frequency and voltage using droop controllers; (ii) secondary control that compensates the steady state deviations in voltage and frequency caused by the primary control; and (iii) tertiary control that takes into account economic considerations and determines power flow between the microgrid and utility grid to achieve an optimal operation [47–49]. In addition to the control structure, control methods are very important as well. Some renewable DGs such as wind and solar PV have fluctuations and do not generate constant power. As a result, microgrid control can be a complex and difficult process. The study in [50] states that there are two main control methods for microgrids: controller/responder and peer‐to‐peer control mechanisms. The former is associated with voltage–frequency (V–f) control, while other DGs are associated with P–Q control to control the active and reactive power to be reached to the planned targets. Peer‐to‐peer control is associated with frequency–active power (f–P) and voltage–reactive power (V–Q) controls. Both controls (controller/responder and peer‐to‐peer) have their own advantages and disadvantages.

Two common control architectures for microgrids are centralized and distributed. Standardized procedures and easy implementations are among the advantages of the centralized approach. The study in [51] presents a microgrid central controller with two major functions for distribution systems that include a communication channel with the distribution system operator and the electricity market and exchanging information with the microgrid local controllers (LCs) and processing them. In the centralized control scheme, the central controller makes decisions about the dispatch of all DGs and ESSs according to the objective function and constraints. In microgrids where each DG has its own controller and pursues distinct objectives, distributed control provides premium applicability. The number of transmitted messages between different individual components and the microgrid controller increases as the size of the microgrid increases, necessitating a larger communication bandwidth. Decentralized control can reduce the number of messages and simplify the optimization with special constraints by reducing it into subproblems and solving them locally [52]. One approach to implement distributed control is based on using multi‐agent systems (MAS). In this approach, each of the controllable elements in the microgrid, such as inverters, loads, and DGs, have agents associated with them, where the communication and coordination of the agents is governed by the multi‐agent theory. MAS includes the microgrid cluster management agent, microgrid control agent, and local agent. The loosely coupled agents forming the MAS are physically or logically dispersed and have a set of distinct characteristics, as follows: (i) their data is distributed; (ii) they have an asynchronous or simultaneous process of computation; (iii) they lack information and capability of problem‐solving; and (iv) they interact and cooperate with each other, hence their problem‐solving capability can be improved [53].

When a microgrid becomes islanded from the utility grid, the primary control keeps the voltage and frequency stable. However, the voltage and frequency can still divert from their nominal values. In order to retrieve the voltage and frequency to nominal values, a secondary control mechanism should be employed. This secondary control can be the distributed cooperative control. “Cooperative” means that all participants cooperate with each other and act as a single group to reach the common goals [54]. Synchronous generators exhibit a self‐stabilizing feature due to their high rotational inertia [55]. Most of the generation units integrated in the microgrid are not classified as synchronous generators and need to mimic the droop characteristic of those generators. When connected to the utility grid, the microgrid voltage will be dictated by the utility grid as it acts as an infinite bus. In the islanded mode, however, voltage control becomes an important and challenging task that requires careful attention. Most of DERs installed in the microgrid generate DC or variable‐frequency power that unlike synchronous generators cannot be relied on for frequency regulation in the islanded operation. The high penetration of power‐electronically interfaced DGs leads to a low inertia in microgrids. Therefore, proper measures need to be implemented to control frequency in the microgrid [56]. Adaptive control schemes can be used to control the systems with varying or uncertain parameters. As microgrid operating modes can unexpectedly change as a result of disturbances in the utility grid, adaptive control schemes are proposed. When the microgrid power exchange with the utility grid is scheduled, it is necessary to establish a control mechanism so that the actual power flow matches the scheduled values. The control of the power flow between the microgrid and the utility grid has been the main discussion in [57–60].

1.4.2 Microgrid Islanding

The salient feature of a microgrid is its ability to be islanded from the utility grid by upstream switches at the PCC. Islanding can be introduced for economic as well as reliability purposes. During utility grid disturbances, microgrids can transition from the grid‐connected to the islanded mode, where a reliable and uninterrupted supply of consumer loads can be provided by local DERs. The microgrid master controller can facilitate optimal operations by maintaining the frequency and voltages within permissible ranges. The islanded microgrid can be resynchronized with the utility grid once the disturbance is eliminated [61–63]. Once the fault is alleviated, the microgrid will be resynchronized with the utility grid. Resynchronization refers to reconnecting the islanded microgrid to the utility grid while ensuring that the microgrid voltage and frequency are synchronized with those of the utility grid [64]. If not ensured, serious damage due to current surges may happen to the microgrid components during the switching process. Although microgrids are infrequently switched to the islanded mode, there could be significant social cost savings and load point reliability enhancements offered by microgrids during major outages.

1.4.3 Microgrid Protection

The unique characteristics of microgrids necessitate changes to the conventional distribution network protection strategies. Connection of DERs, which are normally power electronically interfaced, results in bidirectionality of fault current, reduction in fault current capacity, disruption in fault detection, and protection sensitivity. Furthermore, the dynamic topology of the microgrids due to islanding and sectionalizing necessitates the protection to be able to adapt itself to new conditions. Due to variable microgrid operating conditions and meshed topology of microgrids, it is necessary to use communications to update protection settings [65, 66]. The study in [65] shows that the traditional communication‐less protection schemes are not applicable in a meshed microgrid where a fault at one location is indistinguishable from another. In [67], a protection scheme is presented using digital relays with a communication network for the protection of the microgrid, relying primarily on differential protection based on sampling the current waveform. IEC 61850 is an international standard for substation automation and a part of the International Electrotechnical Commission's Technical Committee 57 (TC57) architecture for electric power systems. These standards will result in very significant improvements in both costs and the performance of utility grids. They are based on abstracting definition of the data items and the services, or, in other words, creating data items/objects and services that are independent of any underlying protocols. The abstract definitions then allow mapping of the data objects and services to any other protocol that can meet the data and service requirements [68]. Due to the existence of different levels of fault current in microgrids, new protective schemes need to be developed that can monitor changes in the microgrid and calculate the operating conditions at any given time. Logical nodes available in IEC 61850 and IEC 61850‐7‐420 communication standards are used to design such versatile schemes in microgrids [69].

1.4.4 Microgrid Communications and Human–Machine Interface (HMI)

The role of communication systems in the microgrid is to provide a means to exchange data and monitor various elements for control and protection purposes. In a centrally controlled microgrid, the communication network is necessary to communicate control signals to the microgrid components. In a microgrid with distributed control, the communication network enables each component to communicate with other components in the microgrid, decide on its operation, and further reach predefined objectives [51]. Communications within the microgrid are necessary to enable rapid fault clearing and increase efficiency in islanding incidences. The communications structure for microgrids includes a three‐layer, hierarchical architecture, as follows: (i) the top layer hosts the energy management system (EMS) that controls the overall operations of the microgrids in both interconnected and islanded modes; (ii) the middle layer is comprised of LCs that regulate the microgrid operations and its interactions with the main grid; and (iii) the bottom layer, which consists of IoT devices (e.g. smart meters, fault recorders, and protective relays), that continuously monitors, records, and transmits the stream of sensed data [70]. An important building block of the EMS is constituted of human–machine interface (HMI), which includes hardware or software through which the microgrid operators interact with the microgrid controller. HMI facilitates on‐demand microgrid monitoring and control on a real‐time basis through a two‐way communication network. On the system operator side, that includes visualizing operations, archiving the collected data, and processing command information. On the customer side, it includes enabling customers to actively participate in and interact with the EMS [51]. An example of a system design for a microgrid EMS that includes details of HMI can be found in [71].

1.5 Overview

This book discusses the engineering economics of microgrids by covering the economic decision‐making processes involved in the system design and operations of these systems. The remainder of the book is organized as follows:

Chapter 2

:

Microgrid Operations Economics

introduces the economics of operations management for microgrids and shows how islanding can economically impact their operations.

Chapter 3

:

Resilience Economics in Microgrids

discusses the economics of resilience in microgrids for optimal operations of these systems during outages and power disturbances.

Chapter 4

:

Community Microgrid Operations Management

presents economic operations scheduling models for community microgrids without compromising the privacy of the users.

Chapter 5

:

Provisional Microgrids for Renewable Energy Integration

introduces the economic decision‐making framework for operations of a novel class of microgrids, namely provisional microgrids, that are important enablers of renewable energy integration in power systems.

Chapter 6

:

Engineering Economics of Microgrid Investments

presents an analytical framework on investment decisions and capital expenditure analysis required for the economic assessment of microgrid projects.

Chapter 7

:

Microgrid Planning Under Uncertainty

presents an economic operations management model that incorporates the uncertainties associated with the prediction of the loads and the market price.

Chapter 8

:

Microgrid Expansion Planning

discusses economic concepts and models for minimizing microgrids' operations costs, including the cost of local generation resources and energy purchases from the main grid to supply local loads.

Chapter 9

:

Microgrids for Asset Management in Power Systems

presents an asset management strategy for distribution networks that incorporates microgrids to maximize the remaining useful life of critical assets in the grid.

Chapter 10

:

Dynamics of Microgrids in Distribution Network Flexibility

presents an economic model for using microgrids to support electricity distribution networks by improving their flexibility and eliminating costly investment alternatives.

Chapter 11

:

Microgrid Operations Under Electricity Market Dynamics

introduces an economic decision‐making model that incorporates the impacts of electricity markets on microgrid operations and planning.

Each chapter of this book is designed to stand on its own and has its own introduction, nomenclature, bibliography, and acronyms.

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