A Comprehensive Review of the Smart Microgrids’ Modeling and Control

Methods for Sustainable Developments

ADENIYI KEHINDE ONAOLAPO1,*, KAYODE TIMOTHY AKINDEJI1,

TEMITOPE ADEFARATI2, KATLEHO MOLOI3

1Smart Grid Research Center, Electrical Power Engineering Department,

Durban University of Technology, Durban,

SOUTH AFRICA

2Department of Electrical and Electronic Engineering,

Federal University Oye Ekiti,

Oye Ekiti, Ekiti State,

NIGERIA

3Electrical Power Engineering Department,

Durban University of Technology,

Durban,

SOUTH AFRICA

*Corresponding Author

Abstract: - Estimation strategies and hierarchical control measures are required for the successful operations of

microgrids. These strategies and measures monitor the processes within the control variables and coordinate the

system dynamics. State-of-the-art frameworks and tools are built into innovative grid technologies to model

different structures and forms of microgrids and their dynamic behaviors. Smart grids' dynamic models were

developed by reviewing different estimation strategies and control technologies. A Microgrid control system is

made up of primary, secondary, and tertiary hierarchical layers. These architectures are measured and monitored by

real-time system parameters. Different estimation schemes and control strategies manage microgrid control layers'

dynamic performances. The control strategies in the developed technologies dynamics were accessed in the grid

environment. The control strategies were modeled for microgrids using six design layers: adaptive, intelligent,

robust, predictive, linear, and non-linear. The estimation schemes were assessed using microgrid controllers'

modeling efficiency. Hierarchical control strategies were also developed to optimize the operation of microgrids.

Hence, this research will inform policy-making decisions for monitoring, controlling, and safeguarding the optimal

design strategies for modeling microgrids.

Key-Words: - Control strategies, estimation schemes, renewable energy system, energy storage system, distributed

energy system, smart grids.

Received: May 12, 2023. Revised: May 15, 2024. Accepted: June 21, 2024. Published: July 30, 2024.

1 Introduction

The practice of incorporating distributed energy

resources (DERs) into the power grids gives rise to

microgrids, which are the prospects of electricity

grids. The DER combines distributed energy

generations (DEGs) and energy storage systems

(ESSs). These standalone or grid-connected

microgrids are the sources of efficient future system

operations because of their suitable control

approaches and estimation structures. Integrating

DERs in either AC or DC form simplifies microgrid

development, [1]. Links between many DERs are

established using power electronic converters. The

factors responsible for converter interfaces vary from

the control loop features, the number of converters

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involved, and the network's topology. Also, the

interactions between converters can affect the

dynamic performance of microgrid control loops, [2].

Microgrids encounter challenges in conforming with

the system's operational requirements and ensuring

safe power-sharing. To ensure microgrids' robustness

and reliability, it is essential to coordinate power-

sharing at every sample interval in grid-connected

mode, [3]. The major challenge is in coordinating

multiple microgrids with many DERs integrated,

ensuring the flexibility of control and protection

systems, managing the unreliability of DER supplies

caused by renewable energy sources (RESs)

fluctuations, and dealing with the uncertainties

around the sizing and placement of ESSs in response

to demand, [4]. These challenges require

harmonizing a real-time demand management system

with an energy management system (EMS). The

research articulates stability models by analyzing

different DERs to evaluate their performances under

different loading conditions. Innovative techniques

are used by smart grid technologies to address these

challenges. Demand-side management gives a

reasonable performance and reduces peak power,

conserves energy, mitigates greenhouse gas

emissions, and produces operations that are cost-

effective, [5]. In [6], strategies such as peak shaving,

load shifting, load development, and conservation are

included in the primary distributed management

system (DMS). Different DMSs are represented

using the smart grid application of the demand

response method.

In [7], current flow in DERs, grid-connected

inverters, and microgrids are controlled using a

developed method. The study analyzed a range of

linear and non-linear controllers. Evaluating the

current control mechanisms can help address grid

synchronization and power quality problems in a

microgrid. The article [8] provides a comprehensive

explanation of a hierarchical control system for a DC

microgrid. This system is designed to effectively

regulate and restore current and voltage, as well as

efficiently manage power across primary, secondary,

and tertiary control layers. In their study, [9]

conducted a comprehensive evaluation of several

control strategies for AC microgrids. They focused

on three key aspects: active and reactive power

control, frequency and voltage control, and droop

control. These control techniques were analyzed

within the microgrids' architectural control hierarchy.

These three control strategies are utilized in the

construction of microgrids' system control. They may

be regarded as methods for designing the control

schemes, as explained in [10] for droop control. The

study evaluates four control strategies, specifically

fuzzy, predictive, robust, and proportional integral

derivative (PID), for their effectiveness in managing

distributed power generation. Hence, this research

seeks to evaluate various control approaches

applicable to all categories of microgrids. The control

approaches utilized in several research endeavors for

the deployment of intelligent microgrids often rely on

control procedures. In addition, a microgrid

controller necessitates precise data to achieve a

higher performance index and guarantee the power

networks' efficiency. A microgrid experiences

different abnormalities and power outages due to

equipment deterioration, cyber security breaches, and

unpredictable power generation. Therefore, it is

necessary to acquire and supervise DEG, distributed

energy storage (DES), and load flexibility in an

efficient manner. Additionally, it is important to

categorize and identify different types of risks, as

well as manage numerous energy supplies to

minimize risks and safeguard microgrid equipment.

This study establishes and categorizes six control

strategies as the primary conceptual foundation for

developing control models for new microgrid

applications. The control approaches mentioned are

adaptive, intelligent, predictive, robust, linear, and

nonlinear. The architectural choice of a certain

control approach takes into account the formulation's

capability to manage microgrids' control strategies.

The estimate strategies for microgrid variables and

parameters involve the use of a measuring and

monitoring system to enhance the dynamic

performance of control procedures with precision.

The design and modeling of estimate approaches in

microgrids enhance the dynamic behavior of system

operation, [11]. The functioning of an intelligent

microgrid is influenced by a range of factors and

characteristics that might vary in different situations.

These include cyber-attacks, erroneous data, power

quality, changing demands, internal disturbances,

external disturbances, faults, line parameters, and

energy supplies. Therefore, an evaluation of crucial

estimation is carried out in an intelligent microgrid

that provides support for these control approaches.

This work also offers a comprehensive viewpoint on

architectural and hierarchical oversight and estimate

strategies for the efficient functioning of microgrids.

These approaches efficiently synchronize microgrids'

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components encompassing the energy resources and

the end-users, irrespective of their system structure or

whether they are AC or DC.

The primary contributions of this study can be

enumerated as follows:

 Evaluation of the key dynamic elements

necessary for the efficient functioning of a

microgrid. Development and execution in the

cutting-edge grid environment, irrespective

of whether they are AC or DC. These rely on

system management and monitoring to

efficiently manage microgrids and create

self-sufficient power networks.

 An examination of the primary control

methods to be utilized in smart grids capable

of managing various control criteria is

established according to the system variables

and microgrids' power quality. Thus, this

addresses the inherent process of system

modeling and the design of optimum control.

 This paper addresses the development of a

perspective approach for optimizing smart

microgrids' operations by integrating control

approaches. This effectively resolves several

issues. The text addresses the difficulties,

sets out the future direction for microgrid

growth, and offers a structure for a digital

thread that can facilitate efficient control

approaches and digital modeling of

microgrids.

2 Insights on Intelligent Microgrid

Systems

Smart grids utilize a diverse range of services and

technology to update conventional power systems.

As a result, an advanced power system is created that

is characterized by sustainability, security,

cooperation, automation, and control, [12]. The

microgrid is an efficient current operating system that

allows for the integration of both AC and DC power

networks. A microgrid links different DERs at a

specific place, known as the point of common

coupling (PCC). A comprehensive categorization

approach is required to elucidate the microgrid

structures, methodologies, and obstacles to

comprehend the operational scheme. The microgrid

topologies consist of islanded and grid-tied operating

modes, [13] and the precise functioning of the

microgrid necessitates the harmonization of the

control systems, the load management system

(LMS), and the EMS, [14]. Hence, the primary

operational difficulties faced by microgrids are the

stability, robustness, resilience, reliability, and

optimization of the system, [15]. These issues need

the use of real-time estimation, which relies on

measuring and monitoring the network to track the

parameters and variables of the system. This

approach may effectively assist control approaches

for the sustainable functioning of microgrids.

2.1 The Distributed Energy Resources and

the Distributed Energy Storage

The DERs refer to a collection of small-scale power

generation units that are located close to the point of

consumption. These units can include renewable

energy sources such as solar panels, wind turbines,

and fuel cells, as well. The most prevalent energy

redessources utilized in microgrid operation are DEG

and DES. The power grid is adopting a new trend

(the DES) that helps to efficiently distribute excess

produced power throughout the network, [16]. DESs

must provide substantial energy capacity and rapid

power response to serve grid support functions

effectively. If the load demand exceeds the maximum

scheduled generation, the DES system can release

electricity to reduce the peak load. If the load

demand is below the minimum scheduled generation,

the DES system can store the surplus energy, [17].

The DEG encompasses both renewable energy

resources (RERs) and non-renewable energy

resources (non-RERs), whereas the DES consists of a

battery energy storage system (BESS) as well as non-

BESS components. BESSs typically utilize

electrochemical technology, but non-BESSs employ

other ESS (Energy Storage System) technologies,

including electrical, mechanical, thermal, and

chemical technologies, [18]. The disparity between

BESS and non-BESS technologies highlights a

significant gap between standard battery-dependent

electrochemical technology and other ESSs based on

different technologies. In certain situations, the

energy demand is included in the category of DERs

because some loads are dynamic and allow for

electricity to flow in both directions within the

electric network. DER operations can have

detrimental and beneficial effects on the system

frequency and voltage profile. Control measures in

power converters can help reduce the detrimental

effects of DERs on the network, [19]. RERs,

specifically variable renewable energies (VREs) like

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solar photovoltaic and wind power, are widely

utilized as the primary source of DEG in microgrids.

BESS technologies are crucial for enhancing the

reliability, stability, and flexibility of the power grid

because of the unpredictability of VRE supplies and

the requirement to meet the whole power demand on

an hourly basis, [20]. In addition, electric vehicles

(EVs) are equipped with a portable ESS technology,

which is widely utilized in many applications of DER

deployment to effectively manage the electric grid,

[21].

2.2 Enhancing the Reliability and Resilience

of the Power Grid using Microgrids

A microgrid is an electrical network that incorporates

DER, either fully or partially. The interdependence

between providers and customers is a pivotal facet of

electrical network advancement. The primary aspect

in maintaining the optimal functioning of any

electrical system is the balance between reliability

and resilience, irrespective of energy security and

equality goals that aim to enhance the power grid's

efficiency. Microgrids enhance the reliability and

resilience of the electricity grid by using the

capabilities of DERs. This benefits several

stakeholders, including distribution network

operators, providers, and customers, [22].

Resilience refers to the ability of the electrical system

to adjust to stressful occurrences effectively. The

system's reliability addresses grid recovery while

considering potential system compromise.

Microgrids provide a chance for client participation,

as their reliable systems allow for sufficient

flexibility in both energy transmission and

distribution, [23]. Ensuring a compelling balance

between the production and usage of energy is a

necessity. An optimal correlation between energy

supply and demand alleviates superfluous strain on

the transportation system. The article [24] introduces

many methodologies and models for monitoring the

reliability of the electricity system. These tactics are

derived from the most pertinent developing

technologies utilizing stochastic processes and

frequency-based approaches. It is crucial to ensure

the resilience of the power grid under certain

circumstances, including system malfunctions, heat

waves, and hydrologic drought conditions. Reliability

ensures the long-term viability of the electrical

system in the face of severe occurrences such as

cyber security threats, climate change, and adverse

weather conditions, [25]. The grid-tied microgrid

effectively enhances the robustness and reliability of

the power network. The integration of DERs

facilitates a strong connection between energy

distribution, transmission, generation, and end users,

[26]. The microgrid provides a method for managing

power consumption in industrial, commercial, and

residential sectors in order to address various grid

functions. In the residential segment, this involves

the three fundamental aspects of building energy

flexibility, which are satisfying the inhabitant's

requirements, adjusting to the surrounding

conditions, and enabling flexible operations.

Dynamic management systems are employed to

create grid-interactive and energy-efficient systems

that provide energy flexibility. These structures are

implemented across all customers to ensure the

efficient functioning of microgrids. Furthermore,

several EMS methods can effectively address the

challenge of real-time coordination and monitoring in

a microgrid. The EMS methods utilize microgrids

with varying power capacities, configurations, and

classifications, [27]. Figure 1 provides a concise

overview of notable characteristics that facilitate the

adoption of microgrids and their creation and

implementation. It contains the energy production

concept, elements, functions, and constituents of

microgrids; the types and advantages of adopting

microgrids are also expressed therein. Microgrids

have the potential to greatly enhance the utility

network and offer a reliable source of electricity in

developing nations.

2.3 Perspective of Smart Grid Technologies

The Smart Grid (SG) is an advanced electricity

system that offers security measures, improved

safety, environmental sustainability, cost-efficiency,

and enhanced reliability. This power grid is

characterized by its lowered cost, specified demand,

optimal utility, reduced emissions, improved energy

efficiency, and revolutionary design. The European

Regulators Group for Gas and Electricity has defined

the smart grid as an energy system that efficiently

integrates the operations of all connected users,

including consumers and producers. The goal is to

create an economically efficient and sustainable

power system with safety, security, exceptional

quality, and minimal losses, [28]. Upgrading the

traditional power grid enhances the electrical

networks' sustainability, security, efficiency, and

connection.

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Fig. 1: Overview of microgrids

Advanced technologies digitize the power grid

infrastructure and convert polluted urban areas into

green landscapes. The shift from traditional power

networks to innovative power grid environments

incurs significant economic expenses. Furthermore, it

has social obstacles due to the entrenched

conventional power structure prevalent in the ancient

cities, to which people are accustomed. Nevertheless,

several benefits are assured in terms of enhancing the

functioning of microgrids in different areas. A

comparison study is discussed in [29] between the

conventional electrical network and an intelligent

power grid. The deployment of cutting-edge grid

technology is intricate and necessitates many stages

to establish an intelligent power grid ecosystem. The

microgrid is a new architectural feature of a power

system that efficiently incorporates many cutting-

edge grid technologies. Using smart grid technology

has the benefit of enhancing the efficiency of

electricity flow and diminishing carbon emissions,

[30]. The advanced electrical power grid incorporates

several applications and technology to address most

microgrids' drawbacks.

The viewpoint of novel grid technologies

encompasses many modeling techniques and

implementation strategies to regulate and efficiently

assess microgrids' dynamic features. The utilization

of cutting-edge grid technology to enhance the

performance of microgrids is illustrated in Figure 2.

This approach aims to achieve microgrids' Phase 5

(independent) functioning. The microgrid is now

functioning in response-predictive Phases 2 and 3.

Phases 1-2 represent the previous stages of the

electrical system, while Phases 4-5 will

correspondingly represent the intelligent grid's

imminent and subsequent future. In Phase 4, artificial

intelligence (AI) effectively manages the dynamic

interactions between components of the power

system, such as power production and transmission,

distributed energy generating sources, electric

vehicle (EV) integration, EMS, DMS, and battery

energy storage systems (BESS). Phase 5 represents

the evolutionary concept of the preceding phase,

characterized by complete full autonomy in the

functioning of all system components.

3 Microgrid Control Modeling and

Design

Dynamic microgrid modeling relies on state, control,

and modified variables with disturbances in the

system. Hence, the control strategy's design must

effectively manage all system disturbances and

variables while adhering to certain limits. The

operation of a microgrid is governed by three control

layers, which work together to manage system

variables and ensure the operation process' optimal

efficiency. The microgrid's architectural control

features are managed by three control layers, namely

the primary, secondary, and tertiary. The control

strategies of microgrids, particularly in the smart grid

setting, may be perplexing to the technological and

scientific communities.

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Fig. 2: Power grid metamorphosis

Fig. 3: Structure of a microgrid control system

Nevertheless, some methods, including active

and reactive power, droop controls, voltage, and

frequency, are essential for developing a design

system to implement control strategies, [31]. Figure 3

displays the microgrid applications' block diagram

for the control design, where the control modeling is

greatly exemplified by distributed generators (DGs).

This demonstrates the integration of the most

pertinent control modeling and design methodologies

with any estimating methodology, [32]. The efficacy

of this control architecture in microgrids is

contingent upon the stages of the digital revolution,

as seen in Figure 2.

3.1 Model of the System

A microgrid model control system applications may

be formulated [33]; the time domain, state space

equation is:

( 1) ( )

( ) ( )

x t A B x t

y t C D u t



     



     

     

(1)

where y(t), t, A, B, x(t), C, D, and u(t) stand for the

manipulated variable, sample time, state matrix, input

matrix, state vector, output matrix, disturbance

matrix, and control variables.

The frequency domain system transfer function,

in the form of (

𝑠

) =

𝑦

(

𝑠

𝑥

(

𝑠

), is expressed as:

( ) ( )G s C SI A B D



  

(2)

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The time domain impulse response is expressed as:

( ) ( ) ( )

y t h t u t dt







(3)

where

ℎ

(

𝑡

) stands for the control system’s impulse

response.

The time domain deterministic model is

expressed as:

( 1) ( )

( ) 0 ( )

x t A B x t

y t C u t



     



     

     

(4)

While the time domain stochastic model is expressed

as:

()

( ) ( ) ( )

( 1) ()

( ) ( ) 0 ()

A t B t B t

xt ut

y t C t I vt





 



 







(5)

where

𝐼𝑣

is an identity matrix,

𝐵𝑣

(

𝑡

) represents the

mixing and scaling matrix for process noise input;

𝑣

(

𝑡

) (defined by

𝑣

(

𝑡

) = [

𝑣

𝑡

𝑣

𝑡

)]), is vector/matrix

noise, where

𝑣

𝑡

), the process noise is attached to

𝑢

(

𝑡

), and

𝑣

𝑡

), the measurement noise attached to

𝑦

(

𝑡

It is important to note that matrix D can also be

included in some design situations of Eq. (5).

Therefore, the deterministic formulation refers to a

traditional model of a state-space model that does not

consider any uncertainty, as shown in Eq. (4).

Nevertheless, the stochastic formulation incorporates

system uncertainty that encompasses probabilistic

characteristics, [33].

Figure 4 illustrates the progress and advancement

of control systems in the intelligent microgrid. The

shown representation is a beneficial framework for

categorizing and designing the system approach, as

seen in Figure 3. The control strategies examined in

this study may be applied and enhanced in any

approach, configuration, and layer, irrespective of the

microgrid's kind and operational structure. The

control system secures critical loads, automatically

detects islanding, enables seamless transition,

provides a safe supply to non-linear loads, maintains

power balance, and verifies voltage and frequency

restoration.

Fig. 4: Hierarchical control strategies

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3.2 System Design

The design modeling technique, comprising the

design (described in section 3.1) of the specified

control system, illustrates microgrid sources, control

hierarchies, and structures. The prevalent design

modeling techniques are primarily derived from the

state-space and transfer function model equations (1)

and (2), respectively. The microgrid control

modeling is structured in several levels and

configurations, as seen in Figure 4. This section

describes the different microgrids' control layers,

including their complexity level, design domain, and

formulation. The microgrids' control layers

encompass the hierarchical control modeling and

design. The optimum control techniques utilized in

the microgrid are explicitly created inside the control

layer's design domain. Figure 3 comprehensively

describes the control implementation process for

establishing a microgrid. Microgrids are designed to

encompass several DERs in terms of structure and

physical components. Hence, the development of the

design model, as depicted in this section, pertains to

the interlinkage and synergy of many DERs to

achieve an effective microgrid operation. For

instance, a specific DEG located at the ith location

interacts with all j DERs, as seen in the distributed

control shown in Figure 3.

The design domain of the primary control layer,

with a low complex framework, encompasses power

quality, power-sharing, voltage, and frequency

stability. The model representations [33] for

frequency stability (AC) are expressed as follows:

( , , , , , , , , , , , )

k vi e i si si pi i i i

f E J J D P P p t

   

(6)

the voltage stability is expressed as:

( , , , , , , , )

i pE iE id i j

f E k k E E e e t



(7)

the power-sharing is expressed as:

* * * *

( , , , , , , , , )

Qi Qi i Pi ij

i j i j

f u C C a Q Q P P



(8)

while the power quality is a function of Eqs. (6)-(8),

expressed as:

( .((6) (8))f Eqs 

(9)

From equations (6)-(9), the primary control layer,

operating inside the microgrid, regulates many

aspects such as frequency, voltage, reactive and

active power. Its main objective is to stabilize

microgrids and ascertain the electricity's high quality.

Most of the variables in this layer are the same and

can also be recognized in the second layer. For

instance, in equation (6), Ek, Jvi, ωe*, ω, Dpi,

and J represent the kinetic energy, virtual inertia,

nominal frequency, rotational speed, virtual friction

coefficient, and the synchronous generator's

rotational inertia, respectively. According to equation

(7)-(8), the voltage compensation provided by the

reference voltage E*, compared to the observer

output, is denoted as δEi1. The ēi and δy are the

voltage-observer output and the transient deviation

produced by the synchronization of the active power,

respectively. CQi and CPi refer to the coupling gain,

while kpE and kiE represent the control gain used in

proportional-integral (PI) control. It should be

emphasized that the extent of these gains relies on the

control approach employed to develop the design

model. The uQi voltage of DEG i is used to control

the reactive power, whereas Pi/P*i represents the

normalized power of the ith DEG.

The secondary layer is expressed mathematically

via equations (10)-(12). The design domain of the

secondary control layer, with a moderately complex

framework, contains frequency restoration, voltage

restoration, and power-sharing improvement. The

model representations [33] for frequency restoration

(AC) is expressed as:

( ) ( )

i i i i

i i ij i j





   



     





(10)

the voltage restoration is expressed as:

() ( ) ( )

ref

i i i i

Eii

i i i ij

E E nQ E

d E Q

k E E a

dt Q Q



   



   





(11)

while the power-sharing improvement is expressed

as:

( ) ( .(10))

( ) ( .(11))

P active power f Eq

Q reactive power f Eq









(12)

In equation (10),

𝜔

𝛺

𝑚

𝑘𝜔𝑖

> 0 and

𝑃

𝜔

𝑚

𝛺

, and

𝑘𝜔𝑖

> 0 represent active power, frequency,

droop coefficient, frequency restoration parameter,

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and the secondary control’s velocity regulation

coefficient. All parameters and variables can be

related to DEG i. The elements of the adjacency

matrix, aij, represent the weights used to assess the

communication architecture. These weights, aij, can

be utilized to estimate the stability of the microgrid.

Node j represents the agent that communicates with

node i. In equation (11), Qi/Qi*, n, δEi, and E are the

normalized reactive power of the DEGi, droop

coefficient, secondary control variable, and the

regulating voltage, respectively. Modification of the

dynamics is achieved using the positive gains

𝛽𝑖

and

𝑘𝐸𝑖

. The objective of the secondary control

mechanism is to address the vulnerability of a

centralized control system to solve the single-point

failure. This concept is implemented to provide

equitable power distribution among microgrids.

Within the secondary control layer, an improved

design of a single domain has the potential to address

the issues present in other domains as well. An

example of a distributed control system in the second

layer is responsible for managing both the restoration

of frequency and voltage in AC microgrids, [34].

The tertiary layer is defined by equations (13)-

(15). The design domain of the tertiary control layer,

with a highly complex framework, is made up of

economic dispatch, energy, and congestion

management. The model representations [33] for

economic dispatch are expressed as:

( ( ), , , , , , )

i i i i i i L i

f C P P P

   

(13)

the energy management is expressed as:

min ( ( ), ( ), ( ), ( ))

i i i i

tF x t u t d t z t

u



(14)

while the congestion management is expressed as:

min ( , , , , , )

i i i s

G ll L L

f pr S P S S t    

(15)

In equation (13), Ci(Pi) represents the operating

expense, related to a certain ith DEG unit. Therefore,

the coefficients of the cost function are represented

𝛼𝑖

𝛽𝑖

, and

𝛾𝑖

, where with

𝛼𝑖

, and

𝛽𝑖

are the values

of the quadratic cost functions that are related with

the generation

𝑖

;

𝜆𝑖

provide the assessment of the

additional cost for each generation. Pi represents the

active power from DEGi, and PL represents the

system’s power demand. In equation (14), xi(t) stands

for the discrete time-varying variables, encompassing

the battery state of charge (SOC) and the energy cost;

ui(t) is the control parameter; di(t) denotes the

parameter vector comprising the most accurate

assessment of demand at a specific time, intermittent

generation, fuel cost, etc.; zI is the time-invariant

variables, such as voltages, phase angles, and

frequency. In equation (15) pr represents the

fluctuating price of electricity in the market,

determined by the bid prices for each load demand

and generation source. From equations (6)-(9), the

primary control layer, operating inside the microgrid,

regulates many aspects such as frequency, voltage,

reactive and active power. Its main objective is to

stabilize microgrids and ascertain the electricity's

high quality. Most of the variables in this layer are

the same and can also be recognized in the second

layer. For instance, in equation (6), Ek, Jvi, ωe*, ω,

Dpi, and J represent the kinetic energy, virtual

inertia, nominal frequency, rotational speed, virtual

friction coefficient, and the synchronous generator's

rotational inertia, respectively. According to equation

(7)-(8), the voltage compensation provided by the

reference voltage E*, compared to the observer

output, is denoted as δEi1. The ēi and δy are the

voltage-observer output and the transient deviation

produced by the synchronization of the active power.

The energy management planning domain is widely

implemented in various tertiary control levels of

various microgrids. In [35], a highly efficient

combinatorial control scheme is developed to

represent the economic dispatch of a freestanding

microgrid. This system depends on an energy

management structure and aims to maximize the use

of ESS. In [36], a decentralized structure is created

by utilizing energy management techniques to

optimize the charging process of electric vehicles in

off-grid microgrids. Moreover, the energy market has

evolved into a complex ecosystem where various

players have substantial influence. Congestion

management establishes a hosting domain that

ensures fair participation of all stakeholders,

including end-users, distribution, transmission

system operators, etc., in the energy market, which

involves the comprehensive integration of DERs

[37].

Optimal functioning necessitates the

consideration of microgrid stability. This is because

microgrids are composed of linked systems where

ensuring dynamic stability is the primary need to

ascertain the stability of DEG. Implementing inertia

control is a viable method to ascertain microgrids'

frequency stability. An efficient primary layer control

ensures optimal power distribution and DC

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microgrids' voltage stability, but frequency stability

in AC microgrids is an additional factor included

[38]. The primary layer serves as the fundamental

basis for the operational functioning of microgrids

[39]. The hierarchical control structure is a

combination of two or more layers; this control

architecture improves the performance of microgrids

in terms of coordination, stability, regulation, and

power quality. Architectural control with two levels,

primary and secondary, is proposed to mitigate

power oscillations of various distributed generators

(DGs) in microgrids [40]. In the first layer, two

current controllers were designed to produce the

current reference and mitigate the active power

oscillations. In addition, the second layer is designed

using an ideal model to effectively reduce the

fluctuating magnitudes of both active and reactive

powers simultaneously. Thus, in most cases, an

effective control design for microgrids relies on a

hierarchical control architecture.

4 Control Methods

The control strategies for a microgrid are built using

the hierarchical design concept. The existing control

architecture of the microgrid is focused on

transitioning the predictive power network (phase 3

of Figure 2) into a prescriptive electrical grid (phase

4 of Figure 2), aligning with the eventual vision of

the autonomous power grid (phase 5 of Figure 2).

The most relevant control methods identified for

microgrid applications are the intelligent, robust,

predictive, adaptive, linear, and non-linear control

methods. The benefits of categorizing control

strategies in this manner are that all design

methodologies may be tailored to the specific needs

of microgrid modeling and enhancement. This may

be accomplished by employing deterministic or

stochastic modeling techniques to manage the

optimum control approach's dynamic feature

effectively.

4.1 Smart Control Method

Smart or intelligent control approaches utilize

hardware and software technologies to accurately

represent the dynamic nature and manage the

operational efficiency of microgrids, [41], [42], [43].

Soft computing technology is commonly called the

programming language, and its models are based on

intelligent control techniques. The data-driven

method is a smart control technique that suggests

many options for both non-linear and linear models,

[44], [45] and the experiment can involve either one

or more state and control variables. Data-driven

approaches provide various advantages, including the

capability to develop a model based on data, the

capacity to learn control behavior, and the flexibility

to place sensors and actuators in systems with

multiple variables. The intelligent control approach is

scalable and resilient, allowing for constant and

precise monitoring of power grid functioning and

user behavior in real-time. This technique also

enables great dynamic control, [46]. The mildest

computing methods employed to regulate intelligent

microgrids are wavelet transform, particle swarm

optimization, machine learning, fuzzy neural

network, fuzzy logic control, deep neural network,

deep learning, artificial neural network, artificial

intelligence, etc., [47], [48], [49], [50], [51], [52].

Soft computing is primarily regarded as a framework

encompassing several intelligent technologies and an

instrument that can enhance the resilience and

efficiency of control procedures. Their applications

are commonly being processed in the realm of power

electronics, as well as active and reactive power, to

maintain the functioning of microgrids and

significantly reduce the complexity of computation in

the system performance index. The hardware

technologies utilize advanced communication

networks based on innovative technology to manage

microgrids intelligently. The Internet of Things (IoT)

technology is a visionary approach to managing

microgrids intelligently. This innovative control

system spans from power generation sources to the

ultimate consumers. The Internet of Energy utilizes

the concept of linking various elements such as

services, devices, individuals, and energy sources

through the Internet of Things (IoT) to develop

intelligent models for energy distribution. These

models aim to address the current difficulties in the

energy sector, [53].

4.2 Robust Control Method

The robust control approach employs many control

mechanisms to facilitate the interconnection control

of DER units and ensure appropriate energy

conversion. This control approach primarily focuses

on the inverter control’s feedback loops of the

microgrid. It regulates frequency and voltage during

imbalanced situations, typically using the concept of

infinite horizon, [10]. The robust control approach

may be used to design slide mode and direct control

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techniques for DGs in microgrids, [54], [55]. The

robust control approach is capable of functioning

well under various loading circumstances. This

control strategy is designed to handle both

unbalanced and balanced loading scenarios. This

resilient control strategy provides precise and rapid

performance irrespective of system fluctuations.

Robust control modeling employs the identical

approach of stochastic formulation as stated in Eq. 4.

Nevertheless, the robust construction only considers

the uncertainty in the system description without

considering any probabilistic characteristics. The

robust control method could be modeled as [33]:

( 1) ( ) ( ) ( )

x t Ax t Bu t B t



   

(16)

where w(t), t, Bd, are the unknown disturbance vector,

time, and the known parameters matrix.

The controller for microgrids often incorporates

many layers to ensure its resilience. Consequently, a

hierarchical control system smoothly transitions

between different microgrid operation modes. A

hierarchical control strategy effectively addresses the

robust control issues that arise in the efficient

functioning of microgrids. The model's structure is

generalized and does not necessitate a mandatory

transition between inverter-interfaced district control

DEGs.

4.3 Predictive Control Method

Predictive control approaches utilize forecasting and

prediction to anticipate the future dynamic behavior

of a particular system. It can be modeled using

quadratic or linear quadratic models, model

predictive control (MPC), statistical or deterministic

time series, neural networks, etc. The predictive

control model can be expressed as [33]:

( | ) ( | )

() ( | ) ( | )

x k i k Qx k i k

Jk u k i k Ru k i k









  





(17)

where

𝑄

and

𝑅

are positive elements diagonal

matrices,

𝑁

and

𝐽

(

𝑘

) are the control horizon and the

objective function of predicted sequences,

respectively.

Authors in [56] discussed the critical trends in

the development of MPC and showcased MPC as a

competitive alternative to conventional techniques in

economic operation optimization, power flow

management, frequency control, and voltage

regulation. Several factors, including demand

constraints, DEG restrictions, output, input, and state

variables, can constrain the MPC performance index.

MPC is commonly implemented at the tertiary

control level. Furthermore, MPC is a resilient and

robust control strategy that may effectively manage

system uncertainty and disturbance. Therefore, the

utilization of predictive control methods that rely on

Model Predictive Control (MPC) and Artificial

Neural Networks (ANN) may be effectively applied

to model all three tiers of smart microgrid controllers,

namely primary, secondary, and tertiary, in both DC

and AC power systems. The dead-beat concept can

likewise be implemented using predictive control

techniques. The predictive control technique offers a

significant advantage, which has resulted in the

development of different execution strategies within

machine learning (ML) methods. These strategies

include using support vector machines, linear

regression, regression trees, and neural networks

(NN).

4.4 Adaptive Control Method

The adaptive control approach encompasses a diverse

array of techniques with varying characteristics. The

adaptive control approach is applicable for creating

the intelligent microgrid controller on many levels,

including primary, secondary, and tertiary, [57], [58].

The accuracy of power-sharing between DGs is

compromised when there is a mismatch in the

impedances of the feeders/lines in islanded

microgrids. This can be attributed to the

disadvantages inherent in decentralized approaches

for managing active power through reactive power

and inverse droop control through conventional

droop control. Virtual impedance is a widely used

solution for addressing this difficulty, necessitating

the use of optimal valuation of the environment.

Hence, a method for adaptive control is devised to

modify the virtual impedance based on the output

current of DGs. This method enhances the power-

sharing capabilities of isolated microgrids without

the need for extra parameters, predictions of network

load, sensors, or communication equipment. The

method further ensures precise power distribution

and attains a balanced state of charge (SOC) among

the distributed energy storage systems (DESS).

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4.5 Linear Control Method

Regarding the development of the control models,

the linear model has the potential to be stated as a

linear time-invariant expression, [33].

( , , )

x f x u t

y h x u t









(18)

Equation (18) functionally represents the state-

space model of Equation (1). A linear control

approach may be used to control the current and

regulate the frequency of a microgrid. Equation (2) is

also used to design a linear control mode. Various

control strategies, including repetitive current

controllers, proportional-resonant, proportional

derivative, and proportional-integral (PI), can be

employed using linear control techniques. This

method efficiently develops a proportional integral

derivative (PID) control strategy. The linear control

approach may create current and voltage control

loops in the microgrids’ primary and secondary

control layers, [59], [60]. Linear control approaches

can be further evolved into a tertiary control layer to

address energy harmonization issues [8] effectively.

Quadratic problems related to the distribution of

DER electricity, including ESSs, may also be

effectively addressed by employing linear quadratic

modeling, [61]. The distinguishing characteristic of

these strategies is their capacity to convert a non-

linear system into a manageable linear system.

4.6 Non-linear Control Method

The control approach of a non-linear state model can

take several shapes based on a specific system model.

Equation (x) serves as an exemplar of a non-linear

design concept:

( ) ( ( )) ( ( )) ( ) ( )

( ) ( )

x t f x t g x t u t t

y t x t



  







(19)

where f(x(t)) and g(x(t)) are the non-linear functions

that may require linearization for the control design’s

optimal solutions and w(t) represents the bounded

disturbance respectively. Utilizing a nonlinear

control approach allows for the creating diverse

control schemes, including hysteresis controllers,

deadbeat (DB), PI, and PID. Fuzzy logic, PI, and PID

controllers are optimal nonlinear strategies for

achieving equilibrium in Energy Storage Systems

(ESSs) and stabilizing the bus voltage of DC

microgrids. In addition, they can manage the

limitations of current distribution and the

synchronization of power transmission. The control

schemes are designed specifically for the primary

control level, [62]. A passivity-based control

approach is considered an efficient and feasible

nonlinear control method for bidirectional DC-DC

converters.

Furthermore, the strategy is easy to put into

practice, [63]. Implementing a droop control

mechanism enables the autonomous recovery of

cluster microgrids. Implementing DER control using

nonlinear primary control stabilizes the system

frequency and voltage at the point of common

coupling (PCC) while regulating the active power. In

addition, this technique provides a solid and

uncomplicated setup.

5 Discussions

Table 1 concisely overviews various control

approaches used in smart microgrids. It is essential to

mention that data-driven approaches are an

alternative term for soft computing techniques. A soft

computing technique encompasses various intelligent

tactics rooted in data-driven and evolutionary

computation approaches. Data-driven refers to the

utilization of data sciences to replicate the behavior

of an ideal control method. Typically, this pertains to

Artificial Intelligence (AI) and Machine Learning

(ML). This technology is a powerful method that will

enable the next generation of the power grid to

function as an independent electrical system, as

presented in Figure 2, and effectively enable the

implementation of Multi-Agent Systems (MASs).

Deterministic optimizations for microgrids primarily

rely on an appropriate optimum control method to

manage the dynamic performance of tertiary control.

A stochastic optimization technique can create an

optimal control scheme. However, it is more

appropriate to categorize it as a fusion of control

methodologies since it can account for the many

uncertainties present in the system. The classification

of deterministic optimization-based optimum control

methods depends on the equations used and may be

categorized as either linear (Eqn. (18)) or non-linear

(Eqn. (19)) control approaches. In addition, it is

possible to create either a deterministic or stochastic

optimization control system based on a framework of

control techniques.

Table 1 displays the most significant research

studies conducted in the past five years that address

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control methodologies for an intelligent microgrid.

This demonstrates the implementation of several

different methods. Intelligent microgrids' precise and

efficient functioning necessitates a meticulous

evaluation of the potential power generated by DERs.

The control hierarchy's bandwidth and time scale

exhibit an inverse relationship with microgrids from

the primary to the tertiary level of control. This

implies that primary control needs a significant

amount of data transfer capacity in a lower range.

Time scale computation is required for tertiary

control structures, which operate at a more

significant time scale with a low bandwidth.

Furthermore, semantic technologies are

employed in cutting-edge grid systems to achieve

precise estimates and optimal efficiency during

runtime [64]. It is essential to recognize that the

manipulated parameters for running microgrids rely

on the control and state variables to provide optimal

control behavior. Monitoring the output reference is

crucial for ensuring optimal performance and reliable

operation of islanded and grid-tied microgrid modes.

Hence, it is crucial to use appropriate control to

ensure an intelligent microgrid's efficient

functioning.

6 Future Works

The advancement of microgrids necessitates control

methods that can independently implement any

dynamic idea depicted in Figure 4 to manage

variables and parameters’ monitoring and protection.

Therefore, microgrids can function at phase 5, as

depicted in Figure 2. The robust management of all

system variables is achieved by implementing control

methodologies. The intelligent grid environment

offers a diverse range of control capabilities for the

power network. In this context, the bidirectional

connection between remote terminal units, phasor

measurement units, intelligent electronic devices, and

control centers enhances the robustness of the power

network. Precision in data is crucial when managing

the integration of innovative power generation with

the growing use of real-time sensor-based decision-

making.

Nevertheless, the intelligent microgrid is

susceptible to cyber-attacks, in which a meticulously

planned assault might introduce inaccurate

information into the microgrid during the process of

state estimate, hence impacting the functioning and

management of the electrical system [95]. This can

lead to significant technological, economic, and

societal issues. Standard approaches like Newton-

Raphson can mitigate the effects of fake data

injection assaults on essential sensors in the

microgrid. Implementing a plan to identify hacked

meters effectively eliminates potential hazards to the

electricity system. The intelligent microgrid

operation should quickly identify false information

and adjust the process according to dynamic

behavior. The internet of energy is a sophisticated

notion within intelligent grids, [96]. This can be

efficiently accomplished and implemented using a

hybrid control framework inspired by the concept of

cutting-edge microgrid control.

The hybrid control structure refers to

amalgamating all three suitable control structures.

The Internet of Energy uses a distributed design to

maintain a balanced power network. Thus, the system

can function autonomously without relying on the

primary transmission and distribution network, [97].

However, this architectural design primarily focuses

on DERs with the potential for large-scale ESSs and

a significant number of integrations into electric

vehicles (EVs). Furthermore, implementing robust

and resilient control in an intelligent grid

environment would face challenges related to many

variables, characteristics, and elements. Hence,

developing a system that can effectively meet all the

requirements is necessary. The proposed system will

incorporate a hierarchical model to manage

protection, monitoring, and system control. It will

handle state estimations, resources, cyber security,

self-healing, fault recognition, and location tasks.

The system will also include an instantaneous

communication network to enable real-time and fast

data transfer within a MAS conglomerate.

7 Conclusion

Cutting-edge grid technology has revolutionized the

power system's modeling and design. These

technologies incorporate innovative ideas for

bidirectional communication and efficiently control

energy distribution across several independent

entities. The smart grid may be defined as the

amalgamation of DERs integrated with optimum

control strategies. The deployment of microgrids

serves as the foundational framework that enables the

adoption of advanced technologies. An intelligent

microgrid operation necessitates hierarchical

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coordination among diverse technologies to assess

and manage several parameters and variables in a

real-time setting, irrespective of the system's

complexity, kind, and structure.

Table 1. Recent trends of control methods in smart microgrids

Year

Application description

Control

method

Methodology

Ref.

2021

A predictive control applying parallel converters in islanded

microgrids

Predictive

Parallel converters

[65]

2020

A power power-sharing control approach for AC microgrids

Predictive

MPC

[66]

2021

A model predictive control in islanded microgrid for distributed

energy sources

Predictive

Finite control model

[67]

2021

A predictive control using virtual inertia emulator in AC

microgrids

Predictive

MPC

[68]

2020

A model predictive control in microgrids’ real-time operation

Predictive

MPC

[69]

2020

Microgrids’ network voltage tracking and damping using robust

control method

Robust

Linear-quadratic-Gaussian (ILQG)

controller

[70]

2020

A robust control method for microgrids using linear matrix

inequality

Robust

Linear

matrix inequality

[71]

2020

A robust control method for DC microgrids using disturbances

and parametric uncertainty

Robust

Lebesque-measurable matrix and

disturbance rejection.

[72]

2020

A robust control method for DC microgrids using communication

network delay

Robust

Lyapunov-Krasovskii theorem and

linear-matrix

[73]

2020

A control method for islanded microgrids using H∞ robust

approach

Robust

linear matrix inequality

[74]

2021

A smart control method for microgrids using distributed fuzzy

cooperative controller

Smart

Model predictive voltage and

current

[75]

2021

A DC microgrid applications of DC/DC converter

Smart

ANN

[76]

2021

Microgrids model reference voltage controller

Smart

Fuzzy PI model

[77]

2021

Smart microgrids battery energy storage energy controller

Smart

ANN

[78]

2021

Frequency and voltage controller in an AC microgrid

Smart

PSO tuned PI

[79]

2021

A microgrids’ voltage controller using droop control

Nonlinear

Load boundary and

negative droop resistances

[80]

2021

A DC microgrids’ controller design

Nonlinear

Lyapunov theory of Linear Matrix

Inequality (LMI).

[81]

2020

An energy storage systems and renewable resources DC

microgrids’ controller

Nonlinear

Integral backstepping

[82]

2021

A distributed event-driven power sharing controller design

Nonlinear

Partial feedback linearization

[83]

2020

A microgrid systems’ droop-like behavior control mapping

Nonlinear

External droop

control architecture

[84]

2022

A DC bus voltage stabilizing controller design

Adaptive

Active damping scheme

[85]

2022

An AC microgrids’ master-slave controller design

Adaptive

Backstepping control (BSC)

scheme,

[86]

2022

A shipboard’s DC microgrids control design

Adaptive

Neural network linear parameter

[87]

2022

A controller design for remote microgrids

Adaptive

Continuous mixed P-norm (CMPN)

algorithm

[88]

2020

An incremental filter-control for rural microgrids

Adaptive

Incremental adaptive filter

[89]

2021

Microgrids’ multiple models’ controller design

Adaptive

Multiple models

[90]

2020

Isolated microgrids’ linear quadratic controller design

Linear

Power sharing control

[91]

2022

A rural PV microgrid controller for DC-DC converters

Linear

Exact

linearization technique

[92]

2020

A hybrid AC-DC microgrid using improved voltage oscillation

damping

Linear

Linear quadratic Gaussian method

[93]

2023

An isolated hybrid AC-DC microgrids frequency controller

Linear

Linear parameter varying (LPV)

method

[94]

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Distinguishing between control methodologies

and modeling of microgrids in the novel grid context

might be challenging. This study evaluates several

control methodologies for advanced microgrids. The

study categorizes the control approaches into six

distinct categories: intelligent, robust, adaptive,

predictive, linear, and non-linear control systems.

This control categorization evaluates the inherent

implementation capabilities of microgrids in terms of

their novel techniques, layers, modeling structure,

and dynamic design. It has been noted that some

control approaches may be applied inside a

framework that simulates two or more methods. The

hierarchical control refers to the organization of the

microgrid into primary, secondary, and tertiary levels

to achieve efficient operational performance. In

addition, this introduced a secondary layer that

examines the plausible assessment of DER potential

since microgrids serve as the driving force behind

DER deployment.

Furthermore, microgrid controllers often consist

of hierarchical control layers that synchronize,

enhance, regulate, and stabilize the system's

behavior. This research proposes a unique control

structure, namely a hybrid, to differentiate itself from

the widely available control structures. The control

structures may be classified into three types:

centralized, decentralized, and distributed. The

hybrid control structure refers to amalgamating all

three suitable control structures. This is the viewpoint

of the entire self-governing electric network

comprised of Multi-Agent Systems (MASs). A

successful hierarchical control architecture requires

exceptional monitoring behavior to safeguard

microgrids against unforeseen occurrences.

Furthermore, the control techniques safeguard

the whole system from various abnormal

occurrences, such as malfunctions and cyber-attacks,

while also offering the chance to identify suitable

areas for adding DERs and ESSs to enhance system

efficiency. In addition, microgrids have yet to be

fully commercialized, and their new applications

need to be integrated into the future of the digital

revolution of the smart grid. Future studies will

investigate the hierarchical coordination and vision

perceptions of novel microgrids.

Acknowledgement:

The authors acknowledge the Smart Grid Research

Centre, Durban University of Technology, Durban,

for providing a conducive environment and research

facility for this study.

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Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

The authors equally contributed in the present

research, at all stages from the formulation of the

problem to the final findings and solution.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

This work was supported by the National Research

Foundation (NRF) grant under Grant Number

PSTD2203301251.

Conflict of Interest

The authors have no conflicts of interest to declare.

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US.

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