A Brief Review of Microgrid Surveys, by Focusing on Energy Management System

Abstract

Microgrids are new concepts in power systems that can upgrade current power systems due to their technical, economic, and environmental advantages. In addition, the increasing penetration of renewable energies and their use in microgrids have increased the complexity of these new grids in terms of planning and operation. Along with numerous research and practical projects built in different countries with multiple applications, countless types of research have also been performed relying on different aspects of MGs. In this paper, based on a review of studies and review articles related to MGs, an attempt has been made to evaluate and report the optimal energy management of MGs, based on what is addressed in the literature. In addition, the most critical surveys on various topics of MGs are introduced as a guide for researchers to draw a road map for future works.

1. Introduction

A microgrid (MG), as the basic structure of the smart grid (SG) concept, can be defined as a local electrical grid, mainly in the low-voltage distribution system, containing renewable and non-renewable energy sources, controllable (dispatch-able) loads, energy storage systems (electrical or thermal), electric vehicles, combined heat and power (CHP) units, control and communication systems, and different strategies such as demand response programs, which can operate in grid-connected or islanded modes [1,2,3].

The microgrid has many advantages for both the consumer and the power generation companies. From the consumer’s point of view, it can simultaneously provide electricity and heat, increase reliability and resiliency, reduce greenhouse gas emissions, and improve quality [4]. In addition, from the point of view of the power companies, using microgrids can manage the demand and, therefore, can postpone the need for new facilities for power system expansion.

Furthermore, with the electricity growth and the need for higher power quality, the electricity industry has moved toward using new technologies. On the other hand, privatization, competition, and restructuring in the electricity market have forced planners and operators to small-scale generation units, increasing energy efficiency and minimizing total investment and operation costs. For this purpose, one of the efficient solutions is using renewable-energy-based distributed generation sources in new formworks, such as microgrids [5]. This also helps in reducing fossil fuels and greenhouse emissions. Furthermore, the power losses will be crucially decreased, creating more flexibility and providing various services to consumers [6].

From the beginning of the introduction of these emergent grids, many types of research and studies have been performed to explain their basics, advantages, and disadvantages, and to examine the challenges ahead in different tasks. Various fields of planning, operation, control, and protection of the MGs are prominent topics that are focused on by scientists, planners, and operators.

SMGs are also the potential power grid composition in many areas, created by integrating RESs and EVs into the power grid [7].

1.1. Motivation

From the beginning of the introduction of these networks, many types of research and studies have been conducted to explain the basics, advantages, and disadvantages and to examine the challenges ahead of this new concept in different fields. For example, there are many reviews or survey articles in other areas of the MG, focusing on different aspects such as planning and design, operation, control, and protection.

In this paper, some of the previous review articles related to MGs, in different fields, are introduced, and the details of the most important topics on the energy management of MGs and some related topics are addressed (see

Table 1. The summary of the survey papers on EM in MGs.

Ref.	Year	Journal	Description	No. of Ref.	Studied Period
[1]	2016	Renewable and Sustainable Energy Reviews	Optimal power flow	283	1962–2016
[8]	2022	Applied Energy		205	2002–2022
[9]	2018	Systems Science & Control Engineering		50	1998–2018
[10]	2019	IEEE Access	Interconnected MMGs	92	2011–2019
[11]	2021	Energies	Considering Energy Storage	158	1985–2021
[12]	2021	Energies	Approaches	176	2002–2021
[13]	2019	International journal of electrical power & energy systems	Optimal Operation of Hybrid AC/DC MG under Uncertainty of RES	150	1988–2019
[14]	2017	Sustainable Cities and Society	considering xEVs	118	1977–2017
[15]	2021	Energies	Campus MGs	145	1994–2021
[16]	2022	Sensors	Sustainable Solutions for Advanced EM in Campus	146	2005–2022
[17]	2016	Renewable and Sustainable Energy Reviews	Optimization objectives, constraints, tools, and algorithms	186	2000–2015
[18]	2021	Renewable and Sustainable Energy Reviews	AI in energy self-management in smart buildings	90	2003–2021

). The scope of the reviewed papers includes AC and DC technologies, blockchain technology, communication issues, community-based MGs, control strategies, cybersecurity, deep-learning-based techniques, demand response, energy storage systems, experiences, forecasting, prediction models, fuel cells, general topics on MGs, grid-tied inverter controllers, information processing, islanding, layers structure, monitoring interfaces, multi-agent systems, multi-microgrids, operation, power electronics, power quality, protection, reactive power compensation, reliability evaluation, resiliency/self-healing, sociotechnical barriers, and solid-state transformers.

1.2. Paper Origination

The rest of this paper is organized as follows: a comprehensive review of the EM problem in an MG is addressed in Section 2. For this purpose, the MG components are described. The EM definition, different EM structures, relevant standards, communications protocols, sample projects, objective functions, constraints, optimization techniques, forecasting algorithms, energy management strategies, uncertainty modeling of EM, used software, blockchain technology, and challenging issues are discussed. The surveys on other fields of MGs are listed in Section 3. Section 4 highlights the conclusions of this work.

2. Energy Management and Review of Surveys

The energy management system (EMS) is a complex system that prepares the necessary actions to minimize/maximize predefined objective functions in an MG, considering the relevant constraints and limitations.

2.1. MG Components

Based on the literature, the MG components are addressed as follows:

• Distributed generators (DGs) [12,19];
• Storage systems (SS), [19], including battery, flywheel, super-capacitor, fuel cell, CES, SMES, and pumped storage [12]; or mechanical (flywheel, compressed air, and pumped hydro), electrochemical (lead-acid (PbA), lithium-ion (Li-ion), sodium-sulfur (NaS), and redox flow batteries, and hydrogen fuel cells (HFC)), and electrical (super-capacitor (SC) and super magnetic) types [11]; or lead acid, lithium-ion, and nickel-cadmium [13];
• Conventional sources: oil, gas, and coal-based generation units [13];
• Point of common coupling (PCC) [19];
• Distribution system [19];
• Protection system [19];
• Monitoring and metering [19];
• Power converters [13,19];
• Control system [19];
• Loads: commercial, residential, industrial, and other types (public and agriculture) [12]; or identification-based (measurement-based and component-based) and controlled-based (controlled and uncontrolled) [12]; or thermal loads and electrical loads (constant power (the most common), constant current, constant impedance, and complex ZIP model) [13];
• EVs: vehicle-to-vehicle (V2V) and vehicle-to-grid (V2G) [12], in four modes of operation, including forward motoring, reserve regeneration, reserve motoring, and forward regeneration [13];
• xEVs: electric vehicles (EVs), battery EVs (BEVs), hybrid EVs (HEVs), fuel cell hybrid vehicles (FCHVs), and plug-in hybrid EVs (PHEVs) [14,20].

In addition, the leading technologies of non-renewable DGs are categorized as follows:

• Reciprocating engines [19];
• Gas turbines [19];
• Micro-turbine [19];
• Fuel cells [19]: Alkaline fuel cell (AFC), Phosphoric acid fuel cell (PAFC), Proton exchange membrane fuel cell (PEMFC), Solid oxide fuel cell (SOFC), Molten carbonate fuel cell (MCFC), and Molten carbonate fuel cell (MCFC) [13];
• Diesel generators [12].

The leading technologies for renewable DGs are listed as follows:

• Wind [12,19]: centralized and decentralized [13];
• Photovoltaic systems [19]: monocrystalline, polycrystalline, and amorphous [13];
• Biomass gasification [19];
• Small (or hydro) hydropower [12,19];
• Geothermal [19];
• Ocean energy [19];
• Solar thermal [19] or solar [12];
• Combined heat and power (CHP) [12].

As an important note, all of the renewable DGs are from AC type, except the fuel cell, solar, or photovoltaic systems [12,19].

In another classification, Muqeet et al. [15] presented the MG components based on what is described in

Table 2. The MG components [15].

Flexible energy sources	Demand response programs (DRP)	Price-based programs: time-of-use; critical peak; real-time pricing. Incentive-based programs: classical; direct load control; capacity market program; ancillary service market; emergency DRP; market-based program.
	ESS	Fuel cell, battery, EV, flywheel, super-capacitor
	Non-RES	Diesel generator, micro-turbine, gas engine, and combustion turbine
Non-flexible energy sources, or RES	PV, hydro, tidal, wind, biomass, geothermal

.

It should be noted that Nawaz et al. [8] addressed incentive-based programs for DRP including direct load control (DLC), interruptible, emergency (EDRP), and capacity market program (CMP).

Battula et al. [12] also divided the supply and demand management strategies into two categories of demand control (price-based and direct load) and energy generation control (with and without RES).

Table 3. Comparative costs and environmental impacts of different energy storage technologies [21,22,23].

ESS Technology	Cost of Energy Capital (USD/kWh)	Cost of Power Capital (USD/kW)	Cost of Operation and Maintenance (USD)	Environmental Impact
Superconducting magnetic energy storage (SMES)	1000–72,000	200–489	0.001–18.5	Low
Flywheel Energy Storage (FES)	1000–14,000	250–350	0.004–20	Very low
Pumped hydro storage (PHS)	5–100	2500–4300	0.004–3.0	High/Medium
Thermal Energy Storage (TES)	20–60	200–400	-	-
Compressed Air Energy Storage (CAES)	2–120	400–1000	0.003–25	Medium/Low
Different types of batteries
Lead acid (Pb-A)	200–400	300–600	50	High
Lithium-ion (Li-ion)	600–3800	1200–4000	−	Medium/Low
Sodium-sulfur (Na-S)	300–500	350–3000	80	High
Nickel–cadmium (Ni-Cd)	800–2400	500–1500	20	High
Vanadium redox (VR)	150–1000	600–1500	70	-
Zinc bromide (ZnBr)	150–1000	400–2500	−	-
Polysulfide bromide (PSB)	150–1000	700–2500	−	-
Capacitors	500–1000	200–400	0.005–13.0	-
Super-capacitors	300–2000	100–450	0.005–6.0	-

compares various costs of energy storage systems [21,22] and their environmental impacts [23].

Table 4. Technical characteristics of some energy storage technologies [23].

ESS Technology	Power Range (MW)	Energy Density (Wh/L)	Power Density (W/L)	Round Trip Efficiency (%)
SMES	0.1–10	0.2–2.5	1000–4000	95, 95–98
FES	<0.25, 0–0.25, 0.01–0.25, 0.1–20	20–80	1000–2000	90–93, 93–95, 90–95
PHS	10–5000	0.5–1.5	0.5–1.5	75–85, 70-85,65–87
CAES	5–300, 5–1000	3–6	0.5–2	50–89, 70–79, 70–89
Different types of batteries
Pb-A	0–20, 0–40	50–80	10–400	75–80, 70–90
Li-ion	0–1, 0-100	200–500	500–2000	85–90, 90–97
Na-S	0.05–8, 0.05–34	150–250	150–230	80–90, 85–90

also describes some technical characteristics of different ESS technologies [23]. It should be noted that these data were recompiled using reported data from various references.

2.2. The Advantages of Microgrids

The MG brings several advantages to the power industry. From a grid point of view, the main advantage of an MG is that it is treated as a controlled entity within the power grid, which can be operated as a single aggregated load [24]. Based on the literature, the following subjects can be addressed in this regard [24,25,26,27]:

• Operation or investment issues: reducing both electrical and physical distance between generating units and consumers may contribute to:

  -

  Improving the reactive power support of the whole system, thus enhancing the voltage profile;

  -

  Operating in both modes of connected to the main grid or islanded, which increases the supply reliability of consumers;

  -

  Separating and isolating itself from the utility, during a grid disturbance, which helps in continuous operation of MG;

  -

  Reduction in distribution and transmission feeder overloading;

  -

  Reducing the power losses in distribution and transmission sub-system;

  -

  Reducing/postponing the investment expansion for large-scale generation and transmission systems;

  -

  Cost saving: utilization of waste heat in CHP units, which increases the energy efficiency and finally leads to a decrease in overall costs;

  -

  Integrating RES low-voltage distribution grids;

  -

  Increasing power system reliability by reducing customer outage and service restoration time.

• Power quality issues: improving the power quality and reliability, due to:

  -

  Better matching of generation and demand;

  -

  Reducing the impacts of large-scale transmission and generation outages;

  -

  Minimizing the downtimes;

  -

  Voltage profile improvement.

• Market issues:

  -

  Significant reduction in market power exercised by established generation companies;

  -

  Using MGs to provide ancillary services, such as power reserve capability, local voltage support, and load frequency control;

  -

  Reduction in energy price due to widespread application of distributed generation units.

• Environmental issues:

  -

  Physical proximity between DGs and consumers may increase consumer information on more rational use of energy;

  -

  Reduce the GHG emissions.

2.3. EM Definition

The energy management system (EMS) is an information and control system that provides the necessary functionality, ensuring that both the generation and distribution systems supply energy at minimized operational costs [28]. It is an important procedure to achieve a stable and economic operation of MGs through some optimization techniques that manage and coordinate the dispatchable distributed generators (DGs), energy storage systems, demand responses, and other applied strategies to optimize the objective function (s) of an MG [8,29,30,31]. It is also responsible for maximizing the utilization of the RES, considering their relevant uncertainties [8,32].

An important point to note is that despite the many definitions of this concept, three sub-systems of power, control, and telecommunication play a fundamental role. The sub-systems’ full coordination and integration are the basis for the development from MGs to smart grids (SGs) and, as a result, smart cities (SCs) and smart societies (SSs) [33].

2.4. Different EM Structures

Generally, the MG structures are divided into [8,12,13]: AC, DC, and AC/DC hybrid MGs.

The operation modes of MGs are addressed as grid-connected and islanded ones [13].

In addition, the interconnection architectures of multi-MGs (MMGs) are addressed as [8,9]: radial, mesh, and daisy-chain.

Furthermore, different EMS schemes or structures are mentioned as:

• Centralized [8,9,11,12];
• Decentralized [8,9,11,12];
• Hybrid [8,9];
• Distributed [8,11,12];
• Hierarchical [11]: primary control: local supervision, voltage and current control, and power-sharing control; secondary control: power quality control, power flow control, and synchronization control; tertiary control: economic dispatching and optimization, MG supervision, and generation forecasting [12];
• Nested [8].

The most important missions of control for EMS in MGs are [11]:

• Power quality/reliability regulation (including low power factor, voltage imbalance, unreliable power supply, and voltage/frequency offsets);
• Spinning reserve;
• Energy shifting;
• Peak shaving [11];
• Energy arbitrage;
• Black start.

The computational stress, privacy, scalability, resilience, and communication infrastructure are important issues for EMS that should be discussed [8]. In addition, the functionality of EMS in different views of information modules, scheduling and control, and resilience operation should be addressed [8].

In addition, it is required to compare the different types of EMS control strategies in terms of information access, communication information, function in real-time, the feature of plug and play, expenditure, the structure of the grid, size (number of nodes), tolerance during the fault, infrastructure final nodes, operation flexibility, bandwidth and latencies, quality of service (QoS), connectivity, safety measures, and individuality [12].

The control configurations for ESSs are aggregated, distributed, and hybrid [11].

The strategies to attain the control objectives are addressed as [11]:

• Active and reactive power (PQ);
• Voltage-frequency (V-f);
• Droop control;
• Conventional control techniques for MGs: PI/PID controller, model predictive controller (MPC), linear quadratic regulator (LQR), and intelligent control techniques for MGs (particle swarm optimization (PSO), fuzzy logic control (FLC), artificial neural networks (ANNs), and reinforcement learning (RL) methods) [11].

2.5. Relevant Standards

Reddy et al. [34] reviewed various developments in SMGs and addressed insights into communication standards and technologies, and challenges. They introduced an advanced wireless technology, called LoRa (Long Range), to establish a communication network for interoperable smart microgrids (ISMs). This reference details the standards and guidelines for SMGs communication: IEC 60870-6-503, IEC TR 62357-200, IEC 62325-503, IEC 62056-4-7, IEC 61851-24, IEC TR 61850-90-1, IEC 61850-8-2, IEC 61850-7-2:2010+AMD1, IEC 61850-7-1:2011+AMD1; IEEE 2030, IEEE 1815, IEEE 1702; ISO/CD 15118-2, ISO 15118-3, ISO/IEC 14908-2, ISO/IEC 14908-4; ITU-T G.9903, ITU-T G.9960; NIST Framework Release 4.0; TR-50 M2M, TR-51, TR-34; ANSI C12.21, ANSI C12.22; MultiSpeak: Versions 1.1, 5.0, 4.x, 3.0, 2.2.

Ahmad et al. [14] focused on the importance of electric vehicles (xEVs) in EM due to their highly positive impacts on describing greenhouse gas (GHG) emissions. Then, the authors addressed many standards related to charging, including IEC 6185123 (2014), IEC 621961 (2014), IEC 621962 (2016), IEC 621963 (2014), IEC 619801 (2015), IEC 626601 (2010), IEC 626602 (2010), IEC 6185124 (2014), SAE J 1772 (2016), SAE J 1773 (2014), SAE J 1715/2 (2013), SAE J 537 (2016), SAE J 1495 (2013), SAE J 1766 (2014), SAE J 1797 (2008), SAE J 1798 (2008), SAE J 2288 (2008), SAE J 2289 (2008), SAE J 2380 (2013), SAE J 2464 (2009), SAE J 2758 (2007), SAE J 2929 (2013), SAE J 2936 (2012), SAE J 2950 (2012), SAE J 2974 (2015), SAE J 2983 (2012), SAE J 2984 (2013), SAE J 2344 (2010), SAE J 2910 (2014), SAE J 2990 (2012), SAE J 2894/1 (2011), SAE J 2894/2 (2015), SAE J 2293/1 (2014), SAE J 1634 (2012), SAE J 2293/2 (2014), SAE J 2711 (2002), SAE J 2841 (2010), SAE J 2889/1 (2015), UL 2202 (2009), UL 22311 (2012), UL 22312 (2012), UL 2089 (2015), UL 2271 (2013), UL 2580 (2013), UL 2251 (2013), UL 2734 (2015), UL 2871 (2014), UL 9741 (2014), UL 2594 (2013), UL 2747 (2012), BS EN 618511 (2011), BS EN 6185121 (2002), BS EN 6185122 (2002), BS EN 621961 (2014), BS DD CLC/TS 504571 (2008), BS DD CLC/TS 504572 (2008), COC GB/T 18487.1 (2015), COC GB/T 18487.2 (2001), COC GB/T 18487.3 (2001), COC GB/T 20234.1 (2015), COC GB/T 20234.2 (2015), COC GB/T 20234.3 (2015), COC GB/T QC/T 895 (2011), COC GB/T QC/T 841 (2010), JIS D 0007 (2012), JIS D 1304 (2004), JIS D 6185123 (2014), JIS D 6185124 (2014), JIS D 621963 (2014), ISO 8714 (2002), ISO 8715 (2001), ISO TR 11954 (2008), ISO 11955 (2008), ISO 232741 (2013), ISO 232742 (2012), ISO 23828 (2013), ISO 64691 (2009), ISO 64692 (2009), ISO 64693 (2011), ISO 64694 (2015), ISO 23273 (2013), ISO 17409 (2015), ISO 151181 (2013), ISO 151182 (2014), ISO 151183 (2015), ISO 124051 (2011), ISO 124052 (2012), ISO 124053 (2014), and ISO PAS 16898 (2012).

Battula et al. [12] addressed the standards of MGs and EVs such as: ISO 15118-1, ISO 15118-2, ISO 15118-3, ISO 15118-4, ISO 15118-8, IEC 62898-1, IEC 62898-2, IEC 62898-3-1, IEC 62898-3-2, IEC 62898-3-3, IEC 62257-9-2, IEC 61850-7-420, IEC 61968, IEC 61851-1, IEC 61851-23, IEC 61851-24, IEEE 1547, IEEE 1547.1, IEEE 1547.2, IEEE 1547.3, IEEE 1547.4, IEEE 1547.6, IEEE 1547.7, IEEE 1547.8, IEEE 2030, IEEE 2030.1, IEEE 2030.2, IEEE 2030.3, IEEE 2030.4, IEEE 2030.5, IEEE 2030.6, IEEE 2030.7, IEEE 2030.8, IEEE 2030.9, IEEE 1646, and IEEE 2413.

2.6. Communications Protocols

Akinwale et al. [35] categorized the applicable communication technologies in MGs as wired (including PLC, ethernet, and fiber optics) and non-wired (including Bluetooth, Wi-Fi, ZigBee/6LoWPAN, Cellular, and LPWAN).

Reddy et al. [34] introduced an advanced wireless technology, called LoRa (Long Range), to establish a communication network for interoperable smart microgrids (ISMs). The system networks were also categorized as: personal area network (PAN), local area network (LAN), metropolitan area network (MAN), and wide area network (WAN).

Battula et al. [12] presented an overview of the communication technologies: GSM (900–1800 MHz, 14.4 Kb/s, 1–10 km), GPRS (900–1800 MHz, 170 Kb/s, 1–10 km), 3G (1.92–1.98 GHz, 2 Mb/s, 1–20 km), 4G (2.11–2.6 GHz, 100 Mb/s, 1–10 km), 5G (3–90 GHz, 10 Gb/s, >1 km), WiMAX (2.5–5.8 GHz, 75 Mb/s, 10–50 km), PLC (1–30 MHz, 2–3 Mb/s, 1–3 km), Zigbee (800 MHz–2.4 GHz, 250 Kb/s, 30–50 m), and Bluetooth (2.4–2.483-GHz, 2.1 Mb/s, 0.1–1 km).

2.7. Sample Projects

Based on the literature [34,36,37], there are many projects in this area: BlueGENs UK grid, CFCL, Ecogrid Project, ERIGrid, Smart Power Hamburg Project, Samso, Danish Edison Project, Ei Hierro, Power Matching City Projects, and GRID4U (in Europe); Hanian, Kitakyushu city project, and Toyota city project (in Asia); STAmi, PRICE, ISOLVES: PSSA-M, ELECTRA, Smart Grid, Hyllie, Model City, Manheim, Vendee, Nice Grid, e-GOTHAM, Arrowhead, and NINES. The details of these projects can be found in [34].

Muqeet et al. [15], by focusing on the market price fluctuations, limited photovoltaic generations, and controlling different loads in managing uncertainties in a campus MG, detailed some campus MG projects in Al-Akhawayn University, Morocco; Aligarh Muslim University, India; American University of Beirut (AUB), Lebanon; Chalmers University of Technology, Sweden; Clemson University, South Carolina; De Vega Zana, Spain; Eindhoven University of Technology, The Netherlands; Federal University of Pará, Brazil; Federal University of Rio de Janeiro, Brazil; Griffith University, Australia; Illinois Institute of Technology, USA; Nanyang Technological University (NTU), Singapore; North China Electric-Power University, Beijing, China; Science and Technology, Algeria; Seoul University, South Korea; Tezpur University, India; University of Central Missouri, USA; University of Connecticut, Mansfield, Connecticut, USA; University of Cyprus (UCY); University of Genova, Savona Campus, Italy; University of Malta; University of Novi Sad, Serbia; University of Southern California, USA; University of Wisconsin-Madison, USA; Valahia University of Targoviste, Romania; Yuan Ze University, Taiwan. All of the mentioned projects were compared in terms of their loads (campus or building) and their components (PV, BESS, Wind, Biomass, DG, MT, EV, SC, FC, and CHP).

Table 5. Details of some campus MGs with their EMS [15].

Location	Components	Optimization Techniques for EMS	Economic Analysis
Al-Akhawayn campus, Morocco	RES, Smart meters Sensors	Energy management system	Minimize energy losses and GHG emissions
AMU (Ali Garh Muslim University), India	PV, Grid, wind	HOMER analysis	Net present cost (NPC), CO2 emissions
Chonnam National University Yongbong Campus, Gwangju, South Korea	ESS, PV, Load controllers, Power load-bank	P2P trading mechanism	Maximizing the performance of every interlinked microgrid
Eindhoven University of Technology, The Netherlands	RES, DGs, ESS	GA	400 kWh energy production
Guangdong University of Technology, China	BESS, PV	NSGA-II (Non-dominated Sorting Genetic Algorithm-II)	Maximizing PV consumption and minimizing the operational cos
Illinois Institute of Technology, Chicago, USA	DERs, DG, ESS	Energy scheduling optimization problem (ESOP)	Power balance reliability sustainability
Jordan University of Science and Technology, Irbid, Jordan	PV, Utility grid	Charging/discharging algorithm	Reducing the energy consumption
Massachusetts Institute of Technology, Cambridge, Massachusetts, USA	Grid, Battery	Forecasting method	Reducing the peak energy consumption
McNeese State University, Lake Charles, Louisiana, USA	PV, CHP	Fast Fourier transform (FFT) algorithm	Control water flow for higher thermal recovery
McNeese State University, Lake Charles, Louisiana, USA	CHP, NG micro turbine, PV plant	HOMER analysis	The efficient system includes CHP, and PV
METU (Middle East Technical University) campus and NCC (Northern Cyprus Campus)	RES, ESS	Generalized reduced gradient (GRG) algorithm	Increasing the RES fraction, demand and supply fraction, cost of electricity (COE)
Multiple Microgrids location such as Nanjing University Microgrid	PV, wind turbines, ESS (EV), diesel generators, Gas turbine	OPF (optimal power flow) technique, Auction algorithm CPLEX solver	Minimizing operation cost
Nanjing University, China	EV, Wind system, PV	Interval optimization	Reducing transmission loss
Nnamdi Azikiwe University, Nigeria	Solar–PV, Diesel generator	HOMER analysis	Net present value (NPV) and levelized cost of energy (LCOE)
Oregon State University, Corvallis, Oregon, USA	Smart meters, 2 Solar–PV arrays	Linear optimization	Energy management and voltage-regulated
Proposed University based in India	Wind system, PV, ESS, Biomass	Newton–Raphson technique, Swarm intelligence approach	Improving the energy exchange among grids, and enhancing power quality
Purdue University, Indiana, USA	Solar–PV grid, 3 lead–acid batteries	EMS technique	Annual return on investment, payback period
Sebelas Maret University, Indonesia	RES, Solar–PV, Energy Storage	HOMER analysis	Net present cost, internal rate of return
University of Coimbra, Portugal	PV, Li-ion batteries, EV, Controllers	LabVIEW analysis	Minimizing energy consumption
University of Connecticut, Mansfield, Connecticut, USA	Wind turbine, fuel cell, PV, ESS, Hydro-kinetic systems	HOMER analysis	The optimal configuration of MG is selected, including solar PV, wind turbine system, and ESS

shows some details for sample campus MGs with their EMS [15]. Some examples of the MG implementations or active experiments were also addressed in [38] for the European Union (EU), Japan, Korea, North America, and Australia.

2.8. Objective Functions and Constraints

Muqeet et al. [16] presented the MG EMS objective functions including costs of energy, net present, emission, reliability, operation, investment, controlling the MG frequency, start-up, shut-down, network security, reserves, and demand response, some of which, in each reported reference, may be considered.

Zou et al. [10] described the optimization objectives of EM in IMMGs including operating cost, customer satisfaction, usage of renewable resources, transmission loss, system flexibility, stability, and environmental benefits, which will be studied in off-line and real-time timescales.

Pourbehzadi et al. [13] divided the objectives into environmental, economic, and technical issues. Furthermore, the primary constraints are load balance, real power, and battery energy. The cost model includes the grid, wind power, PV, battery, FC, and MT.

Khan et al. [17] categorized the OF in EM of SMGs into the environmental cost (carbon emissions and penalties for emissions), capital and operational costs (fuel, fuel cell, capital, maintenance, and electrolysis), energy storage cost (charge/discharge, ultra-capacitor, hydrogen storage, hourly capital and storage, efficiency of charge/discharge, and battery), and miscellaneous (frustration costs, dissatisfaction costs, penalty costs for solar and wind, tracking error penalty, and load shedding costs), and all of the relevant mathematical formulations were discussed based on different references. In addition, different constraints were categorized into supply, demand, storage, operation, prices, wind, solar, fuel cell, and carbon emissions.

Abdi et al. [1] addressed the objective functions of the OPF including active power generation cost, reactive power generation cost, power supplied to the grid from an external utility, active power losses, carbon emission, load curtailment, tap position and capacitor bank switching, social welfare, reserve cost, and load adjustment. Furthermore, the constraints were divided into constraints of active power (generation or power supply to the load), reactive power, voltage, current (thermal rate or maximum capability), voltage angle, tap position, capacitor bank switching, curtailment, Reserve, Flowing AC power to DC grid, and vice versa.

2.9. Optimization Techniques

Khan et al. [17] categorized the optimization methods in EM problems into dynamic programming, mixed integer programming (MIP) (mixed integer non-linear programming, mixed integer quadratic programming, and mixed integer linear programming), stochastic programming, non-linear programming, and integer programming.

Battula et al. [12] presented an overview of the numerical methodologies of EMS including classical (including LP, MILP, MINLP, NLP, DP, Approximate DP, Rule-based, Deterministic-based, and NP-Hard), metaheuristic (non-dominated sorting genetic algorithm II (NSGA-II), particle swarm optimization (PSO), confidence-based velocity-controlled particle swarm optimization (CVCPSO), multi-voxel pattern analysis (MVPA), grey wolf optimization (GWO), artificial bee colony (ABC), enhanced bee colony (EBC), adaptive differential evaluation (ADE), modified PSO (MOPSO), enhanced velocity differential evolutionary PSO (EVDEPSO), rule-based bat optimization (BO), crow search algorithm (CSA), gravitational search algorithm (GSA), genetic algorithm (GA), alternating direction method of multipliers (ADMM) using modified firefly algorithm (MFA), teaching–learning optimization (TLA), social spider algorithm (SSO), and whale optimization algorithm (WOA)), and intelligent (Fuzzy-based models: Neuro-fuzzy, ANN, and RNN; predictive models; game theory and deep learning). In addition, the authors presented a complete review of previous studies, based on the problems addressed, including communication and information exchange, cost minimization, data collection and scenario generation, demand response program, energy scheduling, forecasting-based, generating energy with lower emissions, market-participation-based, on the vehicle-to-grid system (V2G), operating time, optimal storage management, reliability of operation, stability analysis, and time response.

Muqeet et al. [15] addressed the optimization techniques for EM: linear optimization, HOMER analysis, genetic algorithm, energy scheduling optimization problem (ESOP), fast Fourier transform (FFT), charging/discharging algorithm, generalized reduced gradient (GRG) algorithm, forecasting method, P2P trading mechanism, NSGA-II (Non-dominated Sorting Genetic Algorithm-II), interval optimization, OPF (optimal power flow) technique auction algorithm CPLEX solver, LabVIEW analysis, and Newton–Raphson technique swarm intelligence approach. Furthermore, the optimization methods are compared based on their advantages and disadvantages, applications, and objectives. Among the optimization algorithms, we can mention the deterministic methods, including MILP, dynamic programming (DP), and MINLP; metaheuristic methods, including PSO, GA, and artificial fish swarm; artificial intelligence methods, including artificial neural network and Fuzzy logic; other methods such as Manta Ray optimization and Harris Hawks optimization.

Aguilar et al. [18] reviewed the relevant recent research to improve EM for smart buildings using artificial intelligence (AI) methods. For this purpose, they first introduced the concept of “Autonomous Cycles of Data Analysis Tasks” (ACODAT), which defines the need for an autonomous management system for some specialized tasks, including monitoring, analysis, and decision-making to reach defined objectives, such as the energy efficiency. They were previously applied in different domains, such as smart classrooms, smart cities, and industry.

In addition, the methodology for the development of a data mining application (MIDANO) for implementing the ACODAT architectures is presented, which includes three phases: phase 1, which specifies the ACODAT for the problem to be solved; phase 2, which prepares the data for the data analytics tasks (i.e., the extraction and transformation operations of the data); phase 3, which implements all the data analytics tasks of the autonomic cycle. Then, the authors classified the AI techniques in monitoring tasks to K-means, Fuzzy rules or rule-based approaches, ANN, regression algorithms, support vector regression (SVR), density-based spatial clustering of applications with noise (DBSCAN), random forest (RF), evolutionary algorithms, K-nearest neighbor (KNN), and transfer kernel learning. In addition, the main AI techniques used in analysis tasks are classified as: LSTM, auto-encoder, regression neural networks, suggesting convolutional networks (CNN), extreme learning machine (ELM), regression forest, SVR, RF, radial basis functions network, autoregressive integrated moving average (ARIMA), Seasonal autoregressive integrated moving average (SARIMA), and eXtreme gradient boosting (XGBoost). The used AI techniques in control tasks are deep learning (DL), multi-agents, Fuzzy, Fuzzy ANN, and multi-objective. Different techniques in optimization tasks are mentioned as: multi-objective approaches, evolutionary approaches, Fuzzy models, ELM, multi-agent systems, Z-number, reinforcement learning, and bio-inspired approaches. The most important AI techniques in scheduling tasks are bio-inspired approaches, deep reinforcement learning (DRL), and multi-agent systems.

Muqeet et al. [16] focused on the idea of the advanced energy management system (AEMS) to smooth energy flow in a campus MG. For this purpose, the authors introduced some relevant projects and addressed the optimization techniques including high-reliability distribution system (HRDS), control and management system operation, MILP, MICP, PSO, TLBO, multi-agent system (MAS)-based, two-stage stochastic programming, MINLP, and NSGA.

Pourbehzadi et al. [13] addressed some of the optimization methods: AHP, cuckoo search/bat algorithm, DP, genetic optimization/generating sets search algorithm, GWO, hybrid robust/stochastic, imperialist competitive/MCS, multi-objective GA, NSGA II/Fuzzy clustering, decentralized control/multi-agent, MINLP, PSO/Q-learning, stochastic programming, two-step method, and weighted majority algorithm.

Abdi et al. [1] addressed different mathematical-based methods suggested for the OPF problems: distributed and parallel OPF (DPOPF), multiphase OPF (MOPF), OPF approach based on linearization and approximation, OPF approach based on considering storage devices, unbalanced three-phase OPF (TOPF), alternating direction method of multipliers (ADMM), OPF based on the simultaneous formulation of the post-contingency flows, and uncertainty-based OPF models. These approaches can be handled in terms of system type (three phases versus single phase), system balance (balanced versus unbalanced), operational state (islanded versus grid-connected mode), network topology (distribution versus transmission), programming model (dynamic versus static), control strategy (centralized versus decentralized control), multi-agent versus central agent, solution algorithm (mathematical approach versus heuristic algorithm), and realistic description (deterministic versus uncertainty).

2.10. Forecasting Algorithms and Energy Management Strategies

In Ref. [9], focusing on accurately forecasting power generation and load-to-energy management in MGs, forecasting algorithms for the power supply side and load demand were addressed. The forecasting techniques in two categories of hybrid and single models were discussed. In hybrid models, the combination of artificial neural network (ANN) with wavelet, Fuzzy logic, support vector machines (SVMs), and genetic algorithm (GA) were detailed. The single models were classified into artificial intelligence (AI) (including SVM, Fuzzy logic, and ANN) and parametric techniques (including statistic methods and the Kalman filter).

Battula et al. [12] discussed the forecasting techniques for EM of MGs in terms of different parameters (including load, price, and weather). They categorized them into different types based on the required foresting period, including very short-term (from multi seconds to 30 min, used for the dynamic control of RES to the load requirements); short-term (from 30 min to 6 h, used for energy scheduling of different sources and storage devices); medium-term (from 6 h to one day, used for market pricing); long-term (from one day to one week, used for maintenance and load dispatch). In another classification, the forecasting models were categorized based on the used model, linear (including time series; dynamic programming (state space and ARMA)) and non-linear (SVM; Markov chain; stochastic; Fuzzy neural; ANN (supervised, unsupervised, and reinforced)).

2.11. Uncertainty Modeling of EM

Pourbehzadi et al. [13] presented a summary of the proposed approaches in terms of MG Type (AC, DC, AC/DC, grid-connected, and islanded) and solution methodology (deterministic, probabilistic (probability density function/uncertainty model), single/multi-objective, correlation, and EV). They mentioned some methods that were suggested for uncertain optimization such as: analytical hierarchical process, artificial bee colony/improved differential evolution algorithm, clonal selection algorithm, GA, improved teaching-learning optimization algorithm, modified firefly optimization algorithm, modified teaching-learning algorithm/fuzzy-based clustering, NSGA II, PSO/self-adaptive probabilistic mutation, probabilistic load flow, robust optimization, self-adaptive modifies honey bee optimization/fuzzy-based clustering, stochastic programming approach, stochastic programming/cuckoo optimization algorithm, and two-stage stochastic integer programming/robust optimization. Furthermore, different probability density functions/uncertainty models used for modeling the uncertain parameters are: Latin hypercube sampling, real-world values, MCS, Weibull distribution/MCS, 2m-PEM, 2m + 1 PEM, Gaussian mixture model/chance constrained, chance constrained, scenario-based, unscented transformation. In addition, the modeled uncertain parameters were described as power generation of RES, load demand, solar radiation, wind speed, fuel price, load growth, component outage, market price, islanding, a daily-driven distance of PHEVs, electricity price, wind speed, forecast error of active and reactive loads, power loss cost factor, customer interruption cost, failure rate, repair rate, cost function coefficients, the active power output of conventional units, active power flow of transmission lines, and correlated loads.

Abdi et al. [1] stated that deterministic techniques cannot consider the impacts of different uncertainties, arising from the high penetration of RESs, load demand forecasting errors, etc. For this purpose, and in a technical categorization, they suggested using three main methods of MCS, analytical, and approximate methods. The first one is accurate as handling non-linear and complex problems, but it is computationally expensive. Analytical methods, which are based on some mathematical simplifications, can overcome the deficiency of MCS. The first-order second-moment method, Taylor series expansion method, Cumulant method, common uncertain source method, discretization method, and PEM are some methods to model both shortages of the two mentioned methods.

2.12. Used Software

Khan et al. [17] addressed the tools used to solve the EM problem as:

• Anylogic (Developed by XJ Technologies);
• CPLEX (Developed by IBM);
• DigSILENT Power Factory (Developed by DigSILENT);
• DSTATCOM (Developed by S and C Electric Company);
• DSpace (Developed by Dspace Foundation);
• FuseViz (Developed by Oracle Corporation);
• GAMS (Developed by GAMS development Corporation);
• LINDO Global (Developed by LINDO Systems);
• MATLAB/Simulink (Developed by Math-Works);
• MATPOWER (Developed by PSERC at Cornell University);
• PSCAD/EMTDC (Developed by Manitoba HVDC Research Center);
• REST J F (Developed by Roy Fielding);
• SIMPLORER (Developed by Ansoft corporation);
• SCENRED (Developed by GAMS development Corporation);
• VERA (Developed by Visual Technology Applications, Inc).

Muqeet et al. [15] also described the used software: HOMER analysis, Energy scheduling optimization problem (ESOP), CPLEX solver, LabVIEW analysis, PSCAD 4.5, MATLAB/Simulink, MATPOWER, GVMS, RSCAD, JVDE, and HOMER.

In another study, Muqeet et al. [16] mentioned the following software: Hybrid optimization model for electrical renewable (HOMER), IBM ILOG CPLEX, (GAMOM), Gurobi, GAMS, YALMIP toolbox of MATLAB, CPLEX solver 12.4, and MOSEK SOCP.

Pourbehzadi et al. [13] focused on IBM ILOG CPLEXs, Matlab/Simulink, and CPLEX software for EM in an MG:

2.13. Blockchain Technology

Dinesha and Balachandra [39] mentioned that due to the increased penetration of DERs and to overcome climate change, a shift from centralized large-scale generation units to distributed small-sized networks is mandatory. This transition needs to use the distributed ledger technology (DLT) management of energy, information, and money data, by applying blockchain technology due to its numerous advantages of having privacy protection, and facilitating accurate, fast, and real-time settlement of financial transactions. The authors investigated the possibility of developing blockchain-enabled smart microgrids (BSMGs), including the process flows and transaction protocols. For this purpose, after addressing the significance of blockchain technology in SMGs, its different applications in the energy industry were detailed. Then, a review of some projects, including Ethereum, HyperLedger, Tendermint, and open-source projects was performed. Furthermore, the suggested structural (including constituents of a BSMG and layers in BSMGs) and operational (consists of process flow, pricing mechanisms, interoperability between heterogeneous blockchain platforms, and proposed setup of BSMGs) frameworks for BSMGs were discussed. Finally, the authors presented some qualitative and quantitative criteria for the evaluation and benchmarking of the proposed model.

2.14. Challenging Issues

Based on the literature, the most important challenging issues in EM of MGs are categorized as follows:

• Optimization of the large-scale problems of MMGs [8].
• Diverse consumers [8].
• Cyber-secure transactions [8].
• Multi-carrier energy interactions [1,8].
• Multi-energy trading mechanisms [8].
• Forecast both power and load demand in MGs [9].
• The time-varying topologies of MGs [9].
• Modeling the spatial correlation between different renewable power generation sources [10].
• Appropriate modeling of uncertain parameters such as renewable resources, load demand, and electricity price [10,14].
• Modeling the active power trading and sharing in IMMGs [10].
• Applied research on xEVs for worldwide deployments, including the EVs speed, the required standards, the connectivity of the EVs with variable-source-based MGs, and the departure and arrival pattern of xEVs [14].
• Variable market prices [14].
• Need for addressing some well-known problems in traditional power systems in MGs, such as the profit-based unit commitment problem (PBUCP) [40]
• Further investigation of the cost-effective EM techniques for SMG networks [17].
• MG controls at the global MG level: islanding detection, re-synchronization with the upstream network, power quality management, protection from internal faults, and planning new MGs [11].
• MG controls at MG components level: need the critical ESS components for periodic maintenance, minimizing generation fluctuations, communication between nodes and control agents, and limitations in plug-and-play capabilities of different components [11].
• Highly automated and intelligent control systems [11].
• Using machine learning to handle the large amount of front-end data produced by MGs and ESSs [11].
• Manufacturing efficient super-capacitors, batteries, and fuel cells [11,12].
• Customer confidentiality regulations [12].
• Reliability studies in islanded modes [12].
• Management of communication systems [12].
• Demand response integration [12].
• Further investigations on the effect of the conventional grid on greenhouse gas emissions [12].
• Minimizing the operational cost [15,16].
• Maintaining or improving reliability and stability [15,16].
• Reducing power system fluctuations (voltage or frequency) [15].
• Monitoring: the need for real-time semantic features for EM, optimal sensor location strategies, and an occupant detection system [18].
• Analysis: the need for semi-supervised approaches for classification of the loads, consumers, multivariate forecasting time series approaches, and fault detection methods [18].
• Management and decision making: the need for dynamic, adaptive, and distributed control schemes, and smart real-time energy consumption scheduling [18].
• Maximizing the utilization of green sources [16].
• Reduce the use of utility power [16].
• Maximizing the system efficiency [16].
• Different optimization issues in the operation of multi-carrier energy systems, such as integrated optimal power and gas flow (IOPGF) problems [41,42].
• Modeling multiple contingencies [1].
• Load curtailment costs related to different operating conditions [1].
• Constraint modeling, such as topology and stability [1].
• Modeling the impact of the market practice on reactive power costs [1].
• Applying novel and comprehensive methods mainly based on heuristic algorithms [1].
• Comprehensive analysis software [1].
• New objective functions linked to the storage devices allocation problems [1].
• Issues and challenges for implementing an interoperable smart microgrid (ISM), including: alerts and alarms, bandwidth, channel analysis, data fluctuations, data privacy/security, data rate, distance coverage, energy efficiency, latency, link budget, link failures, network topology, node placement, power consumption, receiver sensitivity, scalability, spectrum usage, standards applicability, system cost, system migration, technology access, throughput, and typical framework [34].

3. The Surveys on Other Fields of MGs

Based on the literature, there are many survey papers regarding the MGs. Hereafter, some of these references are addressed. Interested readers are referred to this long list for further investigations.

• AC and DC technologies: [19];
• Solid-state transformer: [43];
• Blockchain technology: [39];
• Communication issues: [34,35,44];
• Grid-Tied inverter controllers: [45];
• Control strategies: [46,47,48,49,50,51,52,53,54,55,56];
• Protection: [55,57];
• Operation: [57];
• Energy storage systems: [11,50,51,58,59];
• Islanding: [54,60];
• Power electronics: [61];
• Cybersecurity: [62,63];
• Deep-learning-based techniques: [64,65];
• Forecasting and prediction models: [65] (power load and RES), [66];
• Fuel cells: [67];
• General topics on MGs: [2,68,69,70,71,72];
• Information processing: [73];
• Layers structure: [74];
• Monitoring interfaces: [75];
• Multi-agent systems: [76,77,78];
• Multi-microgrids: [79];
• Power quality: [80];
• Reactive power compensation: [81];
• Reliability evaluation: [82];
• Resiliency/Self-Healing: [83,84,85,86];
• Community-based MGs: [87,88];
• Experiences: [89] (India), ref. [90] (Latin America: laboratories and test systems), ref. [91] (postindustrial Detroit area), ref. [92] (Victoria, Australia), ref. [93] (university campuses, UK), ref. [94] (The University of Genoa, Italy);
• Socio-technical barriers: [95,96];
• Demand response: [8,58,97,98];
• Microgrid management: [99];
• Virtual power plant concepts: [100];
• Microgrid technology: [101,102];
• Computational optimization techniques and artificial neural networks: [103,104,105,106,107,108];
• Coordination strategies and techniques: [109];
• Decision-making frameworks: [110];
• Dynamic characteristics: [111];
• Microgrid architectures: [112];
• Food, energy, and water nexus: [113,114,115];
• Smart city: [33,116,117];
• Multi-agent communication networks: [118,119].

4. Conclusions

In this paper, a comprehensive review of the EM problem in MGs is addressed by introducing different components of MGs. For this purpose, the classifications of objective functions, constraints, optimization techniques, and used software are mentioned. Furthermore, the main challenges in EM are detailed. Finally, a long list of previous surveys in the field of MGs, including different technologies, communication issues, control strategies, islanding, power electronics, power quality, and reliability evaluation, are highlighted. Based on what has been reported in various references, there are still many aspects of this topic that need more research to implement in all countries and achieve its advantages. Among these challenges, using multiple measuring equipment with their optimal placement, strengthening telecommunication platforms, internet safety and data protection, robust optimization methods, reduction in energy storage costs on a large scale, and modeling multiple uncertainties can be pointed out.