The integration of widely fluctuating distributed generation (such as photovoltaic panels, wind power, electric vehicles, and energy storage systems) puts the stability of power technologies and distribution structures in jeopardy. However, the fundamental reason is that the electrical supply and demand ratio may not be balanced. An excess or scarcity of electricity in the production or consumption of energy can disrupt the system and cause serious difficulties such as voltage drops/rises and, in extreme cases, power outages. Energy management systems can efficiently increase the balance between supply and demand while reducing peak load during unscheduled periods. The energy management system can handle distributing or exchanging energy among the many energy resources available and economically supplying loads in a stable, safe, and effective manner under all power grid operating situations. This article examines the energy control system’s structure, goals, benefits, and challenges through an in-depth investigation of the various stakeholders and participants involved in this system. This review provides a detailed essential analysis of the operation of several programs used inside the power management system, such as demand response, demand management, and energy quality management. It also includes a summary of the smart grid’s functionalities, features, and related techniques and has discovered research gaps, challenges, and issues. Furthermore, in this article, the authors review the literature on the enabling technologies of smart grid and investigate the energy management system, which is among one of the major emerging technologies and quantifications of the various uncertainty techniques. In this paper, the authors also discussed the comprehensive review of researchers’ efforts and contributions to the smart energy management system in the smart grid. It also compares and evaluates the key optimization approaches utilized to achieve the remarkable aims of energy management structures while also fulfilling a variety of constraints. This comprehensive review will be very beneficial for the new researchers, and it would be a great contribution to the research community.

1. Introduction

Electricity is the form of energy that can be utilized to meet the demand of individuals for a balance and developing countries. The genuine and reliable transmission of electrical power is the main element of a nation’s economy [1]. Because CO2 emissions are the primary causes of global warming, focusing on the most contributing factor to these emissions, especially transportation and power generation, is the most effective approach to combat it. The best option for a sustainable future appears to be switching to renewable energy sources (RESs) with electric automobiles. Furthermore, replacing traditional fuel-based transportation with electric vehicle transportation, such as plug-in hybrid electric vehicles (PHEVs) and plug-in electric vehicles (PEVs), and integrating battery energy storage systems (BESSs) or energy storage systems (ESSs) into the current network are two other possible solutions to address the exponential growth of greenhouse gas (GHG) emissions. Renewable energies differ from conventional energy sources in that their output is variable and intermittent [2, 3].

The intermittent nature of RES can be eliminated by combining numerous RES and the ESS and backup sources. On the other hand, this intermittency can dramatically alter the system’s voltage profile, interfere with standard on-load tap changer control systems, and negatively impact the power grid’s performance. As a result, in addition to GHG mitigation, the technologies impose a slew of challenges, such as uncoordinated grid parameters, increased system complexities, intermittent renewable generation, and high PEV price requirements, all of which exacerbate serious issues such as power quality issues, energy imbalance, resilience, loss of reliability, system security, and regulatory issues such as unequal benefit distribution to consumers. Furthermore, with the advancement of renewable energy sources, a shift from a historically passive to an active distribution system has occurred. When there are many energy sources and a storage system, energy flow must be managed. An energy management system (EMS) is critical for maximizing the potential of new resources and new types of loads on the electricity network while minimizing their negative effects, ensuring load continuity in all conditions, and improving the electricity network’s stability. The International Electrotechnical Commission’s IEC 61970 standard defines an EMS as “a computer system that consists of a software platform that provides essential support services and a set of applications that provide the functionalities necessary for the efficient operation of generation and transmission system to ensure energy supply security at a minimal cost.” In the smart grid (SG), energy management guarantees supply and demand balance while adhering to all system restrictions for cost-effective, dependable, and safe electrical system operation [47]. It also contains optimization, which ensures that power generation costs are reduced. Thus, by grouping all systematic procedures, the EMS maintains and reduces the quantity and price of energy required for a particular application to the lowest. Although energy management in a distribution system improves system performance, it also has constraints and obstacles, including client confidentiality, large-scale operations, frequent system upgrades, and EMS dependability issues. A comparative analysis of the smart grid in EMS and its main related technologies is illustrated by the authors (seeFigure 1).

The energy management system of SGs is the subject of this research. This review is chosen to assist the readers in grasping the role and application of each EMS-based method more clearly and have a clearer vision. As a result, it will assist us in determining the scope of our issue. The following issues are addressed in this paper: smart grid and all its technologies and techniques, its challenges and advancements, SG roles and responsibilities of the various parts participating in the EMS, and a detailed study of the DER’s behaviour; general overview of the EMS in the SG and its numerous aspects; and uncertainty management, demand response, demand-side management, and power quality management, which are all subjects of critical analytical investigation. Approaches to EMS solutions are compared and criticized. The definition, benefits, and overall overview of the SG are presented in Sections 1 and 2. Section 3 examines EMS’s structure, benefits, and limitations through a detailed examination of the system’s distracting investors and players. Finally, Section 4 describes the approaches for the EMS that should be considered, including DR, DSM, and PQM. Section 5 discusses several EMS solution approaches. Finally, the concluding remarks are presented in Section 6.

2. Smart Grid

In spite of the popularity and increasing number of related research work, smart grid definitions do not exist. Rather than what occurs in many other developing areas, various other definitions are suggested in the literature. One of its examples is the definition by the Electric Power Research Institute [9] about the smart grid: “the overlaying of unified communications and control system on the existing power delivery infrastructure to provide the right information to the right entity.” Various other authors have different perspectives on the smart grid, such as, e.g., consumers’ commitment to other stakeholders, the part of ICT, the exposure of market opportunities with new value-added services, and so on [1014]. The term smart grid characterizes the combination of sensors, digital technologies, and ICTs to allow and make the system more efficient, reliable, and manageable on the use of electricity. Smart grid is an integration of technologies for the customer and the grid. Smart grid technologies combine software and hardware [8, 15], including creations and essential services, from generation to transmission and distribution. A smart grid is an intelligent and intellectual network in which the current power system blends with information technology [16]. The increasing complexity of the power grid results in the high potential of the smart grid network. The problem is the old infrastructure supporting current energy requirements [1720].

It is necessary to recognize and acknowledge the inheritance of electric systems worldwide. The current controlling infrastructure of electric power systems includes four basic elements, i.e., generation, transmission, distribution, and consumer (see Table 1), and detailed classification is also illustrated by the authors showing the overall concept of smart grid (see Figure 2).

Smart grids are divided into seven categories by the National Institute of Standards and Technology (NIST) [12], which include applications of smart grid. System devices, control systems, programs, stakeholders, and telecom stations can be connected to design a smart grid. Energy management, site automation, and energy storage are also the crucial domains while designing a smart grid (see Figure 3).

3. Technologies of Smart Grid

The current power generation system includes the mechanism of heat production by burning fossil fuels or the division of atoms in nuclear energy [2224]. Excluding solar cells, almost all other forms of power generating include fossil fuel burning, biomass, nuclear, wind, and solar [25, 26]. In addition, several smart grid technologies enable the system to work effectively and are helping designers to have a cost-effective solution [27]. The most important technologies are mentioned briefly and various approaches used in smart grid are also explained by the authors (see Figure 4).

3.1. Energy Management System (EMS)

An energy management system (EMS) is an operator which revitalizes the execution and performance of the system and supervisory control and data acquisition (SCADA). EMS is implemented in many industrial and commercial sectors [27].

3.2. Advanced Metering Infrastructure (AMI)

AMI is the term that enables the gathering and transfer of energy usage information from smart meter to two-way communication networks in near real time. It has several advantages such as AMI upsurging efficiency, decreasing loss and cost, and controlling load and theft protective capability like Vattenfall’s and Fortum’s intelligent system [28]. Automated metering infrastructure employs smart meters in homes that monitor and measure electricity consumption. The main goal of AMI for homes is to take benefit of smart meters to examine the consumption of energy, battery storage, generation of solar or wind connected with an on-site grid, and electric vehicles (plug-in) [2932]. Furthermore, AMI enables configuration of remote meter with dynamic tariffs and monitoring of power quality. AMI also plays an important role in smart homes and smart appliances. Smart homes are outfitted with a home automation system that connects security, lighting, and other appliances using an AMI system that modulates and controls their operation [3336].

3.3. Geographical Information System (GIS)

GIS is the system that controls modelling, data integration, and management of infrastructure. It is one of the important solutions for converting a huge amount of data. The smart grid receives a lot of data from SCADA, AMI, renewable energy, and so on [37].

3.4. Meter Data Management (MDM)

MDM guarantees mechanization of sharing of data and real-time processes that result in useful and effective operations while improving the decision-making process. It also helps in handling a large volume of data.

3.5. Demand-Side Management (DSM)

DSM controls the energy to the consumer’s side of the meter that helps efficient use of system resources without installing new transmission and generation structures. Load and response demand management are the issues solved by DSM [3840].

3.6. Outage Management System (OMS)

OMS is the system that gives solutions for energy restoration and maintains better response time. They also minimalize manual reporting and give a solution for stated problems.

3.7. Wide Area Management System (WAMS)

WAMS is executed in high-voltage power grids to guarantee synchronization, precision, and verification of flexible AC transmission system (FACTS) control and systematic validation of dynamics models. WAMS, through PMUs, also give accurate and time-stamped measurements.

3.8. Battery Energy Storage Systems (BESSs)

BESS employs storage systems based on electrical and mechanical enhanced performance and efficiency [41].

4. Techniques of Smart Grid

The researchers created many artificial intelligence techniques to implement different techniques of smart grid described in Table 2. These techniques included data mining techniques and communication techniques. However, different techniques are extensively used in the data mining technique, such as fuzzy logic, neural networks, expert systems, and artificial intelligence. A communication system is the main component of the smart grid [5759]. A combination of enhanced technologies and applications can generate a huge amount of data from different applications for advanced analysis, control, and methods of real-time pricing. It is very difficult for electric utilities to identify the communication requirements and discover the finest communications infrastructure to deliver a secure, reliable, and cost-effective service [57]. Two wired and wireless media are maintained for data transmission between the electric utilities and smart meters. Wireless communications have a low-cost infrastructure, and wired communication is free from interference problems and is not reliant on batteries. For a flow of information, two types of infrastructure are needed in a smart grid system. The first is from the sensor to smart meters achieved via wired or wireless communications like 6LoWPAN, Zigbee, and so on. The second is between the utility’s centres and smart meters achieved via cellular technologies [17].

5. Functionalities of Smart Grid

The utility companies and government funding development of the grid (United States Department of Energy’s Modern Grid Initiative) defined the smart grid functions required for its modernization, and according to its report, the advance smart grid must have the following.

Customer Participation with Smart Grid Services. Smart grid authorizes customers to alter their actions against fluctuating rates of electricity. Customers can manage the utilization of appliances of smart grid in their homes and businesses. The link between smart grid and energy management systems allows customers to control energy better and examine the pricing of real time (two-way communications). A smart grid is more secure and can identify the attack. The power grid smart monitoring can access and control smart grids to prevent system disruptions. Advance technologies of state monitoring are required to attain the objectives of the smart grids [60].

Recovery Capability from Disruptions of Power. Managers/operators can utilize information based on real time from controls and sensors that predict, detect, and respond to the system’s issues to automatically bypass and diminish blackouts, power outages, and quality problems. In addition, a grid based on smart system will be expected to have a system that can examine its performance by utilizing independent and distributed controllers that can work on successful strategies to overcome challenges like equipment failures [61].

Offering Enhanced and Power Quality Resources. According to the latest research, there is an approximation of $100 billion in losses in US business annually because of electric failure on average. This issue can be resolved through the balanced power offered by the smart grid [62]. Furthermore, smart grid can enhance capital assets by reducing and preserving low costs. This quality of smart grid can utilize the lowest-cost generation and minimize waste.

Empowering Innovative Commodities and Essential Services. The blending of limited and local power generation lets commercial, residential, and business customers produce and offer additional power to the grid with fewer hassles. With this scenario, consistency and quality will enhance, minimizing costs and offering more choices to the customer. In addition, the smart grid will empower small manufacturers to produce and offer electricity to the local market using different sources like solar systems and wind turbines. The authors illustrated all functions of the smart grid (see Figure 5).

5.1. The Current Advancement in Smart Grid Techniques

Various research works are in process for the development of smart grids. These ongoing research works focus on different technologies. Some of them are explained in this paper.

Energy Management System. It is important to work with all components from production to consumer for a reliable grid. The grid contains many tricky components, and they all work and communicate together with the help of software. NIST (National Institute of Standards and Technology) worked on smart grid interoperability (SGIP), having a charge to develop and sustain the components and standards involved with smart grids. It was also responsible for giving power grid stakeholders the platform to work together [63]. In addition, advanced metering infrastructure (AMI) technology makes smart grids more reliable [64].

Internet of Things (IoT). The Internet of Things (IoT) takes our life to the next step by making it more automatic, easier, and handy to communicate with a computer. IoT has the capability of evolution. However, besides all its characteristics, serious issues are like overdosing, authorization, privacy concerns, tapering, and cyberattack. IoT guarantees to accomplish all these features taking the smart grid into a new era. But with advanced technology, some severe security issues developed, including impersonation, data altering, exaggeration, approval issues, privacy issues, and cyberattack [65]. Researchers are trying to deal with these concerns. A smart grid based on IoT should have services like authentication, privacy, and data integrity to prevent any security threat.

Big Data. Smart grid works as a backbone of the grid and is fully dependent on the data it receives. Data collection plays an important role in the smart grid as it receives data from production, channelling, and transmission [66]. Huge information is collected from sensors and devices in the smart grid. This data collection is made possible with the help of algorithms. Secure data and storage are the main challenges in the smart grid [67]. These algorithms made smart grids reliable, but big data still has some issues, like its collection, processing, integration, privacy, and security [67].

Smart Grids with Electric Vehicles. Vehicles are responsible for pollution, causing environmental problems which can be solved by electric vehicles. Electric vehicles also face many problems like controlling, communication, and infrastructure [68, 69]. Smart grids contain advanced communication technologies like control and smart meters, and they can also offer electric vehicles as a load and a flexible energy source [70]. Smart meters have bidirectional communication capability that can smart schedule to improve the grid’s power [71]. Mwasilu et al. [72] presented an overview of communication and power transmission. They gave predictions on the dynamics of the power system. Another important part of charging is the vehicle to grid technology. Researchers have made a lot of contributions to charging and discharging. One of the many types of research was done in Portugal. Research shows excellent communication between the charging of solar energy and EV. Ota et al. [73] proposed a solution that requests battery condition and charging and battery status for the next drive.

5.2. Advanced Challenges in Smart Grid

According to compiled information from different research papers, the smart grid faces many challenges. Some of them are discussed below.

5.2.1. Regulation and Policies

Many countries’ smart grid regulations and policies start from the need to compose innovative policies to assist end user pricing systems and competitive offerings, resulting in improved efficiency while reducing risk in the energy market, thus encouraging investors [74, 75].

Many authors have published their work to examine the regulatory and social issues relating to smart grid development, sustainable development of energy, and advancement in the technologies of smart grids [7679]. Numerous global smart grid policy implementation methods are connected to the power industry [80]. It was proposed by Lin et al. that that the USA choose an approach “environmental side policy” that emphasizes “financial, technical, and scientific development.” With the help of the European Union (EU), 80% of the European Union will deploy smart meters for households in 2020 [81, 82]. With the help of these smart meter programs in many European countries, they became the main part of energy policies. These policies will support other policies of sustainable energy or climate change [74, 83]. In the US, the Act of American Recovery of the year 2009 ordered the advancement of smart grid technologies, and they offer grants for smart grid investment for this purpose [84]. China’s Law on Renewable Energy (2009) focuses on advancing and developing energy storage and smart grid technologies that will automatically improve grid operation [85]. Another policy of China’s “Special Planning of 12th Five-Year Plan on Smart Grid Science and Technology Industrialization Projects” shows China’s interest in developing a smart grid [82]. By 2030, Japan will reduce its emissions by 30% by changing its energy system by constructing “the world’s most advanced next-generation interactive grid network” in their country [86]. Denmark has established a policy on efficiently executing economic, social, and political changes. Developing rules, regulations, and policies for stakeholders to execute smart grids is underway in many other countries. Plan should be created to expand involvement from all sectors, whereas threats should be divided among every stakeholder.

The rules and procedures should consider the following points:(i)Define standards and roadmaps.(ii)Share the cost among the customer, utility, stakeholder, and government.(iii)Meet anticipations of each region participating involving customers.(iv)Outline the objective and time frame.(v)Appropriate attention to workforce innovation and awareness plans.

5.2.2. Technical Challenges

The major technical challenge in the operation of the power system in a competitive environment is to increase the power transfer capability of existing transmission systems. Therefore, various approaches have been planned to handle the technical issues, including generation scheduling based on the finest power flow and utilizing the latest technologies such as distributed generation and FACTS. The authors presented different technical challenges faced by the smart grid briefly (see Table 3). The following factors consider concerns in limitations for the smart grid development.(i)Lack of expert knowledge of engineers and system operators who operate power utility services.(ii)Expanded demand response and distributed generation in the electric market; modification of device parameters in fluctuating conditions prevents utility companies from supplying smart solutions with technical goals.(iii)Complex power system due to the complex tools.(iv)Perception of renewable energy resources and strategies of bidding of participants prevent utility companies from offering solutions with environmental goals.(v)System highly dependent on power and control system planning prevents utility companies from giving technical goal solutions.(vi)Forecasting price and load demand services stop utility companies from giving economic goals and solutions.

5.2.3. Socioeconomic Challenges

Smart grid technologies can utilize advanced inventions and services merged with enhanced technologies for communication, control, and monitoring to optimize generation from renewable energy sources. Moreover, smart grid technology offers consumers instruments to enhance their utilization and performance.

According to Verbong et al., apart from smart grid technologies’ advantages, it is still not clear to customers whether they are interested in them or not. Therefore, smart grids need to introduce more new technologies to let customers rethink them. All this process is not easy because all this process will create more technical issues and socioeconomic issues. The authors presented various challenges faced by the smart grid in terms of social economics briefly (see Table 4). Advancement in smart grids significantly affects the whole value chain and then shifts the consumer practice, behaviour, and whole series of new socioeconomic aspects.

Many researchers have researched socioeconomic aspects; according to Semadeni et al. and Kaldellis, the socioeconomic hurdles are not deeply examined by the social sciences, or maybe it is too early. Still, it could considerably affect smart grid implementation: customers’ satisfactoriness, confidentiality, prices, and cyber security. All of them are the major factors of recognition. Wolsink proposed that obstructions to new infrastructures like wind power are usually related to the syndrome called NIMBY (not in my back yard). For example, there is pretty strong support from the public for wind power, but when the projects come into existence, people suffer the NIMBY syndrome.

In their papers, Anderson and Broman Toft et al. proposed that public opinion is the best way to discover the public’s interest. These opinions were carried out through a survey on smart grid acceptability. Those surveys were on the conception of the smart grid and how electricity customers observe the advancement of smart grids and how their actions shift accordingly. Most report surveys show consumers’ positive attitude towards smart grids, but tariff increment was their main concern. According to the customer, the main issue towards the socioeconomic acceptance of smart grids is privacy and security. The major reason behind the introduction of smart grids is the large collection of data from smart devices, increasing many issues, including privacy concerns. The smart grids can detect every consumption of power of consumers and its utilization, and these data of the customer are very sensitive. The major privacy issue is associated with smart meters. People believe their activities to be confidential because their sensitive data might be utilized by commercial, illegal, and law enforcement agencies (McKenna et al.).

5.3. Impacts of Smart Grid on Electric Utilities

Smart grids cover the overall areas of the electric system, having an impact that is translated into giving promising benefits to different associates. According to the authors, one of the main benefits of the smart grid among all is cost savings surety to end users and its capability of producing potential energy. However, besides having such many capabilities, the smart grid is also facing barriers that decrease its implementation. Figure 6 describes the strength and qualities of the smart grid, which can be reviewed as the main impacts. Smart grid strengths are divided into two parts, enabling strength and primary strength [106]. However, the advantages accomplished by the smart grid include better social assets utilization, decreased costs of operational utilities, and decreased consumer costs [107, 108].

5.3.1. Enhancing Asset Usage

In 2005, investors spent approx. $40 billion on utilities to manage and sustain the power system. Among the advantages of the smart grid, there is a drastic decrease in maintenance costs and a longer maintenance life among some assets. In future, integrated communications technologies will reduce the demand for costly hard assets.

5.3.2. Improving Reliability

The smart grid will reduce the cost of power interruptions and increase reliability. Control and communication technologies employed in the grid will identify faults and allow more rapid repair of identified faults.

5.3.3. Increased Economics

Productivities accompanied by the smart grid should reduce the increasing electricity costs. Real-time price signals will let customers contribute to the electricity market based on existing supply and demand estimating circumstances. Exchange among these consumers and retailers should decrease grid overcrowding and unplanned outages and define the real price for electricity at different times during the day. At present, though, business cases for financing the smart grid developments and technologies are often inadequate when viewed strictly concerning near-term cost-effectiveness. As study after study indicates, the societal case for smart grid adoption is fundamental, lasting, and real: growing energy efficiency, distributed generation, and renewable energy would save a projected $36 billion annually by 2025 [109]. Distributed generation can significantly reduce transmission costs, presently estimated at $4.8 billion annually. Smart appliances costing $600 million can give as much backup capacity to the grid as power plants worth $6 billion [109]. Over 20 years, $46 billion to $117 billion [109] could be saved in the avoided cost of constructing power plants, transmission lines, and substations.

5.4. Current Achievements of Smart Grid on the Economy of Developed Countries

Different countries throughout the globe have advanced in the smart grid and understand its existence. Many smart grid projects are in process in different countries, and some of them are taking initiatives for research and testing to examine probability before implementation. In different countries like the USA, China, Australia, England, Japan, and South Korea, the government considers the smart grid option for reducing CO2 emission [84, 109, 110].

5.4.1. Australia

The government of Australia has been interested in projects of smart grid since 2009 and was willing to invest about $100 million in it. The government raised awareness among consumers about the use of energy and generation management systems. Many parts of Australia, including New South Wales, were nominated to establish a smart grid with GE Energy, IBM, and Grid Net. This project was to create a smart grid based on WIMAX having capabilities of the automatic substation that can support 50,000 smart meters’ networks and adjust electric vehicles. One more project was introduced to assess network fault detection, restoration, power quality monitoring, and isolation.

5.4.2. Europe

In early 2005, the European Union established European Technology Platform 2005 for smart grid advancement to encourage the European electricity vision 2020. Italy is performing a major role in smart grid research and development, and Portugal implemented a control and management system for smart grids in their projects [111113]. The Czech Republic has analyzed the smart grid for cost-benefit [114].

5.4.3. China

The government of China is more interested in the policies of protecting the environment, conservation, relying on domestic resources, and encouraging diverse development [115]. Chinese policy includes improving energy efficiency, increasing renewable energy mix, and reducing CO2 emission. The Chinese agency National Development and Reform Commission is in charge of developing and researching smart grid technologies [115]. In 2009, China declared a context for the smart grid, which was extra transmission centric than other countries like USA and other regions like Europe [116].

5.4.4. Canada

Smart meter installation for businesses and households in Ontario was made compulsory by the Canadian government in 2010 via the 2006 Act of Legislation Energy Conservation Responsibility. This year, the government spent $32 million on different projects on smart grids. For the smart grid campaign for awareness and promotion, an association Smart Grid Canada was established, responsible for research and different smart grid policies [84].

Cost is one of the biggest restrictions on the advancement and execution of the smart grid, especially in the emerging world. Transmission and supply systems, metering, and other technologies are associated with many financial resources. Most of the financial and economic data for this calculation were used from the World Bank’s report of 2015. The summary of these results is illustrated by the authors (see Figure 7).

One day, smart grid will unite the whole world to plentiful, cheap, clean, and effective power anywhere. The smart grid will provide the finest and most reliable electric services. This type of current electric infrastructure will perform a vital role in the future, like substations and power transmission lines. “Smart grid is a fully automated power delivery network that monitors and controls every customer and node, ensuring a two-way flow of electricity and information between the power plants and appliances and all points in between” [117]. Aside from the numerous benefits, smart grids confront other challenges, such as bidirectional communication systems, grid integration with renewable energy resources, inefficient DG utilization, and insufficient existing grid infrastructure and storage, to name a few. Handling electricity generation, energy storage, and loads as a localized group is one technique for optimal DG use. A microgrid is an important part of the smart grid concept. It is a part of a larger grid that includes nearly all of the utility grid’s components, but these are smaller. Microgrids are smaller and can operate independently from the larger utility grid, whereas smart grids take place at a bigger utility level, such as massive transmission and distribution lines.

5.5. Future of Smart Grid

Microgrids can be self-contained or connected to the utility or main grid. If a problem occurs while the microgrid is connected to the grid, it has the potential to separate from the grid and operate independently, supplying its own load. As a result, microgrid operation modes can be divided into grid connected, islanded, and transition from grid-connected to islanded mode and vice versa. The heat generated by some of the microsources can be utilized to meet the heat demand of the local load in any mode of operation.

Various units are integrated into a microgrid. It is made up of a DG unit, an energy storage unit, a controller unit, and a conventional load. The DG unit is made up of a variety of microgenerating devices. As a result, depending on the components employed, microgrid modelling differs from one configuration to the next. While the network is ignored, the dynamics of all the DG units are approximated by a first-order linear model with a time constant and a gain factor. The transfer functions of various components are determined, and time-domain analysis is carried out by taking into account various components at different times.

The following are the transfer functions of various components, and the configuration in one of the cases is illustrated (see Figure 8).

Wind turbine:

PV system:

Fuel cell:

Diesel engine generator:

Aqua electrolyser:

Storage system:

Microgrids can be divided into three categories depending on how the AC and DC buses are connected. The following is the proposed classification: AC microgrids, DC microgrids, and hybrid AC/DC microgrids are all types of microgrids.

5.5.1. AC Microgrids

Mixed loads (DC and AC loads), distributed generation, and energy storage devices are connected to a shared AC bus in AC microgrids. Because most loads and the grid are AC, AC microgrids are simple to integrate into a traditional AC grid. It has increased capacity, controllability, and flexibility as a result. However, connecting DC loads, DC sources, and energy storage devices to the AC bus via a DC/AC inverter reduces efficiency dramatically [119121].

5.5.2. DC Microgrids

A common DC bus is used in DC microgrids to connect to the grid via an AC/DC converter. The operation of a DC microgrid is comparable to that of an AC microgrid. Compared to AC microgrids, DC microgrids are a good way to reduce power conversion losses because they only require one power conversion to connect DC buses. As a result, DC microgrid systems have improved system efficiency, cheaper costs, and smaller systems.

Furthermore, due to the lack of reactive power, DC microgrids are more compatible with integrating distributed energy resources (DERs) and provide superior stability. There are three forms of DC microgrids described in the literature [122124]: monopolar, bipolar, and homopolar.

5.5.3. Hybrid AC-DC Microgrid

A hybrid AC/DC microgrid combines AC and DC microgrids in the same distribution grid, allowing direct integration of both AC and DC-based DG, energy storage system (ESS), and loads. This architecture combines the benefits of both AC and DC microgrids, including a smaller number of interface elements, easier DER integration, fewer conversion stages, lower energy losses and total costs, and increased reliability. Furthermore, when DG, loads, and ESS are directly connected to AC or DC networks, there is no requirement for generation and storage units to be synchronized [125, 126].

Distributed and hybrid RES generators (e.g., PV (photovoltaic) panels and wind turbines) are used in hybrid microgrid systems to create renewable energy (e.g., solar and wind), with energy storage devices built to compensate for the fluctuation between RES generation and load consumption. Depending on the desired and established objectives, these hybrid systems can operate in grid-connected or freestanding modes. However, as the number of distributed generators grows, new energy management systems are needed to ensure their seamless integration into the existing electrical grid. Table 5 shows recent literature on hybrid system implementation, with batteries being the most widely utilized energy storage component cited.

5.6. Optimization and Control Techniques in MG Systems

Numerous studies on MG optimization and control have been conducted based on system topologies, architectures, and operating modes [132134]. For example, optimization and control approaches should handle the stochastic character of installed RES generators by assuring a reliable supply of power to consumers while maintaining appropriate operation conditions for the storage system, electricity bill, and occupants’ comfort.

5.6.1. Comparison of Control Approaches for MG Systems

The selection of method in a microgrid is a prerequisite for the MG system’s reliable and stable operation. An EM can be chosen based on the characteristics of the deployed system (for example, topologies, operation modes, and structure). However, the deployment of one way does not imply that the others are unreliable, and the essential issue in determining the utility of the deployed method is to examine the investigated limitations and the determined target of the control strategy. The remainder of this section discusses the comparative analysis of various control techniques of microgrid (see Table 6).

A successful strategy must take into account the stochastic nature of various control parameters, installation costs, component lifetimes, distributed resources, and the MG system’s dependable and safe operation. In fact, deploying an EM control approach necessitates categorizing the entire system into distinct levels, with each level cooperating with the others from the sources (e.g., maximum power point tracking) to the end consumers, which can be a local or adjacent MG consumer. Smart components are now implemented for each source and each MG system, allowing them to communicate with one another thanks to new ICTs. The actual inverters, in particular, can implement a variety of control schemes, ranging from source power regulation to interconnection with the utility grid or a neighbouring MG. Furthermore, inverters can be constructed for a large number of MG systems, forming a data and electricity exchange cluster, with these inverters connected to the Internet to store historical data in the cloud.

The primary job of each inverter is to ensure uninterrupted power delivery to consumers, regardless of the battery storage system’s lifetime or the cost of electricity. In this case, an EM control technique that takes into account electricity price fluctuations while minimizing the battery cycle is necessary. These two difficulties allow for increased system profitability by lowering the electricity bill and reducing the need for regular battery storage replacement in MG systems. The major goal is to create an intelligent and predictive control strategy that can optimally regulate the distributed resources in the MG while also taking into account different limitations and objective functions. In a microgrid, it is important to sustain the balance between power supply and demand for stability because generating sources such as wind turbines and photovoltaics is tough to forecast. Therefore, it may cause fluctuation in the generation of power. The supply-demand balancing challenge is adversely affected when the microgrid operates in standalone mode, with only a limited supply available to balance the demand. The optimization of energy management in microgrids is typically seen as an offline optimal control problem. Furthermore, energy management inside the smart grid is a critical component in increasing renewable energy usage and energy efficiency. In the smart grid (SG), energy management maintains supply and demand stability while adhering to all system restrictions for cost-effective, dependable, and safe electrical system operation. It also contains optimization, which ensures that power generation costs are reduced. By bringing all systematic procedures together, the EMS controls and decreases to a minimum the quantity and price of energy required for a certain application. However, while energy management in a distribution system improves system performance, it also has constraints and obstacles, including client confidentiality, large-scale operations, frequent system upgrades, and EMS dependability issues.

5.7. Energy Management System in Smart Grid

Power plants generate most of the world’s electricity by burning fossil fuels, which are inadequate in availability and have harmful impacts on the environment [142]. Current research reveals that if the utilization rate stays stable, the fossil fuel reserve of the world will last only 50 to 60 years. Moreover, the Paris Climate Agreement and United Nations Sustainable Development have set targets to decrease the pollution of CO2 to the environment [143]. Balancing energy production and utilization is known as energy management which can have major influences on the journey of electric energy from production to utilization. Energy management in power distribution systems considers various traditional energy sources like energy storage systems, renewable energy sources, critical loads, and energy management system operations and functions illustrated in Figure 9. The scholars have curiosity about the energy management system topic because of numerous reasons, which include(1)Reduction of losses in the distribution systems by the service companies to decrease operational costs, ultimately facilitating the customers by paying fewer electricity bills.(2)Cost reduction by precisely monitoring and observing the loads and energy resources.(3)Decreasing greenhouse gas discharges that affect the society by power and electric companies.

Figure 9 shows that energy management system in the smart grid performs a major role in functioning and management so that the power system strategy works more effective by examining, regulating, and conserving energy. Integration of smart grid with energy management system can evaluate complicated power system data, decrease power utilization, and enhance smart grid reliability and effectiveness.

In this scenario, urgency for a more effective and efficient way to produce and utilize energy is exhibited. It also facilitates giving power to the consumers of critical load in the power lines during scheduled load shedding [144]. Various research papers have been published on the energy management of smart grids for reducing operational costs and system losses; for example, the authors [145] presented a model that shows the schedules and controls of the generators based on diesel and units of battery storage to reduce the cost of the system. They presented an energy management system based on multi-agent that regulates the supply in the existence of high levels of renewable energy sources and electric vehicles [146]. The authors [147], in their paper, presented a model of the optimal dispatch of microgrid distributed generation for system sustainability. Researchers discussed control of the distributed generators to reduce the costs of the system by increasing the use of renewable energy sources. The authors in [148] presented a model to accommodate the output of the generation according to the network’s reasonable scenario. The model in [149] considers the distinct size and increases the allowable distributed generation of the systems. In addition, numerous papers focused on controlling distributed generation for different aspects of smart grid energy management systems [150]. Search optimization technique based on tabu is useful for energy management in multi-microgrids [151], the MINLP technique is functional in a rural distribution system and [152], two-stage optimization technique is practical in microgrids with different levels of penetration of electric vehicles [147]. The energy management system authenticates the schedules, and optimal dispatch of the distributed energy sources in microgrids and hence is accountable for their reliable and economic operation. The authors in [153, 154] also addressed different distributed generation strategies. Moreover, many papers have been published in the literature on energy management and addressed the cost minimization and losses of distribution systems using reconfiguration techniques [155, 156].

Though some research papers have been published on energy management, the literature requires a comprehensive energy management review in smart grid and distribution systems. This paper aims to make a comprehensive literature review of the published research papers on energy management systems in smart grids and distributed networks to address this gap. Furthermore, the objective is to summarize the work concerning various factors, including energy management approaches, objective functions, and solution algorithms, and to recognize the challenges of such research [157161].

5.8. Significance of Energy Management System

Energy demand is directly proportional to the need for the production of power. Growth in the number of consumers is causing several issues for electric companies. First, the system’s performance can be threatened due to high peak demands. To solve this problem, the system operators and electric companies have two solutions:Employ energy management to decrease the high peak demand during peak hours.Expanding the network in terms of size and dimension will require time for implementation and can be costly [162165].

Energy management is considered essential for a smarter grid for many reasons:It increases the efficiency of the system.It preserves the resources.It decreases pollution, which helps to protect the climate.It provides precise outcomes and predictions.It facilitates the end users to manage and reduce their electricity bill.It helps the electric utility to lower the cost of generation.It helps in decreasing the energy losses on the lines and network [166, 167].

5.9. Application of Energy Management System

Energy management has two most important categorizations. One is from the electricity supplier’s perspective, while the other is from the electricity consumer’s perspective. The electricity supplier, which includes power plant operators, electric utility, and production units, can use energy management to control its production units. For example, the electric utility can reduce the production operation cost by turning on some generators, which may have the least operation cost. In contrast, the generators with high operating costs are left to supply extra load demand in specific peak periods [168, 169]. The system operators (distribution and transmission systems) can utilize energy management to adjust the flow of power to reduce the energy losses on the network. Finally, the end users (such as householders, residential and commercial buildings, industries, and faculties) utilize energy management to schedule their load demand and effectively decrease their electricity bills.

5.10. Energy Management System Objectives

In this section, the authors briefly explain the objective of energy management, and different literary works of researchers with different approaches are also addressed in this section of the paper with brief literature (see Table 7).

5.10.1. Cost Management

Energy management supports decreasing the system’s functioning costs and enhancing the system’s production by reducing extra costs during peak hours. For example, in a distribution system, system modification and scheduling lower the losses and costs of the system. However, the load can affect the customers by line losses, which are at a distance from the production station because of environmental, economic, and geographical problems [211].

5.10.2. Controlling Greenhouse Gas Effects

Global warming is one of the most important matters for the Earth, which increases the regular temperature of the Earth’s weather system. Numerous reasons are accountable for global warming, like emissions from fossil fuels, factories, and gas using vehicles. The explanation for utilizing power production based on fossil fuel is their consistency and low cost related to sources of renewable energy. To reduce the effects of greenhouse gas emissions, the energy management system plays a vital role by accurately dealing with and limiting energy sources in distribution systems while focusing on productivity and reliability of the system [212].

5.10.3. Enhancing the Performance of Voltages in the Distribution System

Variability in loads can disturb the voltage permanence in a distribution system. Voltage levels can vary due to sudden changes in loads and production failure. This voltage constancy issue can perhaps be improved by applying reactive sources [213215].

The fluctuations and variations in voltages can increase and be a reason for the ripple effect in the adjacent section if the reactive power cannot supply enough support to the voltage limits. In a current distribution system, the demand response gives the customers the benefit of taking the load in an improved way [216218].

5.10.4. Reliable and Reduced Outage Interval of Energy Management System

The energy management system evaluates the data for all the connected devices to the energy system through data centre consumption. It categorizes the regions with the highest capacity or lowest capacity. The system consistency could be enhanced by controlling the energy storage system and demand response using an energy management system in a distribution system. Researchers are dedicated to energy management systems created for economic development, where the mixture of power generation and storage meets the load requirement in an energy system [219221]. However, the system can suffer outages when it fails to meet the required load demand during peak times. Numerous steps have been taken to reduce outage intervals, and some of them are mentioned in this section [222, 223].

5.10.5. Effective Efficiency

Energy management supports examining the distribution of energy in an enhanced way. With the help of an IoT sensor, the customers can get informed about the data usage of energy from the system and enhance the energy by rescheduling the energy-using devices. An energy management system helps the customers and utility minimize energy use while guaranteeing the efficiency of the power system. Additionally, energy management can supervise and manage the energy reserves situated at the consumer’s side to help minimize the stress of transmission lines and system loss by enhancing the system’s efficiency [224, 225].

5.11. Energy Management System Approaches

To balance energy management in the distribution system, it is necessary to perform different approaches on the distribution system. These approaches are briefly explained by the authors (see Table 8).

5.11.1. Capacitor Bank

Capacitor banks play a vital role in the system. It provides reactive power, which corrects the phase shift inherent and power factor in AC energy supplies and DC energy supplies; it increases the storage energy with an improvement in the ripple current of the power supply.

5.11.2. Managing Energy Storage System

In an energy management system, the energy storage system can reserve additional energy during off-peak times for future use and support improving the system’s productivity and consistency. The energy storage system in energy management includes electric vehicles, flywheels, and batteries for storage.

5.11.3. Demand Response in Energy Management System

Demand response helps in managing time-based utilization from the production side. It helps to reduce the customer’s utilization and shifts the unnecessary load requirement to off-peak time, which reduces the additional costs.

5.11.4. System Modification

System update encourages reducing transmission line losses while posing more challenges for circular distribution networks to synchronise shielding relays [266].

5.12. Enhanced Approaches to the Energy Management System

This section discusses EMS enhanced solution approaches. The machine learning method and its various models are highlighted since they are critical for energy forecasting and highly valuable for the efficient operation of the EMS in the grid. The solution methodologies for EMS are then divided into four categories: mathematical programming, heuristics, metaheuristics, and another approach [267269]. Finally, the applications of various methodologies are discussed, and a table compares each approach’s techniques. Many optimization techniques and programming methods have been developed for energy management schemes, such as RES management, charge controller, LC management, and PEV charge/discharge management [270272]. An EMS strategy’s ultimate purpose is to decrease or maximize the objective function, which could be GHG emissions, cost, power quality, effectiveness, load profile, reliability, etc. As a result, the IoT and deep learning (ML) are rising in popularity simultaneously, and both are extremely beneficial to the network’s EMS’s effective operation. Because of their precision, efficiency, and speed, ML models in EMS are now required to predict production, consumption, and demand analysis [273275]. ML models can also better understand how energy systems work in complicated human relationships [276279]. Therefore, applying machine learning models to traditional power systems and alternative and renewable energy systems seems promising [280, 281]. However, because of the field’s prominence, various policy statements have been produced that provide an overview of existing uses and future difficulties and prospects. Existing synthesis articles [282], on the other hand, either focus on a specific ML model, such as ANNs [283], or a single energy sector, such as solar radiation predictions. Artificial neural network (ANN) [284], support vector machine (SVM), tree-based modelling technique (decision tree), ensemble prediction (EPS), adaptive neuro-fuzzy inference system (ANFIS), wavelet neural network (WNN), multi-layer perceptron (MLP), and deep learning are the most popular machine learning methods.

ANNs are frameworks that allow various machine learning algorithms to process complex data inputs. It can be used for various tasks, including forecasting, regression, and curve fitting.

It can be used in various smart city applications, such as hazard detection, water system, energy, and urban transportation. A neuron that uses a frequency response for output formulation is a basic unit of an ANN. Its key benefit is that it is simpler to solve multi-dimensional issues. Another machine learning method for energy forecasting is SVM [285]. Researchers of paper [286] suggest a new strategy for residential energy management (REM) that differs from previous approaches. They define a REM problem to maximize customers’ utility under numerous practical limitations such as human contact, power supply unavailability, consumer preferences, and priority using advanced mathematical algorithms. In [287], the authors proposed the scheduling algorithm for energy consumption management systems. To construct a smart city, researchers have used DTs to solve challenges such as companies, air pollution, urban transportation, and food [288].

Few enhanced solution approaches such as fuzzy logic, game theory, ant bee colony, multi-agent, and so on have been used in energy management systems, as shown in Table 9, for accomplishing advantages and accuracy in results, and Table 10 summarizes some existing EMS analyses. Each reference uses a distinct scenario and methodology for analysis.

5.13. Aspects of Energy Management System Implementation
5.13.1. Estimating Strategy

Several electricity authorities are trading plans, generally connected to industrial events like offering electricity, metering, procuring, billing, and pricing [301]. Wholesale electricity products are divided into two categories, ensured cost product and the spot cost product. Ensured cost product has characteristics of price allocation for a particular period of the agreement or in advance. Another element related to the end user’s concern is supply capacity. Both cost products have building units like ToU, temporary and fixed billing, and flat rate. A flat-rate tax is more attractive to the consumer as it is simpler to understand. Additional schemes are fixed pricing policy, usage‐based dynamic pricing, distributed demand response, and so on [302] According to Misra et al. [303], in dynamic policy (D2P), there is 34% growth in plug-in hybrid electric vehicles compared to optimal ones [304]. After explicit promotion, renewable energy sources are permitted in the power trade. Demand response and demand-side management increase the consumption of current resources, improving productivity and consistency and smoothing the load profile, among other advantages. Conventional techniques are not productive in dealing with the investors and customers for decision-making steps.

5.13.2. Power Quality Management (PQM)

PQM is the procedure that reduces the effect of external and internal disruptions that can lower the performance of a specific procedure. The power supplied to customers depends on the source and load at the customer’s end. Poor power quality can be caused by the development in electronics technology of power and the increment in nonlinear loads. Problems in power quality differ from high-voltage impulses to wave shape faults, harmonics, and voltage fall and drops. Moreover, the recently appended bidirectional charger with PEV load injects harmonics into the system, deteriorating the power quality. The traditional task of the energy management system was to handle scheduling and transmitting. Therefore, the duty of the energy management system was restricted to the utilization of energy and cost, although the quality of power stayed trackless simultaneously. Quality of power is very important for effectiveness and to attract consumers. Therefore, an energy management system should consider constraints while scheduling significant loads/sources and power quality events. Figure 8 illustrates modern energy management systems’ functioning incorporated with power quality management.

Few researchers suggested the utilization of static VAR compensator (SVC) and unified power quality conditioner (UPQC) to maintain the quality of power in a microgrid instead of using costly and complicated devices [305]. If energy allocation helps reduce power quality issues, it is hopefully an improved method to simultaneously achieve two tasks, such as power quality and energy allocation.

Ovalle et al. [306] presented a scheduling method for optimal charging of PEV and retaining the customers’ benefits in a residential building. In their study centred on sustainable buildings, the authors Luo et al. [307] discussed work on DC microgrid and how PEV batteries are utilised to balance voltage fluctuation. Naidoo et al., in [308], disagreed with an alternative approach to estimating symmetrical components when there are harmonic and noise signals. EMS is itself a difficult task. Combining the energy management system and power quality management would give better grid functioning and an inexpensive solution.

5.13.3. Unpredictable Techniques of Management

With the help of new technologies, the grid became smarter with lots of new limitations through various doubts like ambiguity in the generation, ambiguity in predicted load, or unexpected breakdown of generation or transmission unit equipment. These uncertainties make the forecast and prediction difficult, affecting the system’s consistency and safety. There are two types of uncertainty parameters, economic and technical [309]. Technical one is more categorized in topological and operational parameters. Operational parameters include the info on renewable generation and demand, and topological parameters consist of the info about the trouble of any generation unit. Additionally, the economic parameter can be categorized as macroeconomic.

5.14. Contributions of Energy Management System in Smart Grid

Lots of researchers have done investigation on energy management systems (EMSs) in recent years, including papers on different types of EMS strategies like BEMS (building energy management system), HEMS (home energy management system), SHEMS (smart home energy management system), and EMSA (energy management system aggregator). In this paper, the author presented several comprehensive review papers on energy management systems.

Zakaria et al. [310] proposed a survey paper using unique approaches for renewable energy applications in which the authors included methods for better performance. Azuatalam et al. [311] presented seven different strategies for HEMS, including a forecasting method. They discussed that practical implementation of these approaches is inadequate because of the presupposition of designing different energy management systems. Cheng et al. [312] reviewed a paper on microgrid energy management systems, including microgrid components, consolidated and distributed architecture evaluation, and regulatory approaches. Khan et al. [313] presented a paper on different optimization techniques, including multi-agent systems for distributed microgrid energy management systems. Shareef et al. [314] provided the review paper on the HEMS considering demand responses (DRs), intelligent controllers, and smart technologies. Zou et al. [315] reviewed different topologies of MMG and existing working on the energy management system for unified multi‐microgrids (MMGs). Rafique and Jianhua [316] proposed a review paper on the energy management system and production, including techniques for forecasting for load, wind, and solar.

Salimi and Hammad [317] proposed a critical survey of different control in HVAC to recognize the advantages and challenges of current approaches and future research gaps. Zia et al. [318] presented a survey paper on EMS based on microgrids, including different approaches and communication requirements for a microgrid. Finally, Hannan et al. [319] presented the Internet of Energy (IoE) predictions based on BEMS for better energy utilization, in which they included the concerns and limitations associated with cost, scalability, consistency, and privacy of access data. In this paper, the authors presented an existing literature review in Table 11, which includes energy management types, framework, components, and solution techniques.

5.15. Practical Execution of Energy Management System in Smart Grid

Current and prearranged executions of smart grids offer a wide range of features to achieve the following essential functions.

5.15.1. Minimizing Excess Load

The total load linked among the grids can alter amazingly, indicating that the total load is not stable and has varying power consumption. Generally, the reaction time of a quick rise in power consumption should be greater than the initial start-up time of a large generator. Therefore, a few additional generators are put on standby mode. A smart grid may limit all single devices to minimize the load temporarily. With the help of mathematical prediction algorithms, it is simple to find out the number of standby generators that need to be used to achieve a certain failure rate. Still, on the traditional grid, the failure rate can only be minimized by executing more standby generators [320].

5.15.2. Removal of the Demand Fraction

Grid systems have variable degrees of communication using control systems, such as transmission lines and different parts of substations. Generally, the flow direction is from the users towards the loads, and then they control back to the utilities. The utilities try to distribute the demand and succeed or fail in uncontrolled blackouts and brownouts. Demand response permits generators as well as loads to cooperate in real time. Eradicating the fraction of demand in these spines reduces the cost of additional generators and extends the equipment’s life [320].

5.15.3. Power Generation Allocation

Distribution of generation permits particular customers to generate power in their place by themselves. By this process, customer can manage their load and can make them separate from the public power grid and can avoid power failures.

5.16. Challenges in the Practical Execution of Energy Management Systems in Smart Grid

Energy management system execution looks comparatively easy in the modern era because of development in technology, contemporary sensors, and infrastructure; besides all these advancements, there are still many challenges in the energy management system’s practical implementation. There must be simple maintenance for control architecture. There is still a solution available in a decentralized structure, but it needs synchronization between components, and nonstop two‐way communication makes the system less cost‐effective. Another most important factor for real-time application in energy management systems is communication. Data rate and cost are two factors for the energy management system’s deployment in rural and residential areas. Therefore, Zigbee, Bluetooth, and Wi-Fi are chosen in those rural and residential areas [321]. Data and coverage rates are important for the execution of energy management systems in microgrids and utilities, whereas optical networks, 3G, and 4G are considered less [322]. Nevertheless, ABB, General Electric, Siemens, and Schneider have several energy management system executions.

5.17. A Review of Research Efforts on the Energy Management System for Smart Grid

A smart grid is a way towards the next-generation grid and needs to do more for the researchers, industries, public sector undertakings, policymakers, etc. Therefore, a review of literature has been done to keep in mind the objective of the present study. In this context, some of the reviews of the literature are discussed as follows by the authors (see Table 12) who presented the critical literature review of the researchers’ proposed models in the field of energy management systems in smart grid.

6. Conclusion

The necessity to meet ever-increasing electricity consumption and maintain a sustainable and safe supply of electricity to the power system justifies the global transition to the smart grid. The application of energy management has a bright future. The move from a traditional grid to a smarter grid, on the other hand, necessitates a long-term financial investment. Energy management is crucial to improve the efficiency and reliability of supply and distribution networks. This is accomplished by using clever algorithms and modern control systems to optimize and schedule load demand efficiently. Energy management lowers the cost of electricity by about 20–30%, which is significant and helpful in the long run. This paper provides a thorough and critical examination of the EMS idea, objectives, benefits, types, and difficulties and a thorough examination of the main actors and contributors. It addresses the various uncertainties associated with the numerous loads and sources in SG, as well as effective methods for dealing with them, such as power quality management, DSM, active DR, and optimization solution approaches used in energy management to meet the desired objectives while keeping all constraints in mind. Various parties are currently discussing some topics and concerns linked to EMS deployment, leading to more research and development for a more advanced EMS. The following areas for further research should be considered:(a)The main challenge is to improve the cost-effectiveness of SGs through secure and reliable communications, which can be addressed by developing a multi-agent system that is hybridized with optimization algorithms based on metaheuristics to achieve energy management that meets various objectives and constraints. Numerous applications could be used, such as managing gas power plants with emissions, trading among microgrids, and so on. It is also feasible to factor in (electric vehicles, users, demand management, line losses, etc.) and simulate interconnected networks in real time.(b)Accurate and quick uncertainty modelling is an area that has to be improved further and can be thought of as an alternate optimization paradigm for utility system design that takes into account unpredictable circumstances and gives more operational detail. The framework must be capable of simulating the effects of flexible SG technologies that can effectively alter the demand for traditional solutions. As an example, when considering flexible operational solutions that optimize investment in an SG context, effective modelling of operational restrictions and uncertainty is required in the planning phase.(c)The EMS should be conceived and implemented in a cost-effective, real-time hardware implementation. It is possible to propose an IoT-based system architecture that implements specific communication technologies for connected devices and can be applied to various types of SG simulators, such as communication network simulators, power system simulators, and combined power and communication simulators.(d)Applied to distributed energy grid systems, an integrated system reconfiguration and operation management approach would be a powerful way to achieve high performance, cost-effectiveness, resilience, and sustainability.

Conflicts of Interest

The authors declare that there are no conflicts of interest.