Review on Causes of Power Outages and Their Occurrence: Mitigation Strategies

Abstract

For power plant networks in developing countries like Iraq, balancing electricity demand and generation continues to be a major challenge. Energy management (EM) in either demand-side (DS) or generation-side (GS) strategies, which is frequently utilized in Iraq due to a lack of adequate power generation, has a small impact on the power balancing mechanism. Most previous studies in similar countries discussed only the application of DS strategies. The purpose of this paper is to contrast and review various energy management methodologies being used in developing nations facing power outages, to be able to recommend suitable ones according to the country’s situation. To assess potential EM-based solutions to improve the total energy efficiency of the Iraqi electrical community, a thorough and methodical analysis was carried out. The main objective of this review paper is to discuss the causes of power outages and the energy management strategies addressed here as methods to mitigate or avoid power outages. Unlike existing reviews that concentrated on demand-side energy management, this study specifically focuses on power outage causes in developing countries like Iraq rather than all management strategies. It also introduces the consequences of power outages including analysis of distribution power losses, financial loss from power blackouts, and power blackouts in firms in a typical month. Therefore, it presents readers with state-of-the-art strategies and recommends a generation-based EM strategy to mitigate such issues.

1. Introduction

1.1. Motivations and Overview

The stability of installations and their continuous operation as well as the quality of life of residents, are all highly dependent on the constant supply of energy. The utility company in Iraq, for example, has been unable to continuously supply electricity to all grid-connected customers for the previous 30 years. In addition to having drawbacks related to noise, pollution, and poor performance at partial load, the conventional solutions offered by diesel generators at the neighborhood level, motivated by the necessity for maintenance and fuel supply, also have a significant economic drawback related to a high charge of energy, in the majority cases. Furthermore, conventional battery storage offers expensive, technically constrained, and capacity-constrained technological options. Electricity rates or the prices set by competing generators to power customers change, therefore, from month to month and typically change by an order of size between high and low seasons because electricity is not economically storable. The most significant factor affecting electricity prices is the balance between supply and demand. During high-demand periods, such as hot summer days when air conditioning usage is high, electricity prices tend to rise. Conversely, during low-demand periods, prices may drop. These fluctuations can occur daily, seasonally, or even on an hourly basis.

Electricity is essential to a nation’s economic, social, and political development [1,2]. The lack of sufficient energy supply substantially impacts the rise in the standard of living, industrialization, and other fields (including education and health) [3]. Many studies have linked the role energy consumption plays in a nation’s development in terms of its economy, society, and technology [4]. The demand for electrical energy is rising rapidly, and the available resources are depleting rapidly. Thus, managing energy sources effectively, maximizing consumption, and reducing production costs is crucial [5].

There is a significant increase in household energy use due to the development of electronics and their affordability than years ago, which means that more electricity is used. Electrical energy consumption is rising not only in typical homes but also in various industries [6]. The quality and continuity of electric power is a subject of growing concern for both electric utilities and end customers [7]. It has been predicted for years that developing countries will see an increase in energy demand, a rise in the use of fossil fuels, and a corresponding rise in greenhouse gas emissions, with low-income appliances leading to an increase in energy utilization [8]. Between 2004 and 2040, the consumed energy in developing nations is projected to rise by 3% per year [9]. Demand and supply must often be balanced, and many nations have chosen the tried-and-true strategy of boosting energy supply utilities to keep up with demand [10]. With the growth of renewable resources, the top-down and single-direction energy flow orientations have become problematic, necessitating complex real-time control of distributed energy resources. An option to balance supply and demand is demand-side management (DSM) [10].

The majority of studies have concentrated on customer/demand-side management strategies. Only a few articles have discussed alternative programs for avoiding or mitigating power outages that the utility grids impose to cover the lack of power generation. These studies, like [11,12,13,14,15], discussed multi-agent energy generation networks or their control algorithm for energy optimization [16,17,18,19,20]. However, no generation-side EM-based work has been carried out when the power plant uses one type of energy source (such as natural gas or petroleum in Iraq). In addition, there are no comprehensive studies discussing power outages and their causes in the literature, especially for developing countries; except power outages due to natural disasters and accidents. This work covers this gap by providing a comprehensive understanding of the challenges and opportunities related to addressing power outages in regions like Iraq, and to propose practical solutions that can lead to a more reliable and accessible power supply. Such efforts can have significant positive impacts on the quality of life, economic development, and overall stability in these regions.

1.2. Topology of Grid Utilities

Traditional topologies are often centralized systems built as ring or radial networks to service consumers over a sizable geographic area with a sizable population. With this design, generation is generally kept far from the load centers. In order to allow energy transport from generation to consumers, transmission networks are used, as shown in Figure 1.

The typical generator voltages (34.5 to 5) kV, transmission voltage levels (765 to 66 kV), and distribution voltage levels (33 kV three-phase to 220–230 V 1-phase and 420/220 V), are all handled by this topology. The legacy, utility, or main grids are further names for the topology. Larger global economies with high industrial, commercial, and residential demand currently operate on this topology.

An efficient energy management system of microgrids relies on optimization algorithms and control models that are equipped to handle their assets. Hierarchical control schemes, distributed, decentralized, and centralized are widely known as microgrid managing systems. The development of research questions and microgrid control using hierarchical control architectures and distributed topologies, and decentralized strategies was summarized in [21]. The choice of the control model relies on the type of mode of operation, MG, and operator or user conditions. A review of collaborative control architectures with hierarchical architectures, decentralized, distributed, and centralized, and their processes for DC microgrids was presented in [22,23].

1.3. Review Structure and Contributions

The review structure of this work is as follows. Section 1 describes the rolling blackouts that occur in Iraq as a case study. In Section 2, the causes of power outages are discussed. Section 3 demonstrates the function that EM performs in developing countries. In Section 4, this review outlines and analyses the conditions and governing framework of the Iraqi electricity generation sector. In Section 5, an EM strategy is recommended as a solution for the complex situation in Iraq. Section 6 presents suggestions and conclusions for implementing EM in such underdeveloped countries.

The main objective of this review paper is to discuss the causes of power outages and their occurrence mitigation strategies and take Iraq as a case study. Furthermore, we are mainly focused on the causes of power outages where the energy management strategies are addressed here as methods to mitigate or avoid the power outages. The contributions of this work, when compared to the existing literature, can be summed up as follows:

• To the best of our knowledge, this work is the first review to widely cover the causes of power outages that some countries suffer from, especially the rolling blackouts imposed by national grid companies like in Iraq. This includes power transmission and distribution outages due to technical reasons, natural weather conditions, power plant faults, accidents, over-demand, and bypassing/hacking the power national grid.
• Unlike existing reviews [24,25,26,27] that concentrated on demand-side energy management, we specifically focus on power outage causes for developing countries like Iraq rather than all management strategies.
• This paper covers the most advanced and recent progress to overcome the planned power outages of the power grid. Therefore, it presents readers with state-of-the-art strategies.

Inclusive comparisons of previous strategies in several similar countries are provided with a summary and perceptive discussions being given.

2. Causes of Power Outages

The most frequent reason for power disruptions is typically bad weather. However, there are frequently several causes of a power outage; blackouts can occur for a variety of reasons. Lightning, for instance, can bring down a tree that is in the way of electrical lines. Floods or mudslides can result from persistent rain. Extreme temperatures, such as extreme heat and cold, can harm the electrical grid’s components. According to the survey, severe weather-related large outages occur at the following rates: (1) 3% by lightning, which has the potential to fry wires or zap transformers; (2) 5% by snow and ice, which causes power outages throughout the winter; and (3) 8% by wind, which includes hurricanes, tropical storms, and tornadoes.

Too many individuals using excessive amounts of power in one location at once may be a frequent and unexpected reason for a power outage. The system may become overloaded and experience an outage on a hot summer day when everyone’s air conditioners are running nonstop. All too frequently, avoidable incidents result in power disruptions. In these situations, a human mistake is what results in power outages. Power outages are not just the result of mistakes made by professionals. Deliberate acts of damage and vandalism are more sinister causes of power outages. To steal wire and other components with copper within and sell them for scrap, thieves take severe risks. A sporadic power outage may be the outcome. Three main categories of power interruptions exist:

• Blackout: A blackout happens when the entire system fails. The worst power outage so far is this one. Power restoration can be challenging, particularly when power stations are damaged and the grid is tripped. These disruptions can extend for several hours, days, or even weeks.
• Brownout: Unlike a blackout, which results in a complete loss of electricity, a brownout only results in a temporary reduction in power. This kind of interruption can prevent the grid from becoming overloaded and entering a complete blackout. Rolling brownouts occur when the electricity grid loses power in discrete areas.
• Permanent fault: it does not last forever. Faults include imbalanced voltage or current as well as flow disruptions. The electricity is restored once the fault has been fixed. Because it will not correct itself or reset without intervention, the problem is referred to as permanent.

There are other natural causes of power outages besides the weather. Systems can also be severely damaged by earthquakes, mudslides, floods, and wildfires. Major occurrences like this can knock down transmission lines, damage transformers, and ruin substations. The causes of power outages can be classified and represented with a diagram showing the reason and corresponding related references as shown in Figure 2.

The frequency of occurrence in percentage terms depends on the location and the country. In the USA, for example, the majority of power outages are due to weather conditions, such that 59% are from storms and severe weather, 18% from cold weather and ice storms, 18% from hurricanes and tropical storms, 3% from tornadoes, and 2% from extreme heat and wildfires.

3. Consequences of Power Outages

3.1. Analysis of Distribution Power Losses

The examination of the electrical energy losses in the grid distribution with a voltage of 22 kV, which is brought on by the electrical energy transmission to consumers, is the focus of this section. The primary technical and economic measure that fully captures the management of the power grid, planning, use, production, and design is the line loss rate [73]. The flow of current occurs during the transmission of power plant electrical energy to customers via distribution and transmission lines, which results in voltage drop and PL. In an ideal situation, just the active part generates current, which creates an appliance function and results in fundamental losses.

The topic of power loss analysis and electrical energy computation is fairly broad and necessitates varying effort and difficulty for various voltage levels. The computation and overall analysis become simpler the higher the voltage of the electrical energy being delivered. Calculations based on physical formulas are used to determine the power losses in extra-high voltage and HV networks. These calculations make use of actual network technical parameters as well as active power and voltage measurements on specific network components. Another way of computing the PL is utilized for the LV and M_V networks [74]. The distinction among the electrical energy used for self-consumption (W_V), the electrical energy used by customers (W_O), and electrical energy entering the network (W_I) can be used to calculate the overall electrical energy losses (∆W). ∆W is measured in kWh.

$$∆W=\sum _1^k{W}_I-\left(\sum _1^k{W}_O+\sum _1^k{W}_V\right)$$

(1)

According to Figure 2, distribution power losses can be split into two categories: non-technical and technical losses. Technical losses are losses brought on by the transmission and transformation of electrical energy. For instance, heat is created as electrical energy passes through overhead power lines, transformers, and other components. Additionally, they are brought on by the well-known physical effects of electricity such as low voltage, losses from overloading, unbalanced loading, long single-phase lines, harmonic distortion, etc. These depend on the characteristics and method of operation of the network [75,76,77,78]. Technical losses, according to [79], can be classified into two types:

• In a distribution network, fixed technical losses account for 1/4 to 1/3 of all technical losses. This can happen whenever the transformer is powered and typically manifests as noise and heat. The fixed losses are more affected by leakage current losses, dielectric losses, corona losses, etc., than by the amount of load current flowing.
• Between two-thirds and three-quarters of the distribution system, technical losses are made up of technical variable losses that are proportional to the load current square. Joule heating losses, contact resistance, and line impedance all have an impact on the variable losses.

In LV networks, non-technical losses, often referred to as commercial losses, can occasionally account for a greater portion of overall losses than can technical losses. They result from outside influences acting on the power system or from load situations [80,81,82]. Because system operators frequently fail to account for these losses, there are no records for them, making their measurement more challenging. The difference between calculated technical losses and total losses can be used to compute them [83,84]. Computation errors in technical losses, customer bill non-payment, electricity theft, and flaws in record- and accounting-keeping that falsify technical data are the most likely sources of non-technical losses. The relevance of discussing the causes of power outages also requires discussing the financial losses due to these problems, which are discussed in the next section.

3.2. Reliability Indices

A power system’s reliability refers to its capacity to perform well under predetermined circumstances throughout the desired duration [85]. This definition’s application to distribution systems focuses on how well each part performs under typical conditions and how it affects the end users. The indices evaluate the ability of the distribution network to provide customers with uninterrupted power [86]. The IEEE handbook [87] presents dependability indices, including the system average interruption duration index, system average interruption frequency index, average service availability index, and others. Due to their large effects on utilities’ revenue, system power quality, system stability, and system security indices play a key role in the planning and operation of distribution systems [88]. The following are the indices used in this review study on network reconfiguration.

3.2.1. Average Energy Not Supplied (AENS)

Equation (2), in which L_avg(i) is the average load connected to load point I and N_i is the total number of customers at the load point, mathematically expresses this as the ratio of energy not delivered to the total number of consumers serviced.

$$AENS=\frac{\sum L_{avg\left(i\right)}{U}_i}{\sum N_i}$$

(2)

The dependability indices are often computed with the use of analytical methods and Monte Carlo simulations. The analytical methods use a mathematical model to represent the system and mathematical solutions to compute the indices. Conversely, sequential Monte Carlo simulation techniques consider the unpredictable and time-varying nature of load models when evaluating their reliability [89].

3.2.2. Energy Not Supplied (ENS)

The ENS index, which is calculated using Equation (3), measures the amount of energy that is not provided to customers. Here, L_avg(i) represents the average load connected to load point i, U_i represents the annual unavailability for each load point, and N represents the total number of load points.

$$\mathrm{E}\mathrm{N}\mathrm{S}=\sum _{\mathrm{i}=1}^{\mathrm{N}}{\mathrm{L}}_{\mathrm{a}\mathrm{v}\mathrm{g}\left(\mathrm{i}\right)}{\mathrm{U}}_{\mathrm{i}}$$

(3)

3.2.3. Average Service Availability Index (ASAI)

The availability of power for a given period, as desired by customers, is shown by ASAI. The index is often calculated annually or monthly [90]. In order to calculate ASAI mathematically, Equation (4) is used, where T stands for the entire period under investigation, r_i for the restoration time, N_n for the total number of interrupted customers, and N_s for the total number of customers served.

$$\mathrm{A}\mathrm{S}\mathrm{A}\mathrm{I}=1-\left(\frac{\sum {\mathrm{r}}_{\mathrm{i}}{\mathrm{N}}_{\mathrm{n}}}{\sum {\mathrm{N}}_{\mathrm{s}}\mathrm{T}}\right)$$

(4)

3.2.4. Customer Average Interruption Duration Index (CAIDI)

The average amount of time needed to restart the power supply following an interruption is calculated using the CAIDI index. Equation (5) is used to construct CAIDI, where Ni is the total number of customers at load point i and U_sysi is the system annual outage length at load point i.

$$\mathrm{C}\mathrm{A}\mathrm{I}\mathrm{D}\mathrm{I}=\frac{\sum {\mathrm{U}}_{\mathrm{s}\mathrm{y}\mathrm{s}\mathrm{i}}{\mathrm{N}}_{\mathrm{i}}}{\sum {{\mathsf{\lambda }}_{\mathrm{s}\mathrm{y}\mathrm{s}\mathrm{i}}\mathrm{N}}_{\mathrm{i}}}$$

(5)

3.2.5. System Average Interruption Frequency Index (SAIFI)

A customer’s average number of power outages over a given period of time is represented by SAIFI [91]. Equation (6) can be used to obtain the SAIFI, where λ_sysi is the system failure rate at the load point (ith).

$$\mathrm{S}\mathrm{A}\mathrm{I}\mathrm{F}\mathrm{I}=\frac{\sum {\mathsf{\lambda }}_{\mathrm{s}\mathrm{y}\mathrm{s}\mathrm{i}}{\mathrm{N}}_{\mathrm{i}}}{\sum {\mathrm{N}}_{\mathrm{i}}}$$

(6)

3.2.6. System Average Interruption Duration Index (SAIDI)

This statistic establishes the average consumer’s power outage duration over a given time period [91]. Although SAIDI is not strictly calculated on a monthly or annual basis, any time frame can be included. SAIDI is assessed using Equation (7), where N_i is the total number of customers at load point i and U_sysi is the system yearly outage length at load point i.

$$\mathrm{S}\mathrm{A}\mathrm{I}\mathrm{D}\mathrm{I}=\frac{\sum {\mathrm{U}}_{\mathrm{s}\mathrm{y}\mathrm{s}\mathrm{i}}{\mathrm{N}}_{\mathrm{i}}}{\sum {\mathrm{N}}_{\mathrm{i}}}$$

(7)

3.3. Producing Systems Reliability Indices

The most sophisticated system is probably the one that provides electricity. There are different functional sections of the system. These are referred to as distribution, transmission, and generation. Each component will be examined independently for easier evaluation before being combined to determine the system’s reliability. The resource adequacy metrics explain the incidence of risk over the course of the period, which aids in the evaluation of the power plant. The reliability studies rely on a number of indices [92,93] such as forced outage rate (FOR), expected energy not supplied (EENS), interruption duration index (IDI),energy index of reliability (EIR), expected power not supplied (EPNS), loss of load expectation (LOLE), loss of energy probability (LOEP),loss of load events (LOLEV), loss of load hours (LOLH), expected un-served energy (EUE), and loss of load probability (LOLP).

3.3.1. LOLP

The index LOLP is required to demonstrate the efficacy and performance of an electrical system. The load growth rate, the load duration curve, the plant’s FOR, and the quantity and capacity of generating units all have an impact on this index value. This index measures the likelihood that the system’s hourly or daily demand will outpace the available generating capacity within a specific time frame. A daily peak load curve can be used to determine the LOLP. The LOLP is a projection of how long it will take for the load on a power system to ultimately exceed the capacity of the available generating resources. As a result, the LOLP is based on integrating the daily peak probability with the probability of generation capacity states [94].

3.3.2. EUE and LOLH

This index indicates the quantity of energy that should have been supplied throughout the system load cycle observation period but was not because of a lack of essential energy resources. It can also be described as a measurement of the resource availability to continuously supply the required load at delivery locations. It can be characterized as the anticipated energy demand that will not be met in a specific year. The EUE can offer a measurement of the extent of a particular evaluation region.

At the same time, when a system’s hourly demand is more than its generating capacity, the anticipated number of hours annually (also known as the LOLH) might be used. The load duration curve allows for the calculation of this index. When considering the most recent case, the hourly LOLE is referred to as the LOLH.

The LOLH is divided into several categories, including annual, monthly, and annual EUE, which are both actual and normalized at the same time.

3.3.3. LOLE

Power system planners often analyze future energy demand and then carry out a series of calculations to predict how much and what kind of generation will be required at one or more future dates because constructing and building a power plant is a time-consuming procedure. The use of a resource adequacy measure, often based on loss of load probability (LOLP), or a related metric, is a widespread strategy, despite some variations in approach. A target reliability level is used to assess if resource adequacy will be attained. The industry standard LOLE level is 1 day every 10 years.

The peak hour was used to calculate LOLP historically once each day. The estimate uses a direct convolution of the capacity of each generator and the rate of forced outages. The likelihood is thus determined directly. The formula to obtain LOLE can be given by:

$$LOLE=\sum _{j=1}^{K}P[C_j<L_j]$$

(8)

where P represents the probability function, K is the number of days in a year, C_i is the amount of capacity that is available thanks to the convolution process, and L_i is the daily peak demand.

3.3.4. LOLEV

The situation known as LOLEV occurs when a portion of the system load is not met for an annual length of time [95]. The event could last an hour or several hours. This is defined as the number of occasions throughout the year where a portion of the system load is not handled. A LOLEV may persist for a single hour or several, and it may result in a load loss of one or several hundred megawatts. The frequency of occurrence index is used to describe this [96].

3.3.5. LOEP

An indicator of generation reliability is the loss-of-energy likelihood [97]. The LOEP is a relationship between the overall energy demand during a certain lengthy time of observation and the expected energy not served (EENS) over the same period. Calculating the LOEP for installed capacity is made easier by using the load duration curve. Consequently, the LOEP is given by:

$$LOEP=\sum _i\frac{E_i{P}_i}{E}$$

(9)

where the LOEP is a probability and has no units; energy is E_i, but it cannot be provided because of a capacity problem (O_i); P_i represents the likelihood of a capacity outage (O_i); E for the study period, where E is the overall energy demand.

3.3.6. EPNS

EPNS is identical to the necessary curtailment. The EPNS is defined by:

$$EPNS=\sum _{i=1}^{n}LOL\left(i\right)x.probability \left(i\right)$$

(10)

where LOL is the loss of load, i is the number of cases.

3.4. Economic Losses Due to Non-Delivery of Power

Technical indices with an emphasis on average power interruption frequency, length, and severity, such as CAIDI, SAIDI, and SAIFI, statistically indicate the security of the system. The value of lost load (VoLL) [98] is a significant socioeconomic measure that addresses the financial effects of power outages and the monetary assessment of uninterrupted power supply [70,99].

An important method is the VoLL, which is used to estimate the costs of damage caused by a power outage. VoLL can be viewed as a financial indicator of the stability of the power supply. VoLL is calculated by equating the financial loss brought on by a power outage from the loss of commercial operations to the quantity of kWh that was not provided during the interruption [100]. Costs can be shown in relation to time in addition to monetary units against kWh. Nevertheless, a representation in dollars/kWh is more frequently employed [101]. The reason for the power outage is irrelevant because the VoLL is an economic indicator [102]. VoLL cannot be directly deduced as market performance because, even if it gives the option of expressing the value of power supply security in monetary terms, there is no market on which power interruptions can be exchanged. As a result, VoLL needs to be calculated using accurate measurement methods.

One method for calculating VoLL depends on the stated-preference approach. This includes survey designs such as choice experiments (CE) and contingent valuation methods (CVM) to arrive at the willingness to accept (WTA) and willingness to pay (WTP) and for a hypothetical supply disruption, where estimates of WTA and WTP are normalized using units of time to estimate VoLL [103]. WTA and WTP can be estimated using open-ended CVMs; however, the Atmospheric Administration (NOAA) and National Oceanic Panel headed by Arrow et al. [104] proposed that the open-ended responses to WTA or WTP are unlikely to provide reliable valuations due to associated strategic bias and a lack of realism. Studies that derive WTA and WTP for calculating VoLL using the CE approach have increased significantly in recent years. When one of the alternatives is selected, CE offers the responders two options that are different in some aspects. With each scenario in the survey, the key attributes in each of the alternatives are modified, allowing CEs to estimate marginal WTP/WTA for various attributes.

3.5. Financially Losing from Power Blackouts

Devastating power blackouts that can influence residential and business actions are primarily caused by deteriorating power grids. States that do not succeed in advancing in current technologies for their electricity networks face the danger of experiencing significant economic losses. A power outage may last a few hours or several days. Current electricity grids are more technologically advanced than their predecessors, yet they nonetheless pose problems of their own. However, because a substantial portion of the world lacks access to the most recent technologies, they will inevitably experience the most significant relative losses due to power outages. A list of the first ten countries that are losing financially from power blackouts is displayed in

Table 1. The first ten countries that are losing financially from power blackouts.

Country	Potential Reasons	Economic Lost	Refs.
PAKISTAN	Many outages last up to three hours a day or longer. High temperatures in the summer increase the number of outages.	33.8% of sales value lost	[105,106,107]
YEMEN	Tremendously frequent blackouts have many businesses investing in generation systems.	19.7% of sales value lost	[108,109]
NEPAL	Recent earthquakes have severely damaged an aging electricity grid.	17.0% of sales value lost	[110,111,112]
GHANA	The infrastructure of the country’s electricity utilities is the cause of outages.	15.8% of sales value lost	[113,114,115]
NIGERIA	The mining industry and hospitals are the most affected	15.6% of sales value lost	[116,117,118,119]
TANZANIA	Low water levels in the country’s hydroelectric dams cause blackouts as well. Attempts to update the power grid have caused blackouts for over a month.	15.1% of sales value lost	[120]
SOUTH SUDAN	Only six generators provide power to the capital city (Juba). Power outages are due to fuel shortages.	13.6% of sales value lost	[121,122]
MADAGASCAR	Demonstrations are responsible for a large number of power outages.	13.6% of sales value lost	[122]
UGANDA	Low water levels and poor maintenance contribute to power outages.	11.2% of sales value lost	[120]
AFGHANISTAN	Lack of supplies causes a delay in repairing damaged towers.	9.6% of sales value lost	[123]

.

Regarding the financial loss in Iraq, the government makes assurances that things will improve. A parliamentary committee established to look into the power industry revealed in December 2020 that $81 billion (EUR 68 billion) had been invested in the industry since 2005. Yet, there has not been any notable advancement. An analyst with the International Energy Agency (IEA), which advises governments on policy and analyzes data on the world’s energy supply, mentioned that there are non-technical elements, political and economic ones, in addition to the technical condition [124].

Some of the causes of this crisis include insufficient production, the inability to face demand in tandem with the growing number of residents, the Tehran government’s pronouncement to occasionally cut electricity due to Iraq’s inability to pay debts, and the incapability for importing electricity from Gulf nations because Iraq’s system is only connected to Iran, and terrorist attacks by the Daesh/ISIS grouping. After the defense industry, electricity and energy are regarded as the two most corrupt industries in the nation.

All governments that came into power after 2003 have pledged to develop the energy infrastructure, particularly the electricity grid, but so far there has been no advancement on the matter. In contrast to three territories in northern Iraq in the Kurdish Regional Government zone, notably Erbil, Sulaymaniyah, and Duhok, most regions in Iraq can only currently provide a few hours of energy each day [125]. The remainder of the day is spent on neighborhood generators that are incapable of even powering air conditioners, and are an unsafe network and distribution system, as shown in Figure 3.

3.6. Power Blackouts in Firms in a Typical Month

The power outages of the first 20 countries as reported by firms over a typical month are shown in Figure 4, where the power outages here can be defined as the average number of electricity blackouts that establishment occurrence for a distinctive month [126].

Establishments typically encounter an average of x number of power interruptions each month represented by firm size. Firm size levels are classified as large (100+ employees) (large-sized firms), medium (20–99), and small (5–19). A mapping representation of the power outage in firms in a typical month for 2020 is shown in Figure 5 [127].

The countries with lighter colors refer to a lower rate of monthly power outages, while the darker ones refer to the countries that hardly suffer from monthly power outages. Enterprise Surveys oversample large firms since they tend to be the engines of employment creation despite the fact that small and medium-sized businesses make up the majority of businesses in most nations. Manufacturing, retail, and other services typically make up the breakdown of sectors. Certainly, developed sub-sectors are chosen as new strata in bigger economies based on overall establishment data, value-added, and employment. Geographic areas are chosen within a nation based on the regions and cities that collectively have the greatest concentration of financial activity [127].

4. Power Outage Mitigation Strategies

This section discusses the possible solutions to overcome or mitigate different types of power outages. The limits of the solution and the scope of its effectiveness can be significantly influenced by utilities’ tactics for reducing power outages. Such strategies ought to be comprehensive, covering every aspect of mitigation, from prompt discovery and effective restoration to ongoing public outreach. For minimizing power outages, there are primarily two energy management solutions, demand-side and generation-side energy management.

4.1. Demand Side EM

The classification and terminology of demand-side EM projects have been assessed in this section. The Electric Power Research Institute developed DSM in the 1980s to alter energy consumption patterns and load forms for increased dependability, oversight, and postponement of investments. Customers are placed at the heart of the decision-making method by demand-side EM when deciding how and when to use electricity [128,129]. Demand-side EM has advanced further as a result of the electrical market’s evolution [130]. Although the concept of demand-side EM in the literature is broad, its main objective is to lower energy demand through changing consumer consumption habits.

“Demand-side EM is the term used to describe technology, actions, and programs on the demand-side of energy meters that aim to control or reduce energy use in order to lower total energy system costs or help accomplish policy goals like reducing emissions or balancing supply and demand.” according to Warren [131]. Demand-side EM, in the opinion of Lampropoulos [132], entails the full range of management duties associated with directing demand-side operations, such as program formulation, assessment, execution, and monitoring.

Demand-side EM is described by Strabac [133] in terms of consumers’ reactions to price changes and the shifting of load from on- to off-peak times. The literature often divides DSM into two categories [134]:

• Demand response (DR), which gives end users the ability to vary their load consumption patterns in reaction to an increase or decrease in the price of power over time, lowering the system’s overall peak.
• Energy efficiency places a strong emphasis on encouraging customers to use efficient products as a way to cut demand [129,135,136].

The distribution structures and power technology stabilities are put at risk by the combination of distributed generators that vary greatly (such as energy storage systems, electric vehicles, wind power, and photovoltaic panels). The ratio of supply to demand for power may not be balanced, though, which is the main cause. The consumption or production of too much or too little electricity can disrupt the system and result in serious issues including voltage fluctuations and, in excessive cases, power blackouts. Using EM systems efficiently can reduce peak load during unforeseen periods and improve the supply demand balance. The EM system is capable of efficiently providing loads in an effective, safe, and reliable manner under all circumstances required for the functioning of the power network, in addition to exchanging or sharing energy amongst the various energy resources accessible. These techniques are employed to achieve the outstanding aims of EM structures.

The goals of demand-side EM are to achieve the balanced operations of a utility system [137]. According to real-time supply availability, the demand-side EM may be seen as the application of load management at the consumer side of the DG utility. Energy charge declines are due to rising needs and the avoidance of early breakdowns due to overstretching demands are two goals of demand-side EM deployments in DGs. Such techno-economic objectives have implications for state regulations, governing bodies, system operators, utilities, and consumers. Energy efficiency (EE) and demand response (DR) programs make up demand-side EM as a whole [138]. The DR is a utility-based program created for managing customer demands in the short term. DR programs offer customers the chance to take part in the management of the electric grid by moving or reducing their electricity usage in exchange for time-based energy tariffs or financial incentives. Offerings include critical-peak rebates (CPR), real-time pricing (RTP), critical-peak pricing (CPP), and time-of-use (TOU) pricing to attract customers to participate in DR programs [138]. This is crucial to keep in mind while thinking about the BEMS and its policies for developing and managing real estate, which includes institutional buildings, industrial, agricultural, commercial, and residential.

The paper [139] addressed the significance of emphasizing energy management and efficiencies in building systems. The studies [140,141,142,143] all claim that DSM is applied based on the precisely defined techniques shown in Figure 6.

Load shifting, load leveling, valley filling, and peak shaving are some of the conventionally employed DSM procedures in the literature pertaining to purposes in RE-based DG schemes for load management. Peak shaving takes into account that utility-based DR programs start load shedding for consumers to relieve pressure on resources. Valley filling, on the other hand, tends to increase demands in opposition to surplus generation to lower charges on energy curtailments, as instigated by utility-based DR. Where there are significant changes in PDN, load leveling is a necessary DSM approach. Load shifting takes into account how crucial supply and demand are to changing demands between appliances or consumers. Peak shaving, as a DSM tactic for integrating energy storage systems (ESS) and electric vehicles (EV) with the main grid, is thoroughly reviewed by [27,143]. It schedules the charging and discharging process of an EV parking lot using real-world data of power consumption to achieve the traditional demand-side EM tactics, valley filling and peak shaving, to optimize power consumption profiles at a university construction.

The study [143] schedules the discharging and charging method of an electric vehicle parking lot using parking lot occupancy and real-world power consumption data to achieve the traditional DSM strategies, the valley filling and peak shaving, and to optimize power consumption profiles in a university building. The paper [144], created an algorithm to construct an ESS-based processes program for load leveling and demand peak shaving using demand profile information and a small set of ESS parameters. In order to study the potential of vehicle-to-grid-enabled electric vehicles to carry out reactive power compensations during any of the two topologies of a battery charger with bidirectional property a peak shaving technique [145].

The literature currently available, such as that by [146,147] indicates that flexible load scheduling, strategic conservation, and planned load increase are parts of demand-side EM strategies. Strategic conservation is a utility-initiated program, similar to classic demand-side EM techniques, that primarily focuses on consumer interests in accepting incentives for decreased energy use. A planned rise in energy consumption called “strategic load expansion” aims to boost customer productivity and utility revenue per kWh. In contrast, flexible load scheduling is a program that rewards customers for increasing their load and making curtailments. Customer incentives for load increase and decay are used interchangeably in flexible load scheduling.

Table 2. Comparison for Demand-side EM strategies from the literature.

Demand Side EM	Remarks	Demerits	Merits	Features	References
Fixed load scheduling	Most suitable in integrated systems with multi-tariff systems	This may not be feasible in systems of unified tariffs such as standalone	Good at improving the DG system’s autonomy	A plan with rewards but no clear shapes for how the system’s dependability will change.	[148]
Strategic load growth	The tactic boosts utility sales while enhancing client productivity.	Only possible with dump loading systems It must always be combined with other tactics, such as valley filling, and is never a stand-alone tactic.	Minimises dump energy and energy cost savings	The adoption of smart energy appliances is to blame for the anticipated increase in energy demands.	[149,150,151,152]
Strategic conservation	usually focuses on conserving energy.	Customer tastes affect demand forecasts	A strategy for efficient use of energy	Utility-based DR scheme that encourages users to alter their usage patterns.	[153,154,155,156,157]
Energy arbitrage	Very suitable for intermittent RE systems	It is necessary to handle energy storage effectively. Dump energy is likely to win out in ESS occurrences when they are fully charged.	Boosts the dependability of the supply system and decrease the amount of wasted energy.	Economically saving less expensive energy sources to consume or sell when prices are higher	[158,159,160,161,162,163]
Load leveling	Exhibits characteristics of other DSM strategies	Feasible only through flexible and critical load classifications.	High-level achievement of system autonomy	A method of shifting some demands from one load to another, typically based on the criticality factor.	[164,165,166,167,168]
Load shifting	Resembles a blend of peak shaving and valley filling.	Mostly beneficial to utilities.	And minimizes the need for system growth or updates.	Efforts to reduce differences between high- and low-demand profiles	[168,169,170,171,172,173,174,175,176]
Valley filling	Consumer comfort is put in danger. Valley filling prevents energy losses.	Load classifications are the order of criticality and flexibility needed. Imminent use of storage facilities.	Customers often benefit from the low cost of energy. Burdens of energy curtailments are removed. Dump energy are considerably reduced.	Increasing demand during times of high-power generation	[155,177,178,179,180]
Peak shaving	Mostly appropriate for highly predictable systems, sush as vertically arranged traditional grids	Customer comforts are comforts breached. Economic burdens are normally transferred to customers.	Reduction in per kWh energy cost. Solutions to varying daily electricity needs.	Cutting back on some of the energy used during times of peak demand to prevent overstretching resources.	[9,27,181,182,183,184,185]

lists the comparison of demand-side EM strategies in the literature, while the critical analysis is listed in

Table 3. Critical analysis of implementation methods for demand-side EM.

Algorithm	Further Studies/Shortcomings	Achievements	Objectives/Problem	Components	Year	Refs.
Fuzzy logic control (FLC) integrated EM system (EMS)	This study focuses only on objective functions that are geared toward optimizing the economic balance between the cost and value of MG operation over a certain time.	Reducing energy costs by 7.94% over a 20-year lifetime and an average of 11.87% per day.	Challenges of Demand-side EM during peak periods, such as load shifting, shielding, and delaying appliance operation.	Solar PV systems	2023	[186]
Tri-layer framework	No plan for grid exports.	The thermal generating flexibility index and electrical generating flexibility index are improved by 34.64% and 22.98%.	Comprehensive scheduling that simultaneously considers demand-side flexibility, generation flexibility, and total generation costs.	Renewable-based energy microgrid systems.	2023	[187]
Biogeography Based Optimization (BBO)	More useful information about the amount of consumption of electricity and bill is required for the energy demand curves for Iteration Control BBO and pandemic BBO variants.	The DSM techniques acquire financial savings while lowering and shifting peak load.	To solve the minimization problem and definitions of iteration control BBO and pandemic BBO variants	Smart grids Distribution system operation.	2022	[188]
Load shifting and strategic conservation	The energy management schemes in the stage of load estimation.	The net present cost of $55 263 is reduced to $ 34,009 with the application of DM EM strategies.	The power demand of isolated villages where on-grid power supply is not economical.	Hybrid renewable energy system involves photo-voltaic, wind turbine, diesel generator, and battery.	2022	[189]
Multi-objective genetic algorithm (MOGA)	The proposed demand EM strategy was not tested at the consumer end with multiple balancing constraints for power balance.	Optimal solution amongst the non-dominated solutions in the feasible search area.	Demand EM strategy was used for a day-ahead scheduling problem in SGs with a high penetration of wind energy.	Smart grids, wind turbine.	2022	[190]
Two-tier cloud-based DSM	The proposed systems need high computation and large storage for customers’ data.	The price of consumer consumption dropped. The electrical grid’s peak load and PAR both improved.	Optimizations for both customer and utility costs.	PV-based multiple DGs	2017	[191]
Building energy management system (BEMS)	Only self-consumption is supported. No plan for grid exports.	In total, 23% of average power consumption was reduced without the full cooperation of the residents. 12% peak load was reduced without the full cooperation of the residents.	To develop “a virtuous and flexible load profile” for nearly zero energy building (NZEB).	PV CHP Thermal storage.	2017	[192]
MPC	It is advised that future studies make wise decisions about the prediction and control horizons of the MPC. For a large-scale power system, data management is necessary.	There were cost savings of 12.18% and 6.3% against 1st and 2nd control approaches There was up to 13.9% and 4.9% daily energy utilization as well.	Optimal operation of the market-based wind system.	Wind ESS	2019	[193]
Rolling horizon optimizations	The findings show that the effects of storage capacity, storage efficiency, generator run, and rest times are not significant. The potential for optimization was suggested to be improved by time-shiftable loads.	The achievements involve both supply and demand sides for energy management, unlike reference work. Results indicate a significant in fuel savings without affecting the system performance.	Optimal scheduling of generations and loads in military smart microgrids.	PV Wind Diesel	2019	[194]
multi-objective optimization	A risk assessment should be used to gauge how well the generated solutions work. The quality of the load scheduling may be improved by including load shifting and interruptions in SGs’ operation planning.	The findings highlight the value of this planning approach in terms of techno-economic factors and the best possible power transfer in the functioning of distribution systems.	Grouped microgrids that use a variety of renewable energy sources, including solar systems, wind turbines, microturbines, and electric cars.	Smart grids Distribution system operation.	2022	[195]
multi-objective optimization	A multi-criteria decision-making-based selection technique is used to choose a solution from a non-dominated solution set after the optimization phase.	Determines the best moment for the spread of offshore wind energy technology that is not yet operational.	Levelized cost of the electricity plan is kept to a minimum, and short-term electricity production from renewable energy sources is increased.	Renewable energy resources	2020	[196,197]
Building energy management system (BEMS)	In the domain of stochastic dispatch and planning optimization of RSESs in the presence of responsive loads, in order to identify the major methodological and content gaps.	Gives helpful insights into a variety of prospective new research directions to more fully utilize the promise of responsive loads.	Used a wide range of techniques to jointly quantify uncertainties and purchase demand response services, all the while developing and scheduling RSESs as efficiently as possible.	Energy sources that are sustainable and renewable (RSESs)	2022	[198]
Linearizing nonlinear equations and transforming them into mixed-integer linear programming	It is only possible to support yourself. Grid exports are not planned.	Verifies the effectiveness of the model for ensuring the system security.	Integrated electricity-gas system (IEGS) planning that takes the effects of DSM initiatives into account.	12-node natural gas system and IEEE 39-bus power system.	2019	[199]

.

4.2. Generation-Side EM

Actions made to guarantee that the production, distribution, and transmission of energy are carried out effectively are referred to as generation-side management. The phrase is most frequently used in relation to electricity, while it can also be used to describe operations involving the provision of other energy sources, such as fossil fuels and renewable energy sources. It is possible to describe this type of EM by the following actions:

• Distribution and transmission of electrical power, including substations, lines, and on-site generating, are examples of generation-side EM. Transfer of solid, liquid, and gaseous fuels.
• Energy conversion and power generation, including cogeneration and operational upgrades to existing plants.
• Energy resource supply and use, including the use of renewable energy sources, fuel substitution, and clean coal technology.
• Since the over-demand of electricity forces the national utility company to apply rolling/planned power outages, generation-side-based EM with an appropriate strategy is a method that can help improve management strategy performance. This technique is recommended by this work for cases like Iraq.

Effective generation-side EM will improve the effectiveness with which end consumers are supplied for an energy system, permitting the service company to postpone substantial capital expenditures that might otherwise be necessary for expanding their capacity in expanding markets. SSM minimizes environmental emissions per unit of produced of end-use power, while enabling installed producing capacity to supply electricity at a cheaper cost (allowing for lower pricing to be given to customers). Generation-side EM has the potential to increase a supply system’s dependability. Generation-side EM is becoming increasingly crucial given the present trend of deregulating the supply industry, where the environment, user, and supplier all benefit. An electrical utility may undertake generation EM to:

• Minimize environmental impact;
• Supply the highest value to its clients by reducing energy charges;
• Make sure of consistent availability of energy at the minimum financial cost eventually growing its profits;
• Satisfy rising electricity demand without needless significant capital expenditures for additional producing capacity.

The utility grid may integrate renewable energy sources, and energy storage banks with the distributed energy generation resources. The microgrid can either function as a stand-alone source of energy integration or it can be connected to the grid. The choice is based on the resources that are accessible at the targeted location. The independent microgrid is the best option for all energy needs in distant places. In contrast, a grid-connected microgrid is better suited for metropolitan settings. The main benefit of integration is that energy production would also be performed by the customer [9].

In comparison to the traditional energy setup, the microgrid is more promising, adaptable, and dependable [10]. There are three types of microgrids now in use, with the AC microgrid being the most well-known. A DC microgrid is made up of DC energy-generating sources that are connected to the DC bus and provide energy to the DC loads using DC/DC converter, and a hybrid microgrid is a combination of DC and AC microgrids. In this design, the AC sources are connected to a common bus to supply the necessary power to the AC loads through AC/AC converters or transformers. Using DC and AC bus bars, it makes use of both DC and AC energy sources.

In total, 16.6% of people worldwide have access to electricity, according to the World Energy Outlook report from the International Energy Agency (IEA) [37]. Despite the extravagantness of sources and renewable energy technology, businesses and governments cite significant investments and large distances as the cause [37]. The most practical alternative, according to the aforementioned research, is to support hybrid renewable energy microgrids to meet the demand for energy. Additionally, according to industry standards, the HOMER software (https://www.homerenergy.com/) is a commonly used optimization tool for optimizing and modeling the hybrid renewable energy microgrid.

Economically viable, environmentally friendly, and dependably responsible are all requirements for a microgrid. Due to the dependence of renewable energy resources on atmospheric conditions, the unreliability of the grid, and other numerous factors, the choice of a battery backup is a crucial microgrid element. When energy output is lower than energy consumption, a battery backup will ensure an uninterrupted power supply. The microgrid encourages the energy management system to choose the best suitable combination of resources in terms of power quality, dependability, and cost by allowing multiple energy resources to participate.

The performance evaluation criteria can be measured by renewable fraction (fren), initial capital cost (CC), operating cost (OC), levelized cost of energy (COE), and net present cost (NPC) [200,201].

5. Power Outages in Iraq as a Case Study

When demand for electrical energy exceeds generation, rolling blackouts occur, allowing some clients to obtain power at the right voltage at the price of others who receive no control at all. It is common in various countries and can be organized ahead of time or can happen at any time. Iraq is one of the countries experiencing rolling blackouts with power outages frequently occurring during searing temperatures as a result of long power outages. Iraq’s power sector has a lengthy history of problems. Significant population expansion and the spread of consumer appliances(particularly air conditioners), corruption, and incompetence have slowed development in expanding and rebuilding capacity, while demand has continually climbed rapidly. With continued high levels of related gas flaring, fuel availability to power plants has been a challenge. Privately powered generators fill some of the demand gaps, but they are expensive, noisy, and dirty, and they rarely generate enough electricity to run air conditioning.

A country like Iraq, which has a wealth of natural resources, has one of Asia’s lowest rates of electricity utilization per capita at 1.030 kWh; compared to neighboring countries, it continues to be substantially lower (3.300 kWh in Turkey and 1.900 kWh in Jordan). Following a 17% decline in 2020, the total energy usage increased by 10% to 50 Mtoe in 2021. Since 2015, it has been quickly rising till 2020(+8%/year). Of the nation’s total energy usage in 2021, 73% came from oil; the remaining 24% came from gas; and 3% come from hydropower [202].

Iraq was the fourth-largest energy user in the Middle East in 2021, after Iran, Saudi Arabia, and the United Arab Emirates, consuming an estimated quadrillion British thermal units of total primary energy [203]. The majority of Iraq’s main energy consumption was made up of natural gas and oil, with hydropower and solar energy making only a small contribution (see Figure 7a). Until it builds new pipeline infrastructure and natural gas processing capacity, Iraq will continue to mostly rely on oil to meet demand. A plot of Iraqi electricity supply by source according to the US Energy Information Administration [204] is shown in Figure 7b.

According to a report of the Middle East Institute, the installed capacity of 30 gigawatts (GW) cannot satisfy summer peak demand as of 2023 and numerous users employ rooftop solar panels or small generators due to faulty grid and institutional issues [205], where power outages, both planned and unforeseen, were frequent [206]. The balance between generation and demand is as follows:

• Daily Average Output: 4470 MWh;
• Daily Electricity Demand: 6400 MWh;
• 6900–7800 MWh, or 36–45% of the summer peak demand, cannot currently be satisfied.

Since 2010, Iraq’s net energy generation has increased by an average of nearly 7% annually, reaching a total of more than 93 terawatt-hours (TWh) (see Figure 8).

Iraq generates almost all (almost 95%) of its electricity from oil and natural gas [203]. The International Energy Agency claims that, as a result of Iraq starting to import natural gas from Iran to supplement its own supply, the utilization of natural gas in the electric power sector climbed from 25% in 2016 to around 60% in 2020. The majority of the remaining power production is produced through hydroelectricity [207]. Iraq wants to develop renewable energy projects to replace part of its oil and natural gas-fired capacity and to cut back on natural gas and electricity imports from Iran, even though solar generation made up a small portion of the country’s overall power generation. Iraq has inked contracts with various foreign businesses to create 4.5 gigawatts (GW) of utility-scale solar projects in 2021, and the country intends to install 12 GW of renewable energy capacity by 2030 [208].

For 2021, the federal government of Iraq had a peak power generation capacity of 21 GW. In comparison to the installed capacity of 37 GW and the 33 GW required to meet peak summer demand, the supply that was available in 2021 was significantly less. The highest period for electricity use in Iraq is during the summer. Because of inadequate natural gas supply and infrastructure, inefficient or damaged power plants, poor transmission infrastructure, and low utilization rates of generation units, the available or effective production capacity is substantially lower than the installed capacity. Typically, peak summer demand exceeds actual production, leading to power shortages that, in the summers of 2020, 2021, and 2022, provoked protests in southern Iraq and Baghdad [209].

Iraq’s distribution losses are still a problem. In comparison to a global average of 8% throughout this time, distribution losses from 2011 to 2020 averaged 58% of the total electricity supply. High rates of electricity theft, grid inefficiencies, and subpar system design all contribute to large distribution losses [203].

Iraq is seeking methods to diversify the sources of electricity it imports. The Gulf Cooperation Council (GCC), Saudi Arabia, Turkey, and Jordan are some of the sources being considered. Iraq and the GCC reached a definitive deal under which Iraq will begin receiving 500 MW of electricity from Kuwait starting in the middle of 2024. The project’s maximum capacity, if implemented, will be 1.8 GW [210].

Customer management procedures, strong meter billing and collection systems, checks on energy theft, and effective metering are all critical to making the best use of the country’s accessible energy resources. Customers pay for less than a third of the electricity produced, and income losses related to non-fees at the delivery level reach higher than 60% [211]. A number of things can lead to power outages in an electricity network. Activation of fuses or circuit breakers, short circuits, cascade failures, faults in power plants, and damage to electric transmission lines, substations, or other components of the distribution system are a few instances of these causes [212].

In residences, electricity is mostly utilized to power equipment such as refrigerators and air conditioning schemes, cooking appliances, heating, and lighting systems as well as ICT devices and televisions [213,214,215]. These provide essential services such as preservation and food preparation, academic pursuits and home-based production, security and safety, information access and communication, as well as comfort and air conditioning [213]. All of these services may be affected if there are power outages. As a result, according to the electricity usage, the preferences of particular families, and capacities, the consequences of a power outage can vary [216]. Power outages are more likely to have a bigger impact and cost on higher-electricity-consuming families than on lower-power-consuming appliances [217]. Appliances that rely on electricity for specialized or important needs, such as delivering medical gadgets to provide domestic healthcare, maybe hit even harder.

Iraq is still trying to provide basic services to its citizens. The country’s electricity infrastructure has suffered greatly as a result of years of disagreement, as well as alleged neglect and mismanagement. In today’s Iraq, severe power outages and rolling outages are commonplace. The failure of an electrical power utility to a customer is referred to as a power blackout or outage. Users will possibly make informed EM decisions and save their expenditure by means of an energy monitoring policy that allows information on their usage habits. Therefore, an energy management strategy that includes the operation and planning of energy consumption units and energy production, as well as consumption monitoring, can help to solve power outages in such conditions.

5.1. Formulation of Iraq’s Electricity Problem

In summary of the aforementioned discussion, the Republic of Iraq faces many problems related to the production and distribution of electrical energy, in addition to poor communications and information flow from producer to consumer. The basis of the problem is the deterioration of the production and distribution of electrical energy. The relation between the causes of power outages in Iraq is that the over-demand of electricity enforces the national utility company to apply rolling/planned power outages. However, the main reasons for implementing the rolling power outages policy are not the over-demand exactly but many other causes such as (1) bypassing or hacking of the national power grid, (2) significant population expansion, (3) the spread of end-user household use, mainly through air conditioners, and (4) corruption and incompetence, have slowed growth in expanding and rebuilding capacity, at the same time as demand has continued to climb quickly. As per the above literature review, no study proposed a solution for this particular issue restricted by these conditions.

Although EM in a distribution system aids in enhancing the system’s performance, it also has constraints and difficulties. Researchers have not adequately addressed the privacy of the EMS dependability difficulties, routine system upgrades, operations in a large system, and the customer side. Such restrictions and difficulties may affect EM system operations, hence it is important for the pertinent researchers to develop efficient methods to overcome these restrictions in EM systems.

5.2. Limitations of the Reviewed Studies

The effectiveness of alternative strategies for preventing or lessening the power interruptions that the utility systems impose to make up for the lack of electricity generation has not received much research. Unfortunately, there are no studies in the literature on generation-side EM when the power plant only uses one type of energy source, and the majority of studies focused on customer/demand-side management strategies. Few studies like [11,12,13,14,15] have discussed multi-agent energy generation networks or their control algorithms for energy optimization [16,17,18,19,20]. Apart from power outages caused by accidents and natural catastrophes, there is not thorough research in the literature specifically for developing countries that discusses power outages and their causes. This study fills the gap by examining the reasons for power outages, electricity management strategies, challenges, and the experiences of developed countries. It also assesses the needs and prospects of considering the EM that could work in the power utility in countries like Iraq that experience power outages.

5.3. Statistics for the Reviewed Publications

A Scopus database was used here to offer some statistical analysis on the addressed field of study, where the initial search provided about 573 publication results when using the power outage keyword. Different keywords were used in the search process without restrictions on language, country, etc. The publication source and country are the focused statistical information in this analysis. The term “power outages or blackout” was used as a fundamental keyword, while the primary and secondary keywords “energy management, demand-side, generation-side management, and energy policies/strategies” were used. Therefore, after filtering the results with respect to these keywords and the contents we obtain about 203 publications. Analysis showing the growing number of publications per year by source is depicted in Figure 9, while the statistical analysis of publications with respect to country is shown in Figure 10.

Figure 10 indicates that China followed by the USA are the countries that suffer from power outages and they have great research potential to mitigate these issues. In contrast, Iraq has only four publications in this field with only one focused on demand-side energy management strategies to mitigate power cyclic blackouts with semi-dispatchable reserve generation [218].

5.4. Classification and a Recommended Solution

It is possible at this stage to present a diagram showing the energy management strategies for capping the planned power outages and recommend an appropriate solution under the restrictions present in Iraq. A classification diagram can accordingly be shown in Figure 11. Looking into various indices for determining online stability and reliability [4], power outages can be reduced by coordinating the various control techniques in power systems online [219], and the rate of blackout cascading events can be directly influenced by the online coordination of the various energy management policies. Furthermore, considering real-time monitoring technologies can also help to improve power system supervision in a wide range of scenarios [220].

The red dashed line indicates the proposed method. Here we recommend an interactive EM system for avoiding rolling outages, where the technique limits energy demand only in the event of a power generation constraint that occurs during the summer and winter months. The approach takes into account the equitable distribution of electrical electricity among all social classes of residential customers. This is related to the normal congestion management policies of the transmission system operators, which might not be a new strategy as a demand-side EM, but according to the conditions discussed for Iraq, it will be an effective one when it is imposed by the grid utility as a generation-side solution to mitigate the rolling power blackouts.

An amount of daily energy in kWh is assigned per each house according to daily energy availability and the number of individuals in that house. Customers who are responsible for a considerable portion of the critical peak are forced to lower their use (based on a proposed algorithm) rather than being fully disconnected from the grid. The customers can, with time, manage their consumption by monitoring how much energy there is remaining. However, this method adds more restrictions on the end-user and does not allow them to manage their usage of energy. This method is recommended for Iraq as its power plants run with one type of power source, not a hybrid.

The common demand-side EM strategies would not be effective here because the application of such strategies requires that utility company to impose rules on connecting other sources with the grid, which is very hard for the government.

6. Conclusions

The study presented an overview of power outage causes and the energy management strategies in the literature as solutions to mitigate over-demand or rolling/planned power outages, taking Iraq as a case study. The studies in the literature have discussed these issues mainly through demand-side strategies and energy generation-side methods. The demand-side has been discussed extensively as it guarantees end-user contribution to the system’s management. However, these strategies may not be the appropriate solution for some cases such as in Iraq.

The study found that the main causes for imposing the rolling power outage policy by the national utility company are not over-demand exactly but many other causes such as (1) bypassing or hacking of the national power grid, (2) significant population expansion, (3) the expansion of consumer appliances, especially air conditioners, and (4) corruption and incompetence. These have slowed the advancement of expanding capacity and reconstructing, whilst demand has persisted to climb rapidly.

The generation-side-based Internet of Things is a tool that can help improve management strategy performances. It is a notion that fuses the electronics and computer science disciplines. One or more objects can communicate with one another using IoT technology across the internet network. IoT objects have microcontrollers and sensor hardware that are connected to integrate with software programs and systems that enable interfacing and communication with other objects.

Using IoT-based smart energy meters to address problems associated with prepaid energy metering is indeed a promising solution. These meters leverage the Internet of Things (IoT) technology to offer several advantages in terms of reducing complexity, mitigating non-technical losses, improving data validation, and adding additional capabilities. IoT-based smart energy meters have the potential to significantly improve the efficiency, accuracy, and convenience of prepaid energy metering systems, benefiting both consumers and utilities.