FACTS Controllers’ Contribution for Load Frequency Control, Voltage Stability and Congestion Management in Deregulated Power Systems over Time: A Comprehensive Review

Abstract

Incremental energy demand, environmental constraints, restrictions in the availability of energy resources, economic conditions, and political impact prompt the power sector toward deregulation. In addition to these impediments, electric power competition for power quality, reliability, availability, and cost forces utilities to maximize utilization of the existing infrastructure by flowing power on transmission lines near to their thermal limits. All these factors introduce problems related to power network stability, reliability, quality, congestion management, and security in restructured power systems. To overcome these problems, power-electronics-based FACTS devices are one of the beneficial solutions at present. In this review paper, the significant role of FACTS devices in restructured power networks and their technical benefits against various power system problems such as load frequency control, voltage stability, and congestion management will be presented. In addition, an extensive discussion about the comparison between different FACTS devices (series, shunt, and their combination) and comparison between various optimization techniques (classical, analytical, hybrid, and meta-heuristics) that support FACTS devices to achieve their respective benefits is presented in this paper. Generally, it is concluded that third-generation FACTS controllers are more popular to mitigate various power system problems (i.e., load frequency control, voltage stability, and congestion management). Moreover, a combination of multiple FACTS devices, with or without energy storage devices, is more beneficial compared to their individual usage. However, this is not commonly adopted in small power systems due to high installation or maintenance costs. Therefore, there is a trade-off between the selection and cost of FACTS devices to minimize the power system problems. Likewise, meta-heuristics and hybrid optimization techniques are commonly adopted to optimize FACTS devices due to their fast convergence, robustness, higher accuracy, and flexibility.

1. Introduction

Reforming electrical power systems is a challenging task to carry out. National policies, environmental issues, economic developments, and applications of power networks based on restricted conditions are major factors involved in reformation complexity [1]. In general, there is no unique appropriate solution suitable to all countries [2]. Therefore, every country performs its own deregulation process according to its regional needs. Deregulation of electrical power systems means revolution and conversion of old rules and the reformulation of existing electrical infrastructure into a new competitive form [3]. In recent decades, the electrical power system has moved from a vertically integrated and monopolistic model to an independent, competitive, and restructured model [4,5]. A vertically integrated monopolistic model means a sole entity, i.e., government, has full control of all power system sectors (e.g., generation, transmission, and distribution) [6]. However, an independent restructured competitive model signifies unbundling the generation, transmission, and distribution as a discrete identity [7] which allows the integration of various stakeholders (i.e., private entities, market regulators, and independent power procedures) in terms of developing a competitive, affordable, and efficient power system [8]. In this way, this new competitive restructured power system model changed the operation of traditional control of the power system [9]. The primary objective of deregulation is to create a competitive environment, which leads to lower prices, improved customer services, increased innovation, etc. [10]. In summary, deregulation comprehensively changed the electrical world [11]. At the heart of restructuring is the need for competition in the trading of electrical energy as a commodity. However, it is also necessary to maintain a balance between ever-changing load and generation, keeping voltage and frequency within the defined bounds and coping with various outages [12].

Modernization of electrical networks in terms of upgrading existing networks is affected by several factors such as political influence, high cost, space, efficiency, etc. [13]. Therefore, a flexible alternating current transmission system (FACTS) envisioned by the Electric Power Research Institute (EPRI) is a beneficial option to reconstruct electrical utilities [14]. A major thrust of FACTS technology is the development of power-electronics-based systems that provide dynamic control of the power transfer parameters like transmission voltage (V_T), line impedance (Z_L), and phase angle [15]. Improving power flow is one of the advantages of FACTS devices. This power improvement allows utilities to operate transmission networks nearer to their thermal limits [16]. Similarly, stability enhancement, reliability, congestion management, integrated power networks, and low-efficiency energy resource utilization are driving factors of deregulation. Generally, power systems are interconnected with various power generating units to ensure power supply particularly in high-demand regions [17]. However, this interconnected power system frequently encounters uncertain and unpredictable load demand which can affect the system frequency [18]. In addition, uneven distribution of load adversely affects the voltage profile which increases the interconnected power system’s vulnerability to short circuits [19]. That is why deregulated power systems are faced with a surfeit of various technical issues such as voltage drop, congestion management, power losses, rise in transients, etc. [20,21]. Therefore, transferring power nearer to thermal limits requires comprehensive coordination to ensure the stability and reliability of transmission networks [22]. To ensure stability and ameliorate various power system problems (particularly associated with voltage and frequency), a FACTS is an effective option [23]. Irrespective of the benefit provided by FACTS devices, they can become an economic burden (the sum of investment and maintenance cost, etc. surpassing the energy saving cost) when FACTSs are not employed properly in the power system [19]. Therefore, the deployment of optimization techniques is inevitable to obviate this challenge. Therefore, the main objective of this review paper is to discuss the role of various FACTS devices under different power system control (voltage and frequency) and their technical benefits in restructured networks. In addition, the importance and beneficial role of FACTS controllers to eradicate congestion are also discussed in this paper. Moreover, comparison of several optimization techniques, algorithms, or methods using FACTS devices for improvement in load frequency control, voltage stability, and congestion management will be provided for reference. Therefore, it is essential to provide future researchers with a summarized comparison of various FACTS devices against three major power system challenges, i.e., congestion management and power system profiles (voltage and frequency), and up to date available optimization techniques and their comparison for reference. It is important to mention that optimization techniques irrespective of cost analysis, especially installation cost (because it is out of the scope of this paper), are discussed in this paper. A comprehensive survey on deregulated networks and their role in different areas is shown in Figure A1 (given in Appendix A).

This paper is organized as follows: Section 2 presents general characteristics of electrical power systems. Section 3 discusses the significance of ancillary services in deregulated systems. Similarly, FACTS controllers, their types, and their potential role will be presented in Section 4. Section 5 shows a detailed review about FACTS controllers’ role in load frequency control and voltage stability enhancement. Also, congestion management in restructured power networks by using FACTS controllers is presented in Section 6. Finally, conclusions will be drawn in Section 7.

2. General Characteristics of Electrical Power Systems

Prior to examining the role of FACTS devices, their economic or technical benefits, stability enhancement, and congestion management, it is essential to present several key aspects of power networks [12].

• Generation and load should always be equal. This means that electric energy cannot be stored at the power system scale.
• Ownership of electric energy identity is lost in the transmission network.
• Power systems tend towards instability because of unsustainable electrical market competition. It means stability can only be achieved by sustainable compliance.
• With proper investment both transmission capacity and reliable power supply will be enhanced.

The reliability and quality of electric power delivery are extremely important to customers [24]. Generally, the term “power system reliability” refers to the capability of the power system to execute its intended function, specifically the generation and transmission of electrical energy to consumers without interruption [25]. According to the IEEE, the term “reliability” is defined as “The probability that a system will perform its intended functions without failure, within design parameters, under specific operating conditions, and for a specific period of time”. A power system is a complex entity comprising numerous subsystems and components that collectively function to supply power to consumers continuously or over an extended duration. To achieve the desired functionality, power systems are categorized as short- or long-term performance-based systems. The short-term performance of a power system pertains to its capacity to withstand outages or contingencies, which is referred to as power system security. In essence, security denotes the power system’s capability to manage sudden disturbances, which may include dynamic events (such as voltage stability, frequency stability, and angular stability), static events (such as voltage or thermal limits), or cybersecurity threats (affecting software or hardware infrastructure). Conversely, the long-term performance of a power system is evaluated based on its ability to consistently meet demand, necessitating adequate generation and transmission facilities with the requisite level of reserve. This capability is termed power system adequacy (PSA). In other words, adequacy refers to the availability of sufficient facilities to meet the power system’s load demand, even when one or more components malfunction owing to sudden failure events. Therefore, it is pertinent to assert that power system security is achieved through stable “operation” of the system, while adequacy is ensured through the “planning” of sufficient facilities in the long term. Consequently, reliability is assured when the power system is both secure and adequate, thereby enabling the continuous delivery of energy.

Conversely, the situation in which actual power deviates from the ideal waveform or deviates from an adequate power (voltage and current) level refers to “power quality” [26]. In the literature, various sources employ the term PQ interchangeably with different connotations, including voltage quality (VQ), current quality (CQ), supply reliability, service quality, quality of consumption, quality of supply, and supply reliability. Generally, PQ encompasses both VQ and CQ. VQ denotes deviations from the ideal voltage, characterized by constant amplitude and frequency. Similarly, CQ pertains to deviations from the ideal current waveform. Notably, CQ complements VQ, as the ideal current sine waveform is in phase with the voltage sine wave, maintaining constant amplitude and frequency. Therefore, any deviation in sinusoidal voltage or current waveforms constitutes a PQ disturbance. Such disturbances include three-phase imbalance, voltage sag and swell, voltage flicker, interruptions, harmonics, and switching transients. Moreover, low-frequency swings and power system faults can lead to blackouts, brownouts, and voltage sags that result in dissatisfaction of customers [27]. In other words, the quality and reliability of the power system are severely affected. In the literature [25], terms such as failure, outage, and interruption are employed to characterize the degree of reliability and quality within power systems. Out of these three terms, “interruption” describes the concept of brownout and blackout. If the power system experiences a series of outages or partial system outages (affecting a limited number of customers or a small area), generally, this type of interruption is referred to as a brownout. The term brownout was first used in 1941 in Australia to mean semi-darkening as distinguished from a complete blackout [28]. However, if an interruption lasts for hours or affects a larger part of the country, then such a type of interruption is called a blackout. Consequently, another factor related to the power quality described in [27], leading to customer dissatisfaction, is voltage sags. According to the Institute of Electrical and Electronics Engineers (IEEE) standard (IEEE-Std-125-1995) a voltage sag is defined as “the rapid decrease in effective value of voltage (10–90%) as compared to voltage reference value within the duration of 0.5 cycles to 1 min” [29]. Thus, to mitigate these problems, the introduction of ancillary services in deregulated networks is a reliable option to adopt [30]. In the literature, detailed discussions about power quality [26], power reliability [31,32], blackout [33], and voltage sag [29] are available.

3. Significance of Ancillary Services in Deregulated Systems

Ancillary services are those functions performed to support the basic services of generation capacity, energy supply, and power delivery in deregulated power systems [34]. Ancillary services play an important role in sustaining power flow, reducing differences between demand and supply, and facilitating power systems’ operation during uncertainties (frequency deviation, voltage variations, imbalances between supply and demand, etc.) [35]. Ancillary services may include black start regulation, synchronized operation, contingency reserves, and flexibility reserves [36]. In deregulated power systems, ancillary services investment becomes essential because, without these services, the power system remains uncertain, unstable, and unreliable [37]. Variable load demands, power quality, and uncertainty of fuel price and penetration of renewable energy require ancillary services in power networks [38,39]. Different types of ancillary services for problems associated with power systems are shown in

Table 1. Power system problems and their associated ancillary services.

Sr.	Type of Ancillary Services	Problem Associated with Power System
1	Voltage and reactive power control (Q)	Variations in voltage profiles
2	Scheduling and dispatch (S & D)	Lack of electrical energy storage systems
3	Frequency control	Variation in power system frequency (f)
4	Operating reserves and spinning reserves	Difference in energy demand and generation

.

One of the most economical and efficient solutions of deregulated power system problems is the injection of FACTS controllers [40]. Transient instability, dynamic instability, oscillations, frequency imbalance, or voltage fluctuations can be eliminated by adding ancillary services in power networks [41].

4. FACTS Controllers, Types, and Their Potential Role

Hingorani and Guygui are the pioneers of FACTS controllers. According to the Institute of Electrical and Electronics Engineers (IEEE), FACTS controllers are defined as: “Alternating current transmission systems incorporating power-electronics based and other static controllers to enhance controllability and increase power transfer capability” [42].

FACTS devices are a fast-growing result-oriented technology that is reliable, economical, secure, and efficient in the modern electric world [43]. These fast-acting power-electronics-based controllers dynamically control active (P) and/or reactive (Q) power flow in power systems, exclusively or simultaneously [44]. They can also control transmission lines current and voltage, transfer, or exchange of active power between lines and supply or absorb Q. The primary advantages of utilizing flexible AC transmission system (FACTS) devices are their ability to enhance operational efficiency and regulate power flow within transmission lines. The control of power flow in these lines is contingent upon various factors, including the impedance of the transmission line, the magnitude of bus voltage, and the phase angle between them. Consequently, FACTS devices can modify one of these parameters to achieve power control or augment the limits of transmission lines. Over recent decades, the electricity market has transitioned from a unidirectional to a bidirectional system, evolving from a regulated to a deregulated environment. This transition has intensified competition among market participants, not only concerning electricity costs but also in maintaining power quality. Furthermore, power generation in a deregulated context can vary based on fuel availability, environmental conditions, and economic factors, among others. Simultaneously, the load also fluctuates due to weather conditions, time of day, and expansion. The variability in both load and generation can introduce uncertainty, potentially resulting in congestion. Therefore, optimizing power generation by considering fuel consumption is crucial to meet the increasing electricity demand. Integrating FACTS devices into such deregulated power systems offers several benefits, including reducing the reactive power supply from the source to the load, minimizing voltage sag and swell at bus voltages, lowering harmonics in the source current, and decreasing real power loss. Essentially, there would be an increase in real power delivery to the load without expanding power generation systems. Without the addition of extra lines, FACTS devices can enhance system security by improving voltage regulation and reducing congestion through power flow regulation. Based upon applications, FACTS controllers are classified into three generations and four types [45] as shown in Figure 1 and

Table 2. Classification of FACTS controllers by type.

Types	Functions
Series Controller	Connect in series. Inject voltage (V) in series with transmission line.
Shunt Controller	Parallel connected. Inject current (I) to the transmission lines.
Series–Series Controller	Series connected. Control more than one transmission line. Inject V in series.
Series–Shunt Controller	Connect both in series and parallel. Control more than one transmission line. Inject I in shunt and V in series.

, respectively. In brief, shunt-connected FACTS devices regulate reactive power through the control of susceptance. This indicates that shunt- or parallel-connected controllers manage the voltage at a node by injecting reactive current, regardless of the transmission lines connected to it. Consequently, the operational principle of a parallel controller involves supplying reactive power to the transmission line, with the objective of enhancing active power transfer by improving the power factor while maintaining voltage levels within safe limits under extreme loading conditions [46]. In contrast, series-connected FACTS controllers regulate both active and reactive power by controlling reactance. Essentially, all series controllers inject a voltage in series with the line, as a variable impedance in series, multiplied by the current flow, represents a series voltage injected into the line. Therefore, series-connected controllers may comprise a variable impedance, such as a condenser, reactor, or static converter, operating at fundamental, subsynchronous, and harmonic frequencies [47]. Similarly, the other two types of FACTS controllers, namely, series–series or series–shunt, also regulate both active and reactive power. However, each of these FACTS devices possesses distinct advantages, disadvantages, or limitations, which are beyond the scope of this paper.

As mentioned above, FACTSs are power-electronics-based technologies. This technology consists of thyristors and extends to contemporary advancements such as MOSFETs and IGBTs, which are the basic building block of FACTS controllers. Thyristors used in the FACTS devices are either self-commutated or line commutated [48]. This means that they differ on their turn-off capability. This capability of FACTS devices decides their fast response, reliability, and efficiency. With research and development (R & D) self-commutated controllers replace line-commutated controllers [49] that enhance the power system functionality. Based on turn-off capability, FACTS controllers are also classified as voltage source (VS) or current source (CS) converters [44]. Both VS converters and CS converters have their own merits and demerits [50], but choosing one or the other depends on the type of application and their role in power networks.

Table A1. Application of FACTS controllers in various power system problems.

Type	a	b	c	d	e	f	g	h	i
UPFC	[241]	[242]	[243]	[244]	[245,246]	[151]	[247]	[248]	[249,250]
GUPFC	[251]		[252]				[253]
IPFC	[254]	[255]				[256]	[257,258]	[259]	[91]
GIPFC
SSSC	[260]	[261]	[140]	[262]	[263]	[143]	[264]	[265]	[266,267]
STATCOM	[268]	[269]	[140]		[270]		[271]	[248]
D-STATCOM					[272]
TCSC	[273]	[274,275]	[150]		[245]	[151]	[276]		[266,277]
TCSR							[278]	[279]
TCVR	[280]
TCPS		[281]							[266,267]
SVC	[282]	[275]				[151]	[271,283]

a: transient stability; b: dynamic stability; c: voltage stability; d: active power compensation control; e: reactive power control; f: voltage control; g: damping oscillations; h: fault current limiting; i: automatic generation control.

(given in Appendix B) shows applications of several FACTS controllers and their role in deregulated power networks.

5. Stability Enhancement Using FACTS Devices

Stability is one of many problems associated with interconnected electrical networks [51]. To fulfill the generation and demand gap for electrical power, several methods and techniques have been adopted to enhance power system performance in recent years. Integration of renewable energy and smart grid interaction with existing networks are common examples [52]. Due to these changes, power system stability, reliability, and security are at risk in terms of its smooth operation. Therefore, for reliable operation of power networks, stability enhancement is a necessity in deregulated networks [53].

Power system controls, like voltage (V) and frequency (f) controls, are ancillary services in deregulated networks. Inability to maintain these V and f controls within their established margins can lead to instability. Similarly, independent power producers (IPPs) that are merged into power networks extensively have low capability to handle stability in terms of frequency [54]. Therefore, significant measures should be needed to analyze power system behavior under frequency instability [55,56]. Here, we will discuss both load frequency control (LFC) and voltage control, one by one, for stability enhancement in deregulated networks.

5.1. Load Frequency Control (LFC)

In interconnected power systems, LFC is one of the major issues [57,58]. LFC is defined as regulating the generating unit power in reaction to changes in system frequency or interchange patch line power within their stated limits [53]. LFC has two objectives in deregulated power networks, i.e., power exchange control in interarea tie-lines and maintaining the frequency within their limits [57,59]. With this aim, the role of LFC has become significant in recent years. One of the solutions to tackle LFC is using FACTS devices [60] because classical control methods (P, PI, and PID) are not enough to control sudden variations in deregulated power systems. FACTSs have the capability to maintain the dynamic stability of the system and improve power transfer capability [22]. The main purpose of using FACTS devices in deregulated power networks is to enhance power system stability and security, diminishing transmission losses and increasing their load ability [53]. Therefore, FACTS devices installed in those power systems face overloading power problems at transmission lines [61]. With this aim, the static VAR compensator (SVC) is installed in interconnected power systems to control various parameters and enhance systems capabilities [62]. Similarly, in [63], interarea oscillations (frequency and time domain) are effectively damped out using an SVC, controllable capacitor (CSC), and phase shifter (PS). In [64], an SVC is used to improve LFC by reducing frequency oscillations. To achieve this goal, a feedback signal for an AGC-based SVC was used to stabilize the power system. Furthermore, an SVC is used in the IEEE New England 39 bus, 10 machine power system to analyze and improve its performance for frequency regulation [65]. Moreover, the authors also compare conventional SVC performance with a modified SVC in this restricted system and conclude that the SVC enhanced frequency regulation. However, the modified SVC performs better in case of large disturbances. With this aim, Yong Wan [66] proposes a modified SVC with thyristor firing angle law and implements it on a 150 bus, 27 machine power system. In this study, the author further compares the performance of the conventional SVC with the newly adapted methodology. Results show that the proposed SVC not only outperforms the conventional SVC but also enhances FR in deregulated power systems.

STATCOM is an advanced version of an SVC [67] and superior to an SVC [68]. STATCOM is a shunt-connected FACTS controller. It has the capability to control the frequency or damp out frequency oscillations within their permissible limits [69]. K. Al-Haddad et al. provided a detailed discussion and claimed that the STATCOM controller is a quantitatively superior controller compared to the SVC due to its various attributes [67]. To justify their statement, the authors reviewed 300 research papers to describe the state of the art of STATCOM technology and its future potential and summarized significant conclusions. Furthermore, a detailed review presented in [70] discussed various aspects of STATCOM such as installation, control, stability, and frequency control. Similarly, in [71] STATCOM with superconducting magnetic energy storage (SMES) is used to improve the LFC in deregulated power networks. Results show that settling time in FD and overshoots improved by 40% in the presence of STATCOM-SMES. With this aim there are other FACTS controllers like the thyristor-controlled phase shifter (TCPS), static synchronous series compensator (SSSC), unified power flow controller (UPFC), and interline power flow controller (IPFC) that also play a vital role in LFC.

In [72], TCPS-based LFC in a multi-area network is studied. Results reveal significant support against various problems, such as frequency deviation and tie-line power flow, using TCPS. Rafi et al. [73] discussed TCPSs and SMES in interconnected power to provide LFC. Correspondingly, in [74] a detailed study on ultra-capacitors and TCPSs is presented for deregulated power systems. It is concluded that the TCPS performs better than the ultra-capacitor in suppressing FD and providing LFC. Similarly, a study [75] compares the effects of TCPSs, SMES, and DIGG for LFC in multi-source multi-area power systems.

Furthermore, SMES-TCPS is implemented in a two-area hydrothermal system in a deregulated environment in [76] to improve LFC. In response to such implementation, FD and settling time of the power system significantly reduce. Moreover, it is observed that the actual parameters of the tie-line (power exchange and power generation) are exact matches of theoretical parameters showing the effectiveness of TCPSs and SMES. In addition, under a steady state, the frequency error approached zero. In the literature [77], a study based on SMES and TCPSs also justifies their usefulness in restructured power systems. System dynamics in coordination with SSSCs and TCPSs for a two-area power system has been studied in [78]. Likewise, SSSCs with SMES in interconnected power systems are discussed in [79]. The authors conclude that SSSCs with energy storage devices notably enhanced LFC in the restructured power system. This paper also reveals that SSSCs with SMES not only perform better against realistic situations, generation rate constraint (GRC), and governor dead band but also show effectiveness against step load disturbance. Similarly, an SSSC with capacitive energy storage (CES) for LFC is presented under the open market scenario in [80]. Consequently, in [81] another approach named imperialistic competitive algorithm design is used for SSSCs and CES to provide LFC to multi-area power systems. This proposed approach effectively suppresses FD and justifies the superiority of power systems with SSSCs and CES. Furthermore, the authors of [82] prove that FD in multi-area power systems improve using SSSCs. Other FACTS controllers like UPFCs, generalized UPFCs (G-UPFCs), and IPFCs are also favorable in the electrical power system for LFC.

UPFCs effectively enhanced LFC in an interconnected power system presented in [83]. Additionally, the LFC and robustness of a multi-area power system improve using UPFCs with an energy storage device, i.e., CES. In another study [84], a UPFC with an energy storage device (SMES) is used to assist the proposed controls (PI, fractional-order proportional-fractional filtered derivative action (FOPDF), and fuzzy PI-FOPDF) to provide LFC in an interconnected power system. Results clearly indicate that the proposed approach performed well despite the harsh conditions in the interconnected power system. However, the literature [85] proves that UPFCs and RFBs with the hybrid differential evolution and pattern search (DE-PS) algorithm for LFC are reliable in a deregulated environment. It is observed that FD is zero in multi-area networks when coordinated with UPFCs and RFBs. In the literature [84], UPFCs and SMES are added to the system for improving LFC.

The reformed form of the UPFC is the G-UPFC [86]. The G-UPFC is also known as the multi-line UPFC because it can control the power flowing on more than one line or even on subnetworks [87]. In the literature [88], a fundamental frequency model of G-UPFCs was developed in an electromagnetic transient program (EMTP) and used to demonstrate its performance in a test power system. Also, in [89], the performance of G-UPFCs to suppress frequency oscillations was presented. The trend to adopt UPFCs and G-UPFCs increased in recent decades with the aim of regulating interconnected power systems [19]. However, SVCs and TCSCs are preferred due to the higher installation costs of UPFCs [90].

The authors of [91] concluded that the UPFC is not economically attractive in multi-line power systems due to its high cost. Therefore, the IPFC is considered preferable to the UPFC because it effectively controls the compensation and power flow of multi-area transmission lines. In addition, the authors of [92] used IPFCs to improve the LFC of a multi-unit power system coordinated with redox flow batteries (RFBs). The IPFC also proves its performance in a deregulated environment, as explained in [93]. Table 3 also shows the role of various FACTS devices and their pros and cons. There is no doubt that FACTS devices enhance LFC. However, their performance becomes more efficient by combining various energy storage devices and various optimization techniques. Optimization techniques for deregulated power networks include bacterial foraging [94] and the genetic algorithm (GA) [95,96], simulated annealing algorithm (SA) [97], back propagation algorithm (BPA) [98], strength Pareto honey bee mating optimization (SPHBMO) algorithm [99], partial swarm optimization (PSO) [100], back propagation through time algorithm [101], fuzzy logic [102], hybrid salp swarm algorithm and pattern search algorithm [103], and many more. Generally, meta-heuristics and hybrid optimization techniques are popular nowadays. Meta-heuristics techniques are those optimization techniques that are inspired by natural processes, such as PSO, GA, grey wolf optimization (GWO), etc., used to find optimum solutions of complex optimization problems in a relatively short time [104]. Moreover, meta-heuristic optimization techniques are highly considered for the optimal placement of FACTS devices. Based on these advantages a list of highly adopted meta-heuristic techniques is presented in [47]. Similarly, hybrid optimization techniques, which are a combination of more than two types of optimization technique, have their own importance. Hybrid methods are useful to compensate for the limitations of each other, e.g., a hybrid method may use a classical optimization technique to generate an initial solution and then refine the solution using a meta-heuristic method or analytical method. Moreover, in recent years, machine learning (ML) algorithms have been increasingly adopted to address challenges within power systems. ML algorithms, particularly reinforcement learning (RL) and deep learning (DL) techniques, provide robust solutions by facilitating data-driven, adaptive control strategies [105,106]. These algorithms are capable of learning optimal control policies directly from system interactions, continuously refining their strategies to accommodate uncertainties, non-linear dynamics, and operational constraints without necessitating detailed system modeling. For example, the authors of [105] used RL techniques to implement load frequency control (LFC) without requiring a central authority. Similarly, in [107] IEEE-14, IEEE-57, and IEEE-118 bus testing systems are used to provide LFC using RL. However, there may be a very limited amount of research conducted that jointly represents the ML techniques for LFC studies using FACTS controllers in deregulated power networks. But some significant references that have discussed the various ML techniques in detail can be found in the literature [108,109,110]. However, a detailed discussion about algorithms’ pros and cons is out of the scope of this paper. However, a detailed discussion about optimization techniques for FACTS devices is presented in [104].

Conclusions regarding the role of different FACTS controllers in a deregulated environment are presented below.

• STATCOM performed better than the SVC for LFC problems.
• The G-UPFC is superior to the UPFC because it can control the power flow of more than one line or even of subnetworks.
• Trends to adopt UPFCs and G-UPFCs have increased in recent decades, but SVCs and TCSCs are preferred due to lower installation costs. In other words, between the UPFC and G-UPFC, the UPFC is not an economically viable solution in terms of cost for LFC problems.
• For LFC problems, the IPFC is preferable to the UPFC.
• When comparing 2nd-generation FACTS controllers, the SSSC is preferable in series while STATCOM is preferable in shunt for LFC.
• By comparing 3rd-generation FACTS controllers, IPFC is more beneficial.
• FACTS controllers combined with energy storage devices perform better than alone.
• While comparing strategies for LFC, robust control and artificial-intelligence-based methodologies showed better performance in terms of dealing with various deregulation power system modeling uncertainties, non-linearities, and load disturbances [111].
• Classical control methods are amenable and easy for practical implementation. However, investigation reveals that they exhibit poor performance against various system dynamics, non-linearities, and disturbances because they mostly consider root locus, Bode, and Nyquist approaches to obtain phase margins and gain of the controller, [112,113]. That is why a gap is filled by FACTS devices for complex or deregulated power systems.

In general, the above-mentioned conclusions are helpful for selecting FACTS controllers. However, no specific general conclusion has been drawn for selection of the control methodology, optimization algorithm, and energy storage devices for LFC. One of the reasons is system dynamics, tie-line parameters, location, etc. preventing generalization. Another major factor, cost, plays an important role in the selection of a specific FACTS device. Other factors such as fund availability can also thwart advancement in interconnected systems.

Table 3. FACTS devices and their role in LFC.

Reference	FACTS Devices	Methods/ Optimization Algorithm	Findings	Limitations/Drawbacks/ Future Directions
[62]	SVC	Model analysis Observability and controllability analysis	SVC used to damp the low-frequency interarea oscillations	Lack of controller settings’ optimization
[64]	SVC	Feedback signals are composed of frequency deviation and reactive power variation	Damping the frequency oscillations	In future, proposed methodology can be tested in multi-area power systems
[114]	STATCOM	Artificial rabbits optimizer (ARO)	FR (IEC 60034–1 standard) provided to both New England IEEE-39 bus system and Kundar system using optimized PD with PID-acceleration-based STATCOM during contingencies	It is concluded that a meta-heuristic algorithm, i.e., ARO-based PD-PIDA, is superior to PIDA-based marine predator algorithm. Along with FR, other parameters such as voltage stability will be considered in the future. STATCOM is superior to SVC [68]
[115]	STATCOM-SMES	Genetic algorithm	Improved LFC using STACOM-based SMES in deregulated power systems. Due to lack of mechanical inertia, SMES has an advantage in load leveling applications	GA is more robust than other search techniques because it converges to the optimal values faster [116] and uses probabilistic rules and an encoded set of parameters instead of actual parameters. While comparing SSSC and STATCOM, STATCOM dampens load variations more effectively [71]
[117]	TCPS-CES	Whale optimization algorithm (WOA), hybrid stochastic fractal search–pattern search (hSFS-PS), improved particle swarm optimization (IPSO), modified group search optimization (MGSO), bacterial foraging optimization (BFO), quasi-oppositional harmony search (QOHS), adaptive neuro-fuzzy system (ANFIS)	LFC provided in two-area hydrothermal deregulated power system using TCPS-CES in the presence of WOA. However, WOA comparison with various algorithms highlights its effectiveness	WOA is superior and best for steady-state performance. While comparing with the BFO algorithm, BFO lags due to time-consuming process. Consequently, the QOHS algorithm is not applicable to a real-world power system. The hybrid SFS-PS algorithm is highly complex and works only with a single-area power system. However, IPSO and MGSO algorithms are less effective and not so realistic. ANFIS controller gives better performance than the conventional PI and fuzzy logic controllers for LFC provided by TCPS with energy storage, i.e., SMES [77]. TCPS is more effective with energy storage compared to TCPS for LFC [118]
[119]	SSSC	Modified group search optimization (MGSO), group search optimization (GSO), improved particle swarm optimization (IPSO)	Fractional-order-controller-based SSSC proposed to enhance LFC and improve the restructured AGC performance	Among various heuristics algorithms, MGSO is superior to IPSO and standard GSO
[78,120]	SSSC, SSSC-RFB, SSSC-SMES, TCPS-RFB	PSO	Improved frequency deviation against load variation in deregulated power system	The transient response of SSSC in series with a tie-line is better than TCSC. PSO-based PID tuning of FACTS devices shows better performance than conventional integral controllers. SMES-SMES are superior to stabilize the frequency oscillations compared to coordinated control of SSSC-SMES
[85]	UPFC	GA, differential evolution (DE), hybrid differential evolution and pattern search (hDE-PS) optimization	LFC of multi-area multi-source power system in deregulated environment provided using UPFC and RFB	It is observed that hDE-PS techniques are more effective for optimization compared to DE and GA. MID controllers outperform integral–derivative and integral controllers. Economically, UPFC is not a viable solution for multi-line power systems [91]
[121]	SSSC, IPFC	Fractional order PI (FOPI) controller	LFC provided in three-area hydro-thermal power systems using SSSC and IPFC	It summarized that IPFC is more effective than SSSC
[122]	TCSC	Disrupted oppositional learned gravitational search algorithm (DOGSA)	Frequency regulations are provided using TCSC-based SMES in two-area thermal deregulated power systems. DOGSA effectively regulates the load frequency in the presence of non-linear constraints such as governor deadband, generation rate constraint, and time delay	DOGSA provides faster convergence and takes less execution time to provide optimal solution
[123]	UPFC	Grasshopper optimization algorithm (GOA), moth swarm algorithm (MSA), PSO	Area frequency oscillation stabilization, reduced load disturbance, and enhancement in dynamic power flow using 3 degrees of freedom of proportional, integral, and derivative controller and UPFC in deregulated renewable-based power system. For optimal tuning, GOA, MSA, and PSO algorithms are used. Results show the better performance of GOA in terms of robustness	GOA is better in terms of robustness and efficient to handle acute load perturbation problems. Moreover, it is effective in solving global constrained and unconstrained optimization problems

Table 3. FACTS devices and their role in LFC.

Reference	FACTS Devices	Methods/ Optimization Algorithm	Findings	Limitations/Drawbacks/ Future Directions
[62]	SVC	Model analysis Observability and controllability analysis	SVC used to damp the low-frequency interarea oscillations	Lack of controller settings’ optimization
[64]	SVC	Feedback signals are composed of frequency deviation and reactive power variation	Damping the frequency oscillations	In future, proposed methodology can be tested in multi-area power systems
[114]	STATCOM	Artificial rabbits optimizer (ARO)	FR (IEC 60034–1 standard) provided to both New England IEEE-39 bus system and Kundar system using optimized PD with PID-acceleration-based STATCOM during contingencies	It is concluded that a meta-heuristic algorithm, i.e., ARO-based PD-PIDA, is superior to PIDA-based marine predator algorithm. Along with FR, other parameters such as voltage stability will be considered in the future. STATCOM is superior to SVC [68]
[115]	STATCOM-SMES	Genetic algorithm	Improved LFC using STACOM-based SMES in deregulated power systems. Due to lack of mechanical inertia, SMES has an advantage in load leveling applications	GA is more robust than other search techniques because it converges to the optimal values faster [116] and uses probabilistic rules and an encoded set of parameters instead of actual parameters. While comparing SSSC and STATCOM, STATCOM dampens load variations more effectively [71]
[117]	TCPS-CES	Whale optimization algorithm (WOA), hybrid stochastic fractal search–pattern search (hSFS-PS), improved particle swarm optimization (IPSO), modified group search optimization (MGSO), bacterial foraging optimization (BFO), quasi-oppositional harmony search (QOHS), adaptive neuro-fuzzy system (ANFIS)	LFC provided in two-area hydrothermal deregulated power system using TCPS-CES in the presence of WOA. However, WOA comparison with various algorithms highlights its effectiveness	WOA is superior and best for steady-state performance. While comparing with the BFO algorithm, BFO lags due to time-consuming process. Consequently, the QOHS algorithm is not applicable to a real-world power system. The hybrid SFS-PS algorithm is highly complex and works only with a single-area power system. However, IPSO and MGSO algorithms are less effective and not so realistic. ANFIS controller gives better performance than the conventional PI and fuzzy logic controllers for LFC provided by TCPS with energy storage, i.e., SMES [77]. TCPS is more effective with energy storage compared to TCPS for LFC [118]
[119]	SSSC	Modified group search optimization (MGSO), group search optimization (GSO), improved particle swarm optimization (IPSO)	Fractional-order-controller-based SSSC proposed to enhance LFC and improve the restructured AGC performance	Among various heuristics algorithms, MGSO is superior to IPSO and standard GSO
[78,120]	SSSC, SSSC-RFB, SSSC-SMES, TCPS-RFB	PSO	Improved frequency deviation against load variation in deregulated power system	The transient response of SSSC in series with a tie-line is better than TCSC. PSO-based PID tuning of FACTS devices shows better performance than conventional integral controllers. SMES-SMES are superior to stabilize the frequency oscillations compared to coordinated control of SSSC-SMES
[85]	UPFC	GA, differential evolution (DE), hybrid differential evolution and pattern search (hDE-PS) optimization	LFC of multi-area multi-source power system in deregulated environment provided using UPFC and RFB	It is observed that hDE-PS techniques are more effective for optimization compared to DE and GA. MID controllers outperform integral–derivative and integral controllers. Economically, UPFC is not a viable solution for multi-line power systems [91]
[121]	SSSC, IPFC	Fractional order PI (FOPI) controller	LFC provided in three-area hydro-thermal power systems using SSSC and IPFC	It summarized that IPFC is more effective than SSSC
[122]	TCSC	Disrupted oppositional learned gravitational search algorithm (DOGSA)	Frequency regulations are provided using TCSC-based SMES in two-area thermal deregulated power systems. DOGSA effectively regulates the load frequency in the presence of non-linear constraints such as governor deadband, generation rate constraint, and time delay	DOGSA provides faster convergence and takes less execution time to provide optimal solution
[123]	UPFC	Grasshopper optimization algorithm (GOA), moth swarm algorithm (MSA), PSO	Area frequency oscillation stabilization, reduced load disturbance, and enhancement in dynamic power flow using 3 degrees of freedom of proportional, integral, and derivative controller and UPFC in deregulated renewable-based power system. For optimal tuning, GOA, MSA, and PSO algorithms are used. Results show the better performance of GOA in terms of robustness	GOA is better in terms of robustness and efficient to handle acute load perturbation problems. Moreover, it is effective in solving global constrained and unconstrained optimization problems

5.2. Voltage Stability

Voltage stability is also a major problem associated with multi-network power systems. As we know, increased power demand has led to power network upgrading in recent years. However, upgrading requires a huge amount of reactive compensation to solve problems like regulation and voltage control. Therefore, series and shunt compensation both have the capability to provide reactive compensation and increased voltage stability [124,125]. In other words, FACTS controllers can reduce voltage stability problems in power networks [126,127,128,129,130,131,132]. A series of publications were found in the literature for improvement in voltage stability using FACTS devices [23,133,134,135]. The authors of [136] used SVCs to increase voltage stability in IEEE-14 and 30 bus systems. In addition, researchers also focused on improving the other two system parameters, i.e., increasing loading the margin and reducing active power losses. However, it is concluded that the SVC does not improve overloaded line conditions in case of contingencies (i.e., transmission line outage, generator outage, and overloads). Furthermore, a comparison between phase shifting transformers (PSTs) and SVCs has been made, and the results show that the SVC performs better than the PST in terms of increasing the line loading margin and bus voltage deviation. Furthermore, the genetic algorithm is used to find the optimal location of SVCs in the IEEE-30 bus system to improve the voltage profile and minimize losses [137]. Another study using genetic algorithms was conducted to provide voltage stability, improving voltage profile, by optimally placing an SVC in the IEEE-14 bus system. Similarly, in [138] line-stability-index-based voltage stability analysis for load variation is carried out to find the weaker bus in the IEEE-14 bus system so that the TCSC is optimally placed. Results show that the TCSC significantly contributes to voltage stability enhancement in the power system under study. Consequently, another study [139] based on the line stability index determined the most severe line in the power system (IEEE-14 bus system) and placed a TCSC to enhance voltage stability. The authors of [140] presented a comparison analysis of three FACTS controllers (TCSC, SSSC, and STATCOM) to enhance voltage stability in power systems. A modified IEEE-14 bus system is used to validate the results in this study. It is concluded that STATCOM provides a higher voltage stability margin as compared to the TCSC and SSSC. While comparing the TCSC and SSSC, it is concluded that the SSSC provides a better loading margin and voltage profile. Another comparative study of SVCs, TCSCs, and STATCOM for the voltage stability boundary of IEEE-14 and 30 bus systems is presented in [141]. The results depicted that the effects of the SVC and STATCOM are similar, but TCSC compensation depends on the compensation provided. In [142] a mutual effect of FACTS devices, i.e., SVC and SSSC, is discussed for enhancement of power system stability, which shows that a combination of FACTS devices is more useful than individual ones. Moreover, the authors of [143] used the voltage stability sensitivity factor to find the optimal location of FACTS devices (TCSC and SSSC). Therefore, successful placement of FACTS devices helps to enhance the voltage magnitude profile and mitigate power losses (active and reactive losses) in a Nigerian 48 bus power system. Results show superior performance of TCSCs as compared to SSSCs for steady-state stability. In [144], STATCOM was proposed for improving the voltage profile and load-ability limits of the multi-bus power system using MATLAB. The PSO algorithm is used to find the optimal location of a STATCOM that enhances the upper limit voltage and load-ability. Results show that STATCOM is a better option to adopt regardless of the use of conventional techniques like power system stabilizers (PSSs). Similarly, in a study based on voltage stability conducted by Prof. Gaber [145], modal or eigenvalue and line outage contingency analysis methods were used to identify the weakest bus for STATCOM and SSSCs, respectively. This study reveals that STATCOM achieved a better voltage stability margin than SSSCs.

As discussed previously, penetration of renewable energy into existing infrastructure causes voltage stability problems. Therefore, a comparative study for FACTS shunt devices (SVC and STATCOM) in wind farm generators connected to power systems is discussed in [146]. An IEEE-14 bus system is used in this case to evaluate the significance of FACTS devices for voltage stability. Results show that STATCOM performs better than SVCs. Similarly, the authors presented the impact of the integration of a large-scale PV system to displace the conventional generation and the effects on dynamic voltage stability of a power system [147]. Therefore, the authors used Dominion Virgin Power’s system with various PV scenarios to show how dynamic voltage stability is affected. The same authors propose a novel concept of voltage control together with auxiliary damping control in which inverters of the PV farm are utilized as a STATCOM, called PV-STATCOM, for increasing the power transmission limits during the day and night [148]. Another study, which reveals the significance of PV-STATCOM for enhancing power transmission limits, is discussed in [149]. In [150], the authors used a combination of a TCSC and STATCOM to improve voltage stability in a wind power grid-connected system. The authors concluded that this TCSC-STATCOM combination not only effectively increased voltage stability but also enhanced the low-voltage ride-through capability. In addition, this combination performs better compared to the individual controllers. The authors of [151] summarized a comparison of four different FACTS devices (SVC, TCSC, STATCOM, and UPFC) for providing voltage control. They concluded that STATCOM is less useful than other FACTS devices under study. However, the UPFC is preferable for stability enhancement regardless of its cost. Another comparison of FACTS controllers (SVC, TCSC, SSSC, and UPFC) has been presented in [152]. The results concluded that the TCSC provides low voltage control as compared to other proposed FACTS controllers, but the UPFC secures 1st place to provide stability enhancement. With this aim, another comparative study about various FACTS devices (SVC, SSSC, STATCOM, and UPFC) is discussed in [153] regarding voltage stability in the IEEE-14 bus system. As expected, results in this study also show the superiority of the UPFC over other FACTS controllers. However, the authors of this research study concluded that the worst response is shown by the SSSC. This result contradicts the findings presented in [151], because STATCOM shows the worst response. Therefore, we concluded that the selection of FACTS controllers for power systems not only depends on the parameters of the power system but also on the techniques used to find optimal locations of FACTS devices. Another study [154] is found in the literature with the objective of enhancing system stability and minimizing voltage deviation, line loading index, losses, and cost. These objectives were achieved by allocating FACTS devices (SVC, TCSC, and UPFC) to the IEEE-30 bus system. The authors concluded that UPFC leads.

A detailed investigation on the effects of FACTS devices (SVC, UPFC, SSSC, STATCOM) on the enhancement of voltage stability in multi-network systems is described in [155]. In general, it is concluded that all FACTS devices increased the voltage stability margin. In-depth comparison between FACTS devices shows that the UPFC is superior in this role and the SSSC scores the least. Another comprehensive comparison of FACTS controllers (SVC, STATCOM, SSSC, UPFC) for voltage stability and reactive power sustainability was performed by Dwivedi [156] using PSAT/MATLAB v2.1.2. Results reflect that the UPFC enhanced stability and reduced the loss of reactive power. Consequently, FACTS controllers (TCSC, UPFC, and IPFC) are placed in a Nigerian 58 bus, 330 kV network to enhance power transfer capability by controlling transmission voltage, reducing reactive power losses, and damping power system oscillations [157]. The authors concluded that the UPFC is more efficient than the SSSC and IPFC. Table 4 shows the role of various FACTS devices and their pros and cons. Similarly, several novel techniques like classical trial and error, partial swarm optimization (PSO), ant colony optimization (ACO), and hybrid PSO-ACO for tuning the STATCOM PI controller, to provide voltage regulation support, are presented in [158]. It is concluded that biological methods (PSO, ACO, and hybrid PSO-ACO) have better performance than classical methods (trial and error). Similarly, various conventional optimization techniques such as modal analysis [159], index method [160], numerical optimization [161], sensitivity method [162], and eigenvalue [163] are used to optimize the FACTS devices in voltage stability studies. Furthermore, some other techniques like the analytical approach [164], mixed-integer optimization [165], and linear programming [166] are also used. Moreover, artificial-intelligence-based optimization techniques used for voltage stability analysis are the genetic algorithm (GA) [167], particle swarm optimization (PSO) [160], artificial neural network (ANN) method [168], and fuzzy linear programming [169]. In addition, hybrid techniques used to optimize FACTS devices for voltage stability studies are OPF and GA [170], EP and SA, and much more. Similarly, the authors of [168] concluded that artificial-intelligence-based optimization algorithms such as PSO, GA, and harmony search algorithms are still preferred. A detailed discussion about optimization techniques is provided in the literature [19,171]. But the optimal location of FACTS devices has great importance in power networks. However, one important aspect is cost because FACTS devices require a huge initial investment. Therefore, proper optimal location, size, and capability cannot be found without using optimization techniques and an in-depth study of power networks [172,173]. Therefore, each objective function (voltage stability, transient stability, reactive power planning, cost minimization, congestion management, etc.) for power optimization techniques plays a key role in this regard. That is why optimization techniques can be classified as single or multi-objective based on the objective functions under study. Furthermore, based on objectives’ priority perspectives, multi-objective optimization techniques are categorized into non-dominated or dominated algorithms. Dominated optimization techniques give priority to one specific objective function over other objective functions whereas, in the non-dominated algorithms, the compromise between various objective functions results in a Pareto optimal front of solutions. In general, it is concluded that the selection of optimization techniques and required goals is a trade-off in selecting the FACTS devices and type of algorithm. Regarding optimization techniques, authors [19] have classified them into three groups, i.e., classical analytical-based methods, classical arithmetic-programming-based algorithms, and modern meta-heuristics-based algorithms, as shown in Figure 2. However, in [171] optimization techniques are divided into four different categories, i.e., analytical techniques, conventional techniques, meta-heuristics techniques, and hybrid optimization techniques (a combination of meta-heuristics approaches with analytical approaches or conventional techniques). Irrespective of the advantages of these techniques, the major drawbacks observed in analytical techniques are compromised computational accuracy, lack of solutions about the optimal placement of FACTS devices, and lack of power flow non-linearity consideration [174]. Similarly, conventional optimization techniques are limited due to the difficulty of managing constrained optimization problems. However, most adoptable approaches, to date, are meta-heuristic (stochastic, population-based) optimization techniques. This is because they are vastly efficient in dealing with a highly constrained, multi-modal, multi-objective, and discrete system [175,176]. Additionally, meta-heuristic optimization techniques can be utilized to determine the optimal position and sizing of various types of FACTS devices simultaneously. In the literature a detailed discussion about meta-heuristic optimization, the various categories, and their significance in terms of the minimization of various engineering problems is presented. But the trend shifted towards hybrid optimization techniques because they reduce the search space of meta-heuristics optimization techniques to obtain a simple structure and require less computational time [171].

Voltage stability for deregulated power networks is an important subject in today’s world. Due to several reasons, like limited resources, environmental policies, increased energy demand, etc., policy makers deregulate power networks. This deregulation and advancements in semi-conductor devices open new doors for different methods, algorithms, or techniques. Therefore, several reliable and economical options are available for adopting deregulated networks. In general, FACTS controllers are considered the most reliable, result-oriented (because they provide efficient results in real power networks), and secure options available irrespective of old conventional techniques for today’s deregulated networks.

Useful conclusions about voltage stability using FACTS devices in deregulated power systems are discussed below.

• FACTS devices are preferable and the best option to eradicate voltage stability problems compared to conventional techniques, i.e., PSS, etc.
• When comparing series FACTS devices (TCSC and SSSC) and shunt FACT devices (STATCOM), STATCOM performs better for voltage stability support. In general, it is concluded that shunt FACTS devices are more useful than series FACTS devices to provide voltage stability in deregulated power systems.
• In the series FACTS devices, TCSC and SSSC, the SSSC is superior to the TCSC.
• In shunt-connected FACTS devices (SVC and STATCOM) and series–shunt FACTS devices (UPFC), the UPFC leads to providing solutions for voltage stability. Consequently, STATCOM and the SVC are preferable.
• There is one expectational contradiction observed between two different research studies presented in [151,153]. In [151], STATCOM performs worse than the SVC, TCSC, and UPFC. However, in [153], the SSSC performed worse than the SVC, STATCOM, and UPFC. In both studies, only one series FACTS device was proposed. Generally, series FACTS devices do not perform better than shunt FACTS devices in providing reactive power compensation. However, this is not so in the case of the study presented in [151].
• FACTS devices are more beneficial while being used in combination with another FACTS device irrespective of their individual performance.
• While providing voltage support, TCSCs require bulky capacitors and reactors which is a demerit of TCSCs. The SSSC, on the other hand, does not require bulky capacitors and reactors. But the SSSC has a higher cost and complexity compared to the TCSC. Therefore, selecting a FACTS device is a trade-off among cost, maintenance, and complexity. Similarly, an advantage of the SVC is its lower cost and lower losses as compared to STATCOM. But its slower response due to time delay associated with its thyristor switching is a demerit. Finally, the UPFC is superior to other FACTS devices discussed above, but its complexity and higher cost are considered as disadvantages of its usage [177].
• Regarding optimization techniques, meta-heuristics and hybrid optimization are the most preferable techniques for optimally placed FACTS devices (to provide voltage stability).
• Conventional optimization techniques have difficulties managing constrained optimization problems but have effective convergence characteristics.
• Analytical approaches are unable to provide solutions for optimal placement of FACTS devices but play an important role when combining meta-heuristics optimization techniques.
• The authors of [19] state the conclusion of [90] that, despite providing better performance in reduction of voltage deviation and line loading, the UPFC is less likely to be installed in real-world scenarios due to its higher cost than that of the SVC and TCSC put together. Therefore, more studies will be conducted in future which focus more on price optimization to make its use economically pragmatic.

Table 4. FACTS devices and their role in voltage stability.

Reference	FACTS Devices	Methods/ Optimization Algorithm	Findings	Limitations/Drawbacks/ Future Directions
[136]	SVC	Multi-criteria decision making (MCDM)–analytic hierarchy process	Using MCDM, an optimal location for SVC is found with the aim to provide voltage stability using SVC in IEEE-14 bus, IEEE-30 bus and IEEE-118 bus test systems	Due to the lower cost, SVC was preferred because it is cheaper than UPFC and STATCOM [178] Compared with basic shunt capacitors, SVC and STATCOM are more costly
[146,150]	SVC, STATCOM, TCSC	Cooperative control scheme between FACTS devices	Enhanced voltage stability in renewable energy-based power system	SVC is less effective than STATCOM. Comparative effect of TCSC-STATCOM is more useful as compared to behavior of its individual parts for VS
[179]	SSSC	First-order PID controller	Enhance voltage injection with aim to increase stability using SSSC in deregulated power system	Must enhance this methodology to several buses to increase wider stability of power systems
[180]	STATCOM	Differential evolution (DE)	Reduction in voltage deviation after STATCOM placement in IEEE-30 bus system	Compared with SVC, STATCOM is costly, that is the only disadvantage
[140]	SSSC, TCSC, STATCOM	Loading margin	Provide voltage stability in the presence of various FACTS devices	STATCOM performs better than SSSC and TCSC. Between SSSC and TCSC, voltage profiles are better in the case of SSSC
[181]	SSSC, TCSC, STATCOM, UPFC	Saddle node bifurcation theory	Based on saddle node bifurcation theory, system loading can be determined with the aim to enhance voltage stability using FACTS devices	UPFC outperforms TSCS and STATCOM. UPFC is superior to other FACTS devices discussed above, but its complexity and higher cost are considered disadvantages of its usage [177]
[182]	UPFC	Critical loading margin, decoupled power injection modeling	IEEE-14 bus test system is considered as deregulated power system to enhance voltage stability under (N-1) line outage conditions using UPFC	UPFC enhanced voltage stability margin in contingencies. However, this analysis can be extended to multi-area systems in future
[156]	SVC, UPFC, SSSC, STATCOM	Newton–Raphson method	Standard IEEE-9 bus system is used to test the proposed methodology with the aim to enhance voltage stability with various FACTS devices	UPFC is superior in this study with minimal reactive power loss
[183]	SVC, TCSC	Modified shuffled frog leaping algorithm (MSFLA)	Increased voltage stability index along with generation cost reduction and decreased transmission loss in IEEE-30 bus system using SVC and TCSC. In addition, SFLA is also compared with EGA-DQLF, PSO, and FAPSO. The result shows the superiority of MSFLA	Irrespective of the benefits, one of the drawbacks of MSFLA is it required a large population size and iterative process
[184]	UPFC	Harris hawk optimization (HHO), harmony search (HS)	Enhanced power system stability, particularly voltage stability using L index and line congestion using line utilization factor in the IEEE-30 bus system	HHO trumps HS in terms of providing a better solution for enhancement in quality and voltage profile, reduction in real power losses, and general system robustness
[185]	SVC, TCSC, TCPS	Artificial ecosystem-based optimization (AEO), jellyfish search (JS), marine predators algorithm (MPA), moth flame optimization (MFO), slime mould algorithm (SMA), PSO, GWO	Reduce voltage deviation, power line losses, and generation cost in IEEE-30 bus system equipped with RE using FACTS devices (SVC, TCSC, and TCPS).	The major benefit of AEO compared to other optimization techniques is it requires a lower convergence rate and computational costs. Moreover, it has the ability to solve complex optimal power flow problems and achieve a lower value of cost functions

Table 4. FACTS devices and their role in voltage stability.

Reference	FACTS Devices	Methods/ Optimization Algorithm	Findings	Limitations/Drawbacks/ Future Directions
[136]	SVC	Multi-criteria decision making (MCDM)–analytic hierarchy process	Using MCDM, an optimal location for SVC is found with the aim to provide voltage stability using SVC in IEEE-14 bus, IEEE-30 bus and IEEE-118 bus test systems	Due to the lower cost, SVC was preferred because it is cheaper than UPFC and STATCOM [178] Compared with basic shunt capacitors, SVC and STATCOM are more costly
[146,150]	SVC, STATCOM, TCSC	Cooperative control scheme between FACTS devices	Enhanced voltage stability in renewable energy-based power system	SVC is less effective than STATCOM. Comparative effect of TCSC-STATCOM is more useful as compared to behavior of its individual parts for VS
[179]	SSSC	First-order PID controller	Enhance voltage injection with aim to increase stability using SSSC in deregulated power system	Must enhance this methodology to several buses to increase wider stability of power systems
[180]	STATCOM	Differential evolution (DE)	Reduction in voltage deviation after STATCOM placement in IEEE-30 bus system	Compared with SVC, STATCOM is costly, that is the only disadvantage
[140]	SSSC, TCSC, STATCOM	Loading margin	Provide voltage stability in the presence of various FACTS devices	STATCOM performs better than SSSC and TCSC. Between SSSC and TCSC, voltage profiles are better in the case of SSSC
[181]	SSSC, TCSC, STATCOM, UPFC	Saddle node bifurcation theory	Based on saddle node bifurcation theory, system loading can be determined with the aim to enhance voltage stability using FACTS devices	UPFC outperforms TSCS and STATCOM. UPFC is superior to other FACTS devices discussed above, but its complexity and higher cost are considered disadvantages of its usage [177]
[182]	UPFC	Critical loading margin, decoupled power injection modeling	IEEE-14 bus test system is considered as deregulated power system to enhance voltage stability under (N-1) line outage conditions using UPFC	UPFC enhanced voltage stability margin in contingencies. However, this analysis can be extended to multi-area systems in future
[156]	SVC, UPFC, SSSC, STATCOM	Newton–Raphson method	Standard IEEE-9 bus system is used to test the proposed methodology with the aim to enhance voltage stability with various FACTS devices	UPFC is superior in this study with minimal reactive power loss
[183]	SVC, TCSC	Modified shuffled frog leaping algorithm (MSFLA)	Increased voltage stability index along with generation cost reduction and decreased transmission loss in IEEE-30 bus system using SVC and TCSC. In addition, SFLA is also compared with EGA-DQLF, PSO, and FAPSO. The result shows the superiority of MSFLA	Irrespective of the benefits, one of the drawbacks of MSFLA is it required a large population size and iterative process
[184]	UPFC	Harris hawk optimization (HHO), harmony search (HS)	Enhanced power system stability, particularly voltage stability using L index and line congestion using line utilization factor in the IEEE-30 bus system	HHO trumps HS in terms of providing a better solution for enhancement in quality and voltage profile, reduction in real power losses, and general system robustness
[185]	SVC, TCSC, TCPS	Artificial ecosystem-based optimization (AEO), jellyfish search (JS), marine predators algorithm (MPA), moth flame optimization (MFO), slime mould algorithm (SMA), PSO, GWO	Reduce voltage deviation, power line losses, and generation cost in IEEE-30 bus system equipped with RE using FACTS devices (SVC, TCSC, and TCPS).	The major benefit of AEO compared to other optimization techniques is it requires a lower convergence rate and computational costs. Moreover, it has the ability to solve complex optimal power flow problems and achieve a lower value of cost functions

6. Congestion Management

Congestion management is one of the technical challenges facing deregulated power networks [186,187]. Rapid energy demand and uncertainty in available resources cause problems like outages and disruption of electrical power. Therefore, electrical power transmission capacity is unable to transmit sufficient electrical power under these constraints. This technical phenomenon in deregulated networks requires congestion management.

Christie et al. [155] describe that, in a deregulated market environment, electrical power companies ensure maximum utilization of transmission lines during high-demand periods. This transmission behavior reduced the security margin of the entire system. Also, the presence of any constraints in power networks limits the power transfer capability. In practice, it may not be possible to deliver all bilateral and multi-lateral contracts in full and to supply the entire pool demand at low cost, as it may lead to violations of operating constraints such as voltage limits and line overloads [188,189]. Such transmission limitations are referred to as congestion. Therefore, congestion occurs due to the results of contingencies or lack of coordination between generation and transmission companies.

Congestion management is generally approached by two methods, i.e., cost-free means (CFM) and non-cost-free means (NCFM). The CFM method is called cost free because it has no marginal cost and it includes the operation of power electronics devices or FACTS devices, network configuration, etc. Conversely, the NCFM method takes into consideration the security constraints, generation, redispatch, network sensitivity factor methods, congestion pricing, market base methods, and application of FACTS devices [190]. FACTS controllers play a significant role in the reduction of transmission congestion and allowing better utilization of electric grid infrastructure [191]. Several methods for congestion management in deregulated environments are presented in [192,193]. However, several issues related to FACTS devices are control interactions, modeling, cost, size, optimal location, etc. [191]. Therefore, the authors of [194,195,196] proposed new methodologies for the placement of series FACTS devices to reduce congestion in deregulated networks. To validate the results, proposed methods were tested in IEEE-14, IEEE-30, and IEEE-57 bus systems using TCSCs. Another approach to solving congestion in the IEEE-14 bus network, by optimal placement of TCSCs, is presented in [197]. Furthermore, congestion management is provided by optimal placement of TCSCs in deregulated networks (IEEE-30 and IEEE-57 bus systems) using the gravitational search assisted (GSA) algorithm [198]. However, two methods, i.e., optimal power flow (OPF) and available transfer capability (ATC)-based GSA, are used in this study. Results show that GSA-based OPF reduced congestion in deregulated networks compared to GSA-based ATC, OPF-based GA, and PSO. Similarly, research to reduce congestion (by finding an optimal location) using TCSCs and SVCs in a deregulated environment is presented in [199]. The authors further conclude that locations which present a favorable solution with respect to one of the objectives (branch loading, voltage stability, and loss minimization) are not effective with respect to other objective functions. Later, these objective functions are simultaneously optimized using the strength Pareto evolutionary algorithm (SPEA) and tested in the IEEE-30 bus system. Furthermore, a combination of FACTS devices, i.e., SVC and TCSC, is implemented in restructured networks to provide congestion management [200]. In the literature [201], congestion is reduced by using individual or combined FACTS devices (TCSC and SVC). It is observed that FACTS devices reduced congestion, but a TCSC with an SVC is more effective compared to their individual usage. It is further concluded that the SVC improved the voltage profile of restructured power systems, but the TCSC has more control of line loadings. Similarly, in [202] the authors also validate the effectiveness of combined TCSC and SVC performance to reduce congestion in the IEEE-14 bus system. Similarly, detailed research on the IEEE-30 bus system with the help of a new meta-heuristic algorithm using a combination of TCSC-SVC-HVDC devices, which reduces congestion in terms of reducing transmission line loss and increases load ability, is presented in [203]. The result shows significant improvement in the power system using the proposed approach (the TCSC as series compensator and the SVC as a parallel compensator).

More FACTS devices like the UPFC and IPFC also reduced congestion in power networks. In the literature [204,205], the UPFC and TCSC are implemented to reduce congestion and generation cost. It is further concluded that the TCSC performs better than the UPFC to improve power flow in a deregulated power system [205]. The UPFC also reduced congestion by rescheduling real power of generators in the New England 39 bus network and Indian 75 bus system [206]. Consequently, congestion in power systems increased with the integration of renewable energy resources into existing networks. Based on this, a study of a 110 kV distribution system using UPFCs to reduce congestion was presented in the literature [207]. In addition, a parallel tempering approach as well as a greedy algorithm are used to optimize UPFC devices in terms of their size, their number and placement in the power system, control parameters, and cost. Results show that an optimally placed UPFC power flow control can reduce congestion by 99.13%. Like the UPFC, the IPFC is also used to relieve congestion in power systems. A constriction factor-based particle swarm optimization analysis is used with IPFCs to reduce congestion in the IEEE-30 bus system under different loading and contingency situations. In [208], an IPFC is optimally sized in the IEEE-30 bus system and in [209] an IPFC is optimally placed in the IEEE-30 bus system to reduce cost and mitigate congestion. Similarly, the IPFC reduced congestion (by improving power flow and voltage profile) in the Nigerian 41 transmission network [210] and, in [211], an IPFC is optimally placed in Ethiopian Electric Power 400 kV to enhance the average line severity index. In [212], the authors compare single and combined FACTS devices (UPFC, TCSC, and SVC) that were used to reduce congestion in IEEE-6, 30, and 118 bus systems and the Tamil Nadu Electricity Board (TNEB) 68 bus system by enhancing system load-ability with minimum cost of installation. It is observed that the SVC offers the lowest cost of installation with the lowest system load-ability improvement as compared to other FACTS devices in IEEE-30 and 118 bus systems. However, the cost of installation is high in the case of UPFCs but provides maximum improvement in system load-ability in IEEE systems. However, the TCSC performs better with minimum installation cost. In addition, for TNEB (a practical bus system in India), the TCSC provides maximum load-ability with minimum installation cost compared to the UPFC and SVC. Furthermore, in [213] a GA-based graphical interface is presented to provide congestion management by optimally placing FACTS devices in large power systems. Therefore, researchers implemented five different FACTS devices (UPFC, TCPST, TCVR, TCSC, and SVC) to provide static system load-ability in an IEEE bus network (up to 300 buses). It is observed that the UPFC is the most effective FACTS device as compared to other FACTS devices in terms of increased load-ability while reducing loss at the same time in large power systems. However, it is further concluded that system load-ability increased by increasing the number of FACTS devices in interconnected power systems. Another comparison study [214] for FACTS devices (UPFC, SSSC, and STATCOM) has been carried out by optimal rescheduling of generators’ output and thereby the congestion cost in the IEEE-24 and 57 bus systems. Results show that the UPFC enhanced the load-ability margin of power systems more than the SSSC and STATCOM. Meanwhile, the congestion cost reduction is lowest in the case of the UPFC. However, the SSSC secured second place while STATCOM leads in terms of reducing congestion costs. Therefore, it is concluded that generators are subject to lower values of up and down rescheduling with STATCOM. Consequently, congestion in deregulated power systems was reduced by optimally placed FACTS devices (SVC, TCSC, and UPFC) using sensitivity analysis [215]. It is concluded that the best optimal place for TCSCs is those transmission lines which have a higher value of the line stability index. Similarly, the best optimal location for SVCs in interconnected networks is those weak buses which have higher voltage deviation in response to increases in load-ability. Finally, the same authors concluded that the UPFC is to be placed at those transmission lines having higher active power. Another study was conducted in [214] that was mainly focused on providing congestion management (based on congestion cost by optimal rescheduling of generator outputs) with FACTS devices (SSSC, STATCOM, and UPFC) ensuring an increase in voltage stability and loadability limits in deregulated systems. Results concluded that congestion costs are significantly reduced in the presence of an SSSC. The UPFC also reduces congestion costs (subject to lower rescheduling of generators) but less than the SSSC. However, with STATCOM the reduction in congestion cost is marginal. Furthermore, the same authors conclude that the UPFC provides a greater voltage stability margin, therefore providing more support for load-ability as compared to the SSSC and STATCOM. Furthermore, Table 5 also shows the role of various FACTS devices and their pros and cons in congestion management.

Providing congestion management using FACTS devices is a challenging task itself. Irrespective of choosing the right FACTS device for a particular deregulated power network, the selection of optimization techniques that make FACTS devices beneficial to a deregulated power system plays an important role in this regard. Therefore, in the literature, there are four different groups of optimization techniques that are broadly discussed [104]. These groups include classical optimization methods, analytical or sensitivity index methods, meta-heuristics methods, and hybrid or mixed methods. Major classical optimization techniques include the Newton–Raphson (NR) algorithm, mixed integer linear programming algorithm, mixed integer non-linear programming, sequential quadratic programming algorithm, non-linear programming algorithm, mixed integer programming algorithm, etc. In [216] an SVC is used with the NR algorithm in the IEEE-5 bus system to provide power system security. In the literature [217], the TCSC and UPFC are used with the sequential quadratic programming algorithm in IEEE-14 and 30 bus systems to provide congestion management. In the literature [218], successive-quadratic-programming-technique-based FACTS devices (SSSC and STATCOM) are used in IEEE-14, 30, and 118 bus systems to alleviate congestion by finding the optimal placement and number of FACTS devices. In [219], mixed-integer-linear-optimization-based SVCs and TCSCs are used in the IEEE-118 bus system to reduce congestion (by improving power system security by utilizing power system load-ability). In [220] a mixed-integer-non-linear-programming-model-based TCSC and energy storage are implemented in the IEEE-18 and 30 bus systems to mitigate transmission congestion. In [200] a mixed integer programming optimization technique is used with SVCs, TCSCs, and generators for congestion management in a restructured environment. The authors of [104] concluded that the mixed integer linear programming optimization method and mixed integer non-linear programming methods are widely used in large-scale power networks but are computationally expensive. However, for small-scale power systems the most acceptable optimization techniques are non-linear programming and sequential quadratic methods because they contain power system variables.

Similarly, various analytical or sensitivity-index-based optimization techniques are also used with FACTS devices to mitigate congestion in deregulated systems. In the literature [221], locational marginal price and congestion-rent-contribution-based approaches are used with a series FACTS device, the TCSC, in the IEEE-14, 30, and 57 bus systems for providing congestion management. Results show that the congestion rent contribution approach is more effective than locational marginal price techniques in deregulated electricity markets. Another sensitivity-based approach named the “power flow index” is discussed in [196] to optimally place series FACTS devices and relieve congestion in cases of line outage in deregulated power systems. Similarly, in another study [222] real power flow and transmission line relief security indices in the IEEE-14 bus test system are used to relieve congestion. Some other analytical approaches such as single contingency sensitivity [223], branch overloading line [224], bus voltage violations [213], the extended voltage phasors approach [225], etc. are used to relieve congestion.

However, meta-heuristic optimization algorithms are more popular for FACTS device optimization [104] due to the following reasons [226]: they are simple to implement and rely on simple concepts, effective and adaptable, bypass local optima, do not require gradient information, can be utilized in a wide range of multi-disciplined problems, and useful to solve complex and discrete optimization problems [227]. Meta-heuristics optimization is divided into four different subtechniques [228], shown in Figure 3, because it draws from physical phenomena, animal behavior, and nature-inspired phenomena. Therefore, in the literature [229], the genetic algorithm and differential evolution techniques are used to minimize transmission loss and simultaneously reduce operational cost by optimally placing FACTS devices (SVC, TCSC, and UPFC) in the IEEE-30 network bus system. In addition, in a comparative study [230] the modified whale optimization algorithm, whale optimization algorithm, genetic algorithm, differential evolution, and fast evolutionary programming are used to curtail congestion loss and improve system losses and voltages in the IEEE-118 bus system and New England 39 bus systems. It is concluded that the modified whale optimization programming algorithm superseded the other compared algorithms. In addition, it provides the fastest convergence mobility for congestion management problems. Kumar et al. [231] presented another meta-heuristic approach named the biogeography optimization method for power system security constraints with UPFCs in the IEEE-30 bus system. In addition, the authors calculate various indices such as the sensitivity index, overload index, and voltage violation index with or without optimally placed UPFCs in the IEEE-18 bus system. It is observed that the overload and voltage violation index are significantly reduced using optimally placed UPFCs with biogeography optimization methods. Furthermore, biogeography-based optimization method performance is compared with genetic algorithm and partial swarm optimization methods. It is concluded that biogeography optimization performance is superior. In [232], another meta-heuristic approach, the gravitational search algorithm, was found to be a superior algorithm while comparing it with the genetic algorithm, differential evolution, and partial swarm optimization. Further, it was concluded that gravitational search algorithms are effective to reduce congestion in terms of increasing load-ability and minimizing operating costs and total active power loss in power systems (IEEE-30 and 57 bus systems). Another comparison [233] of three meta-heuristic techniques, simulated annealing, the tabu search method, and the genetic algorithm, made with five FACTS devices (SVC, TCSC, TCVR, TCPST, and UPFC) in the IEEE-118 bus network system concluded that the simulated annealing algorithm is less beneficial compared to the other compared algorithms. In the literature [234], the whale optimization algorithm is compared with grey wolf optimization, differential evolution, the quasi-opposition-based grey wolf optimization algorithm, and quasi-opposition-based differential evolution to minimize active power loss and operating costs while maintaining voltage profiles within permissible limits in IEEE-30 and 57 bus systems. It is observed that the whale optimization algorithm is superior and more effective than other algorithms under consideration because it provides reliable guidance for optimal coordination of FACTS devices (SVC and TCSC). Moreover, in [235], an imperialistic competitive algorithm approach was compared with pattern search, the gravitational search algorithm, non-linear programming, evolutionary programming, bat swarm optimization, asexual reproduction optimization, and the backtracking search algorithm to allocate FACTS devices (TCPST and TCSC) for relieving the power congestion (overloads and voltage deviations) in IEEE-39 bus systems. Results clearly show the usefulness and better performance of the imperialistic competitive algorithm than other meta-heuristic algorithms under study. Furthermore, another meta-heuristic algorithm named the improved harmony search algorithm [236] shows its superior performance compared to the harmonic search algorithm, improved genetic algorithm, tabu search, improved evolutionary programming, gradient method, genetic algorithm, modified differential evolution algorithm, refined genetic algorithm, and evolutionary programming techniques [236]. A detailed review of meta-heuristics optimization techniques to optimally place and size multiple FACTS devices in power systems is presented in [171]. In general, optimization techniques have their own importance for congestion relief. However, the selection of a particular optimization technique for a particular power network is debatable and out of the scope of this paper. But meta-heuristic techniques are preferred by researchers nowadays.

Useful conclusions about congestion management and the role of FACTS devices in deregulated power systems are discussed below.

• FACTS devices are one of the dominant approaches used in practical systems to provide congestion management. However, FACTS devices provide congestion management in three different ways, i.e., optimal placement of FACTS devices, price- or cost-based analysis, and sensitivity index [237].
• Congestion management has two methods, i.e., CFM and NCFM. However, CFM is more popular and easier to implement compared to NCFM because the marginal cost (and not capital cost) is minimal.
• The UPFC is the most effective FACTS controller used for congestion management. In fact, it provides maximum improvement in system load-ability, but the cost of installation is high, which is a major drawback of UPFCs economically.
• The UPFC provides static system load-ability more effectively than the TCPST, TCVR, SVC, TCSC, SSSC, and STATCOM.
• The TCSC is the most popular FACTS device in practical applications because it performs better with minimum installation cost.
• Congestion can be reduced by optimally placed FACTS devices. Therefore, the best place for SVCs is those weak buses that have higher voltage deviation in response to increases in load-ability. Similarly, for TCSCs the best place is those transmission lines that have a higher value of the line stability index. And UPFCs should be placed at those transmission lines having higher active power.
• FACTS devices reduced more congestion in combination with another FACTS devices or other conventional devices as compared to their individual usage.
• In large power systems, the load-ability increased with the increase in the number of FACTS devices.
• There are four different optimization techniques presented in the literature (previously discussed). However, meta-heuristics optimization algorithms are widely adopted and preferably considered in power systems. Out of the various approaches, the improved harmonic search algorithm, imperialistic competitive algorithm, and whale optimization algorithm are the more popular meta-heuristic approaches adopted.
• Classical optimization techniques (mixed integer linear programming and mixed integer non-linear programming optimization) are widely used to optimally place FACTS devices in deregulated networks because such techniques can handle both discrete and continuous variables.

Table 5. FACTS devices and their role in congestion management.

Reference	FACTS Devices	Methods/ Optimization Algorithm	Findings	Limitations/Drawbacks/ Future Directions
[194]	TCSC, SSSC	Power flow model	Reduced congestion in the IEEE-40 bus system by optimal placement of FACTS devices	The authors reduced congestion in terms of improving voltage profile and power transfer capability. However, the best transmission line in the IEEE-40 bus system is presented but no direct comparison about selection of these two FACTS devices is presented. In addition, no simultaneous effect of TCSCs and SSSCs is evaluated
[195]	TCSC	PSO	Alleviate congestion on IEEE-30, IEEE-118, and 33 bus Indian bus network using TCSCs. Therefore, “load flow sensitivity factor” is proposed to optimally place TCSCs. Moreover, robustness of the PSO is also analyzed based on mean time of execution, number of fitness evaluations, success rate, etc.	The PSO algorithm is considered applicable on both small and large power systems. In future, stochastic optimization algorithm performance will be compared with other optimization algorithms. Authors should consider power loss in the future study
[196]	TCSC	Reduction of VAR power losses and real power performance index	Optimally placed the TCSC based on cost reduction and sensitivity index in IEEE-14 bus system	Lack of comparison between various FACTS devices or effects of NCFM on power system
[204]	TCSC, UPFC	Optimal power flow (OPF) using GA	Congestion reduced by OPF method using GA to find the global optimal schedule	Results shows that two UPFCs with a TCSC outperform one UPFC with two TCSCs for reducing line loading. The major drawback is the higher cost of UPFCs
[205]	TCSC, UPFC	Sensitivity analysis	Congestion is removed in IEEE-30 bus system using TCSCs and UPFCs which significantly reduced the loss and cost and resulted in increased load-ability	The TCSC outperformed the UPFC for active power flow improvement. Lack of detailed cost-effective comparison of FACTS devices. Future studies can be extended to power quality in terms of voltage profile improvements using FACTS devices
[209]	IPFC	Expected security cost using PSO	Congestion in IEEE-30 bus system is reduced by simultaneously optimally placing IPFCs and minimizing expected security cost using PSO. Moreover, social welfare maximization and generation rescheduling are also considered without IPFCs to reduce congestion of the IEEE-30 bus system	IPFCs can be placed in more than one line and generation rescheduling is not needed if the IPFC is optimally placed. IPFCs perform better than SSSCs, but cost is a major barrier to their selection
[213]	SVC, TCSC, TCVR, TCPST, UPFC	GA	Graphical user interface (GUI) of more than 300 IEEE bus systems is considered for optimal placement of FACTS devices with aim of reducing congestion	The UPFC reduced congestion better than the SVC, TCSC, TCVR, and TCPST. Multiple FACTS devices perform better than their individual placement in power systems
[218]	SSSC, STATCOM	Neural models based on the averaging technique	Reduced congestion in IEEE-14, 30, and 118 bus systems based on averaging technique and nodal price indices	STATCOM is less effective than SSSCs for alleviating congestion of transmission lines. Increased number of FACTS devices of the same or different types can be more effective than their individual installation. However, FACTS device cost is challenging for their selection
[189]	SVC, TCSC	Mixed integer optimization technique	Congestion (total market cost) in modified IEEE-30 bus system is reduced by a combination of demand response and FACTS devices	A combination of FACTS devices and demand response is more beneficial for congestion management reduction
[229]	SVC, TCSC, UPFC	GA, DE	Optimal placement of FACTS devices in IEEE-30 bus system significantly reduced the active power loss, transmission loss, and operating cost using GA and DE optimization techniques	Lack of optimization techniques’ comparison with individual FACTS devices. It is concluded that the series FACTS device (TCSC) and shunt FACTS device (SVC) with a UPFC outperform their combination without a UPFC
[232]	SVC, TCSC	GA, DE, PSO, GSA	Various constraints such as active power loss and operating cost of IEEE-30 and 57 bus systems are minimized along with enhanced load-ability using FACTS devices (SVC and TCSC)	Lack of comparison about performance of FACTS devices. GSA performance is superior to GA, DE, and PSO. The best place for SVCs is those weak buses that have higher voltage deviation in response to the increase in load-ability. Similarly, for TCSCs the best place is those transmission lines that have a higher value of line stability index [215]
[238]	Modular static synchronous series compensator (M-SSSC)	Correlation coefficient analysis, linear regression	M-SSSC is utilized to manage congestion and increase RES integration and cross-border power flows in the power system by adjusting transmission line reactance in real time	Major advantages of M-SSSC are scalability, rapid deploy-ability, redeployability, and lower cost
[239]	M-SSSC	GA, PSO, teaching–learning-based optimization (TLBO)	Optimal M-SSSC configurations and placement in IEEE-14 bus system and a subarea of the Colombian power grid reduce congestion, enhance voltage stability, and improve overall system efficiency using L-index	The M-SSSC is more beneficial than the traditional SSSC in terms of simultaneously addressing congestion issues, enhancing voltage stability, and optimizing power flow
[240]	SVC, TCSC	Symbiotic organism search (SOS) algorithm, PSO, GA, DE, GSA	Provide congestion management by minimizing the operating cost and system losses in IEEE-57 and 118 bus networks in the presence of SVCs and TCSCs in deregulated regime. Optimization algorithm SOS shows better outcome than PSO, GA, DE, and GSA	The major benefit of using a newer meta-heuristic optimization algorithm, SOS, is that it shows quicker convergence mobility and stronger local search ability
[123]	UPFC	Modified moth flame optimization (MMFO), MFO, PSO, GSA, GA	Reducing congestion and increasing line loading (transmission loss and rescheduling cost) by employing three different curtailment strategies, group, separate, and point-to-point strategies, using UPFCs in modified IEEE-14 bus and IEEE-30 bus systems. MMFO algorithms outperform MFO, PSO, GSA, GA	The major benefit of MMFO is faster convergence and it outperforms MFO, PSO, GSA, and GA in terms of consistency and feasibility for minimizing transaction deviation and loss

Table 5. FACTS devices and their role in congestion management.

Reference	FACTS Devices	Methods/ Optimization Algorithm	Findings	Limitations/Drawbacks/ Future Directions
[194]	TCSC, SSSC	Power flow model	Reduced congestion in the IEEE-40 bus system by optimal placement of FACTS devices	The authors reduced congestion in terms of improving voltage profile and power transfer capability. However, the best transmission line in the IEEE-40 bus system is presented but no direct comparison about selection of these two FACTS devices is presented. In addition, no simultaneous effect of TCSCs and SSSCs is evaluated
[195]	TCSC	PSO	Alleviate congestion on IEEE-30, IEEE-118, and 33 bus Indian bus network using TCSCs. Therefore, “load flow sensitivity factor” is proposed to optimally place TCSCs. Moreover, robustness of the PSO is also analyzed based on mean time of execution, number of fitness evaluations, success rate, etc.	The PSO algorithm is considered applicable on both small and large power systems. In future, stochastic optimization algorithm performance will be compared with other optimization algorithms. Authors should consider power loss in the future study
[196]	TCSC	Reduction of VAR power losses and real power performance index	Optimally placed the TCSC based on cost reduction and sensitivity index in IEEE-14 bus system	Lack of comparison between various FACTS devices or effects of NCFM on power system
[204]	TCSC, UPFC	Optimal power flow (OPF) using GA	Congestion reduced by OPF method using GA to find the global optimal schedule	Results shows that two UPFCs with a TCSC outperform one UPFC with two TCSCs for reducing line loading. The major drawback is the higher cost of UPFCs
[205]	TCSC, UPFC	Sensitivity analysis	Congestion is removed in IEEE-30 bus system using TCSCs and UPFCs which significantly reduced the loss and cost and resulted in increased load-ability	The TCSC outperformed the UPFC for active power flow improvement. Lack of detailed cost-effective comparison of FACTS devices. Future studies can be extended to power quality in terms of voltage profile improvements using FACTS devices
[209]	IPFC	Expected security cost using PSO	Congestion in IEEE-30 bus system is reduced by simultaneously optimally placing IPFCs and minimizing expected security cost using PSO. Moreover, social welfare maximization and generation rescheduling are also considered without IPFCs to reduce congestion of the IEEE-30 bus system	IPFCs can be placed in more than one line and generation rescheduling is not needed if the IPFC is optimally placed. IPFCs perform better than SSSCs, but cost is a major barrier to their selection
[213]	SVC, TCSC, TCVR, TCPST, UPFC	GA	Graphical user interface (GUI) of more than 300 IEEE bus systems is considered for optimal placement of FACTS devices with aim of reducing congestion	The UPFC reduced congestion better than the SVC, TCSC, TCVR, and TCPST. Multiple FACTS devices perform better than their individual placement in power systems
[218]	SSSC, STATCOM	Neural models based on the averaging technique	Reduced congestion in IEEE-14, 30, and 118 bus systems based on averaging technique and nodal price indices	STATCOM is less effective than SSSCs for alleviating congestion of transmission lines. Increased number of FACTS devices of the same or different types can be more effective than their individual installation. However, FACTS device cost is challenging for their selection
[189]	SVC, TCSC	Mixed integer optimization technique	Congestion (total market cost) in modified IEEE-30 bus system is reduced by a combination of demand response and FACTS devices	A combination of FACTS devices and demand response is more beneficial for congestion management reduction
[229]	SVC, TCSC, UPFC	GA, DE	Optimal placement of FACTS devices in IEEE-30 bus system significantly reduced the active power loss, transmission loss, and operating cost using GA and DE optimization techniques	Lack of optimization techniques’ comparison with individual FACTS devices. It is concluded that the series FACTS device (TCSC) and shunt FACTS device (SVC) with a UPFC outperform their combination without a UPFC
[232]	SVC, TCSC	GA, DE, PSO, GSA	Various constraints such as active power loss and operating cost of IEEE-30 and 57 bus systems are minimized along with enhanced load-ability using FACTS devices (SVC and TCSC)	Lack of comparison about performance of FACTS devices. GSA performance is superior to GA, DE, and PSO. The best place for SVCs is those weak buses that have higher voltage deviation in response to the increase in load-ability. Similarly, for TCSCs the best place is those transmission lines that have a higher value of line stability index [215]
[238]	Modular static synchronous series compensator (M-SSSC)	Correlation coefficient analysis, linear regression	M-SSSC is utilized to manage congestion and increase RES integration and cross-border power flows in the power system by adjusting transmission line reactance in real time	Major advantages of M-SSSC are scalability, rapid deploy-ability, redeployability, and lower cost
[239]	M-SSSC	GA, PSO, teaching–learning-based optimization (TLBO)	Optimal M-SSSC configurations and placement in IEEE-14 bus system and a subarea of the Colombian power grid reduce congestion, enhance voltage stability, and improve overall system efficiency using L-index	The M-SSSC is more beneficial than the traditional SSSC in terms of simultaneously addressing congestion issues, enhancing voltage stability, and optimizing power flow
[240]	SVC, TCSC	Symbiotic organism search (SOS) algorithm, PSO, GA, DE, GSA	Provide congestion management by minimizing the operating cost and system losses in IEEE-57 and 118 bus networks in the presence of SVCs and TCSCs in deregulated regime. Optimization algorithm SOS shows better outcome than PSO, GA, DE, and GSA	The major benefit of using a newer meta-heuristic optimization algorithm, SOS, is that it shows quicker convergence mobility and stronger local search ability
[123]	UPFC	Modified moth flame optimization (MMFO), MFO, PSO, GSA, GA	Reducing congestion and increasing line loading (transmission loss and rescheduling cost) by employing three different curtailment strategies, group, separate, and point-to-point strategies, using UPFCs in modified IEEE-14 bus and IEEE-30 bus systems. MMFO algorithms outperform MFO, PSO, GSA, GA	The major benefit of MMFO is faster convergence and it outperforms MFO, PSO, GSA, and GA in terms of consistency and feasibility for minimizing transaction deviation and loss

7. Conclusions

Deregulation of electrical power systems becomes an essential need of the electrical world from time to time because of new challenges and the integration of new power resources or technologies. Therefore, problems related to power system stability, reliability, efficiency, or security arise that adversely affect power networks, both technically and economically. In this review paper, a detailed discussion about features of FACTS controllers, their role in deregulated power networks, and comparison of FACTS controllers against several techniques and power system parameters is provided. With a history of four decades or more, FACTS controllers have proved themselves as a reliable, secure, efficient, and cost-effective solution to some electrical problems. This study aims to elucidate the significance, advantages, disadvantages, and limitations of FACTS devices in LFC, voltage stability, and congestion management within deregulated power networks. In the context of LFC studies, third-generation FACTS devices are generally more prominent than other FACTS devices. However, the G-UPFC and IPFC are commonly integrated into subnetworks, irrespective of the UPFC, STATCOM, or SSSC, owing to their capability to control power flow across multiple transmission lines. An interesting finding is that FACTS devices, when combined with energy storage, are more beneficial than when used alone. Furthermore, when comparing second-generation FACTS devices, it is concluded that STATCOM is preferable in shunt configurations, whereas the SSSC is preferable in series configurations for LFC. Regarding voltage stability, it was concluded that shunt-connected FACTS devices are more beneficial than series-connected FACTS devices. However, among series-connected FACTS devices, the SSSC is superior in providing voltage stability compared to the TCSC. Similarly, series- and shunt-connected FACTS devices, such as the UPFC, offer better reactive compensation or voltage stability than series-only or shunt-only FACTS devices. Another notable finding of this study is that the combination of multiple FACTS devices is more advantageous than their individual performances. However, there is a trade-off between the selection of FACTS devices and their individual proposed benefits; for example, series-connected FACTS devices (TCSC or SSSC) and shunt-connected FACTS devices (SVC or STATCOM) are not economically viable owing to their cost for voltage stability. In terms of congestion management, FACTS devices are a prominent solution for providing congestion management with NCFM methods compared to CFM methods because the marginal cost in NCFM is minimal. Moreover, the load-ability limits in deregulated power systems are increased by installing one or more FACTS devices. The latest generation of FACTS devices, such as the UPFC, is more effective for congestion management than the TCPST, TCVR, SVC, TCSC, SSSC, and STATCOM. However, cost remains a significant drawback for installation. Therefore, the TCSC is more commonly adopted for congestion management in parallel applications, whereas the SVC is selected for installation at weak buses with high voltage deviations in response to increased load-ability.

FACTS devices are crucial for enhancing LFC, maintaining voltage stability, and alleviating congestion in deregulated power systems. Nonetheless, optimization techniques are equally significant for deriving optimal solutions or for the optimal placement of FACTS devices. Typically, meta-heuristic or hybrid optimization techniques are favored over classical, conventional, and analytical methods. Despite the practical advantages of classical control methods, such as ease of use and adaptability, they often demonstrate suboptimal performance against diverse system dynamics, non-linearities, and disturbances. Similarly, conventional optimization techniques, while possessing effective convergence characteristics, often struggle to manage constrained optimization problems. Furthermore, analytical approaches are inadequate for determining the optimal placement of FACTS devices in the networks. However, these aforementioned optimization techniques are effective when integrated with meta-heuristic techniques.

Generally, FACTS controllers utilize existing resources of power networks that reflect their importance for present and future power systems, particularly hybrid or renewable-energy-based power systems. The authors believe that FACTS controllers have the ability to resolve multi-dimensional problems of existing power systems by more appropriate means. In addition, this review article will be very helpful to future researchers for determining the relevant references about the potential and behavior of FACTS controllers in deregulated power networks.

In future, study should be extended to explore the impact of FACTS devices on hybrid or renewable-energy-based power systems. Moreover, exploring various machine-learning-based algorithms and artificial intelligence and their effect with FACTS devices on deregulated power systems is recommended.