Static Security Assessment: A Case Study of the Saudi National Grid

The static security assessment of the Saudi National Network helps to identify the weaknesses and potential threats and provides a comprehensive understanding of the network’s ability to address such incidents due to probable outages. This paper presents the static security assessment of the Saudi national grid with reference to the transmission line overloads and the bus voltage deviations in the system following the initiation of the specified contingencies. Critical contingencies are identified, and these are ranked on the basis of the limit violations of the line overloads and the bus voltage deviations obtained through the Newton-Raphson load flow analysis. The system loading conditions considered correspond to that of a typical day of the Hajj Pilgrimage period, during which millions of pilgrims from various parts of the country and also the world visit the Holy Mosque at Makkah. The preliminary investigations reveal that there are some transmission lines and certain substations requiring special attention to enhance the system security.

Introduction

The Saudi National Grid is a centralized electrical transmission network that connects the various regions of Saudi Arabia. It enables the integration of power generation from diverse sources, including renewable energy installations, conventional thermal power plants, and interconnections with the neighboring countries [1]–[7]. The stable and secure functioning of the grid is overseen by the Saudi Electricity Company (SEC). In addition, it manages and keeps up the grid [8]–[14]. Static security assessment in the electricity network refers to the process of assessing and analyzing the security of systems and infrastructure used in the electricity sector. This evaluation aims to determine the network’s ability to protect electrical transformations and ensure their continuity and safety from various threats due to the identified most probable contingencies that may occur in the system.

The case study of the Saudi National Electricity Grid is an example of the application of static security assessment in this sector. The Saudi National Electricity Grid constitutes the vital infrastructure that provides electrical energy in the Kingdom of Saudi Arabia. It aims to evaluate potential security vulnerabilities and identify risks that may affect the operation of the network [4]–[6].

The static security assessment in the electricity network aims to ensure the continuity of electricity provision and ensure safe and reliable electrical transfers. Recent studies have examined the strategies used to assess and improve the security of the nation’s electrical network [15].

As shown in Fig. 1, the different states of the power system can be divided into five different categories. Normal, alert, emergency, severe, and restorative states are among them. Every state denotes a distinct degree of security in the power system and functions to pinpoint suitable control actions to augment system security [16], [17].

Fig. 1. Operating State Transition diagram [13].

An approach to the analysis of technical systems is the use of ensemble classification techniques. These techniques provide a dependable way to build decision rules for categorizing system features, particularly in situations when there are many possible system states. The implementation of decision tree-based methodologies to assess power system security has been studied in recent literature. To construct an ensemble classification method, this paper suggests a hybrid strategy that incorporates boosting and random forests models. A modified version of the IEEE 118-bus power system is used to test the suggested ensemble classification method in order to evaluate the security of the power system under steady-state operating conditions. The results of the experimental tests showed that important system parameters can be reliably identified as security indicators by using ensemble methods. Additionally, the resulting tree-structure-based security model can sound an alarm to turn on emergency control systems as needed. The accuracy of power system security assessments might theoretically be greatly improved by using an ensemble approach for identification, possibly reaching a level of accuracy as high as 100% [18].

An intelligent system for choosing backup plans during static security analysis of electric power systems was recently presented in Yin et al. [19]. One important consideration when choosing the right control measures to maintain the integrity of system operations is the level of contingency. The study used an ACO metaheuristic to simulate the scenario and presented and evaluated the contingency selection process as a combinatorial optimization problem in order to address this issue. A practical 810-bus network and IEEE 30-bus network were used to assess the system’s performance, with an emphasis on taking double branch outages into account. The simulation results have shown that the findings could identify severe circumstances with a high degree of accuracy. This demonstrates how well the algorithm evaluates and ranks critical contingencies based on their impact [20].

The two primary categories for static security assessment in power systems that have been evaluated in this review were numerical techniques and machine learning-based methods. The review concentrated on the key components of classifying static security status, including the kind of classifier, the static security index, and techniques for feature extraction and selection [21]. This investigation can have a significant impact on future research in the field of static security assessment (SSA), particularly if it concentrates on evaluating the security of microgrids of islanded or power systems that have been combined with battery system [21].

A range of performance indices have been used to represent the extent of limit breaches from security margins; they are often found in the areas of transmission line loading and bus voltage magnitude under particular contingencies. Any security assessment tool must have the ability to evaluate the system security quickly and accurately while accounting for uncertainty in scenarios involving the generation of renewable energy and load demand. Traditional power flow and machine learning methodologies have been examined and compared for static security evaluation [22]. While typical AC power flow yielded accurate results, it was computationally demanding and slow to assess the security of a power system with unknowns and shift future operation possibilities while accounting for concurrent component failures [23]. Several machine learning techniques have been looked into to enable speedy and reasonably accurate assessment [24]. The usage of FACTS devices to improve the static security of a power system has been reviewed. Various optimization and sensitivity strategies have been put out for proper placement and sizing to ensure the effectiveness of FACTS devices [25]. The increasing complexity and uncertainty in power systems brought about by the growing use of renewable energy sources and the introduction of new types of loads, like electric vehicles, suggested the development and deployment of more security assessment tools [26].

Li et al. [27] presents the least relative shrinkage and choosing operator (Lasso) algorithm, to enhance the rapidity and precision of contingency decision and ranking in an electrical system for efficient and secure operation. The focus was on online static security assessment (OSSA), which uses a security index to identify and rate scenarios [28]. The strategy included the use of a multi-step adaptive Lasso (MSA-Lasso) regression technique, which has demonstrated enhanced predicted accuracy. An OSSA module has been developed in order to assess and select backups in different load circumstances. For each power system operation stage, the Lasso algorithm has been used to estimate the security index based on the Newton-Raphson load flow (NRLF) analysis performed in post-contingency states, accounting for bus voltages and line power flow deviations. The numerical results obtained from implementing the suggested strategy to the IEEE 14-bus, 118-bus, and 300-bus test systems has verified the accuracy and speed of OSSA [29].

The recent summary by Bhuiyan et al. [30] has indicated that security assessment in this important domain has moved from deterministic approaches to risk-based methods, which are divided into two main categories: risk assessment and risk identification. This offers a comprehensive study of risk assessment, going over several methods applied to issues with scenario development, probability modeling for equipment failure, and severity assessment. Furthermore, a comprehensive survey and prospective avenues for risk identification have been furnished, offering a roadmap for precisely determining the sources of risk. It is emphasized that by employing strategies like the tracking method, analysis of sensitivity, and risk source verification, operators may effectively avert threats and restore system security [30].

Contingency Selection and Ranking Algorithm

The contingency selection and ranking algorithm in the context of assessing static security in the electricity network, and its application to a case study of the Saudi National Grid, refers to the process in which responses and interventions are prioritized and arranged during emergency situations that affect the security of the electrical network.

The static security requirements depend on power flow and voltage magnitude thresholds, and hence both kinds of indices are to be taken into account in order to develop a composite static security assessment (SSA) index [1]. Accordingly, a number of static security indices have been proposed [12], [21]. In this paper a simple composite security index is utilized with which it is possible to find a single list of critical contingencies in terms of the deviations in both line overload and deviations in bus voltage magnitudes. The proposed SSA index is where w₁ and w₂ are the weighting factors, and LOI_l and VDI_k are defined as: where $\left|V_k^{min}\right|,\left|V_k^{max}\right|and\left|V_k\right|$ are the minimum voltage limit, maximum voltage limit, and bus voltage magnitude of k^th bus, respectively.

(1) $$\begin{array}{r}SSI={\left(\frac{w_1\sum _{i=1}^{N_2+N_1}LO{I}_i+w_2\sum _{i=1}^{N_b}VD{I}_i}{100}\right)}^{1/4}\end{array}$$

(2) $$\begin{array}{r}LO{I}_l=\{\begin{array}{ll}0& if{S}_l\le S_l^{max}\\ {\displaystyle \frac{S_l-S_l^{max}}{S_l}}& if{S}_l>S_l^{max}\end{array}\end{array}$$

(3) $$\begin{array}{r}VD{I}_K=\{\begin{array}{ll}0& at\left|V_k^{max}\right|>\left|V_k\right|>\left|V_K^{min}\right|\\ {\displaystyle \frac{-\left|V_k^{max}\right|+\left|V_k\right|}{\left|V_k^{max}\right|}}\mathrm{\%}& at\left|V_k^{max}\right|<\left|V_k\right|\\ {\displaystyle \frac{-\left|V_k\right|+\left|V_k^{min}\right|}{\left|V_k^{min}\right|}}\mathrm{\%}& at\left|V_k^{min}\right|>\left|V_k\right|\end{array}\end{array}$$

The system is classified into Insecure, Marginally Secure, or Secure based on the value of SSI computed for the respective contingencies, as shown in Table I [21]. As shown in this Table, the higher the value of SSI following a contingency, the higher the level of system insecurity [3], [10], [15].

Table I. System Security Assessment Analysis

SSI	Class category
$0\le \text{ SSI}<5$	Secure
$5\le \text{ SSI}<15$	Marginally secure
$\text{SSI }\ge 15$	Insecure

Test System Description

The security assessment procedure comprises a number of power flow analyses for different scenarios in order to determine the impact of various probable major contingencies.

Fig. 2 shows the flowchart used to determine the SSI using Contingency Analysis.

Fig. 2. Major steps in static security assessment.

Test System Description

The security assessment is performed on the typical 380 kV Saudi power network and 21 substations. The single-line diagram of the system is shown in Fig. 3.

Fig. 3. 380 kV Power Grid in National Saudi Grid.

Three substations in this Grid, Substation 1, Substation 2, and Substation 3, are linked to the network’s generation side. There is one substation that is used in this investigation as the slack bus, which is Substation 1. The voltage at this bus is its predetermined voltage magnitude, 1.01 p.u., and angle, zero degrees. The system line data and bus data are given in Tables II and III, respectively. Each line parameter is in p.u on a 100-MVA basis.

Table II. Line Data

Line no.	Start bus	End bus	R	X
			(p.u.)	(p.u.)
1	1	3	0.00002	0.00033
2	1	3	0.00002	0.00033
3	2	14	0.00124	0.02357
4	2	14	0.00124	0.02357
5	2	4	0.00045	0.00853
6	2	4	0.00045	0.00853
7	2	4	0.00045	0.00853
8	2	4	0.00045	0.00853
9	4	17	0.00109	0.0207
10	4	17	0.00109	0.0207
11	4	17	0.00109	0.0207
12	3	6	0.00067	0.01284
13	3	6	0.00067	0.01284
14	3	7	0.00122	0.02314
15	3	7	0.00122	0.02314
16	1	5	0.00085	0.01609
17	1	5	0.00085	0.01609
18	6	7	0.00054	0.01023
19	7	12	0.00005	0.00092
20	7	12	0.00005	0.00092
21	7	12	0.00005	0.00092
22	7	12	0.00005	0.00092
23	8	9	0.00009	0.00163
24	8	9	0.00009	0.00163
25	8	17	0.0008	0.02404
26	8	17	0.0008	0.02404
27	8	10	0.00013	0.00223
28	8	10	0.00013	0.00223
29	8	15	0.00013	0.00383
30	9	15	0.00008	0.00235
31	9	15	0.00008	0.00235
32	1	11	0.00013	0.00383
33	1	11	0.00013	0.00383
34	11	20	0.00006	0.00179
35	11	20	0.00006	0.00179
36	12	14	0.00006	0.00191
37	12	14	0.00006	0.00191
38	12	19	0.00005	0.00143
39	12	13	0.00013	0.00225
40	12	13	0.00013	0.00225
41	14	18	0.00009	0.00266
42	14	18	0.00009	0.00266
43	14	16	0.00006	0.00181
44	14	16	0.00006	0.00181
45	16	19	0.00011	0.00327
46	20	21	0.00032	0.00956
47	20	21	0.00032	0.00956

Table III. Probable Contingencies

No.	Contingency
1	One of the two lines (No. 1 or 2) between buses 1 and 3
2	One of the three lines (No. 9 or 10 or 11) between buses 4 and 17
3	One of the two lines (No. 14 or 15) between buses 3 and 7
4	Line 18 between buses 6 and 7
5	One of the two lines (No. 25 or 26) between buses 8 and 17
6	One of the two lines (No. 27 or 28) between buses 8 and 10
7	One of the two lines (No. 32 or 33) between buses 1 and 11
8	Two lines (No. 3 and 6) between buses 2,2 and 14,4 respectively
9	Two lines (No. 5 and 7) between buses 2 and 4
10	Two lines (No. 8 and 9) between buses 2,4 and 4,17 respectively
11	Two lines (No. 19 and 20) between buses 7 and 12
12	Two lines (No. 30 and 31) between buses 9 and 15
13	Two lines (No. 36 and 37) between buses 12 and 14
14	Two lines (No. 43 and 44) between buses 14 and 16
15	Generator 2 0utage
16	Generator 3 0utage
17	Bus 7 Generation outage
18	Bus 8 Generation outage
19	Bus 10 Generation outage
20	Bus 14 Generation outage

Simulation Results

The various probable contingencies considered in the Saudi National Grid identified for the security assessment are given in Table III.

The base case Newton-Raphson load flow solution of the 380 kV Saudi Power Grid is given in Table IV. The voltage angle and voltage magnitude values for the various buses in a power system are shown in Fig. 4. It can be seen that Substation 17 has the highest voltage level of any other substations in the system, with the value 1.257 per unit (p.u.). Substation 17 is an important substation and is connected to high demand loads that need a higher voltage supply to operate properly. Bus No. 9 has the lowest voltage angle, −10.1989 degrees. Bus No. 1 has a voltage magnitude of 1.01 p.u. and a voltage angle of zero degrees. Varying between zero degrees and −10.4691 degrees for the voltage angle and 1.01 p.u. to 1.257 p.u. for the voltage magnitude, these values are not constant between the buses 1 to 21. Buses 4, 7, 12, 13, 14, 16, and 18 have relatively high voltage levels that may need to be regulated to maintain system stability and power quality. These buses have voltage magnitudes of more than 1.1 p.u. Buses 12, 13, 14, 15, 16, 18, and 19 have comparatively larger voltage angles, which may suggest portions of the system that require more research or improvement. These buses have voltage angles between −5 and −10 degrees. Buses 1, 3, 5, 6, 10, 11, and 20 have voltage angles that are almost exactly equal to zero degrees, suggesting a better-balanced system in those regions.

Table IV. Load Flow Solution for 380 kV Power Network

S/S name	Bus no.	Voltage magnitude (p.u.)	Voltage angle
			(degree)
Sub 1	1	1.010	0
Sub 2	2	1.060	1.42263
Sub 3	3	1.020	−0.05853
Sub 4	4	1.110	−0.14754
Sub 5	5	1.016	−0.19746
Sub 6	6	1.055	−2.45624
Sub 7	7	1.112	−4.93862
Sub 8	8	1.095	−9.71592
Sub 9	9	1.096	−10.1989
Sub 10	10	1.040	−9.27414
Sub 11	11	1.062	−1.54481
Sub 12	12	1.117	−5.13528
Sub 13	13	1.120	−5.41293
Sub 14	14	1.121	−5.12608
Sub 15	15	1.097	−10.46910
Sub 16	16	1.122	−5.39790
Sub 17	17	1.257	−4.24404
Sub 18	18	1.124	−5.38762
Sub 19	19	1.119	−5.48660
Sub 20	20	1.083	−1.81614
Sub 21	21	1.139	−2.24434

Fig. 4. Voltage magnitude (p.u) for buses in the grid.

Results of the Static security index is shown in Table V in descending order of severity.

Table V. Static Security Assessment Results

Contingency rank	Contingency number	Static security index
1	15	22.01043
2	17	4.788822
3	18	3.542646
4	11	3.343199
5	7	3.327246
6	16	3.156236
7	20	3.031368
8	4	2.525134
9	3	2.377693
10	13	2.341842
11	14	2.222203
12	1	2.17988
13	5	2.126485
14	6	2.126485
15	10	1.954087
16	19	1.885748
17	9	1.624655
18	2	1.425142
19	12	1.280188
20	8	0.683709

A contingency with a higher SSI value influences the behavior of the system more. As shown in Table V and Fig. 5, A higher SSI (Static security index) value of 22.01043 for contingency No. 15 indicates a contingency with a greater impact or sensitivity on the power system’s behavior. A lower Static security index value of 0.683 for Bus No. 4 indicates a contingency with a relatively lower impact or sensitivity on the power system’s behavior.

Fig. 5. Static security assessment results.

A systematic method for identifying, prioritizing, and addressing the most important risks in static security assessment and contingencies is to rank them according to their severity. Table III shows the ranking of contingencies based on their effect on the system. The contingencies are listed in the order of their impact, with the most critical contingency being the outage of Generator 2.

Conclusion

The static security assessment of the Saudi national Grid is the main focus of this research because it is essential to preserve dependable and effective operations. The investigation has shown that the maximum voltage magnitude of Substation 17, an important substation supplying the high-demand loads, is 1.257 p.u. This emphasizes how important it is to maintain a sufficient voltage supply at Substation 17 in order to support connected loads operating as intended. With the highest SSI value of 22.01043, the Outage at Bus 1 was observed to be the most critical contingency in the system corresponding to the current load conditions. Bus No. 4, on the other hand, had the lowest SSI value (0.683), indicating that its effect when it happens at this instant is the least in the system. While lower SSI values suggest less sensitivity, it is still necessary to evaluate and regularly monitor these backup plans in order to maintain overall system security and dependability. The contingency ranking directs the identification and focuses on essential scenarios requiring immediate attention and mitigation actions, with the Generator 2 outage being selected as the most critical; the contingency ranking and SSI results provide important information for assessing the security of the Saudi National Grid, facilitating well-informed decision-making to guarantee its safe and dependable operation.