c ICIC International °2008 ISSN 1881-803X
ICIC Express Letters Volume 2, Number 2, June 2008
pp. 137-142
A PROBABILISTIC RELIABILITY EVALUATION OF KOREA POWER SYSTEM Jeongje Park1 , Sangheon Jeong1 , Jaeseok Choi1 Junmin Cha2 and A.(Rahim)A. El-Keib3 1
Department of Electrical Engineering Gyeonsang National University Gazwadong 900, Jinju, GN, Korea
[email protected];
[email protected];
[email protected] 2
3
Department of Electrical Engineering Daejin University Gyunggi, Pocheon, Korea
[email protected]
Department of Electrical and Computer Engineering The University of Alabama Tuscaloosa, Alabama, 35487-0286, USA
[email protected]
Received January 2008; accepted March 2008 Abstract. Reliability and power quality have been increasingly important in recent years due to a number of black-out events occurring throughout the world. This paper presents a practical method of probabilistic reliability evaluation of Korea Power system by using the Probabilistic Reliability Assessment (P RA) program and Physical and Operational Margins (POM). The case study computes the Probabilistic Reliability Indices (P RI) of Korea Power system as applied P RA and POM. It takes a large number of contingencies in load simulations and combines them with a practical method of characterizing the effect of the availabilities of generators, lines and transformers. The effectiveness and future works are illustrated by demonstrations of case study. The case studies of Korea power system are shown that these packages are effective in identifying possible weak points and root causes for likely reliability problems. The potential for these software packages is being explored further for assisting system operators with managing Korea power system. Keywords: Probabilistic reliability evaluation, P RA, P RI, Korea power system
1. Introduction. This paper presents the result of marginal power flow evaluation performed on Korea power system using two commercial software packages: Physical and Operational Margins (POM V. 3.4) and Probabilistic Reliability Assessment (P RA V. 4.0) developed by V&R Energy System Research and EPRI, respectively [1-3]. The methodology presented in this paper has been implemented in a package of software. The contingency simulation program called Physical Operation Margin (POM) simulates a large number of contingencies. It is described in a paper presented in this conference [4]. An output file of POM which contains the results of the critical contingencies and their impacts is then used as one input to the P RA program package which includes two components, the Probabilistic Reliability Indices (P RI) software and a post processor. The P RI module computes the deterministic and the probabilistic reliability indices. The post processor analyzes the reliability performance of the transmission grid and displays various results which make up a systematic Probabilistic Reliability Assessment (P RA) [5]. The real database for accurate assessment of the outage data of system elements (lines, generators and transformers, etc.) in the Korea system has not completed yet. Thus, the operating outage databases in other countries are used in this paper. The 137
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J. PARK, S. H. JEONG AND J. CHOI, J. CHA AND A. A. EL-KEIB
case study presents application of the concept of POM and a practical method of P RA for Korea power system. It not only demonstrates possibility that the POM and P RA can be applied to the Korea power system but also analyzes the results from P RA. And it shows the direction of future work through P RI in case study. 2. Probabilistic Reliability Assessment (P RA). The program “Probabilistic Reliability Assessment” (P RA) is an effective methodology that was originally used in the nuclear power industry to determine the risk to the general public from the operation of nuclear power plants as shown as Figure 1. P RA combines a probabilistic measure of the likelihood of undesirable events with a measure of the consequence of the events into a single reliability index — Probabilistic Reliability Index (P RI). The P RI is defined as the product of an impact by a probability [6,7]. P RI = P robability ∗ Impact Whereas the probability quantifies the likelihood of the simulated outage configuration, and the physical impact quantifies the severity of the situation.
Figure 1. Probabilistic reliability assessment method The reliability is measured by the Probabilistic Reliability Index (P RI). P RI is defined as product of an impact by a probability. The impacts and the reliability indices are of four distinct types: APRI (amperage or thermal overload), VPRI (voltage violation), VSPRI (voltage instability) and LLPRI (load loss). • Overload Reliability Index: X probabilityi × Aimpacti (1) A − P RI = i∈{Simulated Situations}
Aimpact is the sum of overload above the thermal limit of branches. The overload impact is measured in terms of MVA. • Voltage Reliability Index: X probabilityi × V impacti (2) V − P RI = i∈{Simulated Situations}
Vimpact is the sum of voltage deviations from the upper and lower limits: The voltage impact is measured in terms of kV or pu.
ICIC EXPRESS LETTERS, VOL.2, NO.2, 2008
• Voltage Stability Reliability Index: X V S − P RI =
i∈{Simulated Situations}
probabilityi × V Simpact
139
(3)
The voltage stability impact is measured in binary format. If a contingency situation causes the system into voltage instability, VSimpact is equal to 1. Otherwise, VSimpact is equal to 0. • Load Loss Reliability Index: X probabilityi × LLimpacti (4) LL − P RI = i∈{Simulated Situations}
LLimpact is the sum of load loss at each bus. The load loss impact is measured in terms of MW [11]. Figure 2 shows P RA Flow Chart. In planning context, the probability is a measure of the likelihood that the power system will be in a given situation at a random time in the future, and is a function of the availability of every piece of equipment in the power system. Y Y P robability = u(ci ) a(cj ) (5) i∈U
j∈A
where U is the set of unavailable components, A is the set of available components.
Figure 2. P RA flow chart Overall analysis indicates the overall system reliability level by providing a number of reliability indices. Interaction analysis unveils the cause and effect relationship among user-defined zones. Zone interaction is defined by a zone “cause” where the outage is located and a zone “affected” where the violations are experienced. Each interaction is named as “by Zone Cause on Zone Affected by Zone1 on Zone2” means that the violations encountered in Zone 2 caused by outages in Zone 1.
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J. PARK, S. H. JEONG AND J. CHOI, J. CHA AND A. A. EL-KEIB
Situation analysis ranks the situation according to their contribution to index. It helps the user to identify the scenarios that have high probability or high impact or both. Root cause analysis indicates the facilities (line, transformer, generator etc) that cause critical situation. A root cause facility is a facility (i.e., line, transformer, and generator) that experiences an outage and creates a violation, whether or not it is combined with other outages. In other words, a root cause facility is a facility that experiences an outage that is involved in a critical situation. Weak point analysis identifies the buses and branches that most violated. A weak point component is a component (bus or branch) that experience at least one violation. 3. Case Studies. This case study has simulated Korea power system by using POM and P RA in 2007. The Constraint criteria was set to check system overloads above 100% (Thermal Constraint), and bus violations less than 0.95 p.u (Voltage Constraint). The probabilistic reliability indices of Korea power system were obtained using N-2 contingency. Table 1. Overall analysis with table Affected Zone All Seoul Nam-Seoul Suwon Jecheon Dae-jeon Gwangju Daegu Busan Changwon
1 2 3 4 5 6 7 8 9 10
V-RP I 0.035645 0 0.0262171 0.00045489 0.276897 0.00000073 0.00116018 0.00086992 0.00000314 0.0000421
A-P RI 16.6879 0.197359 0.775887 0.793226 0.0001651 0.407805 14.0715 0.0223218 0.416658 0.0029709
VS-P RI 0.00004678 0 0 0 0 0 0 0 0 0
3.1. Overall analysis. Overall analysis indicates the overall system reliability level by providing a number of reliability indices with chart. Table 1 shows three kinds of Overall Analysis. Jecheon is the most impact area in voltage violation criterion and Gwangju is the most impact area in thermal violation criterion. Table 2. Interaction analysis of voltage violation criterion(×10−6 ) Cause Zone Affected Zone
All
Seoul
NamSeoul
Suwon
Jae
Dae
Gwang
cheon
jeon
ju
Daegu
Busan
Chang won
All
305645
145.805
12830.6
642.115
15000
74.6
1160.18
695.376
252.002
29.1
Seoul
0
0
0
0
0
0
0
0
0
0
Nam-Seoul
26217
144.609
12821.1
214.463
12792.2
58.3
0
186.454
0
0
Suwon
454.89
1.20
9.57
427.652
0.913
15.6
0
0
0
0
Jecheon
276897
0
0
0
2081.73
0
0
0
0
0
Daejeon
0.733
0
0
0
0
0.733
0
0
0
0
Gwangju
1160.2
0
0
0
0
0
1160.18
0
0
0
Daegu
869.92
0
0
0
125.163
0
0
502.41
242.345
0
Busan
3.14
0
0
0
0
0
0
0
3.14
0
Changwon
42.1
0
0
0
0
0
0
6.51
6.51
29.1
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3.2. Interaction analysis. Interaction analysis unveils the cause and effect relationship among user-defined zone. Table 2 shows voltage index in interaction analysis. Korea power system has the most affected by Jecheon in voltage violation. 3.3. Situation analysis. Situation analysis ranks the situation according to their contribution to index. Figure 3 shows situation analysis of thermal violation criterion. The largest bubble means 398.283(MVA) impact, 0.000877481 probabilities and 0.349486 P RI as 1437th contingency.
Figure 3. Situation analysis of thermal violation criterion 3.4. Root cause analysis. The Figure 4 shows root cause analysis of thermal violation criterion. The largest bubble is 0.712902 P RI as outage by itself.
Figure 4. Root cause analysis of thermal violation criterion 3.5. Weak point analysis. The most weakness bus in Korea power system is the YoungdongTP 154kV and Anin 154kV bus of Jecheon control area in voltage violation criterion. The P RI of Weak point analysis is about 0.037646. And, the most weakness branch in Korea power system is the Gwangyang-SK Minja1, 2 345kV branches of Gwangju control
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area in thermal violation criterion. The P RI of Weak point analysis is about 0.775245 as shown in Table III. Table 3. Weak point — overload Branch (Zone)
Cumulated Probability
Probabilistic Impact
P RI
0.775245
8.9884
6.96842
1
L345 Gwangyang3 — Skninja 2 (Gwangju)
2
L345 Gwangyang3 — Skninja 1 (Gwangju)
0.775245
8.9884
6.96842
3
L345 Hwasung — Asan3 1 (Suwon-Daejeon)
0.00215879
94.6918
0.20442
4
L345 Hwasung — Asan3 2 (Suwon-Daejeon)
0.00215879
94.6918
0.20442
5
L154 Suseo — garak 1 (Nam-seoul)
0.00127238
131.413
0.167207
6
L154 DongSeoul1 — Songpa 1 (Nam-seoul)
0.00227065
47.6062
0.108097
7
L154 DongSeoul1 — Songpa 1 (Nam-seoul)
0.00227065
47.6062
0.108097
4. Conclusions. This paper presents the application of POM and P RA what are newly introduced to evaluate probabilistic reliability of Korea power system. The results show new reliability indices which are different type with conventional indices. We simulate to analyze condition of the system under three constraints that are voltage violation, overload violation and voltage stability violation. The OPM/BOR (Optimal Mitigation Measures/Boundary of Operating Region) which is sub-programs in POM didn’t used. If it simulated with both modules, the results will be more correct and reliable. In conclusion, this paper demonstrates the possibility that the POM and P RA can be applied to the Korea power system. Acknowledgment. This study was performed by the EPRRC (Electrical Power Reliability/Power Quality Research Center), KPX, KEPRI funded the Ministry of Commerce, Industry and Energy (MOCIE) of Korea and Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government (MEST) (No.R01-2008-000-10567). The authors are responsible for all conclusions and for any remaining errors. REFERENCES [1] P. Zhang, S. T. Lee and D. Sobajic, Moving toward probabilistic reliability assessment methods, PMAPS-2004, pp.906-913, 2004. [2] L. Navarro, et al., Utility experience computing physical and operational margins: Part I and Part II, IEEE Power System Conference & Exposition, New York, 2004. [3] N. Maruejouls, V. Sermanson, S. T. Lee and P. Zhang, A practical probabilistic reliability assessment using contingency simulation, PSCE, 2044, IEEE PES, vol.3, pp.312-1318, 2004. [4] Physical and operational margins (POM) program manual, V&R Energy System Research. [5] EPRI, Probabilistic Reliability Assessment (PRA) Program User’s Manual, 2006. [6] R. Billinton, Power System Reliability Evaluation, Gordon and Breach Science Publishers, 1979. [7] R. Billinton and R. N. Allan, Reliability Evaluation of Power Systems, Second Edition, Plenum Press, 1996. [8] B. Xu and J. Watada, Observed probability measurement for urbanization development level with errors-in-variables observation, Int. J. of Innovative Computing Information and Control, vol.4, no.5, pp.1233-1242, 2008.
