Application of Risk-based Flexibility Assessment Methods to Evaluate System Expansion Plans

doi:10.4236/epe.2013.54B195

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Energy and Power Engineering, 2013, 5, 1022-1025

doi:10.4236/epe.2013.54B195 Published Online July 2013 (http://www.scirp.org/journal/epe)

Application of Risk-based Flexibility Assessment

Methods to Evaluate System Expansion Plans

Baorong Zhou1, Pei Zhang2, Jian b in C h en1, Xiaoming Jin1, Kai Hou2, Ming Niu3,

Zhao Xu3, Yunkai Lei2

1Electric Power Research Institute of China Southern Power Grid, Guangzhou, China

2Tianjin University, Tianjin, China

3Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China

Email: [email protected], [email protected];

Received April, 2013

ABSTRACT

This paper proposed a flexibility assessment approach based on and risk assessment methodology. System planners

prioritize each planning scheme with consideration of three aspects: reliability, economics, and flexibility. In the past,

there is lack of quantitativ e indices to measure flexibility of a power system. This pap er proposes applying probabilistic

risk assessment method to quantify system flexibility. The proposed approach is demonstrated to compare two trans-

mission plann ing schemes during Guangdong expansion planning process.

Keywords: Flexibility Assessment; Transmission Scheme Prioritization

1. Introduction

Development of power systems is of the foundations of

the national economy. Thus power system planning is

always one of the most important aspects of electric in-

dustry. In order to guarantee the reliability and economy

of the power supply, as well as to meet the rapidly in-

creasing demand of electric industry, power system

planners generally establish several planning schemes,

among which, only the most reliable, economical and

flexible one will be implemented. Therefore, it is great

importance for planners to apply reasonable methods to

perform assessment and prioritization for those schemes.

Regarding assessment and prioritization, there are

clear rules and effective methods currently to conduct

reliability and economy assessment. However, there is no

recognized standard or valid approach associate with the

flexibility assessment. The flexibility of planning

schemes indicates their tolerance for uncertain factors,

major of which are listed as follows:

 Uncertainty of fault probability of components such

as generator, line and transformer;

 Uncertainty of power generation location, capacity,

timing, and availability of new generating facilities;

 Uncertainty of future demand, scope and complex-

ity of transaction;

 Uncertainty of regulatory constraints and rules.

To handle these uncertainties, McCalley and his asso-

ciates have developed a risk-based method for power

system security assessment [1]. This method considers a

predefined set of contingencies, and takes into account

both probabilities and impacts of these events. The im-

pacts are measured in terms of the voltage or current vi-

olations caused by these predefined contingencies. This

innovative method has been successfully implemented in

numerous fields such as network planning, operational

planning and op eration [2-5].

Based on the risk assessment theory, EPRI, together

with some electric power corporations, has developed a

Probabilistic Risk Assessment (PRA) methodology and

further software to perform risk assessment on power

systems [6]. The PRA program computes the probabilis-

tic risk indices and displays the results clearly with forms

and images. Taking into account both probabilities and

physical impacts of contingencies, planners are able to

have a better understanding of planning schemes, as well

as identify potential failure modes. PRA methodology

and software have been implemented in a number of

electric power corporations, such as Southern Company,

American Electric Power, KCP&L, the Eastern Inter-

connection, NYPA, ERCOT and Tri-State. At present,

PRA method is being introduced into over ten utilities for

their own studies [7-9].

This paper proposes using probabilistic risk assess-

ment method to perform flexibility assessment and quan-

tify system flexibility. Quantitative indices can be used

by system planners to prioritize planning schemes. The

second part of this paper presents PRA methodology.

B. R. ZHOU ET AL. 1023

The third part of this paper introduces two expansion

planning schemes of Guangdong Power and study as-

sumptions. The fourth part of this paper presents flexib il-

ity assessment results using PRA methods and prioritiza-

tion based on comparison of ri sk indi ces.

2. Risk-based Flexibility Assessment Method

Flexibility assessment measures the capabilities of the

designed power system to handle future uncertainties. As

shown in Figure 1, system operators need to know where

the potential problems are located, how likely they will

happen, and how far the operating point is close to the

boundaries [7]. Risk assessment should provide detail

information of potential dangers, just like radars. The

traditional deterministic con ting ency an alysis is unab le to

catch the diverse probabilities of events that lead to po-

tential security limit violations. PRA method takes both

likelihood and impacts of each contingency into consid-

eration, and work out a single reliability index – Prob-

abilistic Risk Index (PRI). We proposed to use these

quantitative risk indices to evaluate system flexibility.

2.1. Probabilistic Risk Indices

PRI is defined as the product of an impact by a probabil-

ity.

PRIProbability Impact (1)

where the probability quantifies the likelihood of the

simulated outage configuration, and the physical impact

quantifies the severity of the situation.

2.2. Impacts

According to different types of impacts, there are four

major risk indices: IPRI,N (voltage violation), IPRI,O (am-

perage or thermal overload), IPRI,V (voltage instability)

and IPRI,L (load loss):

 Voltage Violation Index IPRI,N:

PRI,NACC,i IMP,i



V (2)

Figure 1. Operating power system is like navigating a ship

[7].

where VIMP,i is the voltage violation from the bus upper or

lower limits caused by the ith contingency. The voltage

impact is measured in terms of kV or p.u. SVN is a set of

contingencies that causes voltage violations.

 Thermal Overload Index IPRI,O:

PRI,OACC,i IMP,i



A



(3)

where AIMP,i is the thermal overload above the branch

thermal rating caused by the ith contingency. The ther-

mal overload impact is measured in terms of MVA or

KA. SLO is a set of contingencies that cause overload.

 Voltage Instability Index IPRI,V:

PRI,VACC,i IMP,Si

IPV







(4)

where VIMP,Si is the voltage instability impact caused by

the ith critical situation. It is measured in binary format.

If a situation causes the system becoming voltage unsta-

ble, the voltage stability impact value of is equal to 1.

Otherwise, it is equal to 0. SVS is a set of contingencies

that cause voltage instability.

 Load Loss Index IPRI,L:

PRI,LACC,i IMP,Li

IPL



 (5)

where LIMP,Li is the to tal load loss caused b y the ith situa-

tion. The load loss impact is measured in MW. SLR is a

set of contingencies that cause load loss.

2.3. Probabilities

In a planning context, probability is a measure of the

likelihood that the po wer system will be in a given situa-

tion at a random time in the future, and is a function of

the availability of ev ery piece of equipment in the power

system.





iUj A

probabilityu cu c









(6)

where U represents the set of unavailable components; A

represents the set of available components; Ω=U∪A, Ω

represents the complete set of system components. u(ci)

and (1-u(ci)) represent the unavailability and availability

rates of the component i, respectively.

3. Study Data and Assumption

In 2030, the total peak lo ad demands of Guangdong elec-

tric power network will be approximately 175 MW,

among which the load demands of Pearl River Delta will

account for about 74%. Outer area power transfer into

Guangdong electric power network will be about 53.3

MW accounting for about 28% of the total load demands.

86% of 53.3MW will be transferred by HVDC. There are

two expansion planning schemes for 2030 Guangdong

electric pow er network frame .

B. R. ZHOU ET AL.

1024

The first scheme succeeds the existing 500KV double

looped network structure, as shown in Figure 2. The

eastern and western parts of Guangdong electric power

network are connected by two 500kV tie-lines which

enable 2000MW power transfer. This system includes

7972 buses, 63 70 lines a n d 57 36 transformers.

The second scheme, as shown in Figure 3, plans to

build a 1000KV UHV transmission network to connect

major nuclear power plants that are located at eastern and

western parts of Guangdong province. This system con-

sists of 7886 buses, 6349 lines and 5668 transformers.

Contingency analyses of the two planning schemes

were performed using BPA software. The contingency

analysis results provide th e detailed information of phys-

ical impact of each contingency.

The unavailability rate of a line is estimated using the

following equation:

/8760uOutageFreqRepair Time



(7)

The outage frequency is estimated using the following

equation:



/Outage FreqabZZpuPerMile (8)

where a (1/year) is the constant parameter of the forced

outage frequency; b (1/year/mile) is the proportional pa-

rameter of the forced outage frequency; ZpuPerMile

(pu/mile) is the average impedance (p.u.) per mile used

to estimate the line length; RepairTime (hour) is the av-

erage repair time (hour) after a forced outage.

Eastern

Guangdo

ng grid

Northern

Guangdong

grid

Western

guangdo

ng grid

Western

pearl

delta

grid

Eastern

pearl

delta

grid

Western gridEastern grid

Figure 2. High Voltage DC Transmission Scheme.

Figure 3. Ultra high voltage DC transmission scheme.

4. Flexibility Assessment and Prioritization

of Planning Schemes Using PRA Method

We performed risk-based flexibility assessment for two

planning schemes and calculate quantitative risk indices,

which are used as flexibility indices of planning schemes.

Prioritization can be performed by system planners based

on these indices.

Table 1 provides a summary of risk indices for two

planning schemes. With consideration of four risk factors:

overload, voltage and voltage instability, and load loss,

it clearly shows that Scheme A has lower risk comparing

with Scheme B. This indicates Scheme A has a better

flexibility than Scheme B.

Figure 4 show the risk analysis of bus voltage viola-

tions, where it is shown that the risk indices of schemes

A and B are 2.623E-5 and 1.799E-3 respectively. Obvi-

ously, the risk of scheme B is larger than that of scheme

A more than 60 times from a voltage violation risk per-

spective.

Figure 5 shows the risk analysis of transmission line

overloading, where it is shown that the risk indices of

schemes A and B are 9.778 and 10.92 8 respectively. Ob-

viously, the risk of scheme A is larger than that of

scheme B from the overload perspective, because the

overloading risk of scheme B is 1.12 times of scheme A.

Figure 6 shows the risk analysis of voltage instability,

where it is shown that the risk indices of schemes A and

B are 7.379E-3 and 3.014E-2 respectively. From voltage

instability perspective, scheme A has higher risk of volt-

age instability than Scheme B.

Table 1. Overall Risk-baesed Felxibility Analysis ResulT.

Voltage

violation Overload Voltage

instability Load loss

Scheme A2.623E-5 9.778 7.379E-3 0

Scheme B1.799E-3 10.928 3.014E-2 0

5.0E‐04

1.0E‐03

1.5E‐03

2.0E‐03

Figure 4. Risk analysis of bus voltage violations.

5.00E+00

1.00E+01

Figure 5. Risk Analysis of line overloading.

B. R. ZHOU ET AL.

1025

[2] H. Wan, J. D. McCalley and V. Vittal, “Risk Based

Voltage Security Assessment,” Ieee Transactions on

Power Systems, Vol. 15, 2000, pp. 1247-1254.

doi:10.1109/59.898097

1.0E

‐

2.0E‐02

3.0E‐02

4.0E‐02

[3] Y. J. Dai, J. D. McCalley, N. Abi-Samra and V. Vittal,

“Annual Risk Assessment for Overload Security,” Ieee

Transactions on Power Systems, Vol. 16, 2001, pp.

616-623. doi:10.1109/59.962405

Figure 6. Risk analysis of voltage instability. [4] W. H. Fu, J. D. McCalley and V. Vittal, “Risk Assess-

ment for Transformer Loading,” Ieee Transactions on

Power Systems, Vol. 16, 2001, pp. 346-353.

doi:10.1109/59.932267

Considering the results above, scheme A obviously

outperforms scheme B in all three aspects. Therefore it

can be concluded that according to risk-based flexibility

analysis, scheme A is a better flexibility and is recom-

mended to be adopted b y the utility.

[5] M. Ni, J. D. McCalley, V. Vittal and T. Tayyib, “Online

Risk-based Security Assessment,” Ieee Transactions on

Power Systems, Vol. 18, 2003, pp. 258-265.

doi:10.1109/TPWRS.2002.807091

5. Conclusions [6] Z. Pei, S. T. Lee and D. Sobajic, “Moving Toward Prob-

abilistic Reliability Assessment Methods,” in Probabilis-

tic Methods Applied to Power Systems, 2004 Interna-

tional Conference on, 2004, pp. 906-913.

This paper introduced a new risk-based flexibility as-

sessment and prioritization method for planning schemes.

Two planning schemes of Guangdong electric power

network were studied and the risk-based flexibility indi-

ces were computed, including voltage violation index,

thermal overload index, voltage instability index and

load loss index. With this flexibility information, system

planners can evaluate the flexibility and prioritize those

project schemes. The flexibility assessment helps system

planners evaluate the operational risks when designing

the future power system, therefore builds a linkage be-

tween system planning and system operation.

[7] Z. Pei, M. Liang, L. Hopkins and B. Fardanesh, “Utility

Experience Performing Probabilistic Risk Assessment for

Operational Planning,” in Intelligent Systems Applica-

tions to Power Systems, 2007. ISAP 2007. International

Conference on, 2007, pp. 1-6.

[8] Z. Pei, M. Graham and D. Ramsay, “Prioritization of

Transmission Projects using EPRI Probabilistic Risk As-

sessment Program,” in Power and Energy Engineering

Conference, 2009. APPEEC 2009. Asia-Pacific, 2009, pp.

1-5.

[9] Z. Pei, L. Shanshan, X. Ruilin, L. Xinyu and F. Li, “As-

sessing System Risk and Integrating Operation and Plan-

ning Functions for Chongqing Power using EPRI Prob-

abilistic Risk Assessment Program,” in Power and En-

ergy Engineering Conference (APPEEC), 2010

Asia-Pacific, 2010, pp. 1-6.

REFERENCES

[1] J. D. McCalley, V. Vittal and N. Abi-Samra, “An Over-

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