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# Application of Monte Carlo simulation to generating system well-being analysis

IEEE Transactions on Power Systems, no. 3 (2007): 1172-1177

Keywords

Abstract

System well-being analysis is a new approach to power system generation adequacy evaluation which incorporates deterministic criteria in a probabilistic framework and provides system operating information in addition to risk assessment. This approach not only provides a new perspective to generation adequacy studies but can also be useful...More

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Introduction

- The methods adopted by major power utilities for generating capacity adequacy evaluation have slowly moved from deterministic to probabilistic over the last thirty years.
- Monte Carlo simulation (MCS) can be used to estimate the indices by simulating the actual process and random behavior of the system [5]
- It can include system effects which may not be possible without excessive approximation in a direct analytical approach and can generate a wide range of indices within a single study.
- This is no longer a problem for a wide range of studies

Highlights

- The methods adopted by major power utilities for generating capacity adequacy evaluation have slowly moved from deterministic to probabilistic over the last thirty years
- Power system operators and small isolated system planners are, still reluctant to apply probabilistic techniques due to concerns relating to the ability to interpret a single numerical risk index such as loss of load expectation (LOLE) [ l ] and the lack of system operating information in a single risk index
- Monte Carlo simulation (MCS) can be used to estimate the indices by simulating the actual process and random behavior of the system [5]. It can include system effects which may not be possible without excessive approximation in a direct analytical approach and can generate a wide range of indices within a single study
- The indices obtained by the MCS method are not exactly the same when different random number seeds are used
- System well-being analysis provides a bridge between the accepted deterministic and probabilistic methods and defines indices that can be useful in practical power system adequacy assessment
- The approach can be very useful for those situations in which the conventional probabilistic methods have not been accepted and the deterministic techniques are still applied

Methods

**Probabilistic Methods Applied to Small Isolated Power**

Generating Systems", Proceedings of the 5th PMAPS International Conference, Vancouver, Canada, Sept., 1997, pp.457-462.

5.**Probabilistic Methods Applied to Small Isolated Power**.- Generating Systems", Proceedings of the 5th PMAPS International Conference, Vancouver, Canada, Sept., 1997, pp.457462.
- 6. Reliability Test System Task Force of the Application of Probability Methods Subcommittee, "IEEE Reliability Test System", IEEE Transactions on Power Apparatus and Systems, Vol PAS-98, No 6, Nov./Dec. 1979.
- 7. Karki, R., Billinton, R., "SIPSREL (Small Isolated Power System Reliability Software Package) User's Manual", University of Saskatchewan, 1997.

Results

**Evaluation Method**

Analytical Simulation Years

The author Probabilities of Risk I 0.0010 I 0.0010 I 0.0011 I

Table 1 shows that the basic indices estimated by MCS are almost the same as those obtained by the analytical techniques.- The distributions of the basic well-being indices for the IEEE-RTS, obtained when a random number seed of 0.24 was used for 734 simulation years, are shown in Fig. 5 - 7.
- This means that the average duration of the system in a state, that violates the deterministic criterion without actual system failure, is 4.8 1 hours
- Both the EHDUR and EMDUR of the system are useful operating information.
- Fellow of IEEE, the EIC and the Royal Society of Canada and a P.E. in the province of Saskatchewan

Conclusion

- System well-being analysis provides a bridge between the accepted deterministic and probabilistic methods and defines indices that can be useful in practical power system adequacy assessment.
- The approach can be very useful for those situations in which the conventional probabilistic methods have not been accepted and the deterministic techniques are still applied.
- The average values and the distributions of the basic health indices and additional frequency and duration indices can be estimated using the MCS method.
- The MCS method makes it possible to conduct an in depth analysis of a system using the well-being approach

- Table1: Table 1
- Table2: Table 2
- Table3: Effects of Changes in Unit Failure and Repair Rates
- Table4: Basic Well-being Indices

Reference

- Billinton, R. and Allan, R. N., "Reliability Evaluation of Power Systems", Plenum Publishing, New York and London, 1984.
- Newfoundland & Labrador Hydro, "Isolated Systems Generating Planning Practices; A Survey of Canadian Utilities", NOV.1995.
- Billinton, R., Fotuhi-Firuzabad, M., "Reserve capacity assessment in small isolated electric power generating systems", IEE Power Eng. Journal, 1996, Vol. 10, No. 2, pp. 73-80.
- Billinton, R., Karki, R., Fotuhi-Firuzabad M., "Probabilistic Methods Applied to Small Isolated Power Generating Systems", Proceedings of the 5th PMAPS International Conference, Vancouver, Canada, Sept., 1997, pp.457-462.
- Billinton, R. and Ghajar, R., "Utilization of Monte Carlo Simulation in Generating Capacity Adequacy Evaluation", CEA, March 1987.
- Reliability Test System Task Force of the Application of Probability Methods Subcommittee, "IEEE Reliability Test System", IEEE Transactions on Power Apparatus and Systems, Vol. PAS-98, No. 6, Nov./Dec. 1979.
- Karki, R., Billinton, R., "SIPSREL (Small Isolated Power System Reliability Software Package) User's Manual", University of Saskatchewan, 1997.
- R. N. Allen, R. Billinton and N. M. K. Abdel-Gawad, Evaluation of the Generating System", IEEE Winter Power Meeting, Paper No. 86 WM 03804, Jan, 1986.
- Roy Billinton obtained BSc. and MSc. Degrees from the University of Manitoba and Ph.D. and DSc. Degrees from the University of Saskatchewan. Worked for Manitoba Hydro in the System Planning and Production Divisions. Joined the University of Saskatchewan in 1964.Formerly Head of the ElectricalEngineering Department. Presently C.J. Mackenzie Professor of Engineering and Associate Dean of Graduate Studies, Research and Extension of the College of Engineering. Author of papers on Power System Analysis, Stability, Economic System Operation and Reliability. Fellow of IEEE, the EIC and the Royal Society of Canada and a P.E. in the province of Saskatchewan.
- Rajesh Karki was born in Nepal. Received the B.E. degree from the Regional Engineering College, Durgapur, India in 1991 and the M.Sc. degree from the University of Saskatchewan, Saskatoon, Canada in 1997. Worked as an electrical engineer for the Nepal Hydro & Electric, Udayapur Cement Industries and Nepal Telecommunications Corporation and as a lecturer for the Institute of Engineering at the Tribhuvan University, Nepal, from 1991 to 1995. Currently a Ph.D. student at the University of Saskatchewan.

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