Advertisement

A bilevel model for maintenance scheduling of power units including wind farms

  • Amir Naebi Toutounchi
  • SeyedJalal SeyedShenavaEmail author
  • Javier Contreras
  • Hossein Shayeghi
  • Seyed Saeid Taheri
  • Mahdi Nooshyar
Original Paper
  • 12 Downloads

Abstract

This paper proposes a comprehensive bilevel model for the maintenance scheduling of power units including wind farms in a restructured environment. The maintenance-scheduling problem of generating companies (GENCOs) is constrained by technical and security constraints set by the independent system operator (ISO), who has the responsibility of operating the power system. To consider the impact of these constraints on the scheduling problem, we propose a bilevel approach with a strategic GENCO at the upper level and an ISO at the lower level. In the upper level, the scheduling problem considering the participation of wind farms is presented. The lower level, also called the ISO level, maximizes the social welfare with network security constraints. This bilevel problem is modeled as a mathematical program with equilibrium constraints (MPEC), which is then transformed into a mixed-integer linear problem through a linearization procedure. The resultant model is tested on the IEEE 24-Bus Reliability Test System considering various case studies to illustrate the effectiveness of the proposed model. The results show that the proposed model can properly schedule the outages under different strict conditions preserving system’s security.

Keywords

Maintenance scheduling Bilevel model MPEC Wind farms 

List of symbols

Acronyms

GPMS

Generator’s preventive maintenance scheduling

ISO

Independent system operator

GENCO

Generation company

MPEC

Mathematical program with equilibrium constraints

MILP

Mixed-integer linear program

MINLP

Mixed-integer nonlinear problem

KKT

Karush–Kuhn–Tucker

BLPP

Bilevel programming problem

LMP

Locational marginal price

Indices and sets

i

Index for generating units

j

Index for demands

b

Index for generation blocks

t

Index for time

c

Index for demand blocks

n/m

Indices for buses

L

Set of lines between bus n-m

R

Index for the regions in the system

Gp

Set of generating units owned by producer p

DN

Set of demands at bus n

Gw

Set of wind power units

GN

Set of generating units at bus n

GR

Set of generating units in region R

Parameters

rt

Reserve level at time period t

λibt

Offer bid of block b of generating unit i in period t ($/MWh)

Λjct

Price bid of block c of demand j in period t ($/MWh)

\( g_{ibt}^{ {\mathrm{max}} } \)

Maximum capacity of block b of generating unit i in time period t (MW)

\( g_{i}^{ {\mathrm{max}} } \)

Maximum generation limit for unit i (MW)

NCt

Number of crew members to perform maintenance at time period t

ACt

Available number of crew members for generator maintenance at time t

Tduration

Duration of maintenance for each generator

Tend

End maintenance time of each generator

Tstart

Starting time of maintenance for each generator

\( {\text{NPM}}_{R}^{ {\mathrm{max}} } \)

Maximum number of power plants in maintenance allowed in region R

NPMR

Number of power plants under maintenance activities in region R

MWHi

Number of working hours needed for maintenance of unit i

WHt

Total number of working hours

υ

Average percentage of the total number of hours in the time horizon for wind power plants

MWHi.t

Working hours associated with wind power plant i in time period t

MNMi

Maximum number of units simultaneously in maintenance

NPPt

Not permitted maintenance plan in hour t

Bmn

Susceptance of line mn

Variables

yit

Binary variable that is equal to 1 if the generating unit is maintained during time period t and 0 otherwise

θnt

Voltage angle at bus n in time period t (rad)

gibt

Power produced by block b of generating unit i at time period t (MW)

αnt

Price (dual variable) at bus n in time period t ($/MW)

djct

Power consumed by block c of demand j in time period t (MW)

Notes

Supplementary material

202_2019_796_MOESM1_ESM.xlsx (16 kb)
Supplementary material 1 (XLSX 16 kb)

References

  1. 1.
    Shahidehpour M, Marwali M (2012) Maintenance scheduling in restructured power systems. Springer, BerlinGoogle Scholar
  2. 2.
    Gjorgiev B, Kančev D, Čepin M (2013) A new model for optimal generation scheduling of power system considering generation units availability. Int J Electr Power Energy Syst 47:129–139.  https://doi.org/10.1016/j.ijepes.2012.11.001 CrossRefGoogle Scholar
  3. 3.
    Kim J, Geem ZW (2014) Optimal scheduling for maintenance period of generating units using a hybrid scatter-genetic algorithm. IET Gener Transm Distrib 9(1):22–30CrossRefGoogle Scholar
  4. 4.
    Schlünz EB, van Vuuren JH (2013) An investigation into the effectiveness of simulated annealing as a solution approach for the generator maintenance scheduling problem. Int J Electr Power Energy Syst 53:166–174.  https://doi.org/10.1016/j.ijepes.2013.04.010 CrossRefGoogle Scholar
  5. 5.
    El-Sharkh M (2014) Clonal selection algorithm for power generators maintenance scheduling. Int J Electr Power Energy Syst 57:73–78CrossRefGoogle Scholar
  6. 6.
    Fattahi M, Mahootchi M, Mosadegh H, Fallahi F (2014) A new approach for maintenance scheduling of generating units in electrical power systems based on their operational hours. Comput Oper Res 50:61–79MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Elyas SH, Akbari Foroud A, Chitsaz H (2013) A novel method for maintenance scheduling of generating units considering the demand side. Int J Electr Power Energy Syst 51:201–212.  https://doi.org/10.1016/j.ijepes.2013.02.026 CrossRefGoogle Scholar
  8. 8.
    Mollahassani-pour M, Abdollahi A, Rashidinejad M (2015) Investigation of market-based demand response impacts on security-constrained preventive maintenance scheduling. IEEE Syst J 9(4):1496–1506CrossRefGoogle Scholar
  9. 9.
    Conejo AJ, Garcia-Bertrand R, Diaz-Salazar M (2005) Generation maintenance scheduling in restructured power systems. IEEE Trans Power Syst 20(2):984–992.  https://doi.org/10.1109/TPWRS.2005.846078 CrossRefGoogle Scholar
  10. 10.
    Dahal K, Al-Arfaj K, Paudyal K (2015) Modelling generator maintenance scheduling costs in deregulated power markets. Eur J Oper Res 240(2):551–561MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Abiri-Jahromi A, Fotuhi-Firuzabad M, Parvania M (2012) Optimized midterm preventive maintenance outage scheduling of thermal generating units. IEEE Trans Power Syst 27(3):1354–1365CrossRefGoogle Scholar
  12. 12.
    Ji G, Wu W, Zhang B (2016) Robust generation maintenance scheduling considering wind power and forced outages. IET Renew Power Gener 10(5):634–641CrossRefGoogle Scholar
  13. 13.
    Balaji G, Balamurugan R, Lakshminarasimman L (2016) Mathematical approach assisted differential evolution for generator maintenance scheduling. Int J Electr Power Energy Syst 82:508–518CrossRefGoogle Scholar
  14. 14.
    Perez-Canto S, Rubio-Romero JC (2013) A model for the preventive maintenance scheduling of power plants including wind farms. Reliab Eng Syst Saf 119:67–75CrossRefGoogle Scholar
  15. 15.
    Khorramdel H, Khorramdel B, Tayebi Khorrami M, Rastegar H (2014) A multi-objective economic load dispatch considering accessibility of wind power with here-and-now approach. J Oper Autom Power Eng 2(1):49–59Google Scholar
  16. 16.
    Sadeghi Yazdankhah A, Kazemzadeh R (2017) Power management in a utility connected micro-grid with multiple renewable energy sources. J Oper Autom Power Eng 5(1):1–10Google Scholar
  17. 17.
    Stackelberg HV (1952) The theory of the market economy. Oxford University Press, OxfordGoogle Scholar
  18. 18.
    Sinha A, Malo P, Deb K, Korhonen P, Wallenius J (2015) Solving bilevel multi-criterion optimization problems with lower level decision uncertainty. IEEE Trans Evolut Comput 20(2):199–217.  https://doi.org/10.1109/tevc.2015.2443057 CrossRefGoogle Scholar
  19. 19.
    Ruiz C, Conejo AJ (2009) Pool strategy of a producer with endogenous formation of locational marginal prices. IEEE Trans Power Syst 24(4):1855–1866.  https://doi.org/10.1109/TPWRS.2009.2030378 CrossRefGoogle Scholar
  20. 20.
    Dempe S, Kalashnikov V, Pérez-Valdés GA, Kalashnykova N (2015) Bilevel programming problems. Energy systems. Springer, BerlinzbMATHGoogle Scholar
  21. 21.
    Dempe S (2002) Foundations of bilevel programming. Springer, BerlinzbMATHGoogle Scholar
  22. 22.
    Gabriel SA, Conejo AJ, Fuller JD, Hobbs BF, Ruiz C (2012) Complementarity modeling in energy markets, vol 180. Springer, BerlinzbMATHGoogle Scholar
  23. 23.
    Ruiz C, Conejo AJ, Smeers Y (2012) Equilibria in an oligopolistic electricity pool with stepwise offer curves. IEEE Trans Power Syst 27(2):752–761.  https://doi.org/10.1109/TPWRS.2011.2170439 CrossRefGoogle Scholar
  24. 24.
    Naebi Toutounchi A, Seyed Shenava SJ, Taheri SS, Shayeghi H (2018) MPEC approach for solving preventive maintenance scheduling of power units in a market environment. Trans Inst Meas Control 40(2):436–445CrossRefGoogle Scholar
  25. 25.
    Conejo AJ, Baringo L, Kazempour SJ, Siddiqui AS (2016) Investment in electricity generation and transmission. Springer, Cham Zug, SwitzerlandCrossRefGoogle Scholar
  26. 26.
    Grigg C, Wong P, Albrecht P, Allan R, Bhavaraju M, Billinton R, Chen Q, Fong C, Haddad S, Kuruganty S, Li W, Mukerji R, Patton D, Rau N, Reppen D, Schneider A, Shahidehpour M, Singh C (1999) The IEEE Reliability Test System-1996. A report prepared by the Reliability Test System Task Force of the Application of Probability Methods Subcommittee. IEEE Trans Power Syst 14(3):1010–1020.  https://doi.org/10.1109/59.780914 CrossRefGoogle Scholar
  27. 27.
    Garcés LP, Conejo AJ, Garcia-Bertrand R, Romero R (2009) A bilevel approach to transmission expansion planning within a market environment. IEEE Trans Power Syst 24(3):1513–1522.  https://doi.org/10.1109/TPWRS.2009.2021230 CrossRefGoogle Scholar
  28. 28.
    Data—a bilevel maintenace scheduling of power units including wind farms 2017. https://onedrive.live.com/view.aspx?resid=F9E0A01CB65762A0!4058&ithint=file%2cxlsx&authkey=!AGHI1pW41OsowmA
  29. 29.
    GAMS Development Corporation. http://www.gams.com

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical and Computer Engineering, Faculty of EngineeringUniversity of Mohaghegh ArdabiliArdabilIran
  2. 2.E.T.S. de Ingenieros IndustrialesUniversity of Castilla –La ManchaCiudad RealSpain

Personalised recommendations