FUZZY APPROACH TO OPTIMAL GENERATION SCHEDULING FOR GenCos IN COMPETITIVE ELECTRICITY MARKETS

A.F. Al-Ajlouni, H.Y. Yamin, W. Qassem, and S.M. Shahidehpour

References

  1. [1] M. Shahidehpour, H. Yamin, & Z. Li, Market operationsin electric power systems: Forecasting, scheduling and riskmanagement, Hoboken, (New Jersey: John Wiley and Sons.2002).
  2. [2] M. Shahidehpour & M. Marwali, Maintenance scheduling inrestructured power systems, (Boston: Kluwer Publishers, 2000).
  3. [3] K.H. Abdul-Rahman, S.M. Shahidehpour, M. Agangic, & S.Mokhtari, A practical resource scheduling with OPF con-straints, IEEE Transactions on Power Systems, 11 (1), 1996,254–259.
  4. [4] H.Y. Yamin, K. Al-Tallaq, & S.M. Shahidehpour, New ap-proach for dynamic optimal power flow using benders decompo-sition in a deregulated power market, Electric Power SystemsResearch EPSR Journal, 65 (2), New York, 2003, 101–107.
  5. [5] R. Ferrero & S.M. Shahidehpour, Dynamic economic dispatchin deregulated systems, International Journal of ElectricalPower and Energy Systems, 19 (7), 1997, 433–439.
  6. [6] S. Saneifard, N.R. Prasad, & H.A. Smolleck, A fuzzy logicapproach to unit commitment, IEEE Transactions on PowerSystems, 12, 1997, 988–995.
  7. [7] M. Mazumdar & A. Kapoor, Stochastic models for powergeneration system production costs, Operations Research, 35,1995, 93–100.
  8. [8] S. Takriti, J.R. Birge, & E. Long, A stochastic model forthe unit commitment problem, IEEE Transactions on PowerSystems, 11, 1996, 1497–1508.
  9. [9] E.H. Allen & M.D. Ilic, Stochastic unit commitment in aderegulated utility industry, Proceedings of the North AmericanPower Conference, Laramie, WY, 1997, 105–112.
  10. [10] F. Shih, M. Mazumdar, & J.A. Bloom Asymptotic mean andvariance of electric power generation system production costsvia recursive computation of the fundamental matrix of aMarkov chain, Operations Research, 47, 1999, 703–712.
  11. [11] R. Ferrero & S.M. Shahidehpour, Short-term power purchasesconsidering uncertain prices, IEE Proceedings, 144 (5), 1997,423–428.
  12. [12] V.C. Ramesh & X. Li, A fuzzy multiobjective approach tocontingency constrained OPF, IEEE Transaction on PowerSystems, 12 (3), 1997, 1348–1354.
  13. [13] M.E. El-Hawary, Electric power applications of fuzzy systems,(Piscataway, New Jersey: IEEE Press), 1998.
  14. [14] S.M. Shahidehpour & M.I. Alomoush, Decision making ina deregulated power environment based on fuzzy sets, in,Optimization techniques in electric power (Kluwer Publishers,1999).
  15. [15] C.J. Aldridge, M.E. Bradley, K.P. Dahal, S.J. Galloway, J.F.Macqueen, J.R. McDonald, & S. McKee, Knowledge-basedgenetic algorithm for unit commitment, IEE Proceedings onGeneration, Transmission and Distribution, 148 (2), 2001,146–152.
  16. [16] K.S. Swarup & S. Yamashiro, Unit commitment solutionmethodology using genetic algorithm, IEEE Transaction onPower Systems, 17, 2002, 87–91.
  17. [17] C.E. Zoumas, A.G. Bakirtzis, J.B. Theocharis, & V. Petridis,A genetic algorithm solution approach to the hydrothermalcoordination problem, IEEE Transaction on Power Systems,19 (2), 2004, 1356–1364.
  18. [18] P. Attaviriyanupap, H. Kita, E. Tanaka, & J. Hasegawa, Afuzzy-optimization approach to dynamic economic dispatchconsidering uncertainties, IEEE Transaction on Power Sys-tems, 19 (3), 2004, 1299–1306.
  19. [19] R.-H. Liang & J.-H. Liao, A fuzzy-optimization approach forgeneration scheduling with wind and solar energy systems,IEEE Transaction on Power Systems, 22 (4), 2007, 1665–1674.
  20. [20] Y. Ma, C. Jiang, Z. Hou, & C. Wang, The formulation of theoptimal strategies for the electricity producers based on theparticle swarm optimization algorithm, IEEE Transaction onPower Systems, 21 (4), 2006, 1663–1671.
  21. [21] A.Y. Saber, T. Senjyu, N. Urasaki, & T. Funabashi, Unit com-mitment computation–A novel fuzzy adaptive particle swarmoptimization approach, Proceedings of IEEE PSCE, 2006,1820–1828.
  22. [22] P. Bajpai & S.N. Singh, Fuzzy adaptive particle swarm op-timization for bidding strategy in uniform price spot market,IEEE Transaction on Power Systems, 22 (4), 2007, 2152–2160.
  23. [23] E.H. Allen & M.D. Ilic, Reserve markets for power reliability,IEEE Transactions on Power Systems, 15 (1), 2000, 228–233.
  24. [24] M.Y. El-Sharkh, A.A. El-Keib, & H. Chen, A fuzzy evolu-tionary programming-based solution methodology for security-constrained generation maintenance scheduling, Electric PowerSystems Research EPSR Journal, 67 (1), 2003, 67–72.
  25. [25] University of Washington, IEEE 118-bus system, available atthe following web site: http://www.ee.washington.edu, 1996.103Appendix A:List of SymbolsCi(·) Quadratic cost function of unit iCfi(·) Fuel consumption quadratic function of unit iCei(·) Emission quadratic function of unit iDMT GenCo’s decision maker target ($)DR(i) Ramp down rate limit of unit i¯E Upper limit on total emission allowanceF(i, t) Profit of unit i at time t¯F(i) Upper limit on total fuelconsumption for unit iF(i) Lower limit on total fuelconsumption for unit iMSR(i) Maximum sustain ramp rate of uniti(MW/min)N Number of unit in a GenCoP(i, t) Generation of unit i at time tPg(i) Lower limit on generation of unit i¯Pg(i) Upper limit on generation of unit i¯P(t) Upper limit on GenCo’s desired totalgeneration at time tPmin(t) Minimum fuzzy GenCo’s desired demandat time tPmax(t) Maximum fuzzy GenCo’s desired demandat time tR(i, t) Spinning reserve of unit i at time t¯R(t) Upper limit on GenCo’s desired totalspinning reserve at time tRmin(t) Minimum fuzzy GenCo’s desiredspinning reserve at time tRmax(t) Maximum fuzzy GenCo’s desiredspinning reserve at time trs The probability that spinningreserve is called and generatedS(i, t) Startup cost of unit i at time tT Hours in the study horizon (24 hin the day-ahead market)UR(i) Ramp up rate limit of unit iρg(i, t) Forecasted market pricefor energy at bus i and time tρs(i, t) Forecasted market price for spinningreserve at bus i and time tμx Fuzzy membership function fora parameter xμP Fuzzy membership function for thegenerated power PμR Fuzzy membership function for thespinning reserve Rμmin _profit Fuzzy membership function forGenCo’s minimum profitμprofit Fuzzy membership function forGenCo’s optimal profitAppendix B:The IEEE 118-Bus SystemThe IEEE 118-bus system one-line diagram is shown inFig. B1. Figure B2 depicts forecasted bus energy pricesfor the 118-bus system at hour 18. The forecasted systemdemand and reserves are given in Table B1.Table B1Forecasted System Demand and Spinning ReservesHour Demand (MW) Spinning Reserve (MW)1 3,308.8 165.42 3,054.2 152.73 2,884.6 144.24 2,799.7 140.05 2,794.9 139.76 2,797.3 139.97 2,799.7 140.08 2,969.4 148.59 3,393.6 169.710 3,733.0 186.611 3,817.8 190.912 3,860.2 193.013 3,817.8 190.914 3,733.0 186.615 3,690.5 184.516 3,690.5 184.517 3,860.2 193.018 4,242.0 212.119 4,199.6 210.020 4,114.7 205.721 3,987.5 199.422 3,902.6 195.123 3,690.5 184.524 3,436.0 171.8104Figure B1. IEEE118-bus system.Figure B2. Energy price profile for IEEE 118-bus system (at hour 18).105Appendix C:GenCos’ Generation ScheduleTable C1Total GenCos’ Generated Power and Spinning Reserves to be Sold in the Different MarketsHourGenerated Power Spinning ReserveQuantity (MW) Membership Quantity (MW) Membership1 2,415.3 0.978 81.3 0.9682 2,165.3 0.957 76.4 0.9433 1,977.0 0.880 71.2 0.8754 1,885.5 0.841 69.4 0.8335 1,875.5 0.824 65.0 0.8206 1,880.2 0.840 65.0 0.8317 1,894.1 0.845 69.9 0.8368 2,074.0 0.915 73.5 0.9089 2,503.0 0.976 85.7 0.96910 2,633.4 0.985 94.5 0.98111 2,818.2 1.000 97.6 1.00012 2,960.0 1.000 98.6 1.00013 2,916.7 1.000 101.5 1.00014 2,833.0 1.000 94.4 1.00015 2,789.1 0.992 94.2 0.99816 2,790.5 0.993 94.5 0.99917 2,959.6 1.000 97.4 1.00018 3,319.5 1.000 104.6 1.00019 3,299.0 1.000 105.8 1.00020 3,214.2 1.000 101.3 1.00021 3,087.0 1.000 98.6 1.00022 3,002.2 1.000 96.2 1.00023 2,790.2 0.990 94.6 0.99824 2,536.1 0.979 87.4 0.971106

Important Links:

Go Back