Central European Journal of Operations Research

, Volume 24, Issue 4, pp 923–937 | Cite as

Lying generators: manipulability of centralized payoff mechanisms in electrical energy trade

Original Paper


Optimal power flow (OPF) problems are focussing on the question how a power transmission network can be operated in the most economic way. The general aim in such scenarios is to optimize generator scheduling in order to meet consumption requirements, transmission constraints and to minimize the overall generation cost and transmission losses. We use a simple lossless DC load flow model for the description of the transmission network, and assume linearly decreasing marginal cost of generators with different parameters for each generator. We consider a scenario in which the generation values regarding the OPF are calculated by a central authority who is aware of the network parameters and production characteristics. Furthermore, we assume that a central mechanism is applied for the determination of generator payoffs in order to cover their generation costs and assign them with some profit. We analyze the situation when generators may provide false information about their production parameters and thus manipulate the OPF computation in order to potentially increase their resulting profit. We consider two central payoff mechanisms and compare their vulnerability for such manipulations and analyze their effect on the total social cost.


Electricity networks Power Optimal power flow 


  1. Abido M (2002) Optimal power flow using particle swarm optimization. Int J Electr Power Energy Syst 24(7):563–571CrossRefGoogle Scholar
  2. Achterberg T (2009) Scip: solving constraint integer programs. Math Program Comput 1(1):1–41CrossRefGoogle Scholar
  3. Bakirtzis AG, Biskas PN, Zoumas CE, Petridis V (2002) Optimal power flow by enhanced genetic algorithm. Power Syst IEEE Trans 17(2):229–236CrossRefGoogle Scholar
  4. Conejo AJ, Aguado JA (1998) Multi-area coordinated decentralized dc optimal power flow. Power Syst IEEE Trans 13(4):1272–1278CrossRefGoogle Scholar
  5. Contreras J (1997) A cooperative game theory approach to transmission planning in power systems. PhD thesis. University of California, BerkeleyGoogle Scholar
  6. Dommel HW, Tinney WF (1968) Optimal power flow solutions. power apparatus and systems. IEEE Trans 10:1866–1876Google Scholar
  7. Fisher EB, O’Neill RP, Ferris MC (2008) Optimal transmission switching. Power Syst IEEE Trans 23(3):1346–1355CrossRefGoogle Scholar
  8. Fletcher R, Leyffer S, Toint PL (2002) On the global convergence of a filter-sqp algorithm. SIAM J Optim 13(1):44–59CrossRefGoogle Scholar
  9. Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3(2):95–99CrossRefGoogle Scholar
  10. Hedman KW, O’Neill RP, Fisher EB, Oren SS (2008) Optimal transmission switching-sensitivity analysis and extensions. Power Syst IEEE Trans 23(3):1469–1479CrossRefGoogle Scholar
  11. Hedman KW, O’Neill RP, Fisher EB, Oren SS (2009) Optimal transmission switching with contingency analysis. Power Syst IEEE Trans 24(3):1577–1586CrossRefGoogle Scholar
  12. Hedman KW, Ferris MC, O’Neill RP, Fisher EB, Oren SS (2010) Co-optimization of generation unit commitment and transmission switching with n-1 reliability. Power Syst IEEE Trans 25(2):1052–1063CrossRefGoogle Scholar
  13. Hingorani NG (1993) Flexible AC transmission. Spectr IEEE 30(4):40–45CrossRefGoogle Scholar
  14. Hingorani NG, Gyugyi L, El-Hawary M (2000) Understanding FACTS: concepts and technology of flexible AC transmission systems, vol 1. IEEE press, New YorkGoogle Scholar
  15. Huneault M, Galiana F (1991) A survey of the optimal power flow literature. Power Syst IEEE Trans 6(2):762–770CrossRefGoogle Scholar
  16. Johnson SG (2010) The NLopt nonlinear-optimization package. http://ab-initio.mit.edu/nlopt
  17. Kaltenbach J, Hajdu L (1971) Optimal corrective rescheduling for power system security. IEEE Trans Power Appar Syst 90:843–851CrossRefGoogle Scholar
  18. Kirschen D, Strbac G (2004) Fundamentals of power system economics. Wiley, Chichester. doi:10.1002/0470020598 CrossRefGoogle Scholar
  19. Laarhoven PJ (1987) Simulated annealing: theory and applications. In: Mathematics and its applications. Reidel, DordrechtGoogle Scholar
  20. Le Digabel S (2011) Algorithm 909: nomad: nonlinear optimization with the mads algorithm. ACM Trans Math Softw (TOMS) 37(4):44CrossRefGoogle Scholar
  21. Momoh JA, Adapa R, El-Hawary M (1999) A review of selected optimal power flow literature to 1993. i. nonlinear and quadratic programming approaches. Power Syst IEEE Trans 14(1):96–104CrossRefGoogle Scholar
  22. Momoh JA, El-Hawary M, Adapa R (1999) A review of selected optimal power flow literature to 1993. ii. newton, linear programming and interior point methods. Power Syst IEEE Trans 14(1):105–111CrossRefGoogle Scholar
  23. O’Neill RP, Hedman KW, Krall EA, Papavasiliou A, Oren SS (2010) Economic analysis of the n-1 reliable unit commitment and transmission switching problem using duality concepts. Energy Syst 1(2):165–195CrossRefGoogle Scholar
  24. Oren S, Spiller P, Varaiya P, Wu F (1995) Folk theorems on transmission access: proofs and counter examples. Working papers series of the Program on Workable Energy Regulation (POWER) PWP-023, University of California Energy Institute 2539 Channing Way Berkeley, California 94720–5180, www.ucei.berkeley.edu/ucei
  25. Sauma EE, Oren SS (2007) Economic criteria for planning transmission investment in restructured electricity markets. Power Syst IEEE Trans 22(4):1394–1405CrossRefGoogle Scholar
  26. Song YH, Johns AT (1999) Flexible AC transmission systems (FACTS), vol 30. Institution of Electrical Engineers, StevenageGoogle Scholar
  27. Sun DI, Ashley B, Brewer B, Hughes A, Tinney WF (1984) Optimal power flow by newton approach. Power Appar Syst IEEE Trans 10:2864–2880CrossRefGoogle Scholar
  28. Tseng CL, Oren SS, Cheng CS, Li CA, Svoboda AJ, Johnson RB (1999) A transmission-constrained unit commitment method in power system scheduling. Decis Support Syst 24(3):297–310CrossRefGoogle Scholar
  29. Vaz AIF, Vicente LN (2007) A particle swarm pattern search method for bound constrained global optimization. J Glob Optim 39(2):197–219CrossRefGoogle Scholar
  30. Wächter A, Biegler LT (2006) On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Math Program 106(1):25–57CrossRefGoogle Scholar
  31. Yao J, Oren SS, Adler I (2004) Computing cournot equilibria in two settlement electricity markets with transmission constraint. In: System Sciences. Proceedings of the 37th Annual Hawaii International Conference on IEEE, p 9Google Scholar
  32. Yuryevich J, Wong KP (1999) Evolutionary programming based optimal power flow algorithm. Power Syst IEEE Trans 14(4):1245–1250CrossRefGoogle Scholar
  33. Zhu C, Byrd RH, Lu P, Nocedal J (1997) Algorithm 778: L-bfgs-b: Fortran subroutines for large-scale bound-constrained optimization. ACM Trans Math Softw (TOMS) 23(4):550–560CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Faculty of Information Technology and BionicsPázmány Péter Catholic UniversityBudapestHungary
  2. 2.Game Theory Research Group, Centre for Economics and Regional ScienceHungarian Academy of SciencesBudapestHungary

Personalised recommendations