Annals of Operations Research

, Volume 222, Issue 1, pp 389–418 | Cite as

Modelling counter-intuitive effects on cost and air pollution from intermittent generation



In this paper, we first present a market environment with a conventional two settlement mechanism. We show that when we add some wind generation to the system, the steady-state market conditions yield lower social and consumer welfare and higher use of fossil fuels. We also present results of a counterfactual stochastic settlement market which improves social and consumer welfare after the introduction of new intermittent generation. Thus, we conclude that the choice of market mechanism is a critical factor for capturing the benefits of large-scale wind integration.

We also introduce a method to compute analytical equilibria of games in which the payoff functions of players depend on the optimal solution to an optimization problem with inequality constraints.


Electricity markets Uncertainty Wind energy Inefficiency Cost of wind integration Game theory Stochastic optimization Equilibrium models 


  1. Akpinar, A., Kömürcü, M., Kankal, M., Özölçer, I., & Kaygusuz, K. (2008). Energy situation and renewables in Turkey and environmental effects of energy use. Renewable & Sustainable Energy Reviews, 12(8), 2013–2039. CrossRefGoogle Scholar
  2. Brauns, E. (2008). Towards a worldwide sustainable and simultaneous large-scale production of renewable energy and potable water through salinity gradient power by combining reversed electrodialysis and solar power? Desalination, 219, 312–323. CrossRefGoogle Scholar
  3. Cheng, J., & Wellman, M. (1998). The WALRAS algorithm: A convergent distributed implementation of general equilibrium outcomes. Computational Economics, 12(1), 1–24. CrossRefGoogle Scholar
  4. DeMeo, E., Jordan, G., Kalich, C., King, J., Milligan, M., Murley, C., Oakleaf, B., & Schuerger, M. (2007). Accommodating wind’s natural behavior. IEEE Power & Energy Magazine, 5(6), 59–67. CrossRefGoogle Scholar
  5. Eriksen, P., Ackermann, T., Abildgaard, H., Smith, P., Winter, W., & Garcia, J. R. (2005). System operation with high wind penetration. IEEE Power & Energy Magazine, 3(6), 65–74. CrossRefGoogle Scholar
  6. Giebel, G., Brownsword, R., & Kariniotakis, G. (2003). The state-of-the-art in short-term prediction of wind power. A literature overview. Risoe National Laboratory. Google Scholar
  7. Green, R. (1996). Increasing competition in the British electricity spot market. The ICFAI Journal of Industrial Economics, 44, 205–216. CrossRefGoogle Scholar
  8. Green, R., & Vasilakos, N. (2010). Market behaviour with large amounts of intermittent generation. Energy Policy, 38(7), 3211–3220. CrossRefGoogle Scholar
  9. Hiroux, C., & Saguan, M. (2010). Large-scale wind power in European electricity markets: time for revisiting support schemes and market designs? Energy Policy, 38(7), 3135–3145. CrossRefGoogle Scholar
  10. Jafari, A., Greenwald, A., Gondek, D., & Ercal, G. (2001). On no-regret learning, fictitious play, and Nash equilibrium. In Machine learning—international workshop then conference (pp. 226–233). Google Scholar
  11. James, J., & Petersen, E. L. (1999). In 1999 European wind energy conference: wind energy for the next millennium: proceedings of the European wind energy conference, Nice, France 1–5 March 1999. London: Earthscan. 1999. Google Scholar
  12. Kaygusuz, K. (2002). Renewable energy sources: the key to a better future. Energy Sources, 24(8), 787–799. CrossRefGoogle Scholar
  13. Khazaei, J. (2012). Mechanism design for electricity markets under uncertainty. PhD thesis, University of Auckland. Google Scholar
  14. Khazaei, J., Zakeri, G., & Oren, S. S. Single and multi-settlement approaches to market clearing mechanisms under uncertainty. Google Scholar
  15. Kurukulasuriya, M. (1998). Electricity generation from hybrid of hydro power, solar and wind energies for rural development. In Proceedings of the American power conference (Vol. 2, pp. 911–914). Google Scholar
  16. Lin, Y., & Schrage, L. (2009). The global solver in the LINDO-API-PB-Taylor & Francis. Optimization Methods & Software, 24(4), 657. CrossRefGoogle Scholar
  17. Meyer, N.I. (2003). European schemes for promoting renewables in liberalised markets. Energy Policy, 31, 665–676. CrossRefGoogle Scholar
  18. Morales, J., Conejo, A., & Pérez-Ruiz, J. (2009). Economic valuation of reserves in power systems with high penetration of wind power. IEEE Transactions on Power Systems, 24(2), 900–910. CrossRefGoogle Scholar
  19. Morales, J. M., Conejo, A. J., & Pérez-Ruiz, J. (2011). Simulating the impact of wind production on locational marginal prices. IEEE Transactions on Power Systems, 26, 820–828. CrossRefGoogle Scholar
  20. Negrete-Pincetic, M., Wang, G., Kowli, A., & Pulgar-Painemal, H. (2010). Emerging issues due to the integration of wind power in competitive electricity markets. In Proceedings of the 2010 power and energy conference at Illinois, PECI 2010 (pp. 45–50). CrossRefGoogle Scholar
  21. Pritchard, G., Zakeri, G., & Philpott, A. (2010). A single-settlement, energy-only electric power market for unpredictable and intermittent participants. Operations Research. Google Scholar
  22. REN21 (2012). Renewables 2012, global status report. Tech. rep. Google Scholar
  23. Sayigh, A. (1999). Renewable energy—the way forward. Applied Energy, 64, 15–30. CrossRefGoogle Scholar
  24. WWEA (2010). World wind energy report 2009. Tech. rep. World Wind Energy Association, Germany. Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Javad Khazaei
    • 1
  • Anthony Downward
    • 2
  • Golbon Zakeri
    • 2
  1. 1.Department of Operations Research and Financial EngineeringPrinceton UniversityPrincetonUSA
  2. 2.Department of Engineering ScienceUniversity of AucklandAucklandNew Zealand

Personalised recommendations