Assessing Wind Energy Uncertainty Impact on Joint Energy and Reserve Markets by using Stochastic Programming Evaluation Metrics

Abstract

Wind energy is considered as a challenging problem for power systems because of its variable nature. As a result of accommodating wind resources with conventional thermal units in the system, performing of electricity markets will be more complicated. The results of the market clearing problem can be improved by proper wind uncertainty modeling. One of the best methods in order to handle wind uncertainty is to use stochastic programming. The expected value of the perfect information (EVPI) and value of the stochastic solution (VSS) are two well-known indices defined to analyse stochastic programming results compared to a case with having full information and a case disregarding uncertainty, respectively. In this paper, impact of the wind uncertainty on the EVPI and VSS indices is investigated in the joint energy and reserve market clearing problem. Wind uncertainty is characterized by two measures, namely wind penetration level and wind forecasting accuracy level. The wind penetration levels are modeled as a fraction of a basic wind power value while various forecasting accuracy levels are modeled by normal probability distribution function with different variances. The problem is investigated and results are analyzed under different levels of the wind penetration and forecasting accuracies. Based on obtained results, the value of the EVPI metric increases with increment of the wind penetration level or forecasting error variance. Also, because of the higher uncertainty level of the wind power arising from penetration level or variance increment, the value of the VSS metric increases.

Keywords

• Wind Uncertainty Management; Joint Energy and Reserve Markets; Stochastic Programming; Expected Value of Perfect Information; Value of Stochastic Solution.

References

1. V. Fthenakis, H. C. Kim, "Land use and electricity generation: A life-cycle analysis," Renewable and Sustainable Energy Reviews," Vol. 13, No. 6–7, pp. 1465-1474, 2009.
2. G. M. Masters, Renewable and efficient electric power systems, Wiley, 2013.
3. Renewable Energy Policy Network for the 21st Century (REN21), Renewables 2011: Global Status Report.
4. The World Wind Energy Association (WWEA), 2014 Half-year Report,.
5. D.S. Kirschen, and G. Strbac, Fundamentals of power system economics, wiley, 2004.
6. M. Shahidehpour, H. Yamin, and Z. Li, Market operations in electric power systems, Wiley, New York, 2002.
7. J. Wang, N. E. Redondo, and F. D. Galiana, "Demand- side reserve offers in joint energy/reserve electricity markets," IEEE Transactions on Power Systems, Vol. 18, No. 4, pp. 1300-1306, 2003.
8. F. Bouffard, and F. D. Galiana, "Stochastic security for operations planning with significant wind power generation," IEEE Transactions on Power Systems, Vol. 23, No. 2, pp. 306 - 316, 2008.

9. R. Doherty, and M. O'Malley, "A new approach to quantify reserve demand in systems with significant installed wind capacity," IEEE Transactions on Power Systems, Vol.20, No. 2, pp. 587-595, 2005.
10. A. J. Conejo, M. Carrión, and J. M. Morales, Decision making under uncertainty in electricity markets, Springer, New York, 2010. [11]
11. F. Bouffard, F. D. Galiana, and A. J. Conejo,
12. "Market-clearing with stochastic security- part 1:
13. formulation," IEEE Transactions on Power Systems, Vol.
14. , No. 4, pp. 1818-1826, 2005.
15. J. Zhang, J.D. Fuller, and S. Elhedhli, "A stochastic programming model for a day-ahead electricity market with Transactions on Power Systems, Vol. 25, No. 2, pp. 703- 713, 2010. shortage pricing," IEEE
16. C.G. Baslis, and A.G. Bakirtzis, "Mid-term stochastic scheduling of a price-maker hydro producer with pumped storage," IEEE Transactions on Power Systems, Vol. 26, No. 4, pp. 1856-1865, 2011.
17. B. Vatani, N. Amjady, and H. Zareipour, "Stochastic self-scheduling of generation companies in day-ahead multi-auction electricity markets considering uncertainty of units and electricity market prices," IET Generation, Transmission & Distribution, Vol. 7, No. 7, pp. 735-744, 2013.
18. D. Ting, and Q. Wei "Trading wind power in a competitive programming and game theory," IEEE Transactions on Sustainable Energy, Vol. 4, No. 3, pp. 805-815, 2013.
19. H. Akhavan-Hejazi, and H. Mohsenian-Rad, "Optimal operation of independent storage systems in energy and reserve markets with high wind penetration," IEEE Transactions on Power Systems, Vol. 5, No. 2, pp. 1088- 1097, 2014.
20. S. Martin, Y. Smeers, and J.A. Aguado, "Stochastic two settlement equilibrium model for electricity markets with wind generation," IEEE Transactions on Power Systems, Vol. 30, No. 1, pp. 233-245, 2015.
21. Y.H. Wan, Wind Power Plant Behaviors: Analyses of Long-Term Wind Power Data, National Renewable Energy Laboratory (NREL) Report, 2004.
22. J. M. Morales, A. J. Conejo, and J. Perez-Ruiz, "Economic valuation of reserves in power systems with high penetration of wind power," IEEE Transactions on Power Systems, Vol. 24, No. 2, pp. 900-910, 2009.
23. S. S. Reddy, P. R. Bijwe, and A. R. Abhyankar, "Joint energy incorporating uncertainties," IEEE Systems Journals, Vol.9, No. 1, pp. 152-164, 2013. reserve market load forecast power and
24. A. L. Garcia, Probability, statistics, and random processes for electrical engineering, Pearson Education, Inc, 2009.
25. M. Carrión, J. M. Arroyo, and A. J. Conejo, "A Bilevel Stochastic Programming Approach for Retailer Futures Market Trading," IEEE Transactions on Power Systems, Vol. 24, No. 3, pp. 1446-1456, 2009.
26. Y. Yuan, Q. Li, W. Wang, "Optimal Operation Strategy of Energy Storage Unit in Wind Power Integration Based on stochastic Programming," IET Renewable Power Generation, Vol. 5, No. 2, pp. 194–201, 2011.
27. J. C. Lopez, J. Contreras, J. I. Munoz, and J. R. S. Mantovani, "Multi-Stage Stochastic Non-Linear Model for Reactive Power Planning Under Contingencies," IEEE Transactions on Power Systems, Vol. 28, No. 2, pp. 1503-1514, 2013.
28. J. G. Gonzalez, R. M. R. de la Muela, L. M. Santos, and A. M. Gonzalez, "Stochastic Joint Optimization of Wind Generation and Pumped-Storage Units in an Electricity Market," IEEE Transactions on Power Systems, Vol. 23, No. 2, pp. 460-467, 2008.
29. S. Bradley, and A. Hax, and T.L Magnanti, Applied mathematical programming, Addison-Wesley Pub. Co, 1977.
30. M. Carrión, and J. M. Arroyo, "A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem," IEEE Transactions on Power Systems, Vol. 21, No. 3, pp. 1371-1378, 2006.