Published on: April 2026
COMPARATIVE MODELING OF STOCHASTIC AND DETERMINISTIC METHODS FOR ELECTRICITY MARKET CLEARING WITH WIND POWER UNCERTAINTY
Sri K. Naresh
Dr. G.N.Srinivas
Article Status
Available Documents
Abstract
The challenge of wind power variability in modern grids necessitates advanced market clearing methodologies. This paper compares the cost-effectiveness and operational flexibility of Deterministic Market Clearing (DMC) and Stochastic Market Clearing (SMC) in a day-ahead framework. We model both approaches as Linear Programs on a 6-bus power system featuring three conventional generators and two wind farms, explicitly handling wind uncertainty through a four-scenario approach for SMC. The numerical results confirm that the SMC framework yields superior economic performance, achieving an 11.67% reduction in expected total system costs compared to the DMC approach under comparable realization conditions. Critically, SMC eliminates load shedding across all high-stress scenarios, ensuring greater operational security. The analysis of Nodal Marginal Prices (LMPs) demonstrates that SMC generates accurate economic signals reflecting system marginal costs, while DMC results in misleading zero prices. This study provides a compelling quantitative argument for adopting stochastic op-timization to enhance market efficiency and grid reliability in systems with high renewable penetration.
How to Cite this Paper
Naresh, S. K. (2026). Comparative Modeling of Stochastic and Deterministic Methods for Electricity Market Clearing with Wind Power Uncertainty. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.672
Naresh, Sri. "Comparative Modeling of Stochastic and Deterministic Methods for Electricity Market Clearing with Wind Power Uncertainty." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.672.
Naresh, Sri. "Comparative Modeling of Stochastic and Deterministic Methods for Electricity Market Clearing with Wind Power Uncertainty." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.672.
References
- Ben-Tal, L. El Ghaoui, and A. Nemirovski, Robust Optimization. Princeton University Press, 2009.
- R. Birge and F. Louveaux, Introduction to Stochastic Programming, 2nd ed. Springer, 2011.
- Bertsimas et al., “Adaptive robust optimization for the security constrained unit commitment problem,” IEEE Trans. Power Syst., vol. 28, no. 1, pp. 52–63, 2013.
- Pinson and G. Kariniotakis, “Wind power forecasting and electricity markets,” IEEE Power Energy Mag., vol. 14, no. 2, pp. 52–61, 2016.
- Morales, A. Conejo, and J. Pe´rez-Ruiz, “Economic valuation of reserves in power systems with high penetration of wind power,” IEEE Trans. Power Syst., vol. 24, no. 2, pp. 900–910, 2009.
- Jiang, J. Wang, and Y. Guan, “Robust unit commitment with wind power and pumped storage hydro,” IEEE Trans. Power Syst., vol. 27, no. 2, pp. 800–810, 2012.
- Bouffard and F. Galiana, “Stochastic security for operations planning with significant wind power generation,” IEEE Trans. Power Syst., vol. 23, no. 2, pp. 306–316, 2008.
- Papavasiliou and S. Oren, “Multiarea stochastic unit commitment for high wind penetration in large-scale power systems,” IEEE Trans. Power Syst., vol. 28, no. 4, pp. 4621–4632, 2013.
- J. Conejo, M. Carrio´n, and J. M. Morales, Decision Making Under Uncertainty in Electricity Markets. Springer, 2010.
- Zhang, S. Shen, and J. L. Mathieu, “Data-driven chance constrained stochastic program,” Mathematical Programming, vol. 158, no. 1-2, pp. 291–327, 2016.
Ethical Compliance & Review Process
- •All submissions are screened under plagiarism detection.
- •Review follows editorial policy.
- •Authors retain copyright.
- •Peer Review Type: Double-Blind Peer Review
- •Published on: Apr 24 2026
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.

