Thermal Unit Commitment Problem with Wind Power and Energy Storage System

Abstract:

This paper presents a methodology for solving unit commitment (UC) problem for thermal units integrated with wind power and generalized energy storage system (ESS).The ESS is introduced to achieve peak load shaving and reduce the operating cost. The volatility of wind power is simulated by multiple scenarios, which are generated by Latin hypercube sampling. Meanwhile, the scenario reduction technique based on probability metric is introduced to reduce the number of scenarios so that the computational burden can be alleviated. The thermal UC problem with volatile wind power and ESS is transformed to a deterministic optimization which is formulated as the mixed-integer convex program optimized by branch and bound-interior point method. During the branch and bound process, the best first search and depth first search are combined to expedite the computation. The effectiveness of the proposed algorithm is demonstrated by a ten unit UC problem.

• Periodical: Applied Mechanics and Materials (Volumes 347-350)
• Pages: 1455-1461
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.347-350.1455
• Online since: August 2013
• Authors: Rui Wang, Yu Guang Xie, Kai Xie, Ya Qiao Luo
• Keywords: Energy Storage System, Mix-Integer Convex Program, Unit Commitment, Wind Power

References

[1] J. Wang, M. Shahidehpour, Z. Li, Security-constrained unit commitment with volatile wind power generation, IEEE Trans. on Power Systems, Vol. 23, No. 3, pp.1319-1327, Aug. (2008).

DOI: 10.1109/tpwrs.2008.926719

[2] B. Venkatesha, P. Yu, H. B. Gooi, et al., Fuzzy MILP unit commitment incorporating wind generators, IEEE Trans. on Power System, Vol. 23, No. 4, pp.1738-1327, Nov. (2008).

DOI: 10.1109/tpwrs.2008.2004724

[3] M. Carrion, J. M. Arroyo, A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem, IEEE Transaction on Power System, Vol. 21, No. 3, pp.1371-1378, Aug. (2006).

DOI: 10.1109/tpwrs.2006.876672

[4] S. Chakraborty, T. Senjyu, H. Toyama, et al., Determination methodology for optimising the energy storage size for power system, IET Generation, Transmission & Distribution, Vol. 3, No. 3, pp.987-999, (2009).

DOI: 10.1049/iet-gtd.2008.0300

[5] H. Yu, C. Y. Chung, K. P. Wong, et al., Probabilistic load flow evaluation with hybrid Latin hypercube sampling and Cholesky decomposition, IEEE Trans. on Power Systems, Vol. 24, No. 2, pp.661-667, May. (2009).

DOI: 10.1109/tpwrs.2009.2016589

[6] L. Wu, M. Shahidehpour, Stochastic security-constrained unit commitment, IEEE Trans. on Power Systems, Vol. 22, No. 2, pp.800-811, May. (2007).

DOI: 10.1109/tpwrs.2007.894843

[7] Y. G. Xie, H. D. Chiang, A novel solution methodology for solving large-scale thermal unit commitment problems, Electric Power Components and Systems, Vol. 38, pp.1615-1634, (2010).

DOI: 10.1080/15325008.2010.492453

[8] H. Wei, H. Sasaki, J. Kubokawa, R. Yokoyama, An interior point nonlinear programming for optimal power flow problems with a novel data structure, IEEE Transaction on Power System, Vol. 13, No. 3, pp.870-877, Aug. (1998).

DOI: 10.1109/59.708745