Long-Term Unit Commitment Solution Based on Load Pattern

Abstract:

Power production plans are used by power companies to propose a feasible plan regarding the load requirements and system conditions of the following year, in which the arrangement of production material, such as the procurement of fuel required and delivery schedules, is clearly projected. Some of the factors involved in considering power production plans are common to those for short-term unit commitment, including system power balance, system security requirements, and unit characteristics; however, the limit on annual accumulation, which is not considered in short-term unit commitment, is not similar between the two. This study proposes an optimal mathematical model based on mixed-integer programming for long-term unit commitment to minimize the total system costs. To avoid the mixed-integer linear programming problems that increase the duration of computation because of numerous integer variables, the model proposed in this study applies the view of energy and power and integrates the similarities of load patterns and fuzzy logic to reduce the number of variables while adhering to all the constraints. The proposed algorithm can greatly improve the solution time compared with the complete UC model for the Taipower one week simulation case.

• Periodical: Applied Mechanics and Materials (Volumes 284-287)
• Pages: 3604-3611
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.284-287.3604
• Online since: January 2013
• Authors: Tai Ken Lu, Hao Hsuan Hsu, Fung Fei Chen, Meng Fu Kao
• Keywords: Load Pattern, Mixed-Integer Linear Programming, Two-Stage Algorithm, Unit Commitment

References

[1] J. F. Dopazo, H. M. Merrill, Optimal generator maintenance scheduling using integer programming, IEEE Transactions on Power Apparatus and Systems, Vol. 94, No. 5, Part 1, pp.1537-1545, Sep (1975).

DOI: 10.1109/t-pas.1975.31996

[2] H. Daneshi, A. L. Choobbari, M. Shahidehpour, Zuyi Li, Mixed Integer Programming Method to Solve Security Constrained Unit Commitment with Restricted Operating Zone Limits, 2008 IEEE International Conference on Electro/Information Technology, pp.187-192 (2008).

DOI: 10.1109/eit.2008.4554293

[3] V. S. Pappala, I. Erlich, A new approach for solving the unit commitment problem by adaptive particle swarm optimization, IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, 2008 , pp.1-6, Jul (2008).

DOI: 10.1109/pes.2008.4596390

[4] Yun-Won Jeong, Jong-Bae Park, Se-Hwan Jang, K.Y. Lee, A New Quantum-Inspired Binary PSO: Application to Unit Commitment Problems for Power Systems, IEEE Transactions on Power Systems, Vol. 25, Issue 3, p.1486 – 1495, Aug (2010).

DOI: 10.1109/tpwrs.2010.2042472

[5] T. Senjyu, H. Yamashiro, K. Shimabukuro, K. Uezato, T. Finabashi, A fast solution technique for large scale unit commitment problem using genetic algorithm, IEEE Transmission and Distribution Conference and Exhibition: Asia and Pacific 2002, pp.1611-1616 (2002).

DOI: 10.1109/tdc.2002.1176840

[6] J. Alvarez Lopez, R. Nieva Gomez, I. Guillen Moya, Commitment of Combined Cycle Plants Using a Dual Optimization–Dynamic Programming Approach, IEEE Transactions on Power Systems, Vol. 26 , Issue 2, pp.728-737, May (2011).

DOI: 10.1109/tpwrs.2010.2066584

[7] Zhang Li, Xueshan Han, Pengpeng Yang, A new approach for security constrained unit commitment, 2006 International Conference on Power System Technology, pp.1-6 (2006).

DOI: 10.1109/icpst.2006.321629