The effects of demand response on security-constrained unit commitment

Zarei, Ebrahim; Hemmatpour, Mohammad Hasan; Mohammadian, Mohsen

doi:10.24200/sci.2017.4536

The effects of demand response on security-constrained unit commitment

Document Type : Research Article

Authors

¹ Department of Electrical Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

² Department of Electrical Engineering, Faculty of Engineering, Jahrom University, Jahrom, Fars, Iran

10.24200/sci.2017.4536

Abstract

This paper aims to study the effect of the hourly demand response (DR) on security-constraint unit commitment (SCUC) problem with considering economic and security objectives. The demand side participation can solve some of the electricity market problems. Here, DR is take into consideration as one of the demand side management (DSM) parts. The DR is consist of fixed and responsive loads. The fixed loads satisfy in any circumstance and responsive loads can curtail or shift to other operating hours. The combination of SCUC with DR is a complex and the mixed integer non-linear problem. The bender’s decomposition is used as an optimization technique for solve this problem. This technique solves the problem by decompose it into master and sub problems. One of the effective of this manner is reducing the processing time. The performance and effectiveness of the proposed method are demonstrated on 6-, 24- and 118-bus test systems.

Keywords

Main Subjects

Electrical Engineering

References

Refrences:

1.Shahidehpour, M., Yamin, H., and Li, Z. Market operations in electric power systems: Forecasting, scheduling, and risk management", Market Overview in Electric Power Systems, pp. 1-20 (2002).

2. Conejo, A.J., Castillo, E., Minguez, R., and Garcia- Bertrand, R., Decomposition Techniques in Mathematical Programming: Engineering and Science Applications, Springer Science & Business Media (2006).

3. Bakirtzis, A.G., Ziogos, N.P., Tellidou, A.C., and Bakirtzis, G.A. Electricity producer o_ering strategies in day-ahead energy market with step-wise o_ers", IEEE Transactions on Power Systems, 22(4), pp. 1804-1818 (2007). 4. Nikzad, M. and Mozafari, B. Reliability assessment of incentive-and priced-based demand response programs in restructured power systems", International Journal of Electrical Power & Energy Systems, 56, pp. 83-96 (2014). 5. Zhang, D., Li, S., Sun, M., and O'Neill, Z. An optimal and learning-based demand response and home energy management system", IEEE Transactions on Smart Grid, 7(4), pp. 1790-1801 (2016). 6. Nan, S., Zhou, M., and Li, G. Optimal residential community demand response scheduling in smart grid", Applied Energy, 210, pp. 1280-1289 (2017). 7. Larsen, E.M., Pinson, P., Leimgruber, F., and Judex, F. Demand response evaluation and forecasting - methods and results from the ecogrid eu experiment", Sustainable Energy, Grids and Networks, 10, pp. 75-83 (2017). 8. Bui, V.-H., Hussain, A., and Kim, H.-M. A multiagent-based hierarchical energy management strategy for multi-microgrids considering adjustable power and demand response", IEEE Transactions on Smart Grid, 9, pp. 1323-1333 (2018). 9. Dehghanpour, K., Nehrir, H., Sheppard, J., and Kelly, N. Agent-based modeling of retail electrical energy markets with demand response", IEEE Transactions on Smart Grid, 9, pp. 3465-3475 (2018). 1636 E. Zarei et al./Scientia Iranica, Transactions D: Computer Science & ... 26 (2019) 1627{1636 10. Wang, F., Xu, H., Xu, T., Li, K., Sha_e-Khah, M., and Catal~ao, J.P. The values of market-based demand response on improving power system reliability under extreme circumstances", Applied Energy, 193, pp. 220- 231 (2017). 11. David, A. and Li, Y. E_ect of inter-temporal factors on the real time pricing of electricity", IEEE Transactions on Power Systems, 8(1), pp. 44-52 (1993). 12. Samimi, A., Nikzad, M., and Siano, P. Scenariobased stochastic framework for coupled active and reactive power market in smart distribution systems with demand response programs", Renewable Energy, 109, pp. 22-40 (2017). 13. Goel, L., Wu, Q., and Wang, P. Nodal price volatility reduction and reliability enhancement of restructured power systems considering demand-price elasticity", Electric Power Systems Research, 78(10), pp. 1655- 1663 (2008). 14. Zhang, D., Wang, Y., and Luh, P.B. Optimization based bidding strategies in the deregulated market", in, Power Industry Computer Applications, 1999. PICA'99, Proceedings of the 21st 1999 IEEE International Conference IEEE (1999). 15. Arroyo, J.M. and Conejo, A.J. Multiperiod auction for a pool-based electricity market", IEEE Transactions on Power Systems, 17(4), pp. 1225-1231 (2002). 16. Contreras, J., Candiles, O., De La Fuente, J.I., and Gomez, T. Auction design in day-ahead electricity markets", IEEE Transactions on Power Systems, 16(3), pp. 409-417 (2001). 17. Su, C. and Kirschen, D. Direct participation of demand-side in a pool-based electricity market", POWER SYSTEM TECHNOLOGY-BEIJING-, 31(20), p. 7 (2007). 18. Kirschen, D.S., Strbac, G., Cumperayot, P., and de Paiva Mendes, D. Factoring the elasticity of demand in electricity prices", IEEE Transactions on Power Systems, 15(2), pp. 612-617 (2000). 19. Nguyen, D.T., Negnevitsky, M., and De Groot, M. Pool-based demand response exchange-concept and modeling", IEEE Transactions on Power Systems, 26(3), pp. 1677-1685 (2011). 20. Khodaei, A., Shahidehpour, M., and Bahramirad, S. Scuc with hourly demand response considering intertemporal load characteristics", IEEE Transactions on Smart Grid, 2(3), pp. 564-571 (2011). 21. Fu, Y., Shahidehpour, M., and Li, Z. Securityconstrained unit commitment with Ac constraints", IEEE Transactions on Power Systems, 20(3), pp. 1538-1550 (2005). 22. Wu, H., Guan, X., Zhai, Q., and Ye, H. A systematic method for constructing feasible solution to scuc problem with analytical feasibility conditions", IEEE Transactions on Power Systems, 27(1), pp. 526-534 (2012). 23. Bussieck, M.R. and Vigerske, S. MINLP solver software", Wiley encyclopedia of operations research and management science (2010).