Solving realistic reactive power market clearing problem of wind-thermal power system with system security
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Sravanthi Pagidipala
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Sandeep Vuddanti
Abstract
This paper proposes a security-constrained single and multi-objective optimization (MOO) based realistic security constrained-reactive power market clearing (SC-RPMC) mechanism in a hybrid power system by integrating the wind energy generators (WEGs) along with traditional thermal generating stations. Pre-contingency and post-contingency reactive power price clearing plans are developed. Different objective functions considered are the reactive power cost (RPC) minimization, voltage stability enhancement index (VSEI) minimization, system loss minimization (SLM), and the amount of load served maximization (LSM). These objectives of the SC-RPMC problem are solved in a single objective as well as multi-objective manner. The choice of objective functions for the MOO model depends on the load model and the operating condition of the system. For example, the SLM is an important objective function for the constant power load model, whereas the LSM is for the voltage-dependent/variable load model. The VSEI objective should be used only in near-critical loading conditions. The SLM/LSM objective is for all other operating conditions. The reason for using multiple objectives instead of a single objective and the rationale for the choice of the appropriate objectives for a given situation is explained. In this work, the teaching learning-based optimization (TLBO) algorithm is used for solving the proposed single objective-based SC-RPMC problem, and a non-dominated sorting-based TLBO technique is used for solving the multi-objective-based SC-RPMC problem. The fuzzy decision-making approach is applied for extracting the best-compromised solution. The validity and efficiency of the proposed market-clearing approach have been tested on IEEE 30 bus network.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
1. Hasankhani, A, Hakimi, SM. Stochastic energy management of smart microgrid with intermittent renewable energy resources in electricity market. Energy 2021;219:119668. https://doi.org/10.1016/j.energy.2020.119668.Search in Google Scholar
2. Bhattacharya, K, Jin Zhong, J. Reactive power as an ancillary service. IEEE Trans Power Syst 2001;16:294–300. https://doi.org/10.1109/59.918301.Search in Google Scholar
3. Bhattacharya, K, Zhong, J. Toward a competitive market for reactive power. IEEE Trans Power Syst 2002;17:1206–15.10.1109/TPWRS.2002.805025Search in Google Scholar
4. Reddy, SS, Abhyankar, AR, Bijwe, PR. Reactive power price clearing using multi-objective optimization. Energy 2011;36:3579–89. https://doi.org/10.1016/j.energy.2011.03.070.Search in Google Scholar
5. Almeida, KC, Senna, FS. Optimal active-reactive power dispatch under competition via bilevel programming. IEEE Trans Power Syst 2011;26:2345–54. https://doi.org/10.1109/tpwrs.2011.2150765.Search in Google Scholar
6. Reddy, SS, Abhyankar, AR, Bijwe, PR. Optimal day-ahead joint energy and reactive power scheduling with voltage dependent load models. In: IEEE transportation electrification conference and expo Asia-Pacific (ITEC Asia-Pacific), 1–4 June 2016. IEEE, Busan South Korea; 2016.10.1109/ITEC-AP.2016.7512947Search in Google Scholar
7. Saini, A, Saraswat, A. Multi-objective day-ahead localized reactive power market clearing model using HFMOEA. Int J Electr Power Energy Syst 2013;46:376–91. https://doi.org/10.1016/j.ijepes.2012.10.018.Search in Google Scholar
8. Safari, A, Salyani, P, Hajiloo, M. Reactive power pricing in power markets: a comprehensive review. Int J Ambient Energy 2020;41:1548–58. https://doi.org/10.1080/01430750.2018.1517675.Search in Google Scholar
9. Beagam, KSH, Jayashree, R, Khan, MA. A market center based clearing and settlement of pure reactive power market in deregulated power system. Eng Sci Technol, Int J 2018;21:909–21.10.1016/j.jestch.2018.03.019Search in Google Scholar
10. Samimi, A, Nikzad, M, Siano, P. Scenario-based stochastic framework for coupled active and reactive power market in smart distribution systems with demand response programs. Renew Energy 2017;109:22–40. https://doi.org/10.1016/j.renene.2017.03.010.Search in Google Scholar
11. Rabiee, A, Shayanfar, H, Amjady, N. Coupled energy and reactive power market clearing considering power system security. Energy Convers Manag 2009;50:907–15. https://doi.org/10.1016/j.enconman.2008.12.026.Search in Google Scholar
12. Saraswat, A, Saini, A, Saxena, AK. Day-ahead zonal reactive power market clearing model for competitive markets: a multi-objective approach. In: Fourth international conference on computational intelligence and communication networks. IEEE, Mathura; 2012:709–13 pp.10.1109/CICN.2012.78Search in Google Scholar
13. Kargarian, A, Raoofat, M. Stochastic reactive power market with volatility of wind power considering voltage security. Energy 2011;36:2565–71. https://doi.org/10.1016/j.energy.2011.01.051.Search in Google Scholar
14. Ahmadimanesh, A, Kalantar, M. A novel cost reducing reactive power market structure for modifying mandatory generation regions of producers. Energy Pol 2017;108:702–11. https://doi.org/10.1016/j.enpol.2017.06.046.Search in Google Scholar
15. Samimi, A, Kazemi, A, Siano, P. Economic-environmental active and reactive power scheduling of modern distribution systems in presence of wind generations: a distribution market-based approach. Energy Convers Manag 2015;106:495–509. https://doi.org/10.1016/j.enconman.2015.09.070.Search in Google Scholar
16. Yörükoğlu, S, Avşar, ZM, Kat, B. An integrated day-ahead market clearing model: incorporating paradoxically rejected/accepted orders and a case study. Elec Power Syst Res 2018;163:513–22.10.1016/j.epsr.2018.07.007Search in Google Scholar
17. Zhang, T, Elkasrawy, A, Venkatesh, B. A new computational method for reactive power market clearing. Int J Electr Power Energy Syst 2009;31:285–93. https://doi.org/10.1016/j.ijepes.2009.03.015.Search in Google Scholar
18. Papalexopoulos, AD, Angelidis, GA. Reactive power management and pricing in the California market. In: IEEE Mediterranean electromechanical conference. IEEE, Malaga; 2006:902–5 pp.10.1109/MELCON.2006.1653244Search in Google Scholar
19. Ahmadi, H, Akbari Foroud, A. Joint energy and reactive power market considering coupled active and reactive reserve market ensuring system security. Arabian J Sci Eng 2014;39:4789–804. https://doi.org/10.1007/s13369-014-1085-8.Search in Google Scholar
20. Amjady, N, Rabiee, A, Shayanfar, HA. A stochastic framework for clearing of reactive power market. Energy 2010;35:239–45. https://doi.org/10.1016/j.energy.2009.09.015.Search in Google Scholar
21. Reddy, SS, Bijwe, PR, Abhyankar, AR. Joint energy and spinning reserve market clearing incorporating wind power and load forecast uncertainties. IEEE Syst J 2015;9:152–64. https://doi.org/10.1109/jsyst.2013.2272236.Search in Google Scholar
22. Hetzer, J, Yu, DC, Bhattarai, K. An economic dispatch model incorporating wind power. IEEE Trans Energy Convers 2008;23:603–11. https://doi.org/10.1109/tec.2007.914171.Search in Google Scholar
23. Liu, X, Xu, W. Economic load dispatch constrained by wind power availability: a here-and-now approach. IEEE Trans Sustain Energy 2010;1:2–9. https://doi.org/10.1109/tste.2010.2044817.Search in Google Scholar
24. Ullah, NR, Bhattacharya, K, Thiringer, T. Wind farms as reactive power ancillary service providers - technical and economic issues. IEEE Trans Energy Convers 2009;24:661–72. https://doi.org/10.1109/tec.2008.2008957.Search in Google Scholar
25. Ahmidi, A, Guillaud, X, Besanger, Y, Blanc, R. A multilevel approach for optimal participating of wind farms at reactive power balancing in transmission power system. IEEE Syst J 2012;6:260–9. https://doi.org/10.1109/jsyst.2011.2163003.Search in Google Scholar
26. Mishra, S, Mishra, Y, Vignesh, S. Security constrained economic dispatch considering wind energy conversion systems. In: IEEE power and energy society general meeting. IEEE, Detroit, MI, USA; 2011:1–8 pp.10.1109/PES.2011.6039544Search in Google Scholar
27. Ackermann, T. Wind power in power system. Chichester, UK: Wiley; 2005.10.1002/0470012684Search in Google Scholar
28. Mota, LTM, Mota, AA. Load modeling at electric power distribution substations using dynamic load parameters estimation. Int J Electr Power Energy Syst Eng 2014;26:805–11.10.1016/j.ijepes.2004.07.002Search in Google Scholar
29. Thukaram, D, Jenkins, L, Visakha, K. Load modeling at electric power distribution substations using dynamic load parameters estimation. IEE Proc Generat Transm Distrib 2006;153:237–46. https://doi.org/10.1049/ip-gtd:20045210.10.1049/ip-gtd:20045210Search in Google Scholar
30. Ahmadimanesh, A, Kalantar, M. Considering effect of energy market on reactive power market. Int J Smart Electr Eng 2017;6:119–26.Search in Google Scholar
31. García-Román, JI, González-Romera, E. Analysis and decomposition of active and reactive power spot price in deregulated electricity markets. Int J Electr Power Energy Syst 2015;73:539–47. https://doi.org/10.1016/j.ijepes.2015.05.037.Search in Google Scholar
32. Gil, JB, San Roman, TG, Rios, JA, Martin, PS. Reactive power pricing: a conceptual framework for remuneration and charging procedures. IEEE Trans Power Syst 2000;15:483–9.10.1109/59.867129Search in Google Scholar
33. Hasanpour, S, Ghazi, R, Javidi, MH. A new approach for cost allocation and reactive power pricing in a deregulated environment. Electr Eng 2009;91. https://doi.org/10.1007/s00202-009-0113-2.Search in Google Scholar
34. Beagam, KSH, Jayashree, R, Khan, MA. Optimal reactive power allocation and settlement in deregulated power market. Int J Power Energy Convers 2020;11:174–99. https://doi.org/10.1504/ijpec.2020.106269.Search in Google Scholar
35. Vishnu, M, Sunil Kumar, TK. An improved solution for reactive power dispatch problem using diversity-enhanced particle swarm optimization. Energies 2020;13:1–21. https://doi.org/10.3390/en13112862.Search in Google Scholar
36. Wang, Z, Passino, KM, Wang, J. Optimal reactive power allocation in large-scale grid-connected photovoltaic systems. J Optim Theor Appl 2015;167:761–79. https://doi.org/10.1007/s10957-015-0778-9.Search in Google Scholar
37. Bayat, A, Bagheri, A. Optimal active and reactive power allocation in distribution networks using a novel heuristic approach. Appl Energy 2019;233-234:71–85. https://doi.org/10.1016/j.apenergy.2018.10.030.Search in Google Scholar
38. Venkata Rao, R. Teaching learning based optimization algorithm - and its engineering applications. Switzerland: Springer International Publishing; 2016.Search in Google Scholar
39. Rao, RV, Savsani, VJ, Balic, J. Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems. Eng Optim 2012;44:1447–62. https://doi.org/10.1080/0305215x.2011.652103.Search in Google Scholar
40. Zou, F, Wang, L, Hei, X, Chen, D, Wang, B. Multi-objective optimization using teaching-learning-based optimization algorithm. Eng Appl Artif Intell 2013;26:1291–300. https://doi.org/10.1016/j.engappai.2012.11.006.Search in Google Scholar
41. Chinta, S, Kommadath, R, Kotecha, P. A note on multi-objective improved teaching–learning based optimization algorithm (MO-ITLBO). Inf Sci 2016;373:337–50. https://doi.org/10.1016/j.ins.2016.08.061.Search in Google Scholar
42. Lin, W, Yu, DY, Zhang, C, Liu, X, Zhang, S, Tian, Y, et al.. A multi-objective teaching-learning-based optimization algorithm to scheduling in turning processes for minimizing makespan and carbon footprint. J Clean Prod 2015;101:337–47. https://doi.org/10.1016/j.jclepro.2015.03.099.Search in Google Scholar
43. Natarajan, E, Kaviarasan, V, Lim, WH, Tiang, SS, Tan, TH. Enhanced multi-objective teaching-learning-based optimization for machining of Delrin. IEEE Access 2018;6:51528–46. https://doi.org/10.1109/access.2018.2869040.Search in Google Scholar
44. Sayed, F, Kamel, S, Abdel-Rahim, O. Load shedding solution using multi-objective teaching-learning-based optimization. In: International conference on innovative trends in computer engineering. IEEE, Aswan; 2018:447–52 pp.10.1109/ITCE.2018.8316665Search in Google Scholar
45. Power systems test case archive [Online]. Available from: https://labs.ece.uw.edu/pstca/pf30/pg_tca30bus.htm [Accessed 13 May 2021].Search in Google Scholar
46. Salkuti, SR, Abhyankar, AR, Bijwe, PR. Multi-objective day-ahead real power market clearing with voltage dependent load models. Int J Emerg Elec Power Syst 2011;12. https://doi.org/10.2202/1553-779x.2727.Search in Google Scholar
© 2021 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Research Articles
- Solving realistic reactive power market clearing problem of wind-thermal power system with system security
- A novel methodology for power loss allocation of both passive and active power distribution systems
- A simple network reduction technique for large autonomous microgrids incorporating an efficient reactive power sharing
- An adaptive, observer-based switching method for B4 inverters feeding three-phase induction motors
- Analysis and evaluation of two short-term load forecasting techniques
- Power quality improvement in a photovoltaic based microgrid integrated network using multilevel inverter
- Comparison between flexible AC transmission systems (FACTs) and filters regarding renewable energy systems harmonics mitigation
- Evaluating the impact of Khanh Son power plant on Danang Distribution Network
- Fast valving automation setting using HRTSim
- Mathematical modeling of polymer dielectric strength considering filling concentration
- Special action on high quality development of renewable energy in Northeast China: market implementation initiatives and suggestions
- Coordinated power management and control of renewable energy sources based smart grid
Articles in the same Issue
- Frontmatter
- Research Articles
- Solving realistic reactive power market clearing problem of wind-thermal power system with system security
- A novel methodology for power loss allocation of both passive and active power distribution systems
- A simple network reduction technique for large autonomous microgrids incorporating an efficient reactive power sharing
- An adaptive, observer-based switching method for B4 inverters feeding three-phase induction motors
- Analysis and evaluation of two short-term load forecasting techniques
- Power quality improvement in a photovoltaic based microgrid integrated network using multilevel inverter
- Comparison between flexible AC transmission systems (FACTs) and filters regarding renewable energy systems harmonics mitigation
- Evaluating the impact of Khanh Son power plant on Danang Distribution Network
- Fast valving automation setting using HRTSim
- Mathematical modeling of polymer dielectric strength considering filling concentration
- Special action on high quality development of renewable energy in Northeast China: market implementation initiatives and suggestions
- Coordinated power management and control of renewable energy sources based smart grid
