Skip to main content

Advertisement

SpringerLink
Log in

Spinning reserve scheduling in power systems containing wind and solar generations

  • Original Paper
  • Published:
Electrical Engineering Aims and scope Submit manuscript

Abstract

Wind and solar as two renewable energy resources are largely used to generate clean and sustainable energy in the power systems. To integrate these renewable energies in the power system, different aspects of the power system such as reliability and operation are affected that must be investigated. It is due to the variation in the generated power of these resources that are arisen from the variation in the wind speed and solar radiation. To model the uncertainty nature of large-scale wind and photovoltaic farms in the operation studies of the power system, an appropriate multistate reliability model is developed for the wind and photovoltaic farms considering both failure of main components and variation in the wind speed and solar radiation. To determine the optimum number of states in the reliability model of wind and photovoltaic farms, XB index is calculated and based on the fuzzy c-means clustering technique the transition rates among different states are obtained. To calculate the transition rates among different states, the fuzzy numbers are used that results in the accurate and reduced reliability model for wind and photovoltaic farms. To determine the probability of different states in the reliability model of wind and photovoltaic farms in the operation studies, matrix multiplication technique is utilized to determine important indices of the power system such as the unit commitment risk. In this paper, the amount of required spinning reserve of a power system containing wind and solar generation units can be determined based on the reliability criterion. The proposed technique is applied to the two reliability test systems including RBTS and IEEE-RTS and the reliability-based operation indices of these systems are calculated to present the effectiveness of the proposed analytical method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30

Similar content being viewed by others

References

  1. Zeng B, Zhang J et al (2014) Integrated planning for transition to low-carbon distribution system with renewable energy generation and demand response. IEEE Trans Power Systems 29(3):1153–1165

    Article  Google Scholar 

  2. http://www.pvresources.com/en/pvpowerplants/top50pv.php

  3. https://www.power-technology.com/features/feature-biggest-wind-farms-in-the-world-texas/

  4. Zhang X, Gu J, Hua L, Ma K (2019) Enhancing performances on wind power fluctuation mitigation by optimizing operation schedule of battery energy storage systems with considerations of operation cost. IEEE Access 7:94072–94083

    Article  Google Scholar 

  5. Dvorkin Y, Lubin M, Backhaus S, Chertkov M (2015) Uncertainty sets for wind power generation. IEEE Trans Power Syst 31(4):3326–3327

    Article  Google Scholar 

  6. Meng K, Yang H, Dong ZY, Guo W, Wen F, Xu Z (2015) Flexible operational planning framework considering multiple wind energy forecasting service providers. IEEE Trans Sustain Energy 7(2):708–717

    Article  Google Scholar 

  7. Hajibandeh N, Shafie-khah M, Talari S, Dehghan S, Amjady N, Mariano SJ, Catalao JP (2018) Demand response-based operation model in electricity markets with high wind power penetration. IEEE Trans Sustain Energy 10(2):918–930

    Article  Google Scholar 

  8. Liu M, Quilumba FL, Lee WJ (2014) Dispatch scheduling for a wind farm with hybrid energy storage based on wind and LMP forecasting. IEEE Trans Ind Appl 51(3):1970–1977

    Article  Google Scholar 

  9. Yu Y, Luh PB, Litvinov E, Zheng T, Zhao J, Zhao F (2015) Grid integration of distributed wind generation: Hybrid Markovian and interval unit commitment. IEEE Trans Smart Grid 6(6):3061–3072

    Article  Google Scholar 

  10. Quan H, Srinivasan D, Khosravi A (2014) Incorporating wind power forecast uncertainties into stochastic unit commitment using neural network-based prediction intervals. IEEE Trans Neural Netw Learn Syst 26(9):2123–2135

    Article  MathSciNet  Google Scholar 

  11. Sundar K, Nagarajan H, Roald L, Misra S, Bent R, Bienstock D (2019) Chance-Constrained Unit Commitment With N-1 Security and Wind Uncertainty. IEEE Trans Control Netw Syst 6(3):1062–1074

    Article  MathSciNet  Google Scholar 

  12. Naghdalian S, Amraee T, Kamali S, Capitanescu F (2019) Stochastic network constrained unit commitment to determine flexible ramp reserve for handling wind power and demand uncertainties. IEEE Trans Ind Inform. 16(7):4580–4591

    Article  Google Scholar 

  13. Bavafa F, Niknam T, Azizipanah-Abarghooee R, Terzija V (2016) A new biobjective probabilistic risk-based wind-thermal unit commitment using heuristic techniques. IEEE Trans Ind Inf 13(1):115–124

    Article  Google Scholar 

  14. Abedi S, He M, Obadina D (2018) Congestion risk-aware unit commitment with significant wind power generation. IEEE Trans Power Syst 33(6):6861–6869

    Article  Google Scholar 

  15. Yang B, Cao X, Cai Z, Yang T, Chen D, Gao X, Zhang J (2020) Unit commitment comprehensive optimal model considering the cost of wind power curtailment and deep peak regulation of thermal unit. IEEE Access 8:71318–71325

    Article  Google Scholar 

  16. Szymanski BJ et al (2011) Operation of photovoltaic power systems with energy storage. In: 2011 7th international conference-workshop compatibility and power electronics (CPE). IEEE

  17. Bialasiewicz JT (2008) Renewable energy systems with photovoltaic power generators: Operation and modeling. IEEE Trans Ind Electron 55(7):2752–2758

    Article  Google Scholar 

  18. Nwaigwe KN, Mutabilwa P, Dintwa E (2019) An overview of solar power (PV systems) integration into electricity grids. Mater Sci Energy Technol 2(3):629–633

    Google Scholar 

  19. Hosseinabadi M, Rastegar H (2014) DFIG based wind turbines behavior improvement during wind variations using fractional order control systems. Iran J Electr Electron Eng 10(4):314–323

    Google Scholar 

  20. Khalilzadeh E, Fotuhi-Firuzabad M, Aminifar F, Ghaedi A (2014) Reliability modeling of run-of-the-river power plants in power system adequacy studies. IEEE Trans Sustain Energy 5(4):1278–1286

    Article  Google Scholar 

  21. Mirzadeh M, Simab M, Ghaedi A (2019) Adequacy studies of power systems with barrage-type tidal power plants. IET Renew Power Gener 13(14):2612–2622

    Article  Google Scholar 

  22. Mirzadeh M, Simab M, Ghaedi A (2020) Reliability evaluation of power systems containing tidal power plant. J Energy Manag Technolgy 4(2):28–38

    Google Scholar 

  23. Cannon RL, Dave JV, Bezdek JC (1986) Efficient implementation of the fuzzy c-means clustering algorithms. IEEE Trans Pattern Anal Mach Intell 2:248–255

    Article  Google Scholar 

  24. Chen M, Ludwig SA (2014) Particle swarm optimization based fuzzy clustering approach to identify optimal number of clusters. J Artif Intell Soft Comput Res 4(1):43–56

    Article  Google Scholar 

  25. Ghaedi A, Abbaspour A, Fotuhi-Friuzabad M, Parvania M (2014) Incorporating large photovoltaic farms in power generation system adequacy assessment. Scientia Iranica 21(3):924–934

    Google Scholar 

  26. Billinton R, Allan RN (1994) Reliability evaluation of power systems, 2nd edn. Plenum, New York

    MATH  Google Scholar 

  27. Ghaedi A, Abbaspour A, Fotuhi-Firuzabad M, Moeini-Aghtaie M (2013) Toward a comprehensive model of large-scale dfig-based wind farms in adequacy assessment of power systems. IEEE Trans Sustain Energy 5(1):55–63

    Article  Google Scholar 

  28. Billinton R, Kumar S et al (1989) A reliability test system for educational purposes-basic data. Power Eng Rev IEEE 9(8):67–68

    Article  Google Scholar 

  29. Subcommittee PM (1979) IEEE reliability test system. IEEE Trans Power Appar Syst 6:2047–2054

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Dariun Branch, Islamic Azad University, Dariun, Iran

    Amir Ghaedi

  2. Engineering Department, Persian Gulf University, Mahini Ave., P. O. Box 7516913817, Bushehr, Iran

    Hamed Gorginpour

Authors
  1. Amir Ghaedi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamed Gorginpour
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amir Ghaedi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghaedi, A., Gorginpour, H. Spinning reserve scheduling in power systems containing wind and solar generations. Electr Eng 103, 2507–2526 (2021). https://doi.org/10.1007/s00202-021-01239-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00202-021-01239-z

Keywords

Search

Navigation