Skip to main content

Advertisement

SpringerLink
Log in

Optimal power flow with renewable energy resources including storage

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

Abstract

The incorporation of renewable energy resources (RERs) into electrical grid is very challenging problem due to their intermittent nature. This paper solves an optimal power flow (OPF) considering wind–solar–storage hybrid generation system. The primary components of the hybrid power system include conventional thermal generators, wind farms and solar photovoltaic modules with batteries. The main critical problem in operating the wind farm or solar PV plant is that these RERs cannot be scheduled in the same manner as conventional generators, because they involve climate factors such as wind velocity and solar irradiation. This paper proposes a new strategy for the optimal power flow problem taking into account the impact of uncertainties in wind, solar PV and load forecasts. The simulation results for IEEE 30 bus system with genetic algorithm (GA) and two-point estimate method have been obtained to test the effectiveness of the proposed optimal power flow strategy. Results for a sample system with GA and two-point estimate OPF, and GA and Monte Carlo simulation have been obtained to ascertain effectiveness of the proposed 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

Similar content being viewed by others

References

  1. Carpentier J (1979) Optimal power flows. Int J Electr Power Energy Syst 1(1):3–15

    Article  Google Scholar 

  2. Momoh JA, Koessler RJ, Bond MS, Stott B, Sun D, Papalexopoulos A, Ristanovic P (1997) Challenges to optimal power flow. IEEE Trans Power Syst 12(1):444–447

    Article  Google Scholar 

  3. Gayme D, Topcu U (2013) Optimal power flow with large-scale storage integration. IEEE Trans Power Syst 28(2):709–717

    Article  Google Scholar 

  4. Yang G, Zhou M, Lin B, Du W (2013) Optimal scheduling the wind-solar-storage hybrid generation system considering wind-solar correlation. In: IEEE PES Asia-pacific power and energy engineering conference (APPEEC), pp 1–6

  5. Levron Y, Guerrero JM, Beck Y (2013) Optimal power flow in microgrids with energy storage. IEEE Trans Power Syst 28(3):3226–3234

    Article  Google Scholar 

  6. Chandy KM, Low SH, Topcu U, Xu H (2010) A simple optimal power flow model with energy storage. In: 49th IEEE conference on decision and control, pp 1051–1057

  7. Hu Z, Jewell WT (2011) Optimal power flow analysis of energy storage for congestion relief, emissions reduction, and cost savings. In: IEEE power systems conference and exposition (PSCE), pp 1–8

  8. Jabr RA, Pal BC (2009) Intermittent wind generation in optimal power flow dispatching. IET Gener Transm Distrib 3(1):66–74

    Article  Google Scholar 

  9. Zhang Y, Giannakis GB (2013) Robust optimal power flow with wind integration using conditional value-at-risk. In: Proc. of the 4th Intl. Conf. on smart grid communications. arXiv:1310.7292 [math.OC]

  10. Sjodin E, Gayme DF, Topcu U (2012) Risk-mitigated optimal power flow for wind powered grids. In: American control conference, pp 4431-4437

  11. Liu G, Tomsovic K (2012) Quantifying spinning reserve in systems with significant wind power penetration. IEEE Trans Power Syst 27(4):2385–2393

    Article  Google Scholar 

  12. Reddy SS, Bijwe PR, Abhyankar AR (2013) Joint energy and spinning reserve market clearing incorporating wind power and load forecast uncertainties. IEEE Syst J (99):1–13

  13. IEA Wind Energy (2009) Annual Report 2008, International Energy Agency

  14. [Online]. http://www.uwig.org/windinmarketstableOct2011

  15. Chen CL, Lee TY, Jan RM (2006) Optimal wind-thermal coordination dispatch in isolated power systems with large integration of wind capacity. Energy Convers Manag 47(1819):3456–3472

    Article  Google Scholar 

  16. Kundur P (1994) Power system stability and control. McGraw-Hill Inc, New York

    Google Scholar 

  17. Ullah NR, Bhattacharya K, Thiringer T (2009) Wind farms as reactive power ancillary service providers-technical and economic issues. IEEE Trans Energy Convers 24(3):661–672

    Article  Google Scholar 

  18. Ackermann T (2005) Wind power in power system. Wiley, New York

    Book  Google Scholar 

  19. Zhou Y, Kumar P, Bauer P, Ferreira JA (2009) Optimization of hybrid wind park through a design approach- progressive design methodology. In: Proc. of IEEE IPEMC, pp 1092–1098

  20. Cain MB, O’Neill RP, Castillo A (2012) History of optimal power flow and formulations. FERC staff technical paper

  21. Pahwa A, Chavali S, Das S (2003) Intelligent computational methods for power systems optimization problems. In: IEEE power engineering society general meeting, vol 1, p 138

  22. Hong HP (1998) An efficient point estimate method for probabilistic analysis. Reliab Eng Syst Saf 59(3):261–267

    Article  Google Scholar 

  23. Morales JM, Ruiz JP (2007) Point estimate schemes to solve the probabilistic power flow. IEEE Trans Power Syst 22(4):1594–1601

    Article  Google Scholar 

  24. Osman MS, Abo-Sinna MA, Mousa AA (2004) A solution to the optimal power flow using genetic algorithms. Appl Math Comput 155(2):391–405

    Article  MathSciNet  MATH  Google Scholar 

  25. Lai LL, Ma JT, Yokoyama R, Zhao M (1997) Improved genetic algorithms for optimal power flow under both normal and contingent operation states. Electr Power Energy Syst 19(5):287–292

    Article  Google Scholar 

  26. Masters GM (2004) Renewable and efficient electric power systems. Wiley, New Jersey

    Book  Google Scholar 

  27. Bhuiyan FA, Yazdani A (2010) Reliability assessment of a wind-power system with integrated energy storage. IET Renew Power Gener 4(3):211–220

    Article  Google Scholar 

  28. Reddy SS, Bijwe PR, Abhyankar AR (2015) Optimal posturing in day-ahead market clearing for uncertainties considering anticipated real-time adjustment costs. IEEE Syst J 9(1):177–190

    Article  Google Scholar 

  29. Reddy SS, Bijwe PR, Abhyankar AR (2015) Optimum day-ahead clearing of energy and reserve markets with wind power generation using anticipated real-time adjustment costs. Int J Electr Power Energy Syst 71:242–253

    Article  Google Scholar 

  30. Reddy SS, Momoh JA (2015) Realistic and transparent optimum scheduling strategy for hybrid power system. IEEE Trans Smart Grid 6(6):3114–3125

    Article  Google Scholar 

  31. Lu B, Shahidehpour M (2005) Short-term scheduling of battery in a grid-connected PV/battery system. IEEE Trans Power Syst 20(2):1053–1061

    Article  Google Scholar 

  32. Bouffard F, Galiana FD (2008) Stochastic security for operations planning with significant wind power generation. IEEE Trans Power Syst 23(2):306–316

    Article  Google Scholar 

  33. [Online]. http://www.nrel.gov/midc/srrl_bms/

  34. University of Washington (2007) Power system test case archive. [Online]. http://www.ee.washington.edu/research/pstca

Download references

Author information

Authors and Affiliations

  1. Department of Railroad and Electrical Engineering, Woosong University, Daejeon, Republic of Korea

    S. Surender Reddy

Authors
  1. S. Surender Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Surender Reddy.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Reddy, S.S. Optimal power flow with renewable energy resources including storage. Electr Eng 99, 685–695 (2017). https://doi.org/10.1007/s00202-016-0402-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00202-016-0402-5

Keywords

Search

Navigation