Skip to main content

Grid Integration of Renewable Energy Systems

  • Chapter
  • First Online:
Smart Power Systems and Renewable Energy System Integration

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 57))

  • 2844 Accesses

Abstract

The percentage of renewable power demand met by renewable power generators is increasing rapidly. This growth is driven by environmental concerns, government policies and decreasing cost of technologies. However, as the penetration of renewable power sources increases, new challenges in system planning and operation are becoming evident. There are short term operational challenges as well as long term planning challenges due to the intermittent nature of renewable power generation primarily from wind and solar photovoltaics. The study of grid integration of renewables is concerned with determining the optimal technical and regulatory framework that can effectively manage the short term and long term challenges of large scale renewable power penetration. Operational challenges of this chapter include maintaining frequency and voltage stability due to intermittency as well as network congestion. Planning challenges include allocating long term capacity credits of wind and solar power generation. Currently, the cost of a number of balancing technologies is expected to play a major role in overall viability of renewable power generation. This includes energy storage, demand side management, and dynamic ratings of assets. Smart grids are expected to provide the platform for utilizing the full potential of renewable power generation as well as balancing the technologies.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Amelin, M.: Comparison of capacity credit calculation methods for conventional power plants and wind power. IEEE Trans. Power Syst. 24, 685–691 (2009)

    Google Scholar 

  • Androcec, I., Wangensteen, I.: Different methods for congestion management and risk management. In: International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2006, pp. 1–6, 11–15 June 2006

    Google Scholar 

  • Asari, M., Nanahara, T., Maejima, T., Yamaguchi, K., Sato, T.: A study on smoothing effect on output fluctuation of distributed wind power generation. In: Transmission and Distribution Conference and Exhibition 2002: Asia Pacific, IEEE/PES, vol. 2, pp. 938–943, 6–10 Oct 2002

    Google Scholar 

  • Barth, R., Brand, H., Meibom, P., Weber, C.: A Stochastic unit-commitment model for the evaluation of the impacts of integration of large amounts of intermittent wind power. In: International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2006, pp. 1–8, 11–15 June 2006

    Google Scholar 

  • Barth, R., Meibom, P., Weber, C.: Simulation of short-term forecasts of wind and load for a stochastic scheduling model. In: 2011 IEEE Power and Energy Society General Meeting, pp. 1–8, 24–29 July 2011

    Google Scholar 

  • Basso, T.: IEEE 1547 and 2030 Standards for Distributed Energy Resources Interconnection and Interoperability with the Electricity Grid IEEE 1547 and 2030 Standards for Distributed Energy Resources Interconnection and Interoperability with the Electricity Grid. Nrel (2014)

    Google Scholar 

  • Bhaskar, K., Singh, S.N.: AWNN-assisted wind power forecasting using feed-forward neural network. IEEE Trans. Sustain. Energy 3, 306–315 (2012)

    Article  Google Scholar 

  • Billinton, R., Chen, H., Ghajar, R.: Time-series models for reliability evaluation of power systems including wind energy. Microelectron. Reliab. 36, 1253–1261 (1996)

    Article  Google Scholar 

  • Brower, M., Green, D., Hinrichs-rahlwes, R., Sawyer, S., Sander, M., Taylor, R., Giner-reichl, I., Teske, S., Lehmann, H., Alers, M., Hales, D.: Renewables 2014: Global status report. REN 21 Steer. Comm. 215 (2014)

    Google Scholar 

  • Burke, D.J., O’malley, M.J.: Transmission connected wind curtailment with increasing wind capacity connection. In: IEEE Power & Energy Society General Meeting, 2009, PES ’09, pp. 1–5, 26–30 July 2009

    Google Scholar 

  • Burke, D.J., O’Malley, M.J.: Factors influencing wind energy curtailment. IEEE Trans. Sustain. Energy 2, 185–193 (2011)

    Article  Google Scholar 

  • Chuang, A.S., Schwaegerl, C.: Ancillary services for renewable integration. In: 2009 CIGRE/IEEE PES Joint Symposium Integration of Wide-Scale Renewable Resources Into the Power Delivery System, pp. 1–1, 29–31 July 2009

    Google Scholar 

  • Conejo, A.J., Arroyo, J.M., Alguacil, N., Guijarro, A.L.: Transmission loss allocation: a comparison of different practical algorithms. IEEE Trans. Power Syst. 17, 571–576 (2002)

    Article  Google Scholar 

  • Datta, R., Ranganathan, V.T.: Variable-speed wind power generation using doubly fed wound rotor induction machine-a comparison with alternative schemes. IEEE Trans. Energy Convers. 17, 414–421 (2002)

    Google Scholar 

  • de Magalhaes Carvalho, L., da Rosa, M.A., Martins Leite da Silva, A., Miranda, V.: Probabilistic analysis for maximizing the grid integration of wind power generation. IEEE Trans. Power Syst. 27, 2323–2331 (2012)

    Google Scholar 

  • DECC, n.d. Electricity Market Reform: Contracts for Difference - GOV.UK [WWW Document]. https://www.gov.uk/government/collections/electricity-market-reform-contracts-for-difference (Accessed 18 Dec 2015)

  • Ekanayake, J., Jenkins, N.: Comparison of the response of doubly fed and fixed-speed induction generator wind turbines to changes in network frequency. IEEE Trans. Energy Convers. 19, 800–802 (2004)

    Google Scholar 

  • Energy Networks Association: Distributed Generation Connection Guide - A Quick Reference Guide for Connecting Generation to the Distribution Network that Falls Under G59/3 and is 50kW or Less 3-Phase or 17kW or Single-Phase (2014a)

    Google Scholar 

  • Energy Networks Association: Distributed Generation Connection Guide - A Quick Reference Guide for Connecting Generation to the Distribution Network that Falls Under G59/3 (2014b)

    Google Scholar 

  • Energy Networks Association: Distributed Generation Connection Guide - A Quick Reference Guide for Connecting Generation to the Distribution Network in Multiple Premises that Falls Under G83/2 (2014c)

    Google Scholar 

  • Eriksen, P.B., Ackermann, T., Abildgaard, H., Smith, P., Winter, W., Rodriguez Garcia, J.M.: System operation with high wind penetration. IEEE Power Energy Mag. 3, 65–74 (2005)

    Google Scholar 

  • Fang, R.S., David, A.K.: Transmission congestion management in an electricity market. IEEE Trans. Power Syst. 14, 877–883 (1999)

    Article  Google Scholar 

  • Fu, J., Morrow, D.J., Abdelkader, S., Fox, B.: Impact of dynamic line rating on power systems. In: Proceedings of 2011 46th International Universities’ Power Engineering Conference (UPEC), pp. 1–5 (2011)

    Google Scholar 

  • Gao, Y., Billinton, R.: Adequacy assessment of generating systems containing wind power considering wind speed correlation. IET Renew. Power Gener. 3, 217–226 (2009)

    Article  Google Scholar 

  • Glatvitsch, H., Alvarado, F.: Management of multiple congested conditions in unbundled operation of a power system. IEEE Trans. Power Syst. 13, 1013–1019 (1998)

    Article  Google Scholar 

  • Holdsworth, L., Wu, X.G., Ekanayake, J.B., Jenkins, N.: Comparison of fixed speed and doubly-fed induction wind turbines during power system disturbances. IEE Proc. Gener. Transm. Distrib. 150, 343 (2003)

    Google Scholar 

  • Hosek, J., Musilek, P., Lozowski, E., Pytlak, P.: Effect of time resolution of meteorological inputs on dynamic thermal rating calculations. IET Gener. Transm. Distrib. 5, 941–947 (2011)

    Article  Google Scholar 

  • Howington, B.S., Ramon, G.J.: Dynamic thermal line rating summary and status of the state-of-the-art technology. IEEE Trans. Power Delivery 2, 851–858 (1987)

    Article  Google Scholar 

  • IEEE Standard for Calculating the Current-Temperature Relationship of Bare Overhead Conductors. In: IEEE Std 738–2012 (Revision of IEEE Std 738-2006—Incorporates IEEE Std 738-2012 Cor 1-2013), pp. 1–72 (2013)

    Google Scholar 

  • Jabr, R.A., Pal, B.C.: Intermittent wind generation in optimal power flow dispatching. IET Gener. Transm. Distrib. 3, 66–74 (2009)

    Article  Google Scholar 

  • Jiang, W., Xiaohong, G., Xiaoxin, Z., Yuxun, Z.: Estimation and characteristic analysis of aggregated generation of geographically distributed wind farms. In: 2011 IEEE Power and Energy Society General Meeting, pp. 1–6, 24–29 July 2011

    Google Scholar 

  • Jianhua, Z., Yue, Y., Jiayan, Y.: Research on transmission congestion of power system containing wind farms based on genetic algorithm. In: 2011 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), pp. 104–108, 6–9 July 2011

    Google Scholar 

  • Jie, S., Wei-Jen, L., Yongqian, L., Yongping, Y., Peng, W.: Short term wind power forecasting using Hilbert-Huang Transform and artificial neural network. In: 2011 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), pp. 162–167, 6–9 July 2011

    Google Scholar 

  • Kamga, A.F.K., Voller, S., Verstege, J.F.: Congestion management in transmission systems with large scale integration of wind energy. In: 2009 CIGRE/IEEE PES Joint Symposium Integration of Wide-Scale Renewable Resources into the Power Delivery System, pp. 1–1, 29–31 July 2009

    Google Scholar 

  • Katiraei, B.F.: Studies for utility-scale photovoltaic distributed generation. IEEE Power Energy Mag. 9, 62–71 (2011)

    Google Scholar 

  • Kazerooni, A.K., Mutale, J., Perry, M., Venkatesan, S., Morrice, D.: Dynamic thermal rating application to facilitate wind energy integration. In: 2011 IEEE Trondheim PowerTech, pp. 1–7, 19–23 June 2011

    Google Scholar 

  • Khalid, M., Savkin, A.V.: A method for short-term wind power prediction with multiple observation points. IEEE Trans. Power Syst. 27, 579–586 (2012)

    Article  Google Scholar 

  • Khanabadi, M., Ghasemi, H.: Transmission congestion management through optimal transmission switching. In: 2011 IEEE Power and Energy Society General Meeting, pp. 1–5, 24–29 July 2011

    Google Scholar 

  • Khanabadi, M., Ghasemi, H., Doostizadeh, M.: Optimal transmission switching considering voltage security and N-1 contingency analysis. IEEE Trans. Power Syst. 28, 542–550 (2013)

    Article  Google Scholar 

  • London Array Limited, n.d. London Array | Harnessing the power of offshore wind [WWW Document]. http://www.londonarray.com/ (Accessed 18 Dec 2015)

  • Luo, C., Hou, Y., Wen, J., Cheng, S.: Assessment of market flows for interregional congestion management in electricity markets. IEEE Trans. Power Syst. 1–10 (2014)

    Google Scholar 

  • Matus, M., Saez, D., Favley, M., Suazo-Martinez, C., Moya, J., Jimenez-Estevez, G., Palma-Behnke, R., Olguin, G., Jorquera, P.: Identification of critical spans for monitoring systems in dynamic thermal rating. IEEE Trans. Power Delivery 27, 1002–1009 (2012)

    Article  Google Scholar 

  • Moskvitch, K.: In Numbers: The London Array. Eng. Technol. 10, 24–25 (2015)

    Google Scholar 

  • Mountain, B., Szuster, P.: Solar everywhere. IEEE Power Energy Mag. 2–3 (2015)

    Google Scholar 

  • Muljadi, E., Singh, M., Gevorgian, V.: Fixed-speed and variable-slip wind turbines providing spinning reserves to the grid. IEEE Power Energy Soc. Gen. Meet (2013)

    Google Scholar 

  • Muneender, E., Kumar, D.: Optimal rescheduling of real and reactive powers of generators for zonal congestion management based on FDR PSO. In: Transmission & Distribution Conference & Exposition: Asia and Pacific, pp. 1–6, 26–30 Oct 2009

    Google Scholar 

  • National Instruments: Wind Turbine Control Methods 4–7 (2008)

    Google Scholar 

  • Palomares-Salas, J.C., de la Rosa, J.J.G., Ramiro, J.G., Melgar, J., Aguera, A., Moreno, A.: ARIMA vs. neural networks for wind speed forecasting. In: IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, 2009, CIMSA ’09, pp. 129–133, 11–13 May 2009

    Google Scholar 

  • Peiyuan, C., Pedersen, T., Bak-Jensen, B., Zhe, C.: ARIMA-based time series model of stochastic wind power generation. IEEE Trans. Power Syst. 25, 667–676 (2010)

    Google Scholar 

  • Shaker, H., Fotuhi-Firuzabad, M., Aminifar, F.: Fuzzy dynamic thermal rating of transmission lines. IEEE Trans. Power Delivery 27, 1885–1892 (2012)

    Article  Google Scholar 

  • Shirmohammadi, D., Wollenberg, B., Vojdani, A., Sandrin, P., Pereira, M., Rahimi, F., Schneider, T., Stott, B.: Transmission dispatch and congestion management in the emerging energy market structures. IEEE Trans. Power Syst. 13, 1466–1474 (1998)

    Article  Google Scholar 

  • Short, J.A., Infield, D.G., Freris, L.L.: Stabilization of grid frequency through dynamic demand control. IEEE Trans. Power Syst. 22, 1284–1293 (2007)

    Google Scholar 

  • Singh, H., Shangyou, H., Papalexopoulos, A.: Transmission congestion management in competitive electricity markets. IEEE Trans. Power Syst. 13, 672–680 (1998)

    Article  Google Scholar 

  • Styczynski, Z. A., Lombardi, P., Seethapathy, R., Piekutowski, M., Ohler, C., Roberts, B., Verma, S.C.: Electric Energy Storage and its tasks in the integration of wide-scale renewable resources. 2009 CIGRE/IEEE PES Jt. Symp. Integr. Wide-Scale Renew. Resour. Into Power Deliv. Syst. (2009)

    Google Scholar 

  • Vazquez, S., Lukic, S.M., Galvan, E., Franquelo, L.G., Carrasco, J.M.: Energy storage systems for transport and grid applications. IEEE Trans. Ind. Electron. 57, 3881–3895 (2010)

    Google Scholar 

  • Vergnol, A., Sprooten, J., Robyns, B., Rious, V., Deuse, J.: Real time grid congestion management in presence of high penetration of wind energy. In: 13th European Conference on Power Electronics and Applications, 2009, EPE ’09, pp. 1–10, 8–10 Sept 2009

    Google Scholar 

  • Wedde, H.F., Lehnhoff, S., Handschin, E., Krause, O.: Establishing large-scale renewable reserve capacity through distributed multi-agent support. In: 2007 5th IEEE International Conference on Industrial Informatics, pp. 1157–1163, 23–27 June 2007

    Google Scholar 

  • Xiao-Ping, Z., Liangzhong, Y.: A vision of electricity network congestion management with FACTS and HVDC. In: Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, 2008, DRPT 2008, pp. 116–121, 6–9 April 2008

    Google Scholar 

  • Xiaohong, G., Jiang, W., Pai, L.: Analyzing aggregated characteristics of distributed wind farms. In: 2012 IEEE Power and Energy Society General Meeting, pp. 1–1, 22–26 July 2012

    Google Scholar 

  • Xie, L., Carvalho, P.M.S., Ferreira, L.A.F.M., Liu, J., Krogh, B.H., Popli, N., Ilić, M.D.: Wind integration in power systems: operational challenges and possible solutions. Proc. IEEE 99, 214–232 (2011)

    Google Scholar 

  • Yi, Y., Harley, R.G., Divan, D., Habetler, T.G.: Thermal modeling and real time overload capacity prediction of overhead power lines. In: IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, 2009, SDEMPED 2009, pp. 1–7, Aug 31–Sept 3 2009

    Google Scholar 

  • Yi, Z., Chowdhury, A.A., Koval, D.O.: Probabilistic wind energy modeling in electric generation system reliability assessment. IEEE Trans. Ind. Appl. 47, 1507–1514 (2011)

    Article  Google Scholar 

  • Yingzhong, G., Le, X., Rollow, B., Hesselbaek, B.: Congestion-induced wind curtailment: sensitivity analysis and case studies. In: North American Power Symposium (NAPS), pp. 1–7, 4–6 Aug 2011

    Google Scholar 

  • Zhang, X. P., Chong, B., Godfrey, K.R., Yao, L., Bazargan, M., Schmitt, L.: Management of congestion costs utilizing FACTS controllers in a bilateral electricity market environment. In: 2007 IEEE Lausanne Power Tech, pp. 1244–1249, 1–5 July 2007

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Binayak Banerjee .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Banerjee, B., Jayaweera, D., Islam, S. (2016). Grid Integration of Renewable Energy Systems. In: Jayaweera, D. (eds) Smart Power Systems and Renewable Energy System Integration. Studies in Systems, Decision and Control, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-30427-4_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-30427-4_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30425-0

  • Online ISBN: 978-3-319-30427-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics