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Peak shaving: a planning alternative to reduce investment costs in distribution systems?

  • Benoit MartinEmail author
  • Baptiste Feron
  • Emmanuel De Jaeger
  • Francois Glineur
  • Antonello Monti
Original Paper
  • 123 Downloads

Abstract

In the future, the foreseen increase of residential electricity consumption will force the Distribution System Operators to reinforce their networks at great expense. Through the emergence of ICT solutions and the increase of electric consumption flexibility at residential level, peak shaving has become an interesting alternative for reducing the investment costs in a distribution grid facing a load increase. This can be achieved with energy management systems (EMS) installed at residential level. Specifically, this work aims at considering peak shaving as an alternative to network reinforcement in a 20-year distribution planning study. For this purpose, the present work incorporates an optimal peak shaving approach to an accurate Convex DistFlow-based planning approach. Based on this, it quantifies how peak shaving can economically compete with network reinforcements for 12 real UK distribution networks under various flexibility scenarios. The results highlight that peak shaving is a competitive alternative to line reinforcement if the maximum initial line loading at the initial year of the planning study is under 80% of its nominal thermal rating value. It is also shown that EMS devices with a cost between 10 and 250 £/unit are economically competitive with network reinforcements depending on the considered network. Finally, this work proposes a planning decision metric, the initial line loading (ILL), measured at the beginning of the planning study, on the basis of which reinforcement decisions can be made.

Keywords

Distribution planning Energy management system Flexibility Peak shaving 

Notes

Acknowledgements

The authors gratefully acknowledge BMBF (German Federal Ministry of Education and Research) for providing financial support, promotional reference 13N13297.

References

  1. 1.
    Element Energy Limited: Further analysis of data from the household electricity usage study: correlation of consumption with low carbon technologies. In: Final report for Department of Energy and Climate Change and Department for the Environment Food and Rural Affairs (2014)Google Scholar
  2. 2.
    Ganguly, S., Sahoo, N.C., Das, D.: Recent advances on power distribution system planning: a state-of-the-art survey. Energy Syst. 4, 165–193 (2013)CrossRefGoogle Scholar
  3. 3.
    Molzahn, D.K., Hiskens, I.A.: A survey of relaxations and approximations of the power flow equations. Found, Trends Electr Energy Syst (2017)Google Scholar
  4. 4.
    Farivar, M., Clarke, C.R., Low, S.H., Chandy, K.M.: Inverter VAR control for distribution systems with renewables. In: 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 457–462 (2011)Google Scholar
  5. 5.
    Gan, L., Li, N., Topcu, U., Low, S.H.: Exact convex relaxation of optimal power flow in radial networks. IEEE Trans. Autom. Control 60(1), 72–87 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Martin, B., De Rua, P., De Jaeger, E., Glineur, F.: Loss reduction in a windfarm participating in primary voltage control using an extension of the Convex DistFlow OPF (Forthcoming). In: 20th Power Systems Computation Conference, (Dublin) (2018)Google Scholar
  7. 7.
    Ochoa, L., Dent, C., Harrison, G.: Distribution network capacity assessment: variable DG and active networks. IEEE Trans. Power Syst. 25, 87–95 (2010)CrossRefGoogle Scholar
  8. 8.
    Dugan, R.C., McDermott, T.E., Ball, G.J.: Distribution planning for distributed generation. In Rural Electric Power Conference, IEEE, pp. C4–1 (2000)Google Scholar
  9. 9.
    Poudineh, R., Jamasb, T.: Distributed generation, storage, demand response and energy efficiency as alternatives to grid capacity enhancement. Energy Policy 67, 222–231 (2014)CrossRefGoogle Scholar
  10. 10.
    Neimane, V.: On development planning of electricity distribution networks. PhD thesis, Royal Institute of Technology, Department of Electrical Engineering, Stockholm (2001)Google Scholar
  11. 11.
    Pilo, F., Jupe, S., Abbey, C., Baitch, A., Bak-Jensen, B., Carter-Brown, C., Celli, G., El Bakari, K., Fan, M., Georgilakis, P., Hearne, T., Ochoa, L., Petretto, G., Taylor, J.: Planning and optimization methods for active distribution systems. CIGRE, C6.19 working groupGoogle Scholar
  12. 12.
    Gemine, Q., Ernst, D., Cornlusse, B.: Active network management for electrical distribution systems: problem formulation, benchmark, and approximate solution, (2014). ArXiv preprint arXiv:1405.2806
  13. 13.
    Uddin, M., Romlie, M.F., Abdullah, M.F., Abd Halim, S., Abu Bakar, A.H., Chia Kwang, T.: A review on peak load shaving strategies. Renew. Sustain. Energy Rev. 82, 3323–3332 (2018)CrossRefGoogle Scholar
  14. 14.
    Pimm, A.J., Cockerill, T.T., Taylor, P.G.: The potential for peak shaving on low voltage distribution networks using electricity storage. J. Energy Storage 16, 231–242 (2018)CrossRefGoogle Scholar
  15. 15.
    Reihani, E., Motalleb, M., Ghorbani, R., Saoud, L.S.: Load peak shaving and power smoothing of a distribution grid with high renewable energy penetration. Renew. Energy 86, 1372–1379 (2016)CrossRefGoogle Scholar
  16. 16.
    Adika, C., Wang, L.: Smart charging and appliance scheduling approaches to demand side management. Int. J. Electr. Power Energy Syst. 57, 232–240 (2014)CrossRefGoogle Scholar
  17. 17.
    Oudalov, A., Cherkaoui, R., Beguin, A.: Sizing and optimal operation of battery energy storage system for peak shaving application. In: 2007 IEEE Lausanne Power Tech, pp. 621–625 (2007)Google Scholar
  18. 18.
    Rahimi, A., Zarghami, M., Vaziri, M., Vadhva, S.: A simple and effective approach for peak load shaving using battery storage systems. In: 2013 North American Power Symposium (NAPS), pp. 1–5 (2013)Google Scholar
  19. 19.
    Molderink, A., Bakker, V., Bosman, M.G.C., Hurink, J.L., Smit, G.J.M.: Domestic energy management methodology for optimizing efficiency in Smart Grids. In: 2009 IEEE Bucharest PowerTech, pp. 1–7 (2009)Google Scholar
  20. 20.
    Alam, M.J.E., Muttaqi, K.M., Sutanto, D.: A controllable local peak-shaving strategy for effective utilization of PEV battery capacity for distribution network support. IEEE Trans. Ind. Appl. 51, 2030–2037 (2015)CrossRefGoogle Scholar
  21. 21.
    Wang, Z., Wang, S.: Grid power peak shaving and valley filling using vehicle-to-grid systems. IEEE Trans. Power Deliv. 28, 1822–1829 (2013)CrossRefGoogle Scholar
  22. 22.
    Mets, K., Verschueren, T., Turck, F., Develder, C.: Exploiting v2g to optimize residential energy consumption with electrical vehicle (dis)charging. In: 2011 IEEE First International Workshop on Smart Grid Modeling and Simulation (SGMS) (2011)Google Scholar
  23. 23.
    Feron, B.: An agent based approach for virtual power plant valuing thermal flexibility in energy markets. In: IEEE Powertech (2017)Google Scholar
  24. 24.
    Molitor, C.: Residential city districts as flexibility resource analysis simulation and decentralized coordination algorithms. PhD Thesis (2015)Google Scholar
  25. 25.
    Herter, K.: Residential implementation of critical-peak pricing of electricity. Energy Policy 35, 2121–2130 (2007)CrossRefGoogle Scholar
  26. 26.
    Yu, T., Kim, D.S., Son, S.-Y.: Optimization of scheduling for home appliances in conjunction with renewable and energy storage resources. Int. J. Smart Home 7(4), 261–272 (2013)Google Scholar
  27. 27.
    Zhao, Z.: An optimal power scheduling method applied in home energy management system based on demand response. ETRI J. 35, 677–686 (2013)CrossRefGoogle Scholar
  28. 28.
    Mohsenian-Rad, A.-H., Leon-Garcia, A.: Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Trans. Smart Grid 1(2), 120–133 (2010)CrossRefGoogle Scholar
  29. 29.
    Mathieu, J.: Modeling, analysis, and control of demand response resources. PhD thesis, University of California, Berkeley (2012)Google Scholar
  30. 30.
    Martin, B., De Jaeger, E., Glineur, F.: A comparison of convex formulations for the joint planning of microgrids. CIRED-Open Access Proc. J. (2017)Google Scholar
  31. 31.
    Ben-Tal, A., Nemirovski, A.: On polyhedral approximations of the second-order cone. Math. Oper. Res. 26(2), 193–205 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  32. 32.
    Navarro-Espinosa, A., Ochoa, L.F.: Probabilistic impact assessment of low carbon technologies in LV distribution systems. IEEE Trans. Power Syst. 31, 2192–2203 (2016)CrossRefGoogle Scholar
  33. 33.
    Espinosa, A.N.: Dissemination document low voltage networks models and low carbon technology profiles. Technical report, University of Manchester and Electricity North West Limited, UK (2015)Google Scholar
  34. 34.
    Electricity North West Limited: Statement of methodology and charges for connection of Electricity North West Limited’s electricity distribution. Tech, Rep (2015)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Benoit Martin
    • 1
    Email author
  • Baptiste Feron
    • 2
  • Emmanuel De Jaeger
    • 1
  • Francois Glineur
    • 1
  • Antonello Monti
    • 2
  1. 1.Universite Catholique de LouvainLouvain-la-NeuveBelgium
  2. 2.E.ON Energy Research Center - RWTH Aachen UniversityAachenGermany

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