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Robust Optimization for Virtual Power Plants

  • Allegra De FilippoEmail author
  • Michele Lombardi
  • Michela Milano
  • Alberto Borghetti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10640)

Abstract

Virtual Power Plants (VPP) are one of the main components of future smart electrical grids, connecting and integrating several types of energy sources, loads and storage devices. A typical VPP is a large industrial plant with high (partially shiftable) electric and thermal loads, renewable energy generators and electric and thermal storages. Optimizing the use and the cost of energy could lead to a significant economic impact. This work proposes a VPP Energy Management System (EMS), based on a two-step optimization model that decides the minimum-cost energy balance at each point in time considering the following data: electrical load, photovoltaic production, electricity costs, upper and lower limits for generating units and storage units. The first (day-ahead) step models the prediction uncertainty using a robust approach defining scenarios to optimize the load demand shift and to estimate the cost. The second step is an online optimization algorithm, implemented within a simulator, that uses the optimal shifts produced by the previous step to minimize, for each timestamp, the real cost while fully covering the optimally shifted energy demand. The system is implemented and tested using real data and we provide analysis of results and comparison between real and estimated optimal costs.

Keywords

Virtual Power Plants Robust optimization Forecast uncertainty 

References

  1. 1.
    Aloini, D., Crisostomi, E., Raugi, M., Rizzo, R.: Optimal power scheduling in a virtual power plant. In: 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies, pp. 1–7, December 2011Google Scholar
  2. 2.
    Awerbuch, S., Preston, A.: The Virtual Utility: Accounting, Technology and Competitive Aspects of the Emerging Industry, vol. 26. Springer Science & Business Media, Berlin (2012).  https://doi.org/10.1007/978-1-4615-6167-5
  3. 3.
    Bai, H., Miao, S., Ran, X., Ye, C.: Optimal dispatch strategy of a virtual power plant containing battery switch stations in a unified electricity market. Energies 8(3), 2268–2289 (2015). http://www.mdpi.com/1996-1073/8/3/2268
  4. 4.
    Bordin, C., Anuta, H.O., Crossland, A., Gutierrez, I.L., Dent, C.J., Vigo, D.: A linear programming approach for battery degradation analysis and optimization in offgrid power systems with solar energy integration. Renew. Energy 101, 417–430 (2017)CrossRefGoogle Scholar
  5. 5.
    Bracewell, R.N., Bracewell, R.N.: The Fourier Transform and its Applications, vol. 31999. McGraw-Hill, New York (1986)Google Scholar
  6. 6.
    Edwards, R.E., New, J., Parker, L.E.: Predicting future hourly residential electrical consumption: a machine learning case study. Energy Buildings 49, 591–603 (2012)CrossRefGoogle Scholar
  7. 7.
    Espinosa, A., Ochoa, L.: Dissemination document low voltage networks models and low carbon technology profiles. Technical report, University of Manchester, June 2015Google Scholar
  8. 8.
    Gamou, S., Yokoyama, R., Ito, K.: Optimal unit sizing of cogeneration systems in consideration of uncertain energy demands as continuous random variables. Energy Convers. Manag. 43(9), 1349–1361 (2002)CrossRefGoogle Scholar
  9. 9.
    Jain, R.K., Smith, K.M., Culligan, P.J., Taylor, J.E.: Forecasting energy consumption of multi-family residential buildings using support vector regression: investigating the impact of temporal and spatial monitoring granularity on performance accuracy. Appl. Energy 123, 168–178 (2014)CrossRefGoogle Scholar
  10. 10.
    Jurkovi, K., Pandi, H., Kuzle, I.: Review on unit commitment under uncertainty approaches. In: 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1093–1097, May 2015Google Scholar
  11. 11.
    Kaplanis, S., Kaplani, E.: A model to predict expected mean and stochastic hourly global solar radiation I(h;nj) values. Renew. Energy 32(8), 1414–1425 (2007)CrossRefGoogle Scholar
  12. 12.
    Lombardi, P., Powalko, M., Rudion, K.: Optimal operation of a virtual power plant. In: Power and Energy Society General Meeting, PES 2009, pp. 1–6. IEEE (2009)Google Scholar
  13. 13.
    Palma-Behnke, R., Benavides, C., Aranda, E., Llanos, J., Sez, D.: Energy management system for a renewable based microgrid with a demand side management mechanism. In: 2011 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG), pp. 1–8, April 2011Google Scholar
  14. 14.
    Zhao, C., Guan, Y.: Unified stochastic and robust unit commitment. IEEE Trans. Power Syst. 28(3), 3353–3361 (2013)Google Scholar
  15. 15.
    Zheng, Q.P., Wang, J., Liu, A.L.: Stochastic optimization for unit commitment, a review. IEEE Trans. Power Syst. 30(4), 1913–1924 (2015)Google Scholar
  16. 16.
    Zhou, Z., Zhang, J., Liu, P., Li, Z., Georgiadis, M.C., Pistikopoulos, E.N.: A two-stage stochastic programming model for the optimal design of distributed energy systems. Appl. Energy 103, 135–144 (2013). http://www.sciencedirect.com/science/article/pii/S0306261912006599

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Allegra De Filippo
    • 1
    Email author
  • Michele Lombardi
    • 1
  • Michela Milano
    • 1
  • Alberto Borghetti
    • 2
  1. 1.DISIUniversity of BolognaBolognaItaly
  2. 2.DEIUniversity of BolognaBolognaItaly

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