Design of an Autonomous Intelligent Demand-Side Management System by using Electric Vehicles as Mobile Energy Storage Units by Means of Evolutionary Algorithms

Edgar Galván-Lopez, Marc Schoenauer, Constantinos Patsakis

Abstract

Evolutionary Algorithms (EAs), or Evolutionary Computation, are powerful algorithms that have been used in a range of challenging real-world problems. In this paper, we are interested in their applicability on a dynamic and complex problem borrowed from Demand-Side Management (DSM) systems, which is a highly popular research area within smart grids. DSM systems aim to help both end-use consumer and utility companies to reduce, for instance, peak loads by means of programs normally implemented by utility companies. In this work, we propose a novel mechanism to design an autonomous intelligent DSM by using (EV) electric vehicles’ batteries as mobile energy storage units to partially fulfill the energy demand of dozens of household units. This mechanism uses EAs to automatically search for optimal plans, representing the energy drawn from the EVs’ batteries. To test our approach, we used a dynamic scenario where we simulated the consumption of 40 and 80 household units over a period of 30 working days. The results obtained by our proposed approach are highly encouraging: it is able to use the maximum allowed energy that can be taken from each EV for each of the simulated days. Additionally, it uses the most amount of energy whenever it is needed the most (i.e., high-peak periods) resulting into reduction of peak loads.

References

  1. (2007). Pacific Northwest GridWise Testbed Demonstration Projects, Part I. Olympic Peninsula Project.
  2. Bäck, T., Fogel, D. B., and Michalewicz, Z., editors (1999). Evolutionary Computation 1: Basic Algorithms and Operators. IOP Publishing Ltd., Bristol, UK.
  3. Brooks, A., Lu, E., Reicher, D., Spirakis, C., and Weihl, B. (2010). Demand dispatch: Using real-time control of demand to help balance generation and load. IEEE Power & Energy Magazine,, 8:20 - 29.
  4. Cody-Kenny, B., Galván-L ópez, E., and Barrett, S. (2015). locogp: Improving performance by genetic programming java source code. In Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference, GECCO Companion 7815, pages 811-818, New York, NY, USA. ACM.
  5. Eiben, A. E. and Smith, J. E. (2003). Introduction to Evolutionary Computing. Springer Verlag.
  6. Fagan, D., ONeill, M., Galván-L ópez, E., Brabazon, A., and McGarraghy, S. (2010). An analysis of genotype-phenotype maps in grammatical evolution. In Esparcia-Alczar, A., Ekrt, A., Silva, S., Dignum, S., and Uyar, A., editors, Genetic Programming, volume 6021 of Lecture Notes in Computer Science, pages 62-73. Springer Berlin Heidelberg.
  7. Galvan, E., Harris, C., Dusparic, I., Clarke, S., and Cahill, V. (2012). Reducing electricity costs in a dynamic pricing environment. In Proc. Third IEEE International Conference on Smart Grid Communications (SmartGridComm), pages 169 - 174, Tainan, Taiwan. IEEE Press.
  8. Galván-L ópez, E. (2008). Efficient graph-based genetic programming representation with multiple outputs. International Journal of Automation and Computing, 5(1):81-89.
  9. Galván-L ópez, E., Curran, T., McDermott, J., and Carroll, P. (2015). Design of an autonomous intelligent demand-side management system using stochastic optimisation evolutionary algorithms. Neurocomputing, 170:270-285.
  10. Galván-L ópez, E., Dignum, S., and Poli, R. (2008). The effects of constant neutrality on performance and problem hardness in gp. In Proceedings of the 11th European conference on Genetic programming, EuroGP'08, pages 312-324, Berlin, Heidelberg. Springer-Verlag.
  11. Galván-L ópez, E., Harris, C., Trujillo, L., Vázquez, K. R., Clarke, S., and Cahill, V. (2014). Autonomous demand-side management system based on monte carlo tree search. In IEEE International Energy Conference (EnergyCon), pages 1325 - 1332, Dubrovnik, Croatia. IEEE Press.
  12. Galván-L ópez, E., McDermott, J., O'Neill, M., and Brabazon, A. (2010a). Defining locality in genetic programming to predict performance. In IEEE Congress on Evolutionary Computation, pages 1-8. IEEE.
  13. Galván-L ópez, E., Poli, R., and Coello, C. (2004). Reusing code in genetic programming. In Keijzer, M., OReilly, U.-M., Lucas, S., Costa, E., and Soule, T., editors, Genetic Programming, volume 3003 of Lecture Notes in Computer Science, pages 359-368. Springer Berlin Heidelberg.
  14. Galván-L ópez, E., Swafford, J. M., O'Neill, M., and Brabazon, A. (2010b). Evolving a ms. pacman controller using grammatical evolution. In Chio, C. D., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., EsparciaAlcázar, A., Goh, C. K., Guervós, J. J. M., Neri, F., Preuss, M., Togelius, J., and Yannakakis, G. N., editors, EvoApplications (1), volume 6024 of Lecture Notes in Computer Science, pages 161-170. Springer.
  15. Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1st edition.
  16. Kempton, W. and Letendre, S. E. (1997). Electric vehicles as a new power source for electric utilities. Transportation Research Part D: Transport and Environment, 2(3):157 - 175.
  17. Kempton, W. and Tomic, J. (2005). Vehicle-to-grid power fundamentals: Calculating capacity and net revenue. Journal of Power Sources, 144(1):268-279.
  18. Koza, J. R. (1992). Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, MA, USA.
  19. Lohn, J., Hornby, G., and Linden, D. (2005). An evolved antenna for deployment on nasas space technology 5 mission. In OReilly, U.-M., Yu, T., Riolo, R., and Worzel, B., editors, Genetic Programming Theory and Practice II, volume 8 of Genetic Programming, pages 301-315. Springer US.
  20. Masters, G. M. (2004). Renewable and Efficient Electric Power Systems. Wiley-Interscience.
  21. McDermott, J., Galván-L ópez, E., and ONeill, M. (2010). A fine-grained view of gp locality with binary decision diagrams as ant phenotypes. In Schaefer, R., Cotta, C., Koodziej, J., and Rudolph, G., editors, Parallel Problem Solving from Nature, PPSN XI, volume 6238 of Lecture Notes in Computer Science, pages 164-173. Springer Berlin Heidelberg.
  22. Mohsenian-Rad, A., Wong, V., Jatskevich, J., Schober, R., and Leon-Garcia, A. (2010). Autonomous demandside management based on game-theoretic energy consumption scheduling for the future smart grid. Smart Grid, IEEE Transactions on, 1(3):320 -331.
  23. Qin, A. K., Huang, V. L., and Suganthan, P. N. (2009). Differential evolution algorithm with strategy adaptation for global numerical optimization. Trans. Evol. Comp, 13(2):398-417.
  24. Storn, R. and Price, K. (1997). Differential evolution a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4):341-359.
Download


Paper Citation


in Harvard Style

Galván-Lopez E., Schoenauer M. and Patsakis C. (2015). Design of an Autonomous Intelligent Demand-Side Management System by using Electric Vehicles as Mobile Energy Storage Units by Means of Evolutionary Algorithms . In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA, ISBN 978-989-758-157-1, pages 106-115. DOI: 10.5220/0005607401060115


in Bibtex Style

@conference{ecta15,
author={Edgar Galván-Lopez and Marc Schoenauer and Constantinos Patsakis},
title={Design of an Autonomous Intelligent Demand-Side Management System by using Electric Vehicles as Mobile Energy Storage Units by Means of Evolutionary Algorithms},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,},
year={2015},
pages={106-115},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005607401060115},
isbn={978-989-758-157-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,
TI - Design of an Autonomous Intelligent Demand-Side Management System by using Electric Vehicles as Mobile Energy Storage Units by Means of Evolutionary Algorithms
SN - 978-989-758-157-1
AU - Galván-Lopez E.
AU - Schoenauer M.
AU - Patsakis C.
PY - 2015
SP - 106
EP - 115
DO - 10.5220/0005607401060115