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Neural controller for the smoothness of continuous signals: an electrical grid example

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Abstract

In this paper, the use of artificial neural networks (ANNs) is proposed to manage the local demand of different electric grid elements to smooth their aggregated consumption. The ANNs are based on the load automation of the local electric behavior, following a local strategy but affecting to the global system. In an electrical grid, there is no possibility to share information between the users because anonymity must be warranted. Therefore, a solution to the problem is elaborated with the minimum information possible without the need for communication between the users. A grid environment and behavior of different users is simulated.

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References

  1. Akasiadis C, Chalkiadakis G (2017) Mechanism design for demand-side management. IEEE Intell Syst 32(1):24–31

    Article  Google Scholar 

  2. Albadi M, El-Saadany E (2007) Demand response in electricity markets: an overview. In: 2007 power engineering society general meeting, IEEE, Piscataway, NJ, pp 1–5

  3. Behrangrad M (2015) A review of demand side management business models in the electricity market. Renew Sustain Energy Rev 47:270–283

    Article  Google Scholar 

  4. Bharathi C, Rekha D, Vijayakumar V (2017) Genetic algorithm based demand side management for smart grid. Wirel Personal Commun 93(2):481–502

    Article  Google Scholar 

  5. Calpa M, Castillo-Cagigal M, Matallanas E, Caamao-Martín E (2016) Álvaro Gutiérrez: effects of large-scale PV self-consumption on the aggregated consumption. In: Proceedings of the 6th international conference on sustainable energy information technology (SEIT-2016), pp 816–823. Elsevier, Madrid, Spain

  6. Canadian Electricity Association (2010) The smart grid: a pragmatic approach. Canadian Electronic Library, Ottawa

    Google Scholar 

  7. Castillo-Cagigal M (2014) A swarm intelligence approach based on coupled oscillators: an application in demand side management with photovoltaic distributed generation (2014)

  8. Castillo-Cagigal M, Caamaño-Martín E, Matallanas E, Masa-Bote D, Gutiérrez A, Monasterio-Huelin F, Jiménez-Leube J (2011) Pv self-consumption optimization with storage and active dsm for the residential sector. Solar Energy 85(9):2338–2348

    Article  Google Scholar 

  9. Castillo-Cagigal M, Gutiérrez A, Monasterio-Huelin F, Caamaño-Martín E, Masa-Bote D, Jiménez-Leube J (2011) A semi-distributed electric demand-side management system with PV generation for self-consumption enhancement. Energy Convers Manag 52(7):2659–2666

    Article  Google Scholar 

  10. Castillo-Cagigal M, Matallanas E, Gutiérrez A, Monasterio-Huelin F, Caamaño-Martín E, Masa-Bote D, Jiménez-Leube J (2011) Heterogeneous collaborative sensor network for electrical management of an automated house with pv energy. Sensors 11(12):11544–11559

    Article  Google Scholar 

  11. Castillo-Cagigal M, Matallanas E, Monasterio-Huelin F, Martín EC, Gutiérrez A (2016) Multifrequency-coupled oscillators for distributed multiagent coordination. IEEE Trans Ind Inform 12(3):941–951

    Article  Google Scholar 

  12. Dillon TS (1991) Artificial neural network applications to power systems and their relationship to symbolic methods. Int J Electr Power Energy Syst 13(2):66–72

    Article  Google Scholar 

  13. Edenhofer O, Pichs-Madruga R, Sokona Y, Seyboth K, Matschoss P, Kadner S, Zwickel T, Eickemeier P, Hansen G, Schlömer S, von Stechow C (2011) Special report on renewable energy sources and climate change mitigation. Tech. Rep. 1472, IPCC

  14. Elliott SJ, Nelson PA (1993) Active noise control. IEEE Signal Process Mag 10(4):12–35

    Article  Google Scholar 

  15. Elman JL (1990) Finding structure in time. Cognit Sci 14(2):179–211

    Article  Google Scholar 

  16. Commission E (2006) European smartgrids technology platform: vision and strategy for Europe’s electricity networks of the future. EUR-OP, Luxembourg

    Google Scholar 

  17. Gelazanskas L, Gamage KA (2014) Demand side management in smart grid: a review and proposals for future direction. Sustain Cities Soc 11:22–30

    Article  Google Scholar 

  18. Gellings C (1985) The concept of demand-side management for electric utilities. Proc IEEE 73(10):1468–1470

    Article  Google Scholar 

  19. Golberg DE (2006) Genetic algorithms in search. Optimization and machine learning. Addison Wesley, Indiana

    Google Scholar 

  20. Haque MT, Kashtiban AM (2007) Application of neural networks in power systems: a review. Int J Energy Power Eng 1(6):897–901

    Google Scholar 

  21. Haykin S (2009) Neural networks and learning machines. Prentice Hall/Pearson, New York

    Google Scholar 

  22. Kalra PK, Srivastava A, Chaturvedi DK (1992) Possible applications of neural nets to power system operation and control. Electr Power Syst Res 25(2):83–90

    Article  Google Scholar 

  23. Kim JH, Shcherbakova A (2011) Common failures of demand response. Energy 36(2):873–880

    Article  Google Scholar 

  24. Kurbatsky V, Sidorov D, Tomin N, Spiryaev V (2014) Optimal training of artificial neural networks to forecast power system state variables. Int J Energy Optim Eng 3(1):65–82

    Google Scholar 

  25. Li C, Yu X, Yu W, Chen G, Wang J (2017) Efficient computation for sparse load shifting in demand side management. IEEE Trans Smart Grid 8(1):250–261

    Article  Google Scholar 

  26. Mandic DP, Chambers JA (2001) Recurrent neural networks for prediction: learning algorithms, architectures, and stability. Wiley, Chichester

    Book  Google Scholar 

  27. Matallanas E, Castillo-Cagigal M, Gutiérrez A, Monasterio-Huelin F, Caamaño-Martín E, Masa D, Jiménez-Leube J (2012) Neural network controller for active demand-side management with PV energy in the residential sector. Appl Energy 91(1):90–97

    Article  Google Scholar 

  28. Meireles MRG, Almeida PEM, Simoes MG (2003) A comprehensive review for industrial applicability of artificial neural networks. IEEE Trans Ind Electron 50(3):585–601

    Article  Google Scholar 

  29. Mishra SK, Mishra S, Mishra G (2013) Realization of artificial neural network in power system and micro-grids: a review. Int J Eng Comput Sci 2(2):408–415

    MathSciNet  Google Scholar 

  30. Palensky P, Dietrich D (2011) Demand side management: demand response, intelligent energy systems, and smart loads. IEEE Trans Ind Inf 7(3):381–388

    Article  Google Scholar 

  31. Patel F, Prajapati HN (2015) A review on artificial neural network for power system fault detection. Indian J Res 4(1):52–54

    Google Scholar 

  32. Pearlmutter BA (1990) Dynamic recurrent neural networks. Tech. Rep. CMU-CS-90-196, School of Computer Science Carnegie Mellon University

  33. Richter WD, Stelec L, Ahmadinezhad H, Stehlk M (2017) Geometric aspects of robust testing for normality and sphericity. Stoch Anal Appl 35(3):511–532

    Article  MathSciNet  Google Scholar 

  34. Strbac G (2008) Demand side management: benefits and challenges. Energy Policy 36(12):4419–4426

    Article  Google Scholar 

  35. Torriti J, Hassan MG, Leach M (2010) Demand response experience in Europe: policies, programmes and implementation. Energy 35(4):1575–1583

    Article  Google Scholar 

  36. UK Department of Energy & Climate Change (2009) Smarter grids: the opportunity. UK Department of Energy & Climate Change, London

    Google Scholar 

  37. U.S. Department of Energy (2009) Smart grid system report. U.S. Department of Energy, Washington, DC

    Google Scholar 

  38. Ye M, Hu G (2017) Game design and analysis for price-based demand response: an aggregate game approach. IEEE Trans Cybern 47(3):720–730

    Article  Google Scholar 

  39. Yu CN, Mirowski P, Ho TK (2017) A sparse coding approach to household electricity demand forecasting in smart grids. IEEE Trans Smart Grid 8(2):738–748

    Google Scholar 

Download references

Acknowledgements

This work was partially supported by the “DEMS: Sistema Distribuido de Gestión de Energía en Redes Eléctricas Inteligentes”, funded by the Programa Estatal de Investigación Desarrollo e Innovación orientada a los retos de la sociedad of the Spanish Ministerio de Economía y Competitividad (TEC2015-66126-R).

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Authors and Affiliations

  1. E.T.S. Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, 28040, Madrid, Spain

    Eduardo Matallanas, Manuel Castillo-Cagigal & Álvaro Gutiérrez

  2. Instituto de Energía Solar, Universidad Politécnica de Madrid, 28040, Madrid, Spain

    Estefanía Caamaño-Martín

Authors
  1. Eduardo Matallanas
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  2. Manuel Castillo-Cagigal
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  3. Estefanía Caamaño-Martín
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  4. Álvaro Gutiérrez
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Correspondence to Álvaro Gutiérrez.

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Matallanas, E., Castillo-Cagigal, M., Caamaño-Martín, E. et al. Neural controller for the smoothness of continuous signals: an electrical grid example. Neural Comput & Applic 32, 5745–5760 (2020). https://doi.org/10.1007/s00521-019-04139-3

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  • DOI: https://doi.org/10.1007/s00521-019-04139-3

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