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Load forecasting, dynamic pricing and DSM in smart grid: A review

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  • Khan, Ahsan Raza
  • Mahmood, Anzar
  • Safdar, Awais
  • Khan, Zafar A.
  • Khan, Naveed Ahmed

Abstract

Load forecasting (LF) plays important role in planning and operation of power systems. It is envisioned that future smart grids will utilize LF and dynamic pricing based techniques for effective Demand Side Management (DSM). This paper presents a comprehensive and comparative review of the LF and dynamic pricing schemes in smart grid environment. Real Time Pricing (RTP), Time of Use (ToU) and Critical Peak Pricing (CPP) are discussed in detail. Two major categories of LF: mathematical and artificial intelligence based computational models are elaborated with subcategories. Mathematical models including auto recursive, moving average, auto recursive moving average, auto recursive integrated moving average, exponential smoothing, iterative reweighted mean square, multiple regression, etc. used for effective DSM are discussed. Neural networks, fuzzy logic, expert systems of the second major category of LF models have also been described.

Suggested Citation

  • Khan, Ahsan Raza & Mahmood, Anzar & Safdar, Awais & Khan, Zafar A. & Khan, Naveed Ahmed, 2016. "Load forecasting, dynamic pricing and DSM in smart grid: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1311-1322.
    • Handle: RePEc:eee:rensus:v:54:y:2016:i:c:p:1311-1322
    DOI: 10.1016/j.rser.2015.10.117
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    1. Park, S.C. & Jin, Y.G. & Song, H.Y. & Yoon, Y.T., 2015. "Designing a critical peak pricing scheme for the profit maximization objective considering price responsiveness of customers," Energy, Elsevier, vol. 83(C), pages 521-531.
    2. Ozturk, Ilhan, 2010. "A literature survey on energy-growth nexus," Energy Policy, Elsevier, vol. 38(1), pages 340-349, January.
    3. Li, Gang & Qian, Suxin & Lee, Hoseong & Hwang, Yunho & Radermacher, Reinhard, 2014. "Experimental investigation of energy and exergy performance of short term adsorption heat storage for residential application," Energy, Elsevier, vol. 65(C), pages 675-691.
    4. Tripathi, M.M. & Upadhyay, K.G. & Singh, S.N., 2008. "Short-Term Load Forecasting Using Generalized Regression and Probabilistic Neural Networks in the Electricity Market," The Electricity Journal, Elsevier, vol. 21(9), pages 24-34, November.
    5. Li, Gang & Hwang, Yunho & Radermacher, Reinhard & Chun, Ho-Hwan, 2013. "Review of cold storage materials for subzero applications," Energy, Elsevier, vol. 51(C), pages 1-17.
    6. Macedo, M.N.Q. & Galo, J.J.M. & de Almeida, L.A.L. & de C. Lima, A.C., 2015. "Demand side management using artificial neural networks in a smart grid environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 128-133.
    7. Newsham, Guy R. & Bowker, Brent G., 2010. "The effect of utility time-varying pricing and load control strategies on residential summer peak electricity use: A review," Energy Policy, Elsevier, vol. 38(7), pages 3289-3296, July.
    8. Al-Garni, Ahmed Z. & Zubair, Syed M. & Nizami, Javeed S., 1994. "A regression model for electric-energy-consumption forecasting in Eastern Saudi Arabia," Energy, Elsevier, vol. 19(10), pages 1043-1049.
    9. Kim, Jin-Ho & Shcherbakova, Anastasia, 2011. "Common failures of demand response," Energy, Elsevier, vol. 36(2), pages 873-880.
    10. Beaudin, Marc & Zareipour, Hamidreza, 2015. "Home energy management systems: A review of modelling and complexity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 318-335.
    11. Mahmood, Anzar & Javaid, Nadeem & Zafar, Adnan & Ali Riaz, Raja & Ahmed, Saeed & Razzaq, Sohail, 2014. "Pakistan's overall energy potential assessment, comparison of LNG, TAPI and IPI gas projects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 182-193.
    12. Mahmood, Anzar & Javaid, Nadeem & Razzaq, Sohail, 2015. "A review of wireless communications for smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 248-260.
    13. Herter, Karen, 2007. "Residential implementation of critical-peak pricing of electricity," Energy Policy, Elsevier, vol. 35(4), pages 2121-2130, April.
    14. Hippert, H.S. & Bunn, D.W. & Souza, R.C., 2005. "Large neural networks for electricity load forecasting: Are they overfitted?," International Journal of Forecasting, Elsevier, vol. 21(3), pages 425-434.
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