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User-expected price-based demand response algorithm for a home-to-grid system

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  • Li, Xiao Hui
  • Hong, Seung Ho

Abstract

Demand response algorithms can cut peak energy use, driving energy conservation and enabling renewable energy sources, as well as reducing greenhouse-gas emissions. The use of these technologies is becoming increasingly popular, especially in smart-grid scenarios. We describe a home-to-grid demand response algorithm, which introduces a UEP (“user-expected price”) as an indicator of differential pricing in dynamic domestic electricity tariffs, and exploits the modern smart-grid infrastructure to respond to these dynamic pricing structures. By comparing the UEP with real-time utility price data, the algorithm can discriminate high-price hours and low-price hours, and automatically schedule the operation of home appliances, as well as control an energy-storage system to store surplus energy during low-price hours for consumption during high-price hours. The algorithm uses an exponential smoothing model to predict the required energy of appliances, and uses Bayes' theorem to calculate the probability that appliances will demand power at a given time based on historic energy-usage data. Simulation results using pricing structures from the Ameren Illinois power company show that the proposed algorithm can significantly reduce or even eliminate peak-hour energy consumption, leading to a reduction in the overall domestic energy costs of up to 39%.

Suggested Citation

  • Li, Xiao Hui & Hong, Seung Ho, 2014. "User-expected price-based demand response algorithm for a home-to-grid system," Energy, Elsevier, vol. 64(C), pages 437-449.
    • Handle: RePEc:eee:energy:v:64:y:2014:i:c:p:437-449
    DOI: 10.1016/j.energy.2013.11.049
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

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