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

Li, Xiao Hui, Hong, Seung Ho
Energy 2014 v.64 pp. 437-449
algorithms, electricity, energy, energy conservation, energy costs, greenhouse gas emissions, household equipment, infrastructure, models, prices, probability, renewable energy sources, tariffs, Illinois
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%.