Data-Driven Repeated-Feedback Adjustment Strategy for Smart Grid Pricing

Abstract

Applying the optimal problem, we get the optimal power supply and price. However, how to make the real power consumption close to the optimal power supply is still worth studying. This paper proposes a novel data-driven inverse proportional function-based repeated-feedback adjustment strategy to control the users’ real power consumption. With the repeated-feedback adjustment, we adjust the real-time prices according to changes in the power discrepancy between the optimal power supply and the users’ real power consumption. If and only if the power discrepancy deviates the preset range, the real power consumption in different periods will be adjusted through the change of the price, so the adjustment times is the least. Numerical results on real power market show that the novel inverse proportional function-based repeated-feedback adjustment strategy brought forward in the article achieves better effect than the linear one, that is to say, the adjustments times and standard error of the residuals are less. Meanwhile, profit and whole social welfare are more. The proposed strategy can obtain more steady and dependable consumption load close to the optimal power supply, which is conducive to the balanced supply of electric energy.

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