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For the last ten years, utilities have observed on-going transitions onconsumers' load curves. The previously flat load curves have more frequentlyturned into duck-shape. This is jointly caused by the increasing householdloads as well as the popularity of rooftop solar photovoltaic. Such load curvetransition challenges the operational flexibility of the existing systems andgreatly increases the per-MWh energy costs. Peak shaving, in this context,becomes a critical task to demand-side management. Owing to the development ofBattery Energy Storage Systems (BESS), numerous peak shaving strategies havebeen developed and implemented. In this paper, by applying a stackedautoencoder (SAE)-based residential peak load curve forecasting technology, wefurther lift the peaking shaving capabilities of BESSs to a new level. Theproposed strategy takes into account the welfares of both generation-side anddemand-side and reaches an optimal balance. A comprehensive case study usingsmart meter data demonstrates the effectiveness of the proposed method in peakshaving application.

A Stacked Autoencoder Application for Residential Load Curve Forecast and Peak Shaving