The Chinese power grid's peak shaving and carbon emission reduction: Prospects and potential of home energy storage

This paper investigates the potential of home energy storage systems in optimizing electricity consumption and reducing carbon emissions in China. The study utilizes the GAN-Enhanced LSTM (Generative Adversarial Network-Enhanced Long Short-Term Memory) model to predict household energy demand and optimize energy storage scheduling. The model leverages meteorological data and household historical energy consumption data to accurately forecast electricity needs, allowing for efficient energy storage during off-peak periods and discharge during peak hours. The findings demonstrate significant economic and environmental benefits: households can reduce electricity costs and thermal power generation reliance, while contributing to substantial reductions in carbon emissions. Policy recommendations, including subsidy adjustments and electricity price reforms, are proposed to promote the widespread adoption of home energy storage systems. The results highlight the dual value of energy storage systems in cost reduction and carbon emission mitigation, offering a viable path toward achieving sustainable energy goals in China. • This study presents a GAN-LSTM model using weather and past data to predict household energy demand for optimal storage use. • Results show home storage cuts costs and emissions, offering clear economic and environmental gains. • The paper urges subsidy and pricing reforms to boost storage adoption in China. • Findings show home storage lowers costs, supports carbon goals, and drives sustainable energy. • Scalable solutions reveal how storage optimizes use, eases grid load, and cuts thermal reliance.