Quantitative Study on the Impact of Unexpected Events on Electricity Demand Based on the Synthetic Control Method

Le résumé fourni par la source

To accurately quantify the impact of unexpected events on electricity demand and address issues in existing studies such as insufficient assessment of long-term dynamic effects and large estimation biases in small samples, this study improves the Synthetic Control Method (SCM), introduces time-varying weights and LASSO penalty factors, and combines them with a combined grey prediction model. An empirical study is conducted focusing on the charging and swapping service industry in Anhui Province from May 3 to 18, 2023. The results show that the synthetic control group has a high fitting degree with the actual electricity demand of the industry; after the implementation of the policy, the electricity consumption of the industry increases significantly, which verifies the positive impact of the policy on electricity demand. The research method is feasible and can provide scientific support for the power system to respond to unexpected events and formulate relevant policies.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.