Data -Driven Demand Response Strategies for Enhanced Microgrid Energy Management: A Pattern -Based Investigation

The escalating demand across diverse energy consumption sectors necessitates the implementation of effective energy management strategies. Demand response programs (DRPs) emerge as a significant solution for enhancing electricity grid stability through optimized consumption management. This paper introduces a consumer-centric approach to DRP design, leveraging individual energy consumption patterns. Consumer behavior, defined as both energy consumption habits and adherence to optimal consumption schedules, is analyzed to develop DRPs that maximize peak demand reduction. Prioritizing economic feasibility, the proposed methodology aims to prevent increased electricity costs for consumers. To achieve this, a two-stage process is employed: first, consumers are segmented based on their consumption profiles using the K-means clustering algorithm, applied to a dataset of ۵۰ household consumers. Subsequently, tailored DRPs are designed for each consumer cluster, reflecting their specific needs and preferences. The results demonstrate that, compared to conventional DRP methodologies, this consumer-centric approach fosters increased participation, effectively mitigates peak load consumption, and ultimately enhances the efficiency and stability of the microgrid.

Keywords:

Portfolio of demand response programs , Clustering , K-means method , consumers Consumption patterns