Practical Method for Behind-the-Meter Solar PV Disaggregation

Abstract

The increasing adoption of rooftop solar photovoltaic (PV) generation in power distribution systems (PDS) requires innovative methods to estimate behind-the-meter (BTM) energy consumption and generation, given the widespread use of net metering. Existing approaches often rely on extensive historical data, advanced metering infrastructure (AMI), or smart meters. In contrast, we propose a practical energy disaggregation method that operates solely on monthly net energy imports and exports, estimating hourly gross values by leveraging reference generation profiles and typical load curves for residential, commercial, and industrial consumers. A clustering algorithm is used to generate probabilistic power generation for consumer groups within a region, while the sum of the differences between registered and estimated net monthly data is minimized through an iterative process. Validated with synthetic consumers across thirteen different classifications, the proposed method effectively estimates BTM energy consumption and generation. Thus, it provides utilities with a valuable tool for assessing prosumer behavior and understanding self-consumption patterns, helping prevent the underestimation of actual demand during PV generation periods while supporting grid operation and planning.