Enhancing Behind-the-Meter Visibility of grid edge PV Systems and Electric Vehicle Charging Loads through Integration of Compact Low-Cost Sensors

The growing adoption of solar panels, battery storage, and Electric Vehicle (EV) chargers is driving a profound transformation in the distribution grid system. However, limited visibility into Behind-the-Meter (BTM) Distributed Energy Resources (DERs) generation restricts the full integration of these resources as grid assets. This lack of insight hampers efforts to enhance grid reliability, resilience, reduce operational costs, and expand photovoltaic (PV) hosting capacity. By leveraging existing data from distributed inverters, which are already monitored by controllers, it is possible to improve BTM PV generation visibility without incurring additional hardware costs. Furthermore, the introduction of a compact, low-cost bolt-on sensor can further enhance visibility into BTM PV generation. This paper presents a Power Hardware-in-the-Loop (PHIL) testbed designed to evaluate the feasibility of the "virtual inverter sensing node" and "bolt-on sensor" concepts. These concepts aim to improve BTM visibility by collecting data from BTM DERs, loads such as EV charging stations, and micro-Phasor Measurement Units (μ PMU) within a distribution feeder. The paper demonstrates how real-time sensor data can be processed into actionable measurements within both the physical and virtual sensor interfaces using the PHIL testbed. Additionally, the study compares the benefits of cloud-based data aggregation with the use ofμ PMU as data aggregators, as demonstrated through experimental testing.

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Published
2025-03-16
DOI
https://doi.org/10.1109/apec48143.2025.10977290
Primary Topic
Smart Grid Energy Management
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article
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article

Enhancing Behind-the-Meter Visibility of grid edge PV Systems and Electric Vehicle Charging Loads through Integration of Compact Low-Cost Sensors

Mehrnaz Madadi, Paul Richard Ohodnicki, Subhashish Bhattacharya
Smart Grid Energy Management
article

Enhancing Behind-the-Meter Visibility of grid edge PV Systems and Electric Vehicle Charging Loads through Integration of Compact Low-Cost Sensors

Mehrnaz Madadi, Paul Richard Ohodnicki, Subhashish Bhattacharya
article en

Abstract

The growing adoption of solar panels, battery storage, and Electric Vehicle (EV) chargers is driving a profound transformation in the distribution grid system. However, limited visibility into Behind-the-Meter (BTM) Distributed Energy Resources (DERs) generation restricts the full integration of these resources as grid assets. This lack of insight hampers efforts to enhance grid reliability, resilience, reduce operational costs, and expand photovoltaic (PV) hosting capacity. By leveraging existing data from distributed inverters, which are already monitored by controllers, it is possible to improve BTM PV generation visibility without incurring additional hardware costs. Furthermore, the introduction of a compact, low-cost bolt-on sensor can further enhance visibility into BTM PV generation. This paper presents a Power Hardware-in-the-Loop (PHIL) testbed designed to evaluate the feasibility of the "virtual inverter sensing node" and "bolt-on sensor" concepts. These concepts aim to improve BTM visibility by collecting data from BTM DERs, loads such as EV charging stations, and micro-Phasor Measurement Units (μ PMU) within a distribution feeder. The paper demonstrates how real-time sensor data can be processed into actionable measurements within both the physical and virtual sensor interfaces using the PHIL testbed. Additionally, the study compares the benefits of cloud-based data aggregation with the use ofμ PMU as data aggregators, as demonstrated through experimental testing.

North Carolina State University (US), University of Pittsburgh (US)
Affordable and clean energy
Openalex Percentile: Top 93%
Smart Grid Energy Management
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Enhancing Behind-the-Meter Visibility of grid edge PV Systems and Electric Vehicle Charging Loads through Integration of Compact Low-Cost Sensors — Mehrnaz Madadi, Paul Richard Ohodnicki, et al. · (2025) | TGRS Research Map | TGRS