Analysis of Circuit-based Per-Panel Diode Model of Photovoltaic Array

TL;DR

This paper introduces a circuit-based per-panel PV array model that improves accuracy over traditional aggregated models, especially under partial shading and hotspots, by representing each panel individually.

Contribution

The paper develops a novel per-panel PV array model that balances physical detail and system-level control, providing more accurate simulations under real-world conditions.

Findings

Model is 21.2% more accurate under partial shading.

Model is 8.1% more accurate under hotspot conditions.

Establishes mathematical equivalence with traditional models.

Abstract

Solar photovoltaic systems are increasing in size and number on the grid. In regions with high penetration, such as California, PV systems serve multiple functions, including peak shaving and demand response. Therefore, the criticality of PV systems to grid operations calls for accurate models. The current practice is to represent the PV array, composed of multiple PV panels, with an aggregated single-diode model (SDM). The highly abstract model has a limited ability to capture real-world behaviors, such as partial shading and hotspots. Thus, we develop a circuit-based per-panel PV array model that uses a single diode model for each panel and interconnects them to form an array. This approach bridges the tradeoff between cell-level physics and control-dependent system-level behavior. We establish conditions for mathematical equivalence between the proposed per-panel array circuit model and the aggregated single-diode array model. We generate empirical evidence by running simulations using parameters derived from real-world PV panels. Results indicate that the proposed per-panel array model can represent the electrical behavior of the array under non-ideal conditions, such as partial shading, more accurately. With maximum power point tracking control, the proposed model is 21.2% more accurate when estimating the real power output of an array under a partial shading scenario and 8.1% more accurate under a hot spot scenario.