Photovoltaic Module Simulation Using Thermal Coupling Resistances
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Solution Overview
Problem
Current photovoltaic module simulation models fail to accurately predict energy yield under non-steady-state and non-uniform operating conditions, particularly due to limitations in addressing intra-module temperature differences and partial shading effects, leading to inefficiencies in energy conversion.
Innovation Solution
A method utilizing thermal and electrical equivalent circuits connected by thermal coupling resistances to model heat flow between photovoltaic cells, allowing for precise calculation of energy yield under spatial and temporal variations, with a focus on intra-module heat conduction and electrical operation points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single-cell module model is used for simulation, then the model complexity is reduced and ease of operation is improved, but intra-module temperature differences and non-uniform illumination effects cannot be accurately captured, leading to reduced measurement precision
Solution Approach 1:
The photovoltaic module is divided into multiple independently modeled cells, each with its own thermal and electrical equivalent circuits. This segmentation allows the model to capture intra-module temperature differences and non-uniform illumination effects while maintaining the mathematical tractability of individual cell models. The thermal coupling between adjacent cells is modeled through thermal resistances, enabling accurate representation of heat flow patterns without requiring a fully coupled complex model.
2Productivity
If steady-state assumptions are made in the simulation model, then the computational complexity is reduced and productivity is improved, but the model cannot accurately predict energy yield under non-steady-state conditions such as dynamic cloud cover or wind cooling effects
Solution Approach 1:
The model transitions from steady-state to dynamic simulation by incorporating time-dependent thermal and electrical equations. The thermal equivalent circuits include thermal capacitances that capture transient thermal behavior, allowing the model to respond dynamically to changing ambient conditions such as cloud cover variations and wind cooling effects. This dynamic approach maintains computational efficiency while significantly improving the reliability of energy yield predictions under non-steady-state operating conditions.
3Ease of operation
If uniform module temperature is assumed in the simulation, then the model complexity is reduced and ease of operation is improved, but significant inter-module and intra-module temperature differences cannot be captured, leading to reduced accuracy in energy yield prediction
Solution Approach 1:
The module is segmented into multiple cells, each with its own thermal model. This allows temperature to vary spatially across the module while keeping individual cell models relatively simple. The thermal coupling between cells through thermal resistances captures heat flow patterns without requiring a fully coupled complex thermal model, thus maintaining ease of operation while improving temperature prediction accuracy.
Solution Approach 2:
Each cell in the module is assigned its own local temperature variable and thermal model, allowing different regions of the module to have different temperatures based on local illumination conditions, wind cooling effects, and heat flow patterns. This local quality approach enables accurate capture of temperature gradients while maintaining the simplicity of individual cell models.
4Loss of time
If partial shading effects are not modeled in detail, then the model complexity is reduced and computational time is reduced, but energy losses due to non-uniform illumination and cell mismatches cannot be accurately calculated
Solution Approach 1:
The module is divided into multiple cells, each with its own electrical equivalent circuit and illumination model. This segmentation allows partial shading to be modeled at the cell level, capturing non-uniform illumination effects and cell mismatch losses without requiring overly complex computational models. The electrical coupling between cells naturally captures the impact of shading on overall module performance.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides accurate energy yield predictions with a root-mean-square deviation of less than 5%, enabling optimized energy production and module performance evaluation under dynamic conditions.
Implementation Method 1
using a first thermal equivalent circuit of the first photovoltaic cell and a second thermal equivalent circuit of the second photovoltaic cell, wherein at least one node of the first thermal equivalent circuit is connected to a corresponding node of the second thermal equivalent circuit by a thermal coupling resistance
Implementation Method 2
a computer-implemented simulation of a photovoltaic module comprising a plurality of solar cells
Data Source
AI summary
A method is provided for calculating a performance of a photovoltaic module comprising at least a first photovoltaic cell and a second photovoltaic cell. The method comprises calculating a heat flow between the first photovoltaic cell and the second photovoltaic cell using a first thermal equivalent circuit of the first photovoltaic cell and a second thermal equivalent circuit of the second photovoltaic cell, wherein at least one node of the first thermal equivalent circuit is connected to a corresponding node of the second thermal equivalent circuit by a thermal coupling resistance. The method may be used for calculating the influence of spatial and temporal variations in the operation conditions on the performance, such as the energy yield, of a photovoltaic module or a photovoltaic system.