Optimising Solar Asset Performance through Smart Module Installation using Above’s Digital Twin Technology
Year: 2024
Type: conference-paper
Abstract: As PV plants are deployed at a gigawatt scale and without subsidies, even more emphasis is put on yield optimization. Although it is now common industry practice to record the electrical properties of all modules at the factory following flash tests, this data is rarely used to inform the optimal yield layout during construction. This paper examines module distribution and associated mismatch, shading losses and power tolerance gains for recently installed fixed tilt PV plants and analyses the potential performance gains that may be achieved, had an optimal module installation strategy (per Imp grouping) been in place during construction. These interventions do not require any change in plant design or additional CAPEX but uses labour already available on site. Of the 7 case studies examined, the plants with 3 modules in portrait configuration show some yield improvement with 0.3% yield gain when sorting modules by pallet average Imp into strings and installing the pallets of modules with the highest Imp in the top row of the mounting table reducing the impact of shading losses on the higher rated modules. Given the limitations of the modeling software that averages power tolerance and mismatch across all strings in an array it is postulated that higher yield gain may be observed on site. The proposed yield increase relies on stringent installation practices, which may not materialize given field distribution and handling faults. Above Surveying has developed technology to digitally map the location of all installed modules. The Digital Twin allows for better traceability during the construction process and can highlight if the installer has followed the required installation strategy.
Source: EU PVSEC
Authors: Meyer, Imke, Thawanyavitchajit, Chisanupong, Perez, Inaki, Hitchcock, Will, Balchada, Henrique, Porter, Jennifer
Language: English
Field: Energy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6967240497
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