HarvestIT – Advanced monitoring of large-scale solar thermal plants with open-source software solution

Background: Large-scale solar thermal plants (> 500 m² collector area or 350 kW power) are a cost-effective technology for providing renewable heat. The construction of new plants requires a high initial investment, which is repaid over the operating life by the selling the generated solar heat. Thus, the technology is only economically attractive for customers and investors if permanently high operating quality is achieved.

Problem statement: In practice, the key question “How optimal does a solar system work?” is difficult to answer, even for solar experts, because performance and solar yield are a complex interplay of components, system design, control and stochastic influences of weather and customer-side load conditions. Collector manufacturers face the problem that their collectors are lab-tested according to standards, but actual performance and yield proofs to investors and customers are difficult to achieve under field conditions.

Objectives: Increasing availability of data and digitalization technologies offer the opportunity to take the monitoring of large-scale solar plants into the digital future. Building on current scientific results (e.g. FFG project MeQuSo), the project focuses on developing an open-source software for a comprehensive performance and yield analysis of large-scale solar thermal plants by means of an automated test procedure. Effects like soiling, degradation or efficiency losses can be detected with comprehensible KPIs and parameters and clearly distinguished from the respective operating conditions.

Digital products and services: HarvestIT addresses the key stakeholders of large-scale solar thermal systems: The professionally managed open innovation process is designed to engage stakeholders in the development process and align the solution with the entire industry. Integration into existing monitoring tools and business processes enables companies to offer new services such as operational optimization, and to quantify and sell implementation measures.