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The new version of Polysun Simulation Software makes research results available to end customers


Leading software provider Vela Solaris introduces Polysun 6.2, the latest version of its established design and simulation software.

With Polysun 6.2, users have access to the latest developments in the renewable energy sector, resulting from a variety of research projects. So, for example, battery life is also taken into account when it comes to the dimensioning of an electric storage system.

Self-consumption optimization and heat pumps
This year’s EU PVSEC Conference, which was held in Paris at the beginning of October, saw the presentation of the results from a number of current research projects on PV self-consumption optimization (see: http://www.velasolaris.com/…). These results are the product of a collaborative research effort carried out jointly by Vela Solaris and the Swiss Federal University of Technology in Zurich (ETH Zurich) with the funding support of the Federal Office for Energy and Swisselectric Research. Only a few months after their first release, these results are now being made available to a wide audience of users with the Polysun Update 6.2. Simulations show the limits as well as the benefits of these now widely debated storage solutions consisting of a combination of thermal and battery storage. A prerequisite to ensure that reliable results are obtained is the use of programmable controllers (available in Polysun from Version 6.1) as well as realistic profiles (a new feature in the 6.2 version: profiles with 15-minute time-steps). According to the type of solar system, the type of building and consumption profile, a heat pump with intelligent controller or a battery are proposed as the optimal solution.

The life of batteries depends on their use
Further cooperation with the ETH Zurich Power Systems Laboratory resulted in research being conducted on the life of batteries. The latest version of Polysun supports the dimensioning of electrical storage systems with a new battery model and does so by recording the “state of health” (SOH) of batteries during the simulation. The deeper and more frequently a battery is discharged, the lower the parameter that indicates its remaining life will drop. Thus, the following optimization issue arises for the dimensioning: the purchase of a small battery will prove more affordable; however, it will be severely challenged going through repeated deep discharge cycles and will need to be replaced sooner. The additional costs associated with the purchase of a larger battery prove worthwhile in view of its longer duration within a given time span. If the battery is too large, the initial investment will no longer be justified.

 

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