The work analyses thermal and electrical solar cooling systems regarding the influence of energy storages using the open source tool open energy modelling framework (oemof). The systems are optimised with respect to lowest cost, considering various storage configurations and boundary conditions such as a required solar fraction. The results illustrate the importance of the different storage options on the size of the system components and the solar fraction. Optimal solar electrical cooling achieves solar fractions above 65%, optimal solar thermal cooling solar fractions above 87%, with a clear economic advantage for solar electrical cooling. Electrical energy storage suffers from high investment cost (compared to other storage options) and is not part of the cost optimal solutions. A sensitivity analysis shows, that even 50% decreased storage costs and increased electricity prices don't allow a profitable use of large electrical storages. To increase the solar fraction of solar electrical cooling to more than 65 % it is mandatory to use electricity storage. For both concepts, higher than cost optimal solar fractions can be achieved by increasing the respective storage sizes. Solar fractions of up to 95% are still economically reasonable. However, solar fractions above 98% result in an extreme cost increase.

An extended CHP system in a characteristic future market situation with a share of 50% electric energy provided by renewable energy sources is analysed using the open energy modelling framework oemof. Cost-optimal plant configurations are computed based upon assumptions for investments costs and energy prices. The results show that thermal storages are always integrated in the optimum plant layout with little sensitivity to all assumptions. A storage for electrical energy is only considered if sufficiently high price periods occur in the electric energy market. This result is highly sensitive to assumptions regarding storage cost and energy prices. The model is made available open-source for further use.

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