Abstract
Funding policies and legislation by the European Union for the installation of photovoltaic (PV) arrays in the residential sector have led to a steady and successful increase in renewable energy generation by small-scale private producers. In Germany, even the installation of local storage systems is being subsidised. Differing feed-in tariffs, as well as variable production and demand profiles result in very dissimilar amortisation curves for such investments. This paper presents a software tool which allows for the computation of such curves by means of simulation in MATLAB/Simulink. It enables the exploration of a wide search space by manipulating settings on the levels of individual houses, as well as entire neighbourhoods which might want to share in (the cost of) local energy storage. Additionally, a case study underlines the tool’s potential, as well as benefits of shared energy storage systems.
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Notes
- 1.
kWp = Kilowatt-peak, i.e. the power rating of a photovoltaic array in kilowatts (kW) at standard test conditions [2].
- 2.
Technically this is inaccurate, since compensation is only paid starting with the first day that electricity is produced and fed into the grid instead of depending on the date of installation [1].
- 3.
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Biech, M., Bigdon, T., Dielitz, C., Fromme, G., Remke, A. (2016). A Smart Neighbourhood Simulation Tool for Shared Energy Storage and Exchange. In: Wittevrongel, S., Phung-Duc, T. (eds) Analytical and Stochastic Modelling Techniques and Applications. ASMTA 2016. Lecture Notes in Computer Science(), vol 9845. Springer, Cham. https://doi.org/10.1007/978-3-319-43904-4_6
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