Photovoltaics and battery storage—Python-based optimisation for innovation tenders

One of the main concerns in extending variable renewable energy (VRE) is the electric grid stability due to the sources’ volatility. Germany is introducing a new auction mechanism within the German Renewable Energy Sources Act called “Innovationsausschreibung” (innovation tender) to gridand system-supporting VRE-plants operation. The participating hybrid power systems (HPS) must be able to provide one-quarter of their installed power as positive automatic frequency restoration reserve (aFRR). This paper reflects on the optimal operation and design focusing on sizing an HPS consisting of ground-mounted large-scale photovoltaic (PV) and battery energy storage systems (BESS). An optimisation model is developed in Python. It is solved using the Gurobi framework with generation as well as market data for the German spot and balancing market. The optimisation maximises the HPS’s revenue under consideration of the BESS costs for the applications energy arbitrage (EA) and aFRR. A case study for a ground-mounted PV reference project verifies the effectiveness of the model. Ultimately, a sensitivity analysis with long-term market prices and BESS costs along with different bit strategies is conducted. The total revenues less annual BESS costs vary from -3.5% for EA to +10.1% for the sequential combination of EA and participation in the aFRR-market compared to a stand-alone PV system. Considering actual BESS costs and market data, a minimum BESS design is the most economical from today’s perspective. Due to decreasing BESS costs and increasing market volatility, this is expected to change within the next five years. Keywords—Battery storage, Photovoltaics, Optimisation, Operation, Sizing, Innovation tender, Energy arbitrage, Balancing market NOMENCLATURE