Abstract
Modern electricity distribution networks are facilitated with a high share of renewable generation, especially solar photovoltaics (PV). PV source results in fluctuating power injection and bi-directional power flow in a system, which can introduce overvoltage problem in low voltage (LV) networks. Using battery energy storages can mitigate this problem. This paper proposes a tool, which searches for an optimum daily operation strategy and size of batteries so that owners get maximum arbitrage benefit while maintaining voltage constraints. A time-series optimal power flow is formulated and solved in Generic Algebraic Modelling System (GAMS) platform. Day-ahead rooftop PV power profile over a year is studied and categorized by using k-means clustering algorithm. Seasonal load patterns and clustered PV power patterns are then used to execute optimal power flow. The resulting payback period of PV-battery system is also estimated.
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