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In order to handle the fluctuating energy production in distribution grids so-called smart grid components are introduced and storage systems are a key enabling technology to meet energy production and demand. Furthermore they can help to relieve network capacities in regions with weak grid infrastructure. This paper summarises smart-grid components, their interoperability and potentials of centralised control. After describing concepts and limits of data acquisition and transmission in a system with heterogenous components we advocate a central platform for data evaluation based on the infrastructure of the programming language R including a server based installation of RStudio. Here we present three applications in the fields of economy, physics and mathematics. We evaluate a battery electrical storage system (BESS) participating in the German Primary Frequency Responce (PFR) market that proves to give increasing profits esp. with the advent of decreased investment costs for Li-Ion battery systems. Then we describe the method of state-estimation that is used to derive consistent (operational) state-vectors from potentially error-prone (pseudo-) mearurements of an electrical grid. These estimated state-vectors consist of voltage magnitude and phasor for each bus of an electrical grid and are usually derived from incomplete data including voltage magnitude, (re-)active power, current or phasor values of the analysed system. Finally we give a brief outlook towards a global mathematical optimisaton model for a battery storage system that uses economical and physical constraints and objectives. The objectives are maximisation of self-consumption, minimisation of peak loads, minimisation of investment costs and maximisation of profits from trading with energy. Here mathematical optimisation is implemented as a tool in our common platform such that experts may iteratively combine different aspects of an optimal (dis-)charge schedule e.g. input of cost and profit margins computed for participation in PFR market or compute a schedule with maximal self-consumption with respect to a previously computed minimum peak load. Further development towards detailed storage models and variable input values will help to derive and implement controls of BESS that improve the operation of a smart grid and its costs. [ABSTRACT FROM AUTHOR]