  Quantity and price risks are key uncertainties market participants face in
electricity markets with increased volatility, for instance, due to high shares
of renewables. From day ahead until real-time, there is a large variation in
the best available information, leading to price changes that flexible assets,
such as battery storage, can exploit economically. This study contributes to
understanding how coordinated bidding strategies can enhance multi-market
trading and large-scale energy storage integration. Our findings shed light on
the complexities arising from interdependencies and the high-dimensional nature
of the problem. We show how stochastic dual dynamic programming is a suitable
solution technique for such an environment. We include the three markets of the
frequency containment reserve, day-ahead, and intraday in stochastic modelling
and develop a multi-stage stochastic program. Prices are represented in a
multidimensional Markov Chain, following the scheduling of the markets and
allowing for time-dependent randomness. Using the example of a battery storage
in the German energy sector, we provide valuable insights into the technical
aspects of our method and the economic feasibility of battery storage
operation. We find that capacity reservation in the frequency containment
reserve dominates over the battery's cycling in spot markets at the given
resolution on prices in 2022. In an adjusted price environment, we find that
coordination can yield an additional value of up to 12.5%.
