In electricity markets, storage operation and bidding strategies are based on expected price spreads. At the same time, these spreads are affected by the operation of the storages. If many storage units react to an expected price spread in a similar way, their joint operation can significantly reduce the spread realised on the market. Such repercussions are known as “avalanche effects”. This paper examines dispatch planning strategies in agent-based electricity market simulations that counter those avalanche effects. These strategies utilise a dynamic programming algorithm to determine asks and bids. The algorithm can pursue different optimisation targets combined with varying awareness levels for price impacts. We apply these strategy variants to a parametrisation of the German electricity market and compare resulting prices, dispatch, and monetary performance to their historical values. Our findings illustrate that, without price impact awareness, storage units are 220% overused in simulations leading to high monetary losses. System-cost minimisation yields the highest correlation (86%) with the historical dispatch, but electricity prices are reproduced most accurately (87% correlation) using profit maximisation. Disaggregating storage units results in a better fit to historical data than an aggregated single-unit representation. Discharged energies and operational profits vary strongly across the different modelling experiments. Our research highlights the importance of detailed storage modelling to accurately assess storage market values. One identified strategy is based on implicit collusion and requires only minimal data also available in the real world. If storage operators behave accordingly, market monitoring and antitrust regulations may be required.