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A notable feature in the smart grid is the widespread usage of energy storage devices. How to manage those storage devices is a key problem for the smart grid. In this paper, a novel adaptive agent learning ZIPEM algorithm is presented for management of the storage devices. A system with such algorithm allows multi-agent learning that leads to optimal energy storage strategy. The experimental results show that load factor during peak time reduced significantly, and it is possible to save up to 6 percent per consumer on electricity expenses with a storage device of 2 kWh. Moreover, emissions of carbon-dioxide from power generation processes can decrease by 6.3 percent.