Development And Analysis Of An Energy Storage Sizing Tool For Residential Deployment

Decreasing cost of energy storage technologies is driving the need to determine the economic feasibility of deploying a residential energy storage system. An approach utilizing more readily available data sets from home owners and other public datasets is discussed. Using a Mixed-Integer Linear Program (MILP) for optimization, the approach estimates the optimal size of the battery and inverter as well as the cost of the system and the annual estimate financial benefit. This approach is written in python, with an expectation of an open public release to the energy storage community.