An Optimal Approximate Dynamic Programming Algorithm for the Energy Dispatch Problem with Grid- Level Storage

We consider the problem of modeling hourly dispatch and energy allocation decisions in the presence of grid-level storage. The model makes it possible to capture hourly, daily and seasonal variations in wind, solar and demand, while also modeling the presence of hydroelectric storage to smooth energy production over both daily and seasonal cycles. The problem is formulated as a stochastic, dynamic program, where we approximate the value of stored energy. We provide a rigorous convergence proof for an approximate dynamic programming algorithm, which can capture the presence of both the amount of energy held in storage as well as other exogenous variables. Our algorithm exploits the natural concavity of the problem to avoid any need for explicit exploration policies.