Integration of residential-level photovoltaic (PV) power generation and energy storage systems into the smart grid will provide a better way of utilizing renewable power. With dynamic energy pricing models, consumers can use PV-based generation and controllable storage devices for peak shaving on their power demand profile from the grid, and thereby, minimize their electric bill cost. The residential storage controller should possess the ability of forecasting future PV power generation as well as the power consumption profile of the household for better performance. In this paper, novel PV power generation and load power consumption prediction algorithms are presented, which are specifically designed for a residential storage controller. Furthermore, to perform effective storage control based on these predictions, the proposed storage control algorithm is separated into two tiers: the global control tier and the local control tier. The former is performed at decision epochs of a billing period (a month) to globally “plan” the future discharging/charging schemes of the storage system, whereas the latter one is performed more frequently as system operates to dynamically revise the storage control policy in response to the difference between predicted and actual power generation and consumption profiles. The global tier is formulated and solved as a convex optimization problem at each decision epoch, whereas the local tier is analytically solved. Finally, the optimal size of the energy storage module is determined so as to minimize the break-even time of the initial investment in the PV and storage systems.