Accurate component model based control algorithm for residential photovoltaic and energy storage systems accounting for prediction inaccuracies

Integrating residential photovoltaic (PV) power generation and energy storage systems into the Smart Grid is an effective way of utilizing renewable power and reducing the consumption of fossil fuels. This has become a particularly interesting problem with the introduction of dynamic electricity energy pricing models, since electricity consumers can utilize their PV-based energy generation and controllable energy storage devices for peak shaving on their power demand profile from the Smart Grid, and thereby, minimize their electricity bill cost. A realistic electricity pricing function is considered in this chapter with the billing period of a month, which is comprised of both an energy price component and a demand price component. Due to the characteristics of the realistic electricity price function and the energy storage capacity limitation, the residential storage control algorithm should (i) effectively take into account the PV power generation and load power consumption prediction results and mitigate the inevitable inaccuracy in these predictions, and (ii) properly account for various energy loss components during system operation, including the energy loss components due to rate capacity effect in the storage system as well as power dissipation in the power conversion circuitries. A near-optimal storage control algorithm is proposed accounting for these aspects, based on the PV power generation and load power consumption prediction methods in the previous papers. The near-optimal control algorithm, which controls the charging/discharging schemes of the storage system, is effectively implemented by solving a convex optimization problem at the beginning of each day with polynomial time complexity. The optimal size of the energy storage system is determined in order to minimize the break-even time of the initial investment in the PV and storage systems. Experimental results demonstrate the effectiveness of the proposed near-optimal residential storage control algorithm in electricity cost reduction compared with the baseline control algorithm.