Distributed Rate Control for Smart Solar Arrays.

Continued advances in technology have led to falling costs and a dramatic increase in the aggregate amount of solar capacity installed across the world. A drawback on increased solar penetration is the potential for supply-demand mismatches in the grid due to the intermittent nature of solar generation. While energy storage can be used to mask such problems, we argue that there is also a need to explicitly control the rate of solar generation of each solar array in order to achieve high penetration while also handling supply-demand mismatches. To address this issue, we present the notion of smart solar arrays that can actively modulate their solar output based on the notion proportional fairness. We present a decentralized algorithm based on Lagrangian optimization that enables each smart solar array to make local decisions on its fair share of solar power it can inject into the grid, and then present a sense-broadcast-respond protocol to implement our decentralized algorithm into smart solar arrays. Our evaluation on a city-scale dataset shows that our approach enables 2.6x more solar penetration, while causing smart arrays to reduce their output by as little as 12.4%. By employing an adaptive gradient approach, our decentralized algorithm has 3 to 30x faster convergence. Finally, we implement our distributed algorithm on a Raspberry Pi-class processor to demonstrate its feasibility on grid-tied solar inverters with limited processing capability.