Congestion control of smart distribution grids using state estimation

Power utilities worldwide face two major challenges - peak demand and power (supply-demand) imbalance. In the midst of these difficulties faced by utilities, growing fuel costs, environmental awareness and government directives have increased the push to deploy Electric Vehicles (EVs). One single EV being charged at its peak rate imposes an instantaneous load equivalent to that of 10 average households on the grid, making it essential to schedule the EV charging in order to prevent grid failures. Our approach to this problem is motivated by parallels to the development of the internet and in particular internet protocols such as TCP, where agents respond to signals from the central authority to curtail load when the grid is congested. We show that using high resolution measurements from smart meters and distribution feeders and without measurements at any intermediate nodes, we can use recently developed semi-definite programming based state estimation techniques to accurately infer the state of the gird. We then show how to convert this line level congestion information into signal loads to users to curtail usage. In combination with smart home agents that automatically control consumption, we show how this state estimation and signaling protocol leads to reduced congestion and losses while minimizing user inconvenience.