Visualization and Analytics of Distribution Systems with Deep Penetration of Distributed Energy Resources (VADER)

An American future where photovoltaic (PV) energy is a substantial source of power, can only exist if we overcome a number of engineering challenges. In its current state, the distribution system is incapable of handling small to moderate amounts of PV penetration. This is because it was initially designed for handling passive loads, which at the level of a substation, have low variability and are forecasted with high accuracy. It has been an open loop system with little monitoring and control. With the addition of PV energy sources, the overall scenario will change dramatically due to (1) two way power flow on network and (2) high aggregate variability. Additionally, changes on the consumption side lead to the adoption of a number of smart loads, Electric Vehicles (EVs) and increasing use of load flexibility in load management schemes such as demand response (DR).

These fundamental changes in the characteristic of the generation and consumption of power will lead to practical engineering problems that must be overcome to allow increased penetration of Distributed PV. Solving the specific engineering challenges which come at any moderate level of PV penetration requires closed loop integration of data from (1) PV sources, (2) customer load data from smart meters, (3) EV charging data, and (4) local and line mounted precision instruments, and extraction of relevant statistical information from the data to more accurately model distributed energy resources (including management of load and EV) and their behaviors, and actively monitor and manage the emerging grid. The challenges include, for example, analyzing the voltage variations along a feeder when PVs are connected at different locations and with different capacities, and the line loss. A thorough understanding of these engineering challenges provides a foundation for designing smarter and more efficient DER planning schemes, and grid operating paradigms that will be more dynamic and involve more frequent and intelligent controls than the current practice which, for example, adjusts voltage regulators and capacitor banks a few times daily based on experience rather than the actual grid conditions collected from the field.