Some Highlighted Projects

Congestion Management in Electric Transportation Networks

Electric Vehicles (EV) can help improve fuel economy, increase renewable energy penetration in our power grids and reduce emissions. In the past eight years, EV battery costs have decreased 70 percent, and the US has seen an increase in the number of EV charging stations from less than 500 in 2008 to more than 16,000 today - a 40 fold increase. These advancements, along with disruptive technologies fast happening in the area of driverless cars and shared vehicle fleets, are expected to create synergies that can revolutionize our future transportation systems.

Obstacles faced towards significantly increasing the number of EVs in transportation systems are two-fold. First, we lack adequate EV charging stations in less populated areas and practical control mechanisms to allocate charging spots to EVs, leading to range anxiety in drivers on some routes as well as possibly long wait times to find a spot at popular locations. Second, EVs are expected to disrupt the electric utilities’ business due to their significant electricity consumption, which may not be aligned with the grid's generation capacity. Moreover, uncontrolled EV charging can negatively impact electricity distribution networks.

In our research, we propose congestion management schemes that coordinate the operations of the multiple players in this picture with the objective of guiding drivers (or managing autonomous EV fleets) to use infrastructure systems more efficiently. If companies provided workplace charging services to their employees, or if a ride-sharing company were to own a fleet of self-driving electric vehicles to serve their customer base, how can they optimally manage these systems to minimize the carbon footprint of EVs, while considering the mobility needs of customers as well as the limited capacity of charging stations? We provide insights as to how the operations of power and transportation infrastructures can be coordinated through a combination of economic direct control as well as pricing schemes. We have shown that ignoring the interdependence could result in potential instabilities in power grid operations.

Large-Scale Monitoring and Control of Electric Loads for Demand Response

Enabling technologies that harness the intrinsic flexibility of end-use electricity demand (load) is an inevitable step for efficient trading of high levels of non-dispatchable clean generation resources like wind and solar energy. The status-quo of socializing the cost of deploying costly generation reserves whose outputs can ramp up and down quickly and at will, and hence can help compensate for the unpredictable nature of renewable generation outputs, can only go so far. That is why it is important that new paradigms be introduced which actively involve flexible electricity demand in balancing supply and demand in the power grid. We are particularly focused on enabling such paradigms in the residential and commercial sectors as the transformation of wireless communication systems enables vast deployment of smart sensor systems and savvy home appliances. Electricity load is comprised of a large number of heterogeneous subcomponents which represent the load of appliances that are randomly plugged into the grid due to the actions of customers with heterogeneous preferences. Hence, especially for the residential and commercial sectors, active demand response (DR) poses a highly complex problem. This is mainly due to lack of adequate real-time metering, enormous problem size, and the involvement of humans and for-profit retailers in the control loop. To address these complexities, we study the following:

1) We systematically develop reduced-order stochastic models for the aggregate flexibility of a large population of heterogeneous loads based on clustering. Our approach has many benefits, e.g., protecting customer privacy, as well as lowering the computational and communication requirements for aggregate load modeling and control with adjustable margins of error.

2) We study learning schemes that design optimal economic incentives to engage end-use customers in demand response programs.

3) We study the optimal system planning (and wholesale pricing) problem solved by the independent power system operator in the presence of flexible demand.

alt text