Research Directions

Greening Societal Power Consumption

Importance: Power system reliability requirements dictate that supply and demand have to be balanced at all times. The intermittency of renewable energy supplies (e.g., solar and wind) threaten the ability of grid operators to ensure this balance. That is why enabling technologies that harness the intrinsic flexibility of end-use electricity demand is an inevitable step for efficient trading of high levels of non-dispatchable renewable energy resources. The status-quo to deal with supply uncertainty and inflexible demand is to socialize 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. This clearly can only take us so far. That is why price-responsive demand management programs are increasingly gaining more attention.

Challenges: Electricity demand has historically been left completely out of the control system design. In order to make demand more elastic, we need to re-imagine the end-use experience of electricity delivery services and how we operate electricity markets. Engaging demand in the loop poses a highly complex control and communication problem that suffers from a curse of dimensionality. Demand is comprised of a large number of heterogeneous subcomponents that interact through a complex, coupled physical environment operating over many spatial and temporal scales. These subcomponents are also serving the needs of customers with heterogeneous preferences.

Our goal: We aim to design scalable and decentralized protocols that dictate how flexible electricity demand can engage with the grid, with the goal of achieving network-wide near-optimal performance and providing the highest possible quality of service to customers. Think of TCP protocols that help control congestion in the Internet. Can we have a similar scheme for air conditioners? Such protocols need to make financial sense in terms of initial investment and operational costs, should be backward compatible with most existing practices, protect system reliability, and should not depend on unrealistic assumptions such as unlimited computational and communication abilities or full rationality of users.

Smart Urban Infrastructures: How Coupled CHSP Networks Interact

Importance: A city is made up of different infrastructure networks forming a system of systems. However, such city infrastructure elements typically operate in silos. Smart cities need an integrated approach in order to harness the full potential of these complex interdependent networked systems.

Take the concrete examples of Electric Vehicles (EV). EVs are emerging as one of the primary solutions to make electricity demand elastic. 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. Based on their travel needs and the cost of charging at various stations available to them, EV drivers decide on what I call a charge and travel plan. The same can be said about autonomous electric vehicle fleets. At the system level, this joint planning will introduce a connection between intelligent power and transportation systems. In fact, we have shown that ignoring the interdependence may result in instabilities in power grid operations. Similar connections can be found between power networks and data networks, water networks, emergency response systems, etc. Such interdependencies are getting closer to real-time operations and forming elaborate control systems potentially capable of making our urban world safer and more efficient.

Challenges: Clearly, by introducing real-time feedback loops between various processes and services that have been optimized independently until now, we are further increasing their control system design complexity both in terms of analytical modeling, information sharing and optimization as well as economic mechanism design to align the interests of the many organizations involved. Moreover, the ability of failures in one infrastructure to cascade to another may exacerbate the fragility of the overall system.

For the specific case of EVs, 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. Addressing this challenge would require control solutions to affect human behavior and mobility patterns. 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. How can we jointly optimize the control system design to address this second challenge as well?

Our goal: We seek to design methods that improve control, integrity, and overall stability of services and goods provided by interdependent networks. These frameworks aim to provide layered solutions that need minimal coordination between various players. For example, in the case of EVs, we have been working on the design of routing or pricing schemes that allow power and transportation networks to cooperatively minimize the carbon footprint of EVs, while considering the stochastic mobility needs of customers, the limited capacity of charging stations, and the state of the power grid.

Economic/Behavioral Interventions: How Humans Interact with CHPS

Importance: People, unlike sensors and actuators, require incentives to participate in control systems. They are not as predictable, and they cannot be as easily modeled. So how can we reliably engage humans in disruptive engineering technologies?

Challenges: The engineering community lacks a clear understanding of how to model/capture human behavior in the real-time control loop. Moreover, control solutions which require heavy monitoring of personal information have a toll on customers due to loss of privacy.

Our goal: We seek to design methods capable of capturing and reproducing the variability observed in human behavior, and control systems (or what I call real-time human engagement protocols) that decouple the variability of human response from system reliability requirements. As to the protection of users privacy, infrastructure systems mostly follow philosophical and non-quantitative arguments, resulting in measures that are either too mild or very extreme and constraining. A fundamental question here is: even with unlimited sensing and communication power, how much information should be provided to a retailer to enable reliable and competitive operation yet protect the customer's right to privacy?