About the Event
On the distribution side of a power system, there exist many distributed energy resources (DERs) that can be potentially used to provide ancillary services to the grid they are connected to. An example is the utilization of plug-in-hybrid vehicles (PHEV) for providing active power for up and down regulation. Proper coordination and control of DERs is key for enabling their utilization for providing these ancillary services. One solution to this coordination and control problem can be achieved through a centralized control strategy where each DER is commanded from a central decision maker. An alternative solution and the one this talk will focus on is to distribute the decision-making process among the DERs. In order to achieve so, the DERs need to exchange information with a number of other “close-by” DERs, and subsequently making a local decision based on this available information.
In the first part of the talk, we discuss the problem of optimally dispatching a set of distributed energy resources (DERs) without relying on a centralized decision maker. We consider a scenario where each DER can provide a certain resource (e.g., active or reactive power) at some cost (namely, quadratic in the amount of resource), with the additional constraint that the amount of resource that each DER provides is upper and lower bounded by its capacity limits. We propose a low-complexity iterative algorithm for DER optimal dispatch that relies, at each iteration, on simple computations using local information acquired through exchange of information with neighboring DERs. We show convergence of the proposed algorithm to the (unique) optimal solution of the DER dispatch problem.
In the second part of the talk, we introduce a strategic decision-making framework for decentralized DER control. In this framework, there is a set of agents referred to as aggregators who interact with the wholesale electricity market, and through some market-clearing mechanism they are incentivized to provide (or consume) certain amount of energy over some period of time, which they will be compensated for. The objective is for the aggregator to design a pricing strategy for incentivizing DERs to modify their active (or reactive) power consumptions (or productions) so that they collectively provide the amount that the aggregator has targeted for. In order to make a decision, each DER uses the pricing information provided by the aggregator and some estimate of the average energy that neighboring DERs can provide, computed through some exchange of information among them. The focus of this talk is on the DER strategic decision-making process, which we cast as a game. In this context, we provide sufficient conditions on the aggregator's pricing strategy under which this game has a unique Nash equilibrium. Then, we discuss a distributed iterative algorithm for information exchange between DERs and allows for DERs to seek this Nash equilibrium.
Alejandro Dominguez-Garcia is an Assistant Professor in the Electrical and Computer Engineering Department at the University of Illinois, Urbana, where he is affiliated with the Power and Energy Systems area. His research interests lie at the interface of system reliability theory and control, with special emphasis on applications to electric power systems and power electronics. Dr. Dominguez-Garcia received the Ph.D. degree in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology, Cambridge, MA, in 2007 and the degree of Electrical Engineer from the University of Oviedo (Spain) in 2001. After finishing his Ph.D., he spent some time as a post-doctoral research associate at the Laboratory for Electromagnetic and Electronic Systems of the Massachusetts Institute of Technology. Dr. Dominguez-Garcia received the NSF CAREER Award in 2010, and the Young Engineer Award from the IEEE Power and Energy Society in 2012. He is an editor of the IEEE Transactions on Power Systems and the IEEE Power Engineering Letters. He is also a Grainger Associate since August 2011.