We introduce a framework for controlling the charging and discharging processes of plug-in electric vehicles (PEVs) via pricing strategies. Our framework consists of a hierarchical decision-making setting with two layers, which we refer to as aggregator layer and retail market layer. In the aggregator layer, there is a set of aggregators that are requested (and will be compensated for) to provide certain amount of energy over a period of time. In the retail market layer, the aggregator offers some price for the energy that PEVs may provide; the objective is to choose a pricing strategy to incentivize the PEVs so as they collectively provide the amount of energy that the aggregator has been asked for. The focus of this paper is on the decision-making process that takes places in the retail market layer, where we assume that each individual PEV is a price-anticipating decision-maker. We cast this decision-making process as a game, and provide conditions on the pricing strategy of the aggregator under which this game has a unique Nash equilibrium. We propose a distributed consensus-based iterative algorithm through which the PEVs can seek for this Nash equilibrium. Numerical simulations are included to illustrate our results.

%B 2013 American Control Conference (ACC) %I IEEE %C Washington, DC %P 5086 - 5091 %8 06/2013 %@ 978-1-4799-0177-7 %R 10.1109/ACC.2013.6580628 %0 Conference Paper %B IEEE Annual Conference on Decision and Control (CDC) %D 2012 %T Decentralized optimal dispatch of distributed energy resources %A Alejandro D. Dominguez-Garcia %A Stanton T. Cady %A Christoforos N. Hadjicostis %K distributed energy resources (der) %K reliability and markets %K RM11-006 %XIn this paper, we address 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. We also describe a wireless testbed we developed for testing the performance of the algorithms.

%B IEEE Annual Conference on Decision and Control (CDC) %I IEEE %C Maui, HI, USA %P 3688 - 3693 %8 12/2012 %@ 978-1-4673-2065-8 %R 10.1109/CDC.2012.6426665 %0 Conference Paper %B 2012 American Control Conference (ACC) %D 2012 %T Real-time scheduling of deferrable electric loads %A Anand Subramanian %A Manuel J. Garcia %A Alejandro D. Dominguez-Garcia %A Duncan S. Callaway %A Kameshwar Poolla %A Pravin Varaiya %K distributed energy resources (der) %K PEVs %K renewables integration %K RM11-006 %K Thermostatically controlled loads %XWe consider a collection of distributed energy resources [DERs] such as electric vehicles and thermostatically controlled loads. These resources are flexible: they require delivery of a certain total energy over a specified service interval. This flexibility can facilitate the integration of renewable generation by absorbing variability, and reducing the reserve capacity and reserve energy requirements. We first model the energy needs of these resources as tasks, parameterized by arrival time, departure time, energy requirement, and maximum allowable servicing power. We consider the problem of servicing these resources by allocating available power using real-time scheduling policies. The available generation consists of a mix of renewable energy [from utility-scale wind-farms or distributed rooftop photovoltaics], and load-following reserves. Reserve capacity is purchased in advance, but reserve energy use must be scheduled in real-time to meet the energy requirements of the resources. We show that there does not exist a causal optimal scheduling policy that respects servicing power constraints. We then present three heuristic causal scheduling policies: Earliest Deadline First [EDF], Least Laxity First [LLF], and Receding Horizon Control [RHC]. We show that EDF is optimal in the absence of power constraints. We explore, via simulation studies, the performance of these three scheduling policies in the metrics of required reserve energy and reserve capacity.

%B 2012 American Control Conference (ACC) %I IEEE %C Montreal, QC %P 3643 - 3650 %8 06/2012 %@ 978-1-4577-1095-7 %R 10.1109/ACC.2012.6315670