Incentive-based coordinated charging control of plug-in electric vehicles at the distribution-transformer level

Distribution utilities are becoming increasingly aware that their networks may struggle to accommodate large numbers of plug-in electric vehicles (PEVs). In particular, uncoordinated overnight charging is expected to be problematic, as the corresponding aggregated power demand exceeds the capacity of most distribution substation transformers. In this paper, a dynamical model of PEVs served by a single temperature-constrained substation transformer is presented and a centralized scheduling scheme is formulated to coordinate charging of a heterogeneous PEV fleet. We employ the dual-ascent method to derive an iterative, incentive-based and non-centralized implementation of the PEV charging algorithm, which is optimal upon convergence. Then, the distributed open-loop problem is embedded in a predictive control scheme to introduce robustness against disturbances. Simulations of an overnight charging scenario illustrate the effectiveness of the so-obtained incentive-based coordinated PEV control scheme in terms of performance and enforcing the transformer's thermal constraint

Incentive-based coordinated charging control of plug-in electric vehicles at the distribution-transformer level

Distribution utilities are becoming increasingly aware that their networks may struggle to accommodate large numbers of plug-in electric vehicles (PEVs). In particular, uncoordinated overnight charging is expected to be problematic, as the corresponding aggregated power demand exceeds the capacity of most distribution substation transformers. In this paper, a dynamical model of PEVs served by a single temperature-constrained substation transformer is presented and a centralized scheduling scheme is formulated to coordinate charging of a heterogeneous PEV fleet. We employ the dual-ascent method to derive an iterative, incentive-based and non-centralized implementation of the PEV charging algorithm, which is optimal upon convergence. Then, the distributed open-loop problem is embedded in a predictive control scheme to introduce robustness against disturbances. Simulations of an overnight charging scenario illustrate the effectiveness of the so-obtained incentive-based coordinated PEV control scheme in terms of performance and enforcing the transformer's thermal constraint

Seamless Integration of Renewable Generation and Plug-in Electric Vehicles into the Electrical Grid.

An imminent release of plug-in electric vehicles en masse will add substantial load to electrical power grids that are already operating near limits. Coordinated control of vehicle charging, however, can eliminate the need for expensive overhauls of grid infrastructure. Furthermore, the growing penetration of renewable energy sources provides an excellent opportunity to meet the increased electricity demand, but the challenge remains to tackle the variability and intermittency associated with renewable energy. Our research focuses on identifying and analyzing key issues regarding interactions between renewable generation, vehicle charging, and the power grid. In order to address these issues, we are designing control schemes that ensure seamless integration of newer forms of generation and load, while achieving satisfactory grid-level performance in areas such as loss minimization, voltage regulation, generation balancing and valley filling. Feedback control oriented analytical models have been developed to regulate aggregate demand by certain time deferrable loads (thermostatic loads, plug-in electric vehicle chargers). It is shown that, via a hysteresis-based pulse-width modulated type control, a linearized system response model can be established from the evolution of probability distribution of states (thermostat temperature, battery state-of-charge) of loads. It is shown that grid-level objectives, such as generation tracking and valley filling, can be satisfied by using only the aggregate power as measurement. A framework is presented to study the impact of synchronization in plug-in electric vehicle chargers on the voltage resiliency of electrical grid. It is shown that a fault-induced synchronized tripping of chargers can cause critical over-voltage situations in a distribution feeder. A non-linear state-space model is developed that can truly capture the complex, easily synchronizable, dynamics of hysteresis-based loads. It is reported that, under certain control input signals, such load aggregation can display instability in the form of period-adding cascade. A control method is proposed that optimally allocates photo-voltaic inverter output in a de-centralized way to minimize line losses and voltage deviations. While optimality of this de-centralized control law is proved under certain assumptions, its validity in a more practical scenario is also discussed and possible modifications are suggested.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99858/1/soumyak_1.pd

On the Control of Active End-nodes in the Smart Grid

The electrical grid has substantially changed in recent years due to the integration of several disruptive load and generation technologies into low-voltage distribution networks, which are meant to smarten it and improve its efficiency. These technologies have subjected the grid to unprecedented amounts of variability and uncertainty that threaten its reliability and could reduce its efficiency. Even a low penetration of these disruptive technologies may cause equipment overloads, voltage deviations beyond permissible operating thresholds, and bidirectional power flows in distribution networks. The smart grid will comprise a vast number of active end-nodes, including electric vehicle chargers, solar inverters, storage systems, and other elastic loads, that can be quickly controlled to adjust their real and reactive power contributions. Given the availability of inexpensive measurement devices and a broadband communication network that connects measurement devices to controllers, it is possible to incorporate potentially disruptive technologies into distribution networks while maintaining service reliability, using some novel control mechanisms, which are the focus of this thesis. In this thesis, we propose a new paradigm for the control of active end-nodes at scale. This control paradigm relies on real-time measurements of the states of the distribution network and the end-nodes rather than long-term predictions. We use an optimal control framework to design mechanisms that balance a set of system-level and user-level objectives. We study control of active end-nodes in two different contexts: a radial distribution system and a grid-connected public electric vehicle charging station powered by on-site solar generation. We develop both a feedback controller and an open-loop controller, and propose centralized and distributed algorithms for solving optimal control problems. We implement and validate these control mechanisms using extensive numerical simulations and power flow analysis on a standard test system

Semantic Networks for Hybrid Processes.

Simulation models are often used in parallel with a physical system to facilitate control, diagnosis and monitoring. Model based methods for control, diagnosis and monitoring form the basis for the popular sobriquets `intelligent', `smart' or `cyber-physical'. We refer to a configuration where a model and a physical system are run in parallel as a emph{hybrid process}. Discrepancies between the model and the process may be caused by a fault in the process or an error in the model. In this work we focus on correcting modeling errors and provide methods to correct or update the model when a discrepancy is observed between a model and process operating in parallel. We then show that some of the methods developed for model adaptation and diagnosis can be used for control systems design. There are five main contributions. The first contribution is an analysis of the practical considerations and limitations of a networked implementation of a hybrid process. The analysis considers both the delay and jitter in a packet switching network as well as limits on the accuracy of clocks used to synchronize the model and process. The second contribution is a semantic representation of hybrid processes which enables improvements to the accuracy and scope of algorithms used to update the model. We demonstrate how model uncertainty can be balanced against signal uncertainty and how the structure of interconnections between model components can be automatically reconfigured if needed. The third contribution is a diagnostic approach to isolate model components responsible for a discrepancy between model and process, for a structure preserving realization of a system of ODEs. The fourth contribution is an extension of the diagnostic strategy to include larger graphs with cycles, model uncertainty and measurement noise. The method uses graph theoretic tools to simplify the graph and make the problem more tractable and robust to noise. The fifth contribution is a simulation of a distributed control system to illustrate our contributions. Using a coordinated network of electric vehicle charging stations as an example, a consensus based decentralized charging policy is implemented using semantic modeling and declarative descriptions of the interconnection network.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99903/1/danand_1.pd