Changsun Ahn

Synergistic control of plug-in vehicle charging and wind power scheduling

Significant synergy exists between plug-in electric vehicles (PEVs) and wind energy: PEVs can be the demand response to mitigate the intermittent wind power outputs, and wind energy can provide low-carbon electricity to PEVs. This paper presents a hierarchical control algorithm to realize this synergy by integrating the PEV charging and wind power scheduling. The control algorithm consists of three levels: the top-level controller optimizes the scheduling for the conventional power plants and wind power; the middle-level controller plans PEV charging to achieve load following based on the battery state of charge and plug-off time of each vehicle; the bottom-level controller uses grid electricity frequency as the feedback cue to control PEV charging and serves as the ancillary service to the grid. Numerical simulations show that the integrated controller can improve the grid frequency regulation and overall electricity generation cost without sacrificing the PEVs charging completion.

Robust Estimation of Road Frictional Coefficient

Vehicle active safety systems stabilize the vehicle by controlling tire forces. They work well only when the commanded tire forces are within the friction limit. Therefore, knowledge of the tire/road friction is important to improve the performance of vehicle active safety systems. This paper presents two methods to estimate the friction coefficient: one based on lateral dynamics, and one based on longitudinal dynamics. The two methods are then integrated to improve working range of the estimator and robustness. The first method is a nonlinear observer based on vehicle lateral/yaw dynamics and Brush Tire model, the second method is a recursive least squares method based on the relationship between tire longitudinal slip and traction force. The performance of the estimation algorithm is verified using test data under a wide range of friction and speed conditions.

Decentralized charging algorithm for electrified vehicles connected to smart grid

Intelligent management of power generation and dispatching is important when renewable energy sources and electrified vehicles (EV/PHEV) are introduced to the grid. Intermittency of renewable power and vehicle charging loads disturbs power supply and demand and could cause instability. Fortunately, EV/PHEV can be connected as controllable load or even used as energy storage, which makes it possible to reduce their negative impact and can even be explored to improve grid resilience. By coordinating power generation and charging, it is possible to reduce power generation cost and carbon emission. To improve practicality, a decentralized charging algorithm is formulated by emulating the charging pattern identified through linear programming (LP) optimization solutions. The resulting decentralized control algorithm is a function of forecasted total power demand on the grid, estimated number of vehicles, estimated EV/PHEV plug off time, and state of charge of the vehicle battery. Simulation results are presented to demonstrate the performance of the proposed decentralized algorithm.

Robust estimation of road friction coefficient using lateral and longitudinal vehicle dynamics

Vehicle active safety systems stabilise the vehicle by controlling tyre forces. They work well only when the tyre forces commanded by the safety systems are within the friction limit. Therefore, knowledge of the tyre/road friction coefficient can improve the performance of vehicle active safety systems. This study presents four methods to estimate the friction coefficient based on four different excitation conditions: medium lateral excitation, large lateral excitation, small longitudinal excitation, and large longitudinal excitation. For the lateral excitation cases, the estimation is based on vehicle lateral/yaw dynamics and Brush tyre model, whereas for the longitudinal excitation cases, the estimation basis is the relationship between the tyre longitudinal slip and traction force. These four methods are then integrated to increase the working range of the estimator and to improve robustness. The performance of the integrated estimation algorithm is verified through experimental data collected on several surface conditions.

Decentralized Voltage Control to Minimize Distribution Power Loss of Microgrids

Microgrids that integrate renewable power sources are suitable for rural communities or certain military applications such as forward operation bases. For microgrids that are not connected to the large electric grid, new control strategies must be designed to maintain proper grid voltage and frequency. In addition, microgrids with distributed power sources and load nodes may have frequent reconfiguration in grid architecture. Therefore, the control strategies ideally should be “plug-and-play”, i.e., they should not require significant communication or architecture information, and they should work reliably as long as the supply/demand powers are reasonably balanced. Another unique issue of microgrids is the high resistance loss in distribution lines due to the low operating voltage. To reduce power losses, appropriate voltage control at distributed nodes is required which again must work in a plug-and-play fashion. In this paper, we propose a decentralized voltage control algorithm that minimizes power losses for microgrids. Its optimality and plug-and-play nature are demonstrated through comprehensive simulations.

Integration of plug-in electric vehicle charging and wind energy scheduling on electricity grid

Plug-in electric vehicles (PEVs) and wind energy are both key new energy technologies. However, they also bring challenges to the operation of the electricity grid. Charging a large number of PEVs requires a lot of grid energy, and scheduling wind energy is not trivial because of the intermittency. In this paper we propose a hierarchical control algorithm which integrates PEV charging and wind energy scheduling. It explores the controllable nature of PEV charging to accommodate the intermittent wind energy. The algorithm consists of three levels: the top-level controller solves the hourly scheduling of wind energy (and conventional generation) through an optimization problem, the middle-level controller determines sub-hourly scheduling of PEV charging to achieve load following, and the bottom-level controller uses grid frequency deviation as the feedback cue to control PEV charging in real time. The integrated controller achieves multiple goals, including optimal electricity cost, replacing fossil fuel generation by wind energy, reducing ancillary service by controlled PHV charging, and improving the quality of electricity service.

Impact of controlled plug-in EVs on microgrids: A military microgrid example

Increasing concerns about energy security and reliability are intensifying the interest in microgrid and vehicle-to-grid (V2G) technologies. Although the role of V2G technology within the context of optimal scheduling for larger grids has received much attention in the literature, its role within the regulation of microgrids has not yet been studied extensively. In this paper, we focus on the voltage and frequency regulation problem. We develop a microgrid model that is representative of the microgrid architecture considered in the SPIDERS (Smart Power Infrastructure Demonstration for Energy Reliability and Security) project of the Department of Defense. The model is parameterized to reflect the characteristics of Camp Smith, HI, the targeted installation of the SPIDERS project, and the long term Army goals regarding renewable energy penetration and reduction in fuel consumption. The model is augmented by power, frequency, and voltage control algorithms for the inverters that connect microsources to the microgrid. It also incorporates charging/discharging control algorithms for plug-in electric vehicles (PEVs) to take advantage of their capacity as both controllable loads and sources. Using this model, we study the impact of PEVs on the microgrid at different penetration levels and for different control parameters, with the aim of identifying the conditions needed for the vehicle-to-grid technology to have a positive impact on microgrid performance.

Estimation of road friction for enhanced active safety systems: Algebraic approach

Knowledge of tire friction force capacity, i.e. tire-load frictional coefficient, is important for the control of vehicle active safety systems. In this paper we review several methods for friction estimation and develop two robust and cost effective methods based on a nonlinear least square approach and the peak aligning torque. The methods proposed in this paper utilize simple vehicle lateral dynamics, steering system, and front tire dynamics. The first estimator uses direct calculation based on front tire self-aligning torque and the second method is based on a nonlinear least square method. These estimators are verified with Carsim under various conditions.

Robust estimation of road friction coefficient

Vehicle active safety systems stabilize the vehicle by controlling tire forces. They work well only when the commanded tire forces are within the friction limit. Therefore, knowledge of the tire/road friction is important to improve the performance of vehicle active safety systems. This paper presents two methods to estimate the friction coefficient: one based on lateral dynamics, and one based on longitudinal dynamics. The two methods are then integrated to improve working range of the estimator and robustness. The first method is a nonlinear observer based on vehicle lateral/yaw dynamics and Brush Tire model, the second method is a recursive least squares method based on the relationship between tire longitudinal slip and traction force. The performance of the estimation algorithm is verified using test data under a wide range of friction and speed conditions.

Coupling Between Component Sizing and Regulation Capability in Microgrids

Increasing energy security and reliability concerns are intensifying the interest in microgrids. In this setting, design optimization is vital to achieve a reliable infrastructure without overbuilding. This paper considers the impact of frequency and voltage regulation on the optimal design of a conceptual, autonomous military microgrid. This microgrid comprises a solar panel and vehicles as power sources, with each vehicle incorporating a battery and generator. The power output and terminal voltage of these inverter-based sources must be regulated. The paper investigates the effects of battery DC voltage variations on a decentralized regulation scheme, and the resulting influence on optimal component sizing. To this end, controllers are first designed based on the typical assumption that the voltage on the DC side of each inverter is constant. The battery internal resistance is then considered and its impact on regulation performance is investigated. The results show that the battery internal resistance can affect the performance of both frequency and voltage regulation, and consequently must be taken into account in component sizing decisions. Thus, the paper identifies an important coupling between regulation and component sizing problems through battery characteristics, and highlights the need for a combined sizing and regulation framework for microgrid design.