Pardis Khayyer

Renewable Energy Policies: A Brief Review of the Latest U.S. and E.U. Policies

The U.S. Government and the European Union have developed policies to promote microgeneration and smart grid initiatives. Many projects and significant developments have been accomplished, although the momentum has just begun. To accelerate the implementation and to include the private sector, the U.S. states and the E.U. countries have developed their policies to improve their share of green and economic energy production. The main policies have guaranteed longterm profits for the private sector and tax credits for individuals to participate. Although incredible, these policies need to be constantly compared and modified to keep the green energy progress on track. Liquidation of the government-supported renewable energy projects and consequences for the expected sustained growth of this sector show that the government subsidies have not generated enough incentives for the private sector to decrease the cost of renewable energy manufacturing and research and development. The timely and rightful intervention of government must create a strong private sector to achieve the steady momentum of growth in technical skills and manufacturing infrastructures. This article provided a brief review of the latest relevant ideas that central and local governments have developed with the hope of creating more unified and stronger policies.

Fault Diagnosis of Time-Varying Parameter Systems With Application in MEMS LCRs

Multiple-model adaptive estimation (MMAE) is a well-known technique used for model matching of deterministic parameter systems. This technique can be used in fault diagnosis by allocating a model to each type of fault. In each contingency, the model that represents the behavior of the actual system can indicate the type of fault occurrence. Kalman filters are generally used in modeling and residual-signal generation of time-invariant systems. Slowly time-varying parameter systems, however, require a system identification unit in addition to the model-matching core. This paper utilizes the least square forgetting-factor technique in parameter identification of slowly time-varying systems and combines it with MMAE for fault-diagnosis applications in microelectromechanical-systems (MEMS) lateral comb resonators (LCRs). Prescheduled faults were designed for simulations and experimentally examined in real-time implementations of estimation-based diagnosis technique for two fabricated MEMS LCRs. It is shown that the application of a system identification unit significantly increases the performance of the fault diagnosis in MEMS devices.

Decentralized Control of Large-Scale Storage-Based Renewable Energy Systems

Intermittent nature of renewable energies and random nature of moving loads negatively influence the power system transient stability. Energy storage can enhance the system response by providing short-term energy sources. However, fast dynamic storage systems require advanced controllers to stabilize the transients. This paper introduces the application of a decentralized overlapping decomposition controller in transient stability of renewable energy penetrated storage-based power systems. In a battery-based power system there exists strongly coupled state variables shared among areas. Large-scale controllers can decouple the system state variables and stabilize the oscillations. Here, the model of battery storage is obtained considering the batterys state of charge and the droop in power electronic interfaces. The overlapping decomposition decentralized controller demonstrates an enhanced transient stability performance under variable state of charge in various capacity of storage devices.

Analysis of impact factors for plug-in hybrid electric vehicles energy management

Energy management strategies play a critical role in the fuel consumption of hybrid and Plug-in Hybrid Electric Vehicles (PHEV). Most advanced energy management techniques may be further optimized by help of information obtained from Intelligent Transportation Systems (ITS). Following the previously studied impact factors on PHEV energy consumption, in this paper new impact factors are studied. Energy consumption associated with these factors is investigated for subsequent development of energy management strategies in optimizing fuel economy.

Reliability Investigation of a Hybrid Fuel Cell Electric Vehicle Powered by Downsized Fuel Cells

Fuel cell electric vehicles are mostly relying on operation of their fuel cell and battery system. Single power source systems use battery units as backup; however, in heavy loads or instances with low State Of Charge (SOC) levels, there is a need for other mechanisms to provide reliable energy for the system. This paper investigates the natural enhanced reliability of operation in an advanced system configuration with two downsized fuel cells. The two fuel cell configuration brings high fuel efficiency by economic load sharing between two fuel cells. The reliability of this system configuration is investigated and compared with conventional designs of hybrid fuel cell vehicles.

Optimum Adaptive Piecewise Linearization: An Estimation Approach in Wind Power

This paper introduces an effective piecewise linearization technique to obtain an estimation of nonlinear models when their input-output domains include multidimensional operating points. The algorithm of a forward adaptive approach is introduced to identify the effective operating points for model linearization and adjust their domains for the maximum coverage and the minimum model linearization error. The technique obtains a minimum number of linearized models and the continuity of their domains. The algorithm also yields global minimum model linearization error. The introduced algorithm is formulated for a wind power transfer system for a 2-D set of input domains. The linearization error can be arbitrarily minimized in exchange for a higher number of models. The results demonstrate a significant improvement in the linearization of nonlinear models.

Power management strategies for hybrid electric trucks in smart-grids

Smart grid emerging technologies include plug-in hybrid electric vehicles to assist the grid in peak hours and be used as storage devices. However, as the size of the vehicle increases, they may impose heavy loads on the grid during charging hours. To reduce the effect of heavy loads on the grid, accurate planning for vehicle charging, stationary charge sharing, and the usage of onboard generators may be considered. This paper introduces a power management strategy for the use of renewable energy in hybrid electric trucks and smart grids.

Adaptive voltage tracking control of zeta buck-boost converters

Compensators and PI controllers have been designed and used for control of Zeta converters. However, frequent voltage variations in some applications such as maximum power point tracking of solar power require a high profile voltage tracking control. In addition, load resistance and inductor current influence the performance of conventional controllers. This paper illustrates the design and application of a model reference adaptive controller for output voltage tracking of a zeta converter. The results, compared with a PI controller, demonstrate a close tracking profile with minimal control effort and elimination of the load resistance dependencies.

Adaptive Estimation of Energy Factors in an Intelligent Convoy of Vehicles

Energy consumption of a vehicle is a factor of several environmental and driving conditions, such as air flow density, road grade, and vehicle weight. Accurate estimation of these factors influences the control performance, diagnostics, and the vehicles overall energy consumption. Individual vehicle dynamics, as part of a large convoy governing principles, will expand to include the states that are shared between vehicles. The controller performance relies on the estimated parameters to minimize energy consumption. The estimation of environmental and driving conditions for individual vehicles as part of a convoy is a challenging task. This paper introduces an adaptive model-based energy factor estimation in large-scale convoys. These factors are influenced by vehicle parameters and driving condition uncertainties. These uncertainties, if not estimated correctly, shift the predicted energy consumption and result in low control performance. Mathematical formulation of the proposed estimator in the context of large-scale system is studied through several case study scenarios, and their effectiveness is demonstrated.

A study on bus convoy energy consumption using Monte Carlo analysis

This paper introduces an energy consumption model for convoys of busses arriving to a bus stop with limited parking space. As the busses arrive randomly and stay for a fixed amount of time, the Monte Carlo analysis was used to determine the traffic flow and congestions. Simulation results with actualsize busses demonstrate that in a high-density traffic, convoy configurations would always yield higher energy efficiency and lower system energy dissipations. However, in low-density traffic, only for stop times of less than 5 minutes, convoys were more energy efficient compared to non-convoy bus arrivals. It was also observed that when stop times were increased beyond 5 minutes, individual arrival model yielded lower energy consumption. Simulation results and analysis are provided for various bus convoys and stop times.