Zilouchian, Ali

This dissertation proposes a utility-centric peer-to-peer (P2P) energy trading framework as an alternative to traditional net metering, aiming to resolve conflicts between distributed energy resource owners and utilities. It advocates for practical software services and dynamic payment mechanisms tailored to prosumer needs, offering an alternative to reducing net metering incentives. Additionally, it explores game theory principles to ensure equitable compensation for prosumer cooperation, driving the adoption of P2P energy markets. It also builds on demand-side payment mechanisms like NRG-X-Change by adapting it to provide fair payment distribution to prosumer coalitions. The interoperable energy storage systems with P2P trading also presented battery chemistry detection using neural network models. A fuzzy inference system is also designed to facilitate prosumers' choice in participating in P2P markets, providing flexibility for energy trading preferences. The simulation results demonstrated the effectiveness of the proposed design schemes.

This dissertation presents the design, implementation and application of soft
computing methodologies to Proton Exchange Membrane (PEM) Fuel Cell systems.
In the first part of the research work, two distinct approaches for the modeling and
prediction of a commercial PEM fuel cell system are presented. Several Simulink models
are constructed from the electrochemical models of the PEM fuel cells. The models have
been simulated in three dimension (3-D) space to provide the visual understanding of fuel
cell behaviors. In addition, two optimal predictive models, based on back-propagation
(BP) and radial basis function (RBF) neural networks are developed. Experimental data
as well as pre-processing data are utilized to determine the accuracy and speed of the
proposed prediction algorithms. Extensive simulation results are presented to demonstrate
the effectiveness of the proposed method on prediction of nonlinear input-output linear input-output mapping.
In the second part of the study, the design and implementation of several fuzzy
logic controllers (FLCs) as well as classical controllers are carried out. The proposed
real-time controller design is based on the integration of sensory information, Labview
programming, mathematical calculation, and expert knowledge of the process to yield
optimum output power performance under variable load condition. The implementations
of the proposed controllers are carried out for a commercial PEM fuel system at FA U
Fuel Cell Laboratory. The performance of the proposed controllers pertaining to the
oxygen (02) flow rate optimization as well as the actual fuel cell output power under a
variable load bank are compared and investigated. It was found the Fuzzy Logic
Controller design provide a simple and effective approach for the implementation of the
fuel cell systems.

In recent years, many protocols for efficient Multicasting have been proposed.
However, many of the Internet Service Providers (ISPs) are reluctant to use multicastenabled
routers in their networks. To provide such incentives, new protocols are
needed to improve the quality of their services. The challenge is to find a compromise
between allocating Bandwidth (BW) among different flows in a fair manner, and
favoring multicast sessions over unicast sessions. In addition, the overall higher level
of receiver satisfaction should be achieved.
In this dissertation, we propose three new innovative protocols to favor
multicast sessions over unicast sessions. Multicast Favored BW Allocation-
Logarithmic (MFBA-Log) and Multicast Favored BW Allocation-Linear (MFBALin)
protocols allocate BW proportional to the number of down stream receivers.
The proposed Multicast Reserved BW Allocation (MRBA) protocol allocates part of the BW in the links only to multicast sessions. Simulation results show the increase in
the overall level of Receiver Satisfaction in the network.

In thesis work, an innovative design and implementation of a "trybrid" system is
carried out. This new "trybrid" system is the continuation of the solar electric vehicle
which was designed and developed as an undergraduate independent study under the
supervision of Dr. Roger Messenger. The work done on this thesis consists in the
implementation of the existing solar electric vehicle outfitted with a fuel cell system. For
this, two fuel cell systems were analyzed as possible candidates for the retrofit. Also,
different hydrogen storage methods are analyzed in terms of efficiency and safety. This
thesis also covers the control system utilized to manage the energy from the two different
energy sources. Special attention is devoted to the method of determining the state of
charge of the batteries which controls fuel cell turn on time. This first prototype shows
the different functionalities and optimized features that maximize the solar energy
production, and determines exactly when to turn on the fuel cell for minimum hydrogen
usage.

Lower costs of clean energy generation, the need for a more secure grid, and
environmental concerns are leading to create more opportunities for integration of
renewable energy resources utilization in the power systems. The recent concept of
Microgrid (MG), as a part of the development of smart grid, is required in order to
integrate the renewable sources in the utility grid. An MG is described as a small-scale
distribution grid that consists of diversified Distributed Energy Resources (DERs),
Battery Energy Storage Systems (BESSs), and local flexible loads that typically can
either be operated in islanded or grid-connected modes. The optimal utilization control of
such an MG system is a challenging task due to the complexity of coordination among
the DERs, BESSs and load management possibilities. Therefore, in this dissertation,
optimal component sizing and operation of MGs under different operational strategies is
proposed. MGs typically consist of Photovoltaic (PV) systems, wind turbines as well as
microgas turbines, fuel cells, batteries and other dispatchable generating units. Firstly, a methodology to perform the optimal component sizing for DERs in
islanded/grid-tied modes is developed. The proposed optimal algorithm aims to
determine the appropriate configuration among a set of components by taking into
consideration the system’s constraints. An Iterative optimization technique is proposed in
order to minimize the annual cost of energy and cost of emissions including CO2, SO2,
and NOx. A case study from South Florida area, given the local weather data and load
demand is investigated for the modeling verification. Using the results from optimal
component sizes, a day-ahead optimization problem for the operation of an MG under
different scenarios is introduced. Also, the objective function is formulated as a
constrained non-linear problem. The uncertainties of stochastic variables (solar radiation,
wind speed, and load) are modeled and renewable generations and load demand are
forecasted. An advanced dynamic programing procedure is proposed to assess various
operational policies. The simulation results show the efficiency of the proposed method.

FAU's Office of Undergraduate Research and Inquiry hosts an annual symposium where students engaged in undergraduate research may present their findings either through a poster presentation or an oral presentation.

The technical and scientific challenges to provide reliable sources energy for US
and global economy are enormous tasks, and especially so when combined with strategic
and recent economic concerns of the last five years. It is clear that as part of the mix of
energy sources necessary to deal with these challenges, fuel cells technology will play
critical or even a central role. The US Department of Energy, as well as a number of the
national laboratories and academic institutions have been aware of the importance such
technology for some time. Recently, car manufacturers, transportation experts, and even
utilities are paying attention to this vital source of energy for the future. In this thesis, a
review of the main fuel cell technologies is presented with the focus on the modeling, and
control of one particular and promising fuel cell technology, aluminum air fuel cells. The
basic principles of this fuel cell technology are presented. A major part of the study
consists of a description of the electrochemistry of the process, modeling, and simulations
of aluminum air FC using Matlab Simulink™. The controller design of the proposed
model is also presented. In sequel, a power management unit is designed and analyzed as an alternative source of power. Thus, the system commutes between the fuel cell output
and the alternative power source in order to fulfill a changing power load demand. Finally,
a cost analysis and assessment of this technology for portable devices, conclusions and
future recommendations are presented.

In this thesis work, hierarchical control techniques will be used for controlling a robotic manipulator. The hierarchical control will be implemented with fuzzy logic to improve the robustness and reduce the run time computational requirements. Hierarchical control will consist on solving the inverse kinematic equations using fuzzy logic to direct each individual joint. A commercial Micro-robot with three degrees of freedom will be used to evaluate this methodology. A decentralized fuzzy controller will be used for each joint, with a Fuzzy Associative Memories (FAM) performing the inverse kinematic mapping in a supervisory mode. The FAM determines the inverse kinematic mapping which maps the desired Cartesian coordinates to the individual joint angles. The individual fuzzy controller for each joint will generate the required control signal to a DC motor to move the associated link to the new position. The proposed hierarchical fuzzy controller will be compared to a conventional PD controller.

Inferential analysis using neural networks technology is being proposed for the Ras Tanura Refinery crude fractionation section. Plant data for a three month operation period is analyzed in order to construct a neural network model with backpropagation training algorithm. The proposed neural network model can predict various properties associated with crude oil products. The simulation results for modeling Naphtha 95% cut point and Naphtha Reid vapor pressure properties are analyzed. A fuzzy neural network model is also proposed that takes into account the fuzziness in both process variables and the corresponding product quality parameter. The training algorithm is derived based on the backpropagation technique. The results of the proposed study can ultimately enhance the on-line prediction of crude oil product quality parameters for crude fractionation processes in the Ras Tanura Oil Refinery.

In this thesis, a partial fraction expansion of a separable-in-denominator 2-D transfer function is given. Based on this expansion, several novel realizations of separable-in-denominator 2-D filter are provide. These realizations have the properties of highly parallel structure and improved throughput delay. The performance figures are given in the tables. A method of evaluation of quantization error of separable-in-denominator 2-D filter is also derived by using the residue method. Formulas for calculation of roundoff noise of proposed structures are provided. Two programs which can be used to calculate the roundoff noise of proposed structure are listed in the Appendix. To run the programs, we need only to input the constant coefficients of expanded transfer function. At last, an optimal block realization of separable-in-denominator 2-D filter is discussed and the criterion for the absence of limit cycles for a second-order 2-D block is given.