Abtahi, Amir

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.

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.

When a heat pipe operates with a large temperature difference between the two ends, the vapor liquid combination is not in thermodynamic equilibrium. The experiments showed that the vapor liquid mixture is in a higher state of saturation at the condenser as compared to other sections of the pipe. Correlations were obtained that relate the coefficient of thermal coupling alpha to the evaporator flux and mass of NCG (non-condensable gas). As mass of NCG is increased in the heat pipe, the thermal coupling coefficient alpha decreases. The evaporator heat flux is directly proportional to alpha. The coefficient alpha is related to the pressures and temperatures at the evaporator, condenser and the adiabatic sections. In conclusion, for heat pipes that do not operate in thermodynamic equilibrium, correlations were obtained between the operating conditions of the heat pipe, the evaporator heat flux and the mass of NCG.