It is already understood that by preheating the air before it enters the cathode results in better SOFC performance because the temperature difference from inlet to outlet in the stack is much less and therefore can respond faster to load fluctuations. As is already understood, SOFC systems best serve stationary power generation applications. SolidPower recommends using its Engen-2500 system for multi-family houses, hotels, restaurants, public buildings, schools, or small companies. It can be installed in different configurations including as a stand-alone mCHP unit with water storage where it can be combined with heat and power systems that use renewable energy source or as a series of multiple modules, integrated with the heating system of the building. The particular application of the Engen-2500, discussed throughout this thesis, is for providing power to individual server racks, which typically range from a handful of servers to dozens depending on the class of server. Fuel cells are generally suitable for constant power generation applications. They perform best when fuel and oxygen supplies are steady and the load demand remains constant. Therefore, Microsoft’s vision of disconnecting large data center facilities from the electric grid and relying solely on SOFCs for providing power is definitely possible. Data center power consumption has both short and long-term variations due to workload fluctuations and servers turning on and off. The variations in load can be represented by three categories: instantaneous load changes, short term load changes, and long-term load changes. For the first case, the load of a server can change almost instantaneously reacting to a workload. This may be caused by a change in CPU utilization from 0% to 100% and can occur within milliseconds. Some of these types of rapid fluctuations in load can potentially be absorbed by the server’s power supply with its internal capacitors, however, larger instantaneous changes must be handled using an external energy storage system in cooperation with the SOFC system. Short-term load changes occur over several seconds or minutes and can be handled by the SOFC system as it ramps up or down accordingly. Yet, in extreme situations where the server has to be cold rebooted,curing cannabis the SOFC lags behind the sudden spikes in power consumption. For these extreme situations, it is always necessary to include a battery in order to keep up with the drastic load changes.

If the load changes are predictable , the SOFC system can increase its power production ahead of time to follow the load demand. Doing so may lead to reduced end-to-end efficiency of energy usage, therefore this practice is not recommended if trying to maximize production efficiency. The ideal case for applying SOFC systems in data centers is when there are long-term load changes that occur over days and weeks. These changes are typically much slower than all fuel cell’s ramp rates and therefore are no issue for the SOFC’s ability to keep up with the load demand. Under load fluctuations, it would be helpful to understand how much over provisioning of the fuel cell is necessary. The only case where SOFCs struggle is when the servers cause large load changes due to startup and shutdown processes. By staggering server power on and off events over time instead of all at once, a single server sized battery can be shared by multiple servers in a rack. Therefore, the SOFC would not need to be aggressively over provisioned. After receiving the Engen-2500 system from SolidPower and connecting it to the load bank, natural gas, and water pipelines in the testing bay facility of the NFCRC, the initial round of testing began. It was agreed upon with members of the fuel cell team at UCI, Microsoft, and SolidPower that the testing framework would begin with less severe transients in order to avoid causing damage to the stack or system components early on. The first step to characterizing and understanding the performance of any fuel cell system is to subject it to steady-state analysis. A testing matrix was established to neatly organize and document the user-constrained parameters and the corresponding values for other important parameters. For the steady-state analyses, the cathode outlet temperature and fuel utilization were the two parameters that were defined as user-constrained with the two outlet temperature values selected to be 760°C and 770°C and the three fuel utilization values selected to be 0.70, 0.72, and 0.75 as shown in Table 5. In an ideal world, a fuel cell would be able to maintain a constant voltage output for any amount of current demanded. This would appear as a flat line on the current-voltage plot at the peak voltage level, which refers to the open-circuit voltage – defined as the difference of electrical potential between the two electrodes when disconnected from a circuit . However, due to real-world physical limitations, the actual voltage output of a real fuel cell is less than the ideal thermodynamically predicted voltage due to unavoidable losses that is discussed in detail in Section 6 of this thesis.

Another very noticeable fact about the system performance is the slight voltage differences for the same current load demand. From Figures 24 and 25 the slight voltage differences observed between the two stacks in the system are thought to be caused by the slight temperature differences of the two stacks and the manufacturing variation amongst the cells. The benefits of understanding the steady-state performance by looking at the polarization curve is to provide additional information for the overall data center design such as the DC bus, power supply specifications, and DC/DC converters. For all energy conversion devices, it is always of great importance to consider and compare the operational efficiency. The fundamentals of efficiency can be broken down and represented by two important concepts, ideal and real efficiency. Since fuel cell devices produce electric and not mechanical work, thermodynamic theory suggests that the electric work available is limited by the change in Gibbs free energy, therefore, the ideal efficiency of a fuel cell is limited by. The ideal fuel cell efficiency is defined as the amount of useful energy that can be extracted from the process relative to the total energy evolved by that process. For a fuel cell, the maximum amount of energy available to do work is given by the ratio of Gibbs free energy to enthalpy. These losses are illustrated in the polarization plots and the voltage efficiency can be characterized by the ratio of the real operating voltage to the thermodynamically reversible voltage. Note that the operating voltage depends upon the current drawn, therefore the higher current demand, the lower the voltage efficiency. The fuel utilization efficiency accounts for the fact that not all of the provided fuel is used by the fuel cells. Some of the fuel may undergo side reactions that do not produce electric power or the fuel may simply flow through the fuel cell without ever reacting. The fuel utilization efficiency is therefore a ratio of the fuel used by the cells to generate electric current to the total fuel provided. With this understanding of real fuel cell efficiency and considering the initial round of steady-state tests, the steady-state efficiencies of Engen-2500 stack and system were determined. Considering the case for fuel utilization of 75% and a cathode outlet temperature of 770°C, the corresponding stack and system efficiencies are shown in Figure 26. As is evident, the electrical efficiency of just the stack alone is well above 52%, which is a remarkable electrical efficiency for a 2.5 kW electric generator. The additional stack efficiencies for all other cathode outlet temperatures and fuel utilizations listed in Table 5 are displayed in Appendix D at the end of this thesis. From previous fuel cell modeling efforts performed at the NFCRC, a spatially and temporally resolved fuel cell model was developed using the Matlab and Simulink interface environments for experimental verification. The Matlab environment was particularly chosen for modelling development efforts because of its versatility and widespread adoption in the engineering community. Simulink is a graphical block diagram environment for multi-domain simulation and model based design,cannabis dryer which is extremely useful for modeling, controlling and simulating dynamic systems. The SOFC dynamic model developed at the NFCRC and adapted for the purposes of experimental verification must inherently be accurate and sophisticated to achieve reasonable and verifiable results. The NFCRC fuel cell system model incorporates all the necessary components to analyze and assess the dynamic performance for both the SOFC and MCFC fuel cell types. Additional changes can be made to restructure the model to study other fuel cell types like PEMFCs; however, for the purposes of this thesis, the SOFC system model was utilized and adapted to accurately model the Engen-2500 experimental system.

Fabian Mueller, a previous NFCRC graduate researcher, was one of the first students to work on and develop the NFCRC dynamic fuel cell modeling tools. He incorporated modeling strategies from Rivera , Xue et al. , Roberts and Gemmen , Smugeresky, Roberts et al. , and Lim et al. , using control volumes to spatially discretize system components and apply dynamic conservation equations. The preliminary modeling strategies developed by these scholars were shown to accurately capture the dynamics of fuel cell systems. However, additional details were necessary to improve the precision of the system model. Mueller outlines a host of modelling assumptions that are important to consider for a simplified analysis and incorporated them into the NFCRC fuel cell model.An essential feature of the SOFC system model is the capability of spatially resolving each component for varying degrees of resolution, dependent on the desired precision. This capability provides the user with greater accuracy and precision than relying on bulk or equivalent circuit models, especially for scaling analyses. Keep in mind that increasing the model resolution increases the precision of the localized analysis at the expense of significant computing resources and additional time to complete the simulation. Therefore, the user must be aware of the computational power of his or her computer and select a discretization resolution that maximizes the precision of the analysis while minimizing the time to reach completion. Spatial resolution yields descriptive localized analyses of the internal temperature profile and heat transfer across an individual cell and each component of the SOFC system for a given moment in time during the dynamic analysis. For the fuel cell stack, spatial resolution is achieved by taking a single cell and dividing it into a grid of smaller elements, which are referred to as nodes. Each node is broken down further into five distinct segments that comprise a single cell: the oxidant separator plate, cathode gas stream, electrolyte , anode gas stream, and fuel separator plate. Figure 27 indicates the typical structure of one SOFC cell that includes the five segments mentioned.Spatially resolving each segment of a single cell permits the localized dynamic analysis of the conservation of mass, energy, and momentum equations while also locally evaluating the temperature, species mole fractions, pressure, and other required characteristics. The dynamic analysis of one cell is then scaled to the number of cells in the stack, ultimately representing the dynamic characteristics of the entire stack component. The same discretization method just described is applied to all components of the Engen-2500 SOFC system. The transport phenomena and electrochemical reactions evaluated at each locally resolved temperature, species mole fraction, and pressure, determine the performance of each component in the SOFC system. Constructing the system model requires integration of the multiple individual components, which have been resolved following the particular component physics, chemistry and electrochemistry. The complex interactions of the integrated components are captured through simultaneous solutions from the dynamic system analysis. For higher-temperature fuel cells used in combined heat and power systems, the fuel cell stack often appears to be quite a small and insignificant part of the whole system. The extra components required depend greatly on the type of fuel cell and the fuel used. In SOFC systems, fuel and air enter the SOFC stack and electricity, exhaust gas, and hot water or steam exit the system. The difference between an SOFC stack and an SOFC system is generally referred to as the “balance-of-plant” . BOP equipment may differ for each application depending on the size of the system, the operating pressure, and the fuel used. If an SOFC stack is to be tested for commercial applications, the test is usually performed in a complete system with balance-of-plant components included and the stack integrated into the system.