High temperature transport properties of lead chalcogenides and their alloys

This thesis describes a series of experimental studies of lead chalcogenide thermoelectric semiconductors, mainly PbSe. Focusing on a well-studied semiconductor and reporting good but not extraordinary zT, this thesis distinguishes itself by answering the following questions that haven’t been answered: What represents the thermoelectric performance of PbSe? Where does the high zT come from? How (and how much) can we make it better? For the first question, samples were made with highest quality. Each transport property was carefully measured, cross-verified and compared with both historical and contemporary report to overturn commonly believed underestimation of zT. For n- and p-type PbSe zT at 850 K can be 1.1 and 1.0, respectively. For the second question, a systematic approach of quality factor B was used. In n-type PbSe zT is benefited from its high-quality conduction band that combines good degeneracy, low band mass and low deformation potential, whereas zT of p-type is boosted when two mediocre valence bands converge (in band edge energy). In both cases the thermal conductivity from PbSe lattice is inherently low. For the third question, the use of solid solution lead chalcogenide alloys was first evaluated. Simple criteria were proposed to help quickly evaluate the potential of improving zT by introducing atomic disorder. For both PbTe1-xSex and PbSe1-xSx, the impacts in electron and phonon transport compensate each other. Thus, zT in each case was roughly the average of two binary compounds. In p-type Pb1-xSrxSe alloys an improvement of zT from 1.1 to 1.5 at 900 K was achieved, due to the band engineering effect that moves the two valence bands closer in energy. To date, making n-type PbSe better hasn’t been accomplished, but possible strategy is discussed.

Sustainable IT and IT for sustainability

Energy and sustainability have become one of the most critical issues of our generation. While the abundant potential of renewable energy such as solar and wind provides a real opportunity for sustainability, their intermittency and uncertainty present a daunting operating challenge. This thesis aims to develop analytical models, deployable algorithms, and real systems to enable efficient integration of renewable energy into complex distributed systems with limited information.

The first thrust of the thesis is to make IT systems more sustainable by facilitating the integration of renewable energy into these systems. IT represents the fastest growing sectors in energy usage and greenhouse gas pollution. Over the last decade there are dramatic improvements in the energy efficiency of IT systems, but the efficiency improvements do not necessarily lead to reduction in energy consumption because more servers are demanded. Further, little effort has been put in making IT more sustainable, and most of the improvements are from improved "engineering" rather than improved "algorithms". In contrast, my work focuses on developing algorithms with rigorous theoretical analysis that improve the sustainability of IT. In particular, this thesis seeks to exploit the flexibilities of cloud workloads both (i) in time by scheduling delay-tolerant workloads and (ii) in space by routing requests to geographically diverse data centers. These opportunities allow data centers to adaptively respond to renewable availability, varying cooling efficiency, and fluctuating energy prices, while still meeting performance requirements. The design of the enabling algorithms is however very challenging because of limited information, non-smooth objective functions and the need for distributed control. Novel distributed algorithms are developed with theoretically provable guarantees to enable the "follow the renewables" routing. Moving from theory to practice, I helped HP design and implement industry's first Net-zero Energy Data Center.

The second thrust of this thesis is to use IT systems to improve the sustainability and efficiency of our energy infrastructure through data center demand response. The main challenges as we integrate more renewable sources to the existing power grid come from the fluctuation and unpredictability of renewable generation. Although energy storage and reserves can potentially solve the issues, they are very costly. One promising alternative is to make the cloud data centers demand responsive. The potential of such an approach is huge.

To realize this potential, we need adaptive and distributed control of cloud data centers and new electricity market designs for distributed electricity resources. My work is progressing in both directions. In particular, I have designed online algorithms with theoretically guaranteed performance for data center operators to deal with uncertainties under popular demand response programs. Based on local control rules of customers, I have further designed new pricing schemes for demand response to align the interests of customers, utility companies, and the society to improve social welfare.