The federal photovoltaics commercialization program

The dissertation presents a political and economic history of the federal government's program to commercialize photovoltaic energy for terrestrial use. Chapter 1 is a detailed history of the program. Chapter 2 is a brief review of the Congressional roll call voting literature. Chapter 3 develops PV benefit measures at the state and Congressional district level necessary for an econometric analysis of PV roll call voting. Chapter 4 presents the econometric analysis.

Because PV power was considerably more expensive than conventional power, the program was designed to make PV a significant power source in the long term, emphasizing research and development, although sizeable amounts have been spent for procurement (direct government purchases and indirectly through tax credits). The decentralized R and D program pursued alternative approaches in parallel, with subsequent funding dependent on earlier progress. Funding rose rapidly in the 1970s before shrinking in the 1980s. Tax credits were introduced in 1978, with the last of the credits due to expire this year.

Major issues in the program have been the appropriate magnitude of demonstrations and government procurement, whether decentralized, residential use or centralized utility generation would first be economic, the role of storage in PV, and the role of PV in a utility's generation mix.

Roll call voting on solar energy (all votes analyzed occurred from 1975-1980) was influenced in a cross-sectional sense by all the influences predicted: party and ideology, local economic benefits of the technology, local PV federal spending and manufacturing, and appropriations committee membership. The cross-sectional results for ideology are consistent with the strongly ideological character of solar energy politics and the timing of funding increases and decreases discussed in Chapter 1. Local PV spending and manufacturing was less significant than ideology or the economic benefits of the technology. Because time series analysis of the votes was not possible, it is not possible to test the role of economic benefits to the nation as a whole.

Probability in Quantum Mechanics: The Everett Interpretation and the Decision-Theoretic Program

The Everett interpretation of quantum mechanics is an increasingly popular alternative to the traditional Copenhagen interpretation, but there are a few major issues that prevent the widespread adoption. One of these issues is the origin of probabilities in the Everett interpretation, which this thesis will attempt to survey. The most successful resolution of the probability problem thus far is the decision-theoretic program, which attempts to frame probabilities as outcomes of rational decision making. This marks a departure from orthodox interpretations of probabilities in the physical sciences, where probabilities are thought to be objective, stemming from symmetry considerations. This thesis will attempt to offer evaluations on the decision-theoretic program.

Distributed Load Control in Multiphase Radial Networks

The current power grid is on the cusp of modernization due to the emergence of distributed generation and controllable loads, as well as renewable energy. On one hand, distributed and renewable generation is volatile and difficult to dispatch. On the other hand, controllable loads provide significant potential for compensating for the uncertainties. In a future grid where there are thousands or millions of controllable loads and a large portion of the generation comes from volatile sources like wind and solar, distributed control that shifts or reduces the power consumption of electric loads in a reliable and economic way would be highly valuable.

Load control needs to be conducted with network awareness. Otherwise, voltage violations and overloading of circuit devices are likely. To model these effects, network power flows and voltages have to be considered explicitly. However, the physical laws that determine power flows and voltages are nonlinear. Furthermore, while distributed generation and controllable loads are mostly located in distribution networks that are multiphase and radial, most of the power flow studies focus on single-phase networks.

This thesis focuses on distributed load control in multiphase radial distribution networks. In particular, we first study distributed load control without considering network constraints, and then consider network-aware distributed load control.

Distributed implementation of load control is the main challenge if network constraints can be ignored. In this case, we first ignore the uncertainties in renewable generation and load arrivals, and propose a distributed load control algorithm, Algorithm 1, that optimally schedules the deferrable loads to shape the net electricity demand. Deferrable loads refer to loads whose total energy consumption is fixed, but energy usage can be shifted over time in response to network conditions. Algorithm 1 is a distributed gradient decent algorithm, and empirically converges to optimal deferrable load schedules within 15 iterations.

We then extend Algorithm 1 to a real-time setup where deferrable loads arrive over time, and only imprecise predictions about future renewable generation and load are available at the time of decision making. The real-time algorithm Algorithm 2 is based on model-predictive control: Algorithm 2 uses updated predictions on renewable generation as the true values, and computes a pseudo load to simulate future deferrable load. The pseudo load consumes 0 power at the current time step, and its total energy consumption equals the expectation of future deferrable load total energy request.

Network constraints, e.g., transformer loading constraints and voltage regulation constraints, bring significant challenge to the load control problem since power flows and voltages are governed by nonlinear physical laws. Remarkably, distribution networks are usually multiphase and radial. Two approaches are explored to overcome this challenge: one based on convex relaxation and the other that seeks a locally optimal load schedule.

To explore the convex relaxation approach, a novel but equivalent power flow model, the branch flow model, is developed, and a semidefinite programming relaxation, called BFM-SDP, is obtained using the branch flow model. BFM-SDP is mathematically equivalent to a standard convex relaxation proposed in the literature, but numerically is much more stable. Empirical studies show that BFM-SDP is numerically exact for the IEEE 13-, 34-, 37-, 123-bus networks and a real-world 2065-bus network, while the standard convex relaxation is numerically exact for only two of these networks.

Theoretical guarantees on the exactness of convex relaxations are provided for two types of networks: single-phase radial alternative-current (AC) networks, and single-phase mesh direct-current (DC) networks. In particular, for single-phase radial AC networks, we prove that a second-order cone program (SOCP) relaxation is exact if voltage upper bounds are not binding; we also modify the optimal load control problem so that its SOCP relaxation is always exact. For single-phase mesh DC networks, we prove that an SOCP relaxation is exact if 1) voltage upper bounds are not binding, or 2) voltage upper bounds are uniform and power injection lower bounds are strictly negative; we also modify the optimal load control problem so that its SOCP relaxation is always exact.

To seek a locally optimal load schedule, a distributed gradient-decent algorithm, Algorithm 9, is proposed. The suboptimality gap of the algorithm is rigorously characterized and close to 0 for practical networks. Furthermore, unlike the convex relaxation approach, Algorithm 9 ensures a feasible solution. The gradients used in Algorithm 9 are estimated based on a linear approximation of the power flow, which is derived with the following assumptions: 1) line losses are negligible; and 2) voltages are reasonably balanced. Both assumptions are satisfied in practical distribution networks. Empirical results show that Algorithm 9 obtains 70+ times speed up over the convex relaxation approach, at the cost of a suboptimality within numerical precision.

Exploring the Dynamic Radio Sky: The Search for Slow Transients with the VLA

While synoptic surveys in the optical and at high energies have revealed a rich discovery phase space of slow transients, a similar yield is still awaited in the radio. Majority of the past blind surveys, carried out with radio interferometers, have suffered from a low yield of slow transients, ambiguous transient classifications, and contamination by false positives. The newly-refurbished Karl G. Jansky Array (Jansky VLA) offers wider bandwidths for accurate RFI excision as well as substantially-improved sensitivity and survey speed compared with the old VLA. The Jansky VLA thus eliminates the pitfalls of interferometric transient search by facilitating sensitive, wide-field, and near-real-time radio surveys and enabling a systematic exploration of the dynamic radio sky. This thesis aims at carrying out blind Jansky VLA surveys for characterizing the radio variable and transient sources at frequencies of a few GHz and on timescales between days and years. Through joint radio and optical surveys, the thesis addresses outstanding questions pertaining to the rates of slow radio transients (e.g. radio supernovae, tidal disruption events, binary neutron star mergers, stellar flares, etc.), the false-positive foreground relevant for the radio and optical counterpart searches of gravitational wave sources, and the beaming factor of gamma-ray bursts. The need for rapid processing of the Jansky VLA data and near-real-time radio transient search has enabled the development of state-of-the-art software infrastructure. This thesis has successfully demonstrated the Jansky VLA as a powerful transient search instrument, and it serves as a pathfinder for the transient surveys planned for the SKA-mid pathfinder facilities, viz. ASKAP, MeerKAT, and WSRT/Apertif.