Adaptive Constraint Solving for Information Flow Analysis

In program analysis, unknown properties for terms are typically represented symbolically as variables. Bound constraints on these variables can then specify multiple optimisation goals for computer programs and nd application in areas such as type theory, security, alias analysis and resource reasoning. Resolution of bound constraints is a problem steeped in graph theory; interdependencies between the variables is represented as a constraint graph. Additionally, constants are introduced into the system as concrete bounds over these variables and constants themselves are ordered over a lattice which is, once again, represented as a graph. Despite graph algorithms being central to bound constraint solving, most approaches to program optimisation that use bound constraint solving have treated their graph theoretic foundations as a black box. Little has been done to investigate the computational costs or design e cient graph algorithms for constraint resolution. Emerging examples of these lattices and bound constraint graphs, particularly from the domain of language-based security, are showing that these graphs and lattices are structurally diverse and could be arbitrarily large. Therefore, there is a pressing need to investigate the graph theoretic foundations of bound constraint solving. In this thesis, we investigate the computational costs of bound constraint solving from a graph theoretic perspective for Information Flow Analysis (IFA); IFA is a sub- eld of language-based security which veri es whether con dentiality and integrity of classified information is preserved as it is manipulated by a program. We present a novel framework based on graph decomposition for solving the (atomic) bound constraint problem for IFA. Our approach enables us to abstract away from connections between individual vertices to those between sets of vertices in both the constraint graph and an accompanying security lattice which defines ordering over constants. Thereby, we are able to achieve significant speedups compared to state-of-the-art graph algorithms applied to bound constraint solving. More importantly, our algorithms are highly adaptive in nature and seamlessly adapt to the structure of the constraint graph and the lattice. The computational costs of our approach is a function of the latent scope of decomposition in the constraint graph and the lattice; therefore, we enjoy the fastest runtime for every point in the structure-spectrum of these graphs and lattices. While the techniques in this dissertation are developed with IFA in mind, they can be extended to other application of the bound constraints problem, such as type inference and program analysis frameworks which use annotated type systems, where constants are ordered over a lattice

Optimization of energy-constrained resources in radial distribution networks with solar PV

The research objective of the proposed dissertation is to make best use of available distributed energy resources to meet dynamic market opportunities while accounting for AC physics of unbalanced distribution networks and the uncertainty of distributed solar photovoltaics (PV). With ever increasing levels of renewable generation, distribution system operations must shift from a mindset of static unidirectional power flows to dynamic, unpredictable bidirectional flows. To manage this variability, distributed energy resources (DERs; e.g.,solar PV inverters, inverter-based batteries, electric vehicles, water heaters, A/Cs) need to be coordinated for reliable and resilient operation. This introduces the challenge of coordinating such resources at scale and within confines of the existing distribution system. It also becomes important to develop efficient and accurate models of the distribution system to achieve desired operating objectives such as tracking a market reference, reduction in operation cost or voltage regulation. This work surveys, discusses the challenges and proposes solutions to the modeling and optimization of realistic distribution systems with significant penetration of renewables and controllable DERs, including energy storage. To contain this increase in system complexity as result of the large number of controllable DERs available, the distribution system has to be adapted from a passive Volt-Var focused operator to a more active manager of resources. To approach this challenge, in this work, we propose two main approaches. The first is a utility centric approach, where the utility controls the dispatch of flexible resources based on solving an optimization problem. This approach would require the utility to have all the network and resource data and also the control over customer devices. Another approach is a more aggregator centric approach, where an aggregator is an entity that represents an aggregation of many diverse DERs or a Virtual Battery (VB). In this approach, it is the role of the aggregator to dispatch DERs, whereas the utility provides certain bounds and limits (calculated offline), which the aggregator (which dispatches resources in real-time) must operate under. The benefits of such an approach lie in improved data-privacy and real-time dispatch. We present simulation results validating the proposed methods on various standard IEEE and realistic distribution feeders

The deep space network

Progress is reported in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations. The functions and facilities of the Deep Space Network are emphasized