Distributed optimization in power networks and general multi-agent systems

The dissertation studies the general area of complex networked systems that consist of interconnected and active heterogeneous components and usually operate in uncertain environments and with incomplete information. Problems associated with those systems are typically large-scale and computationally intractable, yet they are also very well-structured and have features that can be exploited by appropriate modeling and computational methods. The goal of this thesis is to develop foundational theories and tools to exploit those structures that can lead to computationally-efficient and distributed solutions, and apply them to improve systems operations and architecture.

Specifically, the thesis focuses on two concrete areas. The first one is to design distributed rules to manage distributed energy resources in the power network. The power network is undergoing a fundamental transformation. The future smart grid, especially on the distribution system, will be a large-scale network of distributed energy resources (DERs), each introducing random and rapid fluctuations in power supply, demand, voltage and frequency. These DERs provide a tremendous opportunity for sustainability, efficiency, and power reliability. However, there are daunting technical challenges in managing these DERs and optimizing their operation. The focus of this dissertation is to develop scalable, distributed, and real-time control and optimization to achieve system-wide efficiency, reliability, and robustness for the future power grid. In particular, we will present how to explore the power network structure to design efficient and distributed market and algorithms for the energy management. We will also show how to connect the algorithms with physical dynamics and existing control mechanisms for real-time control in power networks.

The second focus is to develop distributed optimization rules for general multi-agent engineering systems. A central goal in multiagent systems is to design local control laws for the individual agents to ensure that the emergent global behavior is desirable with respect to the given system level objective. Ideally, a system designer seeks to satisfy this goal while conditioning each agent’s control on the least amount of information possible. Our work focused on achieving this goal using the framework of game theory. In particular, we derived a systematic methodology for designing local agent objective functions that guarantees (i) an equivalence between the resulting game-theoretic equilibria and the system level design objective and (ii) that the resulting game possesses an inherent structure that can be exploited for distributed learning, e.g., potential games. The control design can then be completed by applying any distributed learning algorithm that guarantees convergence to the game-theoretic equilibrium. One main advantage of this game theoretic approach is that it provides a hierarchical decomposition between the decomposition of the systemic objective (game design) and the specific local decision rules (distributed learning algorithms). This decomposition provides the system designer with tremendous flexibility to meet the design objectives and constraints inherent in a broad class of multiagent systems. Furthermore, in many settings the resulting controllers will be inherently robust to a host of uncertainties including asynchronous clock rates, delays in information, and component failures.

Synthesis and biological activity of anticoagulant heparan sulfate glycopolymers

Heparin has been used as an anticoagulant drug for more than 70 years. The global distribution of contaminated heparin in 2007, which resulted in adverse clinical effects and over 100 deaths, emphasizes the necessity for safer alternatives to animal-sourced heparin. The structural complexity and heterogeneity of animal-sourced heparin not only impedes safe access to these biologically active molecules, but also hinders investigations on the significance of structural constituents at a molecular level. Efficient methods for preparing new synthetic heparins with targeted biological activity are necessary not only to ensure clinical safety, but to optimize derivative design to minimize potential side effects. Low molecular weight heparins have become a reliable alternative to heparin, due to their predictable dosages, long half-lives, and reduced side effects. However, heparin oligosaccharide synthesis is a challenging endeavor due to the necessity for complex protecting group manipulation and stereoselective glycosidic linkage chemistry, which often result in lengthy synthetic routes and low yields. Recently, chemoenzymatic syntheses have produced targeted ultralow molecular weight heparins with high-efficiency, but continue to be restricted by the substrate specificities of enzymes.

To address the need for access to homogeneous, complex glycosaminoglycan structures, we have synthesized novel heparan sulfate glycopolymers with well-defined carbohydrate structures and tunable chain length through ring-opening metathesis polymerization chemistry. These polymers recapitulate the key features of anticoagulant heparan sulfate by displaying the sulfation pattern responsible for heparin’s anticoagulant activity. The use of polymerization chemistry greatly simplifies the synthesis of complex glycosaminoglycan structures, providing a facile method to generate homogeneous macromolecules with tunable biological and chemical properties. Through the use of in vitro chromogenic substrate assays and ex vivo clotting assays, we found that the HS glycopolymers exhibited anticoagulant activity in a sulfation pattern and length-dependent manner. Compared to heparin standards, our short polymers did not display any activity. However, our longer polymers were able to incorporate in vitro and ex vivo characteristics of both low-molecular-weight heparin derivatives and heparin, displaying hybrid anticoagulant properties. These studies emphasize the significance of sulfation pattern specificity in specific carbohydrate-protein interactions, and demonstrate the effectiveness of multivalent molecules in recapitulating the activity of natural polysaccharides.