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.

Red-shift conical emission by femtosecond pulses at low input power

Red-shift conical emission (CE) is observed by femtosecond laser pulse propagating in BK7 at a low input power (compared to those input powers for generation of blue-shift CE). With the increasing input power the blue-shift CE begins to appear whereas the red-shift CE ring (902 nm in our experiment) disappears accompanied by the augment of the central white spot size synchronously. The disappearing of red-shift CE in our experiment is explained such that the increase of axial intensity is much higher than that of ring emission and the augment of the central white spot size with the increasing input laser power.

Self-compression of femtosecond pulses in argon with a power close to the self-focusing threshold

Self-compression of femtosecond pulses in noble gases with an input power close to the self-focusing threshold has been investigated experimentally and theoretically. It is demonstrated that either multiphoton ionization (MPI) or space time focusing and self-steepening effects can induce pulse shortening, but they predominate at different beam intensities during the propagation. The latter effects play a key role in the final pulse self-compression. By choosing an appropriate focusing parameter, action distance of the space time focusing and self-steepening effects can be lengthened, which can promote a shock pulse structure with a duration as short as two optical cycles. It is also found that, for our calculation cases in which an input pulse power is close to the self-focusing threshold, either group velocity dispersion (GVD) or multiphoton absorption (MPA) has a negligible influence on pulse characteristics in the propagation process.

Development of high-power OPCPA laser at 1064 and 780 nm

A compact 10-TW/100-fs level ultrashort-pulse and ultra-intense laser system at 1064 nm based on optical parametric chirped pulse amplification (OPCPA) scheme is described, at which the pump and seed for the optical parametric amplification (OPA) process is optically synchronized. We investigated the output stability and the conversion efficiency of the system. Moreover, a design toward higher peak power output is given and an optically synchronized amplifier based on the concept of OPCPA at 800 nm is preliminarily explored.

Effects of Climate Change on the European Nuclear Power Sector

4 p.

Impacts of Climate Change on European Critical Infrastructures: The Case of the Power Sector

23 p.

Upconversion luminescence and optical power limiting effect based on two- and three-photon absorption processes of ZnO crystal

Near-infrared to UV and visible upconversion luminescence was observed in single-crystalline ZnO under an 800 nm infrared femtosecond laser irradiation. The optical properties of the crystal reveal that the UV and VIS emission band are due to the exciton transition (D0X) bound to neutral donors and the deep luminescent centers in ZnO, respectively. The relationship between the upconversion luminescence intensity and the pump power of the femtosecond laser reveals that the UV emission belongs to three-photon sequential band-to-band excitation and the VIS emission belongs to two-photon simultaneous defect-absorption induced luminescence. A saturation phenomenon and polarization-dependent effect are also observed in the upconversion process of ZnO. A very good optical power limiting performance at 800 nm has been demonstrated. The two- and three-photon absorption coefficients of ZnO crystal were measured to be 0.2018 cm GW(-1) and 7.102 x 10(-3) cm(3) GW(-2), respectively. The two- and three-photon cross sections were calculated to be 1.189 x 10(-51) cm(4) s and 1.040 x 10(-80) cm(6) s(2), respectively. (c) 2007 Elsevier B.V. All rights reserved.

The power of quantum Fourier sampling

How powerful are Quantum Computers? Despite the prevailing belief that Quantum Computers are more powerful than their classical counterparts, this remains a conjecture backed by little formal evidence. Shor's famous factoring algorithm [Shor97] gives an example of a problem that can be solved efficiently on a quantum computer with no known efficient classical algorithm. Factoring, however, is unlikely to be NP-Hard, meaning that few unexpected formal consequences would arise, should such a classical algorithm be discovered. Could it then be the case that any quantum algorithm can be simulated efficiently classically? Likewise, could it be the case that Quantum Computers can quickly solve problems much harder than factoring? If so, where does this power come from, and what classical computational resources do we need to solve the hardest problems for which there exist efficient quantum algorithms?

We make progress toward understanding these questions through studying the relationship between classical nondeterminism and quantum computing. In particular, is there a problem that can be solved efficiently on a Quantum Computer that cannot be efficiently solved using nondeterminism? In this thesis we address this problem from the perspective of sampling problems. Namely, we give evidence that approximately sampling the Quantum Fourier Transform of an efficiently computable function, while easy quantumly, is hard for any classical machine in the Polynomial Time Hierarchy. In particular, we prove the existence of a class of distributions that can be sampled efficiently by a Quantum Computer, that likely cannot be approximately sampled in randomized polynomial time with an oracle for the Polynomial Time Hierarchy.

Our work complements and generalizes the evidence given in Aaronson and Arkhipov's work [AA2013] where a different distribution with the same computational properties was given. Our result is more general than theirs, but requires a more powerful quantum sampler.

An integrated design approach to power systems : from power flows to electricity markets

Power system is at the brink of change. Engineering needs, economic forces and environmental factors are the main drivers of this change. The vision is to build a smart electrical grid and a smarter market mechanism around it to fulfill mandates on clean energy. Looking at engineering and economic issues in isolation is no longer an option today; it needs an integrated design approach. In this thesis, I shall revisit some of the classical questions on the engineering operation of power systems that deals with the nonconvexity of power flow equations. Then I shall explore some issues of the interaction of these power flow equations on the electricity markets to address the fundamental issue of market power in a deregulated market environment. Finally, motivated by the emergence of new storage technologies, I present an interesting result on the investment decision problem of placing storage over a power network. The goal of this study is to demonstrate that modern optimization and game theory can provide unique insights into this complex system. Some of the ideas carry over to applications beyond power systems.

Ecosystem study Altenwoerth: impacts of a hydroelectric power-station on the River Danube in Austria

The aim of this article is to briefly describe the effects of the Altenwoerth Barrage, on the River Danube, on some physical variables and their consequent effects on water chemistry and the biota of the river. The methods used for biological sampling are summarised, especially those used in the limnological part of the study, and the macroinvertebrate and fish fauna listed. Comparisons are then made between the impounded section of river immediately above the dam and two unimpounded free-flowing sections of the river. Further developments on the Danube are considered.