An Optimal and Distributed Method for Voltage Regulation in Power Distribution Systems

This paper addresses the problem of voltage regulation in power distribution networks with deep-penetration of distributed energy resources, e.g., renewable-based generation, and storage-capable loads such as plug-in hybrid electric vehicles. We cast the problem as an optimization program, where the objective is to minimize the losses in the network subject to constraints on bus voltage magnitudes, limits on active and reactive power injections, transmission line thermal limits and losses. We provide sufficient conditions under which the optimization problem can be solved via its convex relaxation. Using data from existing networks, we show that these sufficient conditions are expected to be satisfied by most networks. We also provide an efficient distributed algorithm to solve the problem. The algorithm adheres to a communication topology described by a graph that is the same as the graph that describes the electrical network topology. We illustrate the operation of the algorithm, including its robustness against communication link failures, through several case studies involving 5-, 34-, and 123-bus power distribution systems.Comment: To Appear in IEEE Transaction on Power System

Quantum information protocols in complex entangled networks

Quantum entangled networks represent essential tools for Quantum Communication, i.e. the exchange of Quantum Information between parties. This work consists in the theoretical study of continuous variables (CV) entangled networks - which can be deterministically generated via multimode squeezed light - with complex topology. In particular we investigate CV complex quantum networks properties for quantum communication protocols. We focused on the role played by the topology in the implementation and the optimization of given characteristics of our entangled resource that are useful for a specific quantum communication task, i.e. the creation of an entanglement link between two arbitrary nodes of the resource we are provided with. We implemented an analytical procedure for the generation of entangled complex networks, their optimization and their manipulation via global linear optics operations. We also developed a numerical procedure, based on an evolutionary algorithm, for manipulating entanglement connections via local linear optics operations. Finally, we analyzed the re-shaping of our entangled resource via homodyne measurements

Towards time-varying proximal dynamics in Multi-Agent Network Games

Distributed decision making in multi-agent networks has recently attracted significant research attention thanks to its wide applicability, e.g. in the management and optimization of computer networks, power systems, robotic teams, sensor networks and consumer markets. Distributed decision-making problems can be modeled as inter-dependent optimization problems, i.e., multi-agent game-equilibrium seeking problems, where noncooperative agents seek an equilibrium by communicating over a network. To achieve a network equilibrium, the agents may decide to update their decision variables via proximal dynamics, driven by the decision variables of the neighboring agents. In this paper, we provide an operator-theoretic characterization of convergence with a time-invariant communication network. For the time-varying case, we consider adjacency matrices that may switch subject to a dwell time. We illustrate our investigations using a distributed robotic exploration example.Comment: 6 pages, 3 figure

Optimal Virtualized Inter-Tenant Resource Sharing for Device-to-Device Communications in 5G Networks

Device-to-Device (D2D) communication is expected to enable a number of new services and applications in future mobile networks and has attracted significant research interest over the last few years. Remarkably, little attention has been placed on the issue of D2D communication for users belonging to different operators. In this paper, we focus on this aspect for D2D users that belong to different tenants (virtual network operators), assuming virtualized and programmable future 5G wireless networks. Under the assumption of a cross-tenant orchestrator, we show that significant gains can be achieved in terms of network performance by optimizing resource sharing from the different tenants, i.e., slices of the substrate physical network topology. To this end, a sum-rate optimization framework is proposed for optimal sharing of the virtualized resources. Via a wide site of numerical investigations, we prove the efficacy of the proposed solution and the achievable gains compared to legacy approaches.Comment: 10 pages, 7 figure

A Review Over Genetic Algorithm and Application of Wireless Network Systems

AbstractTele-communication and network industry are becoming extremely fascinated by the use of evolutionary smart sensor nodes in wireless sensor networks. This technology promises to overcome several challenges within WSNs needed for real time data protection via optimization technique: Genetic Algorithm. This paper reviewedthe use of Genetic Algorithms (GAs) to solve certain limitation of wireless sensor networks. It further presents major application areas of wireless sensors networks. Longerdistance gap between a sensor and destination in a sensor network can remarkably reduce the energy of sensors and can degrade the life of a network. GA can prolong the network lifetime by minimizing the total communication distance

Towards Scalable Network Delay Minimization

Reduction of end-to-end network delays is an optimization task with applications in multiple domains. Low delays enable improved information flow in social networks, quick spread of ideas in collaboration networks, low travel times for vehicles on road networks and increased rate of packets in the case of communication networks. Delay reduction can be achieved by both improving the propagation capabilities of individual nodes and adding additional edges in the network. One of the main challenges in such design problems is that the effects of local changes are not independent, and as a consequence, there is a combinatorial search-space of possible improvements. Thus, minimizing the cumulative propagation delay requires novel scalable and data-driven approaches. In this paper, we consider the problem of network delay minimization via node upgrades. Although the problem is NP-hard, we show that probabilistic approximation for a restricted version can be obtained. We design scalable and high-quality techniques for the general setting based on sampling and targeted to different models of delay distribution. Our methods scale almost linearly with the graph size and consistently outperform competitors in quality