Optimizing the Prioritization of Natural Disaster Recovery Projects

Prioritizing reconstruction projects to recover a base from a natural disaster is a complicated and arduous process that involves all levels of leadership. The project prioritization phase of base recovery has a direct affect on the allocation of funding, the utilization of human resources, the obligation of projects, and the overall speed and efficiency of the recovery process. The focus of this research is the development of an objective and repeatable process for optimizing the project prioritization phase of the recovery effort. This work will focus on promoting objectivity in the project prioritizing process, improving the communication of the overall base recovery requirement, increasing efficiency in utilizing human and monetary resources, and the creation of a usable and repeatable decision-making tool based on Value-Focused Thinking and integer programming methods

Intelligent Decision Support System for Energy Management in Demand Response Programs and Residential and Industrial Sectors of the Smart Grid

This PhD thesis addresses the complexity of the energy efficiency control problem in residential and industrial customers of Smart electrical Grid, and examines the main factors that affect energy demand, and proposes an intelligent decision support system for applications of demand response. A multi criteria decision making algorithm is combined with a combinatorial optimization technique to assist energy managers to decide whether to participate in demand response programs or obtain energy from distributed energy resources

An incentivized auction based group-selling approach for demand response management in V2G systems

Vehicle-to-grid (V2G) system with efficient demand response management (DRM) is critical to solve the problem of supplying electricity by utilizing surplus electricity available at electric vehicles (EVs). An incentivized DRM approach is studied to reduce the system cost and maintain the system stability. EVs are motivated with dynamic pricing determined by the group-selling-based auction. In the proposed approach, a number of aggregators sit on the first-level auction responsible to communicate with a group of EVs. EVs as bidders consider quality of energy (QoE) requirements, and report interests and decisions on the bidding process coordinated by the associated aggregator. Auction winners are determined based on the bidding prices and the amount of electricity sold by the EV bidders. We investigate the impact of the proposed mechanism on the system performance with maximum feedback power constraints of aggregators. The designed mechanism is proven to have essential economic properties. Simulation results indicate that the proposed mechanism can reduce the system cost and offer EVs significant incentives to participate in the V2G DRM operation

Contributions to energy-aware demand-response systems using SDN and NFV for fog computing

Ever-increasing energy consumption, the depletion of non-renewable resources, the climate impact associated with energy generation, and finite energy-production capacity are important concerns worldwide that drive the urgent creation of new energy management and consumption schemes. In this regard, by leveraging the massive connectivity provided by emerging communications such as the 5G systems, this thesis proposes a long-term sustainable Demand-Response solution for the adaptive and efficient management of available energy consumption for Internet of Things (IoT) infrastructures, in which energy utilization is optimized based on the available supply. In the proposed approach, energy management focuses on consumer devices (e.g., appliances such as a light bulb or a screen). In this regard, by proposing that each consumer device be part of an IoT infrastructure, it is feasible to control its respective consumption. The proposal includes an architecture that uses Network Functions Virtualization (NFV) and Software Defined Networking technologies as enablers to promote the primary use of energy from renewable sources. Associated with architecture, this thesis presents a novel consumption model conditioned on availability in which consumers are part of the management process. To efficiently use the energy from renewable and non-renewable sources, several management strategies are herein proposed, such as the prioritization of the energy supply, workload scheduling using time-shifting capabilities, and quality degradation to decrease- the power demanded by consumers if needed. The adaptive energy management solution is modeled as an Integer Linear Programming, and its complexity has been identified to be NP-Hard. To verify the improvements in energy utilization, an optimal algorithmic solution based on a brute force search has been implemented and evaluated. Because the hardness of the adaptive energy management problem and the non-polynomial growth of its optimal solution, which is limited to energy management for a small number of energy demands (e.g., 10 energy demands) and small values of management mechanisms, several faster suboptimal algorithmic strategies have been proposed and implemented. In this context, at the first stage, we implemented three heuristic strategies: a greedy strategy (GreedyTs), a genetic-algorithm-based solution (GATs), and a dynamic programming approach (DPTs). Then, we incorporated into both the optimal and heuristic strategies a prepartitioning method in which the total set of analyzed services is divided into subsets of smaller size and complexity that are solved iteratively. As a result of the adaptive energy management in this thesis, we present eight strategies, one timal and seven heuristic, that when deployed in communications infrastructures such as the NFV domain, seek the best possible scheduling of demands, which lead to efficient energy utilization. The performance of the algorithmic strategies has been validated through extensive simulations in several scenarios, demonstrating improvements in energy consumption and the processing of energy demands. Additionally, the simulation results revealed that the heuristic approaches produce high-quality solutions close to the optimal while executing among two and seven orders of magnitude faster and with applicability to scenarios with thousands and hundreds of thousands of energy demands. This thesis also explores possible application scenarios of both the proposed architecture for adaptive energy management and algorithmic strategies. In this regard, we present some examples, including adaptive energy management in-home systems and 5G networks slicing, energy-aware management solutions for unmanned aerial vehicles, also known as drones, and applicability for the efficient allocation of spectrum in flex-grid optical networks. Finally, this thesis presents open research problems and discusses other application scenarios and future work.El constante aumento del consumo de energía, el agotamiento de los recursos no renovables, el impacto climático asociado con la generación de energía y la capacidad finita de producción de energía son preocupaciones importantes en todo el mundo que impulsan la creación urgente de nuevos esquemas de consumo y gestión de energía. Al aprovechar la conectividad masiva que brindan las comunicaciones emergentes como los sistemas 5G, esta tesis propone una solución de Respuesta a la Demanda sostenible a largo plazo para la gestión adaptativa y eficiente del consumo de energía disponible para las infraestructuras de Internet of Things (IoT), en el que se optimiza la utilización de la energía en función del suministro disponible. En el enfoque propuesto, la gestión de la energía se centra en los dispositivos de consumo (por ejemplo, electrodomésticos). En este sentido, al proponer que cada dispositivo de consumo sea parte de una infraestructura IoT, es factible controlar su respectivo consumo. La propuesta incluye una arquitectura que utiliza tecnologías de Network Functions Virtualization (NFV) y Software Defined Networking como habilitadores para promover el uso principal de energía de fuentes renovables. Asociada a la arquitectura, esta tesis presenta un modelo de consumo condicionado a la disponibilidad en el que los consumidores son parte del proceso de gestión. Para utilizar eficientemente la energía de fuentes renovables y no renovables, se proponen varias estrategias de gestión, como la priorización del suministro de energía, la programación de la carga de trabajo utilizando capacidades de cambio de tiempo y la degradación de la calidad para disminuir la potencia demandada. La solución de gestión de energía adaptativa se modela como un problema de programación lineal entera con complejidad NP-Hard. Para verificar las mejoras en la utilización de energía, se ha implementado y evaluado una solución algorítmica óptima basada en una búsqueda de fuerza bruta. Debido a la dureza del problema de gestión de energía adaptativa y el crecimiento no polinomial de su solución óptima, que se limita a la gestión de energía para un pequeño número de demandas de energía (por ejemplo, 10 demandas) y pequeños valores de los mecanismos de gestión, varias estrategias algorítmicas subóptimos más rápidos se han propuesto. En este contexto, en la primera etapa, implementamos tres estrategias heurísticas: una estrategia codiciosa (GreedyTs), una solución basada en algoritmos genéticos (GATs) y un enfoque de programación dinámica (DPTs). Luego, incorporamos tanto en la estrategia óptima como en la- heurística un método de prepartición en el que el conjunto total de servicios analizados se divide en subconjuntos de menor tamaño y complejidad que se resuelven iterativamente. Como resultado de la gestión adaptativa de la energía en esta tesis, presentamos ocho estrategias, una óptima y siete heurísticas, que cuando se despliegan en infraestructuras de comunicaciones como el dominio NFV, buscan la mejor programación posible de las demandas, que conduzcan a un uso eficiente de la energía. El desempeño de las estrategias algorítmicas ha sido validado a través de extensas simulaciones en varios escenarios, demostrando mejoras en el consumo de energía y el procesamiento de las demandas de energía. Los resultados de la simulación revelaron que los enfoques heurísticos producen soluciones de alta calidad cercanas a las óptimas mientras se ejecutan entre dos y siete órdenes de magnitud más rápido y con aplicabilidad a escenarios con miles y cientos de miles de demandas de energía. Esta tesis también explora posibles escenarios de aplicación tanto de la arquitectura propuesta para la gestión adaptativa de la energía como de las estrategias algorítmicas. En este sentido, presentamos algunos ejemplos, que incluyen sistemas de gestión de energía adaptativa en el hogar, en 5G networkPostprint (published version

Enabling sustainable power distribution networks by using smart grid communications

Smart grid modernization enables integration of computing, information and communications capabilities into the legacy electric power grid system, especially the low voltage distribution networks where various consumers are located. The evolutionary paradigm has initiated worldwide deployment of an enormous number of smart meters as well as renewable energy sources at end-user levels. The future distribution networks as part of advanced metering infrastructure (AMI) will involve decentralized power control operations under associated smart grid communications networks. This dissertation addresses three potential problems anticipated in the future distribution networks of smart grid: 1) local power congestion due to power surpluses produced by PV solar units in a neighborhood that demands disconnection/reconnection mechanisms to alleviate power overflow, 2) power balance associated with renewable energy utilization as well as data traffic across a multi-layered distribution network that requires decentralized designs to facilitate power control as well as communications, and 3) a breach of data integrity attributed to a typical false data injection attack in a smart metering network that calls for a hybrid intrusion detection system to detect anomalous/malicious activities. In the first problem, a model for the disconnection process via smart metering communications between smart meters and the utility control center is proposed. By modeling the power surplus congestion issue as a knapsack problem, greedy solutions for solving such problem are proposed. Simulation results and analysis show that computation time and data traffic under a disconnection stage in the network can be reduced. In the second problem, autonomous distribution networks are designed that take scalability into account by dividing the legacy distribution network into a set of subnetworks. A power-control method is proposed to tackle the power flow and power balance issues. Meanwhile, an overlay multi-tier communications infrastructure for the underlying power network is proposed to analyze the traffic of data information and control messages required for the associated power flow operations. Simulation results and analysis show that utilization of renewable energy production can be improved, and at the same time data traffic reduction under decentralized operations can be achieved as compared to legacy centralized management. In the third problem, an attack model is proposed that aims to minimize the number of compromised meters subject to the equality of an aggregated power load in order to bypass detection under the conventionally radial tree-like distribution network. A hybrid anomaly detection framework is developed, which incorporates the proposed grid sensor placement algorithm with the observability attribute. Simulation results and analysis show that the network observability as well as detection accuracy can be improved by utilizing grid-placed sensors. Conclusively, a number of future works have also been identified to furthering the associated problems and proposed solutions

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes