Optimal Control of Transient Flow in Natural Gas Networks

We outline a new control system model for the distributed dynamics of compressible gas flow through large-scale pipeline networks with time-varying injections, withdrawals, and control actions of compressors and regulators. The gas dynamics PDE equations over the pipelines, together with boundary conditions at junctions, are reduced using lumped elements to a sparse nonlinear ODE system expressed in vector-matrix form using graph theoretic notation. This system, which we call the reduced network flow (RNF) model, is a consistent discretization of the PDE equations for gas flow. The RNF forms the dynamic constraints for optimal control problems for pipeline systems with known time-varying withdrawals and injections and gas pressure limits throughout the network. The objectives include economic transient compression (ETC) and minimum load shedding (MLS), which involve minimizing compression costs or, if that is infeasible, minimizing the unfulfilled deliveries, respectively. These continuous functional optimization problems are approximated using the Legendre-Gauss-Lobatto (LGL) pseudospectral collocation scheme to yield a family of nonlinear programs, whose solutions approach the optima with finer discretization. Simulation and optimization of time-varying scenarios on an example natural gas transmission network demonstrate the gains in security and efficiency over methods that assume steady-state behavior

Intelligent Fault Analysis in Electrical Power Grids

Power grids are one of the most important components of infrastructure in today's world. Every nation is dependent on the security and stability of its own power grid to provide electricity to the households and industries. A malfunction of even a small part of a power grid can cause loss of productivity, revenue and in some cases even life. Thus, it is imperative to design a system which can detect the health of the power grid and take protective measures accordingly even before a serious anomaly takes place. To achieve this objective, we have set out to create an artificially intelligent system which can analyze the grid information at any given time and determine the health of the grid through the usage of sophisticated formal models and novel machine learning techniques like recurrent neural networks. Our system simulates grid conditions including stimuli like faults, generator output fluctuations, load fluctuations using Siemens PSS/E software and this data is trained using various classifiers like SVM, LSTM and subsequently tested. The results are excellent with our methods giving very high accuracy for the data. This model can easily be scaled to handle larger and more complex grid architectures.Comment: In proceedings of the 29th IEEE International Conference on Tools with Artificial Intelligence (ICTAI) 2017 (full paper); 6 pages; 13 figure

Multiperiod Dispatching and Routing for On-Time Delivery in a Dynamic and Stochastic Environment

On-demand delivery has become increasingly popular around the world. Brick-and-mortar grocery stores, restaurants, and pharmacies are providing fast delivery services to satisfy the growing home delivery demand. Motivated by a large meal and grocery delivery company, we model and solve a multiperiod driver dispatching and routing problem for last-mile delivery systems where on-time performance is the main target. The operator of this system needs to dispatch a set of drivers and specify their delivery routes in a stochastic environment, in which random demand arrives over a fixed number of periods. The resulting dynamic program is challenging to solve due to the curse of dimensionality. We propose a novel approximation framework to approximate the value function via a simplified dispatching program. We then develop efficient exact algorithms for this problem based on Benders decomposition and column generation. We validate the superior performance of our framework and algorithms via extensive numerical experiments. Tested on a real-world data set, we quantify the value of adaptive dispatching and routing in on-time delivery and highlight the need of coordinating these two decisions in a dynamic setting. We show that dispatching multiple vehicles with short trips is preferable for on-time delivery, as opposed to sending a few vehicles with long travel times

Dynamic optimization for same-day delivery operations

Same-day delivery (SDD) is a service where consumers place orders online on the same day that these are processed and delivered to the customer. Providing a delivery service requires order management at the stocking location (depot), including request acceptance and processing; and order distribution from the depot to final customer locations, including order dispatch and delivery via vehicle routes. In SDD these processes are highly interrelated, are simultaneously executed, and have a high degree of information dynamism. In this thesis, we formulate and solve dynamic optimization problems to improve the operation of SDD systems. We study how our approaches perform over computationally simulated instances and provide managerial insights for SDD practitioners, including structural solution properties that any common SDD service should have, the trade-offs between common SDD objectives, and the logistics cost of operational constraints in SDD.Ph.D

Competitive power control of distributed power plants

Joint Doctoral Programme in Electric Energy Systems : Universidad de Málaga, Universidad de Sevilla, Universidad del País Vasco y Universitat Politècnica de CatalunyaNowadays, the electrical energy sector is currently found in a dramatic changing paradigm, which moves towards an increasing trend in generating power at distribution levels, where electricity is typically consumed, by means of non-conventional/renewable based generation units. These new generation technologies, termed as distributed generation, not only offers a non-pollutant, cheap and efficient source of energy to cover increasing demand, but also enhance the reliability of supply to critical loads and reduce the need for additional grid reinforcements. Aside of the technical benefits provided, distributed generation will massively integrate renewable energy resources, with new type of loads and end-user actors, such as prosumers, demand responsive loads, or electric vehicles. Where these actors will actively participate in energy and auxiliary service markets, depending on their available or constrained energy needs. For this reason, the work presented in this Thesis deals with designing and implementing advanced hierarchical control solutions to renewable-based power plants with the purpose of achieving advanced grid conection performance while reaching maximum economic benefits from its optimum real-time operation. Initially, an extensive analysis on the main renewable-based power plant hierarchical control solutions currently on the shelf, is performed. This study not only covered the specific case of renewable-based power plants, but also advanced microgrid and smart grid control solutions. Once the main renewable-based power plant hierarchical solutions were analized, a novel Hierarchical Distributed Control Structure (HDCS) is proposed for increased management of renewable-based active distributed plants. This hierarchical control structure comprises all possible functional levels from the higher long-term economic scheduling layer, to the instantaneous supervisory control of the resource, emphasizing the entire operation and control functionalities needed for increasing the integration of active distributed power plants. In order to achieve real-time control capabilities in active distribution systems, the present thesis introduces a novel power sharing control strategy, based on the competitive operation of multiple active participating agents (distributed generators, demand response and energy storage systems) through the implementation of market rules. Such control capabilities are satisfied by applying a price control signal over the entire grid control architecture, being the final-end participating agent, the responsible entity in charge of deciding its own generation/demand involvement based on its marginal or affordable electricity costs. In addition, it reduces the information volume to be transmitted and processing requirements, as the higher control levels do not need to have knowledge on the detailed distribution system topology and contributing actors. In order to have a meaningful evaluation of the proposed competitive control capabilities, a wave power plant application has been selected, which constitutes a challenging scenario for the controller itself to achieve advanced real-time control capabilities in such an oscillating renewable energy resource. In order to suitably characterize the wave energy resource profile resulting from maximum energy absorption, this Thesis introduce a novel adaptive vector controller, which maximizes the energy extraction from the resource regardless of the dominant irregular wave frequency characteristics. For the specific wave power plant application considered, the competitive control does not only ensures real-time optimum resource allocation for satisfying a given production objective, but also provides optimum long term operation of the system. As a result, overall plant costs reductions can be achieved under the competitive operation, since the plant scheduled energy is satisfied by making use of the generation units with cheaper cumulative operation costsActualmente, el sector eléctrico se encuentra inmerso en un profundo proceso de restructuración, donde de cada vez más se tiende a generar energía a nivel de distribución, mediante el uso de generación no convencional/renovable. Estas nuevas tecnologías de generación, referidas como generación distribuida, no proporcionan unicamente una fuente de energía no-contaminante, barata y eficiente para cubrir el incremento de demanda, sinó que también pueden proporcionar seguridad de suministro a cargas críticas, así como reducir la necesidad de expansiones futuras de red. Además de las capacidades técnicas proporcionadas, la generación distribuida hará posible la integración masiva de sistemas de generación renovable, con nuevos tipos de cargas y usuarios finales, como prosumidores, cargas regulables, o vehiculos eléctricos, donde todos estos usuarios participaran activamente en mercados de energía y servicios auxiliares, dependiendo de sus requisitos de uso de energía. Por lo tanto, el trabajo realizado en esta tesis se centra en el diseño e implementación de soluciones jerárquicas de control avanzado en plantas de generación renovable, con el objetivo de obtener un comportamiento harmonioso de intercacción con la red, mientras la operación de la planta maximiza los beneficios derivados de su operación en tiempo real. Inicialmente, se ha llevado a cabo una revisión extensa sobre los sistemas de control jerárquico comunmente implementados en plantas de generación renovable, en microredes y en redes inteligentes. Una vez revisados los principales sistemas de control jerárquico en este tipo de aplicaciones, se propone un una novedosa estructura de control, que cubre todos los niveles de control posibles, desde el más alto nivel de gestión económica, hasta el control detallado del recurso de generación. Para lograr capacidades de control en tiempo real en sistemas activos de distribución, la presente tesis propone una nueva estrategia de control de reparto de potencia, basada en la operación competitiva de múltiples agentes participantes activos (generadores distribuidos, respuesta de demanda y sistemas de almacenamiento de energía) mediante la implementación de reglas del mercado. Dichas capacidades de control se satisfacen aplicando una señal de precio a lo largo de toda la arquitectura de control, siendo el agente de final, el ente responsable de decidir su propia participación en la generación/demanda en función de sus propios costes de electricidad marginales o asumibles. Además, reduce el volumen de información a transmitir y los requisitos de procesamiento de datos, ya que los niveles de control más altos no necesitan tener conocimiento sobre la topología del sistema de distribución detallado ni de la contribución de los actores adyacentes. Para llevar a cabo una evaluación significativa de las capacidades del controlador competitivo propuesto, se ha seleccionado una planta de generación undimotriz, como escenario más desfavorable, ya que el controlador debe asegurar un control estable de la potencia inyectada en un escenario altamente oscilante. Con el fin de caracterizar adecuadamente el perfil de recursos de energía de las olas resultante de la máxima absorción de energía, esta Tesis introduce un nuevo controlador de vector adaptativo, que maximiza la extracción de energía del recurso independientemente de las características dominantes de frecuencia de onda irregular. Para la aplicación de la planta de energía de onda específica considerada, el control competitivo no solo garantiza la asignación óptima de recursos en tiempo real para satisfacer un objetivo de producción dado, sino que también proporciona una operación óptima del sistema a largo plazo. Como resultado, se pueden lograr reducciones generales de los costos de la planta en el marco de la operación competitiva, ya que la energía programada de la planta se satisface haciendo uso de las unidadPostprint (published version

Competitive power control of distributed power plants

Nowadays, the electrical energy sector is currently found in a dramatic changing paradigm, which moves towards an increasing trend in generating power at distribution levels, where electricity is typically consumed, by means of non-conventional/renewable based generation units. These new generation technologies, termed as distributed generation, not only offers a non-pollutant, cheap and efficient source of energy to cover increasing demand, but also enhance the reliability of supply to critical loads and reduce the need for additional grid reinforcements. Aside of the technical benefits provided, distributed generation will massively integrate renewable energy resources, with new type of loads and end-user actors, such as prosumers, demand responsive loads, or electric vehicles. Where these actors will actively participate in energy and auxiliary service markets, depending on their available or constrained energy needs. For this reason, the work presented in this Thesis deals with designing and implementing advanced hierarchical control solutions to renewable-based power plants with the purpose of achieving advanced grid conection performance while reaching maximum economic benefits from its optimum real-time operation. Initially, an extensive analysis on the main renewable-based power plant hierarchical control solutions currently on the shelf, is performed. This study not only covered the specific case of renewable-based power plants, but also advanced microgrid and smart grid control solutions. Once the main renewable-based power plant hierarchical solutions were analized, a novel Hierarchical Distributed Control Structure (HDCS) is proposed for increased management of renewable-based active distributed plants. This hierarchical control structure comprises all possible functional levels from the higher long-term economic scheduling layer, to the instantaneous supervisory control of the resource, emphasizing the entire operation and control functionalities needed for increasing the integration of active distributed power plants. In order to achieve real-time control capabilities in active distribution systems, the present thesis introduces a novel power sharing control strategy, based on the competitive operation of multiple active participating agents (distributed generators, demand response and energy storage systems) through the implementation of market rules. Such control capabilities are satisfied by applying a price control signal over the entire grid control architecture, being the final-end participating agent, the responsible entity in charge of deciding its own generation/demand involvement based on its marginal or affordable electricity costs. In addition, it reduces the information volume to be transmitted and processing requirements, as the higher control levels do not need to have knowledge on the detailed distribution system topology and contributing actors. In order to have a meaningful evaluation of the proposed competitive control capabilities, a wave power plant application has been selected, which constitutes a challenging scenario for the controller itself to achieve advanced real-time control capabilities in such an oscillating renewable energy resource. In order to suitably characterize the wave energy resource profile resulting from maximum energy absorption, this Thesis introduce a novel adaptive vector controller, which maximizes the energy extraction from the resource regardless of the dominant irregular wave frequency characteristics. For the specific wave power plant application considered, the competitive control does not only ensures real-time optimum resource allocation for satisfying a given production objective, but also provides optimum long term operation of the system. As a result, overall plant costs reductions can be achieved under the competitive operation, since the plant scheduled energy is satisfied by making use of the generation units with cheaper cumulative operation costsActualmente, el sector eléctrico se encuentra inmerso en un profundo proceso de restructuración, donde de cada vez más se tiende a generar energía a nivel de distribución, mediante el uso de generación no convencional/renovable. Estas nuevas tecnologías de generación, referidas como generación distribuida, no proporcionan unicamente una fuente de energía no-contaminante, barata y eficiente para cubrir el incremento de demanda, sinó que también pueden proporcionar seguridad de suministro a cargas críticas, así como reducir la necesidad de expansiones futuras de red. Además de las capacidades técnicas proporcionadas, la generación distribuida hará posible la integración masiva de sistemas de generación renovable, con nuevos tipos de cargas y usuarios finales, como prosumidores, cargas regulables, o vehiculos eléctricos, donde todos estos usuarios participaran activamente en mercados de energía y servicios auxiliares, dependiendo de sus requisitos de uso de energía. Por lo tanto, el trabajo realizado en esta tesis se centra en el diseño e implementación de soluciones jerárquicas de control avanzado en plantas de generación renovable, con el objetivo de obtener un comportamiento harmonioso de intercacción con la red, mientras la operación de la planta maximiza los beneficios derivados de su operación en tiempo real. Inicialmente, se ha llevado a cabo una revisión extensa sobre los sistemas de control jerárquico comunmente implementados en plantas de generación renovable, en microredes y en redes inteligentes. Una vez revisados los principales sistemas de control jerárquico en este tipo de aplicaciones, se propone un una novedosa estructura de control, que cubre todos los niveles de control posibles, desde el más alto nivel de gestión económica, hasta el control detallado del recurso de generación. Para lograr capacidades de control en tiempo real en sistemas activos de distribución, la presente tesis propone una nueva estrategia de control de reparto de potencia, basada en la operación competitiva de múltiples agentes participantes activos (generadores distribuidos, respuesta de demanda y sistemas de almacenamiento de energía) mediante la implementación de reglas del mercado. Dichas capacidades de control se satisfacen aplicando una señal de precio a lo largo de toda la arquitectura de control, siendo el agente de final, el ente responsable de decidir su propia participación en la generación/demanda en función de sus propios costes de electricidad marginales o asumibles. Además, reduce el volumen de información a transmitir y los requisitos de procesamiento de datos, ya que los niveles de control más altos no necesitan tener conocimiento sobre la topología del sistema de distribución detallado ni de la contribución de los actores adyacentes. Para llevar a cabo una evaluación significativa de las capacidades del controlador competitivo propuesto, se ha seleccionado una planta de generación undimotriz, como escenario más desfavorable, ya que el controlador debe asegurar un control estable de la potencia inyectada en un escenario altamente oscilante. Con el fin de caracterizar adecuadamente el perfil de recursos de energía de las olas resultante de la máxima absorción de energía, esta Tesis introduce un nuevo controlador de vector adaptativo, que maximiza la extracción de energía del recurso independientemente de las características dominantes de frecuencia de onda irregular. Para la aplicación de la planta de energía de onda específica considerada, el control competitivo no solo garantiza la asignación óptima de recursos en tiempo real para satisfacer un objetivo de producción dado, sino que también proporciona una operación óptima del sistema a largo plazo. Como resultado, se pueden lograr reducciones generales de los costos de la planta en el marco de la operación competitiva, ya que la energía programada de la planta se satisface haciendo uso de las unida

Middle-mile optimization for next-day delivery

We consider an e-commerce retailer operating a supply chain that consists of middle- and last-mile transportation, and study its ability to deliver products stored in warehouses within a day from customer's order time. Successful next-day delivery requires inventory availability and timely truck schedules in the middle-mile and in this paper we assume a fixed inventory position and focus on optimizing the middle-mile. We formulate a novel optimization problem which decides the departure of the last middle-mile truck at each (potential) network connection in order to maximize the number of next-day deliveries. We show that the respective \emph{next-day delivery optimization} is a combinatorial problem that is $NP$-hard to approximate within $(1-1/e)\cdot\texttt{opt}\approx 0.632\cdot\texttt{opt}$, hence every retailer that offers one-day deliveries has to deal with this complexity barrier. We study three variants of the problem motivated by operational constraints that different retailers encounter, and propose solutions schemes tailored to each problem's properties. To that end, we rely on greedy submodular maximization, pipage rounding techniques, and Lagrangian heuristics. The algorithms are scalable, offer optimality gap guarantees, and evaluated in realistic datasets and network scenarios were found to achieve near-optimal results

The Truth About Voter Fraud

Allegations of election-related fraud make for enticing press. Many Americans remember vivid stories of voting improprieties in Chicagoland, or the suspiciously sudden appearance of LBJ's alphabetized ballot box in Texas, or Governor Earl Long's quip: "When I die, I want to be buried in Louisiana, so I can stay active in politics." Voter fraud, in particular, has the feel of a bank heist caper: roundly condemned but technically fascinating, and sufficiently lurid to grab and hold headlines. Perhaps because these stories are dramatic, voter fraud makes a popular scapegoat. In the aftermath of a close election, losing candidates are often quick to blame voter fraud for the results. Legislators cite voter fraud as justification for various new restrictions on the exercise of the franchise. And pundits trot out the same few anecdotes time and again as proof that a wave of fraud is imminent.Allegations of widespread voter fraud, however, often prove greatly exaggerated. It is easy to grab headlines with a lurid claim ("Tens of thousands may be voting illegally!"); the follow-up -- when any exists -- is not usually deemed newsworthy. Yet on closer examination, many of the claims of voter fraud amount to a great deal of smoke without much fire. The allegations simply do not pan out