Binary Search Algorithm for Mixed Integer Optimization: Application to energy management in a microgrid

This paper presents a binary search algorithm to deal with binary variables in mixed integer optimization problems. One example of this kind of problem is the optimal operation of hydrogen storage and energy sale and purchase into a microgrids context. In this work was studied a system composed by a microgrid that has a connection with the external electrical network and a charging station for electric cars. The system modeling was carried out by the Energy Hubs methodology. The proposed algorithm transforms the MIQP (Mixed Integer Quadratic Program) problem into a QP (Quadratic Program) that is easier to solve. In this way the overall control task is carried out the electricity purchase and sale to the power grid, maximizes the use of renewable energy sources, manages the use of energy storages and supplies the charge of the parked vehicles.Ministerio de Economía y Competitividad DPI2013-46912-C2-1-RUniversidad de Sevilla CNPq401126/2014-5Universidad de Sevilla CNPq303702/2011-

Backcasting for transformative water management

This thesis reports on a study on the use of backcasting for climate adaptation in water management. It offers new insights and recommendations for the further development of backcasting approaches for transformative climate adaptation

Backcasting for transformative water management

This thesis reports on a study on the use of backcasting for climate adaptation in water management. It offers new insights and recommendations for the further development of backcasting approaches for transformative climate adaptation

Predicitive control for energy management of renewable energy based microgrids

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2016.O objetivo deste trabalho é abordar a gestão de energia dos sistemas de geração e armazenamento de energia. Estes sistemas são atualmente uma realidade em países onde é desejada uma diversificação das fontes de energia e uma maior penetração das fontes renováveis. Em particular, o Brasil possui uma matriz energética diversificada com base em recursos hídricos, biomassa (etanol), gás natural e petróleo. Mas, expectativa para o futuro próximo é um aumento da utilização de fontes de energia renováveis, principalmente solar e eólica. Dessa forma, este trabalho ataca um problema econômico atual e importante para o País. Uma rede de energia em grande escala pode ser subdividida em subsistemas chamados micro-redes, que podem ser vistos como um conjunto de geradores despacháveis e/ou não-despacháveis que utilizam energias fósseis e/ou renováveis, armazenadores de energia e consumidores. Uma micro-rede pode operar em modo ilha, conectada com a rede principal e interligada com outras micro-redes. Sabe-se que a operação ideal de cada unidade não garante o perfeito funcionamento global da micro-rede, o que leva a um comportamento inaceitável. Assim, torna-se necessária a coordenação entre todos os elementos da micro-rede. A mesma filosofia deve ser aplicada no problema de micro-redes interligadas. Do ponto de vista da modelagem, os modelos de sistemas de gestão de energia no nível superior têm características híbridas, devido à necessidade de introduzir variáveis binárias, por exemplo, para modelar a dinâmica de armazenamento ou preços diferentes para operações econômicas como compra/venda de energia. Assim, uma abordagem natural para controlar esses sistemas é a utilização de controle preditivo, o qual já é amplamente utilizado na indústria de processos e pode lidar com problemas híbridos de optimização. Em uma micro-rede operando em modo conectada à rede principal o desafio de controle é maximizar o uso de fontes renováveis, minimizar o uso de combustíveis fósseis e a quantidade de energia comprada da rede de distribuição, amortecer flutuações de energia e atender a demanda. No caso de micro-redes interligadas é desejada uma estratégia de controle que permita a interação entre micro-redes para compartilhar fontes de energia e reduzir o fluxo de energia com a rede de distribuição. Assim, para uma única micro-rede o controle centralizado parece uma boa opção, enquanto para micro-redes interligadas, quando o intercâmbio de informação é limitado, o controle distribuído aparece como uma solução interessante. Nesta tese é con\-si\-de\-ra\-do o problema de controle de micro-redes em diferentes cenários, dentre eles uma microrrede de laboratório com armazenador de hidrogênio, o estudo de microrredes interligadas e uma planta de geração da indústria da cana de açúcar. O objetivo principal é propor soluções baseadas em controle preditivo para atender os requisitos de operação do sistema.Abstract : The objective of this thesis is to address the energy management of power generation and energy storage systems. These systems are nowadays a reality in countries where a diversification of energy sources and a higher penetration of renewable sources is desired. In particular, Brazil has a diversified energy matrix based on water resources, biomass (ethanol), natural gas and oil. But the expectation for the near future is an increase in the use of renewable sources, mainly solar and wind. Thus, this work attacks a current and important economic problem for the country. A large scale energy network can be subdivided into subsystems called Microgrids, that can be seen as set of dispatchable and/or non-dispatchable generators that uses fossil and/or renewable energy, storage units and consumers. A microgrid can operate in island mode, grid-connected mode and interconnected with other microgrids as the so called networked microgrids. It is known that an ideal operation of each unit does not guarantee the perfect operation of the overall microgrid, which leads to an unacceptable behavior. Thus it becomes necessary coordination between all microgrid elements. The same philosophy should be applied in the networked microgrids problem. From modeling point of view, the top level energy management system models have hybrid characteristics due to the need to introduce binary variables, for example, to model storage dynamics or different prices for economic operations like sell/purchase of energy. Thus, a natural approach to control these systems is the use of predictive control, which is already widely used in the process industry and can deal with hybrid optimization problems. In a microgrid operating in grid connected mode the control challenges are to maximize the use of renewable sources, minimize the use of fossil sources an the amount of energy bought from distribution network operator (DNO), mitigate energy fluctuating and meet the demand. In an interconnected case a control strategy that allows the interaction between microgrids to share energy sources and reduce the energy flow with DNO is desired. Thus for a single microgrid the centralized control seems to be a good option and for networked microgrids, when information sharing is limited, the distributed control appears as an interesting solution. In this thesis the problem of control of microgrids in different scenarios is considered, among them a laboratory microgrid with hydrogen storage, the study of four interconnected microgrids and a sugar cane industry power plant. The main objective is to propose solutions based on model predictive control to attend the system operation requirements

Improving operational flood forecasting using data assimilation

Hoogwatervoorspellingssystemen die betrouwbaar en nauwkeurig overstromingen kunnen voorspellen zijn erg belangrijk, omdat dit het aantal slachtoffers en de economische schade van overstromingen kan beperken. Het begrijpen van het gedrag van extreme hydrologische gebeurtenissen en het vermogen van de modelleur om betere en nauwkeurigere prognoses te krijgen, zijn uitdagingen binnen de toegepaste hydrologie. Omdat modellen slechts een versimpelde weergave van de complexe werkelijkheid geven, kleven er aan hydrologische voorspellingen veel onzekerheden. Dit proefschrift draagt bij aan een verbeterd begrip en kwantificatie van hydrologische modelonzekerheid, vooral gekoppeld aan de initi¨ele condities van het model, en in mindere mate aan de modelstructuur en de parameters

Development and assessment of dynamic storage cell codes for flood inundation modelling

Since 1962 storage cell codes have been developed to simulate flow on fluvial and coastal floodplains. These models treat the floodplain as a series of discrete storage cells, with the flow between cells calculated explicitly using some analytical flow formulae such as the Manning equation. Recently these codes have been reconfigured to use regular Cartesian grids to make full use of widely available high resolution data captured from remote sensing platforms and stored in a raster GIS format. Such raster-based storage cell codes have many of the advantages over full two-dimensional depth averaged schemes but without the computational cost, however their typical implementation results in a number of fundamental limitations. These include an inability to develop solutions that are independent of time step or grid size, and an unrealistic lack of sensitivity to floodplain friction. In this thesis, a new solution to these problems is proposed based on an optimal adaptive time step determined using a Courant-type condition for model stability. Comparison of this new adaptive time step scheme to analytical solutions of wave propagation/recession on flat and sloping planar surfaces and against field measurements acquired for four real flood scenarios demonstrates considerable improvement over a standard raster storage cell model. Moreover, the new scheme is shown to yield results that are independent of grid size or choice of initial time step and which show an intuitively correct sensitivity to floodplain friction over spatially-complex topography. It does, however, incur a prohibitive computation cost at model grid resolutions less than 50 m. This primary research is supplemented by an examination of the data and methods used to apply, and in particular calibrate, distributed flood inundation models in practice. Firstly, different objective functions for evaluating the overall similarity between binary predictions of flood extent and remotely sensed images of inundation patterns are examined. On the basis of the results presented, recommendations are provided regarding the use of various measures for hydrological problems. Secondly, the value of different observational data types typically available for calibrating/constraining model predictions is explored within an extended Generalised Likelihood Uncertainty Estimation (GLUE) framework. A quasi-Bayesian methodology for combining these individual evaluations that overcomes the limitations of calibration against any single measurement source/item is also presented.EThOS - Electronic Theses Online ServiceGBUnited Kingdo