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

Controle avançado de um sistema de separação trifásica e tratamento de água

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia de Automação e SistemasNesse trabalho são abordadas técnicas de controle preditivo não-linear aplicadas ao controle de um sistema de separação e tratamento de água da indústria do petróleo composto por um separador trifásico e uma bateria de hidrociclones. O separador trifásico tem como funções promover a separação das três fases do fluido (água, óleo e gás) proveniente do reservatório de petróleo e amortecer as perturbações de carga atuando como tanque pulmão. Na saída de água do separador se conecta uma bateria de hidrociclones que permitem extrair os resíduos de óleo da água que é recuperada no processo. Usualmente, os separadores industriais contam com três controladores SISO PI, cada um relativo a uma das fases do fluido. A utilização desse esquema de controle, apesar de ser bastante comum na indústria, tem como principal desvantagem o fato de não amortecer as oscilações de carga. Um esquema utilizado pela Petrobras é o controlador PI por bandas que apesar de utilizar técnicas clássicas de controle monovariável proporciona um bom amortecimento das oscilações de carga. O processo estudado é multivariável e tem uma dinâmica complexa, por isso são estudados neste trabalho dois tipos de modelos de predição não-lineares, sendo eles o modelo Hammerstein e um modelo fenomenológico simplificado do separador. Dois sistemas de controle foram desenvolvidos. O primeiro, multivariável, se baseia no controlador preditivo prático, conhecido como PNMPC, desenvolvido em [33] e inclui diversas funcionalidades para melhorar o desempenho do sistema separador-hidrociclones. O segundo consiste de um conjunto de controladores PI com sintonia via MPC, o qual é de grande simplicidade de implementação. Foram realizados ensaios de simulação onde os controladores foram testados no controle do sistema integrado funcionando em regime permanente e sob a presença de perturbações.This work discusses techniques for nonlinear predictive control applied to the control of an oil industry separation system and water treatment which comprises a three-phase separator and a battery of hydrocyclones. The three phase separator function is to promote the three phases separation of the fluid from the oil reservoir (gas, oil and water) and act as a surge tank to attenuate load disturbances. A battery of hydrocyclones is connected to the separator water output to extract the residual oil in the water which is recovered in the process. Usually, industrial separators are controlled with three SISO PI controllers, each in one of fluid phases. This control scheme, though it is quite common in industry, has as main disadvantage that it does not damp load oscillations. A scheme used by Petrobras is the PI Band controller that uses monovariable classical techniques but provides a good damping of load oscillations. As the studied process is multiavariable and has quite complex dynamics, two types of non-linear prediction models are analyzed in this work, the Hammerstein model and a simplified phenomenological model of the separator. Using these models two control systems are developed and tuned. The first one is based on the algorith PNMPC (practical multivariable predictive controller), developed in [33] and includes several features to improve the separator hydrocyclones system performance. The second one consists of a set of PI controllers tuned using a Zone MPC approach, which is simple to implement and use. Several simulations are presented to illustrate the use of the proposed controllers using a complete phenomenological model of the integrated system considering the operation in steady state and in the presence of disturbances

Advanced Control for Energy Management of Grid-Connected Hybrid Power Systems in the Sugar Cane Industry

This work presents a process supervision and advanced control structure, based on Model Predictive Control (MPC) coupled with disturbance estimation techniques and a finite-state machine decision system, responsible for setting energy productions set-points. This control scheme is applied to energy generation optimization in a sugar cane power plant, with non-dispatchable renewable sources, such as photovoltaic and wind power generation, as well as dispatchable sources, as biomass. The energy plant is bound to produce steam in different pressures, cold water and, imperiously, has to produce and maintain an amount of electric power throughout each month, defined by contract rules with a local distribution network operator (DNO). The proposed predictive control structure uses feedforward compensation of estimated future disturbances, obtained by the Double Exponential Smoothing (DES) method. The control algorithm has the task of performing the management of which energy system to use, maximize the use of the renewable energy sources, manage the use of energy storage units and optimize energy generation due to contract rules, while aiming to maximize economic profits. Through simulation, the proposed system is compared to a MPC structure, with standard techniques, and shows improved behavior.Ministerio de Economía y Competitividad CNPq401126/2014-5Ministerio de Economía y Competitividad CNPq303702/2011-7Ministerio de Economía y Competitividad DPI2016-78338-

Gestión Energética de una Micro Red acoplada a un sistema V2G

XXXVI Jornadas de Automática, 2 - 4 de septiembre de 2015. BilbaoEste trabajo presenta un algoritmo para la optimización económica de una micro red basada en el control predictivo. La micro red tiene una conexión de red y una estación de carga para coches eléctricos. El modelado del sistema utilizó la metodología de los Energy Hubs. El algoritmo propuesto tiene la tarea de llevar a cabo la gestión de la compra y venta de electricidad a la red eléctrica, maximizar el uso de fuentes de energía renovables, la gestión del uso de los almacenadores de energía y realizar la carga de los vehículos aparcados. Son presentados resultados de simulación que ilustran el funcionamiento satisfactorio del sistema propuesto.Ministerio de Economía y Competitividad DPI2013-46912-C2-1-

The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management

Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation

Projections of short fiber cellulose production in Brazil

Com base em uma série temporal anual de produção de celulose de fibra curta no Brasil no período de 1950 a 2009, o presente trabalho objetivou analisar a eficiência da metodologia Box & Jenkins em prever a produção. O modelo mais adequado foi escolhido com base nos critérios de AIC e SCH, na significância dos coeficientes, no princípio de parcimônia e no comportamento dos resíduos. Pelos resultados, conclui-se que o modelo ARIMA (2,2,1) é adequado para prever a produção de celulose de fibra curta no Brasil.Palavras-chave: Celulose de fibra curta; séries temporais; metodologia Box & Jenkins. AbstractProjections of short fiber cellulose production in Brazil. Based on an annual production series of hardwood pulp in Brazil from 1950 to 2009, this study aimed to analyze efficiency of the Box & Jenkins methodology to forecast production. The most appropriate model was chosen based on the AIC and SCH criteria, on the significance of coefficients, on the principle of parsimony and residual behavior. The results points to the ARIMA (2,2,1) model as the most adequate to forecast the hardwood pulp production in Brazil.Keywords: Hardwood pulp; time series; Box & Jenkins methodology.Based on an annual production series of hardwood pulp in Brazil from 1950 to 2009, this study aimed to analyze efficiency of the Box & Jenkins methodology to forecast production. The most appropriate model was chosen based on the AIC and SCH criteria, on the significance of coefficients, on the principle of parsimony and residual behavior. The results points to the ARIMA (2,2,1) model as the most adequate to forecast the hardwood pulp production in Brazil

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost