Efficiency and nox emission optimization by genetic algorithm of a coal-fired steam generator modeled with artificial neural networks

This work is part of the development of a decision support model for the operation of a real steam generator. The study proposes a combined optimization that aims to find operating points that achieve the highest efficiency of the steam generator associated with lower NOx emissions, applying genetic algorithm to the output of artificial neural network (ANN) models. The database consists of 10 operating parameters collected over a year and a half with a half-hour step and treated statistically. The behavior of the steam generator is modeled by multilayer Perceptron artificial neural networks with separate outputs for efficiency and NOx emission. The evaluation metrics applied to the ANNs were mean absolute error (MAE), mean square error (MSE), mean percentage error (MAPE) and coefficient of determination (R2). The ANN for predicting efficiency behavior presents test MSE and MAE of 0.7572 and 0.6206, respectively, and RNA for NOx has test MSE and MAE of 312.43 and 12.36. The optimization targets 98% efficiency of the steam generator and 220.00 mg/mN³ of NOx emissions, and approaches these goals with 97.95% efficiency and 222.28 mg/mN³ of NOx emissions.Este trabalho faz parte do desenvolvimento de um modelo de apoio à decisão para a operação de um gerador de vapor real. O estudo propõe uma otimização combinada que visa encontrar pontos de operação que atinjam a maior eficiência do gerador de vapor associada à menor emissão de NOx, aplicando algoritmo genético na saída de modelos de redes neurais artificiais (RNA). A base de dados é formada por 10 parâmetros de operação coletados durante um ano e meio com passo de meia hora e tratados estatisticamente. O comportamento do gerador de vapor é modelado por redes neurais artificiais Perceptron de várias camadas, com saídas separadas para eficiência e emissão de NOx. As métricas de avaliação empregadas nas RNAs foram o erro médio absoluto (MAE), erro quadrático médio (MSE), erro médio percentual (MAPE) e coeficiente de determinação (R2). A RNA para predizer o comportamento da eficiência apresenta MSE e MAE do seu teste de 0,7572 e 0,6206, respectivamente e a RNA para NOx apresenta MSE e MAE do seu teste de 312,43 e 12,36. A otimização tem como alvo atingir 98% de eficiência do gerador de vapor e 220,00 mg/mN³ de emissões de NOx, e se aproxima dessas metas com 97,95% de eficiência e 222,28 mg/mN³ de emissões de NOx

Supervisory model predictive control of building integrated renewable and low carbon energy systems

To reduce fossil fuel consumption and carbon emission in the building sector, renewable and low carbon energy technologies are integrated in building energy systems to supply all or part of the building energy demand. In this research, an optimal supervisory controller is designed to optimize the operational cost and the CO2 emission of the integrated energy systems. For this purpose, the building energy system is defined and its boundary, components (subsystems), inputs and outputs are identified. Then a mathematical model of the components is obtained. For mathematical modelling of the energy system, a unified modelling method is used. With this method, many different building energy systems can be modelled uniformly. Two approaches are used; multi-period optimization and hybrid model predictive control. In both approaches the optimization problem is deterministic, so that at each time step the energy consumption of the building, and the available renewable energy are perfectly predicted for the prediction horizon. The controller is simulated in three different applications. In the first application the controller is used for a system consisting of a micro-combined heat and power system with an auxiliary boiler and a hot water storage tank. In this application the controller reduces the operational cost and CO2 emission by 7.31 percent and 5.19 percent respectively, with respect to the heat led operation. In the second application the controller is used to control a farm electrification system consisting of PV panels, a diesel generator and a battery bank. In this application the operational cost with respect to the common load following strategy is reduced by 3.8 percent. In the third application the controller is used to control a hybrid off-grid power system consisting of PV panels, a battery bank, an electrolyzer, a hydrogen storage tank and a fuel cell. In this application the controller maximizes the total stored energies in the battery bank and the hydrogen storage tank

Energy Management

Forecasts point to a huge increase in energy demand over the next 25 years, with a direct and immediate impact on the exhaustion of fossil fuels, the increase in pollution levels and the global warming that will have significant consequences for all sectors of society. Irrespective of the likelihood of these predictions or what researchers in different scientific disciplines may believe or publicly say about how critical the energy situation may be on a world level, it is without doubt one of the great debates that has stirred up public interest in modern times. We should probably already be thinking about the design of a worldwide strategic plan for energy management across the planet. It would include measures to raise awareness, educate the different actors involved, develop policies, provide resources, prioritise actions and establish contingency plans. This process is complex and depends on political, social, economic and technological factors that are hard to take into account simultaneously. Then, before such a plan is formulated, studies such as those described in this book can serve to illustrate what Information and Communication Technologies have to offer in this sphere and, with luck, to create a reference to encourage investigators in the pursuit of new and better solutions

Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

Optimisation of stand-alone hydrogen-based renewable energy systems using intelligent techniques

Wind and solar irradiance are promising renewable alternatives to fossil fuels due to their availability and topological advantages for local power generation. However, their intermittent and unpredictable nature limits their integration into energy markets. Fortunately, these disadvantages can be partially overcome by using them in combination with energy storage and back-up units. However, the increased complexity of such systems relative to single energy systems makes an optimal sizing method and appropriate Power Management Strategy (PMS) research priorities. This thesis contributes to the design and integration of stand-alone hybrid renewable energy systems by proposing methodologies to optimise the sizing and operation of hydrogen-based systems. These include using intelligent techniques such as Genetic Algorithm (GA), Particle Swarm Optimisation (PSO) and Neural Networks (NNs). Three design aspects: component sizing, renewables forecasting, and operation coordination, have been investigated. The thesis includes a series of four journal articles. The first article introduced a multi-objective sizing methodology to optimise standalone, hydrogen-based systems using GA. The sizing method was developed to calculate the optimum capacities of system components that underpin appropriate compromise between investment, renewables penetration and environmental footprint. The system reliability was assessed using the Loss of Power Supply Probability (LPSP) for which a novel modification was introduced to account for load losses during transient start-up times for the back-ups. The second article investigated the factors that may influence the accuracy of NNs when applied to forecasting short-term renewable energy. That study involved two NNs: Feedforward, and Radial Basis Function in an investigation of the effect of the type, span and resolution of training data, and the length of training pattern, on shortterm wind speed prediction accuracy. The impact of forecasting error on estimating the available wind power was also evaluated for a commercially available wind turbine. The third article experimentally validated the concept of a NN-based (predictive) PMS. A lab-scale (stand-alone) hybrid energy system, which consisted of: an emulated renewable power source, battery bank, and hydrogen fuel cell coupled with metal hydride storage, satisfied the dynamic load demand. The overall power flow of the constructed system was controlled by a NN-based PMS which was implemented using MATLAB and LabVIEW software. The effects of several control parameters, which are either hardware dependent or affect the predictive algorithm, on system performance was investigated under the predictive PMS, this was benchmarked against a rulebased (non-intelligent) strategy. The fourth article investigated the potential impact of NN-based PMS on the economic and operational characteristics of such hybrid systems. That study benchmarked a rule-based PMS to its (predictive) counterpart. In addition, the effect of real-time fuel cell optimisation using PSO, when applied in the context of predictive PMS was also investigated. The comparative analysis was based on deriving the cost of energy, life cycle emissions, renewables penetration, and duty cycles of fuel cell and electrolyser units. The effects of other parameters such the LPSP level, prediction accuracy were also investigated. The developed techniques outperformed traditional approaches by drawing upon complex artificial intelligence models. The research could underpin cost-effective, reliable power supplies to remote communities as well as reducing the dependence on fossil fuels and the associated environmental footprint

Impactos del Cambio Climático en la Generación de Energía Renovable y Evaluación de Escenarios de Generación Energética

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, leída el 26-04-2022This Thesis was titled Climate Change Impacts on Renewable Energy Generation and Energy Generation Scenarios.Climate change is attributed, among other factors, to greenhouse gas emissions produced by the energy sector (including the transport). At the same time, climate change is expected to affect this sector by changing the availability of resources, altering its enabling conditions and transforming demand patterns. This thesis addresses climate change impacts on renewable generation and electricity demand by providing an overview of the most relevant transformations projected in literature and by developing methodologies and quantitative analysis to ascertain the specific infuence in three case studies.The first and second chapters are focus on estimating climate change impacts in wind and photovoltaic generation in specific plants. Both provide physical and economic projections of expected changes, along with conclusions for the development of energy policies. The last chapter delves into how climate change and the scenarios proposed to curb it, can affect the demand for electricity in a region, due to the expected changes in the generation infrastructure and changes on the demand side such as a high penetration of electric vehicles...Esta Tesis se tituló Impactos del Cambio Climático en la Generación de Energía Renovable y Escenarios de Generación de Energía. El cambio climático se atribuye, entre otras variables, a las emisiones de gases de efecto invernadero producidas por el sector energético (incluyendo el transporte). Al mismo tiempo, el cambio climático se espera que pueda afectar a este sector cambiando la disponibilidad de sus recursos, alterando sus condiciones habilitantes y transformando los patrones de la demanda. Esta Tesis aborda los impactos del cambio climático en la generación renovable y cambios en el comportamiento de la demanda de electricidad, proporcionando una introducción a las transformaciones más relevantes proyectadas por la literatura y desarrollando metodologías y análisis cuantitativos que determinan el impacto específico en tres casos de estudio. El primer y el segundo capítulo se centran en determinar los cambios esperados en la generación eólica y fotovoltaica en plantas específicas, con especial atención en el calentamiento global. Ambos proporcionan proyecciones físicas y económicas de los cambios esperados, junto con conclusiones para el desarrollo de políticas energéticas. El último capítulo profundiza en cómo el cambio climático y los escenarios propuestos para frenarlo, pueden afectar a la demanda de electricidad de una región, debido a los cambios esperados en las infraestructuras de generación y en cambios por el lado de la demanda como sería una elevada penetración de los vehículos eléctricos...Fac. de Ciencias Económicas y EmpresarialesTRUEunpu

Grid-Connected Distributed Wind-Photovoltaic Energy Management: A Review

Energy management comprises of the planning, operation and control of both energy production and its demand. The wind energy availability is site-specific, time-dependent and nondispatchable. As the use of electricity is growing and conventional sources are depleting, the major renewable sources, like wind and photovoltaic (PV), have increased their share in the generation mix. The best possible resource utilization, having a track of load and renewable resource forecast, assures significant reduction of the net cost of the operation. Modular hybrid energy systems with some storage as back up near load center change the scenario of unidirectional power flow to bidirectional with the distributed generation. The performance of such systems can be enhanced by the accomplishment of advanced control schemes in a centralized system controller or distributed control. In grid-connected mode, these can support the grid to tackle power quality issues, which optimize the use of the renewable resource. The chapter aims to bring recent trends with changing requirements due to distributed generation (DG), summarizing the research works done in the last 10 years with some vision of future trends

Simulation of power plants steam generators and cooling towers with artificial neural network

A modelagem da operação de equipamentos é uma opção metodológica importante para a melhoria da eficiência de usinas geradoras de energia. Uma dessas metodologias é a rede neural artificial (RNA), que vem ganhando espaço devido à sua capacidade de modelar problemas complexos com base em comportamentos registrados de sistemas reais. O objetivo do presente estudo é desenvolver modelos de RNA capazes de reproduzir o funcionamento do gerador de vapor e da torre úmida de arrefecimento da planta termoelétrica a carvão de PECÉM, no estado do Ceará, Brasil. O modelo de RNA para o gerador de vapor superaquecido a carvão estima a vazão mássica de vapor com base em registros de um ano de operação da Usina. A configuração das RNAs é obtida após uma série de testes com o objetivo de reduzir o erro de predição através do erro absoluto médio (EAM) em diferentes patamares de operação, obtendo-se um MAE de 1,28% para o conjunto total de dados de operação, 8,11% para a faixa de operação de 240 MW e 10,82% para a faixa de operação de 360 MW. O desempenho das redes é comparado ao de modelos de regressão linear múltipla aplicados ao mesmo conjunto de dados, para os quais se têm valores de MAE de 2,05%, 9,47% e 15,76%. Esses resultados mostram a capacidade da RNA de estimar a produção de vapor com erro abaixo daqueles de modelos de regressão. O modelo de RNA é desenvolvido para um dos conjuntos de torres úmidas de resfriamento ligado ao sistema de condensação de uma das plantas do sitio de geração. Essa planta é referenciada como de melhor desempenho e o modelo RNA gerado é aplicado aos dados de operação do segundo conjunto de torres, ajudando na identificação de possíveis desvios ou problemas de desempenho. Ferramentas estatísticas são usadas para avaliar os dois conjuntos de dados referentes as torres de cada usina e identificar correlações de parâmetros. Os modelos de RNA com melhor desempenho são obtidos com um coeficiente máximo de correlação R² de 0,9956 para a taxa de calor rejeitada e 0,8699 para a taxa de vazão mássica de água de reposição para o conjunto de dados de referência. O coeficiente R² encontrado para o segundo conjunto de torres é de 0,748 para a taxa de calor rejeitada e 0,905 para a vazão mássica de água de reposição. Esse resultado ajuda a identificar alguns comportamentos não padronizados da torre. Uma nova simulação sem os pontos de fora da curva (outlier) exibiu valores de R² de 0,98 e 0,99, respectivamente.The modeling of equipment operation is an important methodological option for improving the efficiency of power plants. One of these methodologies is the artificial neural network (ANN), which is gaining space due to its ability to model complex problems based on acquired data from real systems. The objective of the present study is to develop ANN models capable of reproducing the operation of the steam generator and the wet cooling tower of the PECÉM coal-fired power plant in the state of Ceara, Brazil. The ANN model for the coal superheated steam generator estimates the steam mass flow rate based on year-long records of operation. ANN configuration is obtained after a series of tests with the objective of reducing the ANN mean absolute error (MAE) in different levels of operation, obtaining an MAE of 1,28% for the total set of data of operation, 8.11% for the 240 MW operating range and 10.82% for the 360 MW operating range. The network performance is compared to that of multiple linear regression models applied to the same data set, with MAE values of 2.05%, 9.47% and 15.76%. These results show the ability of ANN to estimate the production of vapor with errors below those of regression models. The ANN model is developed for one set of wet cooling towers connected to the condensation system. This plant is referred to present the best performance and the generated ANN model is applied to the operation data of the second plant, helping to identify possible deviations or performance problems. Statistical tools are used to evaluate the two cooling towers and to identify parameter correlations. The best performing ANN models are obtained with a R² correlation coefficient of 0.9956 for the rejected heat rate and 0.8699 for the makeup water mass flow rate for the reference data set. The coefficient R² found for the second set of towers is 0.748 for the rejected heat rate and 0.905 for the makeup water mass flow rate. The coefficient R² found for the second set of towers is 0.748 for the rejected heat rate and 0.905 for the makeup water mass flow rate. This result helps to identify some non-standard behavior of the tower. A new simulation without the outlier points exhibited R² values of 0.98 and 0.99, respectively