38 research outputs found
Predicting Thermoelectric Power Plants Diesel/Heavy Fuel Oil Engine Fuel Consumption Using Univariate Forecasting and XGBoost Machine Learning Models
Monitoring and controlling thermoelectric power plants (TPPs) operational parameters have become essential to ensure system reliability, especially in emergencies. Due to system complexity, operating parameters control is often performed based on technical know-how and simplified analytical models that can result in limited observations. An alternative to this task is using time series forecasting methods that seek to generalize system characteristics based on past information. However, the analysis of these techniques on large diesel/HFO engines used in Brazilian power plants under the dispatch regime has not yet been well-explored. Therefore, given the complex characteristics of engine fuel consumption during power generation, this work aimed to investigate patterns generalization abilities when linear and nonlinear univariate forecasting models are used on a representative database related to an engine-driven generator used in a TPP located in Pernambuco, Brazil. Fuel consumption predictions based on artificial neural networks were directly compared to XGBoost regressor adaptation to perform this task as an alternative with lower computational cost. AR and ARIMA linear models were applied as a benchmark, and the PSO optimizer was used as an alternative during model adjustment. In summary, it was possible to observe that AR and ARIMA-PSO had similar performances in operations and lower error distributions during full-load power output with normal error frequency distribution of −0.03 ± 3.55 and 0.03 ± 3.78 kg/h, respectively. Despite their similarities, ARIMA-PSO achieved better adherence in capturing load adjustment periods. On the other hand, the nonlinear approaches NAR and XGBoost showed significantly better performance, achieving mean absolute error reductions of 42.37% and 30.30%, respectively, when compared with the best linear model. XGBoost modeling was 8.7 times computationally faster than NAR during training. The nonlinear models were better at capturing disturbances related to fuel consumption ramp, shut-down, and sudden fluctuations steps, despite being inferior in forecasting at full-load, especially XGBoost due to its high sensitivity with slight fuel consumption variations
Applications of Computational Intelligence to Power Systems
In power system operation and control, the basic goal is to provide users with quality electricity power in an economically rational degree for power systems, and to ensure their stability and reliability. However, the increased interconnection and loading of the power system along with deregulation and environmental concerns has brought new challenges for electric power system operation, control, and automation. In the liberalised electricity market, the operation and control of a power system has become a complex process because of the complexity in modelling and uncertainties. Computational intelligence (CI) is a family of modern tools for solving complex problems that are difficult to solve using conventional techniques, as these methods are based on several requirements that may not be true all of the time. Developing solutions with these “learning-based” tools offers the following two major advantages: the development time is much shorter than when using more traditional approaches, and the systems are very robust, being relatively insensitive to noisy and/or missing data/information, known as uncertainty
Forecasting tools and probabilistic scheduling approach incorporatins renewables uncertainty for the insular power systems industry
Nowadays, the paradigm shift in the electricity sector and the advent of the smart grid, along with the growing impositions of a gradual reduction of greenhouse gas emissions, pose numerous challenges related with the sustainable management of power systems.
The insular power systems industry is heavily dependent on imported energy, namely fossil fuels, and also on seasonal tourism behavior, which strongly influences the local economy. In comparison with the mainland power system, the behavior of insular power systems is highly influenced by the stochastic nature of the renewable energy sources available.
The insular electricity grid is particularly sensitive to power quality parameters, mainly to frequency and voltage deviations, and a greater integration of endogenous renewables potential in the power system may affect the overall reliability and security of energy supply, so singular care should be placed in all forecasting and system operation procedures.
The goals of this thesis are focused on the development of new decision support tools, for the reliable forecasting of market prices and wind power, for the optimal economic dispatch and unit commitment considering renewable generation, and for the smart control of energy storage systems. The new methodologies developed are tested in real case studies, demonstrating their computational proficiency comparatively to the current state-of-the-art
Decarbonization cost of Bangladesh's energy sector: Influence of corruption
As a rapidly developing lower-middle income country, Bangladesh has been maintaining
a steady growth of +5% in the gross domestic product (GDP) annually since
2004, eventually reaching 7.1% in 2016. The country is targeting to become uppermiddle-
income and developed by 2021 and 2041 respectively, which translates to an
annual GDP growth rate of 7.58% during this period. The bulk of this growth
is expected to come from the manufacturing sector, the significant shift towards
which started at the turn of this century. Energy intensity of manufacturing-based
growth is higher, the evidence of which can be seen in the 3.17 times increase in
national energy consumption between 2001 and 2014. Also, Bangladesh aims to
achieve 100% electrification rate by 2021 against an annual population growth rate
of 1.08%. With the increasing per capita income, there is now a growing middle
class fuelling the growth in demand for convenient forms of energy. Considering
the above drivers, the Bangladesh 2050 Pathways Model suggested 35 times higher
energy demand than that of 2010 by 2050. The government and private sector have
started a substantial amount of investments in the energy sector to meet the signi
ficant future demand. Approximately US250 billion in 2050 under HCS, which can be reduced 23% under
ZCS. The cost of decarbonization would be 3.6, 3.4 and 3.2 times under average
cost of MCS, LCS, and ZCS, than that of HCS. As the energy sector of Bangladesh
is under rapid development, the accumulated capital would be comparatively high
by 2050. However, fuel cost can be significantly reduced under LCS and ZCS which
would also ensure lower emissions. The study suggested that energy mix change,
technological maturity, corruption and demand reduction can influence the cost
of decarbonization. However, the most significant influencer for the decarbonization
of Bangladeshi energy sector would be the corruption. Results showed that if
Bangladesh can minimize the effect of corruption on the energy sector, it can reduce
the cost of decarbonization 45-77% by 2050 under MCS, LCS, and ZCS
Methane emission inventory and forecasting in Malaysia
The increase in global surface temperature by 0.74 ± 0.18 oC between 1901 and 2000 as a result of global warming has become a serious threat. It is caused by the emission of greenhouse gases into the atmosphere due to human activities. The major greenhouse gases are carbon dioxide, methane and nitrous oxide. Records show that only carbon dioxide received detailed investigation but not methane, hence the motive behind this study. This study examined the emission of methane from six main sources in Malaysia. Data for the inventories of the production of these six sources were taken from 1980 – 2011 and were used to forecast emissions from 2012 – 2020. The data were sourced from Ministries, Departments and International Agencies. Six categories of animals were studied under livestock with their corresponding methane emissions from 1980 – 2011 computed as follows: cattle: 1993Gg (6.13%), buffaloes: 341Gg (10.8%), sheep: 24Gg (0.8%), goats: 55Gg (1.8%), horses: 3Gg (0.1%), poultry: 161Gg (5.1%), and pigs: 579Gg (18.3%). Methane emissions from the other sources from 1980 to 2011 are rice production: 1617Gg (0.02%), crude oil production: 8016636Gg (99.8%), Wastewater (POME): 11362Gg (0.14%), municipal solid waste landfills: 3294Gg (0.04%), coal mining: 14Gg (0.0002%). Forecasting of methane emissions from 2012 to 2020 were carried out using the Box-Jenkins ARIMA method. There were close similarities between the observed and forecast values. In the year 2020 predicted methane emissions will be cattle: 113Gg (72.2%), buffaloes: 8.0Gg (5.1%), sheep: 1.2Gg (0.8%), goats: 4.2 Gg (2.7%), horses: 0.2Gg (0.1%), pigs: 13.2Gg (8.4%), and poultry: 16.8Gg (10.7%) for the livestock sector. For other sectors the forecast will be wastewater: 836Gg for wastewater, 4.7 Gg for coal production, 503,208 Gg for crude oil production, 50.6 Gg for rice production, and 167 Gg from municipal solid waste landfills. Population and GDP will rise to 33.26 million and 329US $ billion by 2020, respectively. Optimisation was carried out after running a linear regression to determine the significant parameters. The equation developed was a nonlinear programming problem and was solved using sequential quadratic programming (SQL) and implemented on MATLAB environment. Sensitivity analysis carried out on the constraints showed the need to maintain the present livestock and rice production levels. The amount of meat protein currently available far exceeds the dietary protein requirement by more than five times. Several mitigation measures aimed towards reducing future methane emissions in Malaysia were also suggested for the various sources. These are in line with the country’s commitment to reduce greenhouse gas emissions by 40% over the 2005 level by 2020. The use of renewable energy in the energy mix was suggested in line with the government’s five fuel policy and increase in the number of vehicles using gas was also proposed
Recommended from our members
Design of Smart Energy Generation and Demand Response System in Saudi Arabia
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThough the promising benefits of renewable sources have already pushed many countries into implementing RE units, Saudi Arabia is still highly dependent on fossil fuel. However, the decrease in value of oil reserves has enforced Saudi Arabia to prioritize renewable energy sources in the next decades. Such energy sources are highly dependable on accurate forecasts, due to their intermittence and operability. The present research has the objective to develop models that can accurately forecast energy load for implementation in a decision-making system. The case investigated is the western region of Saudi Arabia. Two modelling approaches were evaluated, linear regression (LR) and artificial neural network (ANN). This last one was chosen because it is a mathematical model able to deal with non-linear relationship among input(s) and output(s) in the data. Time series (past load data) and multivariate data from 2010 until 2016 were investigated A hybrid model structure (combiner) was implemented to analyse the effects of combining outputs of two models in a single one. This hybrid model consisted of a regular average and weighted average of the time series and multivariate model, with calibration through Fuzzy and Particle Swarm Optimisation. These two were selected because, while Particle Swarm Optimization is an optimization algorithm, Fuzzy consists in a complete structured model. The forecasted load and the available input were used in the last chapter for power generation planning and decision-making support. The software used for the modelling and simulation is ETAP®. Different scenarios for replacement of fossil fuel power plants by renewable units were tested considering the network of western Saudi Arabia. The results show that Artificial Neural Network with time series input and 15 neurons in hidden layer shows superior performance (MSE 3.7*105 and R2 equals 99%) compared to other neural networks and linear regression. Though the application of combiner models did not significantly improve model performance, the Fuzzy Combiner shows the best one (MSE 5.8*105 and R2 equals 93%) since it incorporates information from time series and multivariate data. It is important to mention that all the modelling approaches evaluated have some limitations, such as the necessity of accurate input data and they are limited in capability of extrapolating over the training range. In the last section, it was observed that renewable energy sources can be integrated in the grid network without excessive risk regarding demand. This occurs because the current energy management policy of western Saudi Arabia enables the use of energy units with fast compensation (using gas units) in the case of demand increase or decrease in solar or wind power
State of the art of machine learning models in energy systems: A systematic review
Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability
SOLAR IRRADIANCE FORECASTING FOR TROPICAL REGIONS USING NOVEL STATISTICAL ANALYSIS AND MACHINE LEARNING
Ph.DDOCTOR OF PHILOSOPH
Intra-hour solar forecasting for photovoltaic systems integration in weak electric grids
La tesis "Intra-hour solar forecasting for photovoltaic systems integration in weak electric grids" estudia la problemática de la variabilidad del recurso solar en la integración de sistemas fotovoltaicos en redes eléctricas débiles, que es el principal obstáculo que enfrenta esta tecnología para un despliegue masivo. Por un lado, se desarrolla un sistema de predicción de energía fotovoltaica intra-horario basado en dos cámaras de cielo capaz de predecir las rampas de producción causadas por el efecto de las nubes. El sistema hace uso de técnicas de procesamiento de imágenes y deep learning para identificar las nubes y predecir cuando éstas afectaran a la producción de las plantas fotovoltaicas cercanas. Por otro lado, se evalúa el potencial fotovoltaico de las Islas Canarias haciendo uso de técnicas de Big Data. También se estudian los problemas de integración derivados de la inclusión de energía fotovoltaica en las redes eléctricas de distribución, proponiendo un algoritmo para la optimización del control de los inversores fotovoltaicos