Improved modelling of microgrid distributed energy resources with machine learning algorithms

Mención Internacional en el título de doctorRenewable energy technologies are being increasingly adopted in many countries around the world. However, the deployment of these power generation systems is becoming more diverse than ever, from small generation units in individual houses to massive power production plants. In that spectrum, distributed energy resources (DERs) cover systems from the low- to the middle-power ranges. The operation, control and assessment of these technologies is becoming more complex, and structures such as microgrids (MGs) may provide a suitable ecosystem to manage them. The beginning of this thesis covers the fundamentals of MG systems. A review was conducted by analysing the MG in a layer perspective, where each layer corresponded to a topic such as operation, business or standards, among others. The advancements in electronics, computer power and storage capability have created a paradigm in which massive amounts of data are generated and computed. The electrical sector has introduced many data acquisition technologies to assess the grid and its components. Classical modelling approaches have applied physical, chemical or electrical algorithms to model the behaviours of DERs. Nevertheless, with the extensive amount of information at our disposal, data-driven techniques such as machine learning (ML) may provide more individualised models to simulate the behaviour of these power generation technologies with the particularities of both their components and their location. Following the MG review, its power generation technologies were analysed. The information from 1,618 MGs around the world have been aggregated and studied. Also, two MG infrastructure model generators have been proposed (considering the infrastructure as the power generation technology and their rated power of an MG.). One of the models is based on the statistical data aggregated in tables and the other is based on ML techniques. The latter, which provides more particularised results, is able to generate the most typical MG infrastructure for a given location and segment of operation. Ideally, each of the DERs of a MG should be modelled, but, given the time constraints of a PhD, only the principal renewable generation technologies have been studied. Hence, ML models of photovoltaic (PV) systems plus a battery and wind energy conversion systems (WECS) have been proposed. Various ML models for PV systems were developed in two studies. First, an ML model for PV power estimation was performed using data from two real PV farms and validated using deterministic models from the literature. The ML algorithm was performed using neural networks and automatic strategies to clean the data. Neural network accuracy when trained and tested in the same location yields solid results which can be applied in performance ratio tools for PV power stations. In the second study, various mathematical models are proposed. This study provides several models for computing the annual optimum tilt angle for both fixed PV arrays and solar collectors. The optimum tilt algorithm proposed can be calculated in the absence of meteorological or software tools. To generate these models, data were collected from 2,551 sites across the world. A regression analysis with polynomial fitting, neural networks and decision trees was performed. Despite the better performance of the ML models, the ease of use of polynomial algorithms is recommended for those sites with no access to computational tools or meteorological data. The performances of the models were validated using previous research algorithms. Also, an ML algorithm was proposed to estimate the state of charge of a lithium-ion battery. The available capacity in a battery is an important feature when operating these kinds of systems. Given the complex behaviours of a battery, data-driven algorithms are able to capture the dynamic behaviours of a battery. Based on the data obtained in different experiments performed in a laboratory, an ensemble method, gradient boosting algorithm, was trained to model the state of charge of the battery. Even though the state of health of the storage system was below the theoretical life expectancy, the model was able to provide solid results. The model was validated with non-trained data. Finally, data-driven techniques were applied to model different elements of WECS. The first study provided two power coefficient algorithms, one based on polynomial fitting and another based on neural networks. To train the models, data from a corrected blade element momentum algorithm was used and three sets of data representing different wind turbine ranges, from 2 to 10 MW, were generated. Both models were validated with three datasets of real wind turbines and compared with the existing literature equations. Compared to previous equations, errors were reduced by at least 55% with the best numerical approximation from the literature. This type of reduction has a great impact for WECS dynamic and transient studies. The second study proposed for WECS develops three different ML models: one estimates the power of individual WECS, the second aggregates the data from all the WECS and estimates their power and the last one estimates the power of an entire wind farm. Given the stochastic and dynamic behaviours of the systems modelled, data pre-processing should be performed. Along with default cleaning techniques, a Student-t copula has been proposed so outliers can be automatically removed. Results show that the neural network algorithms’ performances for the three models can be improved without excessive manual intervention in the development process. Traditionally, electrical, physical and chemical models have been applied to mimic the behaviour of power systems. Now, with the power of computer and storage systems, a new era of more customised models has begun. It is time to review the existing models and provide better solutions by using ML techniques. In this thesis, only a few DERs have been modelled, but the results show that huge improvements can be made and future work in this subject should be done. The ML models proposed can be applied either as individual models for performance assessment of each DER or as a complementary tool to dynamic or static studies, unit commitment and planning software, among others.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Carlos Veganzones Nicolás.- Secretaria: María Ángeles Moreno López de Saa.- Vocal: Athanasios Kolio

Wind turbine power coefficient models based on neural networks and polynomial fitting

The power coefficient parameter represents the aerodynamic wind turbine efficiency. Since the 1980s, several equations have been used in the literature to study the power coefficient as a function of the tip speed ratio and the pitch angle. In this study, these equations are reviewed and compared. A corrected blade element momentum algorithm is used to generate three sets of data representing different ranges of wind turbines, going from 2 to 10 MW. With this information, two power coefficient models are proposed and shared. One model is based on a polynomial fitting, whereas the other is based on neural network techniques. Both were trained with the blade element momentum model output data and showed good behaviour for all operating ranges. In the results, compared to all the algorithms found in the literature, the proposed models reduced the power coefficient error by at least 55% compared to the best numerical approximation from the literature. An error reduction in the power coefficient parameter may have a large impact on many wind energy conversion system studies, such as those treating dynamic and transient behavioursPublicad

Microgrid and Distributed Energy Resources Standards and Guidelines Review: Grid Connection and Operation Technical Requirements

This article belongs to the Special Issue Analysis of Microgrid Integrated with Renewable Energy SystemIn this review, the state of the art of 23 distributed generation and microgrids standards has been analyzed. Among these standards, 18 correspond mainly to distributed generation while five of them introduce the concept of microgrid. The following topics have been considered: interconnection criteria, operating conditions, control capabilities, power quality, protection functions and reference variables. The revised national standards cover ten countries on four continents, which represents 80% of the countries with the largest installed renewable capacities. In addition, eight other relevant international standards have been analyzed, finding IEEE 1547 as the most comprehensive standard. It is identified a clear need to define a common framework for distributed energy resources (DERs) and microgrid standards in the future, wherein topics, terminology, and values are expressed in a manner that may widely cover the entire diversity in a way similar to how it has already been expressed at the network transport level by the ENTSO-E codes

Microgrid Infrastructure Compendium Analysis with a Model Creation Tool and Guideline Based on Machine Learning Techniques

A microgrid (MG) is an electric power distribution system that may provide a suitable ecosystem for distributed generation. Detailed information about the infrastructure layer in MG projects is available, so this study aimed to propose a compendium and a model creation guideline for MGs. The aggregated information based on 1618 MGs was summarized into different tables and analyzed based on various parameters. Two MG infrastructure model creation tools were developed. First, a simple guideline was created based on the information in the tables, and then a machine learning tool based on decision trees was proposed that generates more accurate MG models using two main inputs: latitude and the segment in which they operate

Optimization and redesign of a production and supply system of compressed air

Grado en Ingeniería en Tecnologías Industriale

Microgrids literature review through a layers structure

Within a distributed generation (DG) system, microgrids (MGs) are an alternative approach that may provide both resiliency and efficiency benefits. In this review, an analysis of both research and industrial documents was done. In order to establish a solid foundation of the MGs concept, a comparison of various definitions written by distinguished authors has been made. Segmenting the information of MGs into layers facilitates its analysis, search, and comparison. Therefore, this paper continuous with a layer approach from other studies and incorporates the concept of the environment as a key element that has a high impact on the microgrid functional structure. With the foundation of the MG concept, an exhaustive literature review has been developed about the main microgrid layers, such as business, standard, climate, infrastructure or control, and operation

Microgrid infrastructure compendium analysis with a model creation tool and guideline based on machine learning techniques

A microgrid (MG) is an electric power distribution system that may provide a suitable ecosystem for distributed generation. Detailed information about the infrastructure layer in MG projects is available, so this study aimed to propose a compendium and a model creation guideline for MGs. The aggregated information based on 1618 MGs was summarized into different tables and analyzed based on various parameters. Two MG infrastructure model creation tools were developed. First, a simple guideline was created based on the information in the tables, and then a machine learning tool based on decision trees was proposed that generates more accurate MG models using two main inputs: latitude and the segment in which they operate

Worldwide annual optimum tilt angle model for solar collectors and photovoltaic systems in the absence of site meteorological data

This study provides several models for accurately computing the annual optimum tilt angle for fixed solar photovoltaic arrays or solar collectors, in any location of the world. The optimum tilt angle that maximizes the annual energy yield can therefore be easily calculated in the absence of meteorological data and simulation software tools. The proposed models are calculated using global horizontal radiation data collected from 2551 sites across the world. In the process, well-established submodels have been selected to estimate the hourly irradiance on any possible inclined surface, and its corresponding annual energy yield. After selecting the optimum angle for each location, through a regression analysis, a mathematical model that calculates annual optimum angles as a function of latitude has been developed. Furthermore, regression techniques such as neural networks and decision trees have been compared with the polynomial models. Finally, the results are compared to those obtained from high-quality 1-min measured irradiance data obtained at 52 research-class stations from the World Radiation Monitoring Center–Baseline Surface Radiation Network, providing a remarkably high number of validation data points. The results are analyzed, validated, and compared with previous research proposals proving the good performance of the proposed models