Solar energy radiation measurement with a low–power solar energy harvester

Solar energy radiation measurements are essential in precision agriculture and forest monitoring and can be readily performed by attaching commercial pyranometers to autonomous sensor nodes. However this solution significantly increases power consumption up to tens of milliwatts and can cost hundreds of euros. Since many autonomous sensor nodes are supplied from photovoltaic (PV) panels which currents depend on solar irradiance, we propose to double PV panels as solar energy sensors. In this paper, the inherent operation of the low-power solar energy harvester of a sensor node is also used to measure the open circuit voltage and the current at the maximum power point (IMPP), which allows us to determine solar irradiance and compensate for its temperature drift. The power consumption and cost added to the original solar energy harvester are minimal. Experimental results show that the relation between the measured IMPP and solar irradiance is linear for radiation above 50¿W/m2, and the relative uncertainty limit achieved for the slope is ±2.4% due the light spectra variation. The relative uncertainty limit of daily solar insolation is below ±3.6% and is hardly affected by the so called cosine error, i.e. the error caused by reflection and absorption of light in PV panel surface.Peer ReviewedPostprint (author's final draft

A solar irradiance estimation technique via curve fitting based on dual-mode Jaya optimization

Solar irradiance is a crucial environmental parameter for optimal control of photovoltaic (PV) systems. However, precise measurements of the solar irradiance are difficult since the irradiation sensors (i.e., pyranometer or pyrheliometer) are expensive and hard to calibrate. This paper proposes a cost-effective and accurate method for estimating the solar irradiance with a PV module via curve fitting. A dual-mode Jaya (DM-Jaya) optimization algorithm is introduced to extract the real-time value of solar irradiance from the measured PV characteristics data by using two search strategies. The step sizes of a random walk are taken from even and Lévy distribution distributions in different searching phases. Compared with the traditional irradiance sensors, the proposed estimator does not require additional circuit and obtains relatively lower error rates. A comparative study of seven population-based optimization algorithms for the optimal design of the estimator is presented. These algorithms include particle swarm optimization (PSO), cuckoo search (CS), Jaya, simulated annealing (SA), genetic algorithm (GA), supply-demand-based optimization (SDO), and the proposed DM-Jaya algorithm. Simulations and experimental results reveal that DM-Jaya outperforms the other optimization algorithms in terms of the estimation speed and accuracy

Physically-based parameterization of spatially variable soil and vegetation using satellite multispectral data

A stochastic-geometric landsurface reflectance model is formulated and tested for the parameterization of spatially variable vegetation and soil at subpixel scales using satellite multispectral images without ground truth. Landscapes are conceptualized as 3-D Lambertian reflecting surfaces consisting of plant canopies, represented by solid geometric figures, superposed on a flat soil background. A computer simulation program is developed to investigate image characteristics at various spatial aggregations representative of satellite observational scales, or pixels. The evolution of the shape and structure of the red-infrared space, or scattergram, of typical semivegetated scenes is investigated by sequentially introducing model variables into the simulation. The analytical moments of the total pixel reflectance, including the mean, variance, spatial covariance, and cross-spectral covariance, are derived in terms of the moments of the individual fractional cover and reflectance components. The moments are applied to the solution of the inverse problem: The estimation of subpixel landscape properties on a pixel-by-pixel basis, given only one multispectral image and limited assumptions on the structure of the landscape. The landsurface reflectance model and inversion technique are tested using actual aerial radiometric data collected over regularly spaced pecan trees, and using both aerial and LANDSAT Thematic Mapper data obtained over discontinuous, randomly spaced conifer canopies in a natural forested watershed. Different amounts of solar backscattered diffuse radiation are assumed and the sensitivity of the estimated landsurface parameters to those amounts is examined

Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations

The increased digitalisation and monitoring of the energy system opens up numerous opportunities to decarbonise the energy system. Applications on low voltage, local networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management. Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties. Despite this urgent need, there has not yet been an extensive investigation into the current state-of-the-art of low voltage level forecasts, other than at the smart meter level. This paper aims to provide a comprehensive overview of the landscape, current approaches, core applications, challenges and recommendations. Another aim of this paper is to facilitate the continued improvement and advancement in this area. To this end, the paper also surveys some of the most relevant and promising trends. It establishes an open, community-driven list of the known low voltage level open datasets to encourage further research and development.Comment: 37 pages, 6 figures, 2 tables, review pape

Data Mining Models for Short Term Solar Radiation Prediction and Forecast-Based Assessment of Photovoltaic Facilities

Solar radiation prediction is useful to integrate photovoltaic power plants into the electrical system. Integrating energy generation in urban environments is interesting because that is where the most energy is consumed and avoids wasting energy in transport infrastructure. Renewable energies are often the easiest to integrate into these environments because they require less infrastructure and cause fewer problems related to noise, dirt, pollution, etc. The overall objective of this thesis is to develop data mining models to forecast solar global radiation 24 hours ahead and to use these predictions to evaluate the performance of photovoltaic systems. The specific objectives are: 1. Propose an index that allows us to remove the seasonal and daily trends observed in global hourly radiation data. 2. Analyze the different sources of meteorological variables that can be used to predict solar radiation and use API's to access external sources of meteorological data. 3. Develop data mining models that allow including the different relationships observed between the radiation values of the next day depending on the values of the current day radiation and other meteorological parameters. 4. Development of a web system that include the proposed models for short-term radiation forescasting and integrate the developed models in the evaluation models of photovoltaic systems. Chapter 3 introduces the methods and models used in this work (Cumulative Probability Distribution Function, Artificial Neural Networks and Support Vector Machines). Also classification methods are presented (Decision Trees and Support Vector Machines for Classification). Performance metrics are presented to measure the accuracy of the proposed models. The data sets and data sources used in this work to test the proposed models are presented, including data from the meteorological station installed at University of Malaga, data from OpenWeatherMap website and data from AEMET (Agencia Estatal de Meteorología). Chapter 4 is dedicated to the solar radiation fundamentals, including astronomical concepts related to Earth-Sun position, characterization of solar radiation hourly series, clearnes index, used to remove seasonal trends, persistence model, used to compare with proposed models and the forecast skill, based on persistence model and used as reference model as well. Chapter 5 introduces a model to model and characterize hourly solar global radiation using statistical methods like CPDF, K-means, and also using the clearness index. This models aims to predict the hourly solar radiation using the daily clearness index as input. Chapter 6 details the proposed model to forecast the hourly global solar radiation using data mining methods and daily profiles of clearness index. K-means is again used to cluster daily solar radiation profiles, then a new variable is defined from the clearness index daily profiles. Support Vector Machines, Decision Trees and Artificial Neural Networks are used to predict the desired hourly solar radiation values. Chapter 7 presents a methodology to assess solar power plants performance based on forecasted solar radiation. A OPC-based system is presented, which is able to obtain data from a large variety of equipment, then an algorithm to assess the performance of the plants is presented

Intelligent Data Analysis for Energy Management

Predictive data analysis has been identified as essential to support intelligent energy management for better energy sustainability and efficiency. Previous studies have showcased that predicted energy information can benefit consumers economically by optimising energy usage while assisting energy suppliers in efficiently planning power distribution and implementing DR energy management. Recent advances in the Internet of Things (IoT) and Information and Communication Technologies (ICT) simplify the collection of desired energy data streams for further informatics analysis. With such energy data, machine learning (ML) prevails to effectively infer future knowledge associated with online energy resource scheduling, e.g., renewable energy generation, load demands and electricity prices. Although some early efforts have been dedicated to incorporating ML into energy management, computation resource limitations and data scarcity are two pressing challenges for on-site predictive energy analysis. Due to privacy concerns, users prefer on-premise model establishment instead of placing the training task in the cloud and sharing sensitive energy data. But most ML algorithms rely heavily on solid computational resources and vast amounts of labelled data to succeed. Users are often unable to fulfil the requirements in real-world scenarios. To this end, this thesis uses different perspectives to propose several affordable solutions for performing on-demand intelligent data analysis on local resource-constrained devices. Also, three algorithm-specific training frameworks have been developed to solve data shortage by leveraging easily obtainable but extensive data sources based on transfer learning and federated learning. We implement our design under practical settings for photovoltaic (PV) power prediction and non-intrusive load monitoring (NILM) as case studies to fully evaluate their performances

Smart Energy Management for Smart Grids

This book is a contribution from the authors, to share solutions for a better and sustainable power grid. Renewable energy, smart grid security and smart energy management are the main topics discussed in this book

Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts-Volume II

The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications, such as hybrid and microgrid power systems based on the Energy Internet, Blockchain technology, and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above

Control and management of energy storage systems in microgrids

The rate of integration of the renewable energy sources in modern grids have significantly increased in the last decade. These intermittent, non-dispatchable renewable sources, though environment friendly tend to be grid unfriendly. This is precisely due to the issues pertaining to grid congestion, voltage regulation and stability of grids being reported as a result of the incorporation of renewable sources. In this scenario, the use of energy storage systems (ESS ) in electric grids is being widely proposed to overcome these issues. However, integrating energy storage systems alone will not compensate for the issue created by renewable generation. The control and management of the ESS should be done optimally so that their full capabilities are exploited to overcome the issues in the power grids and to ensure their lower cost of investment by prolonging ESS lifetime through minimising degradation. Motivated by this aspect this Ph.D work focusses on developing an efficient, optimal control and management strategy for ESS in a microgrid, especially hybrid ESS. The Ph.D work addresses this issue by proposing a hierarchical control scheme comprising of a lower power management and higher energy management stage with contributions in each stage. In the power management stage this work focusses on improving aspects of real time control of power converters interfacing ESS to grid and the microgrid system as whole. The work proposes control systems with improved dynamic behaviour for power converters based on the reset control framework. In the microgrid control the work presents a primary+secondary control scheme with improved voltage regulation performance under disturbances, using an observer. The real time power splitting strategies among hybrid ESS accounting for the ESS operating efficiencies and degradation mechanisms will also be addressed in the primary+secondary control of power management stage. The design criteria, stability and robustness analysis will be carried out, along with simulation or experimental verifications. In the higher level energy management stage, the contribution of this work involves application of an economic MPC framework for the management of ESS in microgrids. The work specifically addresses the problems of mitigating grid congestion from renewable power feed-in, minimising ESS degradation and maximising self consumption of generated renewable energy using the MPC based energy management system. A survey of the forecasting methods that can be used for MPC will be carried out and a neural network based forecasting unit for time series prediction will be developed. The practical issue of accounting for forecasting error in the decision making of MPC will be addressed and impact of the resulting conservative decision making on the system performance will be analysed. The improvement in performance with the proposed energy management scheme will be demonstrated and quantified.La integración de las fuentes de energía renovables en las redes modernas ha aumentado significativamente en la última década. Estas fuentes renovables, aunque muy convenientes para el medio ambiente son de naturaleza intermitente, y son no panificables, cosa que genera problemas en la red de distribución. Esto se debe precisamente a los problemas relacionados con la congestión de la red y la regulación del voltaje. En este escenario, el uso de sistemas de almacenamiento de energía (ESS) en redes eléctricas está siendo ampliamente propuesto para superar estos problemas. Sin embargo, la integración de sistemas de almacenamiento de energía por sí solos no compensará el problema creado por la generación renovable. El control y la gestión del ESS deben realizarse de manera óptima, de modo que se aprovechen al máximo sus capacidades para superar los problemas en las redes eléctricas, garantizar un coste de inversión razonable y prolongar la vida útil del ESS minimizando su degradación. Motivado por esta problemática, esta tesis doctoral se centra en desarrollar una estrategia de control y gestión eficiente para los ESS integrados en una microrred, especialmente cuando se trata de ESS de naturaleza. El trabajo de doctorado propone un esquema de control jerárquico compuesto por un control de bajo nivel y una parte de gestión de energía operando a más alto nivel. El trabajo realiza aportaciones en los dos campos. En el control de bajo nivel, este trabajo se centra en mejorar aspectos del control en tiempo real de los convertidores que interconectan el ESS con la red y el sistema de micro red en su conjunto. El trabajo propone sistemas de control con comportamiento dinámico mejorado para convertidores de potencia desarrollados en el marco del control de tipo reset. En el control de microrred, el trabajo presenta un esquema de control primario y uno secundario con un rendimiento de regulación de voltaje mejorado bajo perturbaciones, utilizando un observador. Además, el trabajo plantea estrategias de reparto del flujo de potencia entre los diferentes ESS. Durante el diseño de estos algoritmos de control se tienen en cuenta los mecanismos de degradación de los diferentes ESS. Los algoritmos diseñados se validarán mediante simulaciones y trabajos experimentales. En el apartado de gestión de energía, la contribución de este trabajo se centra en la aplicación del un control predictivo económico basado en modelo (EMPC) para la gestión de ESS en microrredes. El trabajo aborda específicamente los problemas de mitigar la congestión de la red a partir de la alimentación de energía renovable, minimizando la degradación de ESS y maximizando el autoconsumo de energía renovable generada. Se ha realizado una revisión de los métodos de predicción del consumo/generación que pueden usarse en el marco del EMPC y se ha desarrollado un mecanismo de predicción basado en el uso de las redes neuronales. Se ha abordado el análisis del efecto del error de predicción sobre el EMPC y el impacto que la toma de decisiones conservadoras produce en el rendimiento del sistema. La mejora en el rendimiento del esquema de gestión energética propuesto se ha cuantificado.La integració de les fonts d'energia renovables a les xarxes modernes ha augmentat significativament en l’última dècada. Aquestes fonts renovables, encara que molt convenients per al medi ambient són de naturalesa intermitent, i són no panificables, cosa que genera problemes a la xarxa de distribució. Això es deu precisament als problemes relacionats amb la congestió de la xarxa i la regulació de la tensió. En aquest escenari, l’ús de sistemes d'emmagatzematge d'energia (ESS) en xarxes elèctriques està sent àmpliament proposat per superar aquests problemes. No obstant això, la integració de sistemes d'emmagatzematge d'energia per si sols no compensarà el problema creat per la generació renovable. El control i la gestió de l'ESS s'han de fer de manera _optima, de manera que s'aprofitin al màxim les seves capacitats per superar els problemes en les xarxes elèctriques, garantir un cost d’inversió raonable i allargar la vida útil de l'ESS minimitzant la seva degradació. Motivat per aquesta problemàtica, aquesta tesi doctoral es centra a desenvolupar una estratègia de control i gestió eficient per als ESS integrats en una microxarxa, especialment quan es tracta d'ESS de natura híbrida. El treball de doctorat proposa un esquema de control jeràrquic compost per un control de baix nivell i una part de gestió d'energia operant a més alt nivell. El treball realitza aportacions en els dos camps. En el control de baix nivell, aquest treball es centra a millorar aspectes del control en temps real dels convertidors que interconnecten el ESS amb la xarxa i el sistema de microxarxa en el seu conjunt. El treball proposa sistemes de control amb comportament dinàmic millorat per a convertidors de potència desenvolupats en el marc del control de tipus reset. En el control de micro-xarxa, el treball presenta un esquema de control primari i un de secundari de regulació de voltatge millorat sota pertorbacions, utilitzant un observador. A més, el treball planteja estratègies de repartiment de el flux de potència entre els diferents ESS. Durant el disseny d'aquests algoritmes de control es tenen en compte els mecanismes de degradació dels diferents ESS. Els algoritmes dissenyats es validaran mitjanant simulacions i treballs experimentals. En l'apartat de gestió d'energia, la contribució d'aquest treball se centra en l’aplicació de l'un control predictiu econòmic basat en model (EMPC) per a la gestió d'ESS en microxarxes. El treball aborda específicament els problemes de mitigar la congestió de la xarxa a partir de l’alimentació d'energia renovable, minimitzant la degradació d'ESS i maximitzant l'autoconsum d'energia renovable generada. S'ha realitzat una revisió dels mètodes de predicció del consum/generació que poden usar-se en el marc de l'EMPC i s'ha desenvolupat un mecanisme de predicció basat en l’ús de les xarxes neuronals. S'ha abordat l’anàlisi de l'efecte de l'error de predicció sobre el EMPC i l'impacte que la presa de decisions conservadores produeix en el rendiment de el sistema. La millora en el rendiment de l'esquema de gestió energètica proposat s'ha quantificat

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy