Harnessing the power of neural networks for the investigation of solar-driven membrane distillation systems under the dynamic operation mode

Accurate modeling of solar-driven direct contact membrane distillation systems (DCMD) can enhance the commercialization of these promising systems. However, the existing dynamic mathematical models for predicting the performance of these systems are complex and computationally expensive. This is due to the intermittent nature of solar energy and complex heat/mass transfer of different components of solar-driven DCMD systems (solar collectors, MD modules and storage tanks). This study applies a machine learning-based approach to model the dynamic nature of a solar-driven DCMD system for the first time. A small-scale rig was designed and fabricated to experimentally assess the performance of the system over 20 days. The predictive capabilities of two neural network models: multilayer perceptron (MLP) and long short-term memory (LSTM) were then comprehensively examined to predict the permeate flux, efficiency and gain-output-ratio (GOR). The results showed that both models can efficiently predict the dynamic performance of solar-driven DCMD systems, where MLP outperformed the LSTM model overall, especially in the prediction of efficiency. Additionally, it was indicated that the accuracy of the models for the prediction of GOR can be significantly improved by increasing the size of the dataset

Renewable Energy Resource Assessment and Forecasting

In recent years, several projects and studies have been launched towards the development and use of new methodologies, in order to assess, monitor, and support clean forms of energy. Accurate estimation of the available energy potential is of primary importance, but is not always easy to achieve. The present Special Issue on ‘Renewable Energy Resource Assessment and Forecasting’ aims to provide a holistic approach to the above issues, by presenting multidisciplinary methodologies and tools that are able to support research projects and meet today’s technical, socio-economic, and decision-making needs. In particular, research papers, reviews, and case studies on the following subjects are presented: wind, wave and solar energy; biofuels; resource assessment of combined renewable energy forms; numerical models for renewable energy forecasting; integrated forecasted systems; energy for buildings; sustainable development; resource analysis tools and statistical models; extreme value analysis and forecasting for renewable energy resources

Energy management and guidelines to digitalisation of integrated natural gas distribution systems equipped with expander technology

In a swirling dynamic interaction, digital innovation, environment and anthropological evolution are swiftly shaping the smart grid scenario. Integration and flexibility are the keywords in this emergent picture characterised by a low carbon footprint. Digitalisation, within the natural limits imposed by the thermodynamics, seems to offer excellent opportunities for these purposes. Of course, here starts a new challenge: how digital technologies should be employed to achieve these objectives? How would we ensure a digital retrofit does not lead to a carbon emission increase? In author opinion, as long as it remains a generalised question, none answer exists: the need to contextualise the issue emerges from the variety of the characteristics of the energy systems and from their interactions with external processes. To address these points, in the first part of this research, the author presented a collection of his research contributions to the topic related to the energy management in natural gas pressure reduction station equipped with turbo expander technology. Furthermore, starting from the state of the art and the author's previous research contributions, the guidelines for the digital retrofit for a specific kind of distributed energy system, were outlined. Finally, a possible configuration of the ideal ICT architecture is extracted. This aims to achieve a higher level of coordination involving, natural gas distribution and transportation, local energy production, thermal user integration and electric vehicles charging. Finally, the barriers and the risks of a digitalisation process are critically analysed outlining in this way future research needs

Renewable energy in eco-industrial parks and urban-industrial symbiosis: A literature review and a conceptual synthesis

Replacing fossil fuels with renewable energy sources is considered as an effective means to reduce carbon emissions at the industrial level and it is often supported by local authorities. However, individual firms still encounter technical and financial barriers that hinder the installation of renewables. The eco-industrial park approach aims to create synergies among firms thereby enabling them to share and efficiently use natural and economic resources. It also provides a suitable model to encourage the use of renewable energy sources in the industry sector. Synergies among eco-industrial parks and the adjacent urban areas can lead to the development of optimized energy production plants, so that the excess energy is available to cover some of the energy demands of nearby towns. This study thus provides an overview of the scientific literature on energy synergies within eco-industrial parks, which facilitate the uptake of renewable energy sources at the industrial level, potentially creating urban-industrial energy symbiosis. The literature analysis was conducted by arranging the energy-related content into thematic categories, aimed at exploring energy symbiosis options within eco-industrial parks. It focuses on the urban-industrial energy symbiosis solutions, in terms of design and optimization models, technologies used and organizational strategies. The study highlights four main pathways to implement energy synergies, and demonstrates viable solutions to improve renewable energy sources uptake at the industrial level. A number of research gaps are also identified, revealing that the energy symbiosis networks between industrial and urban areas integrating renewable energy systems, are under-investigated

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

Deployment and control of adaptive building facades for energy generation, thermal insulation, ventilation and daylighting: A review

A major objective in the design and operation of buildings is to maintain occupant comfort without incurring significant energy use. Particularly in narrower-plan buildings, the thermophysical properties and behaviour of their façades are often an important determinant of internal conditions. Building facades have been, and are being, developed to adapt their heat and mass transfer characteristics to changes in weather conditions, number of occupants and occupant’s requirements and preferences. Both the wall and window elements of a facade can be engineered to (i) harness solar energy for photovoltaic electricity generation, heating, inducing ventilation and daylighting (ii) provide varying levels of thermal insulation and (iii) store energy. As an adaptive façade may need to provide each attribute to differing extents at particular times, achieving their optimal performance requires effective control. This paper reviews key aspects of current and emerging adaptive façade technologies. These include (i) mechanisms and technologies used to regulate heat and mass transfer flows, daylight, electricity and heat generation (ii) effectiveness and responsiveness of adaptive façades, (iii) appropriate control algorithms for adaptive facades and (iv) sensor information required for façade adaptations to maintain desired occupants’ comfort levels while minimising the energy use

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

A Review of Solar Hybrid Photovoltaic-Thermal (PV-T) Collectors and Systems

In this paper, we provide a comprehensive overview of the state-of-the-art in hybrid PV-T collectors and the wider systems within which they can be implemented, and assess the worldwide energy and carbon mitigation potential of these systems. We cover both experimental and computational studies, identify opportunities for performance enhancement, pathways for collector innovation, and implications of their wider deployment at the solar-generation system level. First, we classify and review the main types of PV-T collectors, including air-based, liquid-based, dual air–water, heat-pipe, building integrated and concentrated PV-T collectors. This is followed by a presentation of performance enhancement opportunities and pathways for collector innovation. Here, we address state-of-the-art design modifications, next-generation PV cell technologies, selective coatings, spectral splitting and nanofluids. Beyond this, we address wider PV-T systems and their applications, comprising a thorough review of solar combined heat and power (S–CHP), solar cooling, solar combined cooling, heat and power (S–CCHP), solar desalination, solar drying and solar for hydrogen production systems. This includes a specific review of potential performance and cost improvements and opportunities at the solar-generation system level in thermal energy storage, control and demand-side management. Subsequently, a set of the most promising PV-T systems is assessed to analyse their carbon mitigation potential and how this technology might fit within pathways for global decarbonization. It is estimated that the REmap baseline emission curve can be reduced by more than 16% in 2030 if the uptake of solar PV-T technologies can be promoted. Finally, the review turns to a critical examination of key challenges for the adoption of PV-T technology and recommendations