This PDF file contains the front matter associated with SPIE Proceedings Volume 12513, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

In this work, we designed a wavelength-selective thermal meta-emitter consisting of a periodic array of rings. These rings generate localized surface plasmons to provide large emissivity, narrow band, and highly directional thermal radiation. The large emissivity enables large power throughput. The narrow spectrum matches the GaSb PV cell absorption spectrum and thus reduces the power wasted in the cell and increase the power conversion efficiency (PCE). The collimated emission enables large distance between emitter and cell distance to reduce convectional heating of PV cells. The emitter consists of a thin dielectric layer with an etched ring surface, and this surface is covered with a metal. Thermal emission emerges from the planar side of the dielectric surface when the metal cover is heated by a thermal source. We model different dielectric and metal materials to determine the optimum choice of materials for the meta-emitter. The considered materials are SiC and AlN for dielectric and gold, tungsten, rhodium, tantalum, molybdenum, niobium, chromium, and platinum for metal. We found that while AlN provides a larger power selectivity, SiC yields a better overall PCE because of the better matched emission spectrum with the GaSb PV cell. For the metal cover, we found that tantalum has the second largest power selectivity after gold. Since gold has a low melting point unsuitable for high temperature TPV operation, tantalum becomes the most suitable material for the meta-emitter.

Nanoenergetic materials offer high-density energy storage that may be reacted to produce heat and release gaseous products. However, the fundamental reaction mechanisms of isolated nanoenergetic fuel particles (typically aluminum - AL) remain poorly understood. In this study, the structure-property relationships of photothermally heated AL nanoparticles are explored using an optical microscope setup and laser-based photothermal heating. Our research explores optical imaging and computer vision techniques to measure distinctive features from images captured before and after directed energy excitation of nanoenergetic particles. These features are used to describe the reactions in the pursuit of creating an automated nanoenergetic material reaction characterization model. Specifically, optical imagery of nano-aluminum particle clusters is taken before and after the reaction is initiated via laser irradiation. Through image preprocessing and registration, we remove untargeted nanoparticle clusters and align the images. We then classify particle reactions into three classes, Spallation, Sintering, or a Combination of both, through an examination of various features derived from our preprocessed imagery. These techniques serve as tools to aid researchers in quantitatively measuring reaction properties, such as loss of mass, and accelerating the search for optimized reaction parameters.

The majority of air and ground vehicle systems are reliant on specialized diesel fuel. This reliance increases the likelihood that operations may be operating in an energy constrained or contested environment given the state of international relations between global energy providers and consumers. Such a vulnerability has the potential to reduce operational effectiveness or efficiency if logistical supply chains were interrupted or impeded. The most effective and efficient methodology to reduce reliance on specialized diesel fuel is to hybridize our energy and power (E&P) systems, and support more diverse E&P solutions including renewable energy generation (photovoltaic (PV) arrays, wind generation, wave energy converters), nuclear, or decaying isotopes. In this paper/presentation, we present our advances in developing a set of predictive artificial intelligence and machine learning (AI/ML) algorithms that forecast E&P capabilities of a photovoltaic array indirectly and directly. These milestones are a product of two separate types of AI/ML approaches: (1) developing AI/ML based algorithms that predict ambient and panel temperature from various atmospheric-based sensor data which can then be used in combination with an irradiance profile and a MATLAB Simulink model to predict the E&P capabilities of the PV array (indirect method), and (2) developing AI/ML which predicts the resulting E&P capabilities of the PV array, using various atmospheric-based sensor data (direct method).

Converting high-temperature heat to electricity can theoretically provide new sources of energy, but there are several practical barriers to realization. Foremost among these challenges is the lack of data on visibly transparent materials with a suitable set of properties up to high temperatures. In this study, we examine candidate material options and report experimental findings of their key properties across a range of optical wavelengths and temperatures. We then perform a simulation to confirm that we have a correct and consistent understanding of their properties, and use that to design more complex structures for future thermophotovoltaic selective emitters, which can efficiently radiate heat that can be converted into electricity through a device similar to a solar cell.

In this paper we want to illustrate the potential estimation for installing photovoltaic systems on rooftop or horizontal building surfaces in residential areas. Different areas will be measured for residential users, and these will be measured using Google Earth and PVWatts software from NREL. The kWh per Year were calculated from the area in PVWatts and with the real consumption of 6 user’s examples. The results showed for all users the areas on their rooftops have the potential to generate three to four times more than what was calculated from the real consumption of each user. With these results and these six residential random data, it is observed that the basic consumption of each user and a surplus could be generated to share with the network microgrids and other residential users. The next step would be quantifying and modeling the benefits of the use of photovoltaic electrical energy arrangements on the rooftops of the users, as a complementary value in supporting the current grid in Puerto Rico. Also, at a global level, to reduce economic dependence on the consumption of fossil fuels and the emission of greenhouse gases, there has been the development of the use of renewable energies.

The sliding innovation filter (SIF) is a recently developed estimation technique that has gained widespread use. It is a predictor-corrector filter that utilizes a hyperplane and applies a force to allow estimates to fluctuate about it. SIF belongs to the same family as the smooth variable structure filter and sliding mode observer, and it is stable and robust in the face of uncertainties. This paper discusses the use of SIF for estimating the states of Power Converters, which play a crucial role in Electric Vehicles (EVs) by converting high-voltage DC from the battery to low-voltage AC used by the motor. One of the main challenges in Power Converters is accurately estimating their states, such as input voltage, output voltage, and inductor current, which are critical for optimal control and efficient operation. The SIF has demonstrated promising results in addressing this challenge.

Capacitive deionization is a promising electrochemical technology employed in water treatment applications. Among the various water desalination and treatment technologies, capacitive deionization technology has many advantages and appreciably increases desalination efficiency. CDI desalinates the Water via the electrosorption of ions inside the porous structure of two oppositely charged electrodes. The electrodes are considered the core of the CDI system. The carbon flow electrode is a new design for improving salt removal efficiency (SRE). Thus, developing a numerical model to predict CDI salt removal efficiency (SRE) and understanding how electrodes jointly contribute to desalination is crucial for rational FCDI system design. This paper demonstrates the concept of using Artificial intelligence-based modeling to predict the electrosorption capacity of FCDI with reasonable accuracy based on the important flow electrode and process features. The contribution and relative importance of each feature in deionization and the cost analysis framework of FCDI are determined and validated. This study shows that artificial neural networks (ANN) have strong abilities in predicting the nonlinear behavior of the CDI system and in revealing each feature’s role of the electrode in desalination. Two hidden layers with 14 and 11 neurons in the first and second hidden layers have been used. The model has good regression of 100% for training, 99.67% for validation 99.809% for testing, and 99.908% for the overall system. The 𝑅𝑀𝑆𝐸, 𝑀𝐴𝐸, and 𝑅𝑀𝑆𝐸_{%𝐸𝑟𝑟𝑜𝑟} were significantly small.

The temperature difference between the earth (288K) and outer space (3K) can operate a heat engine and generate electricity. We can use an optical emitter to radiate strongly within the atmospheric transparency window (8-13µm) and cool the emitter below the ambient temperature. A thermoelectric generator can then harness the temperature difference between the emitter and ambient to generate electricity. However, parasitic conduction between air and the optical emitter, inefficient thermal contacts, and thermal conduction through the thermoelectric itself severely penalize the maximum electrical power generated. We minimize these three penalty terms by careful engineering while increasing the outgoing optical radiation and experimentally demonstrating a record >100mW/m2 for this technology. We can use this efficient, simple, and cheap electricity generation method to deliver power in remote places and the most efficient ambient energy harvesting scheme.

Multilayer optical coatings are used to control the reflection, transmission, and absorption of light in a variety of applications. However, the design of these coatings is a challenging and computationally expensive task. In this article, we present an inverse-design method that provides an efficient and automated way to design one-dimensional optical coatings with superior optical properties. Our approach involves starting with a desired optical response and working backward to determine the optimal coating structure that can achieve that response. To accomplish this, we define a figure of merit (FOM) that maximizes the reflection or transmission within a desired spectral range and polarization. We identify the design variables, calculate the derivatives of the FOM, and iteratively update the thickness of each layer until the desired FOM is achieved. Our method applies to a broad variety of one-dimensional optical coatings, and we demonstrate its effectiveness by designing broadband mirrors and anti-reflection coatings. Our results show that the optimized structures outperform the initial fixed-thickness structures, highlighting the potential of inverse design to simplify the design and fabrication of complex multilayer optical coatings with greater precision and efficiency.

This paper wants to show the concept of a system that has been designed to automatically detect any voltage fluctuation (high or low) and/or power outage in the electrical network, thus allowing a continuous and more stable supply of electrical energy. The control module will provide a signal to the automatic transfer switch in the event of a power outage and/or voltage fluctuation (≥30 seconds), allowing the emergency generator to provide a constant flow of power to meet utility demand. load. If a voltage variation greater than 30 seconds occurs, the circuit will switch the load to emergency mode. Most commercially available automatic transfer switches are stand-alone units, with voltage monitoring systems purchased separately, whereas here in the engineered system both systems have been combined into a single, very economical control module. The concept designed were similar to those obtained in the simulations, the behavior of the system in the face of changes in the potential difference and electron flow was higher than expected, providing a constant output signal that controlled the devices connected to the system. without hesitation. These results demonstrated what could be achieved if the control module is deployed and used in the areas most affected by power outages and voltage fluctuations. In conclusion, the operation of the designed control module will provide a fundamental advantage, which will be the total independence of the human factor in the event of an interruption and/or fluctuation (low or high) in the energy service.

The demand for renewable energy has been increasing in recent years, including the market of micro wind turbines. However, commercial products lack of a proper control system and emergency braking capabilities. This paper proposes an integrated control system for a micro wind turbine with an electronic braking system. The prototype uses off-the-shelf components and does not need constant maintenance. The three-phase generator output signal is rectified with an AC-DC converter, regulated with a DC-DC converter, and a series of relays will control the power flow. A capacitor bank stabilizes the voltage between the converters. Active filters minimize the noise from the voltage and current sensors while protecting the microcontroller’s Analog-to-Digital Converter (ADC) from voltage spikes. The microcontroller measures the voltage, current, and stop button data to determine the state that the turbine should operate and apply electronic braking if needed. The electronic brake uses a MOSFET that gradually loads the turbine to self-induce an opposing electromotive force that slows down the turbine. This control system can be implemented on commercial micro wind turbines and has been successfully tested on a 400W commercial turbine.

The solar spectrum reaching Earth spans from ultraviolet to infrared wavelengths with a cutoff at around 2.5 μm. About 30% of the irradiance is absorbed by the atmosphere, and the rest is absorbed by the Earth's surface. The radiance available for generating renewable energy ranges from 400 nm to 2.0 μm. Silicon and III-V materials are used for photovoltaic (PV) cells. The PV cells reflect 30% of incoming radiation and to reduce this reflection, antireflection coating (ARC) is being used. Conventional techniques such as single or stacked multi-layer ARC or micro-nanostructures ARC are used. However, they lack in providing broadband and omnidirectional transmission, which limits its conversion efficiency in today’s PV cells. We present broadband ARC achieved with an inverse transfer design, a prospect towards significantly high conversion efficiency PV cells. The ARC exhibit significantly increased transmission more than 96% over the solar spectrum ranges up to 2.5 μm, even with wide angles of incidence from ~ 0° to >70°. This transmission over the same angles of incidence, is significantly higher than that of ARC based on quarter-wavelength optical thickness (QWOT), a state-of-art ARC. The results also rival the transmission performance of state-of-the-art nanostructure-based ARC even at large angles of incidence, but are significantly easier to fabricate using standard e-beam evaporation and/or sputtering. The results show over broadband spectrum ranges, radiation back due to reflection makes to zero, leading to significantly increased conversion efficiency of the PV cells.

With the modernization of cities, the concept of the Internet of Things (IoT) is gaining popularity and becoming a vital source of smart developments. An added advantage of solar energy systems, IoT applications enable automatic and remote sensing, processing, and execution. IoT ensures that information is easily available and accessible from any location around the world. The IoT applications improve the visibility, scalability, and cost-effectiveness of solar energy generation and service. A bibliometric analysis of scientific publications in the field of solar PV and IoT applications was conducted using the Scopus database between the years 2011 and 2023. Many studies of technological development have been discovered, and some insights can still be approached in such a way that the practical implementation of photovoltaic solar systems is improved. Since 2013, there has been an increase in the rate of publications. The majority of these studies were conducted in India, and the most common IoT applications reported were in the fields of computer science and engineering. This article identifies knowledge gaps to inform the community, industry, and government officials about IoT research directions in the solar energy field.