Solar Energy Management Research Articles

AI-based forecasting for optimised solar energy management and smart grid efficiency

This paper considers two pertinent research inquiries: ‘Can an AI-based predictive framework be utilised for the optimisation of solar energy management?’ and ‘What are the ways in which the AI-based predictive framework can be integrated within the Smart Grid infrastructure to improve grid reliability and efficiency?’ The study deploys a Deep Learning model based on Long Short-Term Memory techniques, leading to refined accuracy in solar electricity generation forecasts. Such an AI-supported methodology aids power grid operators in comprehensive planning, thereby ensuring a robust electricity supply. The effectiveness of this framework is tested using performance metrics such as MAE, RMSE, nMAE, nRMSE, and R 2 . A persistent model is utilised as a reference for comparison. Despite a slight decrease in predictive precision with the expansion of the forecast horizon, the proposed AI-based framework consistently surpasses the persistent model, particularly for horizons beyond two hours. Therefore, this research underscores the potential of AI-based prediction in fostering efficient solar energy management and enhancing Smart Grid reliability and efficiency.

Optimizing Energy and Reserve Minimization in a Sustainable Microgrid with Electric Vehicle Integration: Dynamic and Adjustable Manta Ray Foraging Algorithm

The growing presence of EVs in regional microgrids introduces increased variability and uncertainty in the areas’ load profiles. This paper presents a novel approach for optimizing energy and reserve minimization in a sustainable integrated microgrid with electric vehicles (EVs) by the use of the dynamic and adjustable Manta Ray Foraging (DAMRF) algorithm. The DAMRF algorithm harnesses the inherent flexibility of EVs as controllable loads and develops a comprehensive dispatch model for a large-scale EV response. The model takes into account the management, operational, and environmental costs associated with load fluctuations in the microgrid. Simulation evaluations conducted based on a practical microgrid environment validate the effectiveness of our wind–solar energy storage and management strategy. The results showcase significant improvements in energy and reserve minimization, highlighting the potential advantages of integrating EVs into sustainable microgrid systems. In addition, the DAMRF algorithm achieves lower environmental pollution control costs (USD 8000) compared to the costs associated with the Genetic Algorithm (GA) (USD 8654.639) and PSO (USD 8579.546), emphasizing its ability to effectively control and minimize environmental pollution. In addition, the DAMRF algorithm offers a more cost-effective solution for managing the power grid, and the shorter solution running time of the DAMRF is almost the same as PSO’s quicker decision-making and response times, enhancing the overall responsiveness and adaptability of the power grid management system.

Fabrication of functional metamaterials for applications in heat-shielding windows and 6G communications.

Windows with passive multilayer coatings can allow less energy to be used when maintaining comfortable indoor temperatures. As a type of effective solar energy management, these coatings can prevent the generation of excessive heat inside buildings or vehicles by reflecting near-infrared solar radiation (750-2000 nm) while retaining visible light transmission (400-750 nm) over a large range of viewing angles. To prevent overheating, they must also reflect rather than absorb near-infrared radiation. A transparent heat-shielding window is numerically and experimentally demonstrated in this study. High visual transparency (77.2%), near-infrared reflectance (86.1%), and low infrared absorption (<20%) over a wide range of oblique incident angles were achieved using nanometer-scale cross-shaped metamaterials manufactured by electron beam lithography. Furthermore, high terahertz transmittance (up to 82%) was also achieved for 6G communication system applications.

Ensemble Nonlinear Model Predictive Control for Residential Solar Battery Energy Management

Ensemble Nonlinear Model Predictive Control for Residential Solar Battery Energy Management

Spectrally selective nanoparticle-enhanced phase change materials: A study on data-driven optical/thermal properties and application of energy-saving glazing under different climatic conditions

The optical and thermal performances of energy-saving glazing require improvement to reduce peak thermal load and increase solar energy conversion and management efficiency. Herein, it is indispensable to develop novel composite materials that designate the ability to achieve multifunction by thermal load migration as well as radiation transmittance modulation. This study developed a nanoparticle-enhanced phase change material (NEPCM) with spectrally selective and phase change functions. A data-driven algorithm is first developed to evaluate the solar-weighted absorption fraction, integrated irradiance, and spectral irradiance for the considered nanoparticles. The base fluids, nanoparticle diameter, concentrations, and proportions are taken into account in the program, and adjustments to the properties of NEPCM can be easily made. The NEPCM properties exhibit a high blocking performance of near-infrared solar radiation by 30.9%, while keeping visible transmittance at 51.8% using ATO-Al2O3 NEPCM. Furthermore, the photothermal conversion performance of a NEPCM-filled energy-saving prototype window is explored, which yields a heat absorption rate of up to 0.27 °C/min when the solar irradiance is 800 W/m2. In addition, the energy consumption of transparent envelopes with NEPCM filler in a full-scale building is investigated. The results show that NEPCM regulated glazing potentially provides annual heating energy-saving from 5.1 to 41.7 kWh·m−2·year−1 in three representative climatic conditions in different global locations. Outcomes on the spectrally selective capabilities of the energy-saving window provide ideas for advancing the development of low-carbon transparent building envelopes.

Data driven approach for the management of wind and solar energy integrated electrical distribution network with high penetration of electric vehicles

With the increased penetration of fluctuating renewables and growing population of contemporary loads such as electric vehicles, the uncertainties in the generation and demand in the electric power grids are increasing. This makes the efficient operation and management of these systems challenging. Objective of this study is to propose a real-time management system for EV charging, which maximises the renewable energy utilization. An electric power distribution network with an average and peak demands of 1.51 MW, and 3.6 MW respectively, was chosen for the study. The real time power flow through the network components were analyzed using the OpenDSS model. With a wind power density of 574.51 W/m2 and a solar insolation of 4.14 kWh/m2/day, an optimized renewable energy system consisting of a 2.3 MW wind turbine and 2.61 MWp photovoltaic power plant are proposed for the network. Models based on k-Nearest Neighbors algorithms were developed for predicting the performances of these renewable energy systems at the network area. Based on the load profile, power flow analysis, and the predicted generation from solar and wind systems, a demand side management algorithm has been developed for the charge/discharge scheduling of the electric vehicles connected within the network. The basic objective of the algorithm is to maximize the renewable energy utilization by triggering the charging cycle during the periods of excess renewable energy generation. With an annual contribution of renewables is estimated as 12.61 GWh out of which 9.33 GWh is from wind and 3.29 GWh from solar. Wind from wind and solar energy systems, the proposed scheduling algorithm could contribute 71.56 percent of the charging load demand by the EVs.

Design and Development of Solar Drone

Abstract: The harnessing of solar energy during operations of unmanned aerial vehicle(UAV) provides potential solution to combat energy constraints. This Thesis examines the practicality of mini solar drone and provide experimental validation in regards to energy maximization through solar energy management and flight time optimization.The simulations results are consistent with real time measurements during flight test. This project deals with a quadcopter using solar energy as well as lithium polymer battery as power source combination to give maximum flight output. Using solar panels, it collects the energy during day time for immediate use and also store the remaining part for night as well. The objective is to identify , design and analyze such a reusable solar powered quadcopter for high altitude endurance and longer battery life.

On the Economic Viability of Solar Energy When Upgrading Cellular Networks

The massive increase of data traffic, the widespread proliferation of wireless applications and the full-scale deployment of 5G and the IoT, imply a steep increase in cellular networks energy use, resulting in a significant carbon footprint. This paper presents a comprehensive model to show the interaction between the networking and energy features of the problem and study the economical and technical viability of green networking. Solar equipment, cell zooming, energy management and dynamic user allocation are considered in the upgrading network planning process. We propose a mixed-integer optimization model to minimize long-term capital costs and operational energy expenditures in a heterogeneous on-grid cellular network with different types of base station, including solar. Based on eight scenarios where realistic costs of solar panels, batteries, and inverters were considered, we first found that solar base stations are currently not economically interesting for cellular operators. We next studied the impact of a significant and progressive carbon tax on reducing greenhouse gas emissions (GHG). We found that, at current energy and equipment prices, a carbon tax ten-fold the current value is the only element that could make green base stations economically viable.

Improving the accuracy of daily solar radiation prediction by climatic data using an efficient hybrid deep learning model: Long short-term memory (LSTM) network coupled with wavelet transform

Accurate daily solar radiation prediction is a crucial task for the management and generation of solar energy as one of the alternatives to fossil fuels. In this study, the prediction accuracy of new machine learning methods, wavelet long short-term memory (WLSTM), wavelet multi-layer perceptron artificial neural network (WMLPANN), long short-term memory (LSTM), multi-layer perceptron artificial neural network (MLPANN), and multivariate adaptive regression splines (MARS), was assessed for modeling daily solar radiation using various input combinations of climatic data of maximum and minimum relative humidity, potential evapotranspiration, maximum and minimum temperature, precipitation and wind speed from two stations, Brownstown and Carbondale located in Illinois, USA. For accurate assessment of prediction accuracy of the proposed models, four reliable statistical metrics, root mean squared error (RMSE), Nash–Sutcliffe efficiency coefficient (NSE), coefficient of determination (R2), and One-Tailed Wilcoxon Signed-Rank Test were employed. Comparison of results, based on the RMSE values, indicated that the WLSTM method performed better than the WMLPANN, LSTM, MLPANN and MARS methods in the estimation of solar radiation values at both stations. The average RMSE values of WMLPANN, LSTM, MLPANN, and MARS approaches was decreased by 6%, 4%, 7.3%, and 13.5% using WLSTM method at Brownstown Station, by 6%, 5.2%, 13.2%, and 14.3% at Carbondale Station, respectively. The overall results during the testing phase of both stations revealed the successful application of hybridization of the LSTM model with the wavelet transform technique for improving the prediction accuracy of solar radiation based on climatic parameters.

Solar Radiation and Thermal Convection of Hybrid Nanofluids for the Optimization of Solar Collector

This study aims to show the role of the stagnation point flow in solar optimization in the presence of a Riga plate. This requirement is conceivable in the case of solar energy management with a suitable solar collector covering and visual thermal optimization. Solar energy radiation and thermal convection of glycol (C3H8O2)-based aluminum oxide (Al2O3) and copper (Cu) nanoparticles were used for a solar collector, and were studied in terms of the stagnation point flow theoretically. Stagnation refers to the state of a solar thermal system in which the flux varies in the collection loop to control the extra heating. The CVFEM code was used to analyze the flow in the case of represented stagnation using the FEA-Tools multiple physics software that manages partial derivative equations (PDEs). The streamlined patterns and energy contours for different cases were studied in detail. The transformation equations were treated with the numerical method (RK-4 technique) and showed strong agreement of the physical results corresponding to the initial conditions and boundaries. The results showed that hybrid nanofluids have the advanced capability to enhance the thermal performance of the base solvent and provide uniform distribution to the solar panel. The solar optimization and uniform thermal expansion results are displayed graphically.

The Internet of Things for the Intelligent Management of the Heating of a Swimming Pool by Means of Smart Sensors

By using various smart sensors integrated in a global domotic system, a proper solar thermal management is executed. The goal is to properly manage solar energy for heating swimming pool using various devices installed at home. Swimming pools are a necessity in many communities. In summer, they are a source of refreshment. However, maintaining a swimming pool at an optimal temperature can be a challenge even in the summer months. The use of the Internet of Things in homes has enabled proper management of solar thermal energy, thus significantly improving the quality of life by making homes more comfortable and safer without using additional resources. The houses built today have several smart devices that manage to optimize the energy consumption of the house. The solutions proposed in this study to improve energy efficiency in swimming pool facilities include the installation of solar collectors to heat swimming pool water more efficiently. The installation of smart actuation devices (to efficiently control energy consumption of a pool facility via different processes) together with sensors that provide valuable information on energy consumption in the different processes of a pool facility, can optimize energy consumption thus reducing overall consumption (by 90%) and economic cost (by more than 40%). Together, these solutions can help to significantly reduce energy consumption and economic costs and extrapolate it to different processes of similar characteristics in the rest of the society.

Optimization of Coherent Dynamics of Localized Surface Plasmons in Gold and Silver Nanospheres; Large Size Effects

Noble metal nanoparticles have attracted attention in recent years due to a number of their exciting applications in plasmonic applications, e.g., in sensing, high-gain antennas, structural colour printing, solar energy management, nanoscale lasing, and biomedicines. The report embraces the electromagnetic description of inherent properties of spherical nanoparticles, which enable resonant excitation of Localized Surface Plasmons (defined as collective excitations of free electrons), and the complementary model in which plasmonic nanoparticles are treated as quantum quasi-particles with discrete electronic energy levels. A quantum picture including plasmon damping processes due to the irreversible coupling to the environment enables us to distinguish between the dephasing of coherent electron motion and the decay of populations of electronic states. Using the link between classical EM and the quantum picture, the explicit dependence of the population and coherence damping rates as a function of NP size is given. Contrary to the usual expectations, such dependence for Au and Ag NPs is not a monotonically growing function, which provides a new perspective for tailoring plasmonic properties in larger-sized nanoparticles, which are still hardly available experimentally. The practical tools for comparing the plasmonic performance of gold and silver nanoparticles of the same radii in an extensive range of sizes are also given.

Design of phase change composite with hierarchical energy-transfer pathway via laser-induced graphene for efficient energy storage, conversion, and utilization

To achieve efficient energy harvesting and utilization, phase change composites (PCCs) with high energy storage density, thermal conductivity, and photothermal conversion ability have always been a research focus. Herein, a three-dimensional (3D) porous laser-induced graphene supporting matrix with the plant leaf-mimetic network (PLMN) for loading phase change materials (PCMs) was fabricated by direct laser writing on polybenzoxazine resin (Poly(PH-ddm)). PLMN is designed to consist of laser-induced graphene (LIG) with both vein-like and mesophyll-like structures, the former one mainly responsible for efficient energy harvesting as well as fast heat flow transport and the latter one as the dominant part for high encapsulation of polyethylene glycol (PEG) PCM. The obtained plant leaf-mimetic composite (PLMC) exhibits a large latent heat of 162.3 J/g and high thermal conductivity of 1.18 W/m·K (391.67 % increment than pure PEG). PLMC also shows a high solar energy conversion efficiency of 84.1 %, which benefits from the well-designed PLMN structure as well as the thermal insulation effect of the resin layer. In addition, the resin layer provides mechanical support and flame-retardant function, ensuring the use safety of PLMC. The applications of PLMC for efficient utilization of solar energy and thermal management of electronics have been well-demonstrated. This work provides novel insights into the design and fabrication of PCCs with great potential in solar energy utilization and other thermal energy-related processes.

A review of the mechanical and chemical properties of aluminium alloys AA6262 T6 and its composites for turning process in the CNC

The most common material utilized in mechanical applications, particularly in structural applications, is aluminium alloys. The study of aluminium alloys utilized in the aircraft sector, automotive industry, energy production management and many other industrial applications has potential. Due to the benefits of high specific strength, low weight, primarily anti-erosion, increased conductivity, eco-friendly nature, and recoverability, aluminium alloys are widely used in the fields of electric module packaging, electronic technology, automotive body structure, and wind and solar energy management. Reviewing earlier studies of the applications, workability, and usage of aluminium is the study's main goal. According to the study's findings, there are many different types of aluminium alloys that have been used by earlier researchers; nevertheless, only AA6262 T6 has received the least amount of attention. The study's future objectives include examining the effects of machining parameters on the turning process and find out the optimized technique for the process parameters.

Metamaterials: Design, modelling, simulation and implementation

Metamaterials are a new class of man-made compound materials with exceptional properties which are not found in nature. Thanks to their superior features, they are used in a variety of fields and industries such as microwave engineering, dispersion compensation, smart antennas, sensor identification, high-frequency battlefield communications, improved ultrasonic sensors, solar energy management for high-gain antennas, remote aerospace applications, vibration control, acoustic wave guiding, and energy harvesting. The microstructures in acoustic metamaterials are locally alternating. Over the past decade, acoustic metamaterials have gained increasing attention because of the ability to modify the sound wave field or vibration at inputs in ways that cannot be achieved using ordinary materials. In the meantime, acoustic metamaterials can possess negative modulus of elasticity, negative density, or anisotropic mass. These capabilities greatly increase the choice of materials and provide an unprecedented way to manipulate/attenuate wave propagation and vibrational and acoustic behavior of structures. Physiomechanical property improvements yielded major interests in metamaterials and thereby exploring the physics of such mechanisms, leading to excellent essential investigations related to the synthesis, development, and characterization of such advanced materials.

Fluid flow and thermal performance of the pulsating heat pipes facilitated with solar collectors: Experiments, theories and GABPNN machine learning

Solar energy could be effectively exploited and transferred by the solar collectors, where thermal transport efficiency was inhibited by actual operations and applications. In present research, solar collector combined with pulsating heat pipes (PHP) was proposed to enhance solar energy conversion and management. Fluid temperature oscillation, flow patterns and motions, heat transfer mechanisms inside present facility were comprehensively investigated, which aimed to suitably determine the flow stability of working fluid for PHPs, such that the whole system performance could be fully optimized. Firstly, an experimental investigation was comprehensively conducted to obtain the flow motions, temperature oscillation, and thermal resistance of PHPs, under multi-parameter conditions. Our results indicated that heat transfer performance was promoted with an increase in thermally driven force, accompanied by which low frequency with “small amplitude” transformed to high frequency with “bulk amplitude”. In addition, filling ratio of 25% even a minimum input power generally results in dry-out phenomena, superior liquid-vapor patterns and heat transfer performance were observed as filling ratio was 60%. Following that, theoretical modeling results confirmed that, within the critical pipe diameter, larger saturated vapor pressure and lower surface tension were significantly beneficial for the application of PHPs-solar collector. Considering dimensionless parameters and experimental data, Genetic Algorithm-Back Propagation Neural Network (GA-BPNN) was further built to predict the heat transfer performance of PHPs, which was in great agreement with the experimental data. Optimal filling ratios were further confirmed depending on the best Ku performance, within average deviation being no more than 10%. Present theoretical and experimental researches could facilitate the optimization and application of PHP- solar collectors, greatly exploiting solar energy for buildings.

Critical Performance Comparison Between Single-Stage and Two-Stage Incremental Conductance MPPT Algorithms for DC/DC Boost-Converter Applied in PV Systems

Operating with maximum power point tracking (MPPT) strategy is the key functions of the solar energy management system in solar PV deployment. Therefore, in this paper, the strengths and weaknesses of the MPPT based on an incremental conductance (IC) algorithm are analyzed. Furthermore, assessments in terms of performance of two MPPT-IC techniques are presented. More specifically, a two-stage optimized MPPT-IC algorithm in this study is developed and implemented for isolated solar PV system with the conventional single-stage MPPT-IC algorithm. Both MPPT-IC methods are analyzed and compared based on their tracking efficiencies, steady state error and transient speed responses. For the case of the two-mode MPPT-IC algorithm, significant modification is carried-out by combining the conventional IC and modified constant voltage methods. As a result, a better performance under both sunny and cloudy weather scenarios respectively is achieved with higher tracking efficiencies than the conventional IC algorithm. The two stage-algorithm not only rapidly tracks the maximum power point (MPP) under dynamic weather conditions, but also significantly reduces the fluctuations in power around the MPP when subjected to fast and slow speed’s change of irradiations.

Wind speed and global radiation forecasting based on differential, deep and stochastic machine learning of patterns in 2-level historical meteo-quantity sets

AbstractAccurate forecasting of wind speed and solar radiation can help operators of wind farms and Photo-Voltaic (PV) plants prepare efficient and practicable production plans to balance the supply with demand in the generation and consumption of Renewable Energy (RE). Reliable Artificial Intelligence (AI) forecast models can minimize the effect of wind and solar power fluctuations, eliminating their intermittent character in system dispatch planning and utilization. Intelligent wind and solar energy management is essential in load scheduling and decision-making processes to meet user requirements. The proposed 24-h prediction schemes involve the beginning detection and secondary similarity re-evaluation of optimal day-data sequences, which is a notable incremental improvement against state-of-the-art in the consequent application of statistical AI learning. 2-level altitude measurements allow the identification of data relationships between two surface layers (hill and lowland) and adequate interpretation of various meteorological situations, whose differentiate information is used by AI models to recognize upcoming changes in the mid-term day horizon. Observations at two professional meteorological stations comprise specific quantities, of which the most valuable are automatically selected as input for the day model. Differential learning is a novel designed unconventional neurocomputing approach that combines derivative components produced in selected network nodes in the weighted modular output. The complexity of the node-stepwise composed model corresponds to the patterns included in the training data. It allows for representation of high uncertain and nonlinear dynamic systems, dependent on local RE production, not substantially reducing the input vector dimensionality leading to model over simplifications as standard AI does. Available angular and frequency time data (e.g., wind direction, humidity, and irradiation cycles) are combined with the amplitudes to solve reduced Partial Differential Equations (PDEs), defined in network nodes, in the periodical complex form. This is a substantial improvement over the previous publication design. The comparative results show better efficiency and reliability of differential learning in representing the modular uncertainty and PDE dynamics of patterns on a day horizon, taking into account recent deep and stochastic learning. A free available C++ parametric software together with the processed meteo-data sets allow additional comparisons with the presented model results.

Communication frame work in an electric vehicle charging station supporting solar energy management

Exploiting Renewable energy to the maximum extent possible in an electric vehicle charging station (EVCS) is the key in supporting the anticipated carbon reduction from the electric vehicles (EVs). Knowing the expected load and the solar energy in advance at the EVCS can be crucial in framing a proper energy management strategy. Selection of suitable parameters associated with the participating EVs and EVCS are vital in utilizing them for predicting the probable EV load and expected solar energy for a given period under consideration. A prototype EVCS with smart communication infrastructure is developed considering solar pv as the energy source. Real time communication of the parameters between multiple agents has been established effectively using an interactive website, cloud server and an short message service (SMS) application programming interface (API). The data generated from the prototype models have been utilized in a random forest regression (RFR) classifier model in order to predict the probable solar energy and the expected EV load for every minute duration. The integrated communication frame work is found to be less complex to implement for an autonomous direct current (DC EVCS). The details provided at the graphical user interface (GUI) designed at the EVCS can be instrumental in developing a proper energy management strategy.

Infrared‐Reflective Transparent Hyperbolic Metamaterials for Use in Radiative Cooling Windows

AbstractPassive multilayer coatings for windows have potential to improve energy consumption for indoor temperature regulation. The coatings should block the solar IR energy (800–2500 nm) while maintaining visible light transparency (400–700 nm) to prevent unwanted heating of the interior of a building or a vehicle. It should also efficiently radiate thermal energy to prevent excessive heating. Although solar energy management and radiative cooling techniques have been investigated individually, the combination of the two, a transparent radiative cooler, has emerged only recently. This study theoretically and experimentally demonstrates a transparent radiative cooling window using a combination of planar hyperbolic metamaterials and a uniform layer of polydimethylsiloxane, resulting in high visible transparency (&gt;60%), IR reflectivity (&gt;89%), and thermal emissivity (&gt;95%). Daytime temperature experiments confirm that the cooling window efficiently lowers the interior temperature by as much as 7 °C.