Solar Sources Research Articles

Solar Energy Utilization Analysis on Public Street Lighting in Sulur Kulon Progo

he utilization of solar energy as a power source has been widely used in Indonesia. This condition is strongly related to the potential of the sun in Indonesia, which is available throughout the year. Solar-based Public Street Lighting (PJUTS) is one application of utilizing solar energy for street lighting. The use of PJUTS is very appropriate because converting solar energy into electrical energy is still low. Sulur is one of the areas in Kulon Progo Regency with abundant solar energy potential. Meanwhile, street lighting conditions in this region are very low and need an addition of public street lighting. In this study, the ability of solar panels and lamp consumption at PJUTS is compared to analyze the technology sustainability. Observations are made by recording the power absorbed by solar panels using a controller and Solar Station software. The observations found that the solar panels can capture an average solar capacity of 150.73 Wh, while the use of lights at night consumes about 120 Wh. From this observation, the solar energy source in Sulur is enough to fulfill the public road lighting requirement.

Energy and Solar Irradiance Analysis for Hydroponic System

A Simulated impact of partial shadow on PV modules and the different partial shading on the PV panel effect on the PV performance is being analysed. The setting for the report is a hydroponic plant system equipped with a solar energy source and IoT at Pangsapuri Jasa, Mutiara Rini. The system control can reduce the carbon footprint and cause no pollution to the environment, while the information gathering process is faster without requiring a lot of time and energy. By collecting the data at Pangsapuri Jasa, the suitable irradiation data is used. The power consumption cannot be monitored by users. A shadow is casted over a panel which make the amount of sunlight reaching the surface is reduced. In addition, the partial shading effects continue to be one of the most demanding issues that directly influence the effectiveness of PV networks in terms of power outputs, the development of several peaks on the power-voltage (P-V) characteristic curve, and the incidence of hot-phenomena. Furthermore, the effective strategies are not being implemented that could save the energy consumption where analysis of the system performance is not practiced consistently when there are available strategies that can improve the system. The purpose of this project is to focus on analyzing the effect of the partial shading to the PV output performance. Also, to monitor the real time monitoring of electricity usage for hydroponic system. This project also covers the analysis of the effect of different configuration of shaded area of partial shadings on PV output performance. The study focuses on a specific kind of shading where photovoltaic panels as a shading device. The study focusses on the analysis of PV output performance on different partial shading conditions. Different number of shadings are being presented to display the experimental set up by using MATLAB Simulink

A review on the photosensitizers used for enhancing the photoelectrochemical performance of hydrogen production with emphasis on a novel toxicity assessment framework

The significance of hydrogen fuel and its green production is one of the challenges of recent research. This study focuses on photosensitizers, which is the main component of the photoelectrode in the photoelectrochemical water splitting process as a method to produce hydrogen from a solar source. This review article collects, classifies and reviews all the sensitizers used in recent years. Accordingly, photosensitizers include dyes, quantum dots, metal-organic frameworks, conducting polymers, and chalcogenides, which increase optical current density and visible light absorption. Finally, based on the presented framework for evaluating the toxicity of hydrogen production based on the periodic table and RDI, the toxicity of the sensitizers was evaluated.

Thermodynamic and Thermo-Economic Analysis of a Solar-Integrated Double-Turbine Kalina Cycle for Varying Solar Flux Conditions

Abstract Kalina cycle is established as a reliable low-grade energy cycle working on solar, geothermal, and other waste heat recovery sources. This work aims to develop a novel methodology for optimizing a Kalina cycle according to the solar irradiation. A comprehensive analysis of performance is conducted by varying the parameters of the Kalina system, modeled with high- and low-pressure turbines. The present work implements and analyzes the performance of a multi turbine Kalina cycle with cylindrical parabolic collectors for energy input at different time, on a particular day, for a location. The proposed cycle is modeled to simulate the working. The dependency of parameters—separator pressure, concentration of ammonia in boiler, intermediate separator temperature and vapor fraction at condenser side turbine exit—on the system performance is investigated. Optimization is conducted using genetic algorithm with net power as objective function for different solar irradiations. The optimized power values are 282.62, 246.75, 222.31, and 180.0 kW for solar influxes 507.7, 461.8, 413.9, and 321.0 W/m2 respectively. The results show that the proposed model can be adopted for better performance. A thermo-economic analysis of an optimized output is conducted to conclude on capital investment and operation cost for sustainable power production. The analysis yields highest cost rate of exergy destruction of 58936.41$/year for the boiler. The investment cost of the turbines together is 89% of the total capital investment, and hence, thermo-economic factor is highest for these components.

Cleaner production of energy and fuels from a renewable energy-based self-sufficient system with energy storage options

The present study investigates a newly developed solar and wind-driven integrated energy system to achieve self-sufficiency for communities in a sustainable fashion. In order to overcome the fluctuating nature of solar and wind sources, energy storage options are considered through reliable meteorological data and capacities. The non-thermal electricity, heat, and cooling demands of a community with 65,000 people are met by using a 340 MW wind farm and 1500 MWp linear Fresnel concentrated solar system coupled with thermal energy storage. The integrated system consists of a wind farm with 170 wind turbines, linear Fresnel concentrated solar plant, molten salt energy storage tanks, steam Rankine cycle, lithium-bromide absorption refrigeration cycle, anion exchange membrane electrolysis unit, multi-effect distillation desalination unit, district heating system. Both steady-state analysis for different operating conditions and time-dependent analysis with changing source and load data are carried out from energy and exergy aspects of thermodynamics. The proposed system was able to meet 100 % of 363,344.7 MWh of electricity, 544,468.2 MWh of heat, and 192,112.7 MWh of cooling loads in a year; moreover, it generated 24,142 kt of fresh water and 38,843 t of hydrogen in a year, in order to exploit the excess and waste energy. For the overall integrated system, the average energy and exergy efficiencies are found to be 37.69 % and 28.27 %, respectively. In order to benefit from the full potential of wind and solar-based systems in a combined, the current study provides a unique solution with energy storage option and generates multiple useful outputs for the targeted communities in a sustainable manner.

Using machine learning prediction to design an optimized renewable energy system for a remote area in Italy

Due to the lack of fossil fuels, there is a significant demand to employ renewable energy systems (RES) worldwide. This paper proposes designing an optimized RES for a remote microgrid that relies solely on solar and wind sources. The proposed RES aims to provide reliable and efficient energy to the microgrid by using machine learning algorithms to forecast the power output of the solar and wind sources. This forecasting will help the system to anticipate and adjust to changes in the weather patterns that may affect the availability of solar and wind. In addition, the system advisor model (SAM) software is used to design the hybrid solar/wind system, considering factors such as the size of the microgrid and the available resources. The system comprises a 60-kW wind system of ten turbines and a 100-kW PV system spread out over the houses. The results show that random forest regression (RFR) models achieved a high level of accuracy in predicting solar power generation, as evidenced by their low mean squared error (MSE) and high R² values. Additionally, a proposed hybrid system can generate enough energy to meet the area's needs.

An Intelligent Load Balancing Strategy for Energy Cost Minimization in EV Applications

Energy management systems are the most significant field in the potential electric vehicles (EVs). Also, EVs are widely used because of their pollution-free nature worldwide and their specific feature of flexibility. However, the EV charging system’s significant challenges are power loss and high power consumption. Therefore, a robust load-balancing strategy is required to address the EV charging system’s issues. Therefore, a novel intelligent hybrid Buffalo-based Recurrent Energy Management System (BREMS) has been proposed to reduce the power consumption of EVs and mitigate power losses. Here, two primary functions have been done. Initially, hydro and solar sources are modeled with the help of energy storage components and a power grid to enhance the system’s efficiency. Then, the optimization fitness module is initiated to the hidden layer of the neural framework to monitor the loaded grids and distributes the loads to imbalanced grids. Implementation and designing processes have been done on the MATLAB platform. The simulation outcomes of the proposed design have been compared with conventional models in terms of power loss, cost of energy, utilization of power, and execution time. The comparative assessment shows that the presented system obtained better outcomes than other models.

An optimal management architecture based on digital twin for smart solar-based islands incorporating deep learning and modified particle swarm optimization

Smart island (SI) energy management is a type of energy management system used to ensure that energy is used efficiently on islands. It is designed to reduce energy consumption and costs while also improving the sustainability of the island. Smart island energy management systems use a combination of technologies such as solar, wind, and other renewable energy sources, energy storage systems, smart meters, and advanced analytics to monitor and manage energy usage. The system can be used to provide islanders with real-time energy usage data, allowing them to make informed decisions about their energy use. Using the effects on the environment and economics of the SI as a basis for fully modeling the optimum performance of the SI in grid-connected operations, the feasibility and balance of the SI power grid are ensured. In addition, taking into account the impact of interruptible loads (IL) on SI operation costs, a power shift for IL can be performed using storage batteries in a digital twin environment. A new ecology-driven optimization algorithm has been developed that continuously adjusts migration rates with habitat suitability indexes of normalized individuals and adds a differential perturbation to the migration operator of the migration process. The enhanced particle swarm optimization algorithm has been implemented as the SI optimization dispatching algorithm. In order to have a precise prediction of the renewable energy sources, their output power is predicted using the recurrent neural network (RNN) deep learning model. Based on the simulation outcomes, it is evident that the suggested power dispatching model could greatly decrease the overall price of the system by implementing the advanced and effective algorithm and model presented in the study.

A novel framework for integrating solar renewable source into smart cities through digital twin simulations

In this study, the challenges of incorporating PV systems and wind energy sources into smart city power grids are addressed. The fluctuating nature of renewable energy is considered, and microgrid energy management is investigated using probabilistic and deterministic assumptions. To enhance energy management, digital twin simulations are employed, providing a virtual representation of the power grid. This enables accurate analysis and prediction of system behavior in different scenarios. Digital twins help simulate the effects of load profiles on microgrids, facilitating better energy planning and decision-making. The proposed framework considers objective functions related to cost reduction, voltage profile improvement, and voltage stability enhancement. The whale optimization algorithm (WOA) is applied to solve the complex and nonlinear energy management problem, aiming to minimize costs and enhance voltage stability. Simulation results show that an optimally installed PV system and wind turbines reduce energy costs and improve overall system efficiency in smart cities. Furthermore, the objective function of WOA outperforms particle swarm optimization (PSO) and differential evolution algorithm (DEA) in achieving the desired outcomes.

Performance analysis of a novel solar assisted air source heat pump

Solar assisted heat pumps (SAHPs) are energy-efficient and environmentally friendly heating systems that utilize solar energy in different configurations. In this study, the potential benefits of heating the evaporator inlet air using solar energy as a new method were investigated using a developed correlation. The study focused on a 4 kW air conditioner that achieved a COP value of 4.17 for heating with an indoor temperature of 20C and an outdoor temperature of 7C. The aim was to determine how the COP value, evaporator inlet air temperature, and total power consumption changed based on the amount of solar energy transferred when the system was supported using the mentioned method. It was observed that a solar support of 5000 W increased the COP value of the SAHP from 4.17 to 4.99.

Thermal performance analysis of concentric-tube latent heat storage systems integrated with varying energy source under various charging strategies

This study presents a comprehensive assessment of the performance of a vertical concentric tube thermal storage system. The system is charged using constant and variable solar energy sources similar to real-world conditions. The initial part of the work focuses on identifying the suitable optimal phase change material for maximizing energy storage. It later evaluates the impact of using a variable energy source compared to a constant source for charging the system. A performance assessment of the system is carried out for two approximated constant energy source cases (constant time-averaged and constant peak fluid temperatures) and an actual variable energy source (unsteady fluid temperature). The findings indicate that constant time-averaged and peak charging temperatures can lead to under-prediction by up to 25.65 % and over-prediction by up to 32.72 % in the energy storage compared to the actual case considering variable charging temperature. Two charging strategies are proposed and compared with the established cascade methodology, revealing the potential for improving thermal energy storage. The dynamic charging strategy shows promising results with potential energy storage gains of up to 55.57 %. This study emphasizes the importance of considering the variable charging source and highlights effective strategies for enhancing energy storage in the thermal storage systems.

System optimization and mode modification of the solar assisted ground source heat pump system for primary schools in northern rural areas of China

With the increasing application of geothermal and solar energy in northern regions of China, it has become one of the main clean heating technologies for rural primary schools. However, the mismatch in equipment capacity has led to poor operating effect and economic efficiency of such systems. This paper puts forward experimental and analysis on a solar assisted ground source heat pump system in a primary school in Beijing's Daxing District. And then, to address the above issues, a TRNSYS model is established and verified to optimize the system strategy and form during 15 years period. What’ more, the main factors affecting solar collector selection are screened by sensitivity analysis. And through the step-by-step regression analysis, the collector selection correction model based on the coefficient Km is obtained. The results show that the temperature difference of the optimized collector control is 10 °C/1°C, and that of the supplementary heat is 20 °C/1°C. The applicable scenarios of the two system forms are different, and the energy efficiency of the system operation is increased by 23% and 23.4% respectively. In addition, the revised model ensures the balance of soil temperature for 15 years of operation under the condition of considering multiple elements, which provides a reference for the matching and selection of equipment for the same type of project in the future.

Improving the Performance of Solar Thermal Energy Storage Systems

In recent times, renewable energy resources have been greatly researched because of the increasing concern to minimize global warming and meet energy demands. Energy storage systems have become useful tools for sustainability and meeting energy needs. Solar energy has proven in recent times to be the primary and most prevalent option due to its environmental friendliness, availability, and minimal pollution. Effective utilization of available energy resources has led to developing new alternative energy devices like the solar thermal energy storage system (STESS) with a solar energy source. Solar thermal energy systems are efficient systems that utilize solar energy to produce thermal and electrical energy. This review aims to give a detailed overview of solar TESS, different TES application systems, and effective methods of increasing the system performance to provide energy during deficient times. The various classifications, basic components, the principle of operation, application areas of STESSs, prospects, and extensive reviews on these aspects have also been discussed in this review. The different factors to be considered geared towards meeting energy demands and increasing the efficiency of solar TES systems have been duly detailed. This review is a single manuscript with a detailed overview of STESS, the principle of operation and components of STESS, thermal energy storage materials, a description of different application systems, and a discussion of factors responsible for improving the system efficiency.

Thermodynamic performance and heat and mass transfer analysis of air source absorption heat pump for heating

To lower CO2 emission and obtain better heating efficiency compared with conventional gas boilers, this paper proposed a solar air source absorption heat pump (SAAHP) with falling film heat exchanger for district heating. The thermodynamic model is established to analyze the performance of the proposed system. The falling film generator as the key heat exchange component has been designed carefully, and then the numerical models of the falling film generation process which include one and two-dimensional numerical models are developed. According to the thermodynamic analysis, the primary energy efficiency (PEE) and heating capacity of the system are 1.65 and 45.02 kW at the generation temperature of 190 °C and ambient temperature of −7 °C, respectively, which can save 41.69 t of CO2 emissions from heating. The two-dimensional numerical model indicates the main heat and mass transfer occur between the falling film height x = 0 mm and x = 200 mm, and the interface heat transfer decreases from 74.6 kJ/(m2·s) to 40.5 kJ/(m2·s) in a typical vertical falling film generation process. In addition, the energy transfer as the inter-diffusion is corrected in the energy equation which improves the prediction accuracy by about 1.5% of the two-dimensional model. The research provides a reference for the optimization design of the generator, where one-dimensional models can quickly guide the design and selection of equipment, modified two-dimensional numerical models and Computational fluid dynamics (CFD) models can provide references for the refined optimization design, which attributes to the improvement of thermodynamic performance of the solar absorption heat pump system.

Multi-objective optimization of economic emission load dispatch incorporating load forecasting and solar photovoltaic sources for carbon neutrality

In this paper, the future load condition is predicted by proposing an effective single-layer Black Widow Optimization based Functional Link Artificial Neural Network (BWO-FLANN) model. Also, a novel Non-dominated Sorting Multi-Objective Teaching Learning Based Optimization (NS-MOTLBO) has been proposed for obtaining the solution to Economic Emission Load Dispatch (EELD) in a combined operation of the grid with solar photovoltaic source by considering the future predicted load. The EELD is an inherently multi-objective problem with nonlinear constraints, such as the generator power limits, power balance criteria, transmission line capacity, prohibited operating zones, etc., that make it more complex, causing extra computational burden and convergence issues. An effective constraint-handling methodology is also used in this paper to achieve the lowest possible constraint violation. The proposed approach has been implemented in two test situations, each with distinct solar radiation. The Pareto optimal fronts for various hours were obtained, indicating possible scheduling of the corresponding periods. Non-parametric statistical testing is carried out to investigate the dominance of our proposed approach compared to the other state-of-the-art prediction models.

Dynamic Energy Analysis of Different Heat Pump Heating Systems Exploiting Renewable Energy Sources

Renewable energy source-fed heat pumps (HPs) may perform up to very high-efficiency standards, offering a promising tool in the wider residential heat decarbonization effort. In this context, this paper investigates different heating configurations utilizing various renewable thermal sources in conjunction with an HP-based system in order to determine the optimal configuration in terms of efficiency, using an existing, fully functioning residential building in Zaragoza, Spain, as our case study, comprising 40 dwellings. Four different HP configurations are investigated:, (i) an air-source system, (ii) a ground-source system, (iii) a dual-source system with solar thermal collectors, and (iv) a triple-source system based on solar, geothermal, and ambient sources. For the purpose of such investigation, detailed dynamic energy simulations are conducted through the use of the INTEMA.building tool (developed in Modelica), applying a multi-objective optimization process that aims at minimizing both the annual electricity consumption and the net present cost. It is demonstrated that the renewable thermally driven HPs are more efficient than the conventional, air-source ones, with the seasonal coefficient of performance increasing by 9.98% (ground source), 4.57% (dual source), and 17.40% (triple source), compared to the air-source heat pump system. Finally, it is revealed (via integrated techno-economic analyses) that the most effective and economical design is the dual source system, while the most expensive is the ground-source configuration. These findings can guide the ongoing design efforts on green residential heat solutions at both research and commercial implementation level.

What role financial inclusion, green trade and natural resources utilization play in ASEAN economic growth: Evidence from post COVID era

Economic growth of a country indicates that how developed the country is. However, there are various factors such as less resources, destructive lands, more population devaluing the growth and eventually killing it. This is quite challengeable situation and it equally frustrates economists, researchers and financial analysts. Thus, the present study attempts to explore the factors which might be helpful for countries to uplift the economic growth, ASEAN economies are chosen as the region has commendable natural resources. In the light of argument, the study investigates the impact of financial inclusion, green trade and natural resources utilization on the economic growth in ASEAN context from 2011 to 2022. By employing MMQR technique it is exposed that access to clean fuel and technology, access to electricity, electricity production from solar and hydroelectric sources, natural resources utilization, and population growth increase economic growth in case of ASEAN countries.

Cascaded dual fuzzy logic controller for stable microgrid operation mitigating effects of natural uncertainty in solar and wind energy sources

In a microgrid system, the perturbations due to variable solar irradiance, fluctuating wind speed, and even the sudden fluctuation in load lead to the deviation in system voltage, frequency, DC-Link voltage, etc., causing instability. Thus, this paper proposes a robust and adaptive energy management system using the cascaded dual-fuzzy logic-based control approach to mitigate such issues. Besides, the system considers the auto-tuned PI-based voltage regulator with the reference voltage 415 V for inverter control and nine controllable/non-controllable system parameters for the proposed cascaded control approach. The proposed algorithm can control the battery operation, mode of microgrid, and load management to improve stability. It is analyzed for experimental conditions like reduced renewable output, microgrid transition, and unhealthy grid/microgrid conditions. In addition, the proposed method has been compared with the existing control topologies by assessing load voltage, frequency, and DC-Link voltage. The proposed approach has been validated for stable microgrid operation using MATLAB-Software. As per observations, it maintains the limits of 0.25% frequency deviation, 0.57% voltage deviation, and 0.877% DC link voltage deviation, which are acceptable as per IEEE 1547 standard, IEEE 2030 standard, and Indian standard CERC 2011 for the concerned system parameters.

TRNSYS simulation study of the operational energy characteristics of a hot water supply system for the integrated design of solar coupled air source heat pumps

To address the issues with solar water heating systems taking up a lot of space, unstable hot water supply, air source heat pumps susceptible to frost in the winter, and low energy efficiency. The TRNSYS tool is employed in this work to simulate a solar-coupled air source heat pump system. The heat pump operation is first investigated using the inverse Carnot cycle. The performance coefficient is then calculated by the second law of thermodynamics without considering the pipeline's pressure drop and heat loss. The output temperature of the hot water that the heat pump circulates is then determined. The daily hot water needs can be estimated roughly based on information about solar radiation. The heat balance equation for flat plate solar collectors was used to compute the intensity of solar diffused radiation. The Berlage calculation was used to determine the solar radiation received on the collector's surface. After a qualitative analysis of the heat from the heat source, the efficiency of the linked heat pump and the conventional air source heat pump was compared. Analyzing the water temperature change graph for each month's data reveals that the system can achieve 50 °C during the water supply time each month. The heat pump's annual energy consumption is 6252.01 kWh, while the system's annual energy consumption is 9100.47 kWh. The study findings may be used as a guide to improving the design and management of the whole system. In addition, they may improve the solar water supply system's performance.

Improving solar radiation source efficiency using adaptive dynamic squirrel search optimization algorithm and long short-term memory

Artificial intelligence and machine learning are used to optimize the design parameters of renewable energy sources, which are now regarded as vital components in current clean energy sources. As a result, system requirements can be reduced, and a well-designed system can improve performance. Artificial intelligence approaches in renewable energy sources and system design would significantly cut optimization time while maintaining high modeling accuracy and optimum performance. This study examines machine learning in depth, emphasizing how it can be used in developing renewable energy sources because of the vast range of technologies it can use. This paper approximates the hourly tilted solar irradiation using climate factors. The irradiance is estimated using a hybrid ensemble-learning approach. This approach combines a proposed adaptive dynamic squirrel search optimization algorithm (ADSSOA) with long short-term memory (LSTM) methods. To the best of our knowledge, this combination has not been used for solar radiation. The results are analyzed and contrasted with the outcomes of several recent swarm intelligence algorithms, such as the genetic algorithm, particle swarm optimization, and gray wolf optimizer. The binary ADSSOA approach performed as expected, with an average error of 0.1801 and a standard deviation of 0.0656. The ADSSOA–LSTM model had the lowest root mean square error (0.000388) compared to LSTM’s (0.001221). In addition, the statistical analysis uses 10 iterations of each presented and evaluated method to provide accurate comparisons and reliable results.