Solar Plant Research Articles

Life Cycle Assessment of Large-Scale Solar Photovoltaic Plant based in Malaysia

Abstract A cradle-to-grave life cycle assessment (LCA) has been performed on a Large Scale Solar Photovoltaic (LSSPV) plant based in Peninsular Malaysia to study the detailed environmental impact of its life cycle. The LSSPV, with a capacity of 78 MWdc, is currently one of Malaysia’s largest solar power plants. The different life cycle phases of the LSSPV, starting from resource manufacturing and transportation, through plant construction and electricity generation, and to decommissioning of the plant, were assessed using the SimaPro software with the Ecoinvent v3.8 database. Results show that the overall emission rate for the LSSPV is 0.0309 kgCO2eq/kWh. It is estimated that the construction phase of the power plant, which includes activities such as infrastructure works and production of PV modules and Balance of System, yields the largest amount of kgCO2eq/kWh emission at 85.7% and imposes the highest environmental impact as compared to the other phases. The second biggest contributor is attributed to the transportation phase, contributing to 14.0% of the total kgCO2eq/kWh emission, of which most of the emission is emitted during the sea freight transportation of PV components from China port to Malaysia port (12.4%). A sensitivity analysis was performed to compare the environmental impact between open ground installation and the slanted roof installation mounting system of LSSPV. The findings from LCA proposed that open-ground installation has a greater negative impact on the environment because it involves additional land clearing and infrastructure transformation. The findings from this study serve as a benchmark for the LSSPV system in Malaysia and present technical perspectives for the future development of PV projects considering environmental impacts across the project lifetime.

Study on the Optical Coupling Effect of Building-Integrated Photovoltaic Modules Applied with a Shingled Technology

Building-integrated photovoltaics (BIPV) comprise the integration of a solar power generation system into the exterior design and architectural elements of a building to produce electricity, which allows the building itself to generate electricity. By integrating shingled technology into the photovoltaic module with optimization of the optical effect, the output performance of the module can be increased while securing an aesthetic appeal as an architectural exterior material for the building simultaneously. In this research, we studied enhancing the performance of BIPV modules through an analysis of the optical coupling effect for shingled technology using PSpice simulation. Compared to the efficiency of 0.2 cm string spacing, the optical coupling effect was increased by 33.33%, 46.98%, 67.01%, and 193.49% according to the string spacing of 0.5 cm, 1 cm, 2 cm and 4 cm, respectively. To analyze this increase, we focused on studying the increase in current due to the reflection and re-absorption of light in the back layer of the solar cell as the cause of this output enhancement. Additionally, the coupling effect in accord with different layers showed that using white EVA to reflect the incident light from the top layer resulted in 117.14% and 521.90% enhancements in maximum output power (Pm) loss % compared to the conventional and black backsheet applied PV modules, respectively.

Analysis of the operation of a Fresnel solar plant through experimental tests: Energy production and efficiency

Analysis of the operation of a Fresnel solar plant through experimental tests: Energy production and efficiency

Power generation forecasting for solar plants based on Dynamic Bayesian networks by fusing multi-source information

A Dynamic Bayesian network (DBN) model for solar power generation forecasting in photovoltaic (PV) solar plants is proposed in this paper. The key idea is to fuse sensor data, operational indicators, meteorological data, lagged output power information, and model errors for more accurate short-term (e.g., hours) and mid-term (e.g., days to weeks) power generation forecasting. The proposed DBN augments automated data-driven structure learning with expert knowledge encoding using continuous and categorical data given constraints to represent causal relationships within a solar inverter system. In addition, an error compensation mechanism that uses an error node to capture the temporal fluctuation caused by system degradation or failures is proposed to capture temporal fluctuation. The effectiveness of the DBN on solar power generation forecasting was evaluated by rolling window analysis with one-year testing data collected from a local solar plant. The proposed DBN is compared with four state-of-art methods including support-vector regression (SVR), k-nearest neighbors (kNN), artificial neural network (ANN), and long short-term memory (LSTM) models. The results show that the proposed DBN achieves better accuracy in general, and it is not as data-hungry as some neural network-based models. The proposed DBN is also shown to have robust and consistent forecasting power with different forecasting horizons. The accuracy is 92 %–95 % from 1 h to one week ahead forecasting.

Economic, Societal, and Environmental Impacts of Available Energy Sources: A Review

The impacts that the available energy sources have had on society, the environment, and the economy have become a focus of attention in recent years, generating polarization of opinions. Understanding these impacts is crucial for rational evaluation and the development of strategies for economic growth and energy security. This review examines such impacts of the main energy resources currently exploited or in development, including fossil fuels, geothermal, biomass, solar, hydropower, hydrogen, nuclear, ocean, and wind energies on society through analysis and comparison. It is essential to consider how high energy demand influences energy prices, the workforce, and the environment and to assess the advantages and disadvantages of each energy source. One significant finding from this review is that the levelized cost of energy (LCOE) may vary substantially depending on the energy source used and show substantial ranges for different applications of the same energy source. Nuclear energy has the lowest LCOE range whereas ocean energy has the highest LCOE range among the nine energy sources considered. Fossil fuels were found to have the most substantial societal impacts, which involved on the positive side providing by far the largest number of jobs and highest tax revenues. However, on the negative side, fossil fuels, biomass, and nuclear energy sources pose the most significant health threats and social well-being impacts on communities and societies compared to other energy sources. On the other hand, solar, ocean and wind energy pose the lowest risk in terms of health and safety, with solar and wind also currently providing a substantial number of jobs worldwide. Regarding environmental consequences, fossil fuels generate the highest greenhouse gas (GHG) emissions and have the highest adverse impacts on ecosystems. In contrast, nuclear, ocean, solar and wind energies have the lowest GHG emissions and low to moderate impacts on ecosystems. Biomass, geothermal and hydropower energy sources have moderate to high ecosystem impacts compared to the other energy sources. Hydropower facilities require the most materials (mainly concrete) to build per unit of energy generated, followed by wind and solar energy, which require substantial steel and concrete per unit of energy generated. The lack of substantial materials recycling causes associated with solar and wind energy sources. All the energies that use thermal power generation process consume substantial quantities of water for cooling. The analysis and comparisons provided in this review identified that there is an urgent need to transition away from large-carbon-footprint processes, particularly fossil fuels without carbon capture, and to reduce the consumption of construction materials without recycling, as occurs in many of the existing solar and wind energy plants. This transition can be facilitated by seeking alternative and more widely accessible materials with lower carbon footprints during manufacturing and construction. Implementing such strategies can help mitigate climate change and have a positive impact on community well-being and economic growth.

Mathematical model of a battery energy storage for a standalone solar photovoltaic plant

Relevance. One of the priority areas for modern energy development is the active use of renewable energy technologies, the leading position among which in terms of the volume of commissioned generating capacity and areas of practical application is occupied by photovoltaics. In recent years, solar photovoltaic plants are increasingly being used as part of autonomous power supply systems, which is largely facilitated by a significant reduction in the cost of their components due to improved technology. Autonomous power supply systems can vary significantly in power, operating conditions, requirements for uninterrupted power supply and many other factors. This determines the high importance of the task of choosing the composition of the main electrical equipment that ensures optimal technical and economic indicators of the designed energy system. To make a reasonable choice of the equipment of an autonomous photovoltaic power plant, simulation models of all its main components are required that adequately reflect their performance characteristics under real operating conditions. An important component of autonomous photovoltaic plants is the energy storage device, which includes a battery and a solar controller that manages the energy balance of the power plant. The settings of the solar controller largely determine the operating modes of the photovoltaic power plant, on which the service life of the batteries primarily depends. Taking into account the fact that the costs of energy storage constitute a significant share of the costs of the total financial investments in the designed power plant, the problem of reliably assessing the service life of batteries is very relevant. Aim. Development of a mathematical model of energy storage system for the design and optimization of the equipment of autonomous photovoltaic plants. Methods. Mathematical and numerical modeling using the MatLab/Simulink software package. Results. A mathematical model of a battery has been developed, based on the modified Shepherd model and the kinetic model of a rechargeable battery. The model is universal and can be used to simulate the static and dynamic characteristics of different types of batteries. To identify model parameters, only the technical specification data provided by the manufacturer is sufficient. The complex model includes a battery life model, which allows you to dynamically adjust the available maximum battery capacity during operation.

Thermal influence in the design of DC to AC converters due to climatic change for photovoltaic solar plants

This research article discusses the impact of climatic changes in South America, with a case study in a tropical zone, particularly the rise in temperature by 1.15 °C from 1998 to 2022, reaching a maximum of 45.85 °C in 2023. Focusing on the Electronic Conversion Unit, especially IGBT efficiency during El Niño-Southern Oscillation phenomenon in 2023. The case study employs finite element analysis using Navier Stockes-Boussinesq equations, it evaluates the heat dissipation, airflow modeling, and temperature effects, the research proposes enhancements like redesigned air flow grids, centralized air filters, and wind fans. Implementing these improvements reduces the temperature by 5.6 °C, enhances airflow speed, and mitigates the climatic impact on electronic devices over the next two decades.

Advancing short-term solar irradiance forecasting accuracy through a hybrid deep learning approach with Bayesian optimization

The optimization of solar energy integration into the power grid relies heavily on accurate forecasting of solar irradiance. In this study, a new approach for short-term solar irradiance forecasting is introduced. This method combines Bayesian Optimized Attention-Dilated Long Short-Term Memory and Savitzky-Golay filtering. The methodology is implemented to analyze data obtained from a solar irradiance probe situated in Douala, Cameroon. Initially, the unprocessed data is augmented by integrating distinctive solar irradiation variables, and the Savitzky-Golay filter with Bayesian Optimization is used to enhance its quality. Subsequently, multiple deep learning models, including Long Short-Term Memory, Bidirectional Long Short-Term Memory, Artificial Neural Networks, Bidirectional Long Short-Term Memory with Additive Attention Mechanism, and Bidirectional Long Short-Term Memory with Additive Attention Mechanism and Dilated Convolutional layers, are trained and evaluated. Out of all the models considered, the proposed approach, which combines the attention mechanism and dilated convolutional layers, demonstrates exceptional performance with the best convergence and accuracy in forecasting. Bayesian Optimization is further utilized to fine-tune the polynomial and window size of the Savitzky-Golay filter and optimize the hyperparameters of the deep learning models. The results show a Symmetric Mean Absolute Percentage Error of 0.6564, a Normalized Root Mean Square Error of 0.2250, and a Root Mean Square Error of 22.9445, surpassing previous studies in the literature. Empirical findings highlight the effectiveness of the proposed methodology in enhancing the accuracy of short-term solar irradiance forecasting. This research contributes to the field by introducing novel data pre-processing techniques, a hybrid deep learning architecture, and the development of a benchmark dataset. These advancements benefit both researchers and solar plant managers, improving solar irradiance forecasting capabilities.

A techno-economic framework for optimizing multi-area power dispatch in microgrids with tie-line constraints

Fuel-fired power plants are the primary electricity sources in Saudi Arabia. This investigation aims to reduce the overall costs and emissions of electricity production from thermal units operated by the Shuqaiq Water and Electricity Company. It also considers practical thermoelectric constraints, such as power losses, ramp-rate constraints, and environmental optimization functions, to align with the sustainability goals of Saudi Vision 2030. Optimizing electric energy delivery is crucial within an energy control center. The focus of this work is to develop an advanced computational technique that reduces reliance on fossil fuels and minimizes the production cost of electricity, while also considering environmental emissions as an optimization function. An examination was conducted through two distinct practical case studies with power demand of 850 MW—one involving renewable sources and the other without—to assess the effectiveness of the hybrid crow search and JAYA optimization algorithm in optimizing performance. This system includes a multi-area setup with limitations on the tie-line power flow constraints. Additionally, the study also involved RETScreen modelling to assess the practical financial, emission, and benchmark analysis of a proposed integrated solar plant. This analyses offers a thorough assessment of the facility’s performance and reliability in practical situations. This study serves as a motivating factor for energy operators, stakeholders, governmental organizations, and grid administrators to invest resources in environmentally-friendly initiatives. The simulation results showed that the proposed approach successfully reduced the electricity cost by 2.29% and 3.70% in hybrid energy networks while effectively handling the system constraints. In addition, the analysis revealed that the optimization algorithm effectively decreased toxic emissions from power plants while also supporting the sustainability objectives of United Nations SDG-7 and Saudi Vision 2030 through the successful integration of renewable sources.

Comparative analysis of machine learning models of linear Fresnel solar collector

Modeling solar plants, like Linear Fresnel Reflectors, plays an essential role in analyzing plant characteristics, assessing overall efficiency, designing appropriate controllers, and optimizing system's operation. Although physical modeling approaches are widely used, their accuracy when compared to real operational data is deficient. In this context, this paper conducts a novel and comprehensive investigation for an existing Linear Fresnel Reflector using six distinct machine learning algorithms namely, Multilayer Perceptron and LSTM Neural Networks, Random Forest, Decision Trees, Extreme Gradient Boosting, and K-Nearest Neighbours to forecast the useable output power of a linear Fresnel solar plant. Datasets between May 2018 and September 2019 with a 30-s time interval from a linear Fresnel plant located in Nicosia, Cyprus are used in this research. Seven distinct statistical metrics, in conjunction with the computation time are employed to assess the performance of the different machine learning models. The objective of this study is to further improve the prediction of Linear Fresnel Reflectors performance using machine learning model, in order to increase modeling reliability on an annual basis. Outcomes reveal that, among the various models considered, K-Nearest Neighbours demonstrated the most optimal performance with a coefficient of determination 98.81 % and a mean absolute percentage error of 1.975 %.

Robust economic–environmental dispatch problem for BESS and PV generators in MT-HVDC systems: A MI-MPC approach

The research addresses the energy management problem (EMP) in multi-terminal high-voltage direct current (MT-HVDC) systems using a mixed-integer model predictive control (MI-MPC). The MT-HVDC system consists of conventional thermal generation plants (fossil sources), renewable generation sources (solar photovoltaic plants), and battery energy storage systems (BESS). This combination creates a complex nonlinear EMP, as it introduces the power losses of the transmission networks through B-coefficients. The main contributions of this research can be summarized in three elements: (i) the formulation of a measurement- or simulation-based approach to determine B−coefficients in MT-HVDC systems for calculating power losses; (ii) the convexification of the EMP model through MI-MPC approaches, allowing to minimize economic and environmental objective functions; and (iii) the inclusion of uncertainties through the robust convex method. Numerical validations in the 11-bus grid, including the linear transportation model for the MT-HVDC system, confirm the multi-objective nature of the EMP when considering economic and environmental indices. In addition, the effect of uncertainties on generation and demand causes significant variations in the optimal Pareto front when compared to the deterministic scenario.

Experimental isochoric apparatus for bubble points determination: Application to CO2 binary mixtures as advanced working fluids

Carbon dioxide binary mixtures are increasingly considered as working fluids in transcritical power cycles, due to the capability to perform liquid-phase compression even at high environmental temperatures. However, a robust thermodynamic model is essential for optimal and reliable design conditions. It is widely recognised that fine-tuning the equation of state with experimental vapour-liquid equilibrium data of the mixture significantly enhances its reliability.In this work, a new apparatus dedicated to vapour-liquid equilibrium measurements of mixtures is presented. The proposed method consists of a constant-volume system, where bubble points are identified from the divergence of slope of the isochoric lines between the two-phase and liquid regions, in the temperature-pressure plane. The temperature and pressure limits of the apparatus are 503 K and 25 MPa.Bubble points of CO2 binary mixtures with hexafluorobenzene (C6F6) and n-pentane (C5H12) have been measured and compared with previous literature data for validation purposes. Then, the CO2 mixture with octafluorocyclobutane (c-C4F8) is experimentally studied, addressing a literature gap in bubble point data. The data are used to calibrate the thermodynamic model, leading to affordable design conditions of the power cycle compared to the non-optimised thermodynamics scenario, in a concentrated solar power tower plant.

Design of Prototype of Solar Power Based Waste Water Treatment Plant

This research aims to find out an overview of the prototype solar power-based Waste Water Treatment Plant (WWTP), the component requirements of the PLTS system as an energy source for the WWTP, and level of efficiency in using solar power plants in IPAL operations. The type of research used is experimental research. Based on the research results a prototype solar power-based IPAL system has been produced with a capacity of 800 liters per hour or 11,200 liters per day (14 operational hours) with a Biotech/biofilter type modified by adding a water filter after the chloronization process. Besides of that, a source of electrical energy for all electrical components of the WWTP with a solar panel and battery capacity of 200 wattpeak and 100 Ah respectively. The efficiency level of the solar panels used reached 88.36% at 12.00 (very sunny weather conditions) with input power above output power in the range 09.00-16.00. With this efficiency, electrical energy can be stored to operate the WWTP at night.

Performance Optimization and Techno-Economic Analysis of an Organic Rankine Cycle Powered by Solar Energy

Abstract To improve the performance of traditional solar power generation systems, a new solar organic Rankine cycle system that can generate electricity and heat is proposed. The system incorporates the separation-flash process, regenerator, and ejector to enhance its efficiency. The optimization of the working fluid, pinch point temperature difference, evaporator outlet dryness, flash dryness, and entrainment ratio is conducted to achieve optimal performance. Aiming at maximum exergy efficiency and minimum levelized energy cost, the operating parameters are further optimized using a multi-objective optimization algorithm. R245fa is the optimal working fluid for the system, offering maximum net output power and thermal efficiency. The optimal performance can be achieved when the pinch point temperature difference is 1 K, evaporator outlet dryness is 0.6, flash dryness is 0.44, and entrainment ratio is 0.29. Moreover, the photovoltaic subsystem can further increase the net output power and thermal efficiency by 15.52% and 15.45%, achieving a maximum net output power and thermal efficiency of 33.95 kW and 10.61%. Additionally, when the solar hot water temperature is 100 °C, pinch point temperature difference is 1.8 K, evaporator outlet dryness is 0.6, flash dryness is 0.65, and entrainment ratio is 0.16, the system can achieve the optimal state of both performance and economy, exhibiting an optimal exergy efficiency and levelized energy cost of 64.1% and 0.294 $/kWh, respectively. Finally, the payback period of system is 3.43 years, indicating the potential for significant economic benefits.

Energy efficiency and greenhouse gas emission reduction potential of solar PV and biomass-based systems for a food processing plant

The industrial sector is one of the most energy-intensive sectors, with a share of nearly 38 % (156 EJ) of all energy used globally in 2020. Historical energy demand and consumption data from a poultry processing plant have been analyzed to lower greenhouse gas emissions (GHG) and reliance on traditional energy sources. The study includes energy, economic, and emissions analyses for (a) solar photovoltaic (PV) systems, (b) biomass fuels, and (c) coal-fired with wood. Fixed, single, and two-axis tracking solar PV systems that are company-owned and funded by banks are considered. The historical energy consumption data of the plant shows that 52.36 % of energy demand is met by electrical energy and 47.63 % by thermal energy. The yearly electricity demand is 9,938 MWh, costs $1,192,560, and emits 5117.6 tCO2. Thermal energy demand is based on an annual use of 898 tonnes of coal and 71.6 tonnes of liquefied petroleum gas (LPG), which costs $ 143,681 and emits 2977.7 tonnes of carbon dioxide (tCO2). Feasibility of 1.3 MW, fixed axis company-owned, solar PV plant shows an annual electricity production of 1876 MWh, GHG emission reduction of 965.9 tCO2 with a payback period of 3.4 years. Biomass system delivers yearly fuel cost savings of $25,724, reducing 2784 tCO2 with a 1.9-year payback period. Co-firing biomass offers a 3.58 % fuel cost saving and 557 tCO2 with a 0.93-year payback period. It concluded that the wood fuel feedstock biomass option is most suitable renewable energy resource for such large-scale poultry facilities.

Rooftop solar potential in micro, small, and medium size enterprises: An insight into renewable energy tapping by decision-making approach

Rooftop solar micro-power plants mitigate pollution and transmission problems in crowded Indian cities. The Indian government is exploring alternate solutions, and the micro, small, and medium enterprises (MSME) sector is also coming forward to opt for sustainable solutions. This study is focused on the MSME segment in India, which constitutes approximately 63 million entities. Installing a solar system for which a rooftop could be the best place to tap solar energy from these entities is required. Checking the rooftop solar potential of any MSME entity requires a suitable technique to decide which sector to target first and the further order. A unique methodology based on multi-criteria decision-making (MCDM) methods is presented to rank 10 MSME alternatives with seven criteria. It includes the measurement of alternatives and ranking according to compromise solution (MARCOS) to rank MSME sectors in India with rooftop solar potential. Weights of importance are assigned with three objective techniques and combined with the Bonferroni operator. The ranks attained are compared with different MCDM methods, and sensitivity analysis is performed while changing weights. Kendall and Spearman rank correlation coefficients are utilized to access the correlation levels between various strategies. Finally, Garrett’s ranking technique is used to finalize the different ranks. The results reveal that the MARCOS method performed well, and “textiles” ranks first, followed by “auto and engineering products.” As a result, the leading industrial sectors will help improve energy utilization while concentrating on renewable energy resources.

Solar Tracking Control Algorithm Based on Artificial Intelligence Applied to Large-Scale Bifacial Photovoltaic Power Plants

The transition to a low-carbon economy is one of the main challenges of our time. In this context, solar energy, along with many other technologies, has been developed to optimize performance. For example, solar trackers follow the sun’s path to increase the generation capacity of photovoltaic plants. However, several factors need consideration to further optimize this process. Important variables include the distance between panels, surface reflectivity, bifacial panels, and climate variations throughout the day. Thus, this paper proposes an artificial intelligence-based algorithm for solar trackers that takes all these factors into account—mainly weather variations and the distance between solar panels. The methodology can be replicated anywhere in the world, and its effectiveness has been validated in a real solar plant with bifacial panels located in northeastern Brazil. The algorithm achieved gains of up to 7.83% on a cloudy day and obtained an average energy gain of approximately 1.2% when compared to a commercial solar tracker algorithm.

Performance assessment of large-scale rooftop solar PV system: a case study in a Malaysian Public University

Adopting rooftop solar PV systems in various domestic and non-domestic sectors (including commercial, industrial, and agricultural) exhibits their commitment to green energy ventures. This study intends to evaluate the effectiveness of a grid-connected solar system that has been installed so far: a 6.9 MWp photovoltaic (PV) system implemented at University Tun Hussein Onn (UTHM) in Batu Pahat, Johor. This green energy system was installed as a part of the Self-Consumption (SELCO) program utilizing a Supply Agreement for Renewable Energy (SARE) contract. To assess the mounted energy system's efficiency, performance monitoring was carried out between December 2021 and November 2022. By evaluating the enactment of the 6.9 MWp solar system at UTHM, this present work has attempted to compile the achievement of implementation of self-consumption in terms of performance analysis and financial benefits to consumer. Based on International Electrotechnical Commission (IEC) 61724 standard, this study also investigates several performance characteristics of the PV system, such as final yield, clearness index (CI), array yield, PV module efficiency, inverter efficiency, reference yield, and capacity factor (CF) performance ratio (PR). Moreover, an assessment of Carbon Dioxide (CO2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${CO}_{2})$$\\end{document} avoidance was also conducted to quantify the amount of CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${CO}_{2}$$\\end{document} reduction achieved. In addition, the self-consumption ratio and self-sufficiency ratio pattern for solar power plant in UTHM has been assessed. The monthly clearness index at the project location from December 2021 to November 2022 ranges from 0.63 to 0.66. The PV system was monitored throughout this time and generated 8529 MWh of energy. The installed PV system has shown an efficiency of 11.86%, a final yield of 108.28%, and a PR of 0.78, respectively. Further, the installed 6.9 MWp PV system at UTHM will cut CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${CO}_{2}$$\\end{document} emissions by 5450 tons annually. The 6.9 MWp solar PV system is also estimated to reduce 25.1 RM mil in 25 years. Making the system larger to 6.9 MWp would reduce the consumption from the grid during the week, makes it possible to achieve self-consumption levels of up to 100% and self-sufficiency of this solar plant is relatively low ranges from 22 to 35%. The research finding bridges the gap between policy makers, investors, and the public regarding acceptance of the large-scale Self-Consumption (SELCO) and strengthen the strategic collaborations between solar service provider and educational bodies to support of the government’s agenda to achieve carbon emission intensity reduction in Malaysia.Graphical

Analysis of Sodium–Water Reaction as heat source for district heating and cooling

Finding new alternatives is a need to develop disruptive solutions that could be applied to the decarbonized, sustainable, circular systemic change for the energy system transition. In this case, a complete new approach, sodium as energy resource, is described. Traditionally, sodium has been considered a risky element even if it were proposed as coolant in many applications, as nuclear or solar thermal plants. Such applications have been concerned by the explosive reaction of alkali as Na with water. We analysed the alternative of profiting of such highly exothermic reaction for an energy use. Previously, we experimentally tested the utilization of sodium as propellant, as reported in Guerrero et al. (2019), what produced the correlation between the energy release and the sodium consumption in excess of water. We are now developing a boiler fuelled by sodium and water. Based on these previous and ongoing activities, this paper shows the proposal and the process analysis of a sodium–water boiler applied to heating and cooling networks,as one of the sectors to be decarbonized in the energy system. A tentative configuration for the design of such heater, and the heat exchangers to adapt heated water temperatures will be presented. We present the conceptual design of a 13.5 MW district heating plant for a ΔT=10°C heating water from 60 to 70 °C, consuming 1 kg/s Sodium, reaching an efficiency of 95%, comparable with existing boilers.

Development of Low-Cost c-Si-Based CPV Cells for a Solar Co-Generation Absorber in a Parabolic Trough Collector

Concentrator photovoltaics (CPVs) have demonstrated high electrical efficiencies and technological potential, especially when deployed in CPV–thermal (CPV-T) hybrid absorbers, in which the cells’ waste heat can be used to power industrial processes. However, the high cost of tracking systems and the predominant use of expensive multi-junction PV cells have caused the market of solar co-generation technologies to stall. This paper describes the development and testing of a low-cost alternative CPV cell based on crystalline silicone (c-Si) for use in a novel injection-molded parabolic hybrid solar collector, generating both, photovoltaic electricity and thermal power. The study covers two different c-Si cell technologies, namely, passive emitter rear contact (PERC) and aluminum back surface field (Al-BSF). Simulation design and manufacturing are described with special attention to fingerprinting in order to achieve high current carrying capacities for concentrated sunlight. It was determined that Al-BSF cells offer higher efficiencies than PERC for the considered use case. Solar simulator tests showed that the highly doped 4 cm2 cells (50 ohm/sq) reach efficiencies of 16.9% under 1 sun and 13.1% under 60 suns at 25 °C with a temperature coefficient of −0.069%(Abs)/K. Finally, options to further improve the cells are discussed and an outlook is given for deployment in a field-testing prototype.