Performance estimation of photovoltaic energy production

This article deals with the production of energy through photovoltaic (PV) panels. The efficiency and quantity of energy produced by a PV panel depend on both deterministic factors, mainly related to the technical characteristics of the panels, and stochastic factors, essentially the amount of incident solar radiation and some climatic variables that modify the efficiency of solar panels such as temperature and wind speed. The main objective of this work is to estimate the energy production of a PV system with fixed technical characteristics through the modeling of the stochastic factors listed above. Besides, we estimate the economic profitability of the plant, net of taxation or subsidiary payment policies, considered taking into account the hourly spot price curve of electricity and its correlation with solar radiation, via vector autoregressive models. Our investigation ends with a Monte Carlo simulation of the models introduced. We also propose the pricing of some quanto options that allow hedging both the price risk and the volumetric risk

Investigation of a novel solar photovoltaic/loop-heat-pipe heat pump system

With the widespread deployment of solar photovoltaic (PV) and thermal devices imminent, this research aims to resolve some engineering barriers to the existing solar photovoltaic/thermal (PV/T) technologies by incorporating an innovative loop heat pipe (LHP) and a typical heat pump. In addition, a coated aluminium-alloy (Al-alloy) sheet replaces the conventional baseboard for the PV cells to improve heat exportation. As a result, this research has developed a novel solar PV/LHP heat pump system to maximise the electrical output of a PV module and generate an additional amount of heat simultaneously.The overall investigation followed the basic methodology of combined theoretical and experimental analysis, including procedures for a critical literature review, optimal concept design, mathematical derivation, the development of simulation models, prototype fabrication, laboratory-controlled and field testing, simulation model validation and socio-economic analysis. A full range of specialised simulation models was developed to predict the system performance with reasonable accuracy. The proposed LHP device has a maximum heat transfer limit of about 900W. The Al-alloy baseboard improved PV efficiency by nearly 0.26% when compared with a traditional PV baseboard. During the real-time measurement conditions, the mean electrical, thermal and overall energetic/exergetic efficiencies of the PV/LHP module were 9.13%, 39.25% and 48.37%/15.02%, respectively. The basic thermal and advanced system coefficients of performance (COPth/COPPV/T) were almost 5.51 and 8.71, respectively. The test results indicated that this system performed better than conventional solar/air energy systems. The feasibility analysis illustrated that this system could generate a substantial amount of energy in subtropical climatic regions, such as Hong Kong. It is cost effective to operate this system in areas with high energy charging tariffs, such as London and Hong Kong.The research results are expected to configure feasible solutions for future PV/T technologies and develop a new solar-driven heating system. The core technologies may enable a significant reduction in or even elimination of the carbon footprint in the built environment

Comparing the Dynamic Impact of Hybrid Distributed Generation with Single Source DG

Due to the natural intermittent properties of some renewable energies, the grid is subjected to instability, insufficient power delivery and fluctuation. When these renewable energies are combined together to address the challenge of power shortage, increasing energy demand, and voltage drop, the grid is subject to different stabilities issues compare to the single energy source. This paper compares the dynamic behavior of single energy with mixed energy sources. The paper compares the impact of DFIG alone, Solar PV alone and Small Hydro power alone with hybrid type under distributed generation concept on transient stability of power system. To investigate this investigation, a DIgSILENT power factory library models was used as a component model for wind Turbine / Solar PV and small hydropower system. The simulation was carried out on single machine infinite system

Opto-Electro-Thermal Approach to Modeling Photovoltaic Performance and Reliability from Cell to Module

Thanks to technology advancement in recent decades, the levelized cost of electricity (LCOE) of solar photovoltaics (PV) has finally been driven down close to that of traditional fossil fuels. Still, PV only provides approximately 0.5% of the total electricity consumption in the United States. To make PV more competitive with other energy resources, we must continuously reduce the LCOE of PV through improving their performance and reliability. As PV efficiencies approach the theoretical limit, however, further improvements are difficult. Meanwhile, solar modules in the field regularly fail prematurely before the manufacturers 25-year warranty. Therefore, future PV research needs innovative approaches and inventive solutions to continuously drive LCOE down. In this work, we present a novel approach to PV system design and analysis. The approach, comprised of three components: multiscale, multiphysics, and time, aims at systemically and collaboratively improving the performance and reliability of PV. First, we establish a simulation framework for translating the cell-level characteristics to the module level (multiscale). This framework has been demonstrated to reduce the cell-to-module efficiency gap. The framework also enables the investigation of module-level reliability. Physics-based compact models -the building blocks for this multiscale framework are, however, still missing or underdeveloped for promising materials such as perovskites and CIGS. Hence, we have developed compact models for these two technologies, which analytically describe salient features of their operation as a function of illumination and temperature. The models are also suitable for integration into a large-scale circuit network to simulate a solar module. In the second aspect of the approach, we study the fundamental physics underlying the notorious self-heating effects for PV and examine their detrimental influence on the electrical performance (multiphysics). After ascertaining the sources of self-heating, we propose novel optics-based self-cooling methodologies to reduce the operating temperature. The cooling technique developed in this work has been predicted to substantially enhance the efficiency and durability of commercial Si solar modules. In the third and last aspect of the approach, we have established a simulation framework that can forward predict the future energy yield for PV systems for financial scrutiny and inversely mine the historical field data to diagnose the pathology of degraded solar modules (time). The framework, which physically accounts for environmental factors (e.g., irradiance, temperature), can generate accurate projection and insightful analysis of the geographic-and technology-specific performance and reliability of solar modules. For the forward modeling, we simulate the optimization and predict the performance of bifacial solar modules to rigorously evaluate this emerging technology in a global context. For the inverse modeling, we apply this framework to physically mine the 20-year field data for a nearly worn-out silicon PV system and successfully pin down the primary degradation pathways, something that is beyond the capability of conventional methods. This framework can be applied to solar farms installed globally (an abundant yet unexploited testbed) to establish a rich database of these geographic-and technology-dependent degradation processes, a knowledge prerequisite for the next-generation reliability-aware design of PV systems. Finally, we note that the research paradigm for PV developed in this work can also be applied to other applications, e.g., battery and electronics, which share similar technical challenges for performance and reliability

Design and Economic Analysis of a Grid-connected Rooftop Solar PV System for Typical Home Applications in Oman

This paper presents a techno-economic investigation of an integrated rooftop solar PV system for typical home applications in Oman that can reduce the power consumption from the grid and export excess PV generated power back to the gird. Since renewable energy systems design echnically depends on the site, this study selects a typical two-story villa (Home), in a site Al-Hamra, Oman. Temperature is one of the critical parameters in this design as it varies widely over the day and from one season to another in Oman. With the effect of temperature variation, the PV system has designed using system models for the required load of the home. The design process has included two main design constraints, such as the available rooftop space and the grid-connection availability for the selected home.This research also evaluates the economic feasibility of the design system considering the energy export tariff as per the Bulk Supply Tariff (BST) scheme in Oman. The design outcome reveals that the designed PV system can supply the load energy requirement in a year. In addition, the rooftop solar PV system can sell surplus energy back to the grid that generates additional revenue for the owner of the system. The economic performance indices such as payback period, internal rate of return, net present value,and profitability index ensure the financial feasibility of the designed rooftop solar PV system for the selected home.

"Integrating AI and AEM Electrolyzer for Green Hydrogen Production: Optimization of Solar-Powered Electrolysis in Residential Energy Management"

openThis master's thesis presents a comprehensive study on the forecasting of short-term power generation in a grid-connected hybrid solar photovoltaic (PV) system through the utilization of an artificial intelligence (AI) model. The research integrates weather data and solar PV electricity production data to develop and optimize a Long Short-Term Memory (LSTM) based AI model. The year 2021's solar PV and weather data were utilized for training and validating the model. Additionally, AEM electrolyzer was optimized to efficiently produce hydrogen using surplus electricity generated by the solar PV system . The investigation identified notable correlations between solar radiation, solar energy, UV index, and various other weather parameters with solar PV power generation. These correlations played a significant role in enhancing the accuracy of the AI model in predicting power generation. Various LSTM model structures were evaluated, and a two-layer LSTM model demonstrated superior performance, achieving an accuracy of approximately 80%. Furthermore, surplus electricity generated by the system, averaging 10 kWh during the daytime was calculated and analyzed. The economic viability of the hybrid system was also established, as the cost of electricity generated through the hybrid system was less than half of the grid energy price, meeting the regulatory standards.Optimizing the AEM electrolyzer revealed that a configuration with a few standby parallel AEM electrolyzers was optimal for utilizing excess electricity effectively. Further than that scheduling the parallel system in hourly basis for the days ahead, would help to have more conveniently benefit from this system. In conclusion, this research presents promising avenues for future studies aimed at further enhancing the efficiency and sustainability of renewable energy systems. Prospective research includes real-time integration of weather updates for AI models, advanced energy storage systems, demand-side management strategies, comparison of machine learning algorithms, optimized hydrogen production, and the evaluation of the integrated model in a microgrid setting. These future directions aim to contribute to the wider adoption of renewable energy sources and facilitate the transition towards a more sustainable energy future.This master's thesis presents a comprehensive study on the forecasting of short-term power generation in a grid-connected hybrid solar photovoltaic (PV) system through the utilization of an artificial intelligence (AI) model. The research integrates weather data and solar PV electricity production data to develop and optimize a Long Short-Term Memory (LSTM) based AI model. The year 2021's solar PV and weather data were utilized for training and validating the model. Additionally, AEM electrolyzer was optimized to efficiently produce hydrogen using surplus electricity generated by the solar PV system . The investigation identified notable correlations between solar radiation, solar energy, UV index, and various other weather parameters with solar PV power generation. These correlations played a significant role in enhancing the accuracy of the AI model in predicting power generation. Various LSTM model structures were evaluated, and a two-layer LSTM model demonstrated superior performance, achieving an accuracy of approximately 80%. Furthermore, surplus electricity generated by the system, averaging 10 kWh during the daytime was calculated and analyzed. The economic viability of the hybrid system was also established, as the cost of electricity generated through the hybrid system was less than half of the grid energy price, meeting the regulatory standards.Optimizing the AEM electrolyzer revealed that a configuration with a few standby parallel AEM electrolyzers was optimal for utilizing excess electricity effectively. Further than that scheduling the parallel system in hourly basis for the days ahead, would help to have more conveniently benefit from this system. In conclusion, this research presents promising avenues for future studies aimed at further enhancing the efficiency and sustainability of renewable energy systems. Prospective research includes real-time integration of weather updates for AI models, advanced energy storage systems, demand-side management strategies, comparison of machine learning algorithms, optimized hydrogen production, and the evaluation of the integrated model in a microgrid setting. These future directions aim to contribute to the wider adoption of renewable energy sources and facilitate the transition towards a more sustainable energy future

Investigation of renewable, coupled solar-hydrogen fuel generation with thermal management systems suitable for equatorial regions

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Solar Energy and Hydrogen (energy carrier) are possible replacement options for fossil fuel and its associated problems of availability and high prices which are devastating small, developing, oil-importing economies. But a major drawback to the full implementation of solar energy, in particular photovoltaic (PV), is the lowering of conversion efficiency of PV cells due to elevated cell temperatures while in operation. Also, hydrogen as an energy carrier must be produced in gaseous or liquid form before it can be used as fuel; but its‟ present major conversion process produces an abundance of carbon dioxide which is harming the environment through global warming. In search of resolutions to these issues, this research investigated the application of Thermal Management to Photovoltaic (PV) modules in an attempt to reverse the effects of elevated cell temperature. The investigation also examined the effects of coupling the thermally managed PV modules to a proton exchange membrane (PEM) Hydrogen Generator for the production of hydrogen gas in an environmentally friendly and renewable way. The research took place in Kingston, Jamaica. The thermal management involved the application of two cooling systems which are Gravity-Fed Cooling (GFC) and Solar-Powered Adsorption Cooling (SPAC) systems. In both systems Mathematical Models were developed as predictive tools for critical aspects of the systems. The models were validated by the results of experiments. The results of the investigation showed that both cooling systems stopped the cells temperatures from rising, reversed the negative effects on conversion efficiency, and increased the power output of the module by as much as 39%. The results also showed that the thermally managed PV module when coupled to the hydrogen generator impacted positively with an appreciably increase of up to 32% in hydrogen gas production. The results of this work can be applied to the equatorial belt but also to other regions with suitable solar irradiation. The research has contributed to the wider community by the development of practical, environmentally friendly, cost effective Thermal Management Systems that guarantee improvement in photovoltaic power output, by introducing a novel way to use renewable energy that has potential to be used by individual household and/or as cottage industry, and by the development of Mathematical Tools to aid in photovoltaic power systems designs

Experimental and Numerical Investigation of Thermal Performance of a Crossed Compound Parabolic Concentrator with PV Cell

Crossed compound parabolic concentrator (CCPC) is a solar energy device used to increase the photovoltaic (PV) cell electrical power output. CCPC’s thermal and optical performance issues are equally important for a PV cell or module to work under a favourable operating condition. However, most work to-date is emphasised on its optical performance paying a little attention to the thermal characteristics. In this contribution, we investigate the thermal performance of a CCPC with PV cell at four different beam incidences (0o, 10o, 20o, 30o and 40o). Initially, experiment is performed in the indoor PV laboratory at the University of Exeter with 1kW/m2 radiation intensity. 3D simulations are carried out to first validate the predicted data and then to characterise the overall performance. Results show that the temperature in the PV silicon layer is the highest at 0o and 30o, with the top glass cover of CCPC having the lowest temperature at all the incidences. The temperature and optical efficiency profiles at the various incidences predicted by simulation show very good agreement with the measurements, especially at 0o incidence. This study provides useful information for understanding the coupled optical-thermal performance of the CCPC with PV cell working at various conditions

A coupled optical-thermal-electrical model to predict the performance of hybrid PV/T-CCPC roof-top systems

A crossed compound parabolic concentrator (CCPC) is applied into a photovoltaic/thermal (PV/T) hybrid solar collector, i.e. concentrating PV/T (CPV/T) collector, to develop new hybrid roof-top CPV/T systems. However, to optimise the system configuration and operational parameters as well as to predict their performances, a coupled optical, thermal and electrical model is essential. We establish this model by integrating a number of submodels sourced from literature as well as from our recent work on incidence-dependent optical efficiency, six-parameter electrical model and scaling law for outdoor conditions. With the model, electrical performance and cell temperature are predicted on specific days for the roof-top systems installed in Glasgow, Penryn and Jaen. Results obtained by the proposed model reasonably agree with monitored data and it is also clarified that the systems operate under off-optimal operating condition. Long-term electric performance of the CPV/T systems is estimated as well. In addition, effects of transient terms in heat transfer and diffuse solar irradiance on electric energy are identified and discussed