PV System Design and Performance

Photovoltaic solar energy technology (PV) has been developing rapidly in the past decades, leading to a multi-billion-dollar global market. It is of paramount importance that PV systems function properly, which requires the generation of expected energy both for small-scale systems that consist of a few solar modules and for very large-scale systems containing millions of modules. This book increases the understanding of the issues relevant to PV system design and correlated performance; moreover, it contains research from scholars across the globe in the fields of data analysis and data mapping for the optimal performance of PV systems, faults analysis, various causes for energy loss, and design and integration issues. The chapters in this book demonstrate the importance of designing and properly monitoring photovoltaic systems in the field in order to ensure continued good performance

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

Optimization of solar and wind power generation systems with energy storage

This thesis explores the optimization and system configuration of a 100 MW renewable energy facility for a prominent South Asian energy firm currently reliant on fossil-based energy sources. The aim is to integrate 70 MW of solar, 30 MW of wind, and a 10 MW Battery Energy Storage System (BESS) to ensure a dependable power supply and enhance grid stability during peak demand periods. The study involves comprehensive meteorological data analysis, including monthly irradiance patterns and average wind speeds obtained from sources such as Meteonorm 8.1 and WRF, utilizing PVsyst and WindPRO for solar and wind systems respectively. The optimization techniques encompass adjustments to panel tilt angles, interspace distances, turbine positioning, and the synthesis of wind turbines at varying hub heights. Additionally, the study analyses and optimizes site roughness and elevation to mitigate associated energy loss effects. The selection of the lithium-ion BESS was made after a comprehensive literature review tailored to the project's specific requirements and battery chemistry, aligning seamlessly with the objectives of the thesis. This research holds significance for South Asia's energy landscape by advocating sustainable practices and highlighting the utility of simulation tools in renewable energy farm design. The findings underscore the feasibility and advantages of a synergistic approach that combines solar, wind, and advanced energy storage technologies

Performance evaluation of the photovoltaic system

The various renewable energy source technologies, Photovoltaics (PV) transforming sunlight directly into electricity, have become standard practice worldwide, especially in countries with high solar radiation levels. PV systems have been developed rapidly over recent years, and many new technologies have emerged from different producers. For each type of PV module, manufacturers provide specific information on rated performance parameters, including power at maximum power point (MPP), efficiency and temperature factors, all under standard solar test conditions (STC) 1000 W/m2. Air. In addition, the mass (AM) of 1.5 and the cell's temperature was 25 ̊C. Unfortunately, this grouping of environmental conditions is infrequently found in outdoor conditions. Also, the data provided by the manufacturers are not sufficient to accurately predict the performance of photovoltaic systems in various climatic conditions. Therefore, monitoring and evaluating the performance of the off-site systems is necessary. This thesis aims to overview various photovoltaic technologies, ranging from crystalline silicon (c-SI) to thin-film CdTe and GiCs. The following are the main parameters for evaluating the external units' performance to describe the PV systems' operation and implementation. In addition, a review of the impacts of various environmental and operational factors, such as solar radiation, temperature, spectrum, and degradation

Impact of data quality on photovoltaic (PV) performance assessment

In this work, data quality control and mitigation tools have been developed for improving the accuracy of photovoltaic (PV) system performance assessment. These tools allow to demonstrate the impact of ignoring erroneous or lost data on performance evaluation and fault detection. The work mainly focuses on residential PV systems where monitoring is limited to recording total generation and the lack of meteorological data makes quality control in that area truly challenging. Main quality issues addressed in this work are with regards to wrong system description and missing electrical and/or meteorological data in monitoring. An automatic detection of wrong input information such as system nominal capacity and azimuth is developed, based on statistical distributions of annual figures of PV system performance ratio (PR) and final yield. This approach is specifically useful in carrying out PV fleet analyses where only monthly or annual energy outputs are available. The evaluation is carried out based on synthetic weather data which is obtained by interpolating from a network of about 80 meteorological monitoring stations operated by the UK Meteorological Office. The procedures are used on a large PV domestic dataset, obtained by a social housing organisation, where a significant number of cases with wrong input information are found. Data interruption is identified as another challenge in PV monitoring data, although the effect of this is particularly under-researched in the area of PV. Disregarding missing energy generation data leads to falsely estimated performance figures, which consequently may lead to false alarms on performance and/or the lack of necessary requirements for the financial revenue of a domestic system through the feed-in-tariff scheme. In this work, the effect of missing data is mitigated by applying novel data inference methods based on empirical and artificial neural network approaches, training algorithms and remotely inferred weather data. Various cases of data loss are considered and case studies from the CREST monitoring system and the domestic dataset are used as test cases. When using back-filled energy output, monthly PR estimation yields more accurate results than when including prolonged data gaps in the analysis. Finally, to further discriminate more obscure data from system faults when higher temporal resolution data is available, a remote modelling and failure detection framework is ii developed based on a physical electrical model, remote input weather data and system description extracted from PV module and inverter manufacturer datasheets. The failure detection is based on the analysis of daily profiles and long-term PR comparison of neighbouring PV systems. By employing this tool on various case studies it is seen that undetected wrong data may severely obscure fault detection, affecting PV system s lifetime. Based on the results and conclusions of this work on the employed residential dataset, essential data requirements for domestic PV monitoring are introduced as a potential contribution to existing lessons learnt in PV monitoring

Techno-Economic modelling of hybrid renewable mini-grids for rural electrification planning in Sub-Saharan Africa

Access to clean, modern energy services is a necessity for sustainable development. The UN Sustainable Development Goals and SE4ALL program commit to the provision of universal access to modern energy services by 2030. However, the latest available figures estimate that 1.1 billion people are living without access to electricity, with over 55% living in Sub-Saharan Africa. Furthermore, 85% live in rural areas, often with challenging terrain, low income and population density; or in countries with severe underinvestment in electricity infrastructure making grid extension unrealistic. Recently, improvements in technology, cost efficiency and new business models have made mini-grids which combine multiple energy technologies in hybrid systems one of the most promising alternatives for electrification off the grid. The International Energy Agency has estimated that up to 350,000 new mini-grids will be required to reach universal access goals by 2030. Given the intermittent and location-dependent nature of renewable energy sources, the evolving costs and performance characteristics of individual technologies, and the characteristics of interacting technologies, detailed system simulation and demand modelling is required to determine the cost optimal combinations of technologies for each-and-every potential mini-grid site. Adding to this are the practical details on the ground such as community electricity demand profiles and distances to the grid or fuel sources, as well asthe social and political contexts,such as unknown energy demand uptake or technology acceptance, national electricity system expansion plans and subsidies or taxes, among others. These can all have significant impacts in deciding the applicability of a mini-grid within that context. The scope of the research and modelling framework presented focuses primarily on meeting the specific energy needs in the sub-Saharan African context. Thus, in being transparent, utilizing freely available software and data as well as aiming to be reproducible, scalable and customizable; the model aims to be fully flexible, staying relevant to other unique contexts and useful in answering unknown future research questions. The techno-economic model implementation presented in this paper simulates hourly mini-grid operation using meteorological data, demand profiles, technology capabilities, and costing data to determine the optimal component sizing of hybrid mini-grids appropriate for rural electrification. The results demonstrate the location, renewable resource, technology cost and performance dependencies on system sizing. The model is applied for the investigation of 15 hypothetical mini-grids sites in different regions of South Africa to validate and demonstrate the model’s capabilities. The effect of technology hybridization and future technology cost reductions on the expected cost of energy and the optimal technology configurations are demonstrated. The modelling results also showed that the combination of hydrogen fuel cell and electrolysers was not an economical energy storage with present day technology costs and performance. Thereafter, the model was used to determine an approximate fuel cell and electrolyser cost target curve up to the year 2030. Ultimately, any research efforts through the application of the model, building on the presented framework, are intended to bridge the science-policy boundary and give credible insight for energy and electrification policies, as well as identifying high impact focus areas for ongoing further research

Characterisation of spectral and angular effects on photovoltaic modules for energy rating

This thesis presents work aimed at the development of practical and simplified methods for advanced characterisation of PV modules while reducing energy yield estimation uncertainties, focusing on the spectral and angular effects. In this work, practical characterisation method to measure the spectral response (SR) curve of PV modules have been developed based on the polychromatic method. Improvement of the method have been achieved through the development of new measurement setup and detail evaluation of the polychromatic fitting algorithm. Set of coloured plate with unique transmission profiles supplemented with a smaller number of optical bandwidth filters used in the measurement setup resulted in high throughput irradiance (the lowest is measured at 150 W/m2). High uniformity of the throughput irradiance over the measurement plane contribute to low uncertainty in the measurement of short-circuit current where the highest estimated uncertainty lays within the uncertainty margin for the STC measurement, at 2.5%. Measurement of optical/electrical of device under test with associated uncertainty are combined with the fitting algorithm through the Monte-carlo simulation method. The uncertainty in the final determination of SR characteristic gave the value of 7%, with about ±10% agreement between the SR curves obtained through the polychromatic method to the conventional monochromatic method. The measurement of angular response developed in this method employed the indoor measurement setup with the additional turn table attachment. The evaluation of divergent light of the non-ideal light source and the accuracy in angle adjustment of the turn table have been quantified and incorporated into the angular response measurement as uncertainties. Partial illumination method are applied for a reliable extraction of operating current in the measurement of PV modules with the uncertainty estimated at 1%. 4% variation in the measurement of angular dependency of various PV devices at high tilt angle have been realised which translate to about 1.5% difference in the simulated annual energy performance. The application of the same simulator in the development of spectral and angular response measurement in this work creates the potential for the angle-dependent spectral response characterisation on module scale. This have been realised through a simulation. Low uncertainty in energy yield is important as this indicate the risk in the investment of PV project. Detail evaluation with accuracy and uncertainty analysis of the works to be described will further improve the uncertainty in the measurement of spectral and angular response of PV modules, hence better accuracy in the assessment of energy yield can be achieved

Modeling, Simulation and Optimization of Wind Farms and Hybrid Systems

The reduction of greenhouse gas emissions is a major governmental goal worldwide. The main target, hopefully by 2050, is to move away from fossil fuels in the electricity sector and then switch to clean power to fuel transportation, buildings and industry. This book discusses important issues in the expanding field of wind farm modeling and simulation as well as the optimization of hybrid and micro-grid systems. Section I deals with modeling and simulation of wind farms for efficient, reliable and cost-effective optimal solutions. Section II tackles the optimization of hybrid wind/PV and renewable energy-based smart micro-grid systems