Solar Energy and Solar Panels Systems by Carter
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Solar Energy and Solar Panels Systems by Carter

Solar Energy and Solar Panels Systems Performance and Recent Developments by Joel G. Carter | PDF Free Download.

Solar Energy and Solar Panels Contents

  • Chapter 1 Solar-Energy-Driven Bioethanol Production from Carbohydrates for Transportation Applications
  • Chapter 2 PV Panel Modeling and Identification
  • Chapter 3 Statistical Modeling, Parameter Estimation and Measurement Planning for PV Degradation 
  • Chapter 4 Comparison for Policy and Promotion Strategy of Solar Energy Developments between 
  • Chapter 5 The Future of Organic Solar Energy Harvesting Complexes

Preface to Solar Energy and Solar Panels Systems

Indiscriminate extraction and increasing consumption of fossil fuel resources (crude oil, natural gas, and coal) are adversely affecting the major spheres of human activity.

With the depletion of these fuels, efforts are being directed to the use of renewable sources such as solar, wind, and biomass. This book provides new research on the systems, performance and recent developments in solar energy.

As explained in Chapter 1, one of the best alternatives to petroleum, the production of bioethanol has increased since 1990, with a sharp increase from the year 2000 onwards.

Bioethanol also offers an attractive alternative as a fuel in low-temperature fuel cells, as it can be produced in large quantities from agricultural waste and biomass.

Currently, global ethanol is produced mainly from sugar and starch feedstock. The successful utilization of solar energy which is renewable, abundant, and inexpensive, for bioethanol production from biomass, has the potential to solve the fuel shortage problem.

Solar energy provides an important alternative energy source, even if only a portion of this energy is harnessed for heating applications.

The author's work focuses on using solar thermal energy for bioreaction leading to ethanol production.

A solar reactor was developed to perform the conversion of starch (in a batch process) and glucose (in a continuous flow system) to bioethanol by heating the reactor using solar irradiation.

Aqueous starch solution (5 wt%) was charged into the reactor bed loaded with baker’s yeast (Saccharomyces cerevisiae) and enzymes, resulting in the conversion of starch into ethanol in a single-step process, yielding 0.5 M, 25 mL ethanol/day.

A significant amount of ethanol corresponding to 84% of the theoretical yield was obtained after two months.

The process was scaled up to 15 wt% starch, producing 1.3 Methanol, which was demonstrated as a potential and sustainable fuel for direct methanol fuel cells (DEFCs) (310 might-1, 0.75 V).

Additionally, the secondary metabolite glycerol was fully reduced to 1,3- propanediol, which is the first example of a fungal strain that converts glycerol in situ to a value-added product.

The batch process of bioethanol production was further developed into a continuous-flow process.

When aqueous glucose solutions of 10, 20, 30, and 40 wt% were fed into the reactor, high ethanol yields (91, 86, 89, and 88% of the theoretical yield, respectively) were obtained, indicating the atom efficiency of the process.

No loss was observed in the activity of the yeast even after two months of continuous operation of the process.

The ethanol produced from 20 wt% glucose feed (2 M) was demonstrated as a potential fuel for DEFCs with current and power density values as high as 700 mA/cm2 and 330 mW/cm2 at a modest open-circuit voltage of 1.65 V.

Productive utilization of solar energy for driving the fermentation reaction as well as the special design of the reactor that facilitates in situ separation of ethanol from the fermentation broth, make the current process economically feasible and environmentally friendly, and therefore industrially appealing and adaptable.

In Chapter 2, the modeling techniques of PV panels from I-V characteristics are discussed. In the beginning, a necessary review of the various methods is presented, where difficulties in mathematics, drawbacks inaccuracy and challenges in implementation are highlighted.

Next, a novel approach based on linear system identification is demonstrated in detail.

Other than the prevailing methods of using approximation (analytical methods), iterative searching (classical optimization)

Or soft computing (artificial intelligence), the proposed method regards the PV diode model as the equivalent output of a dynamic system, so the diode model parameters can be linked to the transfer function coefficients of the same dynamic system.

In this way, the problem of solving PV model parameters is equivalently converted to system identification in control theory, which can be perfectly solved by a simple integral-based linear least square method.

Graphical meanings of the proposed method are illustrated to help readers understand the underlying principles.

As compared to other methods, the proposed one has the following benefits: 1) unique solution; 2) no iterative or global searching; 3) easy to implement (linear least square); 4) accuracy; 5) extendable to multi-diode models.

The effectiveness of the proposed method has been verified by indoor and outdoor PV module testing results.

In addition, possible applications of the proposed method are discussed like online PV monitoring and diagnostics, non-contact measurement of POA irradiance and cell temperature, fast model identification for satellite PV panels, and etc.

As shown in Chapter 3, photovoltaics (PV) degradation is a key consideration during PV performance evaluation. Accurately predicting power delivery over the course of the lifetime of PV is vital to manufacturers and system owners.

With many systems exceeding 20 years of operation worldwide, degradation rates have been reported abundantly in recent years.

PV degradation is a complex function of a variety of factors, including but not limited to climate, manufacturer, technology and installation skill. As a result, it is difficult to determine the degradation rate by analytical modeling; it has to be measured.

As one set of degradation measurements based on a single sample cannot represent the population nor be used to estimate the true degradation of a particular PV technology, repeated measures through multiple samples are essential.

In Chapter 3, the linear mixed-effects model (LMM) is introduced to analyze longitudinal degradation data.

The framework herein introduced aims to address three issues: 1) how to model the difference in degradation observed in PV modules/systems of the same technology that are installed at a shared location; 2) how to estimate the degradation rate and quantiles based on the data, and 3) how to effectively and efficiently plan degradation measurements.

Solar power is always the ultimate energy source on earth. Solar energy does not drive the hydrologic cycle and wind, but also produces biomass including ancient fossil fuels and present foods.

Solar energy is one of the potential renewable energy and has been actively promoted by many countries.

In Chapter 4, the policy and promotion strategy of solar energy developments between Taiwan and Japan were surveyed and compared.

The results showed that solar power increased significantly in the past ten years. The cumulative capacity of solar energy (CCSE), solar power generation (SPG), and the ratio of SPG to total power generation for Taiwan in 2014 gave on 615.2, 533.1, and 466.2 times than those in 2005.

The CCSE, SPG, and the ratio of SPG to TPG for Japan in 2014 gave on 16.5, 16.4, and 17.6 times than those in 2005.

Besides, an analytic hierarchy process (AHP) structure was proposed for decision-makers to make decisions to prioritize and select policy and promotion strategies of solar energy developments.

Taiwan and Japan have launched solar PV R&D in the 1980s and 1970s, respectively. In the early 2000s, Taiwan enacted the RED Act and rewarded the solar power generation system invested by folk investment to increase the use of renewable energy.

Japan enacted the RPS Law and Feed-in Tariffs policy towards the aim of promoting the new energy electricity. Recent advances in solar harvesting technology are transforming the renewable energy landscape.

Despite the plunging cost of silicon and the ground-breaking efficiencies of new perovskite materials, research into “traditional” biomimetic, organic solar energy harvesting complexes remains important for the future success of solar energy.

In Chapter 5 the authors discuss recent findings from studies of molecular donor-acceptor complexes that show promise as the active light-harvesting components in organic solar energy systems.

In particular, they focus upon self-assembled and covalent complexes of porphyrins (and related molecules) and fullerenes as facile electron transfer partners, and highlight several new results.

Finally, the authors discuss the role of these types of “soft” organic-based materials play in the solar energy marketplace, and explore how that role is likely to change in the future.

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