Purchasing Efficiency Measurement of Selected Chinese PV Panels Using Data Envelopment Analysis (DEA)

As a renewable energy generation, the solar Photovoltaic (PV) system has become a popular technique that converts sunlight to generate electrical power. However, most countries, especially like China met challenges in expanding their domestic market and widespread adoption by their own citizens. One of the major reasons why customers are hesitated to adopt solar panels is the difficulty in comparing a variety of solar panels with respect of multiple different dimensions without the aid of appropriate quantitative models. The PV panel’s purchasing efficiency is determined not only just by those technical factors identified by the current literature and practice, but also by the economy factors such as price and the physical factors including weight. The study overviews all the important purchasing criteria during selecting PV panels and applies a Data Envelopment Analysis (DEA) model to analyze the purchasing efficiency of solar panels for customers during the selection of the most efficient one(s) among a variety of solar panels. In order to demonstrate the model, we used a numerical example to compare a group of thirty five popular solar panels produced by six most famous solar panel manufacturers in China. The main goal of this study is to help customers with purchasing decisions. And the results indicate how the manufacturers can improve the current inefficient products as well

Three essays in international trade : market integration, subsidization and antidumping

This thesis contains three essays on topics in agricultural economics. The research is focused on the economic effects of different trade policies applied within the US, Canada and the European Union. Essay one evaluates the accession of Austria, Finland and Sweden to the single EU common market. The degree of integration of these three countries in agricultural trade in the EU has not previously been evaluated. Trade theory suggests that one of the outcomes resulting from a regional trade agreement is increased market integration among markets in member states. The cointegration of the commodity prices across countries is tested using time-series techniques. This method is important as it can be applied to questions relating to globalization. Essay two examines the biofuel industry in Canada and US from a trade perspective. The development of a large market for biofuels is judged to have two main benefits for North America: environmental benefits in Canada and energy security in the US. A theoretical model is developed using the option value theory to determine whether the two distinct motivating factors can lead to different levels of optimal subsidies in each country. While the development of a biofuel industry is viewed as extremely important in a number of countries, the trade laws on subsidies with respect these products lacks clarity. This research represents an important step in understanding the economics of biofuels and the situations where trade disputes can be expected to appear in the future.Dumping is the subject of the third essay where the strategies of firms in the face of an anti-dumping action are examined using game theory. The possibility of free riding in case of an anti-dumping petition is investigated in two situations: the benefits of the anti-dumping case are considered either a public good or a joint product. The second situation can be applied only for US, because of so-called Byrd Amendment. The theoretical model developed represents an important contribution to trade policy and it can be easily applied when examining the effects of other trade or domestic policies

Modeling and Analysis of Power Processing Systems (MAPPS), initial phase 2

The overall objective of the program is to provide the engineering tools to reduce the analysis, design, and development effort, and thus the cost, in achieving the required performances for switching regulators and dc-dc converter systems. The program was both tutorial and application oriented. Various analytical methods were described in detail and supplemented with examples, and those with standardization appeals were reduced into computer-based subprograms. Major program efforts included those concerning small and large signal control-dependent performance analysis and simulation, control circuit design, power circuit design and optimization, system configuration study, and system performance simulation. Techniques including discrete time domain, conventional frequency domain, Lagrange multiplier, nonlinear programming, and control design synthesis were employed in these efforts. To enhance interactive conversation between the modeling and analysis subprograms and the user, a working prototype of the Data Management Program was also developed to facilitate expansion as future subprogram capabilities increase

Multi-objective Optimization of PV Module Assembly Supply Chain, Concerning Economic, Environmental and Social Impacts

The current concentration of photovoltaic (PV) manufacturing in certain countries gives rise to potential risks, involving supply chain disruptions and increased vulnerabilities. Factors contributing to these risks include geopolitical tensions, trade disputes, and susceptibility to natural disasters. To address this issue, expanding PV manufacturing to various locations can promote diversification within the industry and help reduce current risks. However, promoting local PV manufacturing requires a comprehensive consideration of the economic, environmental, and social aspects associated with it. In this thesis, a novel multi-objective optimization model is developed to examine the various dimensions of local PV manufacturing. The model simultaneously optimizes three key objectives: economic, environmental, and social aspects related to the local PV manufacturing supply chain. The model is initially applied to an Australian case study and then expanded to include other nations such as Germany and the USA. The economic model is developed to account for economies of scale, social factors, and market size, allowing for global application. When considering cost alone, the result shows that 600MWp module manufacturing in Australia would result in a Minimum Sustainable Price (MSP) of USD 0.363/Wp in 2023 and, to be competitive with imports at USD 0.282/Wp, the production capacity needs to reach over 2.8GWp. Extending the model to the global locations reveals that, in case of high labour and electricity costs in nations such as Germany, production capacity would be competitive at 3.7GWp. Whilst the 600MWp production capacity in Germany would result in MSP of USD 0.370/Wp. To promote local manufacturing, a policy framework is introduced based on a comprehensive literature review on the USA, China, and Germany to visualize and develop robust policy mechanisms. The proposed economic model is evaluated against current policies in key markets such as Germany, the US, and Australia, and the costs and benefits are compared. The findings reveal that by developing policies and support mechanisms that cover 75% of upfront expenses and 25% of ongoing costs, the financial burden on stakeholders would be minimized to the greatest extent possible. The model is further extended to capture the environmental impacts of local manufacturing, by optimizing the carbon footprint, and helps find the trade-off between selecting greener suppliers and cost reduction using augmented epsilon constraints. Minimizing the carbon footprint for a 600MWp module leads to a higher MSP of USD 0.509/Wp, with Germany identified as the main supplier and a carbon intensity of 140 kg CO2 per kWp. A balanced weighting of objectives results in an MSP of USD 0.417/Wp and a carbon intensity of 159 kg CO2 per kWp. Finally, a novel socio-economic model is developed to examine the trade-off between production capacity, job creation, and cost reduction. Two options are considered: a single mega-factory with economies of scale or multiple smaller factories to meet local demand. Opting for a 4GWp mega-factory yields an MSP of USD 0.272/Wp and creates 1,642 jobs. Alternatively, seven 600MWp centres create 2,247 jobs with an MSP of USD 0.363/Wp. By employing the augmented epsilon constraints method and equal weighting for social and economic objectives, a compromise point is found. This compromise resulted in an MSP of USD 0.321/Wp and the creation of 1,855 jobs across two centres with a total production capacity of 2GWp. By incorporating these findings, this thesis provides policymakers and stakeholders with opportunities to gain a better understanding of the potential risks and challenges associated with economic, social, and environmental criteria. Informed decisions can then be made regarding the diversification of PV supply chains

Advances in Computational Intelligence Applications in the Mining Industry

This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

Decision Support Tools for Strategic Policy Analysis

New or improved decision analysis tools are developed in this thesis to address strategic policy analysis with specific focus on two topics: strategic conflict analysis and region-performance comparisons. A strategic conflict refers to a situation in which two or more decision makers (DMs) are to make a decision that affects issues over which they have different preferences. Various forms of strategic conflict exist all around us, in areas such as environmental management, international relations, economic competition, and relationships among individuals. The graph model for conflict resolution (GMCR) is an advanced and comprehensive tool to systematically study strategic conflicts. A well-known decision tool, the analytic network process (ANP) is adapted for use in strategic conflict analysis and a comparison of the performance of ANP with GMCR is carried out. Both methods are applied to an international trading conflict between the United States and China over the importation of television sets into the US in order to gain strategic insights about this dispute using the two different but complementary approaches. A country's overall performance comparison with respect to different kinds of indices such as economic, environmental and political indices constitutes another interesting topic for strategic policy analysis. An index aggregation approach is proposed to compare BRICSAM countries, a populous rapidly-growing economic group of nations consisting of Brazil, Russia, India, China, South Africa, ASEAN (Association of South-East Asian Nations), and Mexico with G7 (Group of Seven), the most developed country club including Canada, France, Italy, Japan, Germany, United Kingdom and the United States. A data-envelopment-analysis (DEA) based approach is proposed to aggregate different ranking indices for BRICSAM and the G7 countries. The proposed method can provide a fair overall assessment of a country's standing by maximizing its possibility of obtaining the best evaluation score. Finally, a framework to carry out generic strategic analysis for regions' competence analysis is designed based upon the theory of generic strategic analysis proposed by Porter (1980). This is a well-known approach for use in business competence analysis. The basic idea is to carry out generic strategic analysis in policy studies and two decision tools, DEA and the analytic hierarchy process, are employed to quantify the analysis of competence efficiency and potentiality, respectively. A case study of the competence analysis of provinces in China is used to demonstrate the analysis procedure

Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility

According to the National Research Council, the use of embedded systems throughout society could well overtake previous milestones in the information revolution. Mechatronics is the synergistic combination of electronic, mechanical engineering, controls, software and systems engineering in the design of processes and products. Mechatronic systems put “intelligence” into physical systems. Embedded sensors/actuators/processors are integral parts of mechatronic systems. The implementation of mechatronic systems is consistently on the rise. However, manufacturers are working hard to reduce the implementation cost of these systems while trying avoid compromising product quality. One way of addressing these conflicting objectives is through new automatic control methods, virtual sensing/estimation, and new innovative hardware topologies

Dragline excavation simulation, real-time terrain recognition and object detection

The contribution of coal to global energy is expected to remain above 30% through 2030. Draglines are the preferred excavation equipment in most surface coal mines. Recently, studies toward dragline excavation efficiency have focused on two specific areas. The first area is dragline bucket studies, where the goal is to develop new designs which perform better than conventional buckets. Drawbacks in the current approach include operator inconsistencies and the inability to physically test every proposed design. Previous simulation models used Distinct Element Methods (DEM) but they over-predict excavation forces by 300% to 500%. In this study, a DEM-based simulation model has been developed to predict bucket payloads within a 16.55% error. The excavation model includes a novel method for calibrating formation parameters. The method combines DEM-based tri-axial material testing with the XGBoost machine learning algorithm to achieve prediction accuracies of between 80.6% and 95.54%. The second area is dragline vision studies towards efficient dragline operation. Current dragline vision models use image segmentation methods that are neither scalable nor multi-purpose. In this study, a scalable and multi-purpose vision model has been developed for draglines using Convolutional Neural Networks. This vision system achieves an 87.32% detection rate, 80.9% precision and 91.3% recall performance across multiple operation tasks. The main novelty of this research includes the bucket payload prediction accuracy, formation parameter calibration and the vision system accuracy, precision and recall performance toward improving dragline operating efficiencies --Abstract, page iii

Measurements, Models, Systems and Design

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