Optimal Design and Analysis of Grid-Connected Solar Photovoltaic Systems

Many countries consider utilizing renewable energy sources such as solar photovoltaic (PV), wind, and biomass to boost their potential for more clean and sustainable development and to gain revenues by export. In this thesis, a top-down approach of solar PV planning and optimization methodology is developed to enable high-performance at minimum costs. The first problem evaluates renewable resources and prioritizes their importance towards sustainable power generation. In the second problem, possible sites for solar PV potential are examined. In the third problem, optimal design of a grid-connected solar PV system is performed using HOMER software. A techno-economic feasibility of different system configurations including seven designs of tracking systems is conducted. In the fourth and the final problem, the optimal tilt and azimuth angles for maximum solar power generation are found. Using a detailed estimation model coded in MATLAB software, the solar irradiation on a tilted angle was estimated using a ground measurement of solar irradiation on a horizontal surface. A case study for Saudi Arabia is conducted. The results of our prioritization study show solar PV followed by concentrated solar power are the most favorable technologies followed by wind energy. Using a real climatology and legislation data, such as roads, mountains, and protected areas, land suitability is determined via AHP-GIS model. The overlaid result suitability map shows that 16% (300,000 km2) of the study area is promising for deploying utility-size PV power plants in the north and northwest of Saudi Arabia. The optimal PV system design for Makkah, Saudi Arabia shows that the two-axis tracker can produce 34% more power than the fixed system. Horizontal tracker with continuous adjustment shows the highest net present cost (NPC) and the highest levelized cost of energy (LCOE), with a high penetration of solar energy to the grid. At different tilt and azimuth angles, the solar irradiation, potential power, and system revenue were calculated for 18 cities in Saudi Arabia. For Riyadh city (high suitable site), the monthly adjustment increases the harvested solar energy by 4%. It is recommended to adjust the tilt angle five times per year to achieve near-optimal results and minimize the cost associated with workforce or solar trackers for monthly adjustments. The proposed work can be exploited by decision-makers in the solar energy area for optimal design and analysis of grid-connected solar photovoltaic systems

Modelo de decisión para el diseño conceptual de un sistema de suministro sostenible de energía para la Sede Leticia de la Universidad Nacional de Colombia

ilustraciones, gráficas, tablasThe decentralized model of energy generation has emerged as a solution to provide electricity to isolated areas, ensuring energy security and increasing coverage. This model frequently leads to a dependency on a unique energy source; thus, it is necessary to change the paradigm of energy generation by adding other more sustainable sources. Unfortunately, there is not a well-defined route to establish which energy sources should be linked and in what way, making this restructuring a very complex problem involving a decision-making process. Generally, decisions are made only considering the economic or technical dimensions, ignoring the other dimensions such as environmental, social, and political, which could provide a more contextualized perspective. The aim of this study is to develop and test a methodology to find an optimal arrangement of energy sources in a decentralized electricity production model considering all sustainability dimensions. A methodology as the proposed in this work can support the stakeholders during the planning stages of energy supply systems. The methodology was applied to a specific case in Colombia, the campus Amazonia of the Universidad Nacional de Colombia, located in Leticia, a municipality where on-site generators are employed due to the difficulty of access. As a result, the proposed methodology generated nine different scenarios of energy arrangements according to an evaluation of energy sources using a sustainability approach that considered context aspects along with a carefully selected set of indicators and stakeholders' preferences.El modelo descentralizado de generación de energía surgió como una solución para el suministro de energía en áreas aisladas, asegurando la seguridad energética e incrementando la cobertura. No obstante, este modelo frecuentemente conlleva a una dependencia a una única fuente de energía, por lo que es necesario cambiar el paradigma de la generación de energía añadiendo otras fuentes más sostenibles. Desafortunadamente, no existe una ruta definida para establecer cuales fuentes de energía deben ser agregadas y de qué manera, convirtiendo esta reestructuración en un problema muy complejo que involucra la toma de decisiones. Generalmente, estas decisiones se toman considerando aspectos económicos o técnicos, dejando de lado otras dimensiones como la ambiental, social y política, que podrían proporcionar una perspectiva más contextualizada. El objetivo de este estudio es desarrollar y probar una metodología que permita encontrar un arreglo óptimo de fuentes de energía en un modelo de producción de electricidad descentralizado teniendo en cuenta todas las dimensiones de la sostenibilidad. La metodología propuesta en este trabajo puede ayudar a los principales involucrados durante las fases de planeación de sistemas de suministro de energía. Esta metodología fue aplicada a un caso específico en Colombia, la sede Amazonas de la Universidad Nacional de Colombia, ubicada en Leticia, un municipio donde generadores in situ son empleados debido al difícil acceso. Como resultado, la metodología propuesta generó nueve escenarios diferentes de arreglos energéticos de acuerdo a una evaluación de fuentes de energía en un enfoque de sostenibilidad considerando aspectos de contexto junto a una selección cuidadosa de indicadores y las preferencias de las partes interesadas. (Texto tomado de la fuente).Incluye anexosMaestríaBiorefinerías y biorefinació

Uncertain Multi-Criteria Optimization Problems

Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems

Multi-Criteria Decision Making under Uncertain Evaluations

Multi-Criteria Decision Making (MCDM) is a branch of operation research that aims to empower decision makers (DMs) in complex decision problems, where merely depending on DMs judgment is insufficient. Conventional MCDM approaches assume that precise information is available to analyze decision problems. However, decision problems in many applications involve uncertain, imprecise, and subjective data. This manuscripts-based thesis aims to address a number of challenges within the context of MCDM under uncertain evaluations, where the available data is relatively small and information is poor. The first manuscript is intended to handle decision problems, where interdependencies exist among evaluation criteria, while subjective and objective uncertainty are involved. To this end, a new hybrid MCDM methodology is introduced, in which grey systems theory is integrated with a distinctive combination of MCDM approaches. The emergent ability of the new methodology should improve the evaluation space in such a complex decision problem. The overall evaluation of a MCDM problem is based on alternatives evaluations over the different criteria and the associated weights of each criterion. However, information on criteria weights might be unknown. In the second manuscripts, MCDM problems with completely unknown weight information is investigated, where evaluations are uncertain. At first, to estimate the unknown criteria weights a new optimization model is proposed, which combines the maximizing deviation method and the principles of grey systems theory. To evaluate potential alternatives under uncertain evaluations, the Preference Ranking Organization METHod for Enrichment Evaluations approach is extended using degrees of possibility. In many decision areas, information is collected at different periods. Conventional MCDM approaches are not suitable to handle such a dynamic decision problem. Accordingly, the third manuscript aims to address dynamic MCDM (DMCDM) problems with uncertain evaluations over different periods, while information on criteria weights and the influence of different time periods are unknown. A new DMCDM is developed in which three phases are involved: (1) establish priorities among evaluation criteria over different periods; (2) estimate the weight of vectors of different time periods, where the variabilities in the influence of evaluation criteria over the different periods are considered; (3) assess potential alternatives

A review of CO2 emission reductions due to wind turbines using energy benchmarks: A focus on the Irish electrical energy market

The installed capacity of wind turbines in Ireland increased from a value of 2,250 MW in 2014 to 3,318 MW in 2017, a 43% increase in the four years, supported through climate mitigation policies. The main aim of this study is to determine if the increase in wind turbine installed capacity is impacting on efforts to reduce CO2 emissions. The study utilises a review methodology. The findings show that the steady rise in wind turbine installed capacity year-on-year is not reflected in the Irish CO2 g/kWh energy benchmark. The benchmark value was 457 g CO2/kWh in 2014 and 437 g CO2/kWh in 2017, an improvement of just 5%. There is no consistent correlation between the increase in wind turbine capacity and a reduction in CO2 emissions. Future research into the quality of the wind turbine power output is recommended, in particular, the variability aspect in the power output signal

A Decision Aiding Framework for Concentrated Solar Thermal Power Technologies Assessment in Developing Countries

The diversification of electricity generation is necessary for sustainable development. The planning for renewable energy sources (RESs) integration is an essential goal set by many developing countries. Enormous investments are allocated accordingly to renewable energy projects, including solar power utilities. Concentrated solar thermal power (CSP) technologies are advancing and are expected to play a significant role in energy portfolios in the future. CSP planning is a complex process owing to the involvement of various contradicting factors and players. This thesis proposes a structured aiding framework to assess utility-scale CSP alternatives to support national grids in developing countries. It is common in many fast growing developing countries that the power plants are owned by the state, which enlarges the scope of electric power projects beyond the technical and economic drivers to include environmental, social, and political aspects, which accordingly increases the planning process complexity. The developed methodology consists of three main phases. The first phase is concerned with formulating a value tree for CSP technologies evaluation. This phase is intended to explicitly capture a generic evaluation criteria through a rigorous process of expert deliberation and consensus-seeking. Expert elicitation is conducted through the Delphi method, with a total of 140 experts participating from multidisciplinary solar thermal power fields from 32 countries. Based on participants’ judgments, as expressed during two rounds of Delphi questionnaires, parameters with importance and consensus degrees > 50% are incorporated to construct the final value tree. The recommendations of this phase set a foundation for stakeholders’ assessment of regional CSP utilities planning in developing countries. The second phase considers analyzing, defining, and simulating alternative scenarios. Large-scale CSP deployment is in its infancy with a lack of sufficient data in many developing countries and various available technology combinations. Accordingly, this phase intends to focus the planning process toward practical alternatives given the regional requirements. A techno-economic analysis is conducted that considers the strengths, weaknesses, opportunities, and threats (SWOT) for each technology. As RESs are location dependent, Saudi Arabia defines the scope of this phase. The analysis outcomes are incorporated with the Saudi energy sector requirements and local weather conditions to define alternative scenarios. Six power plant scenarios are defined for performance and financial evaluation. A simulation is subsequently carried out through the System Advisor Model. The alternative scenarios are assessed by defining weather, technical, and financial parameters. Satellite observations and field measured data are integrated to synthesize a typical meteorological year weather profile. The outputs of this phase provide accurate results that represent a solid ground for the assessment of alternative CSP scenarios with consideration of all relevant parameters. The third phase considers a comprehensive assessment of the scenario-based CSP alternatives. A multi-criteria decision-making (MCDM) model is developed in a fuzzy environment to tackle uncertainty, ambiguity, and imprecision. The evaluation is conducted based on extensive analysis of the performances of each alternative scenario in accordance with 4 main criteria and 29 sub-criteria. Quantitative and qualitative data as well as input from 44 local stakeholders are incorporated. The obtained results constitute an accurate basis to derive recommendations for CSP integration to national grids and relate them to stakeholders’ priorities

A knowledge base system for overall supply chain performance evaluation : a multi-criteria decision-making approach

Due to the advancement of technology that allows organizations to collect, store, organize and use data information system for efficient decision making (DM), a new horizon of supply chain performance evaluation starts. Today, DM is shifting from “information-driven” to “data-driven” for more precision in overall supply chain performance evaluation. Based on the real-time information, fast decisions are important in order to deliver product more rapidly. Performance evaluation is critical to the success of the supply chain (SC). In managing SC, there are many decisions to be taken at each level of multi-criteria decision making (MCDM) (short-term or long-term) because of many decisions and decision criteria (attributes) that have an impact on overall supply chain performance. Therefore it is essential for decision makers to know the relationship between decisions and decision criteria on overall SC performance. However, existing supply chain performance models (SCPM) are not adequate in establishing a link between decisions and decisions criteria on overall SC performance. Most of the decisions and decision attributes in SC are conflicting in nature and performance measure of different criteria (attributes) at different levels of decisions (long-term and short-term) is different and makes it more intricate for SC performance evaluation. SC performance heavily depends on how well you design your SC. In other words, it is quite difficult to improve overall SC performance if decisions criteria (attributes) are not embedded or considered at the phase of SC design. The connection between the SC design and supply chain management (SCM) is essential for effective SC. Many companies such as Wal-Mart, Dell, etc. are successful companies and they achieve their success because of their effective SC design and management of SC activities. The purpose of this thesis is in two folds: First is to develop an integrated knowledge base system (KBS) based on Fuzzy-AHP that establish a relationship between decisions and decisions criteria (attributes) and evaluate overall SC performance. The proposed KBS assists organizations and decision-makers in evaluating their overall SC performance and helps in identifying under-performed SC function and its associated criteria. In the end, the proposed system has been implemented in a case company, and we developed a SC performance monitoring dashboard of a case company for top managers and operational managers. Second to develop decisions models that will help us in calibrating decisions and improving overall SC performance

Sustainable Assessment in Supply Chain and Infrastructure Management

In the competitive business environment or public domain, the sustainability assessment in supply chain and infrastructure management are important for any organization. Organizations are currently striving to improve their sustainable strategies through preparedness, response, and recovery because of increasing competitiveness, community, and regulatory pressure. Thus, it is necessary to develop a meaningful and more focused understanding of sustainability in supply chain management and infrastructure management practices. In the context of a supply chain, sustainability implies that companies identify, assess, and manage impacts and risks in all the echelons of the supply chain, considering downstream and upstream activities. Similarly, the sustainable infrastructure management indicates the ability of infrastructure to meet the requirements of the present without sacrificing the ability of future generations to address their needs. The complexities regarding sustainable supply chain and infrastructure management have driven managers and professionals to seek different solutions. This Special Issue aims to provide readers with the most recent research results on the aforementioned subjects. In addition, it offers some solutions and also raises some questions for further research and development toward sustainable supply chain and infrastructure management