Design and Development of a Unified Framework for Selecting Sustainable Renewable Energy through Soft Computing Methods (Research Proposal Sample)

University Research Synopsis Author Date Professor Program Research Synopsis Title: Design and Development of a Unified Framework for Selecting Sustainable Renewable Energy through Soft Computing Methods 1.0Introduction Renewable energy is an unavoidable choice for long-term economic growth and harmonious human-environment coexistence. Renewable energy is non-polluting and may be recycled in the natural world. However, initiatives to promote renewable energy consumption face some obstacles, such as high upfront costs and insufficient cost-effectiveness. The globe is struggling to cope with the consequences of the climate crisis. Even though some research studies have confirmed the technical viability of 100 per cent renewable energy systems, more than 200 GW of unsustainable new coal power capacity was under construction globally in 2018 [1]. A variety of studies have confirmed the technological viability of 100% Renewable Energy Systems in some nations by 2050, such as Hansen et al. [2], who published a state of research study that included 180 published works on the transition to 100% Renewable Energy Systems [1]. Total cumulative CO2 emissions amounted to 2230 GtCO2 through the end of 2017, according to the Intergovernmental Panel on Climate Change (IPCC) special report on Global Warming of 1.5°C [1,3], and the remaining budget to limit the average global temperature rise to 1.5°C until 2050 was 420 GtCO2 (January 2018). 95–130% of the remaining funding will be spent until 2030, based on nationally determined contributions (NDCs). At the same time, the energy sector only accounted for about 44 per cent of overall CO2 emissions in 2018 [1, 4]. These two factors point to the urgent necessity to accelerate the decarbonization of all industries. The concept of 100 per cent renewable energy is currently gaining traction among stakeholders. Sweden, for example, has set a goal of achieving zero net greenhouse gas emissions by 2045 [5,10], and Denmark has set a goal of achieving zero net emissions by 2050 at the latest [6,10]. Barbados, Bangladesh, Colombia, Ethiopia, Cambodia, Vietnam, Ghana, Mongolia, Hawaii, and California aim for 100 per cent renewable electricity by 2045 or 2050 [7,10]. A few nations, such as Costa Rica and Norway [7,10], currently supply almost all of their electricity from renewable sources, while others, such as Uruguay, were the first to reach this goal using a mix of renewables [8,10]. Several cities have also pledged to use 100 per cent renewable energy for overall energy consumption by 2050. Copenhagen, Denmark (2050), Vancouver, Canada (2050), Frankfurt, Germany (2050), Oxford Country, Australia (2050), The Hague, Netherlands (2040), and Malmö, Sweden (2030) are among these cities [7,10]. A similar trend can be seen among significant corporations like IKEA, BMW, and Walmart, as well as technology giants like Apple, Google, eBay, Sony, and Facebook, among many others, and even the first company from the fossil energy industry, Wärtsilä, which has dedicated to 100% renewable energy [9-10]. 2.0Relevant Work and Research Gap Soft computing techniques are valuable instruments for improving the efficiency of energy systems. To choose amongst renewable energy options, fuzzy logic decision-making approaches might be used. Soft Computing in Renewable Energy Systems is a hands-on guide to using gentle computing techniques and hybrid intelligent systems to design, model, characterize, optimize, forecast, and predict the performance of renewable energy systems. Soft Computing Applications for Renewable Energy brings cutting-edge technological studies collectively in computational intelligence and fuzzy logic to take care of our environment. Renewable energy research is moving at a breakneck pace as policymakers, academics, economists, and international organizations join forces to develop alternative sustainable energy solutions to today's crucial environmental, economic, and social concerns. Renewable energy systems use creative models, environmentally friendly procedures, data analytics, and other computationally intensive, non-linear, and complicated techniques, as well as a high degree of uncertainty. Soft computing methods such as neural science and systems, fuzzy sets and systems, genetic programming, evolutionary algorithms, and machine learning are perfect for dealing with data noise, imprecision, and uncertainty while still delivering reliable, low-cost solutions. As an outcome, soft computing and intelligent paradigms are increasingly being used to research renewable energy systems. The Analytic Network Process (ANP) and the Analytic Hierarchy Process (AHP) are two other popular methodologies for evaluating energy options. AHP structures a decision problem as hierarchical, covering a complete goal, a group of alternatives, and criteria. The ANP was created to cope with inner and outer dependencies among options and bars. It is a generalization of the AHP. Ulutaş[11] figured out what Turkey's energy policy should be. The model was used by Erdomuş et al. [12] to assess alternative fuels for domestic heating in Turkey. Dadeviren and Eraslan [13] developed a model for determining the priority of energy policies. Further, Wang et al. [14] developed a model to assess China's energy options. AHP was used by Shen et al. [15] to examine the renewable energy portfolio. With AHP, Yi et al.[16] suggested a BOCR model (Benefits, Opportunities, Costs, and Risks). BOCR is a method of representing choice issues from several angles. The ANP can also be combined with the BOCR method. Ahmad and Tahar [17] analyze the potential of various renewable resources before developing a methodology for ranking renewable solutions. Sola, Hydropower, wind, and biomass (including municipal solid waste and biogas) are all considered. They use AHP, which has 12 sub-criteria and four core criteria: technical, economic, social, and environmental. Fuzzy TOPSIS allows for the use of undefined values in decision-making. Under fuzziness, Kaya and Kahraman[18] developed an approach that combined AHP and VIKOR. They used their methodology to find the most acceptable renewable energy alternatives and energy production locations in Istanbul. Boran et al.[19] intuitionistic fuzzy TOPSIS to evaluate renewable energy options for electricity generation in Turkey. Kaya and Kahraman examined photovoltaic, hydro, wind, and geothermal energy as long-term renewable solutions for Turkey[18]. They suggested a fuzzy TOPSIS technique for energy planning decisions that accounted for technical, economic, environmental, and social factors. They also used fuzzy AHP to determine how to weight the selection criterion. The literature study demonstrates that the subject of renewable energy evaluation is an MCDM problem that can be solved using fuzzy sets. There is a scarcity in using hesitant fuzzy units at the problem. Therefore, there appears to be an urgent need of developing a Sustainable Renewable Energy Framework (SREf) for making Sustainable Renewable Energy through hybrid Soft Computing Techniques. The framework could assess the various Renewable Energy technologies at a utility scale. This research work will try to develop a fuzzy multicriteria approach to the problem, including hesitant fuzzy sets. 3.0Problem of the Statement During the investigation, renewable energy resources will be assessed utilizing hesitant and fuzzy sets. The problem of selecting renewable energy options will be addressed using an integrated technique. The proposed work's novelty will stem from the use of Hybrid Soft Computing Techniques and the application of the MCDM issue to the energy industry. The suggested methodology aims to provide a multicriteria evaluation that incorporates both discrete and continuous fuzzy sets, and this will be done by referring to the past documented articles. Decision-makers will not be forced to employ continuous or discrete fuzzy sets due to the methodology. The fuzzy AHP will allow the decision-maker to compare criteria using pairwise comparison matrices and determine the weights of criteria using continuous fuzzy sets. However, using hesitant fuzzy TOPSIS, the alternatives will be assessed using discrete fuzzy sets, allowing us to gather and manage numerous scores for each option under a sub-criterion. Renewable energy is usually emphasized as a sustainable technology, with particular attention paid to the relationship between renewable energy projects and the overall sustainability of a society or system. As is often believed, renewable energy contributes to the long-term viability of certain regions by delivering a broad range of socioeconomic and environmental advantages. Through the tracking of indicators, these evaluations concentrate on specific aspects of sustainability (ecological, social, and economic), with just a few instances paying attention to the dynamic component of sustainability. Sustainability is a system problem, not a technical or organizational one, although it is seldom treated as such in renewable energy literature. In analyzing the sustainability of an energy system, insights from sustainability science and socio-ecological systems theory emphasize the importance of understanding the system's complexity, adaptive management, and adaptive capacity in the successful adoption of renewable energy technologies. Because of the complexities of sustainability and the necessity to achieve a sustainable future, choices must be made in an organized, transparent, and trustworthy manner, and multiple criterion decision-making (MCDM) may help accomplish this goal. Energy planning and selection, energy policy, energy resource allocation, energy exploitation, building energy management, and energy system transportation are among the energy concerns addressed by soft computing and decision making (DM) methodologies. Therefore, to cater for the need and importance discussed above, the title of...