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South Korea, which has led in “green growth” since 2012, is now focusing on investigating new-growth engine industries such as the gaming industry and mega-resort development. Yeongjong Island is the most representative and promising location for nurturing the gaming industry, which has already generated more than 20 billion USD. However, the const...
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... to the KMA and KAMA [44], annual average wind speed of Yeongjong Island's is 3.65 m/s at 6.9 m of altitude and 10 m of anemometer height. Figure 4 shows the monthly average wind speed of Yeongjong Island, and Figures 5 and 6 shows the annual wind direction of Korean peninsula. Additional data was provided by HOMER and the values used by previous research [14,42]. ...
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... Analyzing the research results of scholars at home and abroad, although some achievements had been made in the fields of economic construction [1,2], sustainable development evaluation [3,4] and tourism development strategy [5,6] in island areas, there were still some deficiencies and limitations to be improved. For example, the research on the unique ecosystem and environmental problems in island areas was not comprehensive enough, and most of them were analyzed from the perspective of certain industry; the analysis of the interaction mechanism between environmental change and economic development was not deep enough [7,8], mostly based on qualitative analysis [9,10], or the research cycle was not long enough [11,12]. In particular, in the research on the relationship between economy and environment by using EKC theory or decoupling model, most of them focused on the national or provincial level, but few analyzed the prefecture-level city as the research unit. ...
Due to the geographical location and resource characteristics of island region, it is important to make its strategic position in the country and even the world. In order to realize the sustainable development of island region, it is necessary to conduct in-depth research on the interaction between the economy and the environment in island region. By combining the decoupling model and EKC theory, this paper adopted the data of economic growth and environmental pollution indexes from 2002 to 2022 to explore the relationship between economic growth and environmental pollution in Zhoushan Island, China. According to research findings, the overall decoupling state between environmental pollution and economic growth in Zhoushan Islands was not very stable, and EKC analysis further revealed that the decoupling of industrial waste gas, industrial solid waste and industrial wastewater was basically consistent with the EKC analysis results. It was not completely consistent with the traditional inverted U-shaped EKC, but showed an incremental relationship, meaning that as the economy grows, so do its emissions. In order to realize the coordinated and sustainable development of the regional economy and society, Zhoushan Islands should further implement industrial optimization and upgrading, adjust the energy structure, improve the scientific and technological level of environmental management, and strengthen inter-departmental cooperation in law enforcement.
... However, many experts have argued that if renewable energy is utilized more for electric power generation, it will not be able to provide highly reliable electricity [30]. Alternative and new technologies that can maintain stable power systems are also costly, as they are fully passed on to the public [31][32][33]. When the proportion of variable renewable energy increases in the power-generation sector, it is likely to make it difficult to maintain a balance between electric power supply and demand in real time. ...
Climate change has been turning into a climate crisis. Thus, we live in an era in which it is important to carry out the promise of 2050 carbon neutrality worldwide. South Korea is a country with a very large private education market. As the online education market has also recently expanded rapidly, interest in the electricity consumed by educational institutions is growing. One way to reduce the power consumption of private educational institutions, which is expected to gradually increase, is to replace the existing power system with a hybrid energy system based on renewable energy. This study aims to investigate an optimized renewable-energy-based hybrid system to supply adequate power to private educational institutions in Korea. We propose an optimal system using the HOMER (Hybrid Optimization Model for Electric Renewables) program. The result is that when private educational institutions adopt a hybrid renewable energy system, the renewable fraction is negligible in the grid-connected type, but it is analyzed that the NPC (Net Present Cost) will greatly increase because it exceeds 70% in the stand-alone type. The difference between on-grid and off-grid is significant, so it must be taken into account when devising renewable energy policies.
... Baek et al. [110] evaluated and proposed the best hybrid renewable power network for Yeongjong Island by using empirical data (load data, meteorological data, equipment data and macroeconomics factors). Table 10 displayed the content of the suggested optimal renewable power generation system. ...
... Table 10. Suggested optimal renewable power generation system [110]. Niazi et al. [111] have been working on technology and economic analysis for small-scale wind/solar combined systems users in domestic applications. ...
Population expansion and increased industrialization are driving up global energy demand. Similarly, the most populous African country, Nigeria generates and transmits electricity far less than is required to meet her basic residential and industrial demands. Alternative means such as fossil fuel-powered generators to complement these demands are still not sufficient to meet these demands with notice to their limitation such as high lifecycle cost and carbon dioxide emission. Renewable energy resources are suitable substitutes for existing electricity sources to fulfil growing demand. Extensively in this paper, a review on the research progress of Hybrid Renewable Energy Systems (HRESs) and Integrated Renewable Energy Systems (IRESs) in the different continents of the world was presented considering methodologies, approaches, and parameters such as technical, economic, and emission limitation in determining the optimal renewable energy system in their present locality. According to the study's findings, about 63% and 22% of the research were conducted in Asia and Africa respectively, from which the research is mostly conducted in rural and remote areas of these continents.
... The cost of energy(CoE) and Loss of Power Supply Probability(LPSP) are used for assessing the cost and reliability to ensure reliability of supply at minimum cost. Optimal sizing for PV/wind/battery based hybrid system for an island in South Korea is presented in ref (Baek et al., 2015). adopting HOMER software. ...
Optimal sizing is necessary in hybrid renewable energy systems for the system to work with highest reliability and minimum cost. The street light system inside Sultan Qaboos University in Oman is considered as a case study for optimal sizing of PV/battery system for three different types of lamps, HPS, LED and discrete LED. The life cycle cost analysis and cost per kWh for two different configurations of panel and battery, the first with larger sized panel/battery and the second, smaller sized panel/battery with and without considering the number of autonomous days(NAD) is compared to investigate the economic feasibility of implementing the system in Oman. Genetic algorithm tool in MATLAB is used for optimal sizing, adopting loss of power supply(LPS) to gauge the reliability using actual data existing in Oman. The cost per kWh of generated energy with large panel/battery system was found to be 0.08 /kWh considering the same case for small panel/battery system proving the economic feasibility of implementing the large panel/battery system in Oman. Results also prove that higher the energy requirement, higher the LCC but lesser the cost per kWh. The conversion of the grid connected street light system extending a stretch of 8.6 km with 285 HPS lamps of 400 W to PV/battery system with LED lamp of 80W is expected to reduce the CO2 emissions by 133.595 tonnes annually. The results can be applied to practical systems worldwide to save millions of dollars and CO2 emissions.
... There have been many studies and demonstrations on such systems (Ataei et al., 2015;Mokhtara et al., 2021). The Korean government is incentivizing hybrid energy system and LTDH implementation to encourage renewable heating systems and reduce network heat losses (Baek et al., 2015;Kim, 2017; South Korea supports District Energy in Cities Initiative, 2019). The Korea Institute of Civil Engineering and Building Technology (KICT) has launched a project to develop an integrated system to satisfy the heating requirements at a building site. ...
Future energy systems will comprise 100% renewable energy and involve high integration of energy systems. District heating (DH) and cooling systems will be an undeniable part of future energy systems, as they facilitate high-efficiency, low-cost, and clean production. Low-temperature district heating (LTDH) is one of the candidates for future district heating systems, where the supply temperature is 60°C or below. Reducing heat losses from the pipe network in DH systems is challenging. Improving the insulation standards in DH pipes can decrease heat and temperature losses in the pipe networks. This study employs computational fluid dynamics to evaluate the optimum insulation thickness based on the material and digging costs in South Korea. A micro hybrid DH system with natural gas run fuel cell, heat pump and solar thermal is proposed in this study. An evaluation of the system with a 500 m pipe network system supplying hot water at 60°C with polyethylene, ethylene propylene diene monomer rubber, and polyurethane as insulation materials using ANSYS Fluent 17.2 shows that the heat losses are minimal when using PU foams. A cost estimation analysis showed that 32 mm was the optimum insulation thickness for achieving heat losses below 20 W/m and minimum material and digging costs when burring the pipeline network in the ground.
... Ref. [109] investigated South Asian and Southwest economies, and the results obtained indicate that the consumption of renewable and nonrenewable energies promotes economic growth [109]. Belt Road countries were investigated by [110,111], SAARC and ASEAN countries [112], as well as, South Korea [113], Bangladesh [114], Malaysia [115][116][117][118], Indonesia [119][120][121][122][123], Vietnam [124], Taiwan [125], Pakistan [126][127][128][129][130], Kazakhstan [131,132], and Thailand [133]. Not many studies dealing with the African continent [134] investigate the continent, [135] sub-Saharan Africa, [136] Rwanda, [137] Cameron, Nigeria [138], [139] Ethiopia and [140] Tunisia. ...
The present research aims to conduct a systemic review on Renewable Energy, Economic Growth and Economic Development and look for links between the papers published between 2008 and May 2021. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, it was possible to reach a sample of 111 articles selected by Web of Science and a sample of 199 academic articles selected by Scopus in that specific period. The analysis of the group of Renewable and Non-renewable Energy Consumption, Economic Growth and Economic Development shows that most of the articles published in this subsample use the quantitative methodology in economic sciences. The results indicate that research on the subject has a growing trend and that most of the articles are post-2015 publications. In addition, China has been the leading nation in published works. The journal Renewable and Sustainable Energy Reviews is considered the most relevant in this category, and Sustainability has the most publications. Finally, a research gap was identified to be explored, lacking studies aimed at understanding the consumption of renewable energies and economic development and studies that focus on renewable energies and economic growth in less developed economies.
... Different countries in ASEAN, especially Malaysia, Thailand, Indonesia, Philippines, and Vietnam have recently initiated devotion for green and sustainable energy deployment [14]. Several researchers presented HRES models including renewable energies such as windbiomass [15], hydro-solar [16], wind-PV-hydro [17], PV-biomass [18] or wind-PV-biomass [19], to plan a power plant in stand-alone [20,21] or grid-connected modes [22,23]. There are many methodologies applied to optimize the hybrid systems including multi-objective [24], artificial intelligence [25], iterative techniques [26], probabilistic methods [25], and so on. ...
This paper presents a comprehensive review of machine learning (ML) based approaches, especially artificial neural networks (ANNs) in time series data prediction problems. According to literature, around 80% of the world’s total energy demand is supplied either through fuel-based sources such as oil, gas, and coal or through nuclear-based sources. Literature also shows that a shortage of fossil fuels is inevitable and the world will face this problem sooner or later. Moreover, the remote and rural areas that suffer from not being able to reach traditional grid power electricity need alternative sources of energy. A “hybrid-renewable-energy system” (HRES) involving different renewable resources can be used to supply sustainable power in these areas. The uncertain nature of renewable energy resources and the intelligent ability of the neural network approach to process complex time series inputs have inspired the use of ANN methods in renewable energy forecasting. Thus, this study aims to study the different data driven models of ANN approaches that can provide accurate predictions of renewable energy, like solar, wind, or hydro-power generation. Various refinement architectures of neural networks, such as “multi-layer perception” (MLP), “recurrent-neural network” (RNN), and “convolutional-neural network” (CNN), as well as “long-short-term memory” (LSTM) models, have been offered in the applications of renewable energy forecasting. These models are able to perform short-term time-series prediction in renewable energy sources and to use prior information that influences its value in future prediction.
... Different countries in ASEAN, especially Malaysia, Thailand, Indonesia, Philippines, and Vietnam have recently initiated devotion for green and sustainable energy deployment [14]. Several researchers presented HRES models including renewable energies such as windbiomass [15], hydro-solar [16], wind-PV-hydro [17], PV-biomass [18] or wind-PV-biomass [19], to plan a power plant in stand-alone [20,21] or grid-connected modes [22,23]. There are many methodologies applied to optimize the hybrid systems including multi-objective [24], artificial intelligence [25], iterative techniques [26], probabilistic methods [25], and so on. ...
This paper presents a comprehensive review of machine learning (ML) based approaches, especially artificial neural networks (ANNs) in time series data prediction problems. According to literature, around 80% of the world’s total energy demand is supplied either through fuel-based sources such as oil, gas, and coal or through nuclear-based sources. Literature also shows that a shortage of fossil fuels is inevitable and the world will face this problem sooner or later. Moreover, the remote and rural areas that suffer from not being able to reach traditional grid power electricity need alternative sources of energy. A “hybrid-renewable-energy system” (HRES) involving different
renewable resources can be used to supply sustainable power in these areas. The uncertain nature of renewable energy resources and the intelligent ability of the neural network approach to process complex time series inputs have inspired the use of ANN methods in renewable energy forecasting. Thus, this study aims to study the different data driven models of ANN approaches that can provide accurate predictions of renewable energy, like solar, wind, or hydro-power generation. Various refinement architectures of neural networks, such as “multi-layer perception” (MLP), “recurrentneural network” (RNN), and “convolutional-neural network” (CNN), as well as “long-short-term memory” (LSTM) models, have been offered in the applications of renewable energy forecasting. These models are able to perform short-term time-series prediction in renewable energy sources and to use prior information that influences its value in future prediction
... But despite the multiplicity of definitions, the literature (e.g. Wong et al., 2005;Wong, 2011;Everest-Phillips, 2014;Baek, Kim & Chang, 2015;Ioannidis & Chalvatzis, 2017) states that most small islands have defining characteristics that include: limited natural resources; colonial legacies; mixed cultures; small populations; high population densities; rapid population growth; high emigration; high biodiversity; high dependencies on tourism, external aid and governmental interventions; diseconomies of scale; high energy and electricity grid connection costs; vulnerable energy infrastructures; high dependencies on fossil fuel imports; costly shipping; environmental fragility; oceanic natures; remote locations; and small land masses. ...
... It must be mentioned that equipment was selected after carefully following the guidelines provided by previous authors [13][14][15][16][17][18][19][20][21][22] except wind machine, which was shortlisted by adopting the technique recommended by the authors [27]. ...
Renewable energy technologies can not only help in mitigating the greenhouse gas (GHG) emissions but it can also be very useful for electricity generation at remote locations, where no other means of power are available. The present study focuses on the techno-economic optimum design of a small hybrid renewable energy system (HRES) consisting of wind-solar as primary energy sources. The HRES was modelled for a remote island (Deokjeok-do Island, South Korea) using real electricity consumption data for one complete year. A daily mean load of 24,720 kWh was entered at Deokjeok-do Island with a peak load of 2291.54 kW. Average annual values of wind speed and daily solar radiations were estimated to be 3.6 m/s (10 m height) and 4.13 kWh/m², respectively. A total of 8760 simulations were performed to achieve the hourly load demand of the mentioned island. In order to deal with the surplus and electricity deficit, two different types of energy storage systems (ESS) were modelled i.e., battery and pumped hydro storage (PHS). Four different HRESs were also evaluated as the most suitable based on levelized cost of energy (LCOE) and net present cost (NPC). A detailed economic break-down of each component and the impact of different sensitivity variables on decision making have also been discussed in detail.
