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This study used the subcritical hydrothermal liquefaction technique (SHLT) in the co- liquefaction of process rejects at a wastepaper-based paper mill (PRWPM) and waste soybean oil (WSO) for the production of biofuels and bio-char material. PRWPM emits complicated waste composed of cellulose, hemicellulose, lignin, and plastic from sealing film. The waste is produced from the recycled paper process of a mill plant located in central Taiwan. The source of WSO is the rejected organic waste from a cooking oil factory located in north Taiwan. PRWPM and WSO are suitable for use as fuels, but due to their high oxygen content, their use as commercial liquid fuels is not frequent, thus making deoxygenation and hydrogenation necessary. The temperature and pressure of SHLT were set at 523–643 K and 40–250 bar, respectively. The experimental conditions included solvent ratios of oil–water, temperature, reaction time, and ratios of solvent to PRWPM. The analysis results contained approximated components, heating values, elements, surface features, simulated distillations, product compositions, and recovery yields. The HHV of the product occurred at an oil–water ratio of 75:25, with a value of 38.04 MJ kg−1. At an oil–water ratio of 25:75, the liquid oil-phase product of SHTL has the highest heating value 42.02 MJ kg−1. Higher WSO content implies a lower heating value of the oil-phase product. The simulated distillation result of the oil-phase product with higher content of alcohol and alkanes obtained at the oil–water ratio of 25:75 is better than the other ratios. Here, the carbon number of the oil product is between C8–C36. The product conversion rate rises with an increase of the WSO ratio. It is proved that blending soybean oil with water can significantly enhance the quality of liquefied oil and the conversion rate of PRWPM. Therefore, the solid and liquid biomass wastes co-liquefaction to produce gas and liquid biofuels under SHLT are quite feasible. [ABSTRACT FROM AUTHOR]

Due to the economic inefficiency of material recycling of general industrial waste and urban waste, the use of solid recovered fuels (SRFs) not only mitigates the environmental loadings from waste incineration plants and sanitary landfills but also creates green electricity and/or heat and thus reduces the use of fossil fuels. In this regard, the Taiwan government formulated the "Solid Recovered Fuel Manufacturing Guidelines and Quality Standards" in 2020 to ensure the manufacturing quality of SRFs. This paper focused on the status of waste management and energy supply, the current regulations for adopting SRFs, and the challenges in the development of SRFs from the viewpoints (or life cycle) of the environmental, economic, and engineering (or technological) characters in Taiwan. Based on the database of the official handbook/yearbook, the energy supply from indigenous biomass and waste was 1678.7 × 103 kiloliters of oil equivalent (KLOE) in 2021, which only accounted for about 1.2% of the total energy supply. Obviously, available indigenous biomass and waste for producing SRFs were mostly from waste wood, sugarcane bagasse, and mixtures containing wood/paper. Finally, some suggestions for the increasing use of SRFs in the energy and industrial sectors were addressed to keep in step with the sustainable development goals (SDGs) in 2030, especially in the mitigation of GHG emissions. [ABSTRACT FROM AUTHOR]

For the advancement of smart grids, solar power generation predictions have become an important research topic. In the case of using traditional modeling methods, excessive computational costs may be incurred and it is difficult for these methods to learn the multi-variable dependencies of the data. Therefore, in this paper, a deep learning model was used to combine convolutional neural networks and long short-term memory recurrent network predictions. This method enables hourly power generation one day into the future. Convolutional neural networks are used to extract the features of multiple time series, while long short-term memory neural networks predict multivariate outcomes simultaneously. In order to obtain more accurate prediction results, we performed feature selection on meteorological features and combined the selected weather features to train the prediction model. We further distinguished sunny- and rainy-day models according to the predicted daily rainfall conditions. In the experiment, it was shown that the method of combining meteorological features further reduced the error. Finally, taking into account the differences in climate conditions between the northern and southern regions of Taiwan, the experimental results of case studies involving multiple regions were evaluated to verify the proposed method. The results showed that training combined with selected meteorological features can be widely used in regions with different climates in Taiwan. [ABSTRACT FROM AUTHOR]

The textile industry accounts for approximately 2% of global greenhouse gas emissions. There is a significant opportunity to decarbonize the textile industry by electrification of process heating where low- or zero-carbon electricity is used. Electrified process heating can be achieved through cross-cutting technologies without modifying the textile process equipment and/or through replacing the existing equipment with technologies that employ electromagnetic or resistance heating techniques for specific end-use applications. This paper aims to investigate the potential for electrification of process heating in the textile wet-processing industry in three of the top textile-producing and exporting regions in the world. To do this, two separate technology pathways, i.e., electrification through (a) industrial heat pumps and (b) textile end-use processes are developed and analyzed. The results show that the total potential final energy and CO2 savings due to electrification in both scenarios could be substantially large due to the lower energy intensity of the electrified heating systems. Moreover, the costs per unit of textile production are found to be lower in the case of industrial heat pumps compared to other systems. It is concluded that wide-scale electrification of process heating in the textile wet-processing industry will require major changes to the electricity system and individual sites, and the coordination efforts among different stakeholders to plan these changes must be intensified. [ABSTRACT FROM AUTHOR]

In recent years, Taiwan has firmly committed itself to pursue the green energy transition and a nuclear-free homeland by 2025, with an increase in renewable energy from 5% in 2016 to 20% in 2025. Offshore wind power (OWP) has become a sustainable and scalable renewable energy source in Taiwan. Maritime Spatial Planning (MSP) is a fundamental tool to organize the use of the ocean space by different and often conflicting multi-users within ecologically sustainable boundaries in the marine environment. MSP is capable of definitively driving the use of offshore renewable energy. Lessons from Germany and the UK revealed that MSP was crucial to the development of OWP. This paper aims to evaluate how MSP is able to accommodate the exploitation of OWP in Taiwan and contribute to the achievement of marine policy by proposing a set of recommendations. It concludes that MSP is emerging as a solution to be considered by government institutions to optimize the multiple use of the ocean space, reduce conflicts and make use of the environmental and economic synergies generated by the joint deployment of OWP facilities and fishing or aquaculture activities for the conservation and protection of marine environments. [ABSTRACT FROM AUTHOR]

The energy conservation achieved by utilizing waste heat in the energy and industrial sectors has became more and more important after the energy crisis in the 1970s because it plays a vital role in the potential energy-efficiency improvement. In this regard, cogeneration (combined heat and power, CHP) systems are thus becoming attractive due to the energy, economic, and environmental policies for pursuing stable electricity supply, sustainable development and environmental pollution mitigation in Taiwan. The objective of this paper is to present an updated analysis of CHP systems in Taiwan during the period from 1990 to 2010. The description in the paper is thus based on an analysis of electricity supply/consumption and its sources from CHP systems during the past two decades, and centered on two important regulations in compliance with CHP systems (i.e., Energy Management Law and Environmental Impact Assessment Act). Based on the total net power generation from CHP systems (i.e., 35,626 GWh) in 2011, it was found that the carbon dioxide reduction benefits were estimated to be around 20,000 Gg. [ABSTRACT FROM AUTHOR]

The objective of this paper was to provide a preliminary analysis of the utilization of energy derived from waste wood in Taiwan, a highly industrialized country with a high dependence (over 99%) on imported energy. The discussion focuses on the status of waste wood generation and its management over the past decade. Findings show that the quantities of biomass waste collected for reuse purposes in the industrial sectors of Taiwan has exhibited an increasing trend, from about 4000 tons in 2001 to over 52,000 tons in 2010. Although waste wood can be reused as a fuel and raw material for a variety of applications based on regulatory promotion, the most commonly used end use is to directly utilize it as an auxiliary fuel in industrial utilities (e.g., boilers, heaters and furnaces) for the purpose of co-firing with coal/fuel oil. The most progressive measure for promoting biomass-to-power is to introduce the feed-in tariff (FIT) mechanism according to the Renewable Energy Development Act passed in June 2009. The financial support for biomass power generation has been increasing over the years from 0.070 US$/kWh in 2010 to 0.094 US$/kWh in 2012. On the other hand, the environmental regulations in Taiwan regarding the hazard identification of wood-combusted ash (especially in filter fly-ash) and its options for disposal and utilization are further discussed in the paper, suggesting that waste wood impregnated with chromated copper arsenate (CCA) and other copper-based preservatives should be excluded from the wood-to-energy system. Finally, some recommendations for promoting wood-to-energy in the near future of Taiwan are addressed. [ABSTRACT FROM AUTHOR]

This paper applies the context-dependent total-factor energy efficiency (CD-TFEE) to determine the multi-layer disaggregate energy efficiency frontiers of twenty administrative regions in Taiwan for the year of 2016. The CD-TFEE overcomes the shortcoming of conventional TFEE index that TFEE is not able to find the "closest target" for each inefficient region in the short run. Furthermore, the CD-TFEE scores here deal with four types of energy inputs (electricity for production, electricity for household and non-household lighting, diesel sales, and gasoline sales), illustrating that multi-layer TFEE frontiers for each energy input in the case of Taiwan can be computed. Empirical results indicate that there are three levels of TFEE frontiers for electricity for production and four levels for other types of energy inputs. In addition, New Taipei City, Taipei City, Keelung City, and Penghu County are at the top level of TFEE frontier for all four energy inputs. This paper also demonstrates that the CD-TFEE procedure generates results different from the CD-DEA introduced by Seiford and Zhu (2003). [ABSTRACT FROM AUTHOR]

This research examines the risk of natural disasters for offshore wind turbines together with their potential wind energy capacity to help the site selection of offshore wind power farms. Through evaluations of expert questionnaires, we use the fuzzy analytic hierarchy process to weight how natural disasters damage the sub-assemblies of an offshore wind turbine, then obtain the natural disaster risk assessment model, and finally utilize ArcGIS Pro 3.2 to map the potential wind farm sites for the waters off Taiwan's west coast. We identify that typhoons are the most threatening type of disaster to generators, rotor blades, and rotor hubs; earthquakes are the most threatening to towers; and lightning is the most threatening to transformers. For the whole wind turbine, wind is ironically the most threatening natural disaster, followed by lightning, sea waves, and then earthquakes. Lastly, we examine the results by overlapping the offshore wind farms developed and planned in Taiwan, which coincide with locations in relatively low risk and high wind speed areas. [ABSTRACT FROM AUTHOR]

Reservoirs in Taiwan often provide hydroelectric power, irrigation water, municipal water, and flood control for the whole year. Taiwan has the climatic characteristics of concentrated rainy seasons, instantaneous heavy rains due to typhoons and rainy seasons. In addition, steep rivers in mountainous areas flow fast and furiously. Under such circumstances, reservoirs have to face sudden heavy rainfall and surges in water levels within a short period of time, which often causes the water level to continue to rise to the full level even though hydroelectric units are operating at full capacity, and as reservoirs can only drain the flood water, this results in the waste of hydropower resources. In recent years, the impact of climate change has caused extreme weather events to occur more frequently, increasing the need for flood control, and the reservoir operation has faced severe challenges in order to fulfil its multipurpose requirements. Therefore, in order to avoid the waste of hydropower resources and improve the effectiveness of the reservoir operation, this paper proposes a real-time 48-h ahead water level forecasting system, based on fuzzy neural networks with multi-stage architecture. The proposed multi-stage architecture provides reservoir inflow estimation, 48-h ahead reservoir inflow forecasting, and 48-h ahead water level forecasting. The proposed method has been implemented at the Techi hydropower plant in Taiwan. Experimental results show that the proposed method can effectively increase energy efficiency and allow the reservoir water resources to be fully utilized. In addition, the proposed method can improve the effectiveness of the hydropower plant, especially when rain is heavy. [ABSTRACT FROM AUTHOR]

There is growing concern regarding air pollutants (NOx, SOx, and PM) and carbon emissions from ocean-going vessels in harbor areas and the role of high-voltage shore connection (HVSC) systems in mitigating these emissions during vessel berthing. The HVSC operates as a TN grounding system in humid environments, and it needs a proper grounding design to ensure safety when faults occur. This article intends to examine the overvoltage resulting from fault currents and its implications for the safety of operators when a single line-to-ground fault takes place within the design of HVSC grounding systems. The assessment is carried out by employing actual scenarios and parameters from a container berth at Kaohsiung Harbor in Taiwan. Considering site conditions, such as the wet ground surface, human body resistance, and electric shock duration, the tolerable safe voltage level is derived using IEEE Std. 80 and IEC 60479-1. Based on the shore power system grounding architecture specified in IEEE/IEC 80005-1, an equivalent circuit model is constructed to calculate the fault currents using symmetrical component analysis. The actual touch voltages generated in various locations are analyzed under scenarios of connecting or disconnecting the equipotential bonding between the ship and the shore using neutral grounding resistor (NGR) designs. This article delves into the scenarios of electric shock that may occur during the operation of an actual container ship's shore power system. It evaluates whether various contact voltage values exceed current international standards and verifies the grounding design and safety voltage specifications of IEEE/IEC 80005-1. According to the results of this study, the use of NGR and protective earthed neutral (PEN) conductors in HVSC is crucial. This can limit fault currents, reduce touch voltage, and ensure the safety of personnel and equipment. Therefore, ensuring and monitoring equipment conductors and adopting NGRs of appropriate sizes are crucial elements in maintaining electrical safety in HVSC systems. [ABSTRACT FROM AUTHOR]

The first global stocktake (GST) at 2023 UN Climate Change Conference (COP28) pointed out that accelerating the phasing down of fossil fuels has become an important mitigation policy to maintain a maximum temperature limit of 1.5 °C. The optimal power portfolio for achieving Taiwan's net-zero emissions by 2050 is evaluated from the perspective of sustainable development. This study is enhances the 2021 research findings of Wang et al. on the sustainable power model, incorporating homogenized cost and technical constraints for empirical analysis. The results indicated that renewable energy sources play a pivotal role in achieving net-zero emissions. Gas power generation requires careful consideration, including early decommissioning or the adoption of carbon capture and storage (CCS) technology to prevent carbon lock-in and compete with hydrogen energy technology. Notably, coal combined with CCS technology offers a viable option for a cost-effective roadmap for a decarburized power generation portfolio by 2050, serving as a reference for national planning strategies for promoting net-zero emissions. [ABSTRACT FROM AUTHOR]

Climate change poses a significant threat to humanity. Achieving net-zero emissions is a key goal in many countries. Among various energy resources, solar power generation is one of the prominent renewable energy sources. Previous studies have demonstrated that post-processing techniques such as bias correction can enhance the accuracy of solar power forecasting based on numerical weather prediction (NWP) models. To improve the post-processing technique, this study proposes a new day-ahead forecasting framework that integrates weather research and forecasting solar (WRF-Solar) irradiances and the total solar power generation measurements for five cities in northern, central, and southern Taiwan. The WRF-Solar irradiances generated by the Taiwan Central Weather Bureau (CWB) were first subjected to bias correction using the decaying average (DA) method. Then, the effectiveness of this correction method was verified, which led to an improvement of 22% in the forecasting capability from the WRF-Solar model. Subsequently, the WRF-Solar irradiances after bias correction using the DA method were utilized as inputs into the transformer model to predict the day-ahead total solar power generation. The experimental results demonstrate that the application of bias-corrected WRF-Solar irradiances enhances the accuracy of day-ahead solar power forecasts by 15% compared with experiments conducted without bias correction. These findings highlight the necessity of correcting numerical weather predictions to improve the accuracy of solar power forecasts. [ABSTRACT FROM AUTHOR]

One of the most important sources of energy is the sun. Taiwan is located at a 22–25° north latitude. Due to its proximity to the equator, it experiences only a small angle of sunlight incidence. Its unique geographical location can obtain sustainable and stable solar resources. This study uses research on solar radiation forecasts to maximize the benefits of solar power generation, and it develops methods that can predict future solar radiation patterns to help reduce the costs of solar power generation. This study built supervised machine learning models, known as a deep neural network (DNN) and a long–short-term memory neural network (LSTM). A hybrid supervised and unsupervised model, namely a cluster-based artificial neural network (k-means clustering- and fuzzy C-means clustering-based models) was developed. After establishing these models, the study evaluated their prediction results. For different prediction periods, the study selected the best-performing model based on the results and proposed combining them to establish a real-time-updated solar radiation forecast system capable of predicting the next 12 h. The study area covered Kaohsiung, Hualien, and Penghu in Taiwan. Data from ground stations of the Central Weather Administration, collected between 1993 and 2021, as well as the solar angle parameters of each station, were used as input data for the model. The results of this study show that different models offer advantages and disadvantages in predicting different future times. The hybrid prediction system can predict future solar radiation more accurately than a single model. [ABSTRACT FROM AUTHOR]

In recent years, the penetration of photovoltaic (PV) power generation in Taiwan has increased significantly. However, most photovoltaic facilities, especially for small-scale sites, do not include relevant monitoring and real-time measurement devices. The invisible power generation from these PV sites would cause a huge challenge on power system scheduling. Therefore, appropriate methods to estimate invisible PV power generation are needed. The main purpose of this paper is to propose an improved fuzzy model for estimating the PV power generation, which includes the clustering processing for PV sites, selection of representative PV sites, and the improvement of the conventional fuzzy model. First, this research uses the K-nearest neighbor (KNN) algorithm to fill in some of the missing data; then, two clustering algorithms are applied to cluster all the photovoltaic sites. Next, the relationship between the power generation of a single PV site and the total generation of all sites at the same cluster is further analyzed to select the representative PV sites. Finally, an improved fuzzy model is implemented to estimate the PV power generation. This research used actual data that were measured from PV sites in Taiwan for the estimation, verification, and comparison study. The numerical results demonstrate that the proposed method can obtain an average estimation error about 7% by using limit measurements from PV sites, highlighting the high efficiency and practicability of the proposed method. [ABSTRACT FROM AUTHOR]

High-technology industries provide opportunities for economic growth, but also raise concerns because of their energy-demanding nature. This paper provides an integrated evaluation of both economic benefits and energy efficiency of high-technology industries based on the real data from one of the globally recognized high-technology industrial clusters, the national science parks in Taiwan. A nation-wide industrial Input-Output Analysis is conducted to demonstrate the positive effects of science parks on national economic developments and industrial upgrades. The concept of energy intensity and an energy-efficient economy index are applied to an integrated assessment of the relationship between economic growth and energy consumption. The proposed case study suggests that economic and energy efficiency objectives can be simultaneously achieved by the development of high-technology industries, while three energy policy implications are considered. First, a nation-wide macro viewpoint is needed and high-technology industries should be considered as parts of the national/regional economies by governmental agencies. Second, a proper industrial clustering mechanism and the shared environmental facilities supported by the government, such as planned land and road usage, electricity and water supply, telecommunications system, sewerage system and wastewater treatments, can improve energy efficiency of high-technology industries. Third, the governmental policies on the taxing and management system in science parks would also direct energy-efficient economy of high-technology industries. [ABSTRACT FROM AUTHOR]

Due to the very complex sets of component systems, interrelated thermodynamic processes and seasonal change in operating conditions, it is relatively difficult to find an accurate model for turbine cycle of nuclear power plants (NPPs). This paper deals with the modeling of turbine cycles to predict turbine-generator output using an adaptive neuro-fuzzy inference system (ANFIS) for Unit 1 of the Kuosheng NPP in Taiwan. Plant operation data obtained from Kuosheng NPP between 2006 and 2011 were verified using a linear regression model with a 95% confidence interval. The key parameters of turbine cycle, including turbine throttle pressure, condenser backpressure, feedwater flow rate and final feedwater temperature are selected as inputs for the ANFIS based turbine cycle model. In addition, a thermodynamic turbine cycle model was developed using the commercial software PEPSE® to compare the performance of the ANFIS based turbine cycle model. The results show that the proposed ANFIS based turbine cycle model is capable of accurately estimating turbine-generator output and providing more reliable results than the PEPSE® based turbine cycle models. Moreover, test results show that the ANFIS performed better than the artificial neural network (ANN), which has also being tried to model the turbine cycle. The effectiveness of the proposed neuro-fuzzy based turbine cycle model was demonstrated using the actual operating data of Kuosheng NPP. Furthermore, the results also provide an alternative approach to evaluate the thermal performance of nuclear power plants. [ABSTRACT FROM AUTHOR]

In the context of the growing emphasis on energy conservation and carbon reduction, the widespread deployment of smart meters in residential and commercial buildings is instrumental in promoting electricity savings. In Taiwan, local governments are actively promoting the installation of smart meters, empowering residents to monitor their electricity consumption and detect abnormal usage patterns, thus mitigating the risk of electrical fires. This safety-oriented approach is a significant driver behind the adoption of smart meters. However, the analysis of the substantial data generated by these meters necessitates pre-processing to address anomalies. Presently, these data primarily serve billing calculations or the extraction of power-saving patterns through big data analytics. To address these challenges, this study proposes a comprehensive approach that integrates a relational database for storing electricity consumption data with knowledge graphs. This integrated method effectively addresses data scarcity at various time scales and identifies prolonged periods of excessive electricity consumption, enabling timely alerts to residents for specific appliance shutdowns. Deep learning techniques are employed to analyze historical consumption data and real-time smart meter readings, with the goal of identifying and mitigating hazardous usage behavior, consequently reducing the risk of electrical fires. The research includes numerical values and text-based predictions for a comprehensive evaluation, utilizing data from ten Taiwanese households in 2022. The anticipated outcome is an improvement in household electrical safety and enhanced energy efficiency. [ABSTRACT FROM AUTHOR]

Due to the worldwide impact of industrialization, renewable sources of energy from solar play a huge role in supplying the domestic demand in Taiwan. This paper aims to impart an unbiased performance evaluation of different solar photovoltaic (PV) power plants around Taiwan. This study utilized data envelopment analysis (DEA) using the epsilon-based measure (EBM) model to compute the performance efficiency and ranking of the solar PV power plants. Additionally, the model is used for its advantage of combined radial and non-radial measures to achieve a more precise measure of efficiency. The results show that among nine selected solar PV plants, the top-performing DMU was I (8200051) from the southern part of Taiwan. Therefore, this study recommends that EBM can be a useful tool to evaluate the performance of solar PV power plants using multiple inputs and outputs. Additionally, this study will contribute to sustaining and developing the existing solar PV power capacity while achieving the installation of 20GW of solar energy as part of Taiwan's goal. Even though the study has achieved the set goals, various restrictions remain present. Future studies could address micro and macro-level factors that influence the performance of solar PV power plants, which are not included in this study. [ABSTRACT FROM AUTHOR]

This paper undertakes liquefaction analysis with simplified procedures with standard penetration test (SPT) data and cone penetration test (CPT) data obtained from an offshore wind farm in the Changhua area. The soil liquefaction resistance calculated by the SPT-based simplified procedure suggested by the Japan Railway Association was in agreement with the laboratory results. The CPT is widely used in the site investigation of offshore wind farms. However, Taiwan's registered professional engineers are still familiar with soil liquefaction analysis for offshore wind farms using SPT-based methods. Hence, a hybrid method that incorporates an SPT–CPT correlation into the New Japan Road Association (NJRA) method is proposed to evaluate the soil liquefaction potential for offshore wind farms in Taiwan. In the case studies of soil liquefaction with five groups of adjacent boreholes in Changhua's offshore wind farms, the hybrid method shows that the soil liquefaction potential with CPT data is consistent with the results calculated with SPT-based simplified procedures. To quantify the risk of soil liquefaction, Monte Carlo simulation is used to calculate the uncertainty of CPT–qc for estimating the probability of soil liquefaction with the hybrid method. [ABSTRACT FROM AUTHOR]

This paper examines the usefulness of statistical methods in forecasting the financial energy of households. The study's objective is to create the innovative ratios that combine both financial and demographic information of households and implement them in the forecasting models. To conduct this objective, six forecasting models are developed using three different methods—discriminant analysis, logit analysis, and decision trees separately for households in Poland and Taiwan. Such a research approach will answer the question whether the implementation of constructed ratios can increase effectiveness of the forecasting model and its' versatility between different economic regions. The research relies on four samples of households—two learning samples and two testing samples (one for each country) consisting of 2400 households from both countries. This study is one of the first attempts in the literature globally to develop forecasting models based on ratios constructed with a combination of two different types of variables—one financial with one demographic variable. Findings confirm the high usability of the proposed innovative approach for forecasting the financial energy of households by taking into account a wide spectrum of diagnostic variables representing the financial strength of consumers. [ABSTRACT FROM AUTHOR]

Frequency control is essential to ensure reliability and quality of power systems. North American Electric Reliability Corporation's (NERC) Control Performance Standard 1 (CPS1) is widely adopted by many operating authorities to examine the quality of the frequency control. The operating authority would have a strong interest in knowing how the frequency-sensitive features affect the CPS1 score and finding out more effective unit-dispatch schedules for reaching the CPS1 goal. As frequency-sensitive features usually possess multi-variable and high-correlated characteristics, this paper employed an ensemble learning technique (the Gradient Boosting Decision Tree algorithm, GBDT) to construct Frequency Response Model (FRM) of the Taipower system in Taiwan to evaluate by CPS1 score. The proposed CPS1 model was then integrated with Unit Commitment (UC) program to determine the unit-dispatch that achieves the targeted CPS1 score. The feasibility and effectiveness of the proposed CPS1-UC platform were validated and compared with the other benchmark model-based UC methods by two operating cases. The proposed model shows promising results: the system frequency could be maintained well, especially in the periods of the early morning or the high renewable penetration. [ABSTRACT FROM AUTHOR]

The Executive Yuan in Taiwan plans to install air-conditioning (A/C) in all elementary schools within two years. However, besides the associated energy consumption and environmental issues, the use of A/C will inevitably result in the doors and windows of the classroom being closed, which will increase the accumulation of carbon dioxide (CO2) within the classroom. An excessive indoor CO2 concentration can result in reduced cognitive performance and an impaired learning efficiency. Therefore, the moderate introduction of external air into the classroom is essential to increase the air exchange rate (AER) and reduce the CO2 concentration level. Accordingly, the present study conducts a numerical investigation into the effects of various A/C operation strategies on the CO2 concentration within the classroom given different proportions of students remaining in the classroom during the recess. Overall, the results indicate that the optimal usage strategy is to operate the A/C over the full school day (08:00~15:50 p.m.) in conjunction with a mechanical ventilation system providing a fresh air exchange rate of 5 l/s for every person in the room. However, the use of a mechanical ventilation system inevitably incurs an additional hardware and energy consumption. Thus, an alternative recommendation is also proposed, in which the windows are opened and the air conditioner is turned off at every recess and during the lunchtime period. It is shown that the resulting CO2 concentration in the classroom is still consistent with the Taiwan Environmental Protection Administration (EPA) regulations and the thermal comfort of the students is achieved for more than three-quarters of the school day. [ABSTRACT FROM AUTHOR]

With the rapidly development of electricity technology and renewable energy, adopting electric vehicles (EVs) not only can efficiently collaborate with renewable energy, but also can help decrease the negative environmental effects of traditional vehicles. However, even though electric vehicles offer many advantages, most consumers still hesitate to buy or even rent them. While there is a large growing body of studies focusing on topics related to electric public transportation and electric cars, there is still a shortage of them examining electric scooters (ESs). Based on the norm activation model (NAM), green consumption effect, and extrinsic motivation, this study aims to present the crucial antecedence of consumers' intention to adopt ESs. As a main transportation for most of the people in Taiwan, this research adopts design of experiments and develops two studies to collect data from Taiwan. The results show that warm glow mediates the positive relationship between awareness of consequences and intention to adopt them. Moreover, the interacting effect of awareness of consequences and extrinsic appeal on intention to adopt ESs is mediated by the warm glow. For academia, the results herein fill the gap related to electric transportation. In real-world practice, this study provides valuable suggestions for governments that want to abate any negative impact from traditional scooters. [ABSTRACT FROM AUTHOR]

Automatic peer-to-peer energy trading can be defined as a Markov decision process and designed using deep reinforcement learning. We consider prosumer as an entity that consumes and produces electric energy with an energy storage system, and define the prosumer's objective as maximizing the profit through participation in peer-to-peer energy trading, similar to that of the agents in stock trading. In this paper, we propose an automatic peer-to-peer energy trading model by adopting a deep Q-network-based automatic trading algorithm originally designed for stock trading. Unlike in stock trading, the assets held by a prosumer may change owing to factors such as the consumption and generation of energy by the prosumer in addition to the changes from trading activities. Therefore, we propose a new trading evaluation criterion that considers these factors by defining profit as the sum of the gains from four components: electricity bill, trading, electric energy stored in the energy storage system, and virtual loss. For the proposed automatic peer-to-peer energy trading algorithm, we adopt a long-term delayed reward method that evaluates the delayed reward that occurs once per month by generating the termination point of an episode at each month and propose a long short-term delayed reward method that compensates for the issue with the long-term delayed reward method having only a single evaluation per month. This long short-term delayed reward method enables effective learning of the monthly long-term trading patterns and the short-term trading patterns at the same time, leading to a better trading strategy. The experimental results showed that the long short-term delayed reward method-based energy trading model achieves higher profits every month both in the progressive and fixed rate systems throughout the year and that prosumer participating in the trading not only earns profits every month but also reduces loss from over-generation of electric energy in the case of South Korea. Further experiments with various progressive rate systems of Japan, Taiwan, and the United States as well as in different prosumer environments indicate the general applicability of the proposed method. [ABSTRACT FROM AUTHOR]

The high utilization of renewable energy to manage climate change and provide green energy requires short-term photovoltaic (PV) power forecasting. In this paper, a novel forecasting strategy that combines a convolutional neural network (CNN) and a salp swarm algorithm (SSA) is proposed to forecast PV power output. First, the historical PV power data and associated weather information are classified into five weather types, such as rainy, heavy cloudy, cloudy, light cloudy and sunny. The CNN classification is then used to determine the prediction for the next day's weather type. Five models of CNN regression are established to accommodate the prediction for different weather types. Each CNN regression is optimized using a salp swarm algorithm (SSA) to tune the best parameter. To evaluate the performance of the proposed method, comparisons were made to the SSA based support vector machine (SVM-SSA) and long short-term memory neural network (LSTM-SSA) methods. The proposed method was tested on a PV power generation system with a 500 kWp capacity located in south Taiwan. The results showed that the proposed CNN-SSA could accommodate the actual generation pattern better than the SVM-SSA and LSTM-SSA methods. [ABSTRACT FROM AUTHOR]

In this study, we allocated soft open points (SOPs) and distributed generation (DG) units simultaneously with and without network reconfiguration (NR), and investigate the contribution of SOP losses to the total active losses, as well as the effect of increasing the number of SOPs connected to distribution systems under different loading conditions. A recent meta-heuristic optimization algorithm called the discrete-continuous hyper-spherical search algorithm is used to solve the mixed-integer nonlinear problem of SOPs and DGs allocation, along with new NR methodology to obtain radial configurations in an efficient manner without the possibility of getting trapped in local minima. Further, multi-scenario studies are conducted on an IEEE 33-node balanced benchmark distribution system and an 83-node balanced distribution system from a power company in Taiwan. The contributions of SOP losses to the total active losses, as well as the effect of increasing the number of SOPs connected to the system, are investigated to determine the real benefits gained from their allocation. It was clear from the results obtained that simultaneous NR, SOP, and DG allocation into a distribution system creates a hybrid configuration that merges the benefits offered by radial distribution systems and mitigates drawbacks related to losses, power quality, and voltage violations, while offering a far more efficient and optimal network operation. Also, it was found that the contribution of the internal loss of SOPs to the total loss for different numbers of installed SOPs is not dependent on the number of SOPs and that loss minimization is not always guaranteed by installing more SOPs or DGs along with NR. One of the findings of the paper demonstrates that NR with optimizing tie-lines could reduce active losses considerably. The results obtained also validate, with proper justifications, that SOPs installed for the management of constraints in LV feeders could further reduce losses and efficiently address issues related to voltage violations and network losses. [ABSTRACT FROM AUTHOR]

As it has an export-oriented economy, Taiwan urgently needs to keep up with the growing trends toward carbon taxation. However, making the institution of a carbon tax a reality in Taiwan has proven to be difficult. Since 1998, Taiwan has explored the possibility of putting a tax on carbon many times. Specifically, three main windows of opportunity emerged to adopt a carbon tax during this period; however, all of them failed. This study mainly explores why these three opportunities failed, what structural factors hindered them, and how those structural factors formed path dependence and locked the entire society back onto the existing development track. Firstly, Taiwan's high-carbon industrial structure has established the rapid growth of energy-intensive industries since the end of the 1990s and has created an economy with high energy consumption and pollution levels. Secondly, this analysis showed that through the combination of government bureaucracy, industry, and the China National Federation of Industries, this brown economy and high-carbon emission structure generated institutional, cognitive, and techno-institutional complex lock-ins, which have led Taiwan to its current path and hindered its transformation. Thirdly, under the above framework, this study further analyzes the contexts and problems that caused the three windows of opportunity to fail. Finally, by linking the economy-first orientation of developmental states, this study identifies structural difficulties and possible breakthrough conditions for newly industrialized/industrializing countries that are undergoing low-carbon transitions. [ABSTRACT FROM AUTHOR]

Fossil fuels have been heavily exploited since the Industrial Revolution. The resulting carbon emissions are widely regarded as being the main cause of global warming and climate change. Key mitigation technologies for reducing carbon emissions include carbon capture and storage (CCS) and renewables. According to recent analysis of the International Energy Agency, renewables and CCS will contribute more than 50% of the cumulative emissions reductions by 2050. This paper presents a new mathematical programming model for multi-footprint energy sector planning with CCS and renewables deployment. The model is generic and considers a variety of carbon capture (CC) options for the retrofit of individual thermal power generation units. For comprehensive planning, the Integrated Environmental Control Model is employed in this work to assess the performance and costs of different types of power generation units before and after CC retrofits. A case study of Taiwan's energy sector is presented to demonstrate the use of the proposed model for complex decision-making and cost trade-offs in the deployment of CC technologies and additional low-carbon energy sources. Different scenarios are analysed, and the results are compared to identify the optimal strategy for the energy mix to satisfy the electricity demand and the various planning constraints. [ABSTRACT FROM AUTHOR]

During the past two decades, Taiwan's average dependence on imported energy was 97.6%, thus pushing the government to promote the indigenous energy supply. In this regard, the energy policy and regulatory incentives for promoting biomass-to-energy or bioenergy have been recently established. In this work, the updated statistics of biomass-derived waste and energy supply from biomass during the period of 2005–2021 were analyzed using national/official reports. It was found that the annual agricultural waste amounts in Taiwan ranged from 4.5 to 5.2 million metric tons, and about 80% of those were generated from rice-derived residues (rice straw and rice husk) and livestock/poultry-derived waste (manure). In addition, a decreasing trend was observed in the indigenous bioenergy supply, mostly from the solid-type biomass resources, including waste wood, rice husk, and sugarcane bagasse. In order to expand bioenergy diversification, the central competent authorities, including the Ministry of Economic Affairs (MOEA), Council of Agriculture (COA), and Environmental Protection Administration (EPA), have announced the relevant policies for bioenergy promotion under the authorization of acts. Among them, the Renewable Energy Development Act is the legal foundation for promoting bioenergy and its industry development through economic incentives like feed-in-tariff (FIT), installation supports (or subsidies), and electrical grid connection. [ABSTRACT FROM AUTHOR]

The proportion of renewable power generation in the world has been increasing in recent years. However, the fluctuations and uncertainties of renewable power generation bring a considerable challenge to future unit scheduling. Therefore, the generation flexibility in power systems becomes more critical as a large amount of renewable generation is integrated into power systems. The use of flexible generators with energy storage systems is one of the most efficient methods of improving power system flexibility. The primary purpose of this study is to explore the effect of generation flexibility on the cost of unit scheduling. A flexibility index is used to evaluate the generation flexibility in the Taiwan power system, and a multi-scenario analysis for renewable power integration is considered. This study also considers various system constraints, such as the unit commitment of actual hydro and thermal units, the scheduling of flexible internal combustion engines (ICEs) and energy storage systems, and possible curtailments of renewable power generation. According to the seasonable characteristics of renewable power generation, this study provides a suitable capacity for flexible ICE units and energy storage systems. Furthermore, this study demonstrates that the cost of unit scheduling is effectively reduced by increasing flexible ICE units and energy storage systems. The results of this study can be used as a reference for power systems in preparing flexible generating units and energy storage systems under the integration of a large amount of renewable power generation in the future. [ABSTRACT FROM AUTHOR]

One of the most critical aspects of integrating renewable energy sources into the smart grid is photovoltaic (PV) power generation forecasting. This ensemble forecasting technique combines several forecasting models to increase the forecasting accuracy of the individual models. This study proposes a regression-based ensemble method for day-ahead PV power forecasting. The general framework consists of three steps: model training, creating the optimal set of weights, and testing the model. In step 1, a Random forest (RF) with different parameters is used for a single forecasting method. Five RF models (RF1, RF2, RF3, RF4, and RF5) and a support vector machine (SVM) for classification are established. The hyperparameters for the regression-based method involve learners (linear regression (LR) or support vector regression (SVR)), regularization (least absolute shrinkage and selection operator (LASSO) or Ridge), and a penalty coefficient for regularization (λ). Bayesian optimization is performed to find the optimal value of these three hyperparameters based on the minimum function. The optimal set of weights is obtained in step 2 and each set of weights contains five weight coefficients and a bias. In the final step, the weather forecasting data for the target day is used as input for the five RF models and the average daily weather forecasting data is also used as input for the SVM classification model. The SVM output selects the weather conditions, and the corresponding set of weight coefficients from step 2 is combined with the output from each RF model to obtain the final forecasting results. The stacking recurrent neural network (RNN) is used as a benchmark ensemble method for comparison. Historical PV power data for a PV site in Zhangbin Industrial Area, Taiwan, with a 2000 kWp capacity is used to test the methodology. The results for the single best RF model, the stacking RNN, and the proposed method are compared in terms of the mean relative error (MRE), the mean absolute error (MAE), and the coefficient of determination (R2) to verify the proposed method. The results for the MRE show that the proposed method outperforms the best RF method by 20% and the benchmark method by 2%. [ABSTRACT FROM AUTHOR]

Vehicle electrification has become an important strategy adopted worldwide, including in Taiwan, as a means to achieving net zero emissions. Taiwan is capable of building a whole light commercial vehicle and has technological strength in producing critical EV parts. This study applies the Bass diffusion model to assess the feasibility of developing eLCV shared architecture in Taiwan and estimates that the annual replacement demand for eLCVs could reach 20,221 units. This exceeds the threshold number of 5000 units, which could motivate the automakers to develop eLCV shared architecture. The simulation result shows that achieving full market penetration would take at least 13 years and would be highly correlated with policy support, the vehicle selling price and the battery pack price. The B2B model is a suitable way of introducing eLCVs into the logistics fleets. In the initial promotion phase, policy support and complementary measures would be needed, e.g., public sectors' purchases, financial incentives and constructing a user-friendly environment. The simulation results further indicate that Taiwan's eLCV market has a high price elasticity, and in the future, a tendency for the battery pack price to decline may speed up the replacement process. [ABSTRACT FROM AUTHOR]

Adequate recognition of the offshore wind energy potential may help coastal states frame proper energy policies for replacing nuclear and fossil fuel energy. In this study, we examined the application potential of the offshore wind energy generated by 31 offshore wind farms designated by the Taiwanese government for future exploitation. Our findings indicate that offshore wind energy (through its substantial power generation volume and capacity factor) can play the most pivotal role in future power generation for Taiwan. A total of 59.3 TWh of electricity produced from offshore wind energy and solar photovoltaics (PVs) each year could replace the power generated from nuclear energy by 2025. Coal-fired power generation could be replaced by offshore wind energy and other renewables by 2032. The full exploitation of offshore wind farms as detailed in this study (103.4 TWh/year), together with other renewables, could reduce the share of liquefied natural gas-fired power generation to 5.6% of the total Taiwanese power supply by 2040. Realizing the ultimate target of 100% carbon-neutral power generation would rely mainly on a further decrease in electricity consumption per unit of gross domestic product and the expansion of offshore wind energy and geothermal energy. [ABSTRACT FROM AUTHOR]

This study determines the energy usage pattern for Penghu, which is an island in the Taiwan Strait, for which electricity is the major energy source. There is an increase in electricity consumption as the number of inhabitants and tourists increases. Water supply from desalination plants represents another major electricity demand. To reduce energy consumption and carbon emissions, subsidy programs are used to encourage the use of LED lighting and energy-efficient appliances (air conditioners and refrigerators), but not for common appliances, such as chest freezers. Abundant solar radiation in summer and the northeast monsoon in autumn/winter make Penghu an ideal location to take advantage of solar energy and wind power. An undersea cable between Penghu and Taiwan is now available and can be used to stabilize the isolated grid system in Penghu. The target for a low-carbon island can be achieved by installing PV systems and onshore and offshore wind turbines to fulfill the energy demand for the tourism industry. [ABSTRACT FROM AUTHOR]

As the competitive market nowadays shortens the life cycle of products, new products should be designed to meet the customer's demand under a dynamic marketing environment so as to efficiently enhance the product strength of new products with maximization of profit. Hence, the key solution for enterprises to succeed will be the precise evaluation of new product development (NPD) performance, particularly for those who manage to survive in the intensely competitive market in Taiwan. This study is to identify the thin film transistor-liquid crystal display (TFT-LCD) touch panel industry and then establish an integrated model of NPD performance evaluation for enterprises. Firstly, literature review and interviews with experts are conducted to select the four aspects and 15 criteria as the main factors affecting NDP performance evaluation. Secondly, Decision Making Trial and Evaluation Laboratory (DEMATEL) is employed to identify the interrelationships among those factors. Finally, a Fuzzy theory is applied to resolve the linguistic hedges and an Analytic Network Process (ANP) is adopted to obtain the weights of all factors. A case study is performed to validate the proposed model in a Taiwanese TFT-LCD company. It not only provides the decision maker with a guidance system but also increases the competitive advantages for the TFT-LCD industry to design new products in the future. [ABSTRACT FROM AUTHOR]

It is of great importance and urgency for Taiwan to develop offshore wind power. However, relevant data on offshore wind energy resources are limited. This study imported wind speeds measured by a tidal station and a buoy into the software WAsP to estimate the high-altitude wind speeds in the two areas. A light detection and ranging (Lidar) system was set up near the tidal station and buoy. High-altitude wind speeds measured by the Lidar system were compared with the WAsP-estimated values, and it was discovered that the two data sets were consistent. Then, long-term wind speed data observed by buoys and tidal stations at various locations were imported into WAsP to forecast wind speeds at heights of 55-200 m on the west coast of Taiwan. The software WAsP Engineering was used to analyze the extreme wind speeds in the same areas. The results show that wind speeds at 100 m are approximately 9.32-11.24 m/s, which means that the coastal areas of west Taiwan are rich in wind energy resources. When a long-term 10-min average wind speed is used, the extreme wind speed on the west coast is estimated to be between 36.4 and 55.3 m/s. [ABSTRACT FROM AUTHOR]

With the advent of new satellite technology, the radiative energy exchanges between Sun, Earth, and space may now be quantified accurately. Nevertheless, much less is known about the magnitude of the energy flows within the climate system and at the Earth's surface, which cannot be directly measured by satellites. This review surveys the basic theories, observational methods, and different surface energy balance algorithms for estimating evapotranspiration (ET) from landscapes and regions with remotely sensed surface temperatures, and highlights uncertainties and limitations associated with those estimation methods. Although some of these algorithms were built up for specific land covers like irrigation areas only, methods developed for other disciplines like hydrology and meteorology, are also reviewed, where continuous estimates in space and in time are needed. Temporal and spatial scaling issues associated with the use of thermal remote sensing for estimating evapotranspiration are also discussed. A review of these different satellite based remote sensing approaches is presented. The main physical bases and assumptions of these algorithms are also discussed. Some results are shown for the estimation of evapotranspiration on a rice paddy of Chiayi Plain in Taiwan using remote sensing data. [ABSTRACT FROM AUTHOR]

Renewable energy is crucial for achieving net zero emissions. Taiwan has abundant wind resources and most major wind farms are offshore over the Taiwan Strait due to a lack of space on land. A thorough study that includes time series modeling of wind speed and sea breeze identification and evaluation for Taiwan's offshore wind farms was conducted. The time series modeling identified two periodic (annual and diurnal) components and an autoregressive model for multiple-year wind speed time series. A new method for sea breeze type identification and magnitude evaluation is proposed. The method (named as EACH) utilizes a vector and an ellipse to represent the wind condition of a day. Verification of the type identification determined by the new method in two cases of different seasons has been conducted by using surface weather charts and wind data measured by lidar. It is a concise, effective, and programmable way to filter a number of dates for type identification and speed change precursor of sea breeze. We found that the typical daily wind power production of corkscrew sea breeze in Central Taiwan is more than 33 times that of pure sea breeze and more than 9 times that of backdoor sea breeze, which highlights the impact of sea breeze types on wind power. [ABSTRACT FROM AUTHOR]

Solar water heaters represent the success story in the development of renewable energy in Taiwan. With increasing public awareness, there are over 0.3 million residential systems in operation. To disseminate solar water heaters in remote islands, economic feasibility and water quality are taken into account in this study. The payback period in Kinmen and Penghu Counties are evaluated, according to effective annual solar energy gain, hot water consumption pattern and cost. Assessment of the scaling and corrosion tendencies for solar water heaters using tap and underground water are also presented. For flat-plate solar collectors with metal components, favorable corrosion resistance and protective anti-corrosion coatings are required. [ABSTRACT FROM AUTHOR]

Taiwan's power system is isolated and not supported by other interconnected systems. Consequently, the system frequency immediately reflects changes in the system loads. Pumped storage units are crucial for controlling power frequency. These units provide main or auxiliary capacities, reducing the allocation of frequency-regulating reserve (FRR) and further reducing generation costs in system operations. Taiwan's Longmen Nuclear Power Plant is set to be converted for commercial operations, which will significantly alter the spinning reserves in the power system. Thus, this study proposes a safe and economic pumped storage unit dispatch strategy. This strategy is used to determine the optimal FRR capacity and 1-min recovery frequency in a generator failure occurrence at the Longmen Power Plant. In addition, this study considered transmission capacity constraints and conducted power flow analysis of the power systems in Northern, Central, and Southern Taiwan. The results indicated that, in the event of a failure at Longmen Power Plant, the proposed strategy can not only recover the system frequency to an acceptable range to prevent underfrequency load-shedding, but can also mitigate transmission line overloading. [ABSTRACT FROM AUTHOR]

The key factors in the energy-saving design of a building's exterior in Taiwan are the thermal performance of the roof and window glazing. This study used the eQUEST software to investigate how different types of roof construction, window glasses and sunshield types affect the energy consumption in residential buildings under common scenarios. The simulation results showed that the use of an appropriate window glass significantly reduced the annual energy consumption, followed by the shading device, whereas the roof construction produced less of an energy-efficiency benefit. By using a low-E glass and a 1.5 × 1.5 m box shading (e.g., balcony), this could save approximately 15.1 and 13.6% of the annual electricity consumption of air conditioners, respectively. Therefore, having control over the dominant factors in the building envelope is indeed an important step in the path to achieving energy savings and carbon reduction in residential houses. [ABSTRACT FROM AUTHOR]

A de-loaded real power control strategy is proposed to decrease the real power output and increase the reactive power output of a grid-connected offshore wind farm in order to improve the voltage profile when the wind farm is subject to a grid fault. A simplified linear model of the wind farm is first derived and a fixed-gain proportional-integral (PI) real power controller is designed based on the pole-zero cancellation method. To improve the dynamic voltage response when the system is subject to a major disturbance such as a three-phase fault in the grid, a self-tuning controller based on particle swarm optimization (PSO) is proposed to adapt the PI controller gains based on the on-line measured system variables. Digital simulations using MATLAB/SIMULINK were performed on an offshore wind farm connected to the power grid in central Taiwan in order to validate the effectiveness of the proposed PSO controller. It is concluded from the simulation results that a better dynamic voltage response can be achieved by the proposed PSO self-tuning controller than the fixed-gain controller when the grid is subject to a three-phase fault. In addition, low voltage ride through (LVRT) requirements of the local utility can be met by the wind farm with the proposed power controller. [ABSTRACT FROM AUTHOR]

The chiller is the major energy consuming HVAC component in a building. Currently, huge chiller data is easy to obtain due to Internet of Things (IoT) technology development. In order to optimize the chiller system, this study presents a data mining technique that utilizes the available chiller data. The data mining techniques used are prediction model, clustering analysis, and association rules mining (ARM) analysis. The dataset was collected every minute for a year from a water-cooled chiller at an institutional building in Taiwan and from meteorological data. The power consumption prediction model was built using deep neural networks with 0.955 of R 2 , 4.470 of MAE, and 6.716 of RMSE. Clustering analysis was performed using the k-means algorithm and ARM analysis was performed using Apriori algorithm. Each cluster identifies those operational parameters that have strong association rules with high performance. The operational parameters from ARM were simulated using the prediction model. The simulation result shows that the ARM operational parameters can successfully save the energy consumption by 22.36 MWh or 18.17% in a year. [ABSTRACT FROM AUTHOR]

We propose an evaluation system to choose appropriate materials for solar cells. A fuzzy DEMATEL information procedure was used for decision-making to gather information and analyze the casual relationship. These data acquired were partitioned into causal and impact bunches, empowering users to gather an improved understanding of the intelligent relationship among them, as well as making recommendations for changes to upgrade their general execution. The proposed approach can deliver a compelling fabric choice assessment with satisfactory criteria that fit the respondent's discernment designs; particularly, these evaluation dimensions are interlaced. Recommendations are given to assist government authorities to plan a Taiwan solar cell industry approach and for industries to develop commerce techniques for improvement in the solar cell field. [ABSTRACT FROM AUTHOR]

Based on the strong similarities between energy-resource-poor and fossil-fuel-centered economies (e.g., Taiwan, Japan, and South Korea) in terms of economy, culture, and energy usage characteristics, they should be analyzed collectively. This study adopted two-tier input-output structural decomposition analysis to identify the driving forces behind CO2 emissions to these countries to the formulation of effective environmental policy. Data from the World Input-Output Database was used to decompose relative changes in CO2 emissions into a range of technological advances, factor substitution, and final demand effects. Technological advances in energy (direct) contributed to a 77% reduction in Taiwan and a 34% reduction in South Korea. This is a clear indication that improving energy efficiency via technological advances should be a priority. In Japan in particular, there was a 22% reduction in CO2 emissions attributable to technological advances in materials; hence, it is recommended that Taiwan and South Korea work to extensively develop eco-industrial parks to create industry clusters to promote resource/energy efficiency and reductions in CO2 emissions. Decomposition results based on factor substitution revealed that a variety of strategies will be required, such as switching to fuels that are less carbon intensive, promoting the adoption of renewable energies, and implementing clean-coal technologies. [ABSTRACT FROM AUTHOR]

With global climate change and resource scarcity becoming increasingly serious issues, the green energy economy is transforming on a global scale. There are an increasing number of large-scale green energy development projects. However, these are often beset with risks; thus, this study conducted an analysis of the renewable energy risks in Taiwan using the analytical hierarchy process technique. Accordingly, a comprehensive study focusing into the current energy situation and potential development is needed. Renewable energy status and the availability by its main types, including, wind, solar, thermal, and biomass energies, were critically reviewed and discussed in this study. The findings of this study include the top 10 key operating risks recognized by specific green energy companies, as well as an assessment of which risks have been addressed and which have caused losses. [ABSTRACT FROM AUTHOR]

Energy transition has become a priority for adaptive policy and measures taken in response to climate change around the world. This is an opportunity and a challenge for the Taiwan government to establish a climate-resilient power generation mixed to ensure electricity security as well as climate change mitigation. This study adopted a sustainable development perspective and applied optimal control theory to establish a cost-effective model to evaluate a long-term (2050), climate-resilient power generation mix for Taiwan. Furthermore, this study applies the STIRPAT approach to predict the demand of electricity by 2050 for the demand side management. The results not only showed the share of various power generation mixed, but also recommended the trajectory of electricity saving by 2050. [ABSTRACT FROM AUTHOR]

The focus of this study is under the auspices of China Steel Corporation, Taiwan, in carrying out the national energy policy of 2025 Non-Nuclear Home. Under this policy, an estimated 600 offshore wind turbines will be installed by 2025. In order to carry out the wind energy project effectively, a preliminary study must be conducted. In this article, we investigated the influence of the wake effect on the efficiency of the turbines' layout in a windfarm. A distributed genetic algorithm is deployed to study the wind turbines' layout in order to alleviate the detrimental wake effect. In the current stage of this research, the historical weather data of weather stations near the site of the 29th windfarm, Taiwan, were collected by Academia Sinica. Our wake effect resilient optimized windfarm showed superior performance over that of the conventional windfarm. Additionally, an operation cost minimization process is also demonstrated and implemented using an ant colony optimization algorithm to optimize the total length of the power-carrying interconnecting cables for the turbines inside the optimized windfarm. [ABSTRACT FROM AUTHOR]

The Taiwanese government has set an energy transition roadmap of 20% renewable energy supply by 2025, including a 20 GW installed PV capacity target, composed of 8 GW rooftop and 12 GW ground-mounted systems. The main trend of feed-in tariffs is downwards, having fallen by 50% over a ten-year period. Predicting the future ten-year equity internal rate of return (IRR) in this study, we examine the investability of PV systems in Taiwan when subsidies and investment costs descend. We have found that the projected subsidies scheme favours investment in small-sized PV systems. Unless the investment costs of medium-sized PV systems fall or subsidies rise over the next decade, investing in medium-sized PV systems will be less attractive. Nonlinear and linear degradation causes slight IRR differences when using higher-reliability modules. [ABSTRACT FROM AUTHOR]