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As the penetration of renewable energy sources into a power system increases, the significance of precise short-term forecasts for wind power generation becomes paramount. However, the erratic and non-periodic nature of wind poses challenges in accurately predicting the output. This paper presents a comprehensive investigation into forecasting wind power generation for the following day, using three machine learning models: long short-term memory (LSTM), convolutional neural network-bidirectional LSTM (CNN-biLSTM), and light gradient boosting machine (LGBM). In addition, this paper proposes a method to improve the prediction performance of LGBM by separating data according to the distribution of features, and training and testing each separated dataset with a distinct model. This study includes a comparative analysis of the performance of the proposed models in predicting wind turbine output, offering valuable insights into their respective efficiencies. The results of this investigation were analyzed for two geographically distinct wind farms (Korea and the UK). The findings of this study are expected to facilitate the selection of efficient prediction models within the forecast accuracy auxiliary service market and assist grid operators in ensuring reliable power supply for the grid. [ABSTRACT FROM AUTHOR]

Recently, the proportion of renewable energy (RE) sources in a power system has been increasing worldwide to reduce carbon emissions. To effectively accommodate renewable energy, there is a growing need to develop integrated planning strategies for both renewable energy sources and transmission lines in a power system, taking into account the location-specific characteristics of renewable energy. Economically viable and rapid system expansion plans are required to resolve the problem of delayed integration of renewable energy into existing power system, which arises due to inadequate transmission facilities. To address these problems, this paper presents an integrated economic evaluation method that considers the inherently uncertain output characteristics of renewable energy, contingent on location, the costs associated with installing transmission lines, and environmental benefits. The case study is conducted using a modified power system in Korea. In the case study, it is assumed that by integrating solar power generators in a low-demand area and wind power generators in a high-demand area, wind power plants have higher construction costs than solar power generators. However, the transmission costs for wind power plants are lower than those for solar power generators due to the regional characteristics of the power grid in the installation areas because renewable energy in high-demand areas typically requires less additional power infrastructure than in low-demand areas. The results of this study show that the net benefit for the wind power plant is estimated at USD 225.4 M, while the solar power plant yields a net benefit of USD 22.9 M in the example system. These results demonstrate the effectiveness of the proposed method, underscoring its potential to mitigate the challenges faced in the practical implementation of renewable energy projects. [ABSTRACT FROM AUTHOR]

This paper proposes a wind power probabilistic model (WPPM) using the reflection method and multi-kernel function kernel density estimation (KDE). With the increasing penetration of renewable energy sources (RESs) into power systems, several probabilistic approaches have been introduced to assess the impact of RESs on the power system. A probabilistic approach requires a wind power scenario (WPS), and the WPS is generated from the WPPM. Previously, WPPM was generated using a parametric density estimation, and it had limitations in reflecting the characteristics of wind power data (WPD) due to a boundary bias problem. The paper proposes a WPPM generated using the KDE, which is a non-parametric method. Additionally, the paper proposes a reflection method correcting for the boundary bias problem caused by the double-bounded characteristic of the WPD and the multi-kernel function KDE minimizing the effect of tied values. Six bandwidth selectors are used to calculate the bandwidth for the KDE, and one is selected by analyzing the correlation between the normalized WPD and the calculated bandwidth. The results were validated by generating WPPMs with WPDs in six regions of the Republic of Korea, and it was confirmed that the accuracy and goodness-of-fit are improved when the proposed method is used. [ABSTRACT FROM AUTHOR]

As the importance of renewable generating resources has grown around the world, South Korea is also trying to expand the proportion of renewable generating resources in the power generation sector. Among the various renewable energy sources, wind generating resources are emerging as a key alternative to conventional power generations in the electricity sector in Korea accounted for 17.7 GW of total capacity by 2030. As wind generating resources are gradually replacing traditional generating resources, the system security and reliability are negatively affected because of the variability, due to intermittent outputs. Therefore, existing power grids will need to be correctly re-measured to cover the large scale of renewable energy, including wind generation. To expand the grid, we must understand the characteristics of renewable energy and the impact of its adoption in the grid. In this paper, we analyze various characteristics of wind power generation, and then we propose a probabilistic power output modeling method to consider the uncertainty of wind power generation. For the probabilistic approach, Monte-Carlo simulation is used in the modeling method. The modeled wind power outputs can help planning for the reinforcement and expansion of power systems to expand the capacity for large-scale renewable energy in the future. To verify the proposed method, some case studies were performed using empirical data, and probabilistic power flow calculation was performed by integrating large-scale wind power generation to the Jeju Island power system. The probabilistic method proposed in this paper can efficiently plan power system expansion and play a key strategy of evaluating the security of the power system through the results of stochastic power flow calculation. [ABSTRACT FROM AUTHOR]

As renewable energy resources such as wind and solar power are developing and the penetration of electric vehicles (EVs) is increasingly integrated into existing systems, uncertainty and variability in power systems have become important issues. The charging demands for EVs and wind power output are recognized as highly variable generation resources (VGRs) with uncertainty, which can cause unexpected disturbances such as short circuits. This can deteriorate the reliability of existing power systems. In response, research is required to identify the uncertainties presented by VGRs and is required to examine the ability of power system models to reflect those uncertainties. The deterministic method, which is the most basic method that is currently in use, does not reflect the uncertainty of system components. Therefore, this paper proposes a probabilistic method to assess the steady-state security of power systems, reflecting the uncertainty of VGRs using Monte Carlo simulation (MCS). In the proposed method, the empirical EVs charging demand and wind power output data are modeled as a probability distribution, and then MCS is performed, integrating the power system operation to represent the steady-state security as a probability index. To verify the method proposed in this paper, a security analysis was performed based on the systems in Jeju Island, South Korea, where the penetration of wind power and EVs is expanding rapidly. [ABSTRACT FROM AUTHOR]

Alkaline electrolysis systems are currently considered to be suitable for large-scale hydrogen production. Previous research has primarily focused on integrating renewable energy sources such as solar and wind into water electrolysis systems. However, intermittent issues stemming from the sporadic nature of renewable energy sources have led to the introduction of energy storage systems (ESSs) to address these intermittent challenges. Extensive research has been conducted on the efficiency and operational aspects of these systems. In contrast to other renewable energy sources, hydropower offers the advantages of stable output and high utilization, making it a promising solution for overcoming intermittent issues. In this study, we propose the design of an optimized alkaline electrolysis system tailored for small-scale hydropower generation. This approach allowed us to confirm the efficiency of a small-scale hydropower-based hydrogen production facility and the analysis of hydrogen production costs under diverse scenarios. Notably, the optimal selling price per kilogram of hydrogen was determined to be USD 15.6 when the operational time exceeded 20 h, albeit indicating a challenging market supply. Under the consideration of various scenarios and government subsidies, this study revealed that a USD 10/kgH2 subsidy or 24 h of continuous operation achieved break-even points in the sixth and eighth years, respectively. Ultimately, the findings underscore the necessity for essential measures, including government backing and technological advancements in small-scale hydropower facilities, to enhance the economic viability of the green hydrogen market in South Korea. [ABSTRACT FROM AUTHOR]

The power industry is rapidly changing as demand for eco-friendly and stable power supply increases along with global greenhouse gas emission regulations. Small-capacity renewable power sources represented by photovoltaics and wind are continuously increasing as a form of microgrid to supply electric power to a community or island. As a result, microgrids based on renewable resources have come into wide usage around small areas or islands in Korea. In particular, the microgrid development policy of Korea is focused on electric power quality, as well as expansion in renewable energy supply for reducing greenhouse gas emissions. From 2009, the government began to develop independent carbon-free microgrids with photovoltaic and wind powers instead of traditional power diesel generators for small islands. The goal of this paper is to investigate a feasible economic microgrid topology for implementing the carbon-free island (CFI) under an acceptable level of reliability. First, we derive three scenarios of power systems including photovoltaics, wind, battery, and fuel cells. Next, we assess economic feasibility on top of the power supply reliability of the scenarios. Then, we perform a sensitivity test to suggest economic conditions for achieving the CFI goals. Finally, we present carbon-free-based microgrid models considering the CFI policy of Korea. [ABSTRACT FROM AUTHOR]

This paper examines the solution to the problem of turbine arrangement in offshore wind farms. The two main objectives of offshore wind farm planning are to minimize wake loss and maximize annual energy production (AEP). There is more wind with less turbulence offshore compared with an onshore case, which drives the development of the offshore wind farm worldwide. South Korea's offshore wind farms, which are deep in water and cannot be installed far off the coast, are affected by land complex terrain. Thus, domestic offshore wind farms should consider the separation distance from the coastline as a major variable depending on the topography and marine environmental characteristics. As a case study, a 60 MW offshore wind farm was optimized for the coast of the Busan Metropolitan City. For the analysis of wind conditions in the candidate site, wind conditions data from the meteorological tower and Ganjeolgot AWS at Gori offshore were used from 2001 to 2018. The optimization procedure is performed by evolutionary algorithm (EA) and particle swarm optimization (PSO) algorithm with the purpose of maximizing the AEP while minimizing the total wake loss. The optimization procedure can be applied to the optimized placement of WTs within a wind farm and can be extended for a variety of wind conditions and wind farm capacity. The results of the optimization were predicted to be 172,437 MWh/year under the Gori offshore wind potential, turbine layout optimization, and an annual utilization rate of 26.5%. This could convert 4.6% of electricity consumption in the Busan Metropolitan City region in 2019 in offshore wind farms. [ABSTRACT FROM AUTHOR]

At present, renewable energy installations are expanding to solve both environmental problems and expensive energy fuel import prices in isolated areas. However, in a small-scale power system, rapid output fluctuations of renewable energy may cause power quality problems such as voltage and frequency fluctuations in the power system. To solve this problem, the local government of Jeju Island in South Korea implemented a megawatt (MW)-class pilot project to stabilize the output power of wind turbines using an energy storage system (ESS). In this project, a 0.5 MWh lithium-ion battery was connected to a 3 MW wind turbine via 1 MW power conversion system (PCS). In this paper, the field test results were divided into four categories as follows. First, the performance of stabilizing the output of the wind turbine using ESS was confirmed. Second, the control performance of the ESS was confirmed when the wind turbine suddenly stopped due to an accident. Third, it was confirmed that the ESS discharged energy into the power system after the stabilization of the wind turbine output. Fourth, the reasons for the failure of the ESS to control output stabilization of the wind turbine were analyzed through MATLAB simulation. [ABSTRACT FROM AUTHOR]

Renewable energy supply is essential for carbon neutrality; however, technologies aiming to optimally utilize renewable energy sources remain insufficient. Seasonal variability in renewable energy is a key issue, which many studies have attempted to overcome through operating systems and energy storage. Currently, hydrogen is the only technology that can solve this seasonal storage problem. In this study, the amount of hydrogen required to circumvent the seasonal variability in renewable energy supply in Korea was quantified. Spatiotemporal analysis was conducted using renewable energy resource maps and power loads. It was predicted that 50% of the total power demand in the future will be met using solar and wind power, and a scenario was established based on the solar-to-wind ratio. It was found that the required hydrogen production differed by approximately four-times, depending on the scenarios, highlighting the importance of supplying renewable energy at an appropriate ratio. Spatially, wind power was observed to be unsuitable for the physical transport of hydrogen because it has a high potential at mountain peaks and islands. The results of this study are expected to aid future hydrogen research and solve renewable energy variability problems. [ABSTRACT FROM AUTHOR]

Although the size of the wind turbine has become larger to improve the economic feasibility of wind power generation, whether increases in rotor diameter and hub height always lead to the optimization of energy cost remains to be seen. This paper proposes an algorithm that calculates the optimized hub height to minimize the cost of energy (COE) using the regional wind profile database. The optimized hub height was determined by identifying the minimum COE after calculating the annual energy production (AEP) and cost increase, according to hub height increase, by using the wind profiles of the wind resource map in South Korea and drawing the COE curve. The optimized hub altitude was calculated as 75~80 m in the inland plain but as 60~70 m in onshore or mountain sites, where the wind profile at the lower layer from the hub height showed relatively strong wind speed than that in inland plain. The AEP loss due to the decrease in hub height was compensated for by increasing the rotor diameter, in which case COE also decreased in the entire region of South Korea. The proposed algorithm of identifying the optimized hub height is expected to serve as a good guideline when determining the hub height according to different geographic regions. [ABSTRACT FROM AUTHOR]

Not only the driving for offshore wind energy capacity of 12 GW by Korea's Renewable Energy 2030 plan but also the need for the rejuvenation of existing world-class shipbuilders' infrastructures is drawing much attention to offshore wind energy in Korea, especially to the diverse substructures. Considering the deep-sea environment in the East Sea, this paper presents detailed modeling and analysis of spar-type substructure for a 5 MW floating offshore wind turbine (FOWT). This process uses a fully coupled integrated load analysis, which was carried out using FAST, a widely used integrated load analysis software developed by NREL, coupled with an in-house hydrodynamic code (UOU code). The environmental design loads were calculated from data recorded over three years at the Ulsan Marine buoy point according to the ABS and DNVGL standards. The total 12 maximum cases from DLC 6.1 were selected to evaluate the structural integrity of the spar-type substructure under the three co-directional conditions (45°, 135°, and 315°) of wind and wave. A three-dimensional (3D) structural finite element (FE) model incorporating the wind turbine tower and floating structure bolted joint connection was constructed in FEGate (pre/post-structural analysis module based on MSC NASTRAN for ship and offshore structures). The FEM analysis applied the external loads such as the structural loads due to the inertial acceleration, buoyancy, and gravity, and the environmental loads due to the wind, wave, and current. The three-dimensional FE analysis results from the MSC Nastran software showed that the designed spar-type substructure had enough strength to endure the extreme limitation in the East Sea based on the von Mises criteria. The current process of this study would be applicable to the other substructures such as the submersible type. [ABSTRACT FROM AUTHOR]

The number of wind-generating resources has increased considerably, owing to concerns over the environmental impact of fossil-fuel combustion. Therefore, wind power forecasting is becoming an important issue for large-scale wind power grid integration. Ensemble forecasting, which combines several forecasting techniques, is considered a viable alternative to conventional single-model-based forecasting for improving the forecasting accuracy. In this work, we propose the day-ahead ensemble forecasting of wind power using statistical methods. The ensemble forecasting model consists of three single forecasting approaches: autoregressive integrated moving average with exogenous variable (ARIMAX), support vector regression (SVR), and the Monte Carlo simulation-based power curve model. To apply the methodology, we conducted forecasting using the historical data of wind farms located on Jeju Island, Korea. The results were compared between a single model and an ensemble model to demonstrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

South Korea greatly depends upon foreign countries to fulfill its energy requirements, and therefore imports billions of barrels of oil every year. For instance, 94.8% of the total primary energy supply (TPES) was imported from other countries in 2015, at the cost of 91.51 billion euros. There is a realistic challenge in front of the government to reduce these oil imports, and to find alternate (local) sources of energy. Renewable energy (RE) technologies can play a vital role in this regard. The South Korean government has shown a great interest in RE, and intends to achieve a target of 11% of the TPES being generated by RE by the end of 2035, as decided in the Korean Renewable Portfolio Standard (RPS); which showed that only 4.9% of TPES was produced by RE at the end of 2015. The present study proposes ten potential onshore wind farm sites. These locations have been identified by using the GIS–MCDM (geographic information system–multi-criteria decision-making) methodology and a detailed techno-economic assessment has also been presented. Furthermore, the appropriate type of wind turbines has been recommended for each site using detailed analysis of wind conditions, 50-year extreme wind speed (EWS) and turbulence intensity (TI). The analysis showed that all the sites have excellent wind conditions, and they are also economically feasible. Parameters such as AEP (annual energy production), CF (capacity factor), LCOE (levelized cost of electricity), and NPV (net present value) have been estimated for each site, using five different wind turbines manufactured in South Korea. The present study can be very useful for the wind energy sector in South Korea. [ABSTRACT FROM AUTHOR]