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    ๋ถˆํ™•์‹ค์„ฑ ํ•˜์—์„œ ์‹œ์Šคํ…œ์˜ ์œ ์ง€ ๋ณด์ˆ˜ ์ตœ์ ํ™” ๋ฐ ์ˆ˜๋ช… ์ฃผ๊ธฐ ์˜ˆ์ธก

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€, 2019. 2. ์ด์›๋ณด.The equipment and energy systems of most chemical plants have undergone repetitive physical and chemical changes and lead to equipment failure through aging process. Replacement and maintenance management at an appropriate point in time is an important issue in terms of safety, reliability and performance. However, it is difficult to find an optimal solution because there is a trade-off between maintenance cost and system performance. In many cases, operation companies follow expert opinions based on long-term industry experience or forced government policy. For cost-effective management, a quantitative state estimation method and management methodology of the target system is needed. Various monitoring technologies have been introduced from the field, and quantifiable methodologies have been introduced. This can be used to diagnose the current state and to predict the life span. It is useful for decision making of system management. This thesis propose a methodology for lifetime prediction and management optimization in energy storage system and underground piping environment. First part is about online state of health estimation algorithm for energy storage system. Lithium-ion batteries are widely used from portable electronics to auxiliary power supplies for vehicle and renewable power generation. In order for the battery to play a key role as an energy storage device, the state estimation, represented by state of charge and state of health, must be well established. Accurate rigorous dynamic models are essential for predicting the state-of health. There are various models from the first principle partial differential model to the equivalent circuit model for electrochemical phenomena of battery charge / discharge. It is important to simulate the battery dynamic behavior to estimate system state. However, there is a limitation on the calculation load, therefore an equivalent circuit model is widely used for state estimation. Author presents a state of health estimation algorithm for energy storage system. The proposed methodology is intended for state of health estimation under various operating conditions including changes in temperature, current and voltage. Using a recursive estimator, this method estimate the current battery state variable related to battery cell life. State of health estimation algorithm uses estimated capacity as a cell life-time indicator. Adaptive parameters are calibrated by a least sum square error estimation method based on nonlinear programming. The proposed state-of health estimation methodology is validated with cell experimental lithium ion battery pack data under typical operation schedules and demonstration site operating data. The presented results show that the proposed method is appropriate for state of health estimation under various conditions. The suitability of algorithm is demonstrated with on and off line monitoring of new and aged cells using cyclic degradation experiments. The results from diverse experimental data and data of demonstration sites show the appropriateness of the accuracy, robustness. Second part is structural reliability model for quantification about underground pipeline risk. Since the long term usage and irregular inspection activities about detection of corrosion defect, catastrophic accidents have been increasing in underground pipelines. Underground pipeline network is a complex infrastructure system that has significant impact on the economic, environmental and social aspects of modern societies. Reliability based quantitative risk assessment model is useful for underground pipeline involving uncertainties. Firstly, main pipeline failure threats and failure modes are defined. External corrosion is time-dependent factor and equipment impact is time-independent factor. The limit state function for each failure cause is defined and the accident probability is calculated by Monte Carlo simulation. Simplified consequence model is used for quantification about expected failure cost. It is applied to an existing underground pipeline for several fluids in Ulsan industrial complex. This study would contribute to introduce quantitative results to prioritize pipeline management with relative risk comparisons Third part is maintenance optimization about aged underground pipeline system. In order to detect and respond to faults causing major accidents, high resolution devices such as ILI(Inline inspection), Hydrostatic Testing, and External Corrosion Direct Assessment(ECDA) can be used. The proposed method demonstrates the structural adequacy of a pipeline by making an explicit estimate of its reliability and comparing it to a specified reliability target. Structural reliability analysis is obtaining wider acceptance as a basis for evaluating pipeline integrity and these methods are ideally suited to managing metal corrosion damage as identified risk reduction strategies. The essence of this approach is to combine deterministic failure models with maintenance data and the pipeline attributes, experimental corrosion growth rate database, and the uncertainties inherent in this information. The calculated failure probability suggests the basis for informed decisions on which defects to repair, when to repair them and when to re-inspect or replace them. This work could contribute to state estimation and control of the lithium ion battery for the energy storage system. Also, maintenance optimization model helps pipeline decision-maker determine which integrity action is better option based on total cost and risk.ํ™”ํ•™๊ณต์žฅ ๋‚ด ์žฅ์น˜ ๋ฐ ์—๋„ˆ์ง€ ์‹œ์Šคํ…œ์€ ๋ฐ˜๋ณต์ ์ธ ์‚ฌ์šฉ์œผ๋กœ ๋ฌผ๋ฆฌํ™”ํ•™์  ๋ณ€ํ™”๋ฅผ ๊ฒช์œผ๋ฉฐ ๋…ธํ›„ํ™”๋˜๊ณ  ์„ค๊ณ„ ์ˆ˜๋ช…์— ๊ฐ€๊นŒ์›Œ์ง€๊ฒŒ ๋œ๋‹ค. ์ ์ ˆํ•œ ์‹œ์ ์— ์žฅ๋น„ ๊ต์ฒด์™€ ๋ณด์ˆ˜ ๊ด€๋ฆฌ๋Š” ์•ˆ์ „๊ณผ ์‹ ๋ขฐ๋„, ์ „์ฒด ์‹œ์Šคํ…œ ์„ฑ๋Šฅ์„ ์ขŒ์šฐํ•˜๋Š” ์ค‘์š”ํ•œ ๋ฌธ์ œ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ๋ณด์ˆ˜ ๋น„์šฉ๊ณผ ์‹œ์Šคํ…œ ์„ฑ๋Šฅ์„ ์œ ์ง€ํ•˜๋Š” ๊ฒƒ ์‚ฌ์ด์—๋Š” ํŠธ๋ ˆ์ด๋“œ ์˜คํ”„๊ฐ€ ์กด์žฌํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์ด์— ๋Œ€ํ•œ ์ตœ์ ์ ์„ ์ฐพ๋Š” ๊ฒƒ์€ ์–ด๋ ค์šด ๋ฌธ์ œ์ด๋‹ค. ๋งŽ์€ ๊ฒฝ์šฐ์— ์šด์˜ํšŒ์‚ฌ์—์„œ๋Š” ๊ฒฝํ—˜์— ๊ธฐ๋ฐ˜ํ•œ ์ „๋ฌธ๊ฐ€ ์˜๊ฒฌ์„ ๋”ฐ๋ฅด๊ฑฐ๋‚˜, ์ •๋ถ€์ฐจ์›์˜ ์•ˆ์ „๊ด€๋ฆฌ ์ •์ฑ… ์ตœ์†Œ ๊ธฐ์ค€์— ๋งž์ถ”์–ด ์ง„ํ–‰ํ•œ๋‹ค. ๋น„์šฉํšจ์œจ์  ๊ด€๋ฆฌ๋ฅผ ์œ„ํ•˜์—ฌ ์ •๋Ÿ‰์ ์ธ ์ƒํƒœ ์ถ”์ • ๊ธฐ๋ฒ•์ด๋‚˜ ์œ ์ง€๋ณด์ˆ˜ ๊ด€๋ฆฌ ๋ฐฉ๋ฒ•๋ก ์€ ํ•„์š”ํ•˜๋‹ค. ๋งŽ์€ ๋ชจ๋‹ˆํ„ฐ๋ง ๊ธฐ์ˆ ์ด ๊ฐœ๋ฐœ๋˜์–ด์ง€๊ณ  ์žˆ๊ณ  ์ ์ฐจ ์‹ค์‹œ๊ฐ„ ์ธก์ • ๋ฐฉ๋ฒ•์ด๋‚˜ ์„ผ์„œ ๊ธฐ์ˆ ์ด ๋ฐœ๋‹ฌ ํ•˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ์—ฌ์ „ํžˆ ์ง์ ‘ ์ธก์ • ๋ฐ ๊ฒ€์‚ฌ ์ด์ „ ์žฅ๋น„์˜ ์ˆ˜๋ช… ์˜ˆ์ธก๊ณผ ์‹œ์Šคํ…œ ๊ด€๋ฆฌ์— ๋Œ€ํ•œ ์˜์‚ฌ๊ฒฐ์ •์„ ๋„์šธ ๋ฐฉ๋ฒ•๋ก ์€ ๋ถ€์กฑํ•œ ์‹ค์ •์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ฆฌํŠฌ ์ด์˜จ ๋ฐฐํ„ฐ๋ฆฌ์˜ ์ˆ˜๋ช…์˜ˆ์ธก ๋ฐฉ๋ฒ•๋ก ๊ณผ ์ง€ํ•˜๋งค์„ค๋ฐฐ๊ด€์˜ ๊ด€๋ฆฌ ์ตœ์ ํ™” ๋ฌธ์ œ๋ฅผ ๋‹ค๋ฃฌ๋‹ค. ์ฒซ ์žฅ์—์„œ๋Š” ์—๋„ˆ์ง€ ์ €์žฅ์‹œ์Šคํ…œ ์šด์ „ํŒจํ„ด์— ์ ํ•ฉํ•œ ๋ฐฐํ„ฐ๋ฆฌ SOH ์ถ”์ • ๋ฐฉ๋ฒ•๋ก ์— ๋Œ€ํ•œ ๊ฒƒ์ด๋‹ค. ๋ฆฌํŠฌ ์ด์˜จ ๋ฐฐํ„ฐ๋ฆฌ๋Š” ์ด๋™๊ฐ€๋Šฅ ์ „์ž์žฅ์น˜์—์„œ๋ถ€ํ„ฐ ์ž๋™์ฐจ ๋ฐ ์‹ ์žฌ์ƒ๋ฐœ์ „ ๋“ฑ์˜ ๋ณด์กฐ ์ „๋ ฅ ์ €์žฅ์žฅ์น˜๋กœ์„œ ํ™œ์šฉ์ด ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋‹ค. ๋ฐฐํ„ฐ๋ฆฌ๊ฐ€ ์ •์ƒ์ ์ธ ์—ญํ• ์„ ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ SOC์™€ SOH์˜ ์ •ํ™•ํ•œ ์ถ”์ •์ด ์ค‘์š”ํ•˜๋‹ค. ์ •ํ™•ํ•œ ๋™์  ๋ชจ๋ธ์€ SOH ์˜ˆ์ธก์„ ์œ„ํ•˜์—ฌ ํ•„์ˆ˜์ ์ด๋‹ค. BMS์—๋Š” ๊ณ„์‚ฐ ๋กœ๋“œ์— ํ•œ๊ณ„๊ฐ€ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ์ƒํƒœ ์ถ”์ •์„ ์œ„ํ•˜์—ฌ ๊ณ„์‚ฐ ๋ถ€ํ•˜๊ฐ€ ๋น„๊ต์  ์ ์€ ๋“ฑ๊ฐ€ํšŒ๋กœ ๋ชจ๋ธ์ด ์‚ฌ์šฉ๋œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” SOH ์˜ˆ์ธก ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์‹œํ•˜๊ณ , ์…€ ๋ฐ ์‹ค์ฆ ์‚ฌ์ดํŠธ ๋ฐ์ดํ„ฐ๋กœ ๊ฒ€์ฆํ•œ๋‹ค. ๋ฐ˜๋ณต ์˜ˆ์ธก๊ธฐ์™€ ๊ด€์ธก๊ธฐ ๊ธฐ๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ SOH๋ฅผ ์ถ”์ •์„ ํ†ตํ•˜์—ฌ ํ˜„์žฌ์˜ ๋ฐฐํ„ฐ๋ฆฌ ์ƒํƒœ๋ฅผ ์ œ์‹œํ•œ๋‹ค. SOH ์˜ˆ์ธก ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์šฉ๋Ÿ‰์„ ์ค‘์š” ์ƒํƒœ๋ณ€์ˆ˜๋กœ ํ•˜์—ฌ ์˜ˆ์ธก๋œ๋‹ค. ์ œ์•ˆ ์•Œ๊ณ ๋ฆฌ์ฆ˜์—์„œ๋Š” SOH๋ฅผ ์ •ํ™•ํžˆ ์ถ”์ •ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ํ™•์žฅ์นผ๋งŒํ•„ํ„ฐ๋ฅผ ๋„์ž…ํ•˜์—ฌ ๋ฐฐํ„ฐ๋ฆฌ ์ƒํƒœ๋ณ€์ˆ˜๋“ค์„ ์ •ํ™•ํžˆ ์˜ˆ์ธกํ•˜๊ณ  ์ด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ SOH๋ฅผ ์ถ”์ •ํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ๋‘๋ฒˆ์งธ ์žฅ์€ ๊ตฌ์กฐ ์‹ ๋ขฐ๋„ ๋ถ„์„์„ ํ†ตํ•˜์—ฌ ์ง€ํ•˜๋ฐฐ๊ด€์˜ ์ •๋Ÿ‰์  ์œ„ํ—˜์„ฑ ๋ชจ๋ธ์„ ์ˆ˜๋ฆฝํ•œ๋‹ค. ๋ฐฐ๊ด€์˜ ์žฅ๊ธฐ ์‚ฌ์šฉ๊ณผ ๋ถˆ๊ทœ์น™ํ•œ ๊ฒ€์‚ฌ/๋ณด์ˆ˜ ํ™œ๋™์— ๋Œ€ํ•œ ๋ถˆํ™•์‹ค์„ฑ์€ ์ง€ํ•˜๋ฐฐ๊ด€ ์•ˆ์ „ ์‚ฌ๊ณ ์˜ ์œ„ํ—˜์„ฑ์„ ์ฆ๋Œ€์‹œํ‚ค๋Š” ์š”์ธ์ด๋‹ค. ์‚ฐ์—…๋‹จ์ง€ ๋‚ด์˜ ์ง€ํ•˜๋ฐฐ๊ด€ ๋„คํŠธ์›Œํฌ๋Š” ๋ณต์žกํ•œ ์ธํ”„๋ผ๋ฅผ ๊ฐ–์ถ”๊ณ  ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ์‚ฌ๊ณ  ๋ฐœ์ƒ์‹œ ๊ฒฝ์ œ์ , ํ™˜๊ฒฝ์ , ์‚ฌํšŒ์ ์œผ๋กœ ํฐ ์œ„ํ˜‘์š”์†Œ๊ฐ€ ๋œ๋‹ค. ์‹ ๋ขฐ๋„ ๊ธฐ๋ฐ˜ ์ •๋Ÿ‰์  ์œ„ํ—˜๋„ ๋ชจ๋ธ์€ ์ง€ํ•˜๋ฐฐ๊ด€์˜ ํฐ ๋ถˆํ™•์‹ค์„ฑ ์š”์†Œ๋ฅผ ๊ณ ๋ คํ•˜๋Š”๋ฐ ์œ ์šฉํ•œ ๋ฐฉ๋ฒ•๋ก ์ด๋‹ค. ๋ฐฐ๊ด€ ์‚ฌ๊ณ  ์œ„ํ˜‘์š”์ธ๊ณผ ์‚ฌ๊ณ  ๋ชจ๋“œ๋ฅผ ์ •์˜ํ•˜๊ณ , ๋ถ€์‹๊ณผ ํƒ€๊ณต์‚ฌ์— ์ด๋ฅด๋Š” ์‹œ๊ฐ„ ์˜์กด์ , ๋น„์˜์กด์  ์š”์†Œ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ํ•œ๊ณ„์ƒํƒœํ•จ์ˆ˜๋ฅผ ๊ฒฐ์ •ํ•œ๋‹ค. ๋ชฌํ…Œ์นด๋ฅผ๋กœ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•˜์—ฌ ์—ฐ๊ฐ„ ์‚ฌ๊ณ ํ™•๋ฅ ์ด ์œ ์ถ”๋˜๋ฉฐ ์‚ฌ๊ณ  ์˜ํ–ฅ๊ฑฐ๋ฆฌ ๋ฐ ๋ˆ„์ถœ๋Ÿ‰ ๊ณ„์‚ฐ ๋ชจ๋ธ๊ณผ ํ•ฉํ•˜์—ฌ ์ •๋Ÿ‰์  ์œ„ํ—˜์„ฑ ๋ถ„์„์„ ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ฐฐ๊ด€์— ์กด์žฌํ•˜๋Š” ๋‹ค์–‘ํ•œ ๋ฌผ์งˆ๋“ค์— ๋Œ€ํ•˜์—ฌ ์ผ€์ด์Šค ์Šคํ„ฐ๋””๋ฅผ ์ง„ํ–‰ํ•˜์—ฌ ์ •๋Ÿ‰ํ™”๋œ ์œ„ํ—˜๋„์— ๊ทผ๊ฑฐํ•˜์—ฌ ๋ฐฐ๊ด€๊ด€๋ฆฌ ์šฐ์„ ์ˆœ์œ„๋ฅผ ์ •ํ•˜๋Š” ์˜์‚ฌ๊ฒฐ์ •์— ๋ฐ˜์˜ํ•  ์ˆ˜ ์žˆ๋‹ค. ์„ธ๋ฒˆ์งธ ์žฅ์€ ๋…ธํ›„ํ™”๋œ ๋ฐฐ๊ด€ ์‹œ์Šคํ…œ์˜ ๊ด€๋ฆฌ ์ตœ์ ํ™”์— ๋Œ€ํ•œ ๋‚ด์šฉ์ด๋‹ค. ์‚ฌ๊ณ ์˜ ์œ„ํ—˜์„ฑ์„ ๋ฏธ์—ฐ์— ๋ฐฉ์ง€ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋‹ค์–‘ํ•œ ๊ฒ€์‚ฌ, ๋ณด์ˆ˜ ๋ฐฉ๋ฒ•๋ก ์ด ์‚ฌ์šฉ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ์ด์— ๋Œ€ํ•œ ํšจ๊ณผ๊ฐ€ ์œ„ํ—˜์„ฑ๊ณผ ์–ด๋–ป๊ฒŒ ๊ด€๋ จ๋˜์–ด์„œ ๋‚˜ํƒ€๋‚˜๋Š”์ง€ ์•Œ๊ธฐ ์–ด๋ ต๋‹ค. ๋Œ€๋ถ€๋ถ„ ๊ฒฝํ—˜์ ์œผ๋กœ ํ˜น์€ ์ œ๋„์ ์ธ ๋ฐฉ์•ˆ์„ ํ†ตํ•˜์—ฌ ๋ณด์ˆ˜์ ์ธ ์•ˆ์ „๊ด€๋ฆฌ๋ฅผ ์ง„ํ–‰ํ•˜๋Š” ํ•œ๊ณ„์„ฑ์ด ์žˆ๋‹ค. ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•๋ก ์„ ํ† ๋Œ€๋กœ ํ•˜์—ฌ ์•ˆ์ „๊ด€๋ฆฌ ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ์‹ค์ œ์ ์ธ ๋ถ€์‹ ๊ด€๋ฆฌ์— ์˜ํ–ฅ ์ •๋„๋ฅผ ์ •๋Ÿ‰ํ™” ํ•œ๋‹ค. ์‹ ๋ขฐ๋„ ๋ชฉํ‘œ์™€ ์ œ์•ˆ ๋˜์–ด์ง„ ์˜ˆ์‚ฐ ๋“ฑ์„ ์ œํ•œ์กฐ๊ฑด์œผ๋กœ ํ•˜๋Š” ์ตœ์ ํ™”๋ฅผ ์‹ค์‹œํ•˜์—ฌ ์ตœ์ ์˜ ๊ฒ€์‚ฌ ์ฃผ๊ธฐ, ์ตœ์ ์˜ ๊ฒ€์‚ฌ ๋ฐฉ๋ฒ•๋ก ์„ ํ™•์ธํ•œ๋‹ค. ์œ„ ์—ฐ๊ตฌ๋ฅผ ํ† ๋Œ€๋กœ ๊ฐœ์„ ๋œ ๋ฆฌํŠฌ์ด์˜จ ๋ฐฐํ„ฐ๋ฆฌ์˜ ์˜จ๋ผ์ธ ์ƒํƒœ์ถ”์ • ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์ œ์‹œํ•˜๊ณ  ์œ„ํ—˜๋„ ํ™˜์‚ฐ ๋น„์šฉ์„ ๊ฒฐํ•ฉํ•œ ๊ตฌ์กฐ ์‹ ๋ขฐ๋„ ๋ชจ๋ธ๋กœ ์ง€ํ•˜๋ฐฐ๊ด€ ๊ด€๋ฆฌ ์ตœ์ ํ™” ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•œ๋‹ค.Abstract i Contents vi List of Figures ix List of Tables xii CHAPTER 1. Introduction 14 1.1. Research motivation 14 1.2. Research objectives 19 1.3. Outline of the thesis 20 CHAPTER 2. Lithium ion battery modeling and state of health Estimation 21 2.1. Background 21 2.2. Literature Review 22 2.2.1. Battery model 23 2.2.2. Qualitative comparative review of state of health estimation algorithm 29 2.3. Previous estimation algorithm 32 2.3.1. Nonlinear State estimation method 32 2.3.2. Sliding mode observer 35 2.3.3. Proposed Algorithm 37 2.3.4. Uncertainty Factors for SOH estimation in ESS 42 2.4. Data acquisition 44 2.4.1. Lithium ion battery specification 45 2.4.2. ESS Experimental setup 47 2.4.3. Sensitivity Analysis for Model Parameter 54 2.5. Result and Discussion 59 2.5.1. Estimation results of battery model 59 2.5.2. Estimation results of proposed method 63 2.6. Conclusion 68 CHAPTER 3. Reliability estimation modeling for quantitative risk assessment about underground pipeline 69 3.1. Introduction 69 3.2. Uncertainties in underground pipeline system 72 3.3. Probabilistic based Quantitative Risk Assessment Model 73 3.3.1. Structural Reliability Assessment 73 3.3.2. Failure mode 75 3.3.3. Limit state function and variables 79 3.3.4. Reliability Target 86 3.3.5. Failure frequency modeling 90 3.3.6. Consequence modeling 95 3.3.7. Simulation method 101 3.4. Case study 103 3.4.1. Statistical review of Industrial complex underground pipeline 103 3.5. Result and discussion 107 3.5.1. Estimation result of failure probability 107 3.5.1. Estimation result validation 118 CHAPTER 4. Maintenance optimization methodology for cost effective underground pipeline management 120 4.1. Introduction 120 4.2. Problem Definition 124 4.3. Maintenance scenario analysis modeling 126 4.3.1. Methodology description 128 4.3.2. Cost modeling 129 4.3.3. Maintenance mitigation model 132 4.4. Case study 136 4.5. Results 138 4.5.1. Result of optimal re-inspection period 138 4.5.2. Result of optimal maintenance actions 144 CHAPTER 5. Concluding Remarks 145 References 147Docto

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    Relationship between Aid and International Migration: Case of Europe

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๊ตญ์ œ๋Œ€ํ•™์› : ๊ตญ์ œํ•™๊ณผ(๊ตญ์ œ์ง€์—ญํ•™์ „๊ณต), 2014. 2. ๊น€์ข…์„ญ.๋ณธ ๋…ผ๋ฌธ์€ ์ด๋ฏผ์ž๋ฅผ ๋งŽ์ด ๋ณด๋‚ด๋Š” ์ถœ์ƒ๊ตญ ์ผ์ˆ˜๋ก ์™ธ๊ตญ์œผ๋กœ๋ถ€ํ„ฐ ์›์กฐ๋ฅผ ๋งŽ์ด ๋ฐ›๋Š”๋‹ค๋Š” ๊ฐ€์„ค ํ•˜์— ์›์กฐ์™€ ๊ตญ์ œ์ด์ฃผ ๊ฐ„ ์ƒ๊ด€๊ด€๊ณ„ ์กด์žฌ์— ๋Œ€ํ•˜์—ฌ ์—ฐ๊ตฌํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋ถ„์„๋Œ€์ƒ์€ ์›์กฐ์ •์ฑ…์˜ ์„ ์ง„๊ตญ์ด๋ฉฐ ํŠนํžˆ ๋งŽ์€ ์ด๋ฏผ์ž์ˆ˜๋ฅผ ๋ณด์œ ํ•˜๊ณ  ์žˆ๋Š” ์œ ๋Ÿฝ๊ณต์—ฌ๊ตญ์„ ์ค‘์‹ฌ์œผ๋กœ ์ด๋ฃจ์–ด์ง€๋ฉฐ 152๊ฐœ๊ตญ์˜ ์–‘์ž์›์กฐ ์ˆ˜์›๊ตญ ์ถœ์‹ ์˜ ์ด๋ฏผ์ž์ˆ˜๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์กฐ์‚ฌํ•œ๋‹ค. ๋™์‹œ์— ์›์กฐ๋ฅผ ํ†ตํ•œ ๊ตญ์ œ์ด์ฃผ ์ฆ๊ฐ€, ๊ตญ์ œ์ด์ฃผ๋ฅผ ํ†ตํ•œ ์›์กฐ ์ฆ๊ฐ€ ๋˜๋Š” ์–‘๋ฐฉํ–ฅ์„ฑ์˜ ๊ด€๊ณ„๊ฐ€ ํ˜•์„ฑ๋˜๋Š”์ง€ ์‚ดํŽด๋ณธ๋‹ค. ํŒจ๋„ ๋ฐ์ดํ„ฐ๋Š” 1995๋…„๋ถ€ํ„ฐ 2010๋…„๊นŒ์ง€์˜ ์ž๋ฃŒ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•˜๋ฉฐ ์ด๋Š” ์ค‘๋ ฅ ๋ชจํ˜•๊ณผ ๊ทธ๋ ˆ์ธ์ € ์ธ๊ณผ๊ด€๊ณ„ ๋ถ„์„๋ฒ•์„ ์‚ฌ์šฉํ•œ๋‹ค. ๋ถ„์„ ๊ฒฐ๊ณผ ์ฒซ์งธ ์œ ๋Ÿฝ๊ณต์—ฌ๊ตญ์€ ์ž๊ตญ์— ๊ฐ€์žฅ ๋งŽ์€ ์ด๋ฏผ์ž์˜ ์ถœ์ƒ๊ตญ์„ ๋Œ€์ƒ์œผ๋กœ ์›์กฐ๋ฅผ ์ง€์›ํ•˜๋ฉฐ ์ด๋Š” ์œ ๋Ÿฝ๊ณต์—ฌ๊ตญ์ด ์›์กฐ๋ฅผ ์ด๋ฏผ ๊ด€๋ฆฌ์˜ ํ•œ ๋„๊ตฌ๋กœ ์‚ฌ์šฉํ•˜๊ณ  ์žˆ์Œ์„ ๋’ท๋ฐ›์นจํ•˜๊ณ  ์žˆ๋‹ค. ๋‘˜์งธ ์›์กฐ์™€ ๊ตญ์ œ์ด์ฃผ ๊ฐ„์˜ ์ƒ๋‹นํ•œ ์ธ๊ณผ๊ด€๊ณ„๊ฐ€ ์กด์žฌํ•˜๋ฉฐ ์›์กฐ๊ฐ€ ์˜คํžˆ๋ ค ์ˆ˜์›๊ตญ์˜ ๊ตญ์ œ์ด์ฃผ๋ฅผ ์ด‰์ง„ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค.This paper investigates the relationship between aid and international migrationthe factors that are widely discussed particularly amongst developed countries in Europe. The main purpose of aid is to enable developing countries to achieve development nevertheless many European countries employ aid as a foreign policy to regulate immigrants. I suggest that donor countries offer more aid to those recipient countries where more immigrants come from and to examine such link, the Gravity model and Granger causality test are applied to panel data. Data is formed with 13 European donor countries that are members of OECD DAC and 152 recipient countries in the time frame from 1995 to 2010. Results from the gravity model show a positive correlation between aid and migration which implies that more aid is given to countries where most immigrants originate from. Moreover results from the causality test prove that aid causes migrants, which indicates that aid actually generates migration from recipient countries, rather than discouraging it. The causality is also tested on 7 different regions to observe whether similar pattern exists and in general, aid to migration relationship is presented. However a bidirectional relationship is established in 5 out of 7 regionsin other words, not only aid causes migration but also migration causes aid. It can thus be suggested that the number of immigrants from a certain recipient country has an impact on aid amount that is given to the country, which further supports the outcome from the gravity model.Abstract Table of Contents List of Figures and Tables Chapter I. Introduction Chapter II. Migration and Aid: Stylized Facts 2.1 Migration 2.2 Aid and Migration Chapter III. Data and Variables 3.1 Dependent and Key Explanatory Variable 3.2 Control Variables Chapter IV. Methodology Chapter V. Empirical Findings and Analysis Chapter VI. Conclusion 6.1 Conclusion 6.2 Limitations Bibliography Appendix Abstract (Korean)Maste
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