1,242 research outputs found

    Planning and Operation of Hybrid Renewable Energy Systems

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    Wind power integration on power system reliability and operating reserve capacity

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    This thesis proposes a WP-HPS cooperation method which utilizes Hydro Pumped Storage (HPS) to cooperate with wind power in order to reduce the impact of wind power characteristics on system reliability, smooth output fluctuation of conventional generation units and maintain system reliability at a required level. Based on the output characteristics of each power generation unit, two control strategies for wind power and pumped storage power stations are proposed.;In addition, the traditional reliability evaluation methods are not applicable to the proposed power system containing HPS and wind power. Therefore, the probability-based reliability assessment method and the reliability cost-benefit analysis method are improved based on the operating characteristics of the proposed power system. These two assessment methods are used to analyze and verify the validity of the two control strategies in improving the reliability of a wind-powered system.;The proposed strategy 2 is applied to three different test systems, and comprehensive analysis in terms of long-term planning and daily operation of the power system at different wind penetration levels is conducted. Simulation experimental results in Chapter 5 show that strategy 2 is more effective than strategy 1 in improving power system reliability. The results in Chapter 6 proved that the WP-HPS cooperation (strategy 2) method can improve the reliability of the power system with a wind penetration level not exceeding 40% to the original level.;The proposed method was finally applied to the actual power system to analyze the proposed method for improving the reliability of the existing system. The evaluation results show that the proposed method not only can meet the requirements of system reliability but also can reduce the wind curtailment rate to the level required by the system.This thesis proposes a WP-HPS cooperation method which utilizes Hydro Pumped Storage (HPS) to cooperate with wind power in order to reduce the impact of wind power characteristics on system reliability, smooth output fluctuation of conventional generation units and maintain system reliability at a required level. Based on the output characteristics of each power generation unit, two control strategies for wind power and pumped storage power stations are proposed.;In addition, the traditional reliability evaluation methods are not applicable to the proposed power system containing HPS and wind power. Therefore, the probability-based reliability assessment method and the reliability cost-benefit analysis method are improved based on the operating characteristics of the proposed power system. These two assessment methods are used to analyze and verify the validity of the two control strategies in improving the reliability of a wind-powered system.;The proposed strategy 2 is applied to three different test systems, and comprehensive analysis in terms of long-term planning and daily operation of the power system at different wind penetration levels is conducted. Simulation experimental results in Chapter 5 show that strategy 2 is more effective than strategy 1 in improving power system reliability. The results in Chapter 6 proved that the WP-HPS cooperation (strategy 2) method can improve the reliability of the power system with a wind penetration level not exceeding 40% to the original level.;The proposed method was finally applied to the actual power system to analyze the proposed method for improving the reliability of the existing system. The evaluation results show that the proposed method not only can meet the requirements of system reliability but also can reduce the wind curtailment rate to the level required by the system

    Optimization Techniques for Modern Power Systems Planning, Operation and Control

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    Recent developments in computing, communication and improvements in optimization techniques have piqued interest in improving the current operational practices and in addressing the challenges of future power grids. This dissertation leverages these new developments for improved quasi-static analysis of power systems for applications in power system planning, operation and control. The premise of much of the work presented in this dissertation centers around development of better mathematical modeling for optimization problems which are then used to solve current and future challenges of power grid. To this end, the models developed in this research work contributes to the area of renewable integration, demand response, power grid resilience and constrained contiguous and non-contiguous partitioning of power networks. The emphasis of this dissertation is on finding solutions to system operator level problems in real-time. For instance, multi-period mixed integer linear programming problem for applications in demand response schemes involving more than million variables are solved to optimality in less than 20 seconds of computation time through tighter formulation. A balanced, constrained, contiguous partitioning scheme capable of partitioning 20,000 bus power system in under one minute is developed for use in time sensitive application area such as controlled islanding

    Electric Power Conversion and Micro-Grids

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    This edited volume is a collection of reviewed and relevant research chapters offering a comprehensive overview of recent achievements in the field of micro-grids and electric power conversion. The book comprises single chapters authored by various researchers and is edited by a group of experts in such research areas. All chapters are complete in themselves but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors on electric power conversion, micro-grids, and their up-to-the-minute technological advances and opens new possible research paths for further novel developments

    Real-time coordinated control of hybrid energy storage systems for floating offshore wind turbine power quality mitigation

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    The characteristic wind power generated by floating turbines includes significant low-frequency harmonics and lower efficiency compared to ideal wind turbine power curves. This implies that floating wind farms will face dual scrutiny regarding the adequacy of power quality and the matching of generated power output with dispatch commands. While optimization through energy storage systems remains one of the most common and effective solutions in wind farms, the achievement of optimal power output in the novel context of wind power from floating turbines necessitates the development of a new control system. With parameter-optimized design of Leveraging Kalman filters, a comprehensive HESS coordinated control scheme is proposed, integrating management of dispatch disparities and SOC health. Experimental data indicate that under the proposed Real-Time Coordinated Control (RTCC), the output power of floating offshore wind turbines (FOWTs) is significantly improved in quality and effectively matches dispatch commands. Additionally, suggestions are concluded for setting Kalman filter parameters for future multi-objective optimization challenges

    ์ „๋ ฅ์‹œ์žฅ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ธฐ๋ฒ•์„ ํ™œ์šฉํ•œ ์žฌ์ƒ์—๋„ˆ์ง€ ํ™•๋Œ€๊ฐ€ ์ „๋ ฅ์‹œ์Šคํ…œ ์œ ์—ฐ์„ฑ ๋ฐ ๊ฒฝ์ œ์„ฑ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ ๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๊ธฐ์ˆ ๊ฒฝ์˜ยท๊ฒฝ์ œยท์ •์ฑ…์ „๊ณต, 2021. 2. ์ด์ข…์ˆ˜.์ „ ์„ธ๊ณ„์ ์œผ๋กœ ์˜จ์‹ค๊ฐ€์Šค ๊ฐ์ถ• ๋ชฉํ‘œ๋ฅผ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•ด์„œ ์žฌ์ƒ์—๋„ˆ์ง€ ๋น„์ค‘์„ ํ™•๋Œ€ํ•˜๋Š” ์—๋„ˆ์ง€ ์ „ํ™˜ ์ •์ฑ…์ด ์‹œํ–‰๋˜๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ, ์ถœ๋ ฅ ๋ณ€๋™์„ฑ๊ณผ ๋ถˆํ™•์‹ค์„ฑ ํŠน์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ์žฌ์ƒ์—๋„ˆ์ง€ ํ™•๋Œ€๋Š” ์ „๋ ฅ์‹œ์Šคํ…œ์˜ ์œ ์—ฐ์„ฑ์— ๋ฌธ์ œ๋ฅผ ์ผ์œผํ‚ฌ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๋‚ฎ์€ ์šด์˜ ๋น„์šฉ๊ณผ ๊ตญ๊ฐ€ ์ •์ฑ…์ƒ์˜ ๋ชฉ์  ๋“ฑ์— ์˜ํ•ด ์ „๋ ฅ ์‹œ์žฅ์—์„œ ์šฐ์„  ๊ตฌ๋งค๋˜๋ฉด์„œ ์ „ํ†ต ๋ฐœ์ „์›์˜ ๊ธ‰์ „ ์šฐ์„ ์ˆœ์œ„ ๊ฒฐ์ •์—๋„ ๋งŽ์€ ์˜ํ–ฅ์„ ์ฃผ๊ฒŒ ๋œ๋‹ค. ์ด์™€ ๊ฐ™์€ ๋งฅ๋ฝ์—์„œ, ๋ณธ ์—ฐ๊ตฌ๋Š” ํ•œ๊ตญ์˜ ์žฌ์ƒ์—๋„ˆ์ง€ ํ™•๋Œ€ ์ •์ฑ…์— ์˜ํ•ด ์žฌ์ƒ์—๋„ˆ์ง€ ๋ฐœ์ „ ๋น„์ค‘์ด 20%๋ฅผ ์ดˆ๊ณผ ํ•˜๋Š” 2031๋…„์„ ๋Œ€์ƒ์œผ๋กœ ์ „๋ ฅ ์‹œ์Šคํ…œ์˜ ์œ ์—ฐ์„ฑ ํ‰๊ฐ€ ๋ฐ ์žฌ์ƒ์—๋„ˆ์ง€ ํ™•๋Œ€๊ฐ€ ์ „๋ ฅ ์‹œ์žฅ์— ๋ฏธ์น˜๋Š” ๊ฒฝ์ œ์  ์˜ํ–ฅ ๋ถ„์„์„ ๋ชฉ์ ์œผ๋กœ ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด์„œ ์šฐ์„ , ํ˜ผํ•ฉ์ •์ˆ˜๊ณ„ํš๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ ํ•˜๋ฃจ ์ „ ๋ฐœ์ „๊ณ„ํš ์ˆ˜๋ฆฝ ๋ชจํ˜•์„ ๊ตฌ์ถ•ํ•˜๊ณ , ์žฌ์ƒ์—๋„ˆ์ง€ ๋ฐœ์ „ ๋น„์ค‘์ด 6.2%๋กœ ์ƒ๋Œ€์ ์œผ๋กœ ๋‚ฎ์€ 2018๋…„์„ ๊ธฐ์ค€์œผ๋กœ 2031๋…„์˜ ์ „๋ ฅ ์‹œ์žฅ ์šด์˜ ์‹ค์ ๊ณผ ๋น„๊ตํ•˜๊ธฐ ์œ„ํ•ด์„œ, ๊ตฌ์ถ•ํ•œ ๋ฐœ์ „๊ณ„ํš ์ˆ˜๋ฆฝ ๋ชจํ˜•์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์ „๋ ฅ ์‹œ์žฅ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. 2031๋…„ ์ „๋ ฅ ์‹œ์Šคํ…œ์˜ ์œ ์—ฐ์„ฑ ํ‰๊ฐ€๋ฅผ ์œ„ํ•ด์„œ 5๊ฐ€์ง€ ์œ ์—ฐ์„ฑ ๊ณต๊ธ‰ ์šฉ๋Ÿ‰ ์‚ฐ์ • ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ์„ค์ •ํ•˜๊ณ  ๊ฐ ์‹œ๋‚˜๋ฆฌ์˜ค์— ๋”ฐ๋ฅธ ์œ ์—ฐ์„ฑ ๊ณต๊ธ‰๋Ÿ‰๊ณผ ์ˆœ์ˆ˜์š” ๋ณ€๋™ ํญ์ธ ์œ ์—ฐ์„ฑ ์š”๊ตฌ๋Ÿ‰์˜ ์‹œ๊ฐ„ ๋‹จ์œ„ ๋น„๊ต๋ฅผ ํ†ตํ•ด์„œ ์ด 8,760์‹œ๊ฐ„์— ๋Œ€ํ•œ ์ฆโˆ™๊ฐ๋ฐœ ์œ ์—ฐ์„ฑ ๋ถ€์กฑ ํšŸ์ˆ˜๋ฅผ ์‚ฐ์ถœํ•˜์˜€๋‹ค. ์œ ์—ฐ์„ฑ ๊ณต๊ธ‰ ์ž์›์œผ๋กœ ์šด์˜ ์˜ˆ๋น„๋ ฅ๋งŒ์„ ๊ณ ๋ คํ•  ๊ฒฝ์šฐ, ์ฆ๋ฐœ ์œ ์—ฐ์„ฑ ์ธก๋ฉด์—์„œ ์žฌ์ƒ์—๋„ˆ์ง€ ๋ณ€๋™์„ฑ์„ ์•ฝ 94%๊นŒ์ง€ ๋Œ€์‘ํ•  ์ˆ˜ ์žˆ์ง€๋งŒ, ์šด์˜ ์˜ˆ๋น„๋ ฅ ํ™•๋ณด๋Ÿ‰๋ณด๋‹ค ํฐ ๋ณ€๋™ ํญ์ธ ์•ฝ 6% ๋ณ€๋™์„ฑ์— ๋Œ€ํ•ด์„œ๋Š” ์†์‘์„ฑ ์ž์›์˜ ์—ญํ• ์ด ํ•„์š”ํ•œ ๊ฒƒ์œผ๋กœ ๋ถ„์„๋˜์—ˆ๋‹ค. ๋ฐ˜๋ฉด์—, ๊ฐ๋ฐœ ์œ ์—ฐ์„ฑ ์ธก๋ฉด์˜ ์œ ์—ฐ์„ฑ ๋ถ€์กฑ ํšŸ์ˆ˜๋Š” ์•ฝ 18ํšŒ ์ˆ˜์ค€์œผ๋กœ ๋งค์šฐ ๋‚ฎ์€ ๋ฐœ์ƒํ™•๋ฅ ์„ ๋ณด์˜€๋‹ค. ์žฌ์ƒ์—๋„ˆ์ง€ ๋ณ€๋™์„ฑ ๋ถ„ํฌ์— ๋Œ€ํ•œ ๋ถ„์„ ๊ฒฐ๊ณผ๋ฅผ ๋ณด๋ฉด, ๊ณ ์ •์ ์œผ๋กœ ์šด์˜ํ•˜๋˜ ์ „ํ†ต์ ์ธ ์šด์˜ ์˜ˆ๋น„๋ ฅ ๊ธฐ์ค€๊ณผ ๋‹ค๋ฅด๊ฒŒ ์žฌ์ƒ์—๋„ˆ์ง€ ๋ณ€๋™์„ฑ ๋Œ€์‘์„ ์œ„ํ•œ ์œ ์—ฐ์„ฑ ์ž์›์€ ํ™•๋ณด ๊ธฐ์ค€์„ ํƒ„๋ ฅ์ ์œผ๋กœ ์šด์˜ํ•  ํ•„์š”๊ฐ€ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚œ๋‹ค. ๋˜ํ•œ, ์œ ์—ฐ์„ฑ ์ธก๋ฉด์—์„œ ํšจ์œจ์  ๋Œ€์‘์„ ์œ„ํ•œ ๋ฌผ๋ฆฌ์  ํŠน์„ฑ์ธ ๋†’์€ ์ฆโ€ข๊ฐ๋ฐœ๋ฅ ๊ณผ ์งง์€ ๊ธฐ๋™ ์ค€๋น„์‹œ๊ฐ„์„ ๋ณด์œ ํ•œ ๋ฐœ์ „์›๋“ค์ด ์šด์˜ ์˜ˆ๋น„๋ ฅ์— ํฌํ•จ๋˜๊ฒŒ ํ•˜๋ ค๋ฉด ํ˜„ํ–‰ ๋ฐœ์ „์ถœ๋ ฅ ์ƒํ•œ์ œ์•ฝ ๋ฐฉ๋ฒ• ๊ฐœ์„  ๋ฐ ์˜ˆ๋น„๋ ฅ ๋ณด์กฐ ์„œ๋น„์Šค ์‹œ์žฅ์˜ ๋ถ„๋ฆฌ ์šด์˜์„ ๊ฒ€ํ† ํ•  ํ•„์š”๊ฐ€ ์žˆ๊ฒ ๋‹ค. ์ด๋•Œ ๊ณ ๋ คํ•  ์ˆ˜ ์žˆ๋Š” ์˜ˆ๋น„๋ ฅ ๋ณด์กฐ ์„œ๋น„์Šค ์‹œ์žฅ ์ตœ์†Œ ๊ทœ๋ชจ๋Š” ์•ฝ 1,620์–ต ์›์œผ๋กœ ์ถ”์ •๋˜์—ˆ๋‹ค. ์žฌ์ƒ์—๋„ˆ์ง€ ํ™•๋Œ€๋กœ ์ธํ•ด 2031๋…„์˜ ๊ณ„ํ†ตํ•œ๊ณ„๊ฐ€๊ฒฉ์ด ํ‰๊ท ์ ์œผ๋กœ 13.7์›/kWh ๋‚ฎ์•„์งˆ ๊ฒƒ์œผ๋กœ ๋ถ„์„๋˜์—ˆ์œผ๋ฉฐ, ๋”์šฑ์ด ์žฌ์ƒ์—๋„ˆ์ง€ ๋ฐœ์ „๋Ÿ‰ ๋น„์ค‘์ด ๋†’์•„์งˆ์ˆ˜๋ก ์ „ํ†ต ๋ฐœ์ „์›์œผ๋กœ ์ถฉ์กฑ์‹œ์ผœ์•ผ ํ•˜๋Š” ์ˆœ์ˆ˜์š” ํฌ๊ธฐ๊ฐ€ ๊ฐ์†Œํ•˜๋ฉด์„œ ๊ณ„ํ†ตํ•œ๊ณ„๊ฐ€๊ฒฉ ํ•˜๋ฝ์€ ๋”์šฑ ์‹ฌํ™”ํ•  ์ˆ˜๋„ ์žˆ๋‹ค. ์ด์™€ ๊ฐ™์€ ์‹œ์žฅ ๊ฐ€๊ฒฉ ํ•˜๋ฝ์€ ํŒ๋งค์‚ฌ์—…์ž์˜ ์ „๋ ฅ ๋„๋งค ์š”๊ธˆ์˜ ๋™๋ฐ˜ ํ•˜๋ฝ์„ ์œ ๋„ํ•  ๊ฒƒ์ฒ˜๋Ÿผ ๋ณด์ด์ง€๋งŒ, ๊ธฐํ›„๋ณ€ํ™” ๋Œ€์‘์„ ์œ„ํ•œ RPS ์ œ๋„์™€ ๋ฐฐ์ถœ๊ถŒ๊ฑฐ๋ž˜์ œ๋ฅผ ๊ณ ๋ คํ•œ ์ „๋ ฅ ๊ตฌ์ž…๋น„ ๋ณ€ํ™”์— ๋Œ€ํ•œ ๋ถ„์„ ๊ฒฐ๊ณผ๋ฅผ ๋ณด๋ฉด, ์ „๋ ฅ๋Ÿ‰ ์ •์‚ฐ๊ธˆ์„ ์ œ์™ธํ•œ ์šฉ๋Ÿ‰ ์ •์‚ฐ๊ธˆ, ๋ฐฐ์ถœ๊ถŒ๊ฑฐ๋ž˜๋น„์šฉ ๋ฐ RPS ์˜๋ฌด์ดํ–‰ ๋น„์šฉ์ด ์ƒ์Šนํ•  ๊ฒƒ์œผ๋กœ ์˜ˆ์ธก๋˜์—ˆ๋‹ค. RPS ์˜๋ฌด์ดํ–‰๋น„์œจ, ๋ฐฐ์ถœ๊ถŒ ์œ ์ƒํ• ๋‹น๋น„์œจ ๋ฐ ๋ฐฐ์ถœ๊ถŒ ๊ฐ€๊ฒฉ ์‹œ๋‚˜๋ฆฌ์˜ค์— ๋”ฐ๋ฅธ ์ „๋ ฅ ์‹œ์žฅ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ์— ์˜ํ•˜๋ฉด, ํ‰๊ท  ์ „๋ ฅ ๊ตฌ๋งค ๋‹จ๊ฐ€๋Š” 2018๋…„ 93.87์›/kWh์—์„œ 2031๋…„ 106.03์›/kWh๊นŒ์ง€ ์ตœ๋Œ€ ์•ฝ 13% ์ƒ์Šนํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ด๋Š” ํ–ฅํ›„ ์ „๋ ฅ ์†Œ๋งค ์š”๊ธˆ์˜ ์ธ์ƒ ์••๋ ฅ ์š”์ธ์œผ๋กœ ์ž‘์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ์ข…ํ•ฉํ•ด๋ณด๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์ •์ฑ…์  ํ•จ์˜๋ฅผ ๋Œ์–ด๋‚ผ ์ˆ˜ ์žˆ๋‹ค. ์ฒซ์งธ, 2031๋…„ ์ „๋ ฅ ์‹œ์Šคํ…œ์˜ ์œ ์—ฐ์„ฑ์„ ์ ์ • ์ˆ˜์ค€์œผ๋กœ ํ™•๋ณดํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์šด์˜ ์˜ˆ๋น„๋ ฅ ํ™•๋ณด ๋ฐฉ๋ฒ•์„ ๋ฐœ์ „์ถœ๋ ฅ ์ƒํ•œ ์ œ์•ฝ ๋ฐฉ์‹ ๋Œ€์‹  ์œ ์—ฐ์„ฑ ์š”๊ตฌ์‚ฌํ•ญ์„ ์ถฉ์กฑํ•˜๋Š” ์ž์›๋“ค์„ ๋Œ€์ƒ์œผ๋กœ ํ•œ ๊ฒฝ์Ÿ ์ž…์ฐฐ์„ ํ†ตํ•ด ํ™•๋ณดํ•˜๋Š” ๋ฐฉ์•ˆ ๋“ฑ ์ƒˆ๋กœ์šด ์šด์˜์˜ˆ๋น„๋ ฅ ํ™•๋ณด ๋Œ€์•ˆ์ด ๊ณ ๋ ค๋˜์–ด์•ผ ํ•œ๋‹ค. ๋˜ํ•œ, ์žฌ์ƒ์—๋„ˆ์ง€ ๋ณ€๋™์„ฑ ๋Œ€์‘ ๋ชฉ์ ์œผ๋กœ ์šด์˜ ์˜ˆ๋น„๋ ฅ๊ณผ ๋ณ„๋„๋กœ ์šด์˜ํ•˜๋Š” ์†์‘์„ฑ ์ž์›์„ ์ฐจ์งˆ์—†์ด ๊ณ„ํš๋Œ€๋กœ ๋ณด๊ธ‰ํ•˜๊ณ , ๊ฐ ์œ ์—ฐ์„ฑ ๊ณต๊ธ‰ ์ž์›๋ณ„ ๋ณ€๋™์„ฑ ๋Œ€์‘ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ๊ณ ๋ คํ•˜์—ฌ ์žฌ์ƒ์—๋„ˆ์ง€ ๋ฐœ์ „๋Ÿ‰ ์˜ˆ์ธก์‹œ์Šคํ…œ์„ ์ •๊ตํ™”ํ•˜์—ฌ ์†์‘์„ฑ ์ž์›์— ๋Œ€ํ•œ ํƒ„๋ ฅ์ ์ธ ์œ ์—ฐ์„ฑ ๊ณต๊ธ‰๋Ÿ‰ ํ™•๋ณด ๊ธฐ์ค€์„ ์ ์šฉํ•ด ๋‚˜๊ฐ€์•ผ ํ•  ๊ฒƒ์ด๋‹ค. ๋‘˜์งธ, ์žฌ์ƒ์—๋„ˆ์ง€ ํ™•๋Œ€์™€ ๊ด€๋ จํ•œ ์ •์ฑ…์„ ๊ฐœ์ •ํ•˜๊ฑฐ๋‚˜ ์‹ ์„คํ•˜๊ณ ์ž ํ•  ๋•Œ๋Š” ์ง์ ‘์ ์ธ ์ •์ฑ…์˜ ๊ธฐ๋Œ€ํšจ๊ณผ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ํŒ๋งค์‚ฌ์—…์ž์˜ ์ „๋ ฅ ๊ตฌ์ž…๋น„ ์ฆ๊ฐ€๋กœ ์ธํ•œ ์ „๊ธฐ ์š”๊ธˆ ์ธ์ƒ ์••๋ ฅ๊ณผ ๊ฐ™์€ ๊ฐ„์ ‘์ ์ธ ํŒŒ๊ธ‰ํšจ๊ณผ๊นŒ์ง€ ํ•จ๊ป˜ ๊ณ ๋ คํ•ด ์ฃผ์–ด์•ผ๊ฒ ๋‹ค. RPS ์˜๋ฌดํ• ๋‹น๋น„์œจ, ๋ฐฐ์ถœ๊ถŒ๊ฑฐ๋ž˜์ œ ์œ ์ƒํ• ๋‹น๋น„์œจ, ๋ฐฐ์ถœ๊ถŒ ๊ฑฐ๋ž˜ ๋น„์šฉ ๋“ฑ์˜ ๋ณ€ํ™”๋กœ ์ตœ๋Œ€ 13%๊นŒ์ง€ ์ „๋ ฅ ๋„๋งค๊ฐ€๊ฒฉ์ด ์ƒ์Šนํ•  ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์…‹์งธ, ์žฌ์ƒ์—๋„ˆ์ง€ ๋ฐœ์ „๋Ÿ‰๊ณผ ์‹œ์žฅ ๊ฐ€๊ฒฉ์ด ์ ์  ์ƒ๋ฐ˜๋œ ํŒจํ„ด์œผ๋กœ ๋ณ€ํ™”ํ•  ๊ฒƒ์œผ๋กœ ์˜ˆ์ธก๋˜๊ธฐ ๋•Œ๋ฌธ์—, ๋ณ€๋™๋น„ ๋ฐ˜์˜ ์‹œ์žฅ์˜ ์ •์‚ฐ ๊ทœ์น™์ด๋‚˜ ์‹œ์žฅ ๊ฐ€๊ฒฉ ์‚ฐ์ • ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ๊ฐœ์„  ๊ฒ€ํ†  ์‹œ ์ด๋Ÿฐ ํŒจํ„ด ๋ณ€ํ™”๋ฅผ ๋ฐ˜๋“œ์‹œ ๊ณ ๋ คํ•ด์•ผ ํ•œ๋‹ค. ๋˜ํ•œ, ์œ ์—ฐ์„ฑ ๊ณต๊ธ‰์— ์ฐธ์—ฌํ•œ ๋ฐœ์ „์‚ฌ์—…์ž๋“ค์˜ ๋ณด์ƒ์ด ์ ์ •ํ•œ ์ˆ˜์ค€์œผ๋กœ ์„ค์ •๋˜์–ด์•ผ ์—๋„ˆ์ง€ ์‹œ์žฅ ๋Œ€๋น„ ๋ณด์กฐ ์„œ๋น„์Šค ์‹œ์žฅ ์ฐธ์—ฌ๊ฐ€ ํ™œ์„ฑํ™” ๋  ๊ฒƒ์ด๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๋ฏธ๋ž˜ ์ „๋ ฅ ์‹œ์žฅ์—์„œ๋Š” ์ˆ˜์š” ํ”ผํฌ์™€ ์‹œ์žฅ ๊ฐ€๊ฒฉ ํ”ผํฌ์˜ ๋ถˆ์ผ์น˜๊ฐ€ ์ ์  ์ฆ๋Œ€๋  ์ˆ˜ ์žˆ์œผ๋ฏ€๋กœ, ์ˆ˜์š” ๊ด€๋ฆฌ, ๊ฒฝ์ œ์„ฑ DR, ์ „๊ธฐ ์š”๊ธˆ ์‚ฐ์ • ๋“ฑ ์ˆ˜์š” ํŒจํ„ด์„ ๊ณ ๋ คํ•˜๋Š” ๋‹ค์–‘ํ•œ ์ •์ฑ…๋“ค์ด ํ–ฅํ›„์—๋Š” ์ˆœ์ˆ˜์š” ํŒจํ„ด๋„ ํ•จ๊ป˜ ๊ณ ๋ คํ•˜๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ์žฌ๊ฒ€ํ† ๋˜์–ด์•ผ ํ•  ๊ฒƒ์ด๋‹ค.To achieve the reduction target of greenhouse gas emissions, energy transition policy is being implemented to expand the share of renewable energy worldwide. However, the expansion of renewable energy not only causes the flexibility problem of the power system due to volatility and uncertainty of renewable energy output, but also affects the merit order of traditional power generation sources due to low operating costs of renewables or national policy objectives. These effects give rise to a huge transformation in power systems with a high share of renewable energy. In this context, this study evaluates the flexibility of the power system and analyzes the economic impact on the power market in 2031, when the share of renewable energy exceeds 20% due to Koreas energy transition policy. First, a mixed-integer linear programming approach was used to formulate the power system day-ahead unit commitment and economic dispatch model, and a power market simulation was conducted to compare the performance of the electricity market in 2031 based on 2018 figures, when the share of renewable energy is relatively low at 6.2%. To assess the flexibility of the power system in 2031, the number of periods of flexibility deficit for 8,760 hours was calculated by comparing the supply of flexibility according to the scenario of available flexibility resources with the flexibility requirement, which is the fluctuation in net load over an hour. The results show that if only the operational reserve is considered as a flexibility supply resource, about 94% of the renewable energy volatility can be dealt with in terms of upward flexibility, but the role of the quick-start generation resources is found to be important for 6% of the ramping event greater than the reserve capacity. On the other hand, the number of times flexibility deficit occurs in terms of downward flexibility is expected to be about 18, showing a very low probability of occurrence. The analysis of the distribution of renewable energy volatility reveals that, unlike the standard for operational reserve, which was traditionally fixed, the resource for responding to flexibility problem in renewable energy needs to operate the flexible securing standard. In addition, it is necessary to review the improvement of the current upper limit method of power output level and the separate operation of the reserve auxiliary service market from the energy service market to ensure that power generation sources suitable for supplying flexibility with physical characteristics for response to flexibility are included in the operational reserve. At this time, the minimum market size of the reserve auxiliary service that could be considered was estimated to be about KRW 162 billion. The expansion of renewable energy will lower the system marginal price by 13.7 KRW/kWh on average in 2031. As the share of renewable energy generation increases, the capacity of net load to be met by traditional power generation decreases, and the drop in the system marginal price may be even worse. Such a decrease in electricity market prices seems to lead to the accompanied decline in the power vendors wholesale electricity price. However, when looking at the result of power purchase cost analysis considering the renewable portfolio standard (RPS) and the emissions trading scheme (ETS) to expand renewable energy, it was predicted that the capacity settlement amount, the emission trading cost, and the RPS obligation fulfillment cost, excluding the electricity settlement amount, would increase. According to the analysis of power market simulation by RPS obligatory rate, paid allocation ratio for emissions trading, and emissions price per unit scenarios, the average power purchase cost may increase up to about 13% from 93.87 KRW/kWh in 2018 to 106.03 KRW/kWh in 2031. This suggests that it could act as a pressure factor to raise electricity rates in the future. The results of this study have the following policy implications. First, to secure the flexibility of the power system to an appropriate level in 2031, it is necessary to consider the alternative method of securing the operating reserve via competitive bidding for flexibility resources that meet the power system requirement instead of the upper limit constraint on generation output. In addition, for the purpose of responding to variability of renewable energy, quick-start generators operated separately from the operational reserve should be implemented as planned. It is also necessary to refine the system for predicting the amount of renewable energy generation in consideration of the mechanism for responding to the variability of each flexibility resource to realize the flexible regulation of flexibility supply amount. Second, if policy makers consider revising or establishing a new policy related to the expansion of renewable energy, it is necessary to examine not only the expected direct effect of the policy but also the indirect ripple effect, such as the pressure to increase electricity rates due to the hike in power purchase costs of vendors. Third, since the amount of renewable energy generation and electricity market price are expected to change in an increasingly inconsistent pattern, it is also important to reconsider the design for the settlement rules of the cost-based pool market or method of deciding the market price. Finally, in the future power market, the pattern difference between the demand peak and the market price peak may increase. Therefore, various policies that consider demand patterns, such as demand management, economical demand response, and electricity fee system, should be reviewed in the direction of considering the net load pattern in the future.Chapter 1. Introduction 1 1.1 Research Background 1 1.2 Research Objectives 6 1.3 Research Outline 9 Chapter 2. Literature Review 12 2.1 Power System Flexibility 12 2.1.1 Sources of Flexibility 15 2.1.2 Studies on Flexibility Evaluation 19 2.2 Generation Scheduling 23 2.2.1 Unit Commitment and Economic Dispatch Model 23 2.2.2 Optimization techniques for solving UC problem with High Renewable Energy Sources Penetration 25 2.3 Research of the Energy policy in Korea 28 2.4 Limitations of previous research and Research Motivation 33 Chapter 3. Methodology 36 3.1 Methodological Framework 36 3.2 Unit Commitment and Economic Dispatch Modeling 40 3.2.1 Generation scheduling using MILP 42 3.2.2 An empirical model for day-ahead unit commitment and economic dispatch 49 3.2.3 Model Input data 58 3.2.4 Evaluation of the power system flexibility 68 3.2.5 Economic impact analysis 72 3.3 Model validation 79 3.3.1 Overview of model validation 79 3.3.2 Model validation result 82 Chapter 4. Empirical Studies 87 4.1 The study on evaluating the power system flexibility 87 4.1.1 Overview of flexibility evaluation and premises of analysis 87 4.1.2 Net load variability and calculation of flexibility requirement 91 4.1.3 Unit commitment and economic dispatch simulation and calculation of flexibility supply amount 96 4.1.4 Empirical results of evaluating the power system flexibility 103 4.2 Composition of flexibility resources and ability to respond to volatility 111 4.2.1 Incentive effect for participation in operational reserve service 112 4.2.2 Composition of operational reserve resources for flexibility supply 117 4.2.3 Volatility response mechanism of operational reserves and quickโ€“start generators 123 4.2.4 Improvement of reserve system and separation of the auxiliary service market 128 4.3 Analysis of the economic impact 132 4.3.1 Premises for economic impact analysis 132 4.3.2 Forecasting SMP and electricity settlement amount 134 4.3.3 Analysis of the impact of policies related to the expansion of renewable energy 138 4.3.4 Empirical results and discussion 143 Chapter 5. Summary and Conclusion 146 5.1 Concluding Remarks and Contribution 146 5.2 Limitations and Future Studies 148 Bibliography 151 Appendix 1: The results of power generation scheduling of pumped-storage power plants 164 Appendix 2: Power market operation performance trend (2001-2019) 166 Abstract (Korean) 168Docto

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