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    ์žฌ์ƒ์—๋„ˆ์ง€ ํ™•๋Œ€๊ฐ€ ํƒ„์†Œ๋ฐฐ์ถœ ๋ฐ ์ง€์—ญ๊ฒฝ์ œ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ํ™˜๊ฒฝ๋Œ€ํ•™์› ํ™˜๊ฒฝ๊ณ„ํšํ•™๊ณผ, 2023. 8. ํ™์ข…ํ˜ธ.์ง€์†๊ฐ€๋Šฅํ•œ ๋ฏธ๋ž˜์„ธ๋Œ€๋ฅผ ์œ„ํ•ด ์—๋„ˆ์ง€ ์‚ฌ์šฉ์œผ๋กœ ์ธํ•œ ํƒ„์†Œ๋ฐฐ์ถœ์„ ๋Œ€ํญ ๊ฐ์ถ•ํ•ด์•ผ ํ•œ๋‹ค. ์ด ๊ณผ์ œ๋Š” ์ˆ˜์ž… ํ™”์„์—ฐ๋ฃŒ์— ์˜์กดํ•˜๋Š” ์—๋„ˆ์ง€ ๋‹ค์†Œ๋น„ ์‚ฐ์—…์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์„ฑ์žฅํ•œ ์šฐ๋ฆฌ๋‚˜๋ผ์—๋Š” ์—„์ฒญ๋‚œ ๋„์ „์ด๋‹ค. ๋”ฐ๋ผ์„œ ์ถฉ๋ถ„ํ•œ ๋…ผ์˜๊ฐ€ ์„ ํ–‰๋˜์–ด์•ผ ํ•œ๋‹ค. ํ’๋ ฅ๊ณผ ํƒœ์–‘๊ด‘ ๋“ฑ์˜ ์žฌ์ƒ์—๋„ˆ์ง€๋Š” ์ž์—ฐ์˜ ํž˜์œผ๋กœ๋งŒ ์ „๋ ฅ์„ ์ƒ์‚ฐํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์ „๋ ฅ ์ƒ์‚ฐ ๊ณผ์ •์—์„œ ์ด์‚ฐํ™”ํƒ„์†Œ๋ฅผ ๋ฐฐ์ถœํ•˜์ง€ ์•Š๋Š” ๋Œ€ํ‘œ์ ์ธ ์ฒญ์ •์—๋„ˆ์ง€์›์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์žฌ์ƒ์—๋„ˆ์ง€๋Š” ์ „ํ†ต์ ์ธ ์ค‘์•™๋ฐœ์ „์›๊ณผ ํฐ ์ฐจ์ด๊ฐ€ ์žˆ์œผ๋ฏ€๋กœ ์ƒ๋‹นํ•œ ๋ณ€ํ™”๋ฅผ ์ˆ˜๋ฐ˜ํ•œ๋‹ค. ์žฌ์ƒ์—๋„ˆ์ง€๋Š” ๊ฐ„ํ—์„ฑ์œผ๋กœ ์ธํ•˜์—ฌ ์œ ์—ฐ์„ฑ ์ž์›์ด ํ•„์ˆ˜์ ์ด๋‹ค. ๋”ฐ๋ผ์„œ ์žฌ์ƒ์—๋„ˆ์ง€์˜ ๊ด‘๋ฒ”์œ„ํ•œ ์ „ํ›„๋ฐฉ์‚ฐ์—…์— ์˜ํ•œ ํƒ„์†Œ๋ฐฐ์ถœ๋Ÿ‰๊นŒ์ง€ ๊ณ ๋ คํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ํ•œํŽธ, ์žฌ์ƒ์—๋„ˆ์ง€๋Š” ๋ถ„์‚ฐ์—๋„ˆ์ง€์›์œผ๋กœ์จ ์ง€์—ญ์ฃผ๋ฏผ ์ƒํ™œ๋ฐ˜๊ฒฝ์— ๊ฐ€๊นŒ์ด ์œ„์น˜ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์ˆ˜์šฉ์„ฑ ๋ฌธ์ œ๊ฐ€ ์ค‘์š”ํ•˜๋ฉฐ, ์žฌ์ƒ์—๋„ˆ์ง€ ํ™•๋Œ€๊ฐ€ ์ง€์—ญ๊ฒฝ์ œ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•œ ์‚ฌํšŒ์  ๊ด€์‹ฌ๋„ ๋งค์šฐ ๋†’๋‹ค. ์ตœ๊ทผ, ๋…น์ƒ‰ ๋ณดํ˜ธ์ฃผ์˜์— ๋”ฐ๋ฅธ ๋ฌด์—ญ๋ถ„์Ÿ์ด ์ „ ์„ธ๊ณ„์ ์œผ๋กœ ์‹ฌํ™”๋˜๊ณ  ์žˆ๊ณ , ๊ตญ๊ฐ€ ์žฌ์ƒ์—๋„ˆ์ง€ ์‚ฐ์—…๊ฒฝ์Ÿ๋ ฅ ์œก์„ฑ ๋˜ํ•œ ๊ฐ•์กฐ๋˜๊ณ  ์žˆ๋Š” ์ถ”์„ธ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์žฌ์ƒ์—๋„ˆ์ง€ ์‹œ์Šคํ…œ์˜ ํƒ„์†Œ์ €๊ฐ ํšจ๊ณผ๋ฅผ ๊ทน๋Œ€ํ™”ํ•˜๊ณ , ์ง€์—ญ์‚ฌํšŒ์™€ ์ƒ์ƒํ•  ๋ฐฉ์•ˆ์„ ๋งˆ๋ จํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์žฌ์ƒ์—๋„ˆ์ง€ ํ™•๋Œ€๊ฐ€ ํƒ„์†Œ๋ฐฐ์ถœ๊ณผ ์ง€์—ญ๊ฒฝ์ œ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์ „์ฃผ๊ธฐ ๊ฐ€์น˜์‚ฌ์Šฌ, ๊ตญ๊ฐ€ ๋ฐ ์ง€์—ญ ์‚ฐ์—…์—ญ๋Ÿ‰ ๊ฐ•ํ™”, ์ง€์—ญ์  ํŠน์„ฑ ๋“ฑ ์žฌ์ƒ์—๋„ˆ์ง€์˜ ๊ณ ์œ ํ•œ ํŠน์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ๋ถ„์„ํ•˜๋Š” ๋ฐ์— ๋ชฉ์ ์„ ๋‘์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ํฌ๊ฒŒ ์„ธ ๊ฐ€์ง€๋กœ ๊ตฌ๋ถ„๋œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ œ์ฃผ๋„์— 211MW ๊ทœ๋ชจ์˜ ์œก์ƒํ’๋ ฅ ๋ฐœ์ „์ด ํ™•๋Œ€๋จ์— ๋”ฐ๋ผ ๋ฐœ์ƒํ•˜๋Š” ์ „์ฃผ๊ธฐ ํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ฐฉ๋ฒ•๋ก ์œผ๋กœ ๋น„์šฉ ๋ถ„์„๊ณผ Environmentally-Extended Input-Output (EEIO) ๋ถ„์„์— ๊ธฐ๋ฐ˜ํ•œ Economic Input-Output Life-cycle Assessment (EIO-LCA)๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์ฃผ์š” ๋ฐฐ์ถœ์›์„ ํŒŒ์•…ํ•˜๊ณ  ํƒ„์†Œ๋ฐฐ์ถœ ์ €๊ฐ ๋ฐฉ์•ˆ์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์—๋„ˆ์ง€์ €์žฅ์‹œ์Šคํ…œ (ESS)์˜ ๋ฐฐํ„ฐ๋ฆฌ ์ œ์กฐ๊ฐ€ ํƒ„์†Œ๋ฐฐ์ถœ์˜ ๊ฐ€์žฅ ํฐ ๋ฐœ์ƒ์›์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ํ„ฐ๋นˆ ๋ถ€ํ’ˆ ์ œ์กฐ๊ฐ€ ๊ทธ ๋’ค๋ฅผ ์ด์—ˆ๋‹ค. ์ด ๊ฒฐ๊ณผ๋Š” ์œ ์—ฐ์„ฑ ์ž์›์˜ ํƒ„์†Œ๋ฐฐ์ถœ ์ €๊ฐ ๋…ธ๋ ฅ์ด ํ•„์š”ํ•จ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋˜ํ•œ ๋ฏผ๊ฐ๋„ ๋ถ„์„์— ๋”ฐ๋ฅด๋ฉด ์œก์ƒํ’๋ ฅ ๋ฐœ์ „์†Œ์˜ ์„ค๋น„ ์ด์šฉ๋ฅ ๊ณผ ์šด์˜๊ธฐ๊ฐ„ ์ฆ๊ฐ€๊ฐ€ ํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰ ๊ฐ์ถ• ์ž ์žฌ๋ ฅ์„ ๋”์šฑ ๋†’์ผ ์ˆ˜ ์žˆ๋Š” ์š”์ธ์œผ๋กœ ํŒŒ์•…๋˜์—ˆ๋‹ค. ์ฃผ์š” ํƒ„์†Œ๋ฐฐ์ถœ ์‚ฐ์—…์œผ๋กœ 1) ์ „๊ธฐ, ๊ฐ€์Šค ๋ฐ ์ฆ๊ธฐ 2) 1์ฐจ ๊ธˆ์†์ œํ’ˆ(์ฒ ๊ฐ• ์ƒ์‚ฐ), 3) ๋น„๊ธˆ์† ๊ด‘๋ฌผ์ œํ’ˆ(์‹œ๋ฉ˜ํŠธ ์ƒ์‚ฐ)์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ๊ถ๊ทน์ ์œผ๋กœ ์—๋„ˆ์ง€์‚ฐ์—…์˜ ์žฌ์ƒ์—๋„ˆ์ง€์˜ ๋น„์ค‘์„ ๋†’์ด๋Š” ๊ฒƒ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์‚ฐ์—…๊ณต์ •์˜ ํƒ„์†Œ ์ €๊ฐ์ด ์ค‘์š”ํ•จ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š” ์œก์ƒํ’๋ ฅ ํ™•๋Œ€๊ฐ€ ์ œ์ฃผ๋„ ์ง€์—ญ๊ฒฝ์ œ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋น„์šฉ ๋ถ„์„๊ณผ Interregional Input-Output (IRIO) ๋ถ„์„์„ ํ™œ์šฉํ•˜์—ฌ ๊ตญ๊ฐ€ ๋ฐ ์ง€์—ญ ์‚ฐ์—…์—ญ๋Ÿ‰์„ ์ˆ˜์ค€์„ ์ˆ˜์น˜ํ™”ํ•œ ๊ตญ๊ฐ€ ๋ฐ ์ง€์—ญ ์ž๊ธ‰๋ฅ (%) ์‹œ๋‚˜๋ฆฌ์˜ค ์•„๋ž˜ ๋ถ„์„ํ•˜์˜€๋‹ค. ์œก์ƒํ’๋ ฅ ๋ฐ ์œ ์—ฐ์„ฑ ์ž์› ํ™•๋Œ€๋Š” ๊ด‘๋ฒ”์œ„ํ•œ ๊ฐ€์น˜์‚ฌ์Šฌ๋กœ ์ธํ•˜์—ฌ ์šด์˜๊ธฐ๊ฐ„์— ๋‹ค์–‘ํ•œ ์‚ฐ์—…์— ๊ฑธ์ณ ๋ถ€๊ฐ€๊ฐ€์น˜ ๋ฐ ๊ณ ์šฉ ๊ธฐํšŒ๋ฅผ ์ฐฝ์ถœํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์šด์˜๊ธฐ๊ฐ„์— ๋ฐœ์ƒํ•˜๋Š” ๋ถ€๊ฐ€๊ฐ€์น˜ ์œ ๋ฐœํšจ๊ณผ๋Š” ์ œ์ฃผ๋„์˜ ๊ฑด์„ค์—…, ์š”์‹์—….์ˆ™๋ฐ•์—…, ๋†์ˆ˜์‚ฐ์—…๊ณผ ๋น„์Šทํ•œ ์ˆ˜์ค€์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์žฌ์ƒ์—๋„ˆ์ง€ ์‹œ์Šคํ…œ์€ ์œก์ƒํ’๋ ฅ๊ณผ ์ง์ ‘์ ์œผ๋กœ ๊ด€๋ จ๋œ ์‚ฐ์—… ์™ธ์—๋„ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์— ๊ฑธ์ณ ์ผ์ž๋ฆฌ๋ฅผ ์ฐฝ์ถœํ•œ๋‹ค๋Š” ์ ์—์„œ ํ™”๋ ฅ๋ฐœ์ „์†Œ์™€๋Š” ์ฐจ๋ณ„๋œ๋‹ค. ์ด๋Š” ํ–ฅํ›„ ๋‹ค์–‘ํ•œ ๊ธฐ์ˆ ์„ ๋ณด์œ ํ•œ ์ธ๋ ฅ์„ ํ™•๋ณดํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•œ ๊ณผ์ œ๊ฐ€ ๋  ๊ฒƒ์ž„์„ ์‹œ์‚ฌํ•œ๋‹ค. ์ง€์—ญ ์ž๊ธ‰๋ฅ ์˜ ์ฆ๊ฐ€๊ฐ€ ๊ตญ๊ฐ€ ์ „์ฒด ์ผ์ž๋ฆฌ ์ฐฝ์ถœ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์€ ๋ฏธ๋ฏธํ•˜์ง€๋งŒ, ๊ตญ๊ฐ€ ์ž๊ธ‰๋ฅ ์˜ ์ฆ๊ฐ€๋Š” ์ง€์—ญ ์ผ์ž๋ฆฌ ์ฆ๊ฐ€๋กœ ์ด์–ด์ง„๋‹ค๋Š” ๊ฒฐ๊ณผ๊ฐ€ ๋„์ถœ๋˜์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ์ผ๋ถ€ ์ง€์—ญ ์ž๊ธ‰๋ฅ  ์ •์ฑ…์€ ์žฌ์ƒ์—๋„ˆ์ง€ ๊ด€๋ จ ์ง€์—ญ์ˆ˜์šฉ์„ฑ ๋ฌธ์ œ๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ๊ฐœ์„ ํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ์‚ฌ๋ฃŒ๋œ๋‹ค. ์„ธ ๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š” ์•ž์„  ๋‘ ์—ฐ๊ตฌ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ IRIO์™€ ๋‹ค์ง€์—ญ EIO-LCA๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๊ตญ๋‚ด 19GW ๊ทœ๋ชจ ์œก์ƒํ’๋ ฅ ๋ฐ ํƒœ์–‘๊ด‘ ๋ฐœ์ „ ํ™•๋Œ€๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰๊ณผ ๊ฒฝ์ œ์  ์˜ํ–ฅ์˜ ๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ์žฌ์ƒ์—๋„ˆ์ง€ ํ™•๋Œ€์‹œ ๊ตญ๊ฐ€ ์ž๊ธ‰๋ฅ ์„ ๋†’์ด๊ณ  ๋งŽ์€ ์œ ์—ฐ์„ฑ ์ž์›์„ ํ†ตํ•ฉํ• ์ˆ˜๋ก ๊ณ ์šฉ์ด ์ƒ๋‹นํžˆ ์ฆ๊ฐ€ํ•˜๋Š” ๋ฐ˜๋ฉด, ํƒ„์†Œ ๋ฐฐ์ถœ๋Ÿ‰๋„ ํ•จ๊ป˜ ์ฆ๊ฐ€ํ•œ๋‹ค. ๋˜ํ•œ, ์†Œ๋น„ ๊ธฐ๋ฐ˜์˜ ๋ฐฐ์ถœ๋Ÿ‰ ๊ด€์ ์—์„œ ์ œ์ฃผ๋„์˜ ์œก์ƒํ’๋ ฅ ๋ฐ ํƒœ์–‘๊ด‘ ๋ฐœ์ „ ์šด์˜์ด ๊ตญ๋‚ด ํƒ€ ์ง€์—ญ์˜ ํƒ„์†Œ๋ฐฐ์ถœ๊ณผ ์ผ์ž๋ฆฌ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ESS ์กฐ๋‹ฌ ์ง€์—ญ์— ๋”ฐ๋ผ ํƒ„์†Œ๋ฐฐ์ถœ ๋ฐ ๊ณ ์šฉ ์ฐฝ์ถœ์˜ ์ง€์—ญ์  ๋ถ„ํฌ ๋ฐ ์ด๋Ÿ‰์ด ์ƒ์ดํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ, ์žฌ์ƒ์—๋„ˆ์ง€ ํ™•๋Œ€๊ฐ€ ์ €ํƒ„์†Œ ๊ณต๊ธ‰๋ง ์ „๋žต๊ณผ ์—ฐ๊ณ„๋œ๋‹ค๋ฉด ํƒ„์†Œ๋ฐฐ์ถœ์„ ์ตœ์†Œํ™”ํ•˜๋ฉด์„œ ์ผ์ž๋ฆฌ ์ฐฝ์ถœ์„ ๊ทน๋Œ€ํ™”ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Š” ๊ธฐ์—…์ด ์ €ํƒ„์†Œ ์‚ฐ์—…ํ™œ๋™์„ ์ฑ„ํƒํ•˜๋„๋ก ํ•˜๋Š” ์œ ์ธ์ฑ…์œผ๋กœ ์ž‘์šฉํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ์ด ์—ฐ๊ตฌ๋Š” ์žฌ์ƒ์—๋„ˆ์ง€ ํ™•๋Œ€์™€ ์ €ํƒ„์†Œ ์‚ฐ์—…ํ™œ๋™์˜ ํ†ตํ•ฉ์„ ๊ฐ•์กฐํ•œ๋‹ค. ํƒœ์–‘๊ด‘๊ณผ ํ’๋ ฅ๋ฐœ์ „์€ ํƒ„์†Œ๋ฐฐ์ถœ์„ ๋งค์šฐ ํšจ๊ณผ์ ์œผ๋กœ ์ €๊ฐํ•  ์ˆ˜ ์žˆ๋Š” ์—๋„ˆ์ง€์›์ด์ง€๋งŒ ๊ฐ„ํ—์„ฑ๊ณผ ๋ถ„์‚ฐ์„ฑ์œผ๋กœ ์ธํ•˜์—ฌ ํƒ„์†Œ๋ฐฐ์ถœ๊ณผ ์ง€์—ญ๊ฒฝ์ œ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์ด ํฌ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์—๋„ˆ์ง€์‚ฐ์—… ๋ฐ ์‚ฐ์—…๊ณต์ •์˜ ํƒˆํƒ„์†Œํ™”, ์žฌ์ƒ์—๋„ˆ์ง€ ์šด์˜ ์กฐ๊ฑด ๊ฐœ์„ , ์ˆœํ™˜๊ฒฝ์ œ ๋ฐ ์ €ํƒ„์†Œ ๊ณต๊ธ‰๋ง ์„ ๋ณ„ ๋“ฑ ์žฌ์ƒ์—๋„ˆ์ง€ ํ™•๋Œ€๋ฅผ ์ €ํƒ„์†Œ ์‚ฐ์—…ํ™œ๋™๊ณผ ๊ธด๋ฐ€ํžˆ ์—ฐ๊ณ„ํ•œ๋‹ค๋ฉด ํƒ„์†Œ๋ฐฐ์ถœ ์˜ํ–ฅ์„ ์ตœ์†Œํ™”ํ•˜๋Š” ๋™์‹œ์— ๊ตญ๊ฐ€ ๊ฒฝ์Ÿ๋ ฅ์„ ํ™•๋ณดํ•˜๊ณ  ์ง€์—ญ ๊ฒฝ์ œ๋ฅผ ํ™œ์„ฑํ™”ํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.The energy sector is the largest contributor to climate change. Drastically reducing carbon emissions from energy use is imperative to ensure a sustainable future. Renewable energy sources, such as wind and solar photovoltaic (PV) emerged as leading clean energy alternatives, However, the transition implicates significant changes due to their major distinctions from the conventional energy system. Renewable energy encompasses a broad range of value chain, particularly requiring flexibility resources due to its intermittency. Consequently, it is essential to evaluate carbon emissions over the complete lifecycle, from development to decommissioning. Moreover, as a distributed energy source, renewable energy is often located close to residential areas raising both public concerns and interest in regional job creation. The recent rise in green protectionism has further amplified the focus on enhancing the national competitiveness of renewable energy industries. The thesis aims to analyze the impact of renewable energy expansion on carbon emissions and regional economy. It considers the inherent characteristics of the renewable energy system, such as the 1) lifecycle value chain, 2) capacity building, 3) regional characteristics such as natural resources, and industry structure. By doing so, it seeks to propose sustainable development strategies related to carbon emission reduction. The study consists of three essays, each exploring different aspects of renewable energy characteristics in the impact assessment. The research employs various analytical methods, including input-output analysis, lifecycle assessment, and cost analysis. The first essay assesses the lifecycle carbon emissions resulting from the expansion of 211MW onshore wind power in Jeju. It utilizes the Economic Input-Output Life-cycle Assessment (EIO-LCA) based on cost analysis and Environmentally Extended Input-Output (EEIO) analysis. The study identifies primary sources of emissions, providing recommendations to mitigate carbon emission. Battery manufacturing of Energy Storage System (ESS) emerges as the single largest source of carbon emission, followed by manufacturing of turbine elements. The results highlight the significance of carbon emission of flexibility resources and the importance of employing various mitigation efforts such as reusing batteries from electric vehicles and other flexible resources such as DR and V2G that require relatively little additional infrastructure. In addition, sensitivity analysis reveals that increasing both the capacity factor and operational period of onshore wind farms could further enhance carbon emission reduction potential. The most notable carbon emission contributing industries are identified as 1) Electricity, gas and steam supply, 2) Primary metal products (Steel production) 3) Non-metallic mineral products (Cement production). Ultimately, it is crucial not only to increase the share of renewable energy in the electricity generation sector but also to actively transform unsustainable industrial processes. The second essay evaluates the impact of onshore wind power expansion on Jejus regional economy based on cost analysis and Interregional Input-Output (IRIO) analysis considering various levels of capacity building in the form of local content (%). The study accentuates the extensive value chain activities of renewable energy, generating value-added and employment opportunities across various sectors. The induced value added by onshore wind Operation& Maintenance (O&M) is comparable to that of Jejus construction, restaurant and hotel, and agriculture and fisheries industries. Moreover, renewable energy system creates jobs across various sectors, even beyond those directly related to onshore wind energy, distinguishing it from conventional power plants. This also implicates that finding personnel with diverse skill sets and talents will be a significant challenge in the future. Furthermore, the results indicate that an increase in regional local content has a minimal effect on the total number of jobs created in the country, while an increase in national manufacturing leads to a rise in regional jobs. Hence, regional local content can effectively address local acceptance issues associated with renewable energy. Building upon preceding studies, the third essay analyzes the relationship between carbon emissions and economic impacts, focusing on onshore wind and solar PV expansion in Korea utilizing IRIO and multi-regional EIO-LCA. By studying the installation of 19GW of renewable energy, the essay highlights that incorporating flexibility options and increasing local content rate substantially increases employment opportunities but can also lead to higher carbon emissions, indicating a trade-off. The case of Jeju Island is examined from a consumption-based emission perspective to analyze the regional distribution of carbon emissions and job creation during the operation of onshore wind and solar PV. It is discovered that there are regional variations in carbon emissions and job creation depending on the ESS production region. Interestingly, the region with the highest carbon emissions does not necessarily create the most jobs. Therefore, aligning renewable energy expansion with sustainable supply chain strategies can ensure job creation while minimizing carbon emissions. This approach could provide incentives for corporations to adopt sustainable practices. In conclusion, this study emphasizes the integration of renewable energy expansion with sustainable industrial activities. The findings have implications for countries facing similar challenges in managing intermittent renewable energy and energy-intensive industries. While renewable energy presents advantages, it also entails complexities due to intermittency and distributed nature. Flexibility resources play a crucial role but can have significant environmental and economic impacts. However, aligning clean energy production with clean industry practices allows for the minimization of carbon emissions impact while ensuring national competitiveness and revitalizing the regional economy. This paves the way for a sustainable future for generations to come.ABSTRACT TABLES FIGURES LIST OF ABBREVIATIONS CHAPTER 1. INTRODUCTION 1 1.1. MOTIVATION 1 1.2. RESEARCH OBJECTIVES 5 1.3. SCOPE AND METHODOLOGY 6 CHAPTER 2. THEORETICAL BACKGROUND 11 2.1. CLEAN ENERGY TRANSITION 11 2.1.1. Sustainable Development and Renewable Energy System 11 2.1.2. Stability of Variable Renewable Energy 16 2.1.3. Regional Perspective 22 2.2. INDUSTRY AND CARBON EMISSIONS 25 2.2.1. Value chain Perspective 27 2.2.2. Lifecycle Perspective 31 2.2.3. Local Capacity Building 32 2.3. LITERATURE REVIEW 39 2.3.1. Carbon Emission Assessment of Renewable Energy 39 2.3.2. Regional Economic Impact Assessment of Renewable Energy 43 2.3.3. Distinction from Previous Studies 49 2.4. SUMMARY 51 CHAPTER 3. DATA AND METHODOLOGY 52 3.1. VALUE CHAIN AND COST ANALYSIS 52 3.1.1. Onshore Wind 52 3.1.2. Solar PV 54 3.1.3. Energy Storage System 55 3.2. VARIABLE PARAMETERS 58 3.2.1. Local Content 58 3.2.2. Operating Conditions 66 3.3. INPUT-OUTPUT ANALYSIS 67 3.3.1. Input-Output Table and Multipliers 67 3.3.2. Demand Vector Creation 74 3.3.3. Economic Input Output Life-cycle Assessment (EIO-LCA) 77 3.3.4. Consumption-based, Production-based Emission Accounting 81 3.4. SUMMARY 86 CHAPTER 4. CARBON EMISSION IMPACT OF WIND ENERGY: A CASE STUDY OF JEJU 88 4.1. ANALYTICAL PROCEDURE 90 4.2. RESULTS 93 4.2.1. Lifecycle Electricity Generation 93 4.2.2. Lifecycle Carbon Emission 94 4.3. DISCUSSION 98 4.3.1. Sensitivity Analysis 98 4.3.2. Emission by Industry 101 4.3.3. Carbon Emission Reduction Potential 105 4.4. CONCLUSION 107 CHAPTER 5. REGIONAL ECONOMIC IMPACT OF WIND ENERGY: A CASE STUDY OF JEJU 109 5.1. ANALYTICAL PROCEDURE 111 5.2. RESULTS 115 5.2.1. Value-added 115 5.2.2. Job Creation 116 5.3. DISCUSSION 117 5.3.1. Industrial Impact 117 5.3.2. Comparison with Conventional Power Source 121 5.3.3. Impact of Local Content 124 5.4. CONCLUSION 127 CHAPTER 6. THE IMPACT OF RENEWABLE ENERGY IN KOREA 129 6.1. ANALYTICAL PROCEDURE 130 6.1.1. Analytical Steps and Assumptions 130 6.1.2. Onshore Wind and Solar PV Potential in Korea 132 6.2. RESULTS & DISCUSSION 136 6.2.1. National Impact of Renewable Energy 136 6.2.2. Regional Distribution Effect of Carbon Emission 140 6.3. CONCLUSION 147 CHAPTER 7. CONCLUSION 149 7.1. SUMMARY AND IMPLICATIONS 149 7.2. POLICY RECOMMENDATIONS FOR JEJU 155 7.3. RESEARCH CONTRIBUTION AND LIMITATIONS 157 REFERENCES 159 APPENDIX 170 1. NEW& RENEWABLE ENERGY CLASSIFICATION 170 2. AVERAGE CAPACITY FACTOR 171 3. NATIONAL GHG INVENTORY SECTOR CLASSIFICATION 173 4. IO INDUSTRY CORRELATION 178 5. JEJU DATA 180 6. MREEIO 182 ๊ตญ๋ฌธ์ดˆ๋ก 186 ACKNOWLEDGEMENTS 189๋ฐ•

    Stability and interaction analysis in islanded power systems including VSC-HVDC and LCC-HVDC power converters

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    Islanded power systems are often connected to larger mainland power systems using HVDC cables. These interconnections are used to import power at lower cost compared to local generation and improve the security of supply. The increase of HVDC interconnectors in islanded systems will allow the reduction of local synchronous generation, which might lead to new interaction and stability problems due to the low inertia and short-circuit power available in the system. Traditionally LCC-HVDC technology has been used to connect island grids, but recently VSCs are presented as an alternative solution that offers more controllability to the islanded grid. Therefore, in order to increase the power transfer to the islands multi-infeed hybrid HVSC systems with VSCs and LCCs might become a common solution. The introduction of VSCs in islanded systems will allow operations in weak grids, but possible interactions with LCCs must be analysed in detail. This paper introduces the potential interactions in multi-infeed HVDC systems with LCCs and VSCs. An initial benchmark model of an islanded power system with a LCC and a VSC-HVDC link is presented to analyse new interaction phenomena between the converters and the islanded AC grid. Simulation results in PSCAD/EMTDC are presented to validate the benchmark model for voltage stability and commutation failure analysis.Postprint (published version

    A study on influence of Putian Port offshore wind farm construction on navigation safety

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    Stochastic Optimization for Network-Constrained Power System Scheduling Problem

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    The stochastic nature of demand and wind generation has a considerable effect on solving the scheduling problem of a modern power system. Network constraints such as power flow equations and transmission capacities also need to be considered for a comprehensive approach to model renewable energy integration and analyze generation system flexibility. Firstly, this paper accounts for the stochastic inputs in such a way that the uncertainties are modeled as normally distributed forecast errors. The forecast errors are then superimposed on the outputs of load and wind forecasting tools. Secondly, it efficiently models the network constraints and tests an iterative algorithm and a piecewise linear approximation for representing transmission losses in mixed integer linear programming (MILP). It also integrates load shedding according to priority factors set by the system operator. Moreover, the different interactions among stochastic programming, network constraints, and prioritized load shedding are thoroughly investigated in the paper. The stochastic model is tested on a power system adopted from Jeju Island, South Korea. Results demonstrate the impact of wind speed variability and network constraints on the flexibility of the generation system. Further analysis shows the effect of loss modeling approaches on total cost, accuracy, computational time, and memory requirement

    FINANCE MODELING OF A FLOATING OFFSHORE WIND PROJECT IN SOUTH KOREA WITHOUT GOVERNMENT SUBSIDES

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    The South Korean government is encouraging the active participation of power generation companies in the offshore wind power project by announcing the renewable energy certificates (REC) weighting plan. However, from a long-term perspective, offshore wind power must be able to generate profits without government support to demonstrate its business feasibility and attract the voluntary participation of power generation companies. This is because government support may be subject to change, depending on the internal and external political circumstances of the country. This report calculates the expected costs for a 495 MW floating offshore wind farm in the South Koreaโ€™s market environment and examine how the feasibility of the project shifts depending on the countryโ€™s current REC weights. Furthermore, this study intends to determine whether floating offshore wind power can generate profits without the Korean governmentโ€™s support by calculating the expected profit in combination with the green hydrogen project. The net present value (NPV), levelized cost of energy (LCoE) and internal rate of return (IRR) indexes are calculated according to the projectโ€™s specific particularities, such as power purchase agreement, REC Weighting, distance from shore and sea depth. Based on this, an index-based comparison is revealed and the margin for profitability for such an investment is discussed

    Modeling and Analysis of an LCC HVDC System Using DC Voltage Control to Improve Transient Response and Short-Term Power Transfer Capability

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    A new control method for a line-commutated converter-based (LCC) high-voltage direct-current (HVDC) system is presented and compared to a conventional strategy. In the proposed method, both the DC voltage and current of an LCC HVDC system are regulated to increase the short-term operating margin of DC power transfer and improve transient responses to DC power references. In particular, an increased operating margin of DC power transfer is achieved via the DC voltage regulation method. To verify the effectiveness of the proposed method, a state space model of an LCC HVDC system is developed considering DC voltage and current references as input variables and analyzed for various values of the DC line inductance and converter controller gains. The state space model can be used for time-efficient analyses of the dynamic characteristics of an LCC HVDC system. Simulation case studies are performed using MATLAB, where the state space model of the Jeju-Haenam HVDC system is implemented as a test case and compared to its comprehensive PSCAD model. The case study results suggest that the proposed method increases the short-term operating margin and speeds up the transient response of the HVDC system. Therefore, it will effectively improve real-time grid frequency regulation.11sciescopu
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