AWS와 MERRA 데이터의 장기간 풍속보정을 통한 풍력터빈 최적배치 및 연간에너지생산량 예측 (Optimal Micrositing and Annual Energy Production Prediction for Wind Farm Using Long-term Wind Speed Correlation Between AWS and MERRA)

서지정보

· 발행기관 : 대한기계학회
· 수록지 정보 : 대한기계학회논문집 B / 40권 / 4호 / 201 ~ 212페이지
· 저자명 : 박미호, 김범석

초록

부산지역 가덕도 일대에 30MW 규모의 육상 풍력발전단지개발을 위한 풍황자원분석과 풍력터빈 최적배치를 수행하였다. 후보지역에 설치되어 운용중인 AWS(KMA)에서 측정된 바람 데이터를 이용하였으며, 데이터 품질분석을 통한 신뢰성 검토를 수행하였다. 1년간 측정된 AWS 데이터는 MERRA 재해석 데이터와 선형희귀(Linear regression method) MCP 기법의 적용을 통해 30년으로 장기 보정되었고, 이를 이용한 풍력터빈 최적배치를 수행하였다. 3MW 풍력터빈을 적용하여 총 25 조건의 풍력터빈 배치에 대한 최적배치를 수행하였으며, 다양한 후류모델을 적용하여 발전량해석을 수행하였다. 단지효율은 97.6%~98.7%, 연간이용률은 37.9%~38.3%로 예측되었고, 후류영향이 고려된 연간발전량이 99,598.4 MWh~100,732.9 MWh로 예측됨에 따라, 우수한 경제성을 갖는 풍력발전단지개발이 가능한 지역임을 확인하였다.

영어초록

A Wind resource assessment and optimal micrositing of wind turbines were implemented for the development of an onshore wind farm of 30 MW capacity on Gadeok Island in Busan, Republic of Korea. The wind data measured by the automatic weather system (AWS) that was installed and operated in the candidate area were used, and a reliability investigation was conducted through a data quality check. The AWS data were measured for one year, and were corrected for the long term of 30 years by using the modern era retrospective analysis for research and application (MERRA) reanalysis data and a measure- correlate-predict (MCP) technique; the corrected data were used for the optimal micrositing of the wind turbines. The micrositing of the 3 MW wind turbines was conducted under 25 conditions, then the best-optimized layout was analyzed with a various wake model. When the optimization was complete, the estimated park efficiency and capacity factor were from 97.6 to 98.7 and from 37.9 to 38.3, respectively. Furthermore, the annual energy production (AEP), including wake losses, was estimated to be from 99,598.4 MWh to 100,732.9 MWh, and the area was confirmed as a highly economical location for development of a wind farm.

참고자료

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