국내 재생에너지 규모가 점차 확대되는 상황에서 효율적인 발전을 위한 재생 에너지 발전량 예측의 중요성이 더욱 부각되고 있습니다. GS E&R은 업계 최초로 클라우드를 활용한 풍력 발전량 예측 플랫폼을 구축, 재생에너지 발전량 예측제도에 참여하고 있으며, 풍력발전 단지 내 발전설비 데이터 및 최신 기상예보 데이터 등 수집 자동화를 위한 파이프라인 구축, 발전 단지 지형 특성에 맞는 자체 개발한 고해상도 기상 예보 모델 수행, 한 자릿수 이하 오차율을 보장하는 풍력 발전량 예측 모델 개발, MLOps 체계 구현, 자체 분석 역량 확보를 위한 노력을 AWS와 함께 하고 있습니다. 아무도 걸어가지 않은 여정이었기에 쉽지 않았던 그 과정을 생생하게 전달해 드리겠습니다.

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S E O U L | M A Y 3 , 2 0 2 3

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© 2023, Amazon Web Services, Inc. or its affiliates. All rights reserved.
GS
박찬희
Account Manager
AWS
Head of DX department
GS E&R
김학민
Head of DX Lab
GS Energy

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© 2023, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Head of DX department
GS E&R

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1.
2.
3.
4.
Agenda

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GS
126MW 74MWh ESS
 1 : 60 MW (3.3 MW x 18 )
• 2015 9
• ESS 50 MWh
 2 : 24 MW (3.45 MW x 7 )
• 2017 12
• ESS 24 MWh
 3 : 42 MW (4.2 MW x 10 )
• 2023 05
 , , ,
·

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4.3%
16.1%
0.0%
10.0%
20.0%
30.0%
2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036
태양광 풍력
***
Phase 3
Phase 2
Phase 1
1.
구분 재생에너지 비중 국제에너지기구(IEA*) 전력계통 관리 권고사항
Phase 1 0~3% 태양광 ∙ 풍력 기술적 요구조건 검토 (가시성, 전력품질 등)
Phase 2 3~15% 태양광 ∙ 풍력 발전량 예측시스템 구축
Phase 3 15~25% 전력 수요/공급 변화 대응을 위한 유연성자원** 확보 (양수발전, 배터리 등)
Phase 4 25~50% 전력 계통 안정성 확보 (수초 단위 계통주파수 급변 대응)
 ( · )
21 4.3%
( 3.8%, 0.5%)
Phase2
* IEA (International Energy Agency) : “Getting Wind and Sun Onto the Grid“ , 2017
** ( , , OCGT(Open Cycle Gas Turbine) ) /
*** , 10

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 →
•
• /
1.
 →
• : 22GWh ( 41%, 15 )
• : 3GWh ( 6%, 2 )
* : ’23.03.12~13* [ (EPSIS)] * 1 (3/13) (3/12)
** : ’22.12.03~04* A: GS Y1-1, B: Y2-7
0
1000
2000
3000
4000
0 3 6 9 12 15 18 21 24
(kWh)
**
12/3
12/4
A B
*
(GWh)
0
10
20
30
40
50
60
70
80
90
100
0 3 6 9 12 15 18 21 24
21.8GW (41%)
3.2GW (6%)
(3 )
3/12
3/13

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구분 현행 향후
제도 재생에너지 발전량 예측 제도 (’21.10월~) 재생에너지 입찰 제도 (’25년 말~)
개요 하루 전 발전량 예측 하루 전 + 실시간 발전량 예측
예측 횟수 일 2회 (하루 전 10시, 17시)
하루 전 시장 : 일 1회
실시간 시장 : 일 96회 (15분 간격)
패널티 없음, 인센티브만 부여 있음, 전력판매대금 손실 발생 가능
1.
3) & ( )
2) & ( )
1)
(-)
=
=
A
<
<
= x
(+)
(=A)
A

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(’23 3 )
0
200
400
600
800
1000
0 3 6 9 12 15 18 21 24
(kW)
 , (’23 2 , 26 )
 (’23 5 ), 1.7GW 0.08GW (4%)
0
500
1000
1500
2000
2500
3000
3500
0 3 6 9 12 15 18 21 24
(’23 3 )
(kW)
2.
• ** ∝ 3
*𝑃 = 𝐾 ↓
[1 − 𝑇𝐶(𝑇 −𝑇𝑟𝑒𝑓)] 𝑃 :solarpower[W], 𝐾 ↓
:solarradiation[Wm-2], TC:temperaturecoefficient[%℃-1], T:moduletemperature[℃],Tref:referencetemperature[25℃]
** 𝑃 = ½𝜌Α𝒗𝟑
𝑃 :windpower[W], 𝜌 :densityofair[kg/m3],𝐴:sweptareaofwindturbine[m2],𝑣 :windspeed[m/s]
• * ∝
,

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300 m
Y1
Y2
12 ~ 25 km
GS
3 km
3 km
 (12 ~25 km) 200 ,
 / ,

2.
,
(forecast field data)

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300 m
Y1
Y2
12 ~ 25 km
GS
3 km
3 km
 (12 ~25 km) 200 ,
 / ,

2.
,
(forecast field data)

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3.
,
,
D+1 day
12 ~ 25 km
(WRF) 300m
D+1 day
12 ~ 25 km

 GS

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 ( 150m, 40 )
, ,
→ / ( , , ) ,
※ (WRF model, Weather Research and Forecasting)
• ( m) ,
( , , ) ,
•
* *
(WRF)
Y1-1
Y2-7
[m]
GS
* Apparatus and Method for Predicting Generated Power of Wind Power Generator based on Atmospheric Model Forecast
3.

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 (WRF)
• , ,
 (WRF)
3.
입력 변수
기상변수
(30개)
var01, var02, var03, var04, var05,
……….
var26, var27, var28, var29, var30
지면, 지형변수
(10개)
var31, var32, var33, var34, var35,
var36, var37, var38, var39, var40
시간, 통계변수
(15개)
var41, var42, var43, var44, var45,
var46, var47, var48, var49, var50,
var51, var52, var53, var54, var55

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3.

•
- 1 (WRF)
4 core → 92 / 1,024 core → 8.5
-
•
- WRF 1TB
• /
- CDK(Cloud Development Kit)

• 1
•
•
- PJT ProServe
-
GS
AWS

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 : 10% & 3 10%
• : 8% 3 /kWh, 6% 4 /kWh
 (’22.09 ~ , 9 )
• 2 , 3
3.
0
2
4
6
8
10
12
'22년 평균 1월 2월 3월 4월 5월 6월 7월 8월 9월 10월 11월 12월
9.62
(%)
10% ( & )

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03.
(VPP )
•
02.
SaaS (Software-as-a-Service) Platform
• (‘23 )
01.
•
4.
VPP

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© 2023, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Head of DX Lab
GS Energy
GS

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1.
2.
3. 2022년도 하반기의 풀었던 문제들
4. Lesson Learned
Agenda

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Amazon
CloudWatch
Amazon
Grafana
AWS IAM
Identity Center
< >
(WRF)
1.
(D+1 day)
(D+1 day)
Amazon
S3
AWS
Lambda
AWS
EventBridge
Amazon
SageMaker
Amazon
EC2
Amazon
S3
AWS
Lambda
Amazon
EC2
AWS
CodeCommit
AWS
CodeBuild
AWS
CodeDeploy
AWS
EventBridge
Amazon
ECR
AWS
Glue
Amazon
SNS
Amazon
Athena
AWS
Glue
Amazon
Athena

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01. SCADA : , ,
02. : ESS
03. LiDAR :
04. :
1.
01
02
03
04

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01.
▪ Agent S3 (PS & Task scheduler)
02. ,
▪
▪ D+1
✓ WRF Golden Image S3
✓ Golden Image Code-Series CI/CD
✓ WRF , Golden Image EC2
,
▪ S3 , Athena
1.
01
02

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01. (Training)
• (WRF) (SCADA)
• 25 10
02. (Prediction)
• ECR
• ECR AWS Lambda Function
• S3 D+1 (Input) ,
D+1 (Output)
1.
MLops
01
02

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1.
01.
▪ CloudWatch Agent
, SNS
✓ Agent
✓ CPU, Memory, Network, Disk
02.
▪ Grafana
, , ,
▪
01
02

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02.
Cost-savings
•
01.
Productivity
•
•
1
04.
Quick decision-making
•
•
03.
Business agility
•
• ,
MLOps
Zero
manual tasks
50%
90 days
to
7 days Real-time
2.
AWS .

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3. 2022년도 하반기의 풀었던 문제들
최적화
,
풍력 발전량 예측 지역난방 열 수요 예측

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DT
DT

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,
모를때는 최대한 판단을 미룬다
최대한 만들어 있는 제품 활용
, , ,
MLOPS
Agile

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Solutions
(1000 )
16 (subject to)
Serverless API : Zappa + Flask + Lambda
DB
,
최적화 프로젝트
Challenges
: aiohttp (async)

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지역난방 열 수요 예측
S3, Athena
SageMaker
MLOPS
Grafana, Slack,
WanDB, Retool
AWS Cloud
Athena
S3
SageMaker
Data
Extraction
Data
Validation
Feature
Engineering
Model
Training
Model
Evaluation
Instance
EventBridge
Lambda
Slack WanDB
Grafana
ECR
Func()
Athena
S3
STAGE / PROD
DEV / TEST
Grafana
SageMaker
WanDB

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4. Lessons Learned
Serverless
•
• ,
• 비용은 얼마 들지 않는다
One team
•
•
(AWS Proserv.)
Agile
• CBO + Prototyping + Sprint
=
• AWS
• Sprint

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감사합니다
© 2023, Amazon Web Services, Inc. or its affiliates. All rights reserved.