Curtailment and Compensation Strategies for Stable Expansion of Renewable Energy in Power Grid = 전력 계통 내 재생에너지의 안정적 확대를 위한 출력제어 및 보상 전략

다국어 초록 (Multilingual Abstract)

In the process of expanding renewable energy to achieve carbon neutrality targets, curtailment occurs inevitably. However, in South Korea, conflicts between renewables producers and grid operators have intensified due to the absence of a clear compensation framework for curtailment. This dissertation proposes a policy development approach for curtailment compensation through three phases: curtailment cause identification, cause-specific compensation criteria, and estimation of ingenerated power during curtailment.
First, a methodology was developed that systematically categorizes renewable energy curtailment causes into power supply-demand imbalance, transmission constraints, voltage constraints, and emergency response utilizing Unit Commitment (UC) constraints. This methodology quantitatively calculates cause-specific curtailment amounts by analyzing curtailment volume changes according to UC constraint variations.
Second, a cause-specific compensation criteria based on power grid reliability types was constructed. Curtailment causes were classified into planning phase issues (adequacy) and operational issues (security), with differentiated compensation eligibility criteria proposed for each category. This presents an equilibrium point for power industry stakeholders to equitably share losses resulting from renewable energy curtailment.
Third, a reconstruction methodology was developed to reconstruct potential generation from curtailed renewable facilities. This methodology integrated weather-based and time-based profiles through adaptive binning statistical analysis combined with dynamic integration mechanisms. The integrated approach enables more accurate potential generation reconstruction by considering both weather-dependent generation variations and characteristic diurnal patterns of PV generation.
The research outcomes establish a systematic and quantitative framework for renewable energy curtailment management that integrates cause identification, compensation determination, and potential generation reconstruction. These integrated methodologies provide quantitative technical foundations for curtailment management systems and are expected to improve operational efficiency in power systems with high renewable energy penetration.

국문 초록 (Abstract)

탄소중립 목표 달성을 위한 재생에너지 확대 과정에서 출력제어가 불가피하게 발생하고 있으나, 한국의 경우 출력제어에 관한 명확한 보상 체계의 부재로 인해 재생에너지 발전사업자와 계통운영자 간 갈등이 심화되고 있다. 이에 본 연구는 재생에너지 출력제어 원인 판별, 원인별 보상 기준, 그리고 출력제어 시 미발전량 추정이라는 세 단계의 연구를 통해 출력제어 보상 정책 수립방안을 제시한다.
첫째, Unit Commitment (UC)의 constraints를 활용하여 재생에너지 출력제어의 원인을 전력수급 불균형, 송전망 제약, 전압 제약, 그리고 비상상황 대응으로 체계적으로 구분하는 방법론을 개발하였다. 이 방법론은 UC의 제약조건 변화에 따른 출력제어량 변화를 분석하여 원인별 출력제어량을 정량적으로 산출할 수 있게 한다.
둘째, 전력계통 신뢰도 유형을 기반으로 한 원인별 보상 기준 체계를 구축하였다. 출력제어 원인을 계획 단계 문제(적정성)와 운영 문제(안전성)로 분류하고, 각 범주에 대한 차별화된 보상 메커니즘을 제시하였다. 이는 재생에너지 출력제어로 인한 손실을 전력산업 이해관계자들이 고루 분담할 수 있는 균형점을 제시하였다.
셋째, 출력제어된 재생에너지 설비의 출력제어량을 산출하기 위한 잠재 발전량 재구성 방법론을 개발하였다. 이 방법론은 적응적 binning 기반 통계 분석과 동적 통합 메커니즘을 결합하여 기상 기반(WB) 및 시간 기반(TB) 발전 프로필을 통합한다. 통합 접근법은 기상 조건에 따른 발전량 변동과 태양광 발전의 특성적 일변화 패턴을 모두 고려하여 더 정확한 잠재 발전량 재구성을 가능하게 한다.
본 연구의 결과는 원인 판별, 보상 결정, 그리고 잠재 발전량 재구성을 통합한 체계적이고 정량적인 재생에너지 출력제어 관리 프레임워크를 구축한다. 이러한 통합된 방법론들은 출력제어 관리 시스템을 위한 정량적 기술적 기반을 제공하며, 높은 재생에너지 보급률을 가진 전력계통의 운영 효율성 개선을 기대한다.

목차 (Table of Contents)

• LIST OF FIGURES v
• LIST OF TABLES vi
• ABSTRACT vii
• 1. Introduction 1
• 1.1 Problem Statements 1
• 1.1.1 Increase of Renewable Energy Connected to the Power Grid 1
• 1.1.2 Curtailment of Renewable Energy in South Korea and the Absence of Compensation 2
• 1.2. Objectives of Research 3
• 2. Analysis of Curtailment Experiences 5
• 2.1. International Experiences of Renewable Energy Curtailment 5
• 2.2. South Korean Experiences of Renewable Energy Curtailment 7
• 2.3. Key Causes of Curtailment 9
• 2.3.1 Transmission Limitation 10
• 2.3.2 Grid Security and Stability 11
• 2.3.3 Oversupply 11
• 2.3.4 Regulatory Approaches to Curtailment 12
• 2.4. International Approaches to Renewable Energy Curtailment Compensation 12
• 2.4.1 Full Compensation Schemes 12
• 2.4.2 Conditional Compensation Schemes 13
• 2.4.3 Non-Compensation Schemes 16
• 2.4.4 International Comparison Analysis 17
• 2.4.5 Key Insights from Policy Analysis 23
• 2.5. Summary 24
• 3. Cause-based Renewable Energy Curtailment Calculation 26
• 3.1. Objective function 26
• 3.2 Constraints Setup 29
• 3.2.1 Constraints Related to Supply and Demand Balance 30
• 3.2.2 Constraints Related to Transmission Limit 34
• 3.2.3 Constraints Related to Voltage 35
• 3.3 Curtailed Power Calculation of Renewable Energy by Cause 36
• 3.4 Calculation of Curtailment Volume 41
• 3.4.1 Contingency-induced Curtailment Volume Pcont,j(t) 41
• 3.4.2 Voltage Issues-induced Curtailment Volume Pov,j(t) 42
• 3.4.3 Transmission Constraints-induced Curtailment Volume Pol,j(t) 42
• 3.4.4 Balance-induced Curtailment Volume Pbal,j(t) 43
• 3.4.5 Emergency Response-induced Curtailment Volume Pfault,j(t) 43
• 3.5 Methodology Verification 44
• 3.5.1 Analysis of Curtailment Causes by Renewable Energy Source 47
• 3.5.2 Comparison of Annual Renewable Energy Curtailment by Cases 51
• 3.6 Summary 54
• 4. Proposal for Curtailment Compensation Criteria 56
• 4.1. Compensation Eligibility Criteria 56
• 4.1.1 Renewable Energy Curtailment for Supply–Demand Balance 58
• 4.1.2 Renewable Energy Curtailment owing to Transmission Constraint 59
• 4.1.3 Renewable Energy Curtailment for Preventive Measures 61
• 4.1.4 Real-time Grid Emergency Response 62
• 4.2. Calculation of Compensable Curtailment Energy for Renewables 63
• 4.2.1 Compensable Curtailment Energy due to Contingency Analysis Econt,j(t) 63
• 4.2.2 Compensable Curtailment Energy due to Voltage Constraints Eov,j(t) 64
• 4.2.3 Compensable Curtailment Energy due to Transmission Constraints Eol,j(t) 65
• 4.2.4 Compensable Curtailment Energy due to Supply-Demand Balance Ebal,j(t) 65
• 4.2.5 Compensable Curtailment Energy due to Emergency Response Efault,j(t) 66
• 4.3. Calculation of Compensation Payment 67
• 4.4. Eligibility Assessment for Renewable Energy Curtailment Compensation in Day-Ahead Market 68
• 4.4.1 Data Foundation and Scenario Configuration 68
• 4.4.2 Application of Compensation Eligibility Criteria 69
• 4.4.3 Multiple Probability Simulation 70
• 4.4.4 Results - Analysis of Compensation Eligibility Distribution 72
• 4.4.5 Results - Analysis of Compensation-Eligible vs Non-Eligible Curtailment Energy 74
• 4.4.6 Results - Compensation Payment Analysis 76
• 4.5 Eligibility Assessment for Renewable Energy Curtailment Compensation in Emergency response 77
• 4.5.1 Data configuration and multiply probability simulations 77
• 4.5.2 Analysis of Simulation Results 80
• 4.6. Policy Implications of Curtailment Compensation Eligibility Assessment 81
• 5. Reconstruction of Potential Power Generation for Renewable Energy under Curtailment 83
• 5.1 Binning-based Statistical Method for Photovoltaic Generation 83
• 5.1.1 Configuration of Adaptive Binning Structure 84
• 5.1.2 Statistical Analysis by Bin for Generation Data 90
• 5.2. Reconstruction Method of Potential Generation Profile 92
• 5.2.1 Component 1: Weather-Based Profile Modeling 92
• 5.2.2 Component 2: Time-Based Profile Modeling 95
• 5.2.3 Integration: Development of Performance-Driven Practical Reconstruction Profile 97
• 5.3 Simulation 101
• 5.3.1 Data Setup 101
• 5.3.2 Performance Validation of the Proposed Binning Method 104
• 5.3.3 Performance Validation of the Proposed PD-PRP 109
• 5.4 Summary 112
• 6. Conclusion 114
• 6.1. Summary of Research 114
• 6.2. Contributions and Advantages 115
• References 117
• Abstract in Korean 124