Stratified Latin Hypercube Sampling Assisted Stochastic Optimization in Portfolio Planning of Distributed Energy Resources for Virtual Power Plants
- 주제(키워드) DER Allocation , Distributed Energy Resources , Scenario Generation , Stratified Latin Hypercube Sampling , Two-stage Stochastic Mixed-Integer Programming , VPP Bidding Strategy
- 발행기관 고려대학교 대학원
- 지도교수 Sung-Kwan Joo
- 발행년도 2024
- 학위수여년월 2024. 8
- 학위명 박사
- 학과 및 전공 대학원 전기전자공학과
- 세부전공 전기에너지
- 원문페이지 81 p
- 실제URI http://www.dcollection.net/handler/korea/000000287929
- UCI I804:11009-000000287929
- DOI 10.23186/korea.000000287929.11009.0001563
- 본문언어 영어
초록/요약
The emergence of Virtual Power Plants (VPP) marks a significant transformation in the energy sector, moving towards more decentralized and sustainable energy systems. A VPP aggregates distributed energy resources (DER), including energy storage systems (ESS), distributed generator (DG), photovoltaic (PV) generators and wind turbines (WT). This integration allows for an improved system efficiency, as the VPP operator can submit an operational schedule of the DER to the system operator, eliminating the need to individually monitor and control each DER. The allocation of DERs within a VPP is becoming increasingly important due to the growing penetration of Renewable Energy Sources (RES), and the dynamic nature of electricity markets. Effective DER allocation strategies enable VPPs to maximize financial returns, and participate competitively in various energy markets, including the day-ahead market (DAM). However, it is challenging, primarily because of the variability in RES’s power generation and fluctuating market prices. To represent the variability in RES’s power generation and market price fluctuations, Kernel Density Estimation method combined with Stratified Latin Hypercube Sampling (KDE-SLHS) is employed for modeling RES scenario generation in this dissertation. Furthermore, a copula function based on SLHS is used for modeling forecast errors that consider the spatial and temporal correlations of RES generators. A two-stage mixed-inter programming (TSMIP) is utilized to optimize portfolio planning of DER within a VPP for maximizing the revenue and minimizing the imbalance penalty. The numerical results of the portfolio planning method show that optimal resource allocation is achieved through TSMIP, taking into account the RES’s power generation and SMP uncertainty. Consequently, it successfully maximizes SMP and REC revenues while minimizing imbalance penalties and satisfying the constraints of DERs.
more목차
CHAPTER 1. INTRODUCTION 1
1.1 Background 1
1.2 Literature Review 3
1.3 Contributions 8
1.4 Organization of the Dissertation 9
CHAPTER 2. RES SCENARIO GENERATION USING SLHS 10
2.1 The Market Environment 11
2.2 Distributed Energy Resources 13
2.3 RES Scenario Generation 15
2.3.1 Kernel Density Estimation Functions 16
2.3.2 Stratified Latin Hypercube Sampling 19
2.3.2 Copula Functions 24
CHAPTER 3. PORTFOLIO PLANNING OF DER FOR VPP USING TWO-STAGE STOCHASTIC MIXED INTEGER PROGRAMMING 28
3.1 First Stage Problem 32
3.2 Second Stage Problem 34
CHAPTER 4. NUMERICAL RESULTS 39
4.1 Case Study I: RES Scenario Generation 40
4.1.1 RES Scenario Generation Using SLHS 40
4.1.2 Forecast Error Modeling Using Copula Functions 48
4.2 Case Study II: DER Portfolio Planning 52
4.2.1 DER Allocation Results 54
4.2.2 DER Scheduling Results 56
CHAPTER 5. CONCLUSIONS 62
REFERENCES 64
