臺灣博碩士論文加值系統

有鑑於分佈式再生能源近年來越見顯著的重要性，太陽能發電不僅成為快速增長的再生能源，更已成為各國重視的能源議題之一。然而，隨著高滲透率的太陽能發電電源併入電網，可能引起潛在的電壓議題、系統過熱、以及諧波的產生。此外，太陽能發電的不穩定性將影響系統的控制，致使電容器開關和電壓調節器操作頻繁，進而損耗設備及增加系統損失。換言之，隨著日益增加的太陽能發電源併入系統，評估太陽能發電的潛在風險越顯重要，因此迫切地需要相關研究來探討饋線的太陽能承載容量，即饋線能接受且不會違反系統正常操作的太陽能最大併網裝置容量。
本論文提出隨機分析方法以決定配電饋線的太陽能承載容量。在本研究中，太陽能承載容量根據以下五個標準被定義:過電壓、電壓偏差量、動態電壓、電壓不平衡量、以及功率逆送。考量到未來饋線中太陽能配置的不確定性，本論文使用隨機分析法在饋線中多次模擬太陽能配置情況和容量，進行多次電壓品質影響的模擬以量化和統計太陽能的承載容量，並在太陽能模型中加裝智慧型變流器以改善電力品質和提升承載容量，經由研究這些影響，估算出不會危害饋線穩定度和安全性的太陽能的承載容量。並使用資料視覺化的方式呈現出饋線中太陽能裝置的最大承載容量。
本論文使用OpenDSS與Matlab共進行共同模擬，估配電饋線中的電壓品質，其中OpenDSS可提供快速負載潮流計算，且不僅可單獨運行亦可透過COM介面與多種其他軟體平台共同模擬，在每次OpenDSS模擬結束後，Matlab透過COM介面取其所需的資料加以分析計算並以圖形化呈現結果，以供進一步分析。

The integration of distributed energy resources (DERs), especially solar photovoltaics (PV), has been gaining place in the past decade, making solar the fastest growing renewable energy source. A high PV penetration can potentially lead to voltage issues, thermal stress, and system harmonics. Additionally, the variability in power generation due to PVs can affect system controls, resulting in capacitors switching and regulators tap changing frequently, and further deteriorate the equipment and increase the system losses. In other words, with adoption of solar energy rising, it is increasingly important for utilities to easily assess potential risks of PV systems. This calls for a study to determine the maximum installed capacity of PV; A condition for a given distribution feeder that can accommodate and without violating the nominal system operations is defined as the feeder’s PV hosting capacity.
In this thesis, a stochastic approach is applied to determine the PV hosting capacity in a distribution feeder. The hosting capacity is determined with respect to the following five power quality criteria: overvoltage, voltage deviation, dynamic voltage drop, voltage unbalance, and reversing power flow. By applying a stochastic approach, a large number of potential PV deployment scenarios are simulated in a distribution feeder. While considering the unpredictability of future PV deployments, both in terms of size and location are the randomly generated factors. The voltage quality impacts of multiple simulations are quantified and statistically representative PV hosting capacities. In addition, smart inverter strategy is adopted to improve power quality and enhance PV hosting capacity. After studying these impacts, estimation of the PV hosting capacity is made which will not endanger feeder stability and safety. Furthermore, a visualization manner is used to present the maximum capacity for possible PV installations for distribution feeders.
This thesis uses Open Distribution System Simulator (OpenDSS, or simply, DSS) in conjunction with Matlab to estimate voltage quality on distribution system feeders. OpenDSS offers the advantage of fast power flow analysis and it is implemented as both a stand‐alone executable program and an in‐process COM server DLL designed to be driven from a variety of existing software platforms. When simulation is done, Matlab is used to collect the needed data by COM interface and arrange to plot the results in order to do the further analysis.

中文摘要 i
ABSTRACT iii
TABLE OF CONTENTS v
LIST OF FIGURES viii
LIST OF TABLES x
I. INTRODUCTION 1
1.1 Background and Motivations 1
1.2 Research Methods 3
1.3 Contributions 4
1.4 Organization 5
II. HIGH PENETRATION PHOTOVOLTAIC SYSTEM IMPACT STUDIES 6
2.1 Challenges 6
2.1.1 Voltage Rise 7
2.1.2 Reverse Power Flow 7
2.1.3 Voltage Fluctuations 7
2.1.4 Interaction between Protection and control devices 8
2.1.5 Feeder Loading and Power Losses Increase 8
2.2 Regulation Standards of Grid-connected PV System 9
2.2.1 Definition 9
2.2.2 Classification of Grid-connect Renewable System 9
2.3 Mitigation Approaches 10
2.3.1 Voltage Regulators 10
2.3.2 On-Load Tap Changers (OLTC) 11
2.3.3 Static Var Compensators (SVC) 12
2.3.4 Photovoltaic Smart Inverter 12
III. PHOTOVOLTAIC AND SMART INVERTER FUNCTION 13
3.1 Brief Overview of PV System 13
3.1.1 Introduction of OpenDSS 14
3.1.2 Generic Model in OpenDSS 16
3.2 Smart Inverter Control 17
3.2.1 Overview of Smart Inverters 17
3.2.2 Intelligent Volt-Var Control 18
3.2.3 Intelligent Volt-Watt Control 20
3.2.4 Intelligent Volt-Var cooperates with Volt-Watt Control 22
IV. HOSTING CAPACITY STUDY 23
4.1 Hosting Capacity Concept 23
4.2 Impacting Factors 23
4.3 Limiting Factors 25
V CASE STUDY 29
5.1 Proposed Stochastic Analysis 29
5.2 Power System Simulation 35
5.3 Case Study 37
5.3.1 Results of Over-Voltage Criterion 40
5.3.2 Results of Voltage Deviation Criterion 42
5.3.3 Results of Dynamic Voltage Criterion 44
5.3.4 Results of Voltage Unbalance Criterion 46
5.3.5 Comparing Results 47
5.3.6 Results of Reversing Power Flow Criterion 48
5.3.7 Results of System Losses 52
5.3.8 Discussions 53
VI. CONCLUTION AND FUTURE WORKS 56
6.1 Conclusion 55
6.2 Future works 56
REFERENCES 57
 

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