عنوان

مدل‌سازی ظرفیت دینامیکی خطوط انتقال به منظور برنامه‌ریزی بهینه شبکه‌های هوشمند انرژی,‮‭Dynamic line rating modeling for optimal scheduling smart grid‬

Number

‭۲۲۲۳۰پ‬

.Language of Text, Soundtrack etc

per

Title Proper

مدل‌سازی ظرفیت دینامیکی خطوط انتقال به منظور برنامه‌ریزی بهینه شبکه‌های هوشمند انرژی

Parallel Title Proper

‮‭Dynamic line rating modeling for optimal scheduling smart grid‬

First Statement of Responsibility

/سجاد مددی یگانه

Name of Publisher, Distributor, etc.

: مهندسی برق و کامپیوتر

Date of Publication, Distribution, etc.

، ‮‭۱۳۹۸‬

Name of Manufacturer

، افشاری

Specific Material Designation and Extent of Item

‮‭۱۱۵‬ص‬

Text of Note

چاپی - الکترونیکی

Dissertation or thesis details and type of degree

دکتری

Discipline of degree

مهندسی برق قدرت

Date of degree

‮‭۱۳۹۸/۰۷/۱۷‬

Body granting the degree

تبریز

Text of Note

در سالیان اخیر با توجه به افزایش چشمگیر مصرف انرژی و توسعه روز افزون منابع انرژی تجدیدپذیر، شبکه قدرت با چالش‌های جدیدی روبه رو شده است .خطوط هوایی ضعیف به عنوان مهمترین چالش در استفاده بیشینه از منابع انرژی تجدید پذیر به خصوص مزارع بادی شناخته می‌شوند .استفاده از ظرفیت دینامیکی خطوط انتقال به عنوان پاسخی اقتصادی و فنی برای غلبه بر محدودیت ظرفیت خطوط ارائه شده است .این ظرفیت معمولا به عوامل آب و هوایی وابسته است به خصوص در سرعت باد زیاد و دمای محیط کم خط انتقال بیشترین ظرفیت دینامیکی خود را دارا می‌باشد .در نتیجه این روش می‌تواند با تولیدات مزارع بادی به خوبی هماهنگ شوند .با این حال در برنامه‌ریزی شبکه، ظرفیت دینامیکی خطوط انتقال همانند ظرفیت تولیدی نیروگاه‌های تجدیدپذیر قابل دستیابی نمی‌باشد .با این حال استفاده از ظرفیت دینامیکی تخمین زده شده خطوط انتقال برای برنامه‌ریزی بهینه و قابل اطمینان شبکه نیازمند پیش‌بینی مناسب از مقدار آینده آن می‌باشد .در این رساله ابتدا مروری بر کارهای صورت گرفته در زمینه پیش‌بینی ظرفیت حرارتی خطوط انتقال و برنامه ریزی شبکه با در نظر گرفتن این محدودیت انجام می‌شود و در ادامه تاثیر ماهیت متناوب این ظرفیت در برنامه‌ریزی شبکه بررسی می‌شود .همچنین یک مدل پیش‌بینی جدید برای پیش‌بینی ظرفیت دینامیکی ارائه می‌شود .به منظور توسعه شبکه خطوط انتقال با توجه به این محدودیت، یک مدل مناسب توسعه خطوط نیز ارائه می‌گردد

Text of Note

Dynamic line rating (DLR) technologies are fast becoming a key instrument in power systems with high penetration of wind farms, because power companies can reduce the value of wind power spillage and also can postpone the investments of transmission lines by using DLR equipment. In other words, weather data plays an important role in determining the value of the transmission line capacities. Moreover, wind power generations are related to weather data. Therefore, it can be concluded that the increase of wind power generations occurs when transmission lines operate with higher capacity. However, what we know about DLR is largely based on empirical studies that investigate how to change the capacity of transmission lines with changing the weather data. Therefore, it can be concluded that considering DLR constraint can be increased the stochastic set. The increase of stochastic variables in power systems has a significant effect on power system scheduling; therefore, the impact of stochastic variables on power systems should be carefully taken into account. This can be realized by probabilistic evaluation. In this thesis, a probabilistic approach is proposed to calculate the expected value of ATC and depict cumulative distribution function (CDF) of each variable. The proposed probabilistic model is based on dividing the stochastic set into smaller groups similar to clustering techniques. However, the significant differences between the proposed probabilistic model and cluster-based model are related to detect the optimal value of the reduced number of stochastic sets and find members of each cluster set. In other words, a sequential game-theoretic approach is applied to divide data into a smaller set. Also, Due to the intermittent nature of dynamic rating (DLR) of overhead lines, DLR forecasting plays an important role in the scheduling of power networks. In the DLR forecasting, the trend and fluctuation of past data are modeled and future DLR values are estimated. Autoregressive model and its variants are expanded to reach accurate forecasting. Such methods apply white noise assumption to account for the DLR fluctuations. Since DLR fluctuations are related to weather condition, the white noise assumption cannot model fluctuations correctly. The Brownian motion has been implemented to meet data fluctuation issues in time series prediction. The Ornstein-Uhlenbeck (OU) process is one of the most widely groups of forecasting methods by considering Brownian motion. However, this approach is able to model a single factor that has never driven over the time. Therefore, implementing this factor is not suitable for forecasting DLR. In this paper, the OU process is extended into an integrated factorized Ornstein-Uhlenbeck to model and predict DLR values by considering hidden factors of DLR such as the weather conditions and other latent factors. lack of a mathematical model for estimating the capacity of lines by considering the weather conditions across the line and wind farm generations can be accounted as a research gap of DLR problems. This thesis proposes a solution for transmission expansion planning (TEP) considering DLR technologies. The operational costs, penalty cost of wind power spillage and investment costs depending on constructing new lines and reinforcing lines are considered in the objective function of the proposed TEP model. In addition, this study seeks to develop a model which will help to address the research gap of DLR problems. The geostatistical analysis is used to develop a mathematical model, which can provide the relationship between wind farms as well as the capacity of lines calculated based on the weather conditions across the line. The efficiency of the proposed model is examined on a modified IEEE reliability test system

Parallel Title

‮‭Dynamic line rating modeling for optimal scheduling smart grid‬

مددی یگانه، سجاد

Madadi Yeganeh, Sajad

Public note

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