Thesis Title: Modeling of Networked-Microgrids and Comparision of Their Dynamic Characteristic with Reduced Moldel
Abstract: In recent years, the concept of microgrid has gained attention as part of the energy infrastructure of smart grids. To improve reliability, stability margins, and flexibility, microgrids can interconnect to each other and form a multiple microgrid system. The integration of distributed energy resources and the formation of a multiple microgrid system create various technical challenges and issues, including uncertainty and variable power from wind and solar-based distributed generation, reduction of system inertia, voltage and frequency stability control in islanded operation, and the need for coordination among microgrids. Studying the effects of these problems and providing suitable analytical solutions requires access to appropriate and detailed models that accurately represent the dynamic behavior of the microgrid and its components. In this thesis, a detailed model of distributed generation resources such as DFIG wind turbines and battery storage systems, along with load models and network lines, is presented, and by integrating these models, a multi-microgrid model is obtained. With the expansion of microgrid dimensions, the use of detailed and high-order models leads to an increase in computational burden and the time required for simulations, making it difficult to evaluate the dynamic characteristics of these systems. When studying a specific microgrid, the external microgrids and its elements can be replaced with simpler models. For this purpose, this thesis presents a reduced-order model for external microgrids titled ”Black Box External Grid”. In this method, external microgrids are replaced by a black box model that has been trained using a nonlinear exogenous self-organizing neural network method (NARX). For each microgrid, two neural networks related to the injected active and reactive power, named NN-P and NN-Q, are trained using data obtained from the detailed model simulation, and their estimated output values are injected as a controllable current source at the boundary bus of the microgrid. The effectiveness of the proposed reduced-order model is evaluated by comparing it with the results obtained from the detailed model.
عنوان پایان نامه: مدل سازی شبکه ای از ریزشبکه ها و مقایسه مشخصات دینامیکی آن با مدل کاهش یافته
چکیده: ﺩﺭ ﺳﺎﻝﻫﺎﻱ ﺍﺧﻴﺮ ﻣﻔﻬﻮﻡ ﺭﻳﺰﺷﺒﻜﻪ ﺑﻪﻋﻨﻮﺍﻥ ﺑﺨﺸﻲ ﺍﺯ ﺯﻳﺮﺳﺎﺧﺖ ﺍﻧﺮﮊﻱ شبکه ﻫﻮﺷﻤﻨﺪ ﻣﻮﺭﺩ ﺗﻮﺟﻪ ﻗﺮﺍﺭ ﮔﺮﻓﺘﻪ ﺍﺳﺖ. ﺑﻪﻣﻨﻈﻮﺭ ﺑﻬﺒﻮﺩ ﻗﺎﺑﻠﻴﺖ ﺍﻃﻤﻴﻨﺎﻥ، ﺣﺎﺷﻴﺔ ﭘﺎﻳﺪﺍﺭﻱ ﻭ ﺍﻧﻌﻄﺎﻑ ﭘﺬﻳﺮﻱ، ﺭﻳﺰﺷﺒﻜﻪﻫﺎ ﻣﻲﺗﻮﺍﻧﻨﺪ ﺑﻪﻳﻜﺪﻳﮕﺮ ﻣﺘﺼﻞ ﺷﺪﻩ ﻭ ﻳﻚ ﺭﻳﺰشبکه ﭼﻨﺪﮔﺎﻧﻪ ﺗﺸﻜﻴﻞ ﺩﻫﻨﺪ. ﻳﻜﭙﺎﺭﭼﻪ ﺳﺎﺯﻱ ﻣﻨﺎﺑﻊ ﺍﻧﺮﮊﻱ ﭘﺮﺍﻛﻨﺪﻩ ﻭ ﺗﺸﻜﻴﻞ ﺭﻳﺰشبکه ﭼﻨﺪﮔﺎﻧﻪ، ﭼﺎﻟﺶﻫﺎ ﻭ ﻣﺸﻜﻼﺕ ﻓﻨﻲ ﻣﺨﺘﻠﻔﻲ ﺍﺯ ﺟﻤﻠﻪ ﻋﺪﻡ ﻗﻄﻌﻴﺖ ﻭ ﺗﻮﺍﻥ ﻣﺘﻐﻴﺮ ﺗﻮﻟﻴﺪ ﭘﺮﺍﻛﻨﺪه ﻣﺒﺘﻨﻲ ﺑﺮ ﺑﺎﺩ ﻭ ﺧﻮﺭﺷﻴﺪ، ﻛﺎﻫﺶ ﺍﻳﻨﺮﺳﻲ ﺳﻴﺴﺘﻢ، ﻛﻨﺘﺮﻝ ﭘﺎﻳﺪﺍﺭﻱ ﻭﻟﺘﺎﮊ ﻭ ﻓﺮﻛﺎﻧﺲ ﺩﺭ ﺣﺎﻟﺖ ﻛﺎﺭ ﺟﺰﻳﺮﻩﺍﻱ ﻭ ﻟﺰﻭﻡ ﻫﻤﺎﻫﻨﮓﺳﺎﺯﻱ ﺑﻴﻦ ﺭﻳﺰﺷﺒﻜﻪﻫﺎ ﺭﺍ ﺍﻳﺠﺎﺩ ﻣﻲﻛﻨﺪ. ﻣﻄﺎلعه ﺍﺛﺮ ﺍﻳﻦ ﻣﺸﻜﻼﺕ ﻭ ﺍﺭﺍﺋﻪ ﺭﺍﻩ ﺣﻞﻫﺎﻱ ﺗﺤﻠﻴﻠﻲ ﻣﻨﺎﺳﺐ، ﻧﻴﺎﺯﻣﻨﺪ ﺩﺳﺘﺮﺳﻲ ﺑﻪ ﻣﺪﻝﻫﺎﻱ ﻣﻨﺎﺳﺐ ﻭ ﺑﺎﺟﺰﺋﻴﺎﺗﻲ ﺍﺳﺖ ﻛﻪ ﺭﻓﺘﺎﺭ ﺩﻳﻨﺎﻣﻴﻜﻲ ﺭﻳﺰﺷﺒﻜﻪ ﻭ ﻋﻨﺎﺻﺮ ﻣﻮﺟﻮﺩ ﺩﺭ ﺁﻥ ﺭﺍ ﺑﻪﺧﻮﺑﻲ ﻣﺪﻝ کند. ﺩﺭ ﺍﻳﻦ ﭘﺎﻳﺎﻥﻧﺎﻣﻪ، ﻣﺪﻝ ﺑﺎﺟﺰﺋﻴﺎﺕ ﻣﻨﺎﺑﻊ ﺗﻮﻟﻴﺪ ﭘﺮﺍﻛﻨﺪﻩ ﻣﺜﻞ ﺳﺎﻣﺎنه ﺑﺎﺩﻱ ﻭ ﺳﻴﺴﺘﻢ ﺫﺧﻴﺮﻩﺳﺎﺯ ﺑﺎﺗﺮﻱ ﺑﻪﻫﻤﺮﺍﻩ ﻣﺪﻝ ﺑﺎﺭ ﻭ ﺧﻄﻮﻁ ﺷﺒﻜﻪ ﺍﺭﺍﺋﻪ ﻭ ﺑﺎ ﻳﻜﭙﺎﺭﭼﻪﺳﺎﺯﻱ ﺍﻳﻦ ﻣﺪﻝﻫﺎ، ﻣﺪﻝ ﻳﻚ ﺭﻳﺰشبکه ﭼﻨﺪﮔﺎﻧﻪ ﺣﺎﺻﻞ ﻣﻲﺷﻮﺩ. ﺑﺎ ﮔﺴﺘﺮﺵ ﺍﺑﻌﺎﺩ ﺭﻳﺰﺷﺒﻜﻪ، ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﻣﺪﻝﻫﺎﻱ ﺑﺎﺟﺰﺋﻴﺎﺕ ﻭ ﻣﺮﺗﺒﺔ ﺑﺎﻻ، ﻣﻨﺠﺮ ﺑﻪ ﺍﻓﺰﺍﻳﺶ ﺑﺎﺭ ﻣﺤﺎﺳﺒﺎﺕ ﻭ ﻣﺪﺕ ﺯﻣﺎﻥ ﻻﺯﻡ ﺑﺮﺍﻱ ﺍﻧﺠﺎﻡ ﺷﺒﻴﻪﺳﺎﺯﻱ ﺷﺪﻩ ﻭ ﺍﺭﺯﻳﺎﺑﻲ ﻣﺸﺨﺼﺎﺕ ﺩﻳﻨﺎﻣﻴﻜﻲ ﺍﻳﻦ ﺳﻴﺴﺘﻢﻫﺎ ﺭﺍ ﺩﺷﻮﺍﺭ ﻣﻲﻛﻨﺪ. ﺩﺭ ﻫﻨﮕﺎﻡ ﻣﻄﺎﻟﻌﻪ ﺭﻭﻱ ﻳﻚ ﺭﻳﺰشبکه ﺧﺎﺹ، ﻣﻲﺗﻮﺍﻥ ﺭﻳﺰﺷﺒﻜﻪ ﻭ ﻋﻨﺎﺻﺮ ﺧﺎﺭﺟﻲ ﺭﺍ ﺑﺎ ﻣﺪﻝﻫﺎﻱ ﺳﺎﺩﻩﺗﺮﻱ ﺟﺎﻳﮕﺰﻳﻦ ﻧﻤﻮﺩ. ﺑﻪﻫﻤﻴﻦ ﻣﻨﻈﻮﺭ ﺩﺭ ﺍﻳﻦ ﭘﺎﻳﺎﻥﻧﺎﻣﻪ، ﻳﻚ ﻣﺪﻝ ﻛﺎﻫﺶ ﻣﺮﺗﺒﻪ ﺑﺮﺍﻱ ﺭﻳﺰﺷﺒﻜﻪﻫﺎﻱ ﺧﺎﺭﺟﻲ ﺗﺤﺖ ﻋﻨﻮﺍﻥ «ﺟﻌﺒﻪ ﺳﻴﺎﻩ شبکه ﺧﺎﺭﺟﻲ» ﺍﺭﺍﺋﻪ ﺷﺪﻩ ﺍﺳﺖ. ﺩﺭ ﺍﻳﻦ ﺭﻭﺵ ﺭﻳﺰﺷﺒﻜﻪﻫﺎﻱ ﺧﺎﺭﺟﻲ ﺑﻮسیله ﺟﻌﺒﻪ ﺳﻴﺎﻫﻲ ﻛﻪ ﺑﺎ ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﺭﻭﺵ شبکه ﻋﺼﺒﻲ ﺑﺮﻭﻥﺯﺍﺩ ﺧﻮﺩﭘﻨﺪﺍﺭﻧﺪه ﻏﻴﺮﺧﻄﻲ (NARX) ﺁﻣﻮﺯﺵ ﺩﺍﺩﻩ ﺷﺪﻩﺍﻧﺪ، ﺟﺎﻳﮕﺰﻳﻦ ﻣﻲﺷﻮﻧﺪ. ﺑﺮﺍﻱ ﻫﺮ ﺭﻳﺰﺷﺒﻜﻪ، ﺩﻭ شبکه ﻋﺼﺒﻲ ﻣﺮﺑﻮﻁ ﺑﻪ ﺗﻮﺍﻥ ﺍﻛﺘﻴﻮ ﻭ ﺭﺍﻛﺘﻴﻮ ﺗﺰﺭﻳﻘﻲ، ﺑﻪﻧﺎﻡﻫﺎﻱ NN-P ﻭ NN-Q ﺑﺎ ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﺩﺍﺩﻩﻫﺎﻱ ﺑﻪﺩﺳﺖ ﺁﻣﺪﻩ ﺍﺯ ﺷﺒﻴﻪﺳﺎﺯﻱ ﻣﺪﻝ ﺑﺎﺟﺰﺋﻴﺎﺕ ﺁﻣﻮﺯﺵ ﺩﺍﺩﻩ ﺷﺪﻩ ﻭ ﻣﻘﺎﺩﻳﺮ ﺗﺨﻤﻴﻨﻲ ﺧﺮﻭﺟﻲ ﺁﻥﻫﺎ ﺑﻪ ﺻﻮﺭﺕ یک ﻣﻨﺒﻊ ﺟﺮﻳﺎﻥ ﻛﻨﺘﺮﻝﺷﻮﻧﺪﻩ ﺩﺭ ﺑﺎﺱ ﻣﺮﺯﻱ ﺭﻳﺰﺷﺒﻜﻪ ﺗﺰﺭﻳﻖ ﻣﻲﺷﻮﻧﺪ. ﻛﺎﺭﺁﻣﺪﻱ ﻣﺪﻝ ﻛﺎﻫﺶ ﻣﺮﺗﺒﻪ ﻳﺎفته ﺍﺭﺍﺋﻪ ﺷﺪﻩ ﺑﻮسیله ﻣﻘﺎﻳﺴﻪ ﺑﺎ ﻧﺘﺎﻳﺞ ﺣﺎﺻﻞ ﺍﺯ ﻣﺪﻝ ﺑﺎﺟﺰﺋﻴﺎﺕ ﺑﺮﺭﺳﻲ ﻣﻲﺷﻮﺩ.
