ﮐﻨﺘﺮل ﻓﺮﮐﺎﻧﺲ در رﯾﺰﺷﺒﮑﻪﻫﺎي ﭼﻨﺪ-ﺣﺎﻣﻠﯽ ﺑﺎ ﺣﻀﻮر ﺧﻮدروﻫﺎي اﻟﮑﺘﺮﯾﮑﯽ ﻣﺒﺘﻨﯽ ﺑﺮ ﮐﻨﺘﺮلﮐﻨﻨﺪه ﺳﯿﺴﺘﻢ اﺳﺘﻨﺘﺎج ﻓﺎزي ﻋﺼﺒﯽ ﺗﻄﺒﯿﻘﯽ

Frequency Control in Multi-Carrier Microgrids with the Presence of Electric Vehicles Based on Adaptive Neuro Fuzzy Inference System Controller

اﻣﺮوزه اﻓﺰاﯾﺶ ﻗﯿﻤﺖ ﺳﻮﺧﺖﻫﺎي ﻓﺴﯿﻠﯽ و ﮐﺎﻫﺶ ﻣﻨﺎﺑﻊ از ﯾﮏ ﺳﻮ و آﻟﻮدﮔﯽﻫﺎي زﯾﺴﺖ ﻣﺤﯿﻄﯽ از ﺳﻮي دﯾﮕﺮ ﺑﺎﻋﺚ اﻓﺰاﯾﺶ اﺳﺘﻔﺎده از ﻣﻨﺎﺑﻊ ﺗﺠﺪﯾﺪﭘﺬﯾﺮ ﮔﺮدﯾﺪه اﺳﺖ. در اﯾﻦ ﻣﻘﺎﻟﻪ رﯾﺰﺷﺒﮑﻪ ﻣﻮرد ﻣﻄﺎﻟﻌﻪ از ﻣﻨﺎﺑﻊ ﺑﺎدي و ﺧﻮرﺷﯿﺪي، ذﺧﯿﺮهﺳﺎز ﺑﺎﺗﺮي و ﻓﻼﯾﻮﯾﻞ( ﺧﻮدرو اﻟﮑﺘﺮﯾﮑﯽ، دﯾﺰل ژﻧﺮاﺗﻮر و ﺣﻀﻮر ﺳﯿﺴﺘﻢﻫﺎي اﻧﺮژي ﭼﻨﺪ ﺣﺎﻣﻠﯽ )MCH( ﺑﻪﻋﻨﻮان اﻧﺮژي ﺗﺮﮐﯿﺒﯽ ﺑﺮق و ﺣﺮارت )CHP( ﺗﺸﮑﯿﻞ ﺷﺪه اﺳﺖ. ﻓﺮﮐﺎﻧﺲ رﯾﺰﺷﺒﮑﻪ ﺑﺎﺗﻮﺟﻪ ﺑﻪ ﺷﺒﮑﻪ ﮔﺎز و ﭘﯿﮏ ﻣﺼﺮف ﮐﻨﺘﺮل ﻣﯽﺷﻮد. در رﯾﺰﺷﺒﮑﻪ ﭼﻨﺪ ﺣﺎﻣﻠﯽ ﭘﺨﺶ ﺑﺎر ﺷﺒﮑﻪ ﮔﺎز ﻫﻤﺰﻣﺎن ﺑﺎ ﭘﺨﺶ ﺑﺎر اﻟﮑﺘﺮﯾﮑﯽ ﻣﻨﻈﻮر ﻣﯽﮔﺮدد. ﻫﻤﭽﻨﯿﻦ ﻓﺮﮐﺎﻧﺲ ﺑﻪﺻﻮرت ﻏﯿﺮﺧﻄﯽ ﮐﻨﺘﺮل ﻣﯽﺷﻮد. از ﻃﺮف دﯾﮕﺮ روﻧﺪ رو ﺑﻪ رﺷﺪ ﺗﻮﻟﯿﺪ و ﺑﻪﮐﺎرﮔﯿﺮي ﺧﻮدروﻫﺎي ﺑﺮﻗﯽ )V2G( ﺑﺎرﻫﺎي ﺟﺪﯾﺪي ﺑﺮاي ﺷﺒﮑﻪ ﺑﺮق اﯾﺠﺎد ﮐﺮده اﺳﺖ ﮐﻪ در ﺻﻮرﺗﯽ ﮐﻪ ﻣﺪﯾﺮﯾﺖ ﺻﺤﯿﺤﯽ ﺑﺮ روي ﻧﺤﻮه ﺷﺎرژ آﻧﻬﺎ ﺻﻮرت ﻧﮕﯿﺮد اﻧﺤﺮاف ﻓﺮﮐﺎﻧﺲ ﺷﺒﮑﻪ اﻓﺰاﯾﺶ ﯾﺎﻓﺘﻪ و ﻣﯽﺗﻮاﻧﺪ ﺳﺒﺐ ﻓﺮوﭘﺎﺷﯽ ﺷﺒﮑﻪ ﮔﺮدد. ﻟﺬا از ﺧﻮدروﻫﺎي ﺑﺮﻗﯽ ﺑﻪ ﻣﻨﻈﻮر ﻣﺸﺎرﮐﺖ در ﻋﻤﻠﯿﺎت ﺗﻨﻈﯿﻢ ﻓﺮﮐﺎﻧﺲ رﯾﺰﺷﺒﮑﻪ ﺑﺎ روش ﺳﯿﺴﺘﻢ اﺳﺘﻨﺘﺎج ﻓﺎزي ﻋﺼﺒﯽ ﺗﻄﺒﯿﻘﯽ )ANFIS( اﺳﺘﻔﺎده ﻣﯽﺷﻮد. ﺑﻪﻣﻨﻈﻮر ﻣﻘﺎﯾﺴﻪ روش ﭘﯿﺸﻨﻬﺎدي در ﺷﺒﯿﻪﺳﺎزيﻫﺎ از ﮐﻨﺘﺮلﮐﻨﻨﺪه ﻓﺎزي اﺳﺘﻔﺎده ﺷﺪه اﺳﺖ. ﻧﺘﺎﯾﺞ ﺣﺎﺻﻞ از ﺷﺒﯿﻪﺳﺎزيﻫﺎ در ﭘﻨﺞ ﻣﻄﺎﻟﻌﻪ ﻣﻮرد ﺑﺮرﺳﯽ ﻗﺮار ﻣﯽﮔﯿﺮﻧﺪ ﮐﻪ ﺑﯿﺎنﮔﺮ ﻋﻤﻠﮑﺮد ﻣﻄﻠﻮب روش ﭘﯿﺸﻨﻬﺎدي در ﮐﺎﻫﺶ اﻧﺤﺮاف ﻓﺮﮐﺎﻧﺲ، اﺳﺘﺤﮑﺎم در ﺑﺮاﺑﺮ اﻏﺘﺸﺎﺷﺎت و ﻣﻘﺎوم ﺑﻮدن در ﺑﺮاﺑﺮ ﻋﺪم ﻗﻄﻌﯿﺖﻫﺎي ﻣﻮﺟﻮد در ﺳﯿﺴﺘﻢ اﺳﺖ. ﻫﻤﭽﻨﯿﻦ روش ﭘﯿﺸﻨﻬﺎدي ﺗﻮان ﺧﺮوﺟﯽ ﭘﺎﯾﺪارﺗﺮي در ﻣﻨﺎﺑﻊ ﺗﻮﻟﯿﺪ رﯾﺰﺷﺒﮑﻪ دارد.

Nowadays, the use of renewable resources has increased because of fossil fuel price growth, resource shortage, and environmental pollution. This study investigates a microgrid composed of wind and solar systems with battery storage sources and flywheel, diesel generator, and multi-carrier energy systems (MCH) as combined electricity and heat (CHP). The microgrid frequency is controlled based on the gas network and its consumption peak. In a multi-carrier network, the load distribution in the gas network is simultaneously considered with the electric charge distribution. Besides, the frequency is controlled nonlinearly. On the other hand, the growing trend of producing and using electric vehicles has generated new loads on the electricity network. In this regard, if these loads are not properly managed to charge them, the network’s frequency deviations will increase and cause the collapse of the electricity network. Therefore, electric vehicles (V2G) are considered in microgrid frequency tuning operations through ANFIS adaptive fuzzy control method. In order to compare the proposed method in the simulations, a fuzzy controller is used. The results of the simulations are examined in five studies that express the optimal performance of the proposed method in reducing frequency deviations, strength against disturbances and resistance Uncertainties in the system. The proposed method also has a more stable output power in microgrid production resources.