اﺳﺘﺮاﺗﮋي ﭘﯿﺸﻨﻬﺎددﻫﯽ ﺑﻬﯿﻨﻪ ﻣﺸﺎرﮐﺖ ﯾﮑﭙﺎرﭼﻪ ذﺧﯿﺮهﺳﺎز ﻫﻮاي ﻓﺸﺮده و ﺗﮑﻨﻮﻟﻮژي ﺑﺮق ﺑﻪ ﮔﺎز در ﺑﺎزار روز ﭘﯿﺶ ﺗﺤﺖ روﯾﮑﺮد ﺗﺮﮐﯿﺒﯽ ﺗﺼﺎدﻓﯽ-ﻣﻘﺎوم

Hybrid Robust Stochastic Bidding Strategy for Wind Farms Integrated with Compressed Air Energy Storage and Power to Gas Technology

اﻓﺰاﯾﺶ ﻧﻔﻮذ ﻣﻨﺎﺑﻊ اﻧﺮژي ﺗﺠﺪﯾﺪﭘﺬﯾﺮ ﺑﻪ وﯾﮋه ﻣﻨﺎﺑﻊ ﺑﺎدي ﺑﺎ ﻣﺎﻫﯿﺖ اﺣﺘﻤﺎﻻﺗﯽ، ﺷﺒﮑﻪﻫﺎي ﻗﺪرت را ﺑﺎ ﭼﺎﻟﺶﻫﺎي ﻣﺘﻌﺪدي از ﻧﻈﺮ ﺑﺮﻧﺎﻣﻪرﯾﺰي و ﺑﻬﺮهﺑﺮداري ﻣﻮاﺟﻪ ﻧﻤﻮدهاﺳﺖ. از ﺳﻮي دﯾﮕﺮ، ﮔﺎز ﻃﺒﯿﻌﯽ ﺑﻪ ﻋﻨﻮان ﯾﮏ ﺣﺎﻣﻞ اﻧﺮژي ارزان و ﭘﺎك، ﻧﻘﺸﯽ ﻣﻮﺛﺮ در ﭼﺸﻢاﻧﺪازﻫﺎي ﺑﻠﻨﺪ ﻣﺪت اﻧﺮژي دارد. ﻣﻨﺎﺑﻊ اﻧﻌﻄﺎفﭘﺬﯾﺮ ﻧﻮﻇﻬﻮر و ﺗﮑﻨﻮﻟﻮژيﻫﺎي ﻧﻮﯾﻦ ﻫﻤﺎﻫﻨﮓﺷﺪه، ﻣﯽﺗﻮاﻧﻨﺪ ﺑﺎ ﺷﺮﮐﺖ در ﺑﺎزار ﺑﺮق وﮔﺎز روي ﻗﯿﻤﺖ ﺑﺮق ﺗﺄﺛﯿﺮﮔﺬار ﺑﺎﺷﻨﺪ. ﺑﺮ اﯾﻦ اﺳﺎس، در اﯾﻦ ﻣﻘﺎﻟﻪ اﺳﺘﺮاﺗﮋي ﭘﯿﺸﻨﻬﺎددﻫﯽ ﺑﻬﯿﻨﻪ ﻗﯿﻤﺖدﻫﯽ ﺑﺮاي ﯾﮏ ﺳﯿﺴﺘﻢ ﺗﺮﮐﯿﺒﯽ ﻣﺘﺸﮑﻞ از ﻣﺰرﻋﻪ ﺑﺎدي، ذﺧﯿﺮه ﺳﺎز ﻫﻮاي ﻓﺸﺮده و ﺗﮑﻨﻮﻟﻮژي ﺑﺮق ﺑﻪ ﮔﺎز ﻣﻮرد ﺑﺮرﺳﯽ ﻗﺮار ﻣﯽﮔﯿﺮد. وﺟﻮد ﻋﺪمﻗﻄﻌﯿﺖﻫﺎي ﻧﺎﺷﯽ از ﺗﻮان ﺑﺎدي و ﻗﯿﻤﺖ ﺑﺎزار روز ﺑﻌﺪ ﻣﻮﺟﺐ ﻣﯽﺷﻮد ﮐﻪ ﺑﺮﻧﺎﻣﻪرﯾﺰي ﭘﯿﺸﻨﻬﺎدي ﺗﺤﺖ ﯾﮏ روﯾﮑﺮد اﺣﺘﻤﺎﻻﺗﯽ- ﻣﻘﺎوم ﺑﻬﯿﻨﻪ ﮔﺮدد. ﺗﻮان ﺗﻮﻟﯿﺪي ﻣﺰرﻋﻪ ﺑﺎدي ﺑﺎ اﺳﺘﻔﺎده از روﯾﮑﺮد اﺣﺘﻤﺎﻻﺗﯽ ﻣﺒﺘﻨﯽ ﺑﺮ ﺗﻮﻟﯿﺪ ﺳﻨﺎرﯾﻮ ﻣﺪل ﺷﺪه اﺳﺖ، ﺣﺎلآﻧﮑﻪ ﺟﻬﺖ ﻣﺪل-ﺳﺎزي ﻗﯿﻤﺖ ﺑﺎزار روز ﺑﻌﺪ، ﯾﮏ ﭼﺎرﭼﻮب ﻣﻘﺎوم اراﺋﻪ ﮔﺮدﯾﺪه اﺳﺖ. ﻣﺪل ﭘﯿﺸﻨﻬﺎدي ﺟﻬﺖ ﺣﺪاﮐﺜﺮ ﺳﺎزي ﺳﻮد ﺑﺮ روي ﯾﮏ ﺳﯿﺴﺘﻢ ﻧﻤﻮﻧﻪ ﭘﯿﺎده ﺷﺪهاﺳﺖ ﮐﻪ ﻧﺘﺎﯾﺞ ﺣﺎﺻﻞ از ﺷﺒﯿﻪ ﺳﺎزي ﻧﺸﺎندﻫﻨﺪه اﻓﺰاﯾﺶ ﺳﻮد ﻣﺎﻟﮏ ﺳﯿﺴﺘﻢ و اﺳﺘﻔﺎده ﺣﺪاﮐﺜﺮ از ﻇﺮﻓﯿﺖ ﻧﯿﺮوﮔﺎه ﺑﺎدي در ﺣﻀﻮر ﺗﮑﻨﻮﻟﻮژي ﺑﺮق ﺑﻪ ﮔﺎز و ذﺧﯿﺮهﺳﺎز ﻫﻮاي ﻓﺸﺮده اﺳﺖ.

With the high penetration of renewable energy, especially wind power generation with probabilistic nature, the power system is faced with multiple challenges from planning and operation point of view. The need for highly flexible emerging sources like energy storage systems for the integration of renewable energy is essential. Furthermore, the natural gas network as a clean and low-cost energy carrier has an impressive role in the long-term energy perspective. The emergence of new technologies like Power-to-Gas (P2G) and compressed air energy storage (CAES) technologies cause the interdependency between electricity and natural gas networks. An optimal bidding strategy of the hybrid wind farms, CAES and P2G facilities to participate in the day-ahead market is investigated in this paper. The hybrid robust-stochastic approach is implemented to handle the system uncertainties including wind power generation, as well as power market prices to maximize profit. The power generated by the wind farm is modeled by a scenario-based stochastic approach, while day-ahead power price uncertainty to find bidding/ offering curves is modeled by a robust optimization approach. Numerical results indicate an increase in profit, as well as maximum utilization of wind power capacity in the presence of P2G and CAES, simultaneously.