ﻣﺪلﺳﺎزي ﺑﺮﻧﺎﻣﻪﻫﺎي ﭘﺎﺳﺦﮔﻮﯾﯽ ﺑﺎر در ﻣﺪﯾﺮﯾﺖ ﭼﻨﺪﮔﺎﻧﻪ اﻧﺮژي و ﺳﺎﺧﺘﺎر در رﯾﺰﺷﺒﮑﻪﻫﺎي ﻣﺠﻬﺰ ﺑﻪ ﺗﻮﻟﯿﺪﮐﻨﻨﺪه ﻫﻤﺰﻣﺎن ﺑﺮق و ﺣﺮارت

Demand Response Programs Modeling in Multiple Energy and Structure Management in Microgrids Equipped by Combined Heat and Power Generation

ﻣﺪﯾﺮﯾﺖ اﻧﺮژي در رﯾﺰﺷﺒﮑﻪﻫﺎ ﺑﺎ اﻫﺪاف ﻣﺘﻔﺎوﺗﯽ ﻧﻈﯿﺮ ﮐﺎﻫﺶ ﻫﺰﯾﻨﻪﻫﺎي ﺑﻬﺮهﺑﺮداري اﻧﺠﺎم ﻣﯽﺷﻮد. در اﯾﻦ روﯾﮑﺮد، ﺑﻬﺮهﺑﺮدار رﯾﺰﺷﺒﮑﻪ ﺑﺎ ﺗﻌﯿﯿﻦ ﻣﯿﺰان ﺗﻮﻟﯿﺪ اﻧﺮژي ﺗﻮﺳﻂ ﻫﺮ ﯾﮏ از ﻣﻨﺎﺑﻊ اﻧﺮژي در دﺳﺘﺮس، ﺳﻌﯽ در ﻣﺪﯾﺮﯾﺖ اﻧﺮژي ﺑﻪﻣﻨﻈﻮر ﺗﺎﻣﯿﻦ اﻧﺮژي ﻣﻮرد ﻧﯿﺎز ﻣﺼﺮفﮐﻨﻨﺪﮔﺎن ﺑﺎ ﺣﺪاﻗﻞ ﻫﺰﯾﻨﻪ ﻣﻤﮑﻦ دارد. اﯾﻦ ﻣﻨﺎﺑﻊ ﻣﯽﺗﻮاﻧﻨﺪ ﺗﻮﻟﯿﺪﮐﻨﻨﺪه اﻧﺮژي اﻟﮑﺘﺮﯾﮑﯽ، ﺣﺮارﺗﯽ ﯾﺎ ﺗﺮﮐﯿﺒﯽ ﺑﺎﺷﻨﺪ. در اﯾﻦ ﻣﻘﺎﻟﻪ ﻣﺪﯾﺮﯾﺖ اﻧﺮژي و ﺳﺎﺧﺘﺎر در رﯾﺰﺷﺒﮑﻪ ﺑﺎ اﻫﺪاﻓﯽ ﻧﻈﯿﺮ ﮐﺎﻫﺶ ﻫﺰﯾﻨﻪﻫﺎي ﺑﻬﺮهﺑﺮداري، ﮐﺎﻫﺶ آﻟﻮدﮔﯽﻫﺎي زﯾﺴﺖﻣﺤﯿﻄﯽ و ﺑﻬﺒﻮد ﺷﺎﺧﺺﻫﺎي ﻓﻨﯽ ﻧﻈﯿﺮ ﮐﺎﻫﺶ ﺗﻠﻔﺎت ﺗﻮان ﺣﻘﯿﻘﯽ ﺻﻮرت ﮔﺮﻓﺘﻪ اﺳﺖ. ﺑﻪ اﯾﻦ ﻣﻨﻈﻮر از اﺑﺰارﻫﺎﯾﯽ ﻣﺎﻧﻨﺪ ﺑﺮﻧﺎﻣﻪﻫﺎي ﭘﺎﺳﺦﮔﻮﯾﯽ ﺑﺎر، ذﺧﯿﺮهﺳﺎزﻫﺎ، ﻣﻨﺎﺑﻊ ﺗﻮﻟﯿﺪ ﭘﺮاﮐﻨﺪه ﻣﺎﻧﻨﺪ ﺗﻮﻟﯿﺪﮐﻨﻨﺪهﻫﺎي ﻫﻤﺰﻣﺎن ﺑﺮق و ﺣﺮارت، ﻣﻨﺎﺑﻊ ﺗﺠﺪﯾﺪﭘﺬﯾﺮ ﻣﺎﻧﻨﺪ واﺣﺪﻫﺎي ﺑﺎدي و ﻓﺘﻮوﻟﺘﺎﯾﯿﮏ و ﺑﺎزآراﯾﯽ ﻓﯿﺪرﻫﺎي رﯾﺰﺷﺒﮑﻪ ﺑﻬﺮه ﮔﺮﻓﺘﻪ ﺷﺪه اﺳﺖ. از اﻟﮕﻮرﯾﺘﻢ ژﻧﺘﯿﮏ ﮐﻪ از ﻣﻌﺘﺒﺮﺗﺮﯾﻦ اﻟﮕﻮرﯾﺘﻢﻫﺎي ﻓﺮااﺑﺘﮑﺎري اﺳﺖ ﺑﺮاي ﺣﻞ ﻣﺴﺌﻠﻪ ﺑﻬﯿﻨﻪﺳﺎزي ﻣﺪﻧﻈﺮ اﺳﺘﻔﺎده ﺷﺪه اﺳﺖ. ﻧﺘﺎﯾﺞ ﻋﺪدي ﮐﺎراﯾﯽ ﻣﺪل اراﺋﻪ ﺷﺪه را ﻧﺸﺎن ﻣﯽدﻫﺪ.

Energy management in microgrids is done with different goals such as reducing operation costs. In this approach, the microgrid operator tries to manage energy in order to supply the energy required by consumers at the lowest possible cost, by determining the amount of energy generation by each of the available energy sources. These resources can generate electrical, thermal or combined energy. In this paper, energy and structure management in microgrids have been done with objectives such as reducing operation costs, reducing environmental pollution and improving technical indices such as reducing real power losses. For this purpose, it has been used from options such as demand response programs, storage devices, distributed generation resources such as combined heat and power generators, renewable sources such as wind and photovoltaic units and feeder reconfiguration. In this paper, the genetic algorithm, which is one of the most valid meta-heuristic algorithms, is used to solve the optimization problem. Numerical results show the efficiency of the proposed model.