ﺑﺮﻧﺎﻣﻪرﯾﺰي ﺑﻬﯿﻨﻪ ﺳﯿﺴﺘﻢﻫﺎي ﺗﻬﻮﯾﻪ ﻣﻄﺒﻮع ﺑﺎ ﮐﻨﺘﺮل ﻫﻤﺰﻣﺎن ﻧﻘﻄﻪ ﺗﻨﻈﯿﻢ ﺗﺮﻣﻮﺳﺘﺎت و ﭘﻬﻨﺎي ﺑﺎﻧﺪ ﻣﺮده ﺑﺎ در ﻧﻈﺮ ﮔﺮﻓﺘﻦ ﻣﺪﯾﺮﯾﺖ رﯾﺴﮏ

Optimal scheduling of HVAC systems with simultaneously control of set point and dead band width and consideration of risk management

ﭼﮑﯿﺪه: ﺳﯿﺴﺘﻢﻫﺎي ﺗﻬﻮﯾﻪ ﻣﻄﺒﻮع ﺑﻪ ﻋﻨﻮان ﺑﺰرگﺗﺮﯾﻦ ﻣﺼﺮفﮐﻨﻨﺪﮔﺎن ﺗﺠﺎري و ﺧﺎﻧﮕﯽ، ﻧﻘﺶ ﻣﻬﻤﯽ در ﭘﺎﺳﺨﮕﻮﯾﯽ ﺑﺎر دارﻧﺪ. در اﯾﻦ ﭘﮋوﻫﺶ ﺑﺎ اﺳﺘﻔﺎده از دادهﻫﺎي ﭘﯿﺶﺑﯿﻨﯽ دﻣﺎي ﻫﻮا و ﭘﯿﺶﺑﯿﻨﯽ ﻗﯿﻤﺖ ﺑﺮق، ﺑﺮﻧﺎﻣﻪرﯾﺰي ﺑﻬﯿﻨﻪ ﻧﻘﻄﻪ ﺗﻨﻈﯿﻢ ﺗﺮﻣﻮﺳﺘﺎت ﺑﻪ ﻧﺤﻮي اﻧﺠﺎم ﺷﺪه اﺳﺖ ﮐﻪ ﻫﺰﯾﻨﻪ ﺑﺮق ﻣﺼﺮﻓﯽ ﮐﺎرﺑﺮ ﺣﺪاﻗﻞ ﺷﻮد. ﭘﯿﺶﺑﯿﻨﯽ ﻗﯿﻤﺖ ﺑﺮق ﺑﺎ اﺳﺘﻔﺎده از ﻣﺪل آرﯾﻤﺎ و ﭘﯿﺶﺑﯿﻨﯽ دﻣﺎي ﻫﻮا ﺑﺎ اﺳﺘﻔﺎده از زﻧﺠﯿﺮه ﻣﺎرﮐﻒ ﺻﻮرت ﮔﺮﻓﺘﻪ اﺳﺖ. از آﻧﺠﺎ ﮐﻪ ﭘﯿﺶﺑﯿﻨﯽﻫﺎ داراي ﻗﻄﻌﯿﺖ ﻧﯿﺴﺘﻨﺪ، ﺑﺎﯾﺪ رﯾﺴﮏ در ﻧﻈﺮ ﮔﺮﻓﺘﻪ ﺷﻮد. ﺑﺪﯾﻦ ﻣﻨﻈﻮر ﺑﺎ ﺗﻮﻟﯿﺪ ﺳﻨﺎرﯾﻮﻫﺎي ﻣﺨﺘﻠﻒ و ﺑﺎ اﺳﺘﻔﺎده از CVaR ﺗﻼش ﺷﺪه اﺳﺖ ﺗﺎ رﯾﺴﮏ ﻣﺪﯾﺮﯾﺖ ﺷﻮد. ﺑﺎ ﮐﻤﮏ دادهﻫﺎي ﻓﻮق و ﺑﺎ اﺳﺘﻔﺎده از اﻟﮕﻮرﯾﺘﻢ ﻓﺮااﺑﺘﮑﺎري GWO، ﺑﺮﻧﺎﻣﻪرﯾﺰي ﺑﻬﯿﻨﻪ ﻧﻘﻄﻪ ﺗﻨﻈﯿﻢ ﺗﺮﻣﻮﺳﺘﺎت و ﭘﻬﻨﺎي ﺑﺎﻧﺪ ﻣﺮده اﻧﺠﺎم ﺷﺪه اﺳﺖ. ﻧﺘﺎﯾﺞ ﻧﺸﺎن ﻣﯽدﻫﻨﺪ ﮐﻪ اﯾﻦ ﺷﯿﻮه ﺑﺮﻧﺎﻣﻪرﯾﺰي ﻣﯽﺗﻮاﻧﺪ اﺛﺮ ﻣﻬﻤﯽ در ﻣﺸﺎرﮐﺖ ﺳﯿﺴﺘﻢﻫﺎي ﺗﻬﻮﯾﻪ ﻣﻄﺒﻮع در ﭘﺎﺳﺨﮕﻮﯾﯽ ﺑﺎر داﺷﺘﻪ ﺑﺎﺷﺪ. ﻫﻤﭽﻨﯿﻦ ﻧﺸﺎن داده ﺷﺪه اﺳﺖ ﮐﻪ ﻣﺪﯾﺮﯾﺖ ﺑﺎﻧﺪ ﻣﺮده ﻧﻘﺶ ﻣﺆﺛﺮي در ﻣﺪﯾﺮﯾﺖ ﻣﺼﺮف اﻧﺮژي اﯾﻔﺎ ﻣﯽﮐﻨﺪ. ﺑﺎ ﻣﻄﺎﻟﻌﻪ ﺳﻄﺢ رﯾﺴﮏﭘﺬﯾﺮي اﻓﺮاد ﻣﺨﺘﻠﻒ، ﻧﺸﺎن داده ﺷﺪه اﺳﺖ ﮐﻪ اﻓﺮاد رﯾﺴﮏﭘﺬﯾﺮﺗﺮ ﻣﺸﺎرﮐﺖ ﺑﯿﺸﺘﺮي در ﺑﺮﻧﺎﻣﻪﻫﺎي ﭘﺎﺳﺨﮕﻮﯾﯽ ﺑﺎر دارﻧﺪ و ﻫﺰﯾﻨﻪ اﻧﺮژي ﮐﻤﺘﺮي را ﻣﺘﺤﻤﻞ ﻣﯽﺷﻮﻧﺪ. ﻫﻤﭽﻨﯿﻦ اﺛﺮ دﻣﺎي ﻣﻄﻠﻮب ﮐﺎرﺑﺮ ﺑﺮ اﻧﺮژي ﻣﺼﺮﻓﯽ و ﻫﺰﯾﻨﻪ ﮐﺎرﺑﺮ ﺳﻨﺠﯿﺪه ﺷﺪه اﺳﺖ.

HVAC systems, as the largest commercial and household power consumers, play an important role in demand response programs. In this research, using weather forecast data and using electricity price forecast, optimal scheduling for HVAC systems set points is performed in such a way that the user’s electricity consumption cost is minimized. Electricity price forecasting is achieved using an ARIMA model. A Markov’s chain has been employed for temperature forecasting. As these forecasts are not deterministic, the risk must be considered. To accomplish this, several scenarios were considered and CVaR was utilized to handle the risk aspect. In this research, the above-mentioned data was employed, and GWO algorithm was invoked to schedule the optimal set points of the HVAC systems and symmetrical dead band width. The results show that this planning method can have an important effect on the participation of the HVAC systems in demand response programs. Also, it has been shown that the dead-band width management plays an important role in energy consumption management. Different users with different attitudes toward the risk were analyzed; the results show that risk-averse users are more involved in demand response programs; and hence, they bear lower energy costs.