Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)
Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
ÃÖ´ë ¼ö¿ä Àü·Â Àú°¨À» À§ÇÑ LSTM ±â¹Ý ESS ¿î¿µ ½ºÄÉÁÙ¸µ ±â¹ý |
¿µ¹®Á¦¸ñ(English Title) |
ESS Operation Scheduling Scheme Using LSTM for Peak Demand Reduction |
ÀúÀÚ(Author) |
¼¿µ¿õ
¹Ú½Â¿µ
±è¸íÁø
ÀÓ¼ººó
Yeongung Seo
Seungyoung Park
Myungjin Kim
Sungbin Lim
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¿ø¹®¼ö·Ïó(Citation) |
VOL 46 NO. 11 PP. 1165 ~ 1173 (2019. 11) |
Çѱ۳»¿ë (Korean Abstract) |
ÃÖ±Ù ¿ì¸®³ª¶óÀÇ ÃÖ´ë ¼ö¿ä Àü·Â ºÎÇÏ°¡ ±Þ°ÝÈ÷ Áõ°¡ÇÔ¿¡ µû¶ó Á¤Àü È®·üÀÌ ¿Ã¶ó°¡°í ÀÖ´Ù. ÀÌ¿¡ ´ëÀÀÇϱâ À§ÇØ energy storage system (ESS)¿¡ ÀúÀåÇÑ Àü·ÂÀ» È°¿ëÇÏ¿© ÃÖ´ë ¼ö¿ä Àü·ÂÀ» Àú°¨ÇÏ´Â ESS ¿î¿µ ½ºÄÉÁÙ¸µ ±â¹ýÀÌ ¿¬±¸µÇ°í ÀÖ´Ù. ¼ö¿ä Àü·Â Á¤º¸¸¦ ¹Ì¸® ¾Ë°í ÀÖ´Ù¸é, ESS¿¡ ÀúÀåµÈ Àü·Â°ú ¾ÕÀ¸·Î ¹ß»ýÇÒ ¼ö¿ä Àü·ÂÀ» ¸ðµÎ °í·ÁÇÏ¿© ÃÖÀûÀÇ ESS ¿î¿µ ½ºÄÉÁÙ¸µ ±â¹ýÀ» Àû¿ëÇÒ ¼ö ÀÖÀ» °ÍÀÌ´Ù. ±×·¯³ª, ÃÖ´ë ¼ö¿ä Àü·ÂÀº »ó´ëÀûÀ¸·Î ªÀº ½Ã°£ ±¸°£¿¡¼¸¸ ¹ß»ýÇÏ¸ç ¹ß»ý ½Ã°£µµ ÀÏÁ¤ÇÏÁö ¾Ê¾Æ ¿¹ÃøÀÌ ¸Å¿ì ¾î·Æ´Ù. µû¶ó¼, ¹Ì·¡ÀÇ ¼ö¿ä Àü·Â Á¤º¸¸¦ ¹Ì¸® ¾Ë°í ÀÖ¾î¾ß¸¸ ±¸Çö °¡´ÉÇÑ ÃÖÀûÀÇ ESS ¿î¿µ ½ºÄÉÁÙ¸µ±â¹ýÀº ½ÇÁúÀûÀ¸·Î Àû¿ëÀÌ ¾î·Æ´Ù. º» ³í¹®¿¡¼´Â °ú°Å¿¡ ÃøÁ¤µÈ ¼ö¿ä Àü·Â Á¤º¸¸¸À» ÀÌ¿ëÇÏ´Â ESS ¿î¿µ½ºÄÉÁÙ¸µ ±â¹ýÀ» Á¦¾ÈÇÏ¿´´Ù. ±¸Ã¼ÀûÀ¸·Î, °ú°Å¿¡ ÃøÁ¤µÈ ¼ö¿ä Àü·Â°ú ÀÌ¿¡ ´ëÀÀµÇ´Â ESSÀÇ ÃÖÀû ¹æÀü Àü·ÂÀ» ÀÔ・Ãâ·Â µ¥ÀÌÅÍ·Î È°¿ëÇÏ¿© long short-term memory (LSTM) ½Å°æ¸ÁÀ» ÈÆ·ÃÇÏ°í À̸¦ ESS ¿î¿µ ½ºÄÉÁÙ¸µ¿¡ Àû¿ëÇÏ¿´´Ù. Á¦¾È ±â¹ýÀÇ À¯È¿¼ºÀ» °ËÁõÇϱâ À§ÇØ, 4°÷ÀÇ Àü·Â ¼ö¿ë°¡µé¿¡ ´ëÇÑ ¼ö¿ä Àü·Â µ¥ÀÌÅ͸¦ ÀÌ¿ëÇÏ¿© ½ÇÇèÀ» ¼öÇàÇÏ¿´´Ù. ±¸Ã¼ÀûÀ¸·Î, Á¦¾È ±â¹ýÀº Á¤È®ÇÑ Àü·Â ¼ö¿ä Á¤º¸¸¦ ¹Ì¸® ¾Ë°í ÀÖ¾î¾ß¸¸ ±¸Çö °¡´ÉÇÑ ÃÖÀû ¿î¿µ½ºÄÉÁÙ¸µ ±â¹ý ´ëºñ ÃÖ´ë ¾à 82.42%±îÁö ¿¬°£ ÃÖ´ë ¼ö¿ä Àü·Â °¨¼Ò¸¦ ´Þ¼ºÇÒ ¼ö ÀÖÀ½À» È®ÀÎÇÏ¿´´Ù.
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¿µ¹®³»¿ë (English Abstract) |
In recent years, blackouts have become more likely in South Korea as the peak demand has sharply increased. In order to address this issue, an energy storage system (ESS) operation scheduling technique has been investigated for its ability to reduce the peak demand by utilizing the power stored in the ESS. If the power demand information is known in advance, an optimal ESS operation scheduling technique can be applied in consideration of both the power stored in the ESS and the power demand to be generated in the future. However, it is difficult to predict the peak demand in advance because it only occurs in a relatively short time period, and the instance of its occurrence differs substantially from day-to-day. Therefore, it is very difficult to implement an optimal ESS operation scheduling technique that requires exact information on power demands in advance. Thus, in this paper, we proposed an ESS operation scheduling method with which to reduce the peak demand by using only historical power demands. Specifically, we employed a long short-term memory (LSTM) network and trained it using the historical power demands and their corresponding optimal ESS discharge powers. Then, we applied the trained network to approximate the optimal ESS operation scheduling. We showed the validity of the proposed method through computer simulations using historical power demand data from four customers. In particular, it was shown that the proposed scheme reduced the peak demand per year by up to about 82.42% compared to the optimal scheme that is only feasible when the exact future power demands are available.
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Å°¿öµå(Keyword) |
¿¡³ÊÁö ÀúÀå ½Ã½ºÅÛ
ESS
ÃÖ´ë ¼ö¿ä Àü·Â
long short-term memory
LSTM
±â°èÇнÀ
½Ã°è¿ µ¥ÀÌÅÍ
energy storage system
ESS
eak demand
long short-term memory
LSTM
machine learning
time-series data
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