TY - GEN
T1 - Distributed demand scheduling method to reduce energy cost in smart grid
AU - Imamura, Akiyuki
AU - Yamamoto, Soushi
AU - Tazoe, Takashi
AU - Onda, Harunaga
AU - Takeshita, Hidetoshi
AU - Okamoto, Satoru
AU - Yamanaka, Naoaki
PY - 2013/12/1
Y1 - 2013/12/1
N2 - Smart grid has attracted attentions and expresses a view for the future power systems in the world. Demand Side Management(DSM) is a significant method in smart grid that helps the energy providers to shift behind the peak load. Some methods to reduce peak load have been studied in dynamic pricing. One of the purposes is to delay the demand to periods of low electricity price. The conventional scheduling method makes load curve that is inversely proportional to electricity prices as an objective load curve, and this strategy delay the expected demand curve as close as to the objective load curve. There is a problem, however, the scope of controlled object is limited and the whole load situation is not considered. This paper presents a method to smooth the demand situation of each house to reduce electricity prices by a Genetic Algorithm, in consideration of the amount of the whole demand and coordination between each group. The proposed method is able to give a higher value of objective to the group which has more shiftable demand, by adjusting the objective curve considering the delayable time. This feature makes the load as close as the object and reduces electricity price. Simulation results show that the proposed algorithm achieve the reduction of the peak load and the utility bill in smart grid.
AB - Smart grid has attracted attentions and expresses a view for the future power systems in the world. Demand Side Management(DSM) is a significant method in smart grid that helps the energy providers to shift behind the peak load. Some methods to reduce peak load have been studied in dynamic pricing. One of the purposes is to delay the demand to periods of low electricity price. The conventional scheduling method makes load curve that is inversely proportional to electricity prices as an objective load curve, and this strategy delay the expected demand curve as close as to the objective load curve. There is a problem, however, the scope of controlled object is limited and the whole load situation is not considered. This paper presents a method to smooth the demand situation of each house to reduce electricity prices by a Genetic Algorithm, in consideration of the amount of the whole demand and coordination between each group. The proposed method is able to give a higher value of objective to the group which has more shiftable demand, by adjusting the objective curve considering the delayable time. This feature makes the load as close as the object and reduces electricity price. Simulation results show that the proposed algorithm achieve the reduction of the peak load and the utility bill in smart grid.
KW - Demand Side Management
KW - Dynamic Pricing
KW - Genetic Algorithm
KW - Smart Grid
UR - http://www.scopus.com/inward/record.url?scp=84893401947&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893401947&partnerID=8YFLogxK
U2 - 10.1109/R10-HTC.2013.6669032
DO - 10.1109/R10-HTC.2013.6669032
M3 - Conference contribution
AN - SCOPUS:84893401947
SN - 9781467359634
T3 - 2013 IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2013
SP - 148
EP - 153
BT - 2013 IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2013
T2 - 2013 IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2013
Y2 - 26 August 2013 through 29 August 2013
ER -
