TY - GEN
T1 - Stochastic residential energy resource scheduling by multi-objective natural aggregation algorithm
AU - Luo, Fengji
AU - Ranzi, Gianluca
AU - Liang, Gaoqi
AU - Dong, Zhao Yang
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2018/1/29
Y1 - 2018/1/29
N2 - This paper studies the coordinated scheduling of residential energy resources in a smart home environment. The particularity of this paper is to consider the uncertainties of the must-run appliance load demand forecast errors and to addresses the residential energy resource scheduling through a multi-objective optimization approach. Multiple 1-day must-run appliance power demand scenarios are firstly generated from the house's historical energy consumption data. Based on this, a stochastic day-ahead appliance scheduling model is formulated, aiming to minimize the 1-day energy costs while maximizing the preference of the homeowner simultaneously. A new multi-objective optimization tool, i.e. Multi-Objective Natural Aggregation Algorithm (MONAA), is proposed to solve the stochastic day-ahead appliance scheduling model. Simulations are designed for the validation of the proposed method. © 2017 IEEE.
AB - This paper studies the coordinated scheduling of residential energy resources in a smart home environment. The particularity of this paper is to consider the uncertainties of the must-run appliance load demand forecast errors and to addresses the residential energy resource scheduling through a multi-objective optimization approach. Multiple 1-day must-run appliance power demand scenarios are firstly generated from the house's historical energy consumption data. Based on this, a stochastic day-ahead appliance scheduling model is formulated, aiming to minimize the 1-day energy costs while maximizing the preference of the homeowner simultaneously. A new multi-objective optimization tool, i.e. Multi-Objective Natural Aggregation Algorithm (MONAA), is proposed to solve the stochastic day-ahead appliance scheduling model. Simulations are designed for the validation of the proposed method. © 2017 IEEE.
KW - Demand response
KW - Multi-objective optimization
KW - Natural aggregation algorithm
KW - Smart grid
KW - Smart home
UR - https://www.scopus.com/pages/publications/85046366499
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85046366499&origin=recordpage
U2 - 10.1109/PESGM.2017.8274308
DO - 10.1109/PESGM.2017.8274308
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781538622124
VL - 2018-January
T3 - IEEE Power and Energy Society General Meeting
SP - 1
EP - 5
BT - 2017 IEEE Power and Energy Society General Meeting, PESGM 2017
PB - IEEE Computer Society
T2 - 2017 IEEE Power and Energy Society General Meeting, PESGM 2017
Y2 - 16 July 2017 through 20 July 2017
ER -