Name
Title | ||
|---|---|---|
A Computational Intelligence Approach For Residential Home Energy Management Considering Reward Incentives |
Abstract | ||
|---|---|---|
The residential sector's energy consumption share is increasing in recent years. Demand response (DR) is a typical way to schedule consumers' energy consumption and help utility to reduce the peak load demand. This paper proposes a new incentive-based DR program for residential houses based on a coordinated two-level optimization framework. A central controller is used to consider the demand response potential (DRP) of the local controllers and allocates the demand reduction according to the DRP information. A mixed integer linear programming (MILP) algorithm is employed to optimize the residential appliances. The thermal comfort of resident is evaluated using comfort indicator, and rewards are used to evaluate the demand response performance. The results demonstrate the feasibility and advantages of the proposed system. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI) | Computational Intelligence, demand response, residential home energy management, comfort indicator, multi-level control, mixed integer linear programming |
Field | DocType | Citations |
Energy management,Control theory,Demand reduction,Computational intelligence,Incentive,Computer science,Operations research,Demand response,Integer programming,Energy consumption | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhen Ni | 1 | 525 | 33.47 |
| Priti Paudyal | 2 | 0 | 0.34 |
| Xiangnan Zhong | 3 | 346 | 16.35 |