.png){kind=icon}
.png){kind=icon}
Return
A model predictive control framework for reliable microgrid energy management
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ijepes.2014.03.017.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes a framework for reliable microgrid energy management based on receding horizon control. A microgrid is considered for exemplification, connected to an external grid via a transformer and containing a local consumer, a renewable generator (wind turbine) and a storage facility (battery). Optimal scheduling of battery is sought for minimizing costs. To this aim, a predictive control framework is proposed, which allows taking into consideration cost values, power consumption and generation profiles, and specific constraints. Uncertainty due to variations in the generator model parameters is taken into account. The efficiency of the proposed approach is validated through simulation results and comparisons using real numerical data for a test system often considered in bulk power system reliability evaluation studies. The obtained results show that predictive control is a viable approach for providing optimal energy management solutions accounting for costs, profiles and constraints. (C) 2014 Elsevier Ltd. All rights reserved.
Keywords:
Microgrid energy management
Uncertainty
Constrained Model Predictive Control (MPC)
Mixed-Integer Linear Programming (MILP)
Fault Tolerant Control (FTC)
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
5
Papers:
1.1W
Citations:
3.1W