Title :
Multiobjective Intelligent Energy Management for a Microgrid
Author :
Chaouachi, A. ; Kamel, R.M. ; Andoulsi, R. ; Nagasaka, K.
Author_Institution :
Dept. of Electron. & Inf. Eng., Tokyo Univ. of Agric. & Technol., Koganei, Japan
fDate :
4/1/2013 12:00:00 AM
Abstract :
In this paper, a generalized formulation for intelligent energy management of a microgrid is proposed using artificial intelligence techniques jointly with linear-programming-based multiobjective optimization. The proposed multiobjective intelligent energy management aims to minimize the operation cost and the environmental impact of a microgrid, taking into account its preoperational variables as future availability of renewable energies and load demand (LD). An artificial neural network ensemble is developed to predict 24-h-ahead photovoltaic generation and 1-h-ahead wind power generation and LD. The proposed machine learning is characterized by enhanced learning model and generalization capability. The efficiency of the microgrid operation strongly depends on the battery scheduling process, which cannot be achieved through conventional optimization formulation. In this paper, a fuzzy logic expert system is used for battery scheduling. The proposed approach can handle uncertainties regarding to the fuzzy environment of the overall microgrid operation and the uncertainty related to the forecasted parameters. The results show considerable minimization on operation cost and emission level compared to literature microgrid energy management approaches based on opportunity charging and Heuristic Flowchart (HF) battery management.
Keywords :
battery management systems; distributed power generation; energy management systems; expert systems; fuzzy logic; fuzzy set theory; learning (artificial intelligence); linear programming; neural nets; photovoltaic power systems; power engineering computing; power generation scheduling; wind power plants; HF battery management; artificial intelligence techniques; artificial neural network ensemble; battery scheduling process; emission level; enhanced learning model; heuristic flowchart battery management; linear-programming-based multiobjective optimization; load demand; machine learning; microgrid energy management approach; multiobjective intelligent energy management; operation cost; optimization formulation; photovoltaic generation; renewable energies; time 1 h; time 24 h; wind power generation; Artificial neural networks; Batteries; Energy management; Forecasting; Optimization; Training; Vectors; Fuzzy logic (FL); microgrid; multiobjective intelligent energy management (MIEM); neural network ensemble (NNE); short-term forecasting;
Journal_Title :
Industrial Electronics, IEEE Transactions on
DOI :
10.1109/TIE.2012.2188873