Energy Management Maturity Model Based on Fuzzy Logic

2019 ◽  
pp. 1034-1041
Author(s):  
Veysel Çoban ◽  
Sezi Çevik Onar
Keyword(s):  
Fuzzy Logic ◽  
Energy Management ◽  
Maturity Model ◽  
Model Based

Hybrid-Trip-Model-Based Energy Management of a PHEV With Computation-Optimized Dynamic Programming

2018 ◽  
Vol 67 (1) ◽  
pp. 338-353 ◽  
Author(s):  
Jichao Liu ◽  
Yangzhou Chen ◽  
Wei Li ◽  
Fei Shang ◽  
Jingyuan Zhan
Keyword(s):  
Dynamic Programming ◽  
Energy Management ◽  
Model Based

scholarly journals Experts versus Algorithms? Optimized Fuzzy Logic Energy Management of Autonomous PV Hybrid Systems with Battery and H2 Storage

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1777
Author(s):  
Lisa Gerlach ◽  
Thilo Bocklisch
Keyword(s):  
Fuzzy Logic ◽  
Energy Management ◽  
Fuzzy Logic Controller ◽  
Expert Knowledge ◽  
Large Power ◽  
Hybrid Energy ◽  
Knowledge Based ◽  
Long Term Storage ◽  
Fuzzy Logic Approach

Off-grid applications based on intermittent solar power benefit greatly from hybrid energy storage systems consisting of a battery short-term and a hydrogen long-term storage path. An intelligent energy management is required to balance short-, intermediate- and long-term fluctuations in electricity demand and supply, while maximizing system efficiency and minimizing component stress. An energy management was developed that combines the benefits of an expert-knowledge based fuzzy logic approach with a metaheuristic particle swarm optimization. Unlike in most existing work, interpretability of the optimized fuzzy logic controller is maintained, allowing the expert to evaluate and adjust it if deemed necessary. The energy management was tested with 65 1-year household load datasets. It was shown that the expert tuned controller is more robust to changes in load pattern then the optimized controller. However, simple readjustments restore robustness, while largely retaining the benefits achieved through optimization. Nevertheless, it was demonstrated that there is no one-size-fits-all tuning. Especially, large power peaks on the demand-side require overly conservative tunings. This is not desirable in situations where such peaks can be avoided through other means.

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