Abstract
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.
Highlights
Residential off-grid applications relying on intermittent renewable sources for their energy supply benefit greatly from hybrid energy storage systems
This article makes the following contributions: Firstly, it aims at giving a detailed account of the expert knowledge used to design the fuzzy logic controller in the context of autonomous PV hybrid systems with battery and H2 storage path
In the case of a low state of charge (SOC) and a large deficit in the power balance ( Pnet is negative big (NB)), the fuel cell should operate at its maximum, which corresponds to its nominal operation point
Summary
Residential off-grid applications relying on intermittent renewable sources for their energy supply benefit greatly from hybrid energy storage systems. In order to broaden the perspective, further studies using fuzzy logic control in comparable applications were reviewed They all have in common that the net power flow is to be divided between a short-term storage system—in most cases a battery—and a storage system or power source covering intermediate- and long-term load fluctuations. Applying metaheuristic algorithms for optimization leads generally to a loss of interpretability, Energies 2021, 14, 1777 which is considered one of fuzzy logic’s main strengths and thirdly, the verification of fuzzy logic energy management is based on very limited data sets, not accounting for the large variety found in load and generation profiles. This article makes the following contributions: Firstly, it aims at giving a detailed account of the expert knowledge used to design the fuzzy logic controller in the context of autonomous PV hybrid systems with battery and H2 storage path.
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