http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-110021947-A

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filingDate 2019-04-23-04:00^^<http://www.w3.org/2001/XMLSchema#date>
inventor http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_af8ff6f5d60a74000de0c2784ed8521d
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publicationDate 2019-07-16-04:00^^<http://www.w3.org/2001/XMLSchema#date>
publicationNumber CN-110021947-A
titleOfInvention An operation optimization method of distributed energy storage power system based on reinforcement learning
abstract The invention discloses an operation optimization method of a distributed energy storage power system based on reinforcement learning. The energy storage aggregator will centrally control the DBESS that is willing and capable of low storage and high power generation, combined with a large amount of historical electricity price data in the region. , and the energy storage constraints, use the self-optimizing Q-learning algorithm to obtain the charging and discharging strategy of the distributed energy storage, and periodically send the strategy information to each distributed energy storage (DBESS) through the Internet. After receiving the control command, its power control system adjusts the charge and discharge power in the control cycle through the bidirectional DC-DC converter to achieve low storage and high power generation; it is of great significance to promote the autonomous operation of distributed energy storage in the microgrid community.
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