Privacy preserving integrated energy load joint clustering method
With the diverse development of the energy system,a thorough exploration of the potential inherent in integrated energy costumer and the enhancement of the energy system adaptability are important.The foundation for this endeavor lies in costumer load clustering,which enables the precise delineation of shared energy usage traits a-mong various costumer types and the extraction of distinct energy consumption patterns.Contrasting traditional pow-er costumers,integrated energy costumers present multiple challenges for clustering due to their diverse energy re-quirements and the interconnection of data from different energy types.Additionally,the intricate structural rela-tionships within integrated energy systems further complicate the matters,with energy data being the purview of multiple operational entities,each of which maintains strict data privacy protocols.This paper introduces an inte-grated energy load joint clustering method based on parameter consensus.This approach takes into account costu-mer data privacy concerns and is especially suited for decentralized data storage scenarios marked by information i-solation among distinct load aggregators,producing results equivalent to those achieved via centralized clustering.By collectively clustering data from multiple energy sources,the resultant clustering outcomes can effectively cap-ture user energy consumption habits within the context of multi-energy interplay.These findings offer a valuable foundation for formulating integrated energy demand-side management strategies and optimizing system operations.The efficacy of the proposed methodology is substantiated through experimentation with the San Francisco building energy consumption dataset.
integrated energy loadload joint clusteringprivacy preservingintegrated demand response
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