Title: Hierarchical Microgrid Energy Management in an Office Building

Citation
Jin X, Wu J, Mu Y, et al. (2017). Hierarchical Microgrid Energy Management in an Office Building. Cardiff University. http://doi.org/10.17035/d.2017.0041619600


Access Rights: Data can be made available only subject to certain contractual terms
Access Method: Click to email a request for this data to opendata@cardiff.ac.uk

Cardiff University Dataset Creators

Dataset Details
Publisher: Cardiff University
Date (year) of data becoming publicly available: 2017
Data format: .docx, .xlsx
Estimated total storage size of dataset: Less than 100 megabytes
Number of Files In Dataset: 2
DOI: 10.17035/d.2017.0041619600

Description

A two-stage hierarchical Microgrid energy management method in an office building is proposed, which considers uncertainties from renewable generation, electric load demand, outdoor temperature and solar radiation. In stage 1, a day-ahead optimal economic dispatch method is proposed to minimize the daily Microgrid operating cost, with the virtual energy storage system being dispatched as a flexible resource. In stage 2, a two-layer intra-hour adjustment methodology is proposed to smooth the power exchanges at the point of common coupling by coordinating the virtual energy storage system and the electric vehicles at two different time scales. A Vehicle-to-Building control strategy was developed to dispatch the electric vehicles as a flexible resource. Numerical studies demonstrated that the proposed method is able to reduce the daily operating cost at the day-ahead dispatch stage and smooth the fluctuations of the electric power exchanges at the intra-hour adjustment stage.

The dataset contains details of the test office building with electric vehicles integrated (thermal parameters, volume, outdoor temperature data, solar radiation data, electrical load, internal heat gain data and real-time electricity purchasing price, etc). It also contains the optimal dispatch results of the virtual energy storage system and the electric vehicles at both day-ahead and intra-hour stages.

Research results based upon these data are published at http://dx.doi.org/10.1016/j.apenergy.2017.10.002




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Last updated on 2019-02-08 at 10:37