Research ArticleELECTRICAL POWER

Data-driven modeling of solar-powered urban microgrids

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Science Advances  15 Jan 2016:
Vol. 2, no. 1, e1500700
DOI: 10.1126/sciadv.1500700
  • Fig. 1 Temporal patterns of electric energy demand.

    (A) Map of a portion of Cambridge, MA. The colors represent the monthly electricity consumption. (B) Monthly electricity consumption of 4683 users over the course of 3 years. (C) Distribution of the daily consumption for an average day in July. The solid red curve denotes the lognormal fit. (D) Hourly demand profiles for a typical day in July, with representative daily curves marked with colors and respective daily consumption values. (E) Hourly solar generation profiles for typical residential-size installations.

  • Fig. 2 Microgrid and its network representation.

    The microgrids are part of the distribution grid, downstream of distribution substations. Users with and without solar PVs are modeled as load and generator nodes, respectively, equipped with smart grid electronics to govern bidirectional flows and voltage fluctuations. The microgrid is connected to the distribution network at one point via the point of common coupling (PCC).

  • Fig. 3 The role of microgrids in consumption of electric energy.

    (A) Proposed microgrids in Cambridge. The colors denote the grid demand per user in the microgrid at 1 p.m. for the no–solar adoption (top) and the 20% solar adoption (bottom) case. (B) Distributions of grid demand for the 200 microgrids for the whole day (top) and daylight hours (bottom) for the no-adoption (red) and the 20% adoption (blue) case. The red and blue solid lines indicate the lognormal fits for the no-adoption and 20% adoption cases, respectively. (C) Total flow in the microgrid as a function of time of day for no solar adoption (top) and 20% solar adoption (bottom). The colors denote the total daily grid demand of each microgrid.

  • Fig. 4 Effects of microgrid configurations in costs and energy flow.

    (A) Example microgrid and the resulting optimized topologies for different rewiring parameters α. The darker shade of red denotes higher load. (B) Distributions of flow ratio and cost for different values of α over all microgrids in Cambridge. The solid red lines indicate the lognormal fits. (C) Total flow and cost with respect to different values of α for the 10 largest microgrids in Cambridge.

  • Fig. 5 Effects of microgrid configurations on their resilience.

    (A) Topologies and qc values of the 10 largest microgrids in Cambridge, MA. (B) Size of the second largest connected component S2 with respect to q for different values of α. The microgrids are color-coded according to (A). (C) Size of the largest and second largest connected component, S1 and S2, respectively, as a function of q for a sample synthetic microgrid of size N = 50. (D) Percolation threshold qc as a function of α for ensembles of synthetic microgrids of size N = 50, averaged over 20 realizations. The solid lines follow Eq. 6.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/2/1/e1500700/DC1

    S1. Cambridge and Pecan Street data set selection

    S2. DC versus AC power flow

    S3. Microgrid size distributions

    S4. Theoretical qc as a function of α

    Fig. S1. The monthly usage distributions of Cambridge for January (triangles) and July (circles).

    Fig. S2. The hourly demand profiles of Pecan Street users for 17 months from December 2012 to April 2014.

    Fig. S3. The hourly solar PV generation profiles of Pecan Street users for 17 months from December 2012 to April 2014.

    Fig. S4. The monthly usage distributions of Cambridge in July (circles) and Pecan Street for 17 months (squares) from December 2012 to April 2014.

    Fig. S5. The topology of the building microgrid used in the power flow sensitivity analysis.

    Fig. S6. The % error (Perr) between AC and DC power flow as a function of the X/R ratio for the proposed topology for different values of R.

    Fig. S7. Size distributions of the microgrids in Cambridge partitioned using k-means.

    Fig. S8. Theoretical qc calculated over the whole range of α values, averaged over 10 realizations.

  • Supplementary Materials

    This PDF file includes:

    • S1. Cambridge and Pecan Street data set selection
    • S2. DC versus AC power flow
    • S3. Microgrid size distributions
    • S4. Theoretical qc as a function of α
    • Fig. S1. The monthly usage distributions of Cambridge for January (triangles) and July (circles).
    • Fig. S2. The hourly demand profiles of Pecan Street users for 17 months from December 2012 to April 2014.
    • Fig. S3. The hourly solar PV generation profiles of Pecan Street users for 17 months from December 2012 to April 2014.
    • Fig. S4. The monthly usage distributions of Cambridge in July (circles) and Pecan Street for 17 months (squares) from December 2012 to April 2014.
    • Fig. S5. The topology of the building microgrid used in the power flow sensitivity analysis.
    • Fig. S6. The % error (Perr) between AC and DC power flow as a function of the X/R ratio for the proposed topology for different values of R.
    • Fig. S7. Size distributions of the microgrids in Cambridge partitioned using k-means.
    • Fig. S8. Theoretical qc calculated over the whole range of α values, averaged over 10 realizations.

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