TY - GEN
T1 - A Multi-Objective Optimization for Clustering Buildings into Smart Microgrid Communities
AU - Ghorbani-Renani, Nafiseh
AU - Odonkor, Philip
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Smart microgrid communities are small groups of buildings and distributed energy resources (DERs) that collectively work to jointly enhance energy security. As a collective system, their energy performance relies on the energy consumption and generation characteristics of constituent buildings. Yet, in practice, the geographical proximity of buildings is often used as the primary clustering criteria for microgrids. In this study, we proposed a novel multi-objective formulation that expands this criterion to consider the heterogeneity and complimentary nature of building load profiles in the clustering process. Specifically, the proposed model clusters households by (i) minimizing the variation in net-energy across the building cluster, and (ii) minimizing the physical distance between buildings contained in the cluster. The study uniquely leverages the augmented ϵ-constraint method to efficiently populate and analyze Pareto optimal solutions within the resulting tradeoff space. To illustrate the application of the model, it is applied to cluster households within an imagined neighborhood in Ithaca, New York using the Pecan Street Inc. Dataport database. The results illustrate the ability of the proposed model to efficiently design building clusters that balance net energy and physical distance, allowing for increased utilization of DER resources.
AB - Smart microgrid communities are small groups of buildings and distributed energy resources (DERs) that collectively work to jointly enhance energy security. As a collective system, their energy performance relies on the energy consumption and generation characteristics of constituent buildings. Yet, in practice, the geographical proximity of buildings is often used as the primary clustering criteria for microgrids. In this study, we proposed a novel multi-objective formulation that expands this criterion to consider the heterogeneity and complimentary nature of building load profiles in the clustering process. Specifically, the proposed model clusters households by (i) minimizing the variation in net-energy across the building cluster, and (ii) minimizing the physical distance between buildings contained in the cluster. The study uniquely leverages the augmented ϵ-constraint method to efficiently populate and analyze Pareto optimal solutions within the resulting tradeoff space. To illustrate the application of the model, it is applied to cluster households within an imagined neighborhood in Ithaca, New York using the Pecan Street Inc. Dataport database. The results illustrate the ability of the proposed model to efficiently design building clusters that balance net energy and physical distance, allowing for increased utilization of DER resources.
KW - Community microgrid
KW - cluster design
KW - multi-objective optimization
KW - net energy
UR - http://www.scopus.com/inward/record.url?scp=85143823974&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85143823974&partnerID=8YFLogxK
U2 - 10.1109/IGESSC55810.2022.9955342
DO - 10.1109/IGESSC55810.2022.9955342
M3 - Conference contribution
AN - SCOPUS:85143823974
T3 - 2022 IEEE Green Energy and Smart Systems, IGESSC 2022
BT - 2022 IEEE Green Energy and Smart Systems, IGESSC 2022
T2 - 2022 IEEE Green Energy and Smart Systems, IGESSC 2022
Y2 - 7 November 2022 through 8 November 2022
ER -