Optimal Renewable Resource Allocation and Load Scheduling of Resilient Communities

Jing Wang, Kaitlyn Garifi, Kyri Baker, Wangda Zuo, Yingchen Zhang, Sen Huang, Draguna Vrabie

Research output: Contribution to journalArticlepeer-review

18 Scopus Citations

Abstract

This paper presents a methodology for enhancing community resilience through optimal renewable resource allocation and load scheduling in order to minimize unserved load and thermal discomfort. The proposed control architecture distributes the computational effort and is easier to be scaled up than traditional centralized control. The decentralized control architecture consists of two layers: The community operator layer (COL) allocates the limited amount of renewable energy resource according to the power flexibility of each building. The building agent layer (BAL) addresses the optimal load scheduling problem for each building with the allowable load determined by the COL. Both layers are formulated as a model predictive control (MPC) based optimization. Simulation scenarios are designed to compare different combinations of building weighting methods and objective functions to provide guidance for real-world deployment by community and microgrid operators. The results indicate that the impact of power flexibility is more prominent than the weighting factor to the resource allocation process. Allocation based purely on occupancy status could lead to an increase of PV curtailment. Further, it is necessary for the building agent to have multi-objective optimization to minimize unserved load ratio and maximize comfort simultaneously.

Original languageAmerican English
Article number5683
Number of pages29
JournalEnergies
Volume13
Issue number21
DOIs
StatePublished - 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020 by the authors.

NREL Publication Number

  • NREL/JA-5500-79413

Keywords

  • Load scheduling
  • Mixed-integer linear program
  • Model predictive control
  • Optimal operation
  • Renewable resource allocation
  • Resilient community

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