Optimal Renewable Resource Allocation and Load Scheduling of Resilient Communities

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

doi:10.3390/en13215683

Title: Optimal Renewable Resource Allocation and Load Scheduling of Resilient Communities

Journal Article · Fri Oct 30 00:00:00 EDT 2020 · Energies (Basel)

DOI:https://doi.org/10.3390/en13215683· OSTI ID:1755192

Wang, Jing ^[1];

^[1];

^[2]; Zuo, Wangda ^[3];

^[4]; Huang, Sen ^[5]; Vrabie, Draguna ^[5]

Univ. of Colorado, Boulder, CO (United States)
Univ. of Colorado, Boulder, CO (United States); Renewable and Sustainable Energy Inst. (RASEI), Boulder, CO (United States)
Univ. of Colorado, Boulder, CO (United States); National Renewable Energy Lab. (NREL), Golden, CO (United States)
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

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.

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Research Organization:: Pacific Northwest National Lab. (PNNL), Richland, WA (United States); National Renewable Energy Lab. (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office

Grant/Contract Number:: AC05-76RL01830; AC36-08GO28308

OSTI ID:: 1755192

Alternate ID(s):: OSTI ID: 1770907

Report Number(s):: PNNL-SA-156963; NREL/JA-5500-79413

Journal Information:: Energies (Basel), Vol. 13, Issue 21; ISSN 1996-1073

Publisher:: MDPI AGCopyright Statement

Country of Publication:: United States

Language:: English

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32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
resilient community
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mixed-integer linear program

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