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Article

Optimal Renewable Resource Allocation and Load Scheduling of Resilient Communities

1
Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder, 1111 Engineering Dr, Boulder, CO 80309, USA
2
Department of Electrical, Computer and Energy Engineering, University of Colorado Boulder, 425 UCB #1B55, Boulder, CO 80309, USA
3
Renewable and Sustainable Energy Institute, 027 UCB Suite N321, Boulder, CO 80309, USA
4
National Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401, USA
5
Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99354, USA
*
Author to whom correspondence should be addressed.
Energies 2020, 13(21), 5683; https://doi.org/10.3390/en13215683
Received: 9 October 2020 / Revised: 28 October 2020 / Accepted: 29 October 2020 / Published: 30 October 2020
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. View Full-Text
Keywords: resilient community; optimal operation; load scheduling; renewable resource allocation; model predictive control; mixed-integer linear program resilient community; optimal operation; load scheduling; renewable resource allocation; model predictive control; mixed-integer linear program
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MDPI and ACS Style

Wang, J.; Garifi, K.; Baker, K.; Zuo, W.; Zhang, Y.; Huang, S.; Vrabie, D. Optimal Renewable Resource Allocation and Load Scheduling of Resilient Communities. Energies 2020, 13, 5683. https://doi.org/10.3390/en13215683

AMA Style

Wang J, Garifi K, Baker K, Zuo W, Zhang Y, Huang S, Vrabie D. Optimal Renewable Resource Allocation and Load Scheduling of Resilient Communities. Energies. 2020; 13(21):5683. https://doi.org/10.3390/en13215683

Chicago/Turabian Style

Wang, Jing, Kaitlyn Garifi, Kyri Baker, Wangda Zuo, Yingchen Zhang, Sen Huang, and Draguna Vrabie. 2020. "Optimal Renewable Resource Allocation and Load Scheduling of Resilient Communities" Energies 13, no. 21: 5683. https://doi.org/10.3390/en13215683

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