Optimal Renewable Resource Allocation and Load Scheduling of Resilient Communities

Jing Wang, Kaitlyn Garifi, Kyri Baker, Wangda Zuo, Yingchen Zhang, Sen Huang and Draguna Vrabie
Additional contact information
Jing Wang: Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder, 1111 Engineering Dr, Boulder, CO 80309, USA
Kaitlyn Garifi: Department of Electrical, Computer and Energy Engineering, University of Colorado Boulder, 425 UCB #1B55, Boulder, CO 80309, USA
Kyri Baker: Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder, 1111 Engineering Dr, Boulder, CO 80309, USA
Wangda Zuo: Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder, 1111 Engineering Dr, Boulder, CO 80309, USA
Yingchen Zhang: National Renewable Energy Laboratory, 15013 Denver W Pkwy, Golden, CO 80401, USA
Sen Huang: Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99354, USA
Draguna Vrabie: Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99354, USA

Energies, 2020, vol. 13, issue 21, 1-29

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.

Keywords: resilient community; optimal operation; load scheduling; renewable resource allocation; model predictive control; mixed-integer linear program (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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