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.
Highlights
In the past several decades, the degrading power grid infrastructure has been faced with higher stress
The optimal resource allocation and load scheduling model predictive control (MPC) algorithm for the community when disconnected from the grid was simulated for 48 h within Florida’s hurricane season on August 4 and 5
This section first discusses the allocation factors αti generated by the operator layer, which later serves as the input for the smart controllers at the building layer
Summary
In the past several decades, the degrading power grid infrastructure has been faced with higher stress. Studies have proven that communities with on-site PV power and batteries have the potential to sustain power outages for a certain period if the energy resources are properly managed and the controllable loads are well scheduled [4]. The critical loads are given higher priority and can operate at their scheduled time while the uncritical loads can be shifted to other times This type of method is not suitable for problems with a large number of variables (i.e., controllable appliances), where manually defining the algorithms becomes difficult. This paper proposes a new methodology for optimal renewable resource allocation and load scheduling in resilient communities. The innovation of this work is that it proposes an easy-to-deploy optimization scheme for large-scale communities, which embraces both high-level resource allocation and low-level high-fidelity building energy modeling.
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