LAPSE:2024.1557
Published Article

LAPSE:2024.1557
Optimal Design and Control of Behind-the-Meter Resources for Retail Buildings with EV Fast Charging
August 16, 2024. Originally submitted on July 9, 2024
Abstract
The growing electrification of buildings and vehicles, while a natural step towards achieving global decarbonization, poses some challenges for the electric grid in terms of power consumption. One way of addressing them is by deploying onsite, behind-the-meter resources (BTMR), such as battery energy storage and solar PV generation. The optimal design of these systems, however, is a demanding task that depends on the integration of multiple complex subsystems. In this work, the optimal integrated design and dispatch of BTMR systems for retail buildings with electric vehicle fast charging stations is addressed. A framework is proposed, combining high-fidelity simulation (of buildings, electric vehicle fast charging stations, and BTMR), predictive control strategies with closed-loop implementation, and a derivative-free design method that explores parallelization and high-performance computing. Focus is given to the design layer, highlighting the effect of parallelization on the choice of the method, computational effort, and types of results. A case study of a big-box grocery store with an EV fast charging station is presented, and its optimal BTMR system is identified in terms of equipment sizes, costs (capital, utility, lifecycle, and levelized) and resiliency against outages, demonstrating great potential for real-world applications.
The growing electrification of buildings and vehicles, while a natural step towards achieving global decarbonization, poses some challenges for the electric grid in terms of power consumption. One way of addressing them is by deploying onsite, behind-the-meter resources (BTMR), such as battery energy storage and solar PV generation. The optimal design of these systems, however, is a demanding task that depends on the integration of multiple complex subsystems. In this work, the optimal integrated design and dispatch of BTMR systems for retail buildings with electric vehicle fast charging stations is addressed. A framework is proposed, combining high-fidelity simulation (of buildings, electric vehicle fast charging stations, and BTMR), predictive control strategies with closed-loop implementation, and a derivative-free design method that explores parallelization and high-performance computing. Focus is given to the design layer, highlighting the effect of parallelization on the choice of the method, computational effort, and types of results. A case study of a big-box grocery store with an EV fast charging station is presented, and its optimal BTMR system is identified in terms of equipment sizes, costs (capital, utility, lifecycle, and levelized) and resiliency against outages, demonstrating great potential for real-world applications.
Record ID
Keywords
Battery Energy Storage, Derivative-free Optimization, Distributed Generation, Electric Vehicle Fast Charging, Model Predictive Control
Subject
Suggested Citation
Campos G, Vercellino R, Guittet D, Mann M. Optimal Design and Control of Behind-the-Meter Resources for Retail Buildings with EV Fast Charging. Systems and Control Transactions 3:417-425 (2024) https://doi.org/10.69997/sct.150240
Author Affiliations
Campos G: National Renewable Energy Laboratory (NREL), Golden, CO, USA
Vercellino R: National Renewable Energy Laboratory (NREL), Golden, CO, USA
Guittet D: National Renewable Energy Laboratory (NREL), Golden, CO, USA
Mann M: National Renewable Energy Laboratory (NREL), Golden, CO, USA
Vercellino R: National Renewable Energy Laboratory (NREL), Golden, CO, USA
Guittet D: National Renewable Energy Laboratory (NREL), Golden, CO, USA
Mann M: National Renewable Energy Laboratory (NREL), Golden, CO, USA
Journal Name
Systems and Control Transactions
Volume
3
First Page
417
Last Page
425
Year
2024
Publication Date
2024-07-10
Version Comments
DOI Assigned
Other Meta
PII: 0417-0425-676337-SCT-3-2024, Publication Type: Journal Article
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LAPSE:2024.1557
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External Link

https://doi.org/10.69997/sct.150240
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