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Optimal power management of battery energy storage systems (BESS) is crucial for their safe and efficient operation. Numerical optimization techniques are frequently utilized to solve the optimal power management problems. However, these techniques often fall short of delivering real-time solutions for large-scale BESS due to their computational complexity. To address this issue, this paper proposes a computationally efficient approach. We introduce a new set of decision variables called power-sharing ratios corresponding to each cell, indicating their allocated power share from the output power demand. We then formulate an optimal power management problem to minimize the system-wide power losses while ensuring compliance with safety, balancing, and power supply-demand match constraints. To efficiently solve this problem, a parameterized control policy is designed and leveraged to transform the optimal power management problem into a parameter estimation problem. We then implement the ensemble Kalman inversion to estimate the optimal parameter set. The proposed approach significantly reduces computational requirements due to 1) the much lower dimensionality of the decision parameters and 2) the estimation treatment of the optimal power management problem. Finally, we conduct extensive simulations to validate the effectiveness of the proposed approach. The results show promise in accuracy and computation time compared with explored numerical optimization techniques.
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Vendor-neutral and Production-grade Job Power Management in High Performance Computing
Power management and energy efficiency are critical research areas for exascale computing and beyond, necessitating reliable telemetry and control for distributed systems. Despite this need, existing approaches present several limitations precluding their adoption in production. These limitations include, but are not limited to, lack of portability due to vendor-specific and closed-source solutions, lack of support for non-MPI applications, and lack of user-level customization. We present a job-level power management framework based on Flux. We introduce flux-power-monitor and demonstrate its effectiveness on the Lassen (IBM Power AC922) and Tioga (HPE Cray EX235A) systems with a low average overhead of 0.4%. We also present flux-power-manager, where we discuss a proportional sharing policy and introduce a hierarchical FFT-based dynamic power management algorithm (FPP). We demonstrate that FPP reduces energy by 1% compared to proportional sharing, and by 20% compared to the default IBM static power capping policy.
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- Award ID(s):
- 1942182
- PAR ID:
- 10566577
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-5554-3
- Page Range / eLocation ID:
- 1845 to 1855
- Format(s):
- Medium: X
- Location:
- Atlanta, GA, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SCW63240.2024.00231
