# Microgrid Optimal Energy Scheduling Considering Neural Network based Battery Degradation

arXiv: https://arxiv.org/abs/2202.12416 · HTML: https://mlsanity.com/paper/2202.12416/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 43 references that are in the map.
- First posted on arXiv 24 Feb 2022.

## Builds on (most cited first, 4 of 4)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [A Convex Cycle-based Degradation Model for Battery Energy Storage Planning and Operation](https://mlsanity.com/paper/1703.07968/) (2017)
- [A Novel Real-Time Energy Management Strategy for Grid-Supporting Microgrid: Enabling Flexible Trading Power](https://mlsanity.com/paper/2104.14635/) (2021)
- [A Novel Real-Time Energy Management Strategy for Gird-Friendly Microgrid: Harnessing Internal Fluctuation Internally](https://mlsanity.com/paper/2006.11521/) (2020)

## Built on by (most cited first, 3 of 3)

- [Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit Commitment](https://mlsanity.com/paper/2306.01570/) (2023)
- [Graph Neural Network-based Power Flow Model](https://mlsanity.com/paper/2307.02049/) (2023)
- [Linearization of ReLU Activation Function for Neural Network-Embedded Optimization: Optimal Day-Ahead Energy Scheduling](https://mlsanity.com/paper/2310.01758/) (2023)

## Abstract (arXiv)

Battery energy storage system (BESS) can effec-tively mitigate the uncertainty of variable renewable generation. Degradation is unpreventable and hard to model and predict for batteries such as the most popular Lithium-ion battery (LiB). In this paper, we propose a data driven method to predict the bat-tery degradation per a given scheduled battery operational pro-file. Particularly, a neural network based battery degradation (NNBD) model is proposed to quantify the battery degradation with inputs of major battery degradation factors. When incorpo-rating the proposed NNBD model into microgrid day-ahead scheduling (MDS), we can establish a battery degradation based MDS (BDMDS) model that can consider the equivalent battery degradation cost precisely with the proposed cycle based battery usage processing (CBUP) method for the NNBD model. Since the proposed NNBD model is highly non-linear and non-convex, BDMDS would be very hard to solve. To address this issue, a neural network and optimization decoupled heuristic (NNODH) algorithm is proposed in this paper to effectively solve this neural network embedded optimization problem. Simulation results demonstrate that the proposed NNODH algorithm is able to ob-tain the optimal solution with lowest total cost including normal operation cost and battery degradation cost.