# Adaptive BESS and Grid Setpoints Optimization: A Model-Free Framework for Efficient Battery Management under Dynamic Tariff Pricing

arXiv: https://arxiv.org/abs/2408.09989 · HTML: https://mlsanity.com/paper/2408.09989/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 12 of its 69 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Continuous Deep Q-Learning with Model-based Acceleration](https://mlsanity.com/paper/1603.00748/) (2016)
- [A Sufficient Condition for Convergences of Adam and RMSProp](https://mlsanity.com/paper/1811.09358/) (2018)
- [Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges](https://mlsanity.com/paper/2102.01168/) (2021)
- [Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU](https://mlsanity.com/paper/1611.06256/) (2016)
- [Deep Reinforcement Learning with Quantum-inspired Experience Replay](https://mlsanity.com/paper/2101.02034/) (2021)
- [Digital twin of a MWh-scale grid battery system for efficiency and degradation analysis](https://mlsanity.com/paper/2206.10245/) (2022)

## Abstract (arXiv)

This paper introduces an enhanced framework for managing Battery Energy Storage Systems (BESS) in residential communities. The non-convex BESS control problem is first addressed using a gradient-based optimizer, providing a benchmark solution. Subsequently, the problem is tackled using multiple Deep Reinforcement Learning (DRL) agents, with a specific emphasis on the off-policy Soft Actor-Critic (SAC) algorithm. This version of SAC incorporates reward refinement based on this non-convex problem, applying logarithmic scaling to enhance convergence rates. Additionally, a safety mechanism selects only feasible actions from the action space, aimed at improving the learning curve, accelerating convergence, and reducing computation times. Moreover, the state representation of this DRL approach now includes uncertainties quantified in the entropy term, enhancing the model's adaptability across various entropy types. This developed system adheres to strict limits on the battery's State of Charge (SOC), thus preventing breaches of SOC boundaries and extending the battery lifespan. The robustness of the model is validated across several Australian states' districts, each characterized by unique uncertainty distributions. By implementing the refined SAC, the SOC consistently surpasses 50 percent by the end of each day, enabling the BESS control to start smoothly for the next day with some reserve. Finally, this proposed DRL method achieves a mean reduction in optimization time by 50 percent and an average cost saving of 40 percent compared to the gradient-based optimization benchmark.