# Optimal Control of Renewable Energy Communities subject to Network Peak Fees with Model Predictive Control and Reinforcement Learning Algorithms

arXiv: https://arxiv.org/abs/2401.16321 · HTML: https://mlsanity.com/paper/2401.16321/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 45 references that are in the map.
- First posted on arXiv 29 Jan 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, 10 of 10)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Differentiable Convex Optimization Layers](https://mlsanity.com/paper/1910.12430/) (2019)
- [OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real World](https://mlsanity.com/paper/1709.07643/) (2017)
- [How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies](https://mlsanity.com/paper/1512.02011/) (2015)
- [Generalization, Mayhems and Limits in Recurrent Proximal Policy Optimization](https://mlsanity.com/paper/2205.11104/) (2022)
- [Scalable Planning with Tensorflow for Hybrid Nonlinear Domains](https://mlsanity.com/paper/1704.07511/) (2017)
- [Multi-agent Deep Reinforcement Learning for Zero Energy Communities](https://mlsanity.com/paper/1810.03679/) (2018)

## Built on by (most cited first, 4 of 4)

- [Behind the Myth of Exploration in Policy Gradients](https://mlsanity.com/paper/2402.00162/) (2024)
- [Off-Policy Maximum Entropy RL with Future State and Action Visitation Measures](https://mlsanity.com/paper/2412.06655/) (2024)
- [Gym-TORAX: Open-source software for integrating reinforcement learning with plasma control simulators in tokamak research](https://mlsanity.com/paper/2510.11283/) (2025)
- [Maximum-Entropy Exploration with Future State-Action Visitation Measures](https://mlsanity.com/paper/2603.18965/) (2026)

## Abstract (arXiv)

We propose in this paper an optimal control framework for renewable energy communities (RECs) equipped with controllable assets. Such RECs allow its members to exchange production surplus through an internal market. The objective is to control their assets in order to minimise the sum of individual electricity bills. These bills account for the electricity exchanged through the REC and with the retailers. Typically, for large companies, another important part of the bills are the costs related to the power peaks; in our framework, they are determined from the energy exchanges with the retailers. We compare rule-based control strategies with the two following control algorithms. The first one is derived from model predictive control techniques, and the second one is built with reinforcement learning techniques. We also compare variants of these algorithms that neglect the peak power costs. Results confirm that using policies accounting for the power peaks lead to a significantly lower sum of electricity bills and thus better control strategies at the cost of higher computation time. Furthermore, policies trained with reinforcement learning approaches appear promising for real-time control of the communities, where model predictive control policies may be computationally expensive in practice. These findings encourage pursuing the efforts toward development of scalable control algorithms, operating from a centralised standpoint, for renewable energy communities equipped with controllable assets.