# Empowering Distributed Solutions in Renewable Energy Systems and Grid Optimization

arXiv: https://arxiv.org/abs/2310.15468 · HTML: https://mlsanity.com/paper/2310.15468/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 36 references that are in the map.
- First posted on arXiv 24 Oct 2023.

## Builds on (most cited first, 1 of 1)

- [Three-Party Energy Management With Distributed Energy Resources in Smart Grid](https://mlsanity.com/paper/1406.5794/) (2014)

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

- [Strategizing EV Charging and Renewable Integration in Texas](https://mlsanity.com/paper/2310.17056/) (2023)
- [LOOP-MAC: Learning-Based Multi-Agent Coordination with Guaranteed Local Feasibility in a Virtual Power Plant](https://mlsanity.com/paper/2310.17882/) (2023)
- [Toward Rapid, Optimal, and Feasible Power Dispatch through Generalized Neural Mapping](https://mlsanity.com/paper/2311.04838/) (2023)
- [Accelerating L-shaped Two-stage Stochastic SCUC with Learning Integrated Benders Decomposition](https://mlsanity.com/paper/2311.10835/) (2023)

## Abstract (arXiv)

This study delves into the shift from centralized to decentralized approaches in the electricity industry, with a particular focus on how machine learning (ML) advancements play a crucial role in empowering renewable energy sources and improving grid management. ML models have become increasingly important in predicting renewable energy generation and consumption, utilizing various techniques like artificial neural networks, support vector machines, and decision trees. Furthermore, data preprocessing methods, such as data splitting, normalization, decomposition, and discretization, are employed to enhance prediction accuracy. The incorporation of big data and ML into smart grids offers several advantages, including heightened energy efficiency, more effective responses to demand, and better integration of renewable energy sources. Nevertheless, challenges like handling large data volumes, ensuring cybersecurity, and obtaining specialized expertise must be addressed. The research investigates various ML applications within the realms of solar energy, wind energy, and electric distribution and storage, illustrating their potential to optimize energy systems. To sum up, this research demonstrates the evolving landscape of the electricity sector as it shifts from centralized to decentralized solutions through the application of ML innovations and distributed decision-making, ultimately shaping a more efficient and sustainable energy future.