Abstract
Optimal power flow (OPF) is a crucial tool in the operation and planning of modern power systems. However, as power system optimization shifts towards larger-scale frameworks, and with the growing integration of distributed generations, the computational time and memory requirements of solving the alternating current (AC) OPF problems can increase exponentially with system size, posing computational challenges. In recent years, machine learning (ML) has demonstrated notable advantages in efficient computation and has been extensively applied to tackle OPF challenges. This paper presents five commonly employed OPF transformation techniques that leverage ML, offering a critical overview of the latest applications of advanced ML in solving OPF problems. The future directions in the application of machine learning to AC OPF are also discussed.
| Original language | English |
|---|---|
| Article number | 1381 |
| Pages (from-to) | 1 |
| Number of pages | 17 |
| Journal | Energies |
| Volume | 17 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 13 Mar 2024 |
Keywords
- active set
- artificial neural network
- machine learning
- optimal power flow
- optimization method
- reinforcement learning
ASJC Scopus subject areas
- Renewable Energy, Sustainability and the Environment
- Fuel Technology
- Engineering (miscellaneous)
- Energy Engineering and Power Technology
- Energy (miscellaneous)
- Control and Optimization
- Electrical and Electronic Engineering