# Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges

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## Abstract

**:**

## 1. Introduction

## 2. Review Methodology

- Are there any publications that have implemented various forms of distributed learning in power systems?
- What applications can distributed learning frameworks have in power systems?
- What are the main benefits of distributed learning for power systems?
- What kind of data is exchanged in distributed learning methods in power systems?
- What are some possible research areas for implementing distributed learning in power systems?

- 1.
- The article should have a learning-based structure. This could include any type of learning algorithm where the aim is to construct a mathematical representation for an unknown model.
- 2.
- The article should focus on solving a power system-related problem.
- 3.
- It should use a distributed structure where there is data exchange between multiple agents or between agents and a central server.
- 4.
- Only research articles that have tested their algorithms on a case study and have presented the results should be included.

- Multidisciplinary databases:

- – MDPI;
- – Elsevier;
- – Springer;
- – Arxiv; and
- – Wiley Online Library.

- Specific databases:

- – ACM Digital Library; and
- – IEEE Xplore Library.

- Machine learning keywords: [learning, distributed learning, federated learning, assisted learning, ADMM, dual decomposition, primal decomposition, consensus gradient, and privacy.]
- Power system keywords: [power system, voltage control, resiliency, renewable energy, energy, energy management, electric vehicle, and agent.]

## 3. Distributed Learning Overview

## 4. Applications of Distributed Learning

#### 4.1. Voltage Control

#### 4.2. Renewable Energy Forecast

#### 4.3. Demand Prediction

#### 4.4. Energy Management

#### 4.5. Transient Stability Enhancement

#### 4.6. Resilience Enhancement

#### 4.7. Economic Dispatch

#### 4.8. Energy Storage Systems Control

#### 4.9. Other Applications

## 5. Research Gaps and Challenges

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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Method | Data Source | Communication with Central Server | Communication between Agents |
---|---|---|---|

Distributed Learning | Central server | ✓ | ✓ |

Federated Learning | Agents | ✓ | × |

Assisted Learning | Agents | × | ✓ |

Ref. | Application | Agents | Central Server | Machine Learning Algorithm | Exchanged Data |
---|---|---|---|---|---|

[48] | Voltage control | STATCOMs | - | Q-learning | Rewards, value functions |

[49] | Voltage control | Voltage control units | - | Actor–critic framework | Powerflow information |

[50] | Voltage control | Synchronous generators | Virtual server | Multilayer perceptron | Control actions |

[51] | Voltage control | FACTs | - | SARSA Q-learning | Rewards, value functions |

[53] | Wind power forecast | Neighbor wind turbine operators | Wind turbine operator | ADMM, mirror-descent | Partial power predictions, model coefficients of sites encryption matrix |

[54] | Wind power forecast | Neighbor wind turbine operators | Wind turbine operator | ADMM | Partial power predictions |

[56,57] | Wind power forecast | Wind farm operators | Power system operator | ADMM | Partial power predictions |

[59] | Wind power maximization | Wind turbine operators | Transmission system operator | Deep Q-learning | Rewards |

[60,61] | EV demand prediction | Charging stations | Charging station provider | Federated learning | Gradient information |

[62] | Energy sharing among households | Households | Utility | Q-learning | Rewards |

[64] | Microgrid energy management | Element controllers | Microgrid management server | Hamiltonians | Control variables |

[65] | Microgrid energy management | Element controllers | Virtual server | Reinforcement learning | Load ratio |

[66] | Wind–PV management | Wind turbines PV systems | - | Reinforcement learning | Action history |

[69] | Increasing power system stability margins | Generator excitation systems, power system stabilizers | - | Reinforcement learning | States, rewards |

[72] | Resiliency enhancement | Network regions | - | Ensemble learning | Rotor angle |

[73,74,75,76] | Resiliency enhancement | Feeder agents | Substation agent | Q-learning | Measurements |

[77] | Economic dispatch | Generators | Transmission system operator | Primal-dual decomposition | Lagrange multipliers |

[78,79] | Energy Storage Control | Energy storage systems | Virtual server | Q-learning | Rewards |

[80] | Wide-area monitoring | Synchrophasors | Virtual server | Incremental learning | Measured data |

[81] | Optimal allocation | Generation units | - | Log-linear learning | Generation types |

[82] | Technology deployment | Technology types | Market agent | Q-learning, Actor–critic framework | Energy prices, production prices |

[83] | Mode shapes estimation | Local estimators | - | Linear regression | Electro-mechanical states |

[84,85] | OPF | Microgrids | Central critic server | Deep reinforcement learning | Loss gradients |

[87] | NILM | Households | Utility | Federated learning | Gradient information |

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**MDPI and ACS Style**

Gholizadeh, N.; Musilek, P.
Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges. *Energies* **2021**, *14*, 3654.
https://doi.org/10.3390/en14123654

**AMA Style**

Gholizadeh N, Musilek P.
Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges. *Energies*. 2021; 14(12):3654.
https://doi.org/10.3390/en14123654

**Chicago/Turabian Style**

Gholizadeh, Nastaran, and Petr Musilek.
2021. "Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges" *Energies* 14, no. 12: 3654.
https://doi.org/10.3390/en14123654