# Optimizing Power Demand Side Response Strategy: A Study Based on Double Master–Slave Game Model of Multi-Objective Multi-Universe Optimization

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## Abstract

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## 1. Introduction

## 2. Demand Response Revenue Model for Individual Market Players

#### 2.1. Demand Response Revenue Model for Grid

#### 2.2. Demand Response Revenue Model for Electricity Retailers

#### 2.3. User Demand Response Revenue Model

## 3. Master–Slave Game Model and Solution of Demand-Side Response

#### 3.1. Master–Slave Game Model

#### 3.2. Game Model Solving

## 4. Example Analysis

#### 4.1. Parameter Settings

#### 4.2. Scene Settings

- Scenario 1 did not consider price elasticity on user demand response and used the MOMOV algorithm to solve the game model’s optimal solution.
- Scenario 2 considered price elasticity and used the MOMOV algorithm.
- Scenario 3 considered price elasticity and used the MOPSO algorithm to solve the game model’s optimal solution.
- Scenario 4 considered price elasticity and used the NSGA-II algorithm to solve the game model’s optimal solution.

#### 4.3. Comparative Analysis

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Tripartite demand response decision model of “power grid, electricity retailers and consumers”.

**Figure 4.**Convergence process of the optimal response quantity of the electricity retailers in Scenario 1.

**Figure 6.**Convergence process of the optimal response quantity of the electricity retailers in Scenario 2.

**Figure 8.**Response income with the participation of power grid and electricity retailers with the change of income weight of power grid.

Type | Period of Time |
---|---|

Valley period | 1, 2, 3, 4, 5, 6, 23, 24 |

Normal period | 7, 8, 13, 14, 15, 16, 17, 18 |

Peak period | 9, 10, 11, 12, 19, 20, 21, 22 |

Valley Period | Normal Period | Peak Period | |
---|---|---|---|

electricity retailers A ($/MWh) | 28.26 | 34.42 | 38.33 |

electricity retailers B ($/MWh) | 26.21 | 33.65 | 39.21 |

Type of Load | Coefficient of Self-Elasticity | Cross Elastic Coefficient | ||
---|---|---|---|---|

Peak-Normal | Peak-Valley | Normal-Valley | ||

user1 | −0.05 | 0.02 | 0.04 | 0.02 |

user2 | −0.12 | 0.02 | 0.03 | 0.11 |

user3 | −0.38 | 0.03 | 0.23 | 0.03 |

user4 | −0.56 | 0.02 | 0.04 | 0.02 |

user5 | −0.14 | 0.15 | 0.03 | 0.18 |

user6 | −0.23 | 0.03 | 0.08 | 0.06 |

User | Response Cost Factor $\mathit{\beta}$ | Response Cost Factor $\mathit{\gamma}$ |
---|---|---|

user1 | 0.2 | 60 |

user2 | 0.2 | 62 |

user3 | 0.2 | 58 |

user4 | 0.2 | 58 |

user5 | 0.2 | 60 |

user6 | 0.2 | 56 |

Electricity Retailers | Period | Response Amount ($/MW) | Subsidy Unit Price ($/MW·h) |
---|---|---|---|

electricity retailers A | 19:00–20:00 | 48.044 | 24.64 |

20:00–21:00 | 49.068 | 25.07 | |

electricity retailers B | 19:00–20:00 | 51.417 | 27.09 |

20:00–21:00 | 51.057 | 26.35 |

Scenario | Response Cost Factor Grid Revenue ($) | Total Response (MW) | Load Transfer Amount (MW) |
---|---|---|---|

Scenario 1 | 3267.31 | 193.06 | 0 |

Scenario 2 | 3345.33 | 198.64 | 13.36 |

MOPSO | NSGA-II | MOMOV | |||
---|---|---|---|---|---|

GD | IGD | GD | IGD | GD | IGD |

4.18 $\times {10}^{-3}$ | 1.32 $\times {10}^{-2}$ | 3.27 $\times {10}^{-3}$ | 2.12 $\times {10}^{-2}$ | 1.60 $\times {10}^{-4}$ | 4.10 $\times {10}^{-3}$ |

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**MDPI and ACS Style**

Hu, D.; Wang, T.
Optimizing Power Demand Side Response Strategy: A Study Based on Double Master–Slave Game Model of Multi-Objective Multi-Universe Optimization. *Energies* **2023**, *16*, 4009.
https://doi.org/10.3390/en16104009

**AMA Style**

Hu D, Wang T.
Optimizing Power Demand Side Response Strategy: A Study Based on Double Master–Slave Game Model of Multi-Objective Multi-Universe Optimization. *Energies*. 2023; 16(10):4009.
https://doi.org/10.3390/en16104009

**Chicago/Turabian Style**

Hu, Diandian, and Tao Wang.
2023. "Optimizing Power Demand Side Response Strategy: A Study Based on Double Master–Slave Game Model of Multi-Objective Multi-Universe Optimization" *Energies* 16, no. 10: 4009.
https://doi.org/10.3390/en16104009