# Two-Layer Optimal Operation of AC–DC Hybrid Microgrid Considering Carbon Emissions Trading in Multiple Scenarios

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## Abstract

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## 1. Introduction

_{2}tax, evaluation criteria were converted into cost. In [19], a closed-loop hierarchical operation (CLHO) algorithm was proposed that potentially helps the real-time optimal operation of the microgrids, showing that a low emissions allowance (EA) and a high emissions trading price reduced the total amount of carbon emissions. In [20], a novel collaborative coordination scheme was proposed for facilitating electricity and heat interaction among multistakeholder distributed energy systems. The total cost and carbon dioxide emissions were reduced obviously. In [21], a mathematical model was created that allows the user to arrive at an optimal trade-off between energy generation and carbon production in each scenario.

- A two-layer optimization model has been established considering the benefits of each part of the hybrid microgrid.
- The carbon trading mechanism has been integrated into the upper layer optimization model. Moreover, the different carbon trading prices on carbon emissions and operation costs have been investigated.
- The demand response and dynamic conversion efficiency have been taken into account.
- The uncertainties of WT power and PV power output and AC and DC load have been evaluated.
- The effect of different electricity price models on total operating costs and carbon emissions and power output have been evaluated.

## 2. The Structure and Operation Method of the AC–DC Hybrid Microgrid

## 3. Mathematical Model for AC–DC Hybrid Microgrid Double Layer Optimization

#### 3.1. Upper Layer Model

#### 3.1.1. Comprehensive Operating Cost Model

#### 3.1.2. Carbon Trading Mechanism

_{2}emission rights based on the carbon emission allowances allocated by the government. In order to simulate the motivation of players of interest to save energy and reduce emissions, the baseline method is used to determine the unpaid carbon emission quotas of each interest player. For the AC–DC hybrid microgrid optimization model established in this paper, the carbon emission distributing power sources are diesel generators, microcombustion engines, and fuel cells [24,25,26]. The carbon emission cost model under the carbon trading mechanism is as follows [20]:

#### 3.2. Lower Layer Model

#### 3.2.1. AC Sub-Microgrid Optimization Model

_{2}, SO

_{2}, and NO

_{2}; ${\lambda}_{i,\alpha}$ denotes the emission factor of the $\alpha $ pollutant for the $i$ DG; and ${c}_{\alpha}$ denotes the discounted cost factor of the $\alpha $ pollutant.

#### 3.2.2. DC Sub-Microgrid Optimization Model

## 4. Scenario Analysis Method

#### 4.1. Scene Generation

#### 4.2. Scene Reduction

## 5. Case Study

#### 5.1. System Structure and Data

#### 5.2. Comparison Analysis of Different Scenarios and Strategies

#### 5.3. Day-Ahead Forecast Optimization Results Analysis

#### 5.4. The Impact of Carbon Trading Price on System Operation

## 6. Conclusions

- (1)
- The optimization model proposed in this paper has good results in terms of the economic benefits of microgrids as well as carbon emissions, realizing a reasonable allocation and use of resources and effectively coordinating the economy and low carbon of system operation.
- (2)
- In this paper, the impact of various uncertainties on the system was verified by simulating and analyzing the uncertainties of wind, PV, load, and tariff. A reasonable time-sharing tariff strategy can not only reduce system operating costs but also reduce carbon emissions to a certain extent.
- (3)
- By comparing the impact of the price of carbon trading costs on the optimized operation results, this paper concludes that the total operating cost of the system steadily increases with the increase in carbon trading price, and the carbon emissions show a phased decrease with the change of carbon trading price; furthermore, setting a reasonable carbon trading price can synergize low carbon and economy.

## Author Contributions

## Funding

## Informed Consent Statement

## Conflicts of Interest

## References

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Literatures | Microgrid Type | Planning Horizon | Title | Consider Carbon Constraint | Considered Uncertainties | Model |
---|---|---|---|---|---|---|

Ref. [6] | AC–DC hybrid microgrid | 24-h | Grid-connected | No | No | Multi-objective optimization |

Ref. [7] | AC–DC hybrid microgrid | 24-h | Island | No | No | Dynamic economic dispatch |

Ref. [8] | AC–DC hybrid microgrid | 24-h | Grid-connected | No | Renewable energy and load | The intraday rolling energy management |

Ref. [9] | AC microgrid | 24-h | Island | No | Wind power, PV power, and load | MILP |

Ref. [10] | Residential Microgrid | 24-h | Grid-connected | No | Noncontrollable load and Renewable energy source | MILP |

Ref. [15] | AC–DC hybrid microgrid | 24-h | Island | No | Wind power and PV power | MILP |

Ref. [16] | AC–DC hybrid microgrid | 24-h | Grid-connected | No | Wind power and PV power | MILP |

Ref. [17] | AC–DC hybrid microgrid | 24-h | Grid-connected | No | Wind power and PV power | Copula model |

Ref. [18] | AC microgrid | 1-year 24-h | Grid-connected | Yes | PV power | Multi-objective optimization |

Ref. [19] | AC microgrid | 24-h | Grid-connected | Yes | No | MILP |

Ref. [20] | Distributed energy system | 24-h | Grid-connected | Yes | No | analytical target cascading |

Ref. [21] | AC microgrid | 24-h | Grid-connected | Yes | No | Energy Blockchain |

This paper | AC–DC hybrid microgrid | 24-h | Grid-connected | Yes | WT, PV, AC and DC load | MILP |

Area | Unit | P_{min} (kW) | P_{max} (kW) | δ_{i} | k_{i} |
---|---|---|---|---|---|

AC sub-microgrid | WT | 0 | 40 | -- | 0.0296 |

DEG | 10 | 80 | 50 | 0.0880 | |

MT | 10 | 80 | 60 | 0.0474 | |

DC sub-microgrid | PV | 0 | 40 | -- | 0.0096 |

FC | 10 | 80 | 60 | 0.0260 | |

SB | −30 | 30 | -- | 0.0175 |

No. | Scenario Probability (%) | Total Cost (CNY) | Carbon Emissions (kg) |
---|---|---|---|

Predict Scenario | -- | 1794.89 | 2548.93 |

Scenario 1 | 10.7 | 1790.15 | 2511.79 |

Scenario 2 | 34.4 | 1797.31 | 2551.44 |

Scenario 3 | 9.1 | 1779.72 | 2524.09 |

Scenario 4 | 29.1 | 1756.69 | 2528.71 |

Scenario 5 | 16.7 | 1874.93 | 2559.32 |

No. | C_{ACyx} (CNY) | C_{DCyx} (CNY) | C_{GRID} (CNY) | Total Cost (CNY) | E_{d} (kg) | |
---|---|---|---|---|---|---|

Strategy 1 | 976.17 | 459.47 | 232.96 | 1794.89 | 2548.93 | |

Strategy 2 | a | 986.91 | 560.23 | 120.49 | 1710.51 | 2681.63 |

b | 945.24 | 459.47 | 263.61 | 1876.71 | 2516.68 | |

Strategy 3 | a | 975.27 | 273.75 | 489.02 | 1835.54 | 2564.42 |

b | 900.41 | 460.34 | 287.75 | 1760.53 | 2512.18 | |

c | 989.14 | 591.77 | 166.37 | 1876.62 | 2721.64 |

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**MDPI and ACS Style**

Yan, L.; Zhao, Y.; Xue, T.; Ma, N.; Li, Z.; Yan, Z.
Two-Layer Optimal Operation of AC–DC Hybrid Microgrid Considering Carbon Emissions Trading in Multiple Scenarios. *Sustainability* **2022**, *14*, 10524.
https://doi.org/10.3390/su141710524

**AMA Style**

Yan L, Zhao Y, Xue T, Ma N, Li Z, Yan Z.
Two-Layer Optimal Operation of AC–DC Hybrid Microgrid Considering Carbon Emissions Trading in Multiple Scenarios. *Sustainability*. 2022; 14(17):10524.
https://doi.org/10.3390/su141710524

**Chicago/Turabian Style**

Yan, Laiqing, Yulin Zhao, Tailin Xue, Ning Ma, Zhenwen Li, and Zutai Yan.
2022. "Two-Layer Optimal Operation of AC–DC Hybrid Microgrid Considering Carbon Emissions Trading in Multiple Scenarios" *Sustainability* 14, no. 17: 10524.
https://doi.org/10.3390/su141710524