# Optimizing Energy and Reserve Minimization in a Sustainable Microgrid with Electric Vehicle Integration: Dynamic and Adjustable Manta Ray Foraging Algorithm

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## Abstract

**:**

## 1. Introduction

## 2. Microgrid Scheduling Model of Large-Scale EVs

## 3. Proposed Method

#### 3.1. Problem Formulation

_{n}is cost of the maintenance; P

_{n}is output of the power generation at time t; C

_{n}is cost of the power generation; I is the cost of incentive EVs; E

_{t+}represents the electricity purchased by the microgrid management center from users at time t; S

_{t+}represents the electricity sold by the microgrid management center to EVs at time t.

_{homt}and Δt

_{t+}are the continuous time required for EV users to discharge and charge to the regional microgrid, respectively; P

_{t+}is the charging price for the user; P

_{homt}is the on-grid price at time t; and N is the total number of EVs.

_{x}, SO

_{2}, and carbon during the power generation process. The pollutant penalty costs associated with microgrid operation can be calculated as:

_{m}is the pollutant cost per kilogram; and E

_{n+}is the emission coefficient.

#### 3.2. Constraints

_{nm}is the emission coefficient, ${\mathrm{E}}_{{\mathrm{grid}}_{\mathrm{m}}}$ is the emission coefficient from the grid, ${\mathrm{P}}_{\mathrm{g}\mathrm{r}\mathrm{i}\mathrm{d}}$ is the power imported from the grid, and ${\mathrm{C}}_{\mathrm{p}\mathrm{e}\mathrm{n}\mathrm{a}\mathrm{l}\mathrm{t}\mathrm{y}\_\mathrm{m}\mathrm{a}\mathrm{x}}$ is the maximum allowable pollutant.

#### 3.3. Dynamic and Adjustable Manta Ray Foraging (DAMRF) Algorithm

## 4. Simulation Results

#### 4.1. Simulation Parameters Setting

#### 4.2. Effectiveness of Proposed Model

#### 4.3. Optimization of Model

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**A large-scale EV response to microgrid dispatch. Included: wind turbine, PV panel, PHEV, and microgrid.

**Figure 6.**Case 1: (

**a**) DG units power. (

**b**) FC power production and electricity tariff. (

**c**) The schedule of DG units output power.

Types | Power Range (KW) | ST/SD (USD) | Bid (kW/USD) |
---|---|---|---|

MT 1 and 2 | 100–1500 | 0.9408 | 0.44786 |

Fuel cell | 80–1000 | 1.617 | 0.28812 |

PV | - | - | 2.53232 |

WT 1 and 2 | - | - | 1.05154 |

Method | Operating Cost (USD) | |||
---|---|---|---|---|

BS | WS | Mean | Std | |

GA | 49,335.31 | 49,390.25 | 49,345.32 | 14.23 |

PSO | 49,246.24 | 49,278.82 | 49,262.83 | 8.79 |

DE | 49,240.32 | 49,270.13 | 49,255.97 | 8.19 |

DAMRF | 49,181.65 | 49,192.73 | 49,184.00 | 5.04 |

Method | Operating Cost (USD) | |||
---|---|---|---|---|

BS | WS | Mean | Std | |

GA | 48,891.83 | 48,914.50 | 48,917.04 | 15.34 |

PSO | 48,926.26 | 48,946.41 | 48,946.27 | 12.12 |

DE | 48,887.63 | 48,907.63 | 48,912.02 | 11.12 |

DAMRF | 48,879.77 | 48,857.66 | 48,855.19 | 6.21 |

Method | Operating Cost (USD) | |||
---|---|---|---|---|

BS | WS | Mean | Std | |

GA | 48,823.81 | 48,857.76 | 48,839.03 | 18.98 |

PSO | 48,789.63 | 48,819.18 | 48,814.18 | 14.56 |

DE | 48,785.36 | 48,816.08 | 48,805.11 | 12.40 |

DAMRF | 48,731.79 | 48,738.46 | 48,740.66 | 7.41 |

Cases | Without Optimization | Energy (kW.h) | Reserve (%) | ||||||
---|---|---|---|---|---|---|---|---|---|

GA | PSO | DE | DAMRF | GA | PSO | DE | DAMRF | ||

Case 1 | 1968.15 | 1903.43 | 1900.00 | 1899.77 | 1897.51 | 0.137 | 0.142 | 0.144 | 0.145 |

Case 2 | 1937.96 | 1884.28 | 1887.65 | 1886.16 | 1885.86 | 0.111 | 0.107 | 0.108 | 0.111 |

Case 3 | 1923.35 | 1884.29 | 1883.33 | 1882.98 | 1880.49 | 0.85 | 0.86 | 0.87 | 0.95 |

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**MDPI and ACS Style**

Abed, A.A.; Suwaed, M.S.; Al-Rubaye, A.H.; Awad, O.I.; Mohammed, M.N.; Tao, H.; Kadirgama, K.; Karah Bash, A.A.H.
Optimizing Energy and Reserve Minimization in a Sustainable Microgrid with Electric Vehicle Integration: Dynamic and Adjustable Manta Ray Foraging Algorithm. *Processes* **2023**, *11*, 2848.
https://doi.org/10.3390/pr11102848

**AMA Style**

Abed AA, Suwaed MS, Al-Rubaye AH, Awad OI, Mohammed MN, Tao H, Kadirgama K, Karah Bash AAH.
Optimizing Energy and Reserve Minimization in a Sustainable Microgrid with Electric Vehicle Integration: Dynamic and Adjustable Manta Ray Foraging Algorithm. *Processes*. 2023; 11(10):2848.
https://doi.org/10.3390/pr11102848

**Chicago/Turabian Style**

Abed, Adnan Ajam, Mahmood Sh. Suwaed, Ameer H. Al-Rubaye, Omar I. Awad, M. N. Mohammed, Hai Tao, Kumaran Kadirgama, and Ali A. H. Karah Bash.
2023. "Optimizing Energy and Reserve Minimization in a Sustainable Microgrid with Electric Vehicle Integration: Dynamic and Adjustable Manta Ray Foraging Algorithm" *Processes* 11, no. 10: 2848.
https://doi.org/10.3390/pr11102848