# Sustainable Energy Management and Control for Variable Load Conditions Using Improved Mayfly Optimization

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## Abstract

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## 1. Introduction

- Usage of RES in conjunction with a battery reduces operating costs since batteries are inexpensive, enhancing their adaptability. As a result, by inserting the batteries into the system, a higher energy density is produced.
- The improved mayfly optimization-based MP&O optimized the step size and produced the required duty cycle ratios due to the overall decrease in step size in the exploitation stage. The corrected duty cycle ratio provides a greater voltage to alleviate the overvoltage problems in the grid.
- The IMO and MP&O methods are intended to create an efficient EMS that can meet the ever-increasing load needs. By activating the converter switching, IMO is utilized to manage the wind/battery and generate steady energy.
- The expense of an IMO-MP&O method with grid-connected RES is calculated using different temperature and illumination levels.

## 2. Literature Review

## 3. Problem Formulation

#### Load Generation Stability and Its Boundaries

## 4. Objectives

- A hybrid method name called improved mayfly optimization-based MP&O is applied to evaluate the EMS between renewable sources (solar and wind) and batteries at different load conditions;
- To assess the technical feasibility of a hybrid solar-wind power system to meet the load requirements;
- To evaluate a strategy for optimizing the size of the energy generation and storage (battery) subsystems;
- By extending the combination of the hybrid energy systems, to analyze the effect of load size or load variation.

## 5. Modelling of Energy Resources

#### 5.1. Modelling of PV

#### 5.2. Wind Energy

#### 5.3. Battery

## 6. Proposed Method

#### 6.1. Mayfly Optimization Algorithm

#### 6.1.1. Movements of Male Mayflies

#### 6.1.2. Movements of Female Mayflies

#### 6.1.3. Mating of Mayflies

#### 6.2. Improved MO Algorithm

#### 6.3. Modified P&O Algorithm

## 7. Results and Discussion

#### 7.1. Performance Study

#### 7.1.1. MPPT Voltage and Current

#### 7.1.2. Grid Voltage and Current

#### 7.1.3. Real Power and Reactive Power

#### 7.1.4. Performance of Power Generation

#### 7.1.5. Performance of THD

#### 7.2. Comparative Analysis

## 8. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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Constraints | Rate |
---|---|

Type | Sunpower SPR-305E-WHT-D |

Voltage at MPP (Vmpp) | 54.7 |

Temperature coefficient of Voc (%/deg.C) | −0.27269 |

Temperature coefficient of Isc (%/deg.C) | 0.061745 |

Short-circuit current (A) | 5.96 |

Open circuit voltage (V) | 64.2 |

Maximum power (W) | 305.226 |

Ideality factor | 0.94504 |

Current at MPP (A) | 5.58 |

Cells per module (Ncell) Panel Efficiency (%) | 96 18.7 |

Constraints | Rate |
---|---|

Rotational speed | 1 |

Wind speed (m/s) | 9 |

Magnetizing inductance (H) | 7.14 |

Nominal output power (W) | 50 × 10^{3} |

Pitch angle controller gain | 4 |

Rotor (pu) | 0.047 |

Stator (pu) | 0.048 |

Power Efficiency (%) | 59 |

Constraints | Rate |
---|---|

Energy density (Wh/L) | 200–250 |

Rated capacity (Ah) | 6.7 |

Voltage (v) | 500 |

Discharge Current (A) | 1.4 |

Model | Nickel Metal Hydride |

Initial State of Charge (%) | 15 |

Fully Charged Voltage (V) | 575.81 |

Charge/Discharge Efficiency (%) | 66–92 |

Constraints | Rate |
---|---|

X/R ratio | 5 |

Short circuit level | 2 × 10^{3} |

Voltage (Vrms) | 2 × 10^{3} |

Frequency (fn) | 50 |

Base voltage | 2 × 10^{3} |

Active Power P (KW) | 10 × 10^{3} |

Conditions | Time (Min) | PV Power (W) | Wind Power (W) | Battery Power (W) | |||
---|---|---|---|---|---|---|---|

MPPT Controller [26] | Proposed IMO-MP&O | MPPT Controller [26] | Proposed IMO-MP&O | MPPT Controller [26] | Proposed IMO-MP&O | ||

Radiation-1000, Temperature—35′ Wind Speed—9 m/s. | 8.30–9.20 | 50 | 79.37 | 35 | 65.97 | 2 | 29.97 |

9.30–10.20 | 54.26 | 82.93 | 42.18 | 66.24 | −22.2 | 5.80 | |

10.30–11.20 | 54.26 | 83.91 | 42.18 | 67.81 | −43.14 | 0.99 | |

11.30–12.20 | 54.26 | 83.99 | 42.18 | 68.23 | −43.14 | 8.08 | |

12.30–1.20 | 54.26 | 84.36 | 42.18 | 68.88 | −22.17 | 10.20 | |

1.30–2.20 | 54.26 | 84.83 | 42.18 | 69.34 | 2.9 | 18.40 | |

2.30–3.20 | 54.26 | 86.27 | 42.18 | 71.55 | 16.13 | 26.87 | |

3.30–4.20 | 54.26 | 86.27 | 42.18 | 71.55 | 22.24 | 29.98 |

Techniques | THD (%) |
---|---|

ANN-Z-Source [28] | 1.26 |

MO-P&O | 1.36 |

MO-MP&O | 1.32 |

IMO-P&O | 0.92 |

IMO-MP&O | 0.77 |

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**MDPI and ACS Style**

Subramani, P.; Mani, S.; Lai, W.-C.; Ramamurthy, D.
Sustainable Energy Management and Control for Variable Load Conditions Using Improved Mayfly Optimization. *Sustainability* **2022**, *14*, 6478.
https://doi.org/10.3390/su14116478

**AMA Style**

Subramani P, Mani S, Lai W-C, Ramamurthy D.
Sustainable Energy Management and Control for Variable Load Conditions Using Improved Mayfly Optimization. *Sustainability*. 2022; 14(11):6478.
https://doi.org/10.3390/su14116478

**Chicago/Turabian Style**

Subramani, Prabu, Sugadev Mani, Wen-Cheng Lai, and Dineshkumar Ramamurthy.
2022. "Sustainable Energy Management and Control for Variable Load Conditions Using Improved Mayfly Optimization" *Sustainability* 14, no. 11: 6478.
https://doi.org/10.3390/su14116478