#
Optimal Coordinated Control of DC Microgrid Based on Hybrid PSO–GWO Algorithm^{ †}

^{1}

^{2}

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^{†}

## Abstract

**:**

## 1. Introduction

_{2}emissions, degradation of the ozone layer, biomass fuel reliance, and global warming, as summarized in Figure 1, have all contributed to a greater awareness of the need for safe, renewable, environmentally friendly, and clean energies. The direct integration of available renewable energy sources, involving photovoltaic (PV) and wind turbine generation (WTG), into the utility grid, is not easy due to their sporadic nature [3]. In this regard, the standalone MGs power system can combine such resources in one place as an excellent choice for isolated locations where grid power is unavailable [4]. In the literature, several categories of MGs, comprising AC, DC, and hybrid AC/DC MGs, have been employed to coordinate both renewable energy resources and energy storage systems to cater to required demand [5]. However, DC MGs are expanding more rapidly as compared to conventional AC MGs [6]. There are no harmonics or frequency conflicts, no synchronization is needed in the islanded mode, and no concerns about reactive power regulation exist [7,8]. In order to gain the full utilization of this type of microgrid, there are a few obstacles that need to be considered, including a seamless transition from islanded mode to on-grid mode operation, along with compatibility with AC loads [9]. Furthermore, microgrid protection is a problematic issue owing to the unavailability of zero-crossing current in addition to grounding [10,11]. The stability of the DC microgrid is a significant problem during fault circumstances, because of the resistive impedance characteristic of DC microgrid schemes and the absence of physical inertia [12]. Standardization seems to be another obstacle to the adoption of DC MGs [13]. Regardless of the abovementioned issues, DC MGs have a bright future owing to their improved compatibility with distributed renewable energy sources (DRES), better efficiency, and increased reliability [4]. It is essential to mention that power electronics converters are commonly employed as interfaces in each MG, to link each source to the shared bus [14].

## 2. Modeling of the Local Control Layer in DCMG

#### 2.1. DC MG Control

#### 2.2. PV Side Control Strategy

#### 2.3. Hybrid Battery/SC Side Control Strategy

## 3. Design Considerations of the DC–DC Power Converters

#### 3.1. Boost Converter

#### 3.2. Buck-Boost Converter

## 4. Proposed Control Method

#### 4.1. HPSO–GWO Algorithm

#### 4.2. Problem Formulation

- Initialize the grey wolf’s populations, X
_{1}, X_{2}, X_{3}, etc., which indicates that each wolf (X) represents Kp and Ki. - Initialize parameters $\overrightarrow{\mathrm{A}}$, $\overrightarrow{\mathrm{C},}$ and $\overrightarrow{\mathrm{a}}$, as their capabilities for exploration and development may be leveraged to achieve a better balance in the GWO algorithm.
- Compute the fitness value of each agent (grey wolf) to determine the best three wolves.
- The placements of the best three wolves regarding targeted prey can be determined, based on Equations (11)–(17).
- The locations and velocities of the best wolves are updated, based on Equations (18) and (19), respectively.
- In case the current iteration is less than the maximum iterations limit, based on step 3, all other wolves (ω) will update the positions. Otherwise, the optimal values of X agents (Kp and Ki) will be obtained to be applied in the system.
- Based on the first condition in step 4, $\overrightarrow{\mathrm{A}}$, $\overrightarrow{\mathrm{C}},$ and $\overrightarrow{\mathrm{a}}$ will be updated accordingly. Then, the value of each search agent (wolf) is recalculated.
- Based on the previous updates, the best position is updated. This process continues until the best values of Kp and Ki are obtained.
- Figure 4 depicts all these steps.

## 5. Results and Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 5.**Results with the conventional PI method: (

**a**) DC-link voltage; (

**b**) power exchange; (

**c**) battery current; (

**d**) SOC of battery; (

**e**) SOC of supercapacitor.

**Figure 6.**Results with the GWO: (

**a**) DC-link voltage; (

**b**) power exchange; (

**c**) battery current; (

**d**) SOC of battery; (

**e**) SOC of supercapacitor.

**Figure 7.**Results with the hybrid PSO–GWO: (

**a**) DC-link voltage; (

**b**) power exchange; (

**c**) battery current; (

**d**) SOC of battery; (

**e**) SOC of supercapacitor.

Controller | Kp | Ki |
---|---|---|

PI controller 1 | 10 | 200 |

Battery controller | 50 | 166.4 |

SC controller | 80 | 350 |

Voltage controller (PV) | 4.2956 | 0.6284 |

Current controller (PV) | 100 | 250 |

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**MDPI and ACS Style**

Al-Tameemi, Z.H.A.; Lie, T.T.; Foo, G.; Blaabjerg, F.
Optimal Coordinated Control of DC Microgrid Based on Hybrid PSO–GWO Algorithm. *Electricity* **2022**, *3*, 346-364.
https://doi.org/10.3390/electricity3030019

**AMA Style**

Al-Tameemi ZHA, Lie TT, Foo G, Blaabjerg F.
Optimal Coordinated Control of DC Microgrid Based on Hybrid PSO–GWO Algorithm. *Electricity*. 2022; 3(3):346-364.
https://doi.org/10.3390/electricity3030019

**Chicago/Turabian Style**

Al-Tameemi, Zaid Hamid Abdulabbas, Tek Tjing Lie, Gilbert Foo, and Frede Blaabjerg.
2022. "Optimal Coordinated Control of DC Microgrid Based on Hybrid PSO–GWO Algorithm" *Electricity* 3, no. 3: 346-364.
https://doi.org/10.3390/electricity3030019