This paper introduces a renewable-energy-based microgrid that includes Photovoltaic (PV) energy and wind energy generation units. Also, an energy storage system is present. The proposed microgrid is loaded with a constant load impedance. To improve the performance of the proposed microgrid, an optimal control algorithm utilizing Cuckoo Search Algorithm (CSA) is adapted. It has many merits such as fast convergence, simple tunning, and high efficiency. Commonly, the PV and wind energies are suitable for supplying loads under normal conditions. However, the energy storage system recovers the excess load demand. The load frequency and voltage are regulated using the CSA optimal controller. The microgrid responses with the introduced optimal controller are measured under step changes in load demand, wind power, and the PV irradiation level. Matlab simulations are carried out to test the proposed system performance. The simulation results showed that the proposed microgrid fed the load with Alternating Current (AC) power of constant amplitude and frequency for all disturbances. Moreover, the required load demand has been perfectly compensated. Moreover, the performance of the storage system is excellent with the unstable wind speed and variable solar irradiation. Also, the results with the optimal CSA controller are compared to those with the Particle Swarm Optimization (PSO) algorithm at the same conditions. It is also found that the optimal CSA controller provides better responses.

Due to the growing electricity demand, renewable energy sources have become significantly important nowadays. Relying on fossil fuels for energy production increased environmental concerns to use alternative clean energy sources. Therefore, solar and wind energy sources are considered a viable choice for clean energy production in the future. Despite being emission-free solar and wind energy sources are available at no cost. Moreover, they can deliver power to remote areas that are not accessible by the electricity companies or not connected to the grid. Also, they can offer a solution for countries that are suffering a shortage of fossil fuel energies [

The development of the microgrids concept is vital to deal with renewable energy penetration nowadays. It has the facility that the final user can store, control, generate, and manage a portion of the consumed energy, which makes the customers be a part of the grid and not only a consumer [

Various types of microgrids have been explored in the literature. However, hybrid wind/PV microgrids are commonly utilized. Authors in [

The control scheme of the microgrid can be classified based on the modes of operation into grid-connected and isolated modes [

Many control techniques have been applied to wind/PV microgrids [

In this paper, an electrical microgrid is optimally controlled and managed using the CSA algorithm. The objectives of this study are enhancing the performance of the wind/PV microgrid, improving energy management, and optimizing the response using the CSA. The introduced microgrid includes an impedance that represents the static load. The sources are solar PV and wind energy systems. Moreover, it contains an ESS, uncontrolled rectifier, and 3-φ inverter.

In normal circumstances, the energy from the PV and wind generation are acclimatized to feed the loads. The excess energy is stored in the ESS during the peak power generation periods. During the generation drop periods, the ESS returns the stored energy to the loads.

From the results, it has been investigated that the proposed renewable microgrid can feed the loads skillfully and manage the stored energy carefully. Additionally, the response of the inverter controller is very precise as it tracks the required load demand perfectly. The major contributions of this work are:

Developing an analytical model of the proposed wind/PV microgrid that is optimally controlled and managed using the CSA algorithm.

Improving the energy management of the proposed wind/PV microgrid.

Optimizing the performance of the proposed wind/PV microgrid using the CSA against disturbances in the load power, wind speed, and solar insolation.

The responses of the system with CSA are compared to that with the PSO.

The manuscript is prepared as follows: the structure of the proposed microgrid is included in Section 2. Section 3 investigates the microgrid dynamic model. Section 4 presents the details of the proposed microgrid energy management and control. Section 5 talks about the simulation results. The research conclusions are included in Section 6.

The proposed, presented in

The DC link represents the input of the 3-φ inverter that converts the DC voltage into AC voltage with the utility frequency. The inverter output feeds the system loads. The system has a static load that can be represented by an impedance. The objectives of the inverter controller are supplying the load with AC power with constant voltage amplitude and frequency.

Commonly, the wind speed varies from time to time. Hence, the PMSG output power varies as well. As well, the PV-generated power varies from time to time. Hence, the ESS compensates for the power drop for the load if the power generated by the hybrid wind/PV decreases. Therefore, the ESS represents the slave energy source to recompense any decay in the generated energy of the wind/PV. Also, it helps supply additional loads.

An inverter controller is adapted to regulate the inverter frequency and voltage. The variations in the voltage occur frequently due to solar insolation and wind speed variations.

To realize the design, control, and simulation of the proposed microgrid, modeling the system is commonly the first step. The models’ parameters of the microgrid parts have been chosen to be as close to the practical case. Hence, all possible losses, voltage drops, and snubber circuits have been taken into consideration. The dynamic models of the whole microgrid components are explained in the next paragraphs.

The PV panel contained in the proposed microgrid is established from 18 PV modules. It has 3 series cells and 6 parallel strings. _{p}, _{sh}). However, (_{sh}) is the PV panel short circuit current.

Notation | Description | Notation | Description |
---|---|---|---|

_{sh} |
PV panel short circuit current | Coefficient of inertia of the whole mechanical system | |

_{p}, _{sh} |
PV array parallel and series equivalent resistances | _{e} |
PMSG torque |

_{m} |
Turbine mechanical torque | Viscous friction coefficient | |

The swept area of the blades | _{ph}, _{ph} |
PMSG phase current and voltage in root mean square values | |

Air density | _{dc}, _{dc} |
Output average voltage and current of the rectifier | |

_{w} |
The velocity of the wind | _{l}, _{b} |
Inductor current and capacitor voltage |

Pitch angle of the blade | _{b}, _{b} |
Internal voltage and resistance of the ESS | |

_{m} |
The turbine angular speed | _{1} |
Digital number (0,1) equivalent to the switching state |

The ratio of the tip-speed | Filter inductance and capacitance | ||

_{p} |
Turbine performance coefficient | _{d} |
DC bus voltage |

_{a}, _{b}, _{c} |
The 3-φ variables, | _{o} |
Output current vector |

Space vector | _{f} |
filter current vector | |

_{f}, _{f} |
Filter capacitance and inductance | Number of iterations | |

Switching states space vector | An integer representing the iterations | ||

_{c} |
Output Voltage vector | α | Scaling step size of the optimization problem |

_{i} |
Inverter voltage vector | Le’vy flight of the local searching and global searching | |

(_{1p}, _{1i}, _{2p}, _{2i}) |
PI controller parameters |

The function of the turbine is to convert the wind power into rotating mechanical power. A list of symbols used is shown in _{m}) of the wind turbine is determined by [

where; (_{w}) is the velocity of the wind; (_{m}) is the mechanical angular-speed of the turbine, and

The turbine performance coefficient (_{p}) is given by:

The mechanical system formed by the turbine and the PMSG generator may be modeled by the differential equation:

_{e}) is the PMSG torque, and

It is well known that the PMSG speed is proportional to the wind speed. Nevertheless, the wind speed varies continuously. As the PMSG output voltage and frequency depend mainly on its speed, therefore the PMSG output voltage and frequency are unregulated. However, the electrical loads usually need regulated supplies. To solve this problem, the PMSG output voltage is rectified and connected to the DC bus. Hence, an inverter is used to supply regulated voltage to the loads. An uncontrolled 3-φ rectifier bridge is utilized for this job. The average model of the rectifier is presented by [

where; (_{ph}, _{ph}) are the PMSG phase current and voltage in root mean square values and (_{dc}, _{dc}) are the output average voltage and current of the rectifier. The source impedance is neglected,

The ESS is charged/discharged via a two-directional converter shown in _{2} is off and the transistor Q_{1} is switched, this case represents the buck mode. In this case, the ESS is charged. However, it acts in the boost mode when transistor Q_{1} is off and the transistor Q_{2} is switched. Therefore, the ESS discharging process is indicated.

The DC/DC converter is modeled as follows:

The buck mode:

where (_{l}, _{b}) are the inductor current and capacitor voltage, (_{b}, _{b}) are the internal voltage and resistance of the ESS, _{1} is a digital number (0,1) equivalent to the switching state, (_{d}) is the DC bus voltage.

The boost mode:

where _{2} is a digital number (0,1) equivalent to the switching state.

The 3-φ DC/AC converter circuit diagram incorporating an L-C filter is shown in

where; (_{a}, _{b}, and _{c} ) are the 3-φ variables,

According to the operating rules of the 3-φ inverter, there are 8 switching states. These states can be represented as space vectors shown in

where; (_{f}, _{f}) is the capacitance and inductance of the filter; (_{c}) is the voltage of the filter output; (_{i}) is the terminal voltage of the inverter; (_{o}) is the output current; and (_{f}) is and the filter.

Two control loops are forming the control system of the proposed wind/PV microgrid. The DC/DC converter controller which controls the DC bus voltage is the first controller. The DC/AC inverter controller that controls the frequency and voltage of the load is the second controller. The controllers are explained in the next subsections.

The function of this controller is to control the ESS charging/discharging processes. Additionally, it regulates the DC bus voltage. A simple Proportional-Integral (PI) controller is assigned for controlling the current and voltage of the ESS using two nested loops, as shown in

The inverter utilized in the proposed system is a current-controlled voltage source inverter. Hence, the converter has two nested control loops as shown in

The CSA optimization concept is derived from the searching process of the Le’vy flight. It has been used to get the optimized solution to many problems. The random walk of the Le’vy flight is divided into steps. It is assumed that the random length of the step has a probability distribution using [

where; (

The main idea of this optimization algorithm is to get the nest that has the optimal solutions. All the nests are considered potential nominees for the optimization. The CSA reproduction process of the cuckoo is based on the following rules [

Every cuckoo lays an egg in a random nest.

The optimal solution is produced by the best nest

The best nest is kept for carrying the next generation of cuckoos.

The foreign eggs may be discovered by a host bird with a probability (_{α}).

The new nest of the cuckoos is generated using the global random walk or Le’vy flights:

where (

An objective function is usually utilized with the CSA algorithm. It helps to test the parameters’ optimization. Many objective functions are introduced in the literature [

where; _{s} is the sampling time and the PI controllers parameters (_{1p}, _{1i}, _{2p}, _{2i}) are to be determined with the following constraints:

The optimal CSA controlling parameters of the proposed microgrid are given in

Parameter | Value |
---|---|

No. of nests | 60 |

Max iterations | 1500 |

Discovery of alien egg | 0.4 |

Tolerance | e^{−6} |

The paper idea is validated using Matlab simulations for the proposed microgrid. The wind turbine and PV-panel parameters are presented in

Parameter | Value | Parameter | Value |
---|---|---|---|

_{SC} of the PV |
36.54 A | 4 m | |

V_{OC} of the PV |
255.9 V | Blade swept area (A) | 4 m^{2} |

7466 W | DC-link voltage | 250 V | |

1.25 kg/m^{2} |
Load voltage | 110 V | |

1 m | Load frequency | 50 Hz |

Since our concerns are also in robust stability against various model uncertainties, some of the system parameters are changed. Where the PV shunt resistance is decreased by 10%, the PV series resistance is increased by 10%, and the wind blade radius is increased by 8%. For the perturbed system, the responses are shown in

The response of the system with the proposed CSA approach is compared with the PSO algorithm at the same conditions as shown in

This research introduces the modeling and energy management of an isolated wind/PV microgrid power system supported by an optimal controller design using the CSA algorithm. The system incorporates an energy storage system to enhance the power system’s efficiency and sustainability. The whole system components are modeled and simulated using the Matlab/Simulink platform. The proposed microgrid response is simulated under step variations in load demand, wind power, and the PV irradiation level. The results showed that the proposed renewable-energy-based microgrid system fed the load perfectly and tracked the required load demand under all disturbances. Moreover, the DC bus voltage is kept at its desired value and so is the AC load voltage and frequency for the applied disturbances. The performance of the storage system is excellent with the unstable wind speed and variable solar irradiation. The charging and discharging processes of the ESS are perfectly controlled to compensate for the variations in the wind and PV energies Also, the results with the optimal CSA controller are compared to that with the PSO algorithm at the same conditions. It is also found that the optimal CSA controller provides better responses regarding the overshoots and settling times. The main limitations of the proposed controller may be the trapping of CSA at local minima or having premature convergence.

The authors extend their appreciation to the Deanship of Scientific Research of the University of Tabuk for funding this work through Research Group Grant number S-1441-0055.