# Design of a Partially Grid-Connected Photovoltaic Microgrid Using IoT Technology

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## Abstract

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## 1. Introduction

- A proposed design of a partially grid-connected microgrid based on the IoT communication network.
- A comprehensive simulation model using Matlab/Simulink was designed to evaluate the performance of the load-shedding algorithm and the associated communication protocol.
- A prototype design of IoT-based smart meter using ZigBee communication protocol.
- A LoRa based communication network is proposed to link the smart meters with the microgrid controller.

## 2. Microgrid Operation and Control

_{G}and P

_{L}are the generated and load demand active power, respectively. Q

_{G}and Q

_{L}are the generated and load reactive power, respectively. N

_{g}and N

_{L}are numbers of distributed generators and distributed loads, respectively. P

_{loss}and Q

_{loss}are the active and reactive power losses, respectively. Also, there is a considerable power generation reserve that is used to compensate for any violation in generation scheduling during the operational planning phase. Basically, the power system involves continuous transition among three different states as shown in Figure 1. The steady state describes the normal operation of the electrical power system in which there will be a perfect balance of active and reactive powers between generation and demand. The frequency and voltage are used as the indicators for this balance and set to operate within specific allowable limits. The system operates in the emergency state for a specific time according to the type and nature of the disturbance. The emergency state involves a change in either voltage or frequency beyond the allowable limits. The power system control includes mechanisms that respond by fixing the problems and providing restorative actions that may include isolating faulted parts or shedding some loads from the system.

## 3. Microgrid Modeling and Design

#### 3.1. PV Array

_{S}), in addition to a number of strings connected in parallel (N

_{P}) [50]. The building block of the array is the single-diode equivalent circuit of solar cells. The performance of this circuit mainly depends on a five-parameter model including diode ideality factor (n), light-induced current (I

_{L}), diode reverse saturation current (I

_{o}), series resistance (R

_{s}), and shunt resistance (R

_{sh}). Therefore, the irradiance- and temperature-dependent current-voltage (I-V) characteristics of a solar cell can be computed using the equation [51]:

_{PV}) and voltage (V

_{PV}) with N

_{S}series cells/string and N

_{P}parallel strings are calculated by [51]:

_{S}is set to be 480 cells/string (96 cells × 5 modules) and N

_{P}is 64 strings. The I-V and power-voltage (P-V) characteristics of one module and the whole array, are plotted as shown in Figure 5.

_{PVm}) is calculated using [52]:

^{2}, P

_{ref}represents the maximum output power measured by the module manufacture at the STC, λ is a correction factor, and T

_{C}represents the temperature in degrees Celsius.

#### 3.2. Boost Converter

_{L}) using the following equation [53]:

_{min}< D < D

_{max}). Consequently, the load resistance limits are calculated by [53]:

_{mpp}and I

_{mpp}are the PV array voltage and current at the MPP. In this design, the duty-cycle ratio was set from 0.1 to 0.9 and the load resistance limits are obtained to be 0.93 to 73.3 Ω, computed at the STC. The converter parameters considered here are listed in Table 2. Regarding the control algorithm of the circuit, the Perturb and Observe (P&O) approach was selected here due to its simplicity, robustness, and small numbers of computed variables required to implement the power tracking process. The P&O tracking method can continuously track the MPP, in different weather conditions, regardless of the kind and location of the PV panels. This is accomplished by analyzing real-time monitoring of PV voltage and current measurements. Because the needed circuitry for implementing MPPTs is more expensive, they are often used for large projects. A pseudocode of the P&O is shown in Algorithm 1. A slight change (perturbation) in the duty-cycle (ΔD) is injected into the circuit throughout the gate of the switch, then PV voltage and current are read (observed) by the sensors. After the change of the power (ΔP) is continuously computed and its sign is checked if it is positive (ΔP > 0) or negative (ΔP < 0), the perturbation continues to increase (D

_{k}+ ΔD) or decrease (D

_{k}− ΔD). The performance of the MPPT algorithm is commonly evaluated based on several criteria including (1) time response to rapid and slow variations in irradiance, (2) amount of power fluctuations around the maximum power point and tracking efficiency (TE). The latter parameter can be computed by [54]:

_{PV-average}is the average output power of the array that is actually collected by the tracking circuit, and P

_{PV-available}is the available power of the array at a certain level of irradiance, which is the maximum power that may be obtained and targeted by the MPPT controller.

Algorithm 1 Pseudocode of P&O control | |

1: | Initialization; D_{min}, D_{max}, ΔD (duty cycle step), D_{k}, V_{k}, I_{k}, P_{k} |

2: | Read input values of voltage and current V_{k}_{+1}, I_{k}_{+1} |

3: | Calculate: P_{k}_{+1} = V_{k}_{+1} * I_{k}_{+1}; ΔV = V_{k}_{+1} − V_{k}; ΔP = P_{k}_{+1} − P_{k}; |

4: | If ΔP ≠ 0 → If ΔP < 0 → If ΔV < 0 → D_{k}_{+1} = D_{k} − ΔD |

5: | else D_{k}_{+1} = D_{k} + ΔD end |

6: | else → If ΔV < 0 → D_{k}_{+1} = D_{k} + ΔD |

7: | else D_{k}_{+1} = D_{k} +ΔD end |

8: | End |

9: | else D_{k}_{+1} = D_{k} end |

10: | If D ≤ D_{min} or D ≥ D_{max} → D_{k+1} = D_{k} end |

11: | D_{k} = D_{k+1}; V_{k} = V_{k+1}; P_{k} = P_{k+1}; end |

12: | Goto step 2 |

#### 3.3. Battery Array and Bidirectional Converter

_{0}is the open-circuit voltage of a battery, AH is battery capacity in Ah unit, k denotes the polarization voltage (V), A is the exponential zone amplitude (V), B is the exponential-zone time-constant inverse (in the unit (Ah)

^{−1}), I

_{batt}is the battery current (A), R

_{o}is the battery internal resistance (Ω), and the integral ∫idt is the charge drawn or supplied to the battery. The state-of-charge (SOC) is a key variable of a rechargeable battery, representing the percentage of the charge level of a battery as compared to its total capacity. Ampere-hour (Ah) counting is a simple and low-complexity method for estimating a battery SOC. To integrate the discharging or charging current and compute the remaining charge in the battery, the Ah counting estimate technique is employed. Therefore, the SOC of a battery is computed as follows [56]:

_{init}is the initial value of SOC, α represents the usable capacity of the battery. I

_{Batt}represents the current which is, by definition, negative during charge and positive during the discharge state. The discharge characteristics of a battery bank with a rated capacity of 3 kAh are illustrated in Figure 7. The typical discharge curve includes three distinguished regions. The first region, at the initial time, represents the exponential decay of the battery voltage when the battery is charged. The second one indicates the battery nominal voltage. The third represents the region at which the voltage drops rapidly, and the battery is discharged.

_{p}= 10 and k

_{i}= 100 s

^{−1}, and the controller output was limited by the maximum charging/discharging current of the battery. The obtained current was used as a reference value and compared with the monitored battery current (I

_{Batt}). The error signal was fed to another PI controller to obtain the duty-cycle required to complementary drive the two switching transistors (Q1 and Q2). The parameters of the latter PI controller were optimally tuned at k

_{p}= 1 and k

_{i}= 10 s

^{−1}with duty-cycle limits of 0.1 and 0.9.

#### 3.4. Three-Phase Inverter

_{f}in the LCL filter circuit, can be calculated as follows:

_{o a}

_{,b,c}are output voltages of the inverter, r

_{f}and L

_{f}are the resistance and inductance values of the inverter filter, and i

_{a}

_{,b,c}are the inverter currents. In a rotating direct-quadrate d-q reference frame, the vector representation of a balanced three-phase system and their corresponding vectors can be written as:

_{d-ref}= |V| representing the required peak voltage at the AC-Bus feeder (|V| =$\sqrt{2}$ V

_{rms}), and that of the q-axis (v

_{q-ref}) is set to zero. Then the inverter active and reactive power can be calculated by:

_{p}= 0.1 and k

_{i}= 10 s

^{−1}and those of the current PI controllers were set to k

_{p}= 30 and k

_{i}= 200 s

^{−1}. Table 4 shows the most important design parameters of the inverter.

#### 3.5. Solid-State Transfer Switches

_{a}

_{,b,c}(t) into a bi-axial coordinate system v

_{d}

_{,q,0}(t). The following equations are used to express the transfer switch operation [60]:

_{d}

_{,q,0}(t) vector. The voltage v

_{dq}(t) is the continuously monitored voltage that has to be compared to a predetermined threshold voltage in the control algorithm of the microgrid operation. In case the microgrid fails to power the connected loads, a control signal is used to start the transfer process of the assigned loads from the microgrid to the utility grid. Algorithm 2 illustrates the pseudocode used to activate the transfer process by the SSTS.

Algorithm 2 Pseudocode of SSTS transfer process | |

1: | Initialization; threshold voltage of v_{dq-th} |

2: | Read actual v_{dq} voltage of the microgrid |

3: | Is V_{dq} ≤ V_{dq-min}? |

4: | If YES → select the loads to be shed from the microgrid → generate transfer |

signals → transfer the loads | |

5: | Goto step 2 |

6: | If NO → Goto step 2 |

#### 3.6. Smart Meters

_{m}denotes the maximum voltage amplitude and ω denotes the voltage’s angular frequency, which should be fixed by the utility provider. I

_{m}is the current amplitude, and θ is the phase angle between the voltage and current. The apparent power (S) can be calculated using the root-mean square (rms) values of the voltage and current as follows:

_{n}and i

_{n}, respectively. On the other hand, the real power (P) is simply the average value of the multiplied discrete values of the voltage and current:

#### 3.7. Microgrid Control

_{µG-min}= v

_{dq-min}) that can be extracted from voltage-power characteristics (V-P) of the microgrid, the real-time monitored microgrid voltage at the AC-bus (V

_{µG}), and a power margin (ΔP) used to adjust the amount of load shedding, as described in Algorithm 3. At regular intervals, the algorithm checks the power demands (P

_{D}) and compares them with the power produced by the renewable resources (P

_{DER}). If the demands exceed the microgrid capacity and the microgrid voltage falls below the predetermined threshold voltage, the algorithm classifies the loads and calculates the number and location of loads that must be removed from the microgrid. Upon receiving the transfer signal from the microgrid controller, the selected loads are momentary and seamlessly transferred to the utility grid. The LoRa communication network is used to communicate between the transfer switches, power smart-meters, and grid controllers. When the operating conditions of the microgrid get improved or the demand is reduced, the transferred load(s) can be restored to the microgrid.

Algorithm 3 Pseudocode of loads selecting and transferring to the utility-grid | |

1: | Initialization N, V_{µG-min}, V_{uG–nominal}, ΔP |

2: | Read V_{µG}, P_{DER}, [P_{L1},P_{L2},…P_{Li},..,P_{LN}] |

3: | Calculate present power demand P_{D} ←$\sum}_{i=1}^{N}{P}_{Li$ |

4: | If V_{µG} ≤ V_{uG–min} then |

5: | Calculate P_{diff} = P_{D} − P_{DER} + ΔP |

6: | Find the load m such that P_{Lm} is the nearest value to P_{diff} |

7: | Disconnect Load m from the microgrid and transfer it to the utility-grid |

8: | Else if V_{µG} > V_{uG–nominal} then |

9: | Restore the Load m to microgrid |

10: | Else Return |

## 4. Design of the IoT Communication Network

_{b}) in bps for the LoRa network is given by:

## 5. Results and Discussion

_{PV}), voltage (V

_{PV}), and current (I

_{PV}) waveforms are shown in Figure 15a. The system performance was initially tested at a sudden reduction in the solar irradiance from 1000 to 200 W/m

^{2}in three steps and then suddenly raised from 200 to 1000 W/m

^{2}in one step. The results show that the designed tracking circuit exhibits a high tracking efficiency, TE, that reached 99.5% at P

_{PV}= 100 kW in addition to a fast response that is less than 25 ms to reach the steady-state conditions. At these abrupt changes in the irradiance, the simulation results proved that the system achieved minimal fluctuations in tracked P

_{PV}that is less than 4% in the worst case. Moreover, regardless of the irradiance level, the output power fluctuations are mostly eliminated at the steady-state operating point. In most applications, the progressive increase or decrease of irradiation is the practical condition. Therefore, the tracking performance of the designed MPPT circuit was also tested for gradual change in the irradiance as shown in Figure 15b. The irradiance was gradually reduced from 1000 to 200 W/m

^{2}and then increased from 200 to 1000 W/m

^{2}with raising and falling rates of +1600 and 800 W/m

^{2}/s, respectively. As a result, the designed controller clearly accomplished the slow variations of the irradiance with a considerable tracking accuracy in a short period of time, of less than 25 ms, with a minimal fluctuated power, that is less than 2.5% in the worst case.

^{2}) and lower power consumption by the loads, PV power is maximized to 100 kW, while the battery power is negative indicating the charging state of the batteries. When the load power increases and at the same time the irradiance level decreases, the power delivered from the PV array is reduced and compensated by the batteries. At a very low irradiance level and full load, most of the power is delivered from the batteries and its state is mainly discharging. The main objective of the battery in this system is to supply critical loads during the night and temporarily support the load demand during cloud trainsets which usually take a few minutes.

## 6. Conclusions

## 7. Future Work

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 5.**(

**a**) I-V and (

**b**) P-V characteristics of one module, (

**c**) I-V and (

**d**) P-V characteristics of 100-kW PV array, computed at different irradiance levels of 0.1, 0.5, and 1 kW/m

^{2}.

**Figure 11.**(

**a**) Prototype design of smart power meter based on ZigBee communication protocol, (

**b**) display dashboard, and (

**c**) appliances remote control using Blynk IoT platform.

**Figure 12.**Detailed architecture of microgrid operating in partially grid-connected mode. The solid lines denote to power flow and dashed lines denote to communication flow.

**Figure 15.**Variation of PV power, voltage, and current in cases of (

**a**) abrupt and (

**b**) progressive variations of solar irradiance.

**Figure 16.**(

**a**) Waveform of instantaneous voltage and current of microgrid computed at a load power of 100 kW, (

**b**,

**c**) THD analysis of load voltage and current, respectively.

**Figure 17.**(

**a**) Variation of PV power, SOC and Battery power with changing of load power computed for (

**a**) variable irradiance and (

**b**) 24 h absence of irradiance with load profile of an academic institute.

**Figure 19.**Change of microgrid voltage, microgrid power and utility-grid power versus time. Five non-critical loads were sequentially connected to the microgrid, a selected load (load4) was disconnected from the microgrid and momentary connected to utility grid.

Parameter | Value (Unit) |
---|---|

Rate power | 315 W |

Short-circuit current | 6.14 A |

Open-circuit voltage | 64.6 V |

Peak current | 5.75 A |

Peak voltage | 54.7 V |

Peak efficiency | 19.3% |

Surface area | 1.63 m^{2} |

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

Rated power | 100 kW |

Input voltage range | 200~300 V |

Output voltage (V_{o}) | 800 V |

Input current at MPP | 363 A |

Switching frequency | 10 kHz |

Inductor value (L_{1}) | 5 mH |

Input capacitor (C_{1}) | 1000 µF |

Output capacitor (C_{2}) | 1000 µF |

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

Rated Power | 100 kW |

Battery nominal voltage | 192 V (4 × 48 V) |

Battery maximum charging/discharging current | 500 A |

Output voltage (V_{DC-ref}) | 800 V |

Switching frequency | 10 kHz |

Inductor value (L1) | 0.5 mH |

Input capacitor (C_{1}) | 500 µF |

Output capacitor (C_{2}) | 500 µF |

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

Rated Power | 100 kW |

Line-to-line output voltage (Vrms) | 400 V |

Switching frequency | 10 kHz |

Filter Inductor values (L_{f}) | 0.25 mH |

Filter capacitor values (C_{f}) | 100 µF |

Modulation | Spread Spectrum |
---|---|

Transmission mode | Half-duplex |

Frequency band | ISM |

Maximum data rate | 50 Kbps |

Payload length | 243 bytes |

Transmission range | Up to 20 Km |

Class | A | B | C |
---|---|---|---|

Energy consumption | Low | Moderate | High |

Down link latency | High | Low | No latency |

Mode of operation | Bi-directional with the gateway | Bidirectional with scheduled receive slots | Bidirectional communication |

Source of energy | Battery | Battery | Main |

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## Share and Cite

**MDPI and ACS Style**

Shaban, M.; Ben Dhaou, I.; Alsharekh, M.F.; Abdel-Akher, M. Design of a Partially Grid-Connected Photovoltaic Microgrid Using IoT Technology. *Appl. Sci.* **2021**, *11*, 11651.
https://doi.org/10.3390/app112411651

**AMA Style**

Shaban M, Ben Dhaou I, Alsharekh MF, Abdel-Akher M. Design of a Partially Grid-Connected Photovoltaic Microgrid Using IoT Technology. *Applied Sciences*. 2021; 11(24):11651.
https://doi.org/10.3390/app112411651

**Chicago/Turabian Style**

Shaban, Mahmoud, Imed Ben Dhaou, Mohammed F. Alsharekh, and Mamdouh Abdel-Akher. 2021. "Design of a Partially Grid-Connected Photovoltaic Microgrid Using IoT Technology" *Applied Sciences* 11, no. 24: 11651.
https://doi.org/10.3390/app112411651