# Real-Time Minimization Power Losses by Driven Primary Regulation in Islanded Microgrids

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## Abstract

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## 1. Introduction

- The primary control is usually designed to use a droop-control method to stabilize voltage and frequency and regulate the power sharing between distributed generators in microgrids. This control level is also used to mitigate the circulating currents between paralleled three-phase generators’ converters that cause over-current phenomenon in the power electric devices and damage the capacitors in milliseconds.
- The secondary control is designed to compensate for the voltage and frequency deviation caused by the primary control. This control level has a slower dynamics response than the primary control level and is explicated in the range of seconds. In this way, the secondary control level can also be implemented to satisfy the power quality requirements.
- The tertiary control is the last and slower control level. It manages the power flows inside the MG and between the MG and the main grid providing the distributed energy resources the operating set-points. The tertiary control level also provides optimal operation setting by solving optimization problems for minimizing power losses and operating costs.

_{0}and V

_{0}are the rated frequency and voltage respectively corresponding at the operating points (chosen at the tertiary level) P

_{Gi}

_{0}, Q

_{Gi}

_{0}; K

_{Gi}, and K

_{di}are the frequency and voltage droop coefficients chosen based on the rated power of the DGs. Typically, K

_{Gi}and K

_{di}are kept at constant values in the computational process.

- A structure of an online driven droop regulation system is presented to decrease operating energy losses. The designed controller relies on the real-time measurements and online power flow optimization within microgrids by adjusting the droop coefficients of inverter interfaced units.
- Improving the real-time dynamic response of distributed energy resources and maintaining real-time stability for the microgrid. Indeed, the volumes of secondary and tertiary control taken over from primary control are relieved, thus getting a more reliable operation for microgrids.
- In the application part of the work, experimental validation scenarios for a laboratory platform with optimization controllers and power-hardware-in-the-loop setups have been implemented to test the online operating characteristics of the system. During the experiments, the P-f droop coefficients have changed to adapt to load changing conditions. It is proved that the proposed architecture under realistic conditions achieves improved operation.

## 2. Optimization Program

_{Gi}are variable and will be chosen optimally in the range [K

_{Gmini}; K

_{Gmaxi}] to minimize an objective function. To improve the clarity of the presentation of the problem formulation, the Q-V droop coefficients will be considered as fixed quantities.

_{i(KG)}at the i-th bus can be expressed as:

_{Gi}can be evidenced, as P

_{i(KG)}can be written as the difference between generated (P

_{Gi}) and consumed (P

_{Li}) power at the i-th bus, as reported by Equation (1). In (3), as expressed in [25], V

_{i}and V

_{j}are the i-th and j-th phasor bus voltages; δ

_{i}and δ

_{j}are the phase angles of the i-th and j-th bus voltages. Although not shown here, the values of V

_{i}and V

_{j}, δ

_{i}and δ

_{j}are the results of the optimized power flow process that is depending on the value of K

_{di}and K

_{Gi}at droop buses; Y

_{ij}is the admittance of branch ij; θ

_{ij}is the argument of Y

_{ij}; n

_{branch}is the number of branches connected to bus i.

_{loss}of the system:

_{bus}is the number of buses in the system.

_{G}is the number of generators in the microgrid, n

_{d}is the number of load buses, and n

_{branch}is the number of transmission branches. Figure 1 shows the flowchart of the GSO algorithm.

## 3. Structure of the Online Driven Droop Regulation for Minimum Power Losses Operation

_{Gi}for minimizing power losses of the system in the current time slot. Then the results are sent to the inverters controllers to adjust their power outputs. In this particular case, there is only one optimized generator, which is DGi. Figure 3 expresses the pseudocode of the online optimization process. The features of the computer used for simulation are shown in Figure 4 below.

## 4. Simulations and Results

#### 4.1. Simulation in an Optimization Program

#### 4.2. Hardware in the Loop Simulation

_{G2}obtained from the resolution of the OPF.

_{G1}is fixed while K

_{G2}in this work is chosen optimally in the range (9–11.25) and then in a wider range (6.75–11.25).

- Scenario 1: Test system is operated with the conventional droop control method;
- Scenario 2: Test system is operated with the proposed optimized droop control method to see how the system operates when K
_{G2}is selected optimally in the range (9–11.25); - Scenario 3: Test system is operated with the proposed optimized control method to see how the system operates when K
_{G2}is selected optimally in a wider range (6.75–11.25).

_{G2}of DG2 is changed in a range to adjust the output power. The constraints for frequency are set to f

_{min}= 49 Hz and f

_{max}= 51 Hz. The voltage can vary in the range from 360 V to 440 V. The frequency f

_{0}of DG1 and DG2 is set to the same value and equal to 50 Hz. In this case, generator DG1 was non-optimized while generator 2 was optimized. K

_{G1}is kept constantly at 8.75 while the range of variation of K

_{G2}was set as already said above.

## 5. Experimental Results and Analysis

#### 5.1. The Simulation Results of the Transient Responses

_{G2}is expected to change to produce a new operating point for minimizing power losses in the microgrid. The simulation results of transient response for a K

_{G2}changing are expressed in Figure 12. Figure 13 shows the output power of generators while frequency and bus voltages of the system are described specifically in Figure 14, Figure 15 and Figure 16.

^{−5}to −7.3 × 10

^{−5}to optimize the microgrid, the power sharing of generators is regulated. The output power from DG2 is re-established and reduced from 1.88 × 10

^{4}to 1.46 × 10

^{4}W and the generated power of DG1 is changed from 1.48 × 10

^{4}to 1.86 × 10

^{4}W. The frequency fluctuates in the range from 49.9 Hz to 50.16 Hz. This changing only occurs in 0.5 s and within the frequency range of interest. The voltage of three phases at DG1 and DG2 are also monitored and showed in Figure 13; Figure 14.

_{G2}to a new optimized value corresponding to the new loading condition.

#### 5.2. The Simulation Results in 24 H

_{G2}is fixed at 11.25. In scenario 2, K

_{G2}is optimized in the range (9–11.25) and changed slightly around 9. In scenario 3, because K

_{G2}is chosen in a wider range (6.75–11.25), the adjustment of K

_{G2}in different load conditions is described clearly, especially in peak hours from 8 a.m. to 1 p.m. and from 6 p.m. to 9 p.m. In the off-peak hours from 0 a.m. to 6 a.m., the power demand is not too large, the energy flow through the transmission system is small. Thus the regulation and improvement are not obvious although K

_{G2}is chosen optimally. From the results in Figure 17, the DG2 output is adjusted to inject enough power to the system in a way that minimizes power losses for the network. The power generated by DG1 is also regulated to manage the differences between generation and consumption. It shows that the wider adjustment range of K

_{G2}, the better improvement for power losses P

_{loss}.

## 6. Conclusions

_{G2}in different ranges. The operating characteristics are checked in both transient response and steady-state operation. The results of scenarios have been compared with each other to prove the effectiveness and show the advantages of the proposed configuration. The power losses improvement is verified in the HIL simulations, which provides near reality testing conditions and thus, ensures applicability and reproducibility of the results. 16.15% improvement of energy losses is a good result to illustrate the effectiveness of the proposed method while all constraints of problems are satisfied.

## Author Contributions

## Funding

## Conflicts of Interest

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Digital I/O | FPGA |
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Number of channels: 256 input/output configurable in 1- to 32-bit groups Compatibility: 3.3 V Power-on state: High impedance | Device: Xilinx Spartan 3 I/O Package: fg676 Embedded RAM available: 216 Kbytes Clock: 100 MHz Platform options: XC3S5000 Logic slices: 33,280 Equivalent logic cells: 74,880 Available I/O lines: 489 |

Bus | |

Dimensions (not including connectors): PCI-Express x1 Data transfer: 2.5 Gbit/s |

From | To | R (Ohm/km) | X (Ohm/km) | L (km) |
---|---|---|---|---|

1 | 3 | 0.43 | 0.14444 | 1 |

2 | 3 | 0.43 | 0.14444 | 2.5 |

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**MDPI and ACS Style**

Tran, Q.T.T.; Riva Sanseverino, E.; Zizzo, G.; Di Silvestre, M.L.; Nguyen, T.L.; Tran, Q.-T. Real-Time Minimization Power Losses by Driven Primary Regulation in Islanded Microgrids. *Energies* **2020**, *13*, 451.
https://doi.org/10.3390/en13020451

**AMA Style**

Tran QTT, Riva Sanseverino E, Zizzo G, Di Silvestre ML, Nguyen TL, Tran Q-T. Real-Time Minimization Power Losses by Driven Primary Regulation in Islanded Microgrids. *Energies*. 2020; 13(2):451.
https://doi.org/10.3390/en13020451

**Chicago/Turabian Style**

Tran, Quynh T.T, Eleonora Riva Sanseverino, Gaetano Zizzo, Maria Luisa Di Silvestre, Tung Lam Nguyen, and Quoc-Tuan Tran. 2020. "Real-Time Minimization Power Losses by Driven Primary Regulation in Islanded Microgrids" *Energies* 13, no. 2: 451.
https://doi.org/10.3390/en13020451