# Optimized Self-Adaptive Power Distribution for Microgrids in a Typical Tourism Water Village of Northern China under COVID-19 Background

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Object

#### 2.1.1. Microgrid Topology

#### 2.1.2. Specific Conditions under COVID-19 Background

#### 2.2. Optimized Self-Adaptive Power Distribution Scheme

#### 2.2.1. Microgrid Model

- Model of wind generator;

_{WT}is the output power of the wind generation, ρ is the air density, R is the blade radius of the wind generator, v is the wind speed, θ is the blade pitch angle, γ is the blade tip ratio, and C

_{p}(θ, γ) denotes the power coefficient, which depend on θ and γ. According to (1), the output power can be maintained at maximum by adjusting the key parameter γ to control the max power C

_{p}

_{max}(θ, γ) coefficient.

- Photovoltaic;

_{d}is the output current of the solar panel, U is the output voltage of the solar panel, I denotes the output current flowing through the series resistor R

_{s}, I

_{sh}denotes the leakage current through the parallel resistor, R

_{sh}, I

_{ph}, I

_{sc}, and I

_{o}are the diode current, the photocurrent, the short circuit current, and the diode reverse saturation current respectively. G is the solar intensity, A represents the diode ideal factor, q stands for the electronic quantity, generally 1.602

^{−19}, K stands for Boltzmann constant and K

_{t}is the temperature coefficient.

- Battery.

_{b}is internal resistance of the battery, V

_{o}is the open circuit voltage of the battery, i

_{b}is battery charging current, V

_{b}is polarization voltage, Q is battery capacity, A is exponential voltage, and B is exponential capacity.

#### 2.2.2. Boundary Condition and Limitation

_{i}

_{,min}and P

_{i}

_{,max}represent the minimum allowable output power and the maximum allowable output power of each DG at time t, respectively. P

_{i}(t) denotes the output power of each DG at time t.

_{wt}

_{,t}and Q

_{pv}

_{,t}represent the reactive power output of the wind generator and the photovoltaic generator grid-connected inverter at time period t; S

_{wt}and S

_{pv}represent the rated capacity of the wind generator inverter and the photovoltaic generator inverter; P

_{wt}and P

_{pv}represent the output of the wind generator and the photovoltaic generator.

_{svc}is the set of static reactive power compensators in the distribution network. Q

_{svci}(t) is the compensation power of the svci discrete capacitor bank at time t, ${Q}_{svci}^{\mathrm{min}}$ and ${Q}_{svci}^{\mathrm{max}}$ are respectively the lower and upper limits of the svci operational capacity of the SVC station.

_{t}is the SOC at period t, while S

_{min}and S

_{max}are the minimum value and the maximum value of SOC, respectively. Usually, S

_{min}is set to 0.3 while S

_{max}is set to 0.9.

_{PV}(t) represents the output power of PV, P

_{BAT}(t) denotes the charge and discharge power of BAT. P

_{b}(t) and P

_{s}(t) mean that the whole system buys and sells electric power from the primary grid, respectively, And P

_{load}(t) indicates the power consumed by the load. Q

_{load}(t) is the reactive load power during period t, Q

_{CB}(t), Q

_{SVC}(t) are the reactive power of the capacitor banks and the static reactive compensator at period t respectively. I

_{ij}(t) represents the branch ij current value at time period t, and x

_{ij}is the reactance of branch ij.

#### 2.2.3. Improved PSO Algorithm for Typical Condition under COVID-19 Background

- Improved PSO algorithm application;

- elite strategy;
- The elite strategy is used to generate elite reverse individuals. The fitness value of each particle is calculated. Meanwhile, the fitness value of the particle is stored in p
_{Best}. Further, the next generation population is generated based on the best value of the objective function. Moreover, the fitness value of the best individual is stored in g_{Best}. - Weight factor and acceleration updating.

_{1}, c

_{2}) are very important parameters that have an obvious effect on the algorithm performance in PSO algorithm. The traditional parameter selection based on selecting constant is easy to obtain the local optimal solution, but not the global optimal solution. Aiming at the shortcoming, PSO algorithm is improved from the inertial weight factor and the learning factor. The improved strategy is as follows:

_{s}and w

_{e}are the initial value and the end value, respectively. t and T are the current number and the maximum number during the iteration, respectively. c

_{1s}and c

_{1e}are the beginning and final value of c

_{1}, respectively, while c

_{2s}and c

_{2e}are the beginning and final value of c

_{2}, respectively.

- Objective functions and optimization scheme.

_{(t)}denotes the energy yield of each distributed generation, and g

_{x}represents the relationship between the generating capacity of each distributed generation and it corresponds to the operation and maintenance cost, usually taking the empirical value. g

_{PV}and g

_{WT}are 0.61 rmb/kWh and 0.75 rmb/kWh, respectively.

_{i}represents the available duration of the device.

_{(n)}denotes the output power of the pollutant, E

_{n}represents the cost of treating the pollution (rmb/kg), α

_{n}represents the pollutant emission, n represents the pollutant number.

_{n}(t) denotes the output power of the distributed power supply, β

_{n}represents the power losses rate of device n, γ represents the economic loss caused by unit power loss. N and T indicate the total number of devices and periods.

_{buy}represents the price of the electricity bought from the grid; B

_{sell}represents the price of the electricity sold to the large grid, P

_{b}is the amount of electricity bought from the grid; P

_{s}represents the amount of electricity sold to the grid. The corresponding electricity price within a period is shown in Figure 7. The electric price is the same for the same period of time every day. Hence, the price is supposed to be constant.

_{n}(t) represents the output power of device n in period t, and N and T are the total number of devices in microgrid and the total number of hours. U

_{n}, r

_{n}, and L

_{n}are the output power of equipment n, the resistance of device n, and the distance between device n and the main line of the grid.

_{1C}to ω

_{6C}are the weighted factors for the economic operation. In different modes, based on (13) and (14), the weighted factors are dynamic in order to better find the optimal solution.

#### 2.3. Power Dispatching Optimization Case Study

#### 2.3.1. Five Modes

_{3C,m1}to ω

_{3C,m5}are the weighted factors ω

_{3C}for mode 1 to mode 5. In different cases, the weighted factors are assigned with different values. For example, ω

_{3C,m1}is smaller than ω

_{3C,m3}and much smaller than ω

_{3C,m5}. C

_{B}is the basic cost including the maintenance costs C

_{rm}, the depreciable costs C

_{DEP}, and the environment costs C

_{ENV}. Moreover, ω

_{1C}, ω

_{2C}, ω

_{4C}are the weighted factors for the economic operation. C

_{P}(t), C

_{P}

_{,C}(t), C

_{P,B}(t), C

_{P,CB}(t), and C

_{P,CBL}(t) are the electricity transaction cost for different mode, respectively, and P

_{load}(t), P

_{WT}(t), P

_{PV}(t), and P

_{BAT}(t) are the load power, the output power of wind generator, photovoltaic generator and batteries at period t. µ(t) represents the electricity transaction coefficient, which contains A

_{buy}(t), and B

_{sell}(t) indicates the purchase price of electricity per unit in period t and the selling price per unit in period t. P

_{buy}(t) and P

_{sell}(t) donate the power sold to the grid and the power bought from the gird. In addition, P

_{exc,}

_{max}represents the upper limit of power exchange between microgrid and grid. C

_{LOSS,PV}, C

_{LOSS,WT}, and C

_{LOSS,BAT}are the power loss and the line loss from photovoltaic power generation, wind power generation, and batteries. In general, a identifies the abandon of the wind generator, a = {0, 1}; and b identifies the abandon of the photovoltaic generator.

#### 2.3.2. Power Dispatching Optimization without Epidemic

#### 2.3.3. Power Dispatching Optimization with Epidemic

## 3. Results and Discussion

#### 3.1. Comparison among Five Modes

#### 3.2. Impact on Microgrid Economic Due to Epidemic

## 4. Conclusions

## Author Contributions

## Funding

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 8.**Power dispatching under different mode in microgrid without pandemic (

**a**) mode 1; (

**b**) mode 2; (

**c**) mode 3; (

**d**) mode 4; (

**e**) mode 5.

**Figure 9.**Power dispatching under different mode in microgrid (

**a**) mode 1; (

**b**) mode 2; (

**c**) mode 3; (

**d**) mode 4; (

**e**) mode 5.

**Figure 10.**Power dispatching comparison without pandemic respectively for (

**a**) wind generation; (

**b**) photovoltaic generation; (

**c**) exchanged power from primary grid; (

**d**) battery discharging.

**Figure 11.**Power dispatching comparison respectively for (

**a**) wind generation; (

**b**) photovoltaic generation; (

**c**) exchanged power from primary grid; (

**d**) battery discharging.

The Serial Number | Time Serial Number | Corresponding Period of Time |
---|---|---|

1 | 0–24 | 0:00~6:00 |

2 | 25–40 | 6:00~10:00 |

3 | 41–60 | 10:00~15:00 |

4 | 61–72 | 15:00~18:00 |

5 | 73–84 | 18:00~21:00 |

6 | 85–96 | 21:00~24:00 |

Mode | Description | ||
---|---|---|---|

Use of Battery | Use of New Energy Generation | Exchange between Microgrid and Primary Grid | |

mode 1 | no | mandatory | no |

mode 2 | no | free | no |

mode 3 | yes | mandatory | no |

mode 4 | yes | free | no |

mode 5 | yes | free | yes |

Mode | Without Pandemic | With Pandemic | ||||||
---|---|---|---|---|---|---|---|---|

Total Cost (10^{5} RMB) | Change Ratio | Average Price (RMB/kWh) | Change Ratio | Total Cost (10^{5} RMB) | Change Ratio | Average Price (RMB/kWh) | Change Ratio | |

mode 1 | 8.58 | / | 0.78 | / | 4.81 | / | 0.82 | 0.00% |

mode 2 | 8.01 | 6.64% | 0.69 | 11.54% | 3.94 | 18.01% | 0.72 | 12.20% |

mode 3 | 8.39 | 2.21% | 0.72 | 7.69% | 4.15 | 13.72% | 0.75 | 8.54% |

mode 4 | 7.68 | 10.49% | 0.60 | 23.08% | 3.61 | 24.95% | 0.63 | 23.17% |

mode 5 | 7.81 | 8.97% | 0.65 | 16.67% | 3.79 | 21.21% | 0.65 | 20.73% |

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## Share and Cite

**MDPI and ACS Style**

Zhao, J.-L.; Zeng, S.-M.; Xu, W.-T.; Du, X.-D.; He, Y.-L.
Optimized Self-Adaptive Power Distribution for Microgrids in a Typical Tourism Water Village of Northern China under COVID-19 Background. *Sustainability* **2022**, *14*, 9839.
https://doi.org/10.3390/su14169839

**AMA Style**

Zhao J-L, Zeng S-M, Xu W-T, Du X-D, He Y-L.
Optimized Self-Adaptive Power Distribution for Microgrids in a Typical Tourism Water Village of Northern China under COVID-19 Background. *Sustainability*. 2022; 14(16):9839.
https://doi.org/10.3390/su14169839

**Chicago/Turabian Style**

Zhao, Jian-Li, Si-Ming Zeng, Wen-Tao Xu, Xiao-Dong Du, and Yu-Ling He.
2022. "Optimized Self-Adaptive Power Distribution for Microgrids in a Typical Tourism Water Village of Northern China under COVID-19 Background" *Sustainability* 14, no. 16: 9839.
https://doi.org/10.3390/su14169839