# An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand

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## Abstract

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## 1. Introduction

- Proposing a novel operation and management framework for the reconfigurable microgrids considering the high penetration of HEVs (with two different charging schemes) and renewable energy sources;
- Developing a novel hybrid prediction model based on SVR and MDA for accurate estimation of the total charging demand of HEVs in the microgrid;
- Proposing a new optimization method based on DA and a three phase modification method to enhance its search ability for the optimization applications.

## 2. Machine Learning Based Policy Development and Energy Management Framework

#### 2.1. Machine Learning Based on SVR

#### 2.2. Improved Optimization Based on MDA

_{best}. The enemy situation is also updated in accordance with the worst candidate in the population. All the above motivation factors are then mixed up with different weighting factors to make a united factor as below [27]:

_{1}≠ θ

_{2}≠ θ

_{3}≠ θ

_{i}):

_{s}= 0 and it is updated as below:

## 3. Charging Modeling of HEVs

_{c}and efficiency η

_{c}.

## 4. Problem Formulations

- -
- Active and reactive power limit of DGs:$$\underset{\_}{{P}_{m}^{G}}{x}_{m,t}^{G}\le {P}_{m,t}^{G}\le \overline{{P}_{m}^{G}}{x}_{m,t}^{G}\hspace{1em}\forall m\in {\mathsf{\Omega}}^{BG},\forall t\in {\mathsf{\Omega}}^{T}$$$$\underset{\_}{{Q}_{m}^{G}}{x}_{m,t}^{G}\le {Q}_{m,t}^{G}\le \overline{{Q}_{m}^{G}}{x}_{m,t}^{G}\hspace{1em}\forall m\in {\mathsf{\Omega}}^{BG},\forall t\in {\mathsf{\Omega}}^{T}$$
- -
- Ramp up/down limits:$${P}_{m,t}^{G}-{P}_{m,t-1}^{G}\le R{U}_{m}^{G}\hspace{1em}\forall m\in {\mathsf{\Omega}}^{BG},\forall t\in {\mathsf{\Omega}}^{T}$$$${P}_{m,t-1}^{G}-{P}_{m,t}^{G}\le R{D}_{m}^{G}\hspace{1em}\forall m\in {\mathsf{\Omega}}^{BG},\forall t\in {\mathsf{\Omega}}^{T}$$
- -
- Minimum up and down time limit:$${T}_{m,t}^{G-on}\ge U{T}_{m}^{G}\left({x}_{m,t}^{G}-{x}_{m,t-1}^{G}\right)\hspace{1em}\forall m\in {\mathsf{\Omega}}^{BG},\forall t\in {\mathsf{\Omega}}^{T}$$$${T}_{m,t}^{G-off}\ge D{T}_{m}^{G}\left({x}_{m,t-1}^{G}-{x}_{m,t}^{G}\right)\hspace{1em}\forall m\in {\mathsf{\Omega}}^{BG},\forall t\in {\mathsf{\Omega}}^{T}$$
- -
- Charging/discharging limit of battery storage:$$\underset{\_}{{P}_{m}^{Ch}}{y}_{m,t}^{Ch}\le {P}_{m,t}^{Ch}\le \overline{{P}_{m}^{Ch}}{y}_{m,t}^{Ch}\hspace{1em}\forall m\in {\mathsf{\Omega}}^{BS},\forall t\in {\mathsf{\Omega}}^{T}$$$$\underset{\_}{{P}_{m}^{Disch}}{y}_{m,t}^{Disch}\le {P}_{m,t}^{Disch}\le \overline{{P}_{m}^{Disch}}{y}_{m,t}^{Disch}\hspace{1em}\forall m\in {\mathsf{\Omega}}^{BS},\forall t\in {\mathsf{\Omega}}^{T}$$

- -
- Adjustable load demand:

- -
- Bus voltage limit:$$\underset{\_}{V}\le {V}_{m,t}\le \overline{V}\hspace{1em}\forall m\in {\mathsf{\Omega}}^{B},\forall t\in {\mathsf{\Omega}}^{T}$$
- -
- Main grid power limit:$$-\overline{{P}_{m}^{M}}\le {P}_{m,t}^{M}\le \overline{{P}_{m}^{M}}\hspace{1em}\forall m\in {\mathsf{\Omega}}^{B},\forall t\in {\mathsf{\Omega}}^{T}$$
- -
- Reconfiguration using the remote switches:Optimal switching is a strategic tool for altering the power flow path in the microgrid using the tie (normal open) switches and sectionalizing (normal closed). To this end the binary variable ${w}_{mn,t}^{L}$ is used, which can get 0 and 1 to show the closed or open status of a line:$$0\le {I}_{mn,t}^{L}\le \overline{{I}^{L}}{w}_{mn,t}^{L}\hspace{1em}\forall mn\in {\mathsf{\Omega}}^{L},\forall t\in {\mathsf{\Omega}}^{T}$$$$\sum _{lm\in {\mathsf{\Omega}}^{L}}{w}_{lm,t}^{L}}=1\hspace{1em}\forall m\in \left({\mathsf{\Omega}}^{BAD}\cup {\mathsf{\Omega}}^{BCD}\right),\forall t\in {\mathsf{\Omega}}^{T$$$$\sum _{lm\in {\mathsf{\Omega}}^{L}}{\theta}_{lm,t}^{L}}-{\displaystyle \sum _{mn\in {\mathsf{\Omega}}^{L}}{\theta}_{mn,t}^{L}}+{\theta}_{m,t}^{M}={\theta}_{m,t}^{D}\hspace{1em}\forall m\in {\mathsf{\Omega}}^{B},\forall t\in {\mathsf{\Omega}}^{T$$$$0\le {\theta}_{mn,t}^{L}\le \left|{\mathsf{\Omega}}^{B}\right|{w}_{mn,t}^{L}\hspace{1em}\forall mn\in {\mathsf{\Omega}}^{L},\forall t\in {\mathsf{\Omega}}^{T}$$

## 5. Simulation Results

_{v}shows the number of forecast samples and ${\tilde{y}}_{i}/{y}_{i}$ is the forecast/real value of the total charging demand sample of HEV [47]. Table 6 shows the prediction using different methods. According to these results, the proposed hybrid method based on SVR-MDA could provide much higher accuracy in terms of MAPE, MARPE and RMSE concurrently. This shows not only the high accuracy of the proposed prediction model for estimating the total charging demand of the HEVs, but also shows the appropriate search ability of the MDA by effective adjustment of the parameters in SVR. The superiority of the search optimization algorithm of MDA over the original version of DA can be deduced from the low values of MAPE, RMSE and MARPE [48].

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Nomenclature

${C}_{m,t}^{S}$ | Amount of energy stored in m^{th} storage at time t |

${C}_{bat}$ | Capacity of battery in HEVs |

$C{T}_{m}^{S}$ | Minimum charging time |

$DOD$ | Depth of discharge in HEVs |

$D{T}_{m}^{G}$ | Minimum down time limits for DG |

$D{T}_{m}^{S}$ | Minimum discharging time for storage |

E_{loc} | The enemy’s scene |

${E}_{m}^{AD}$ | Total energy required by the adjustable load |

ER | Total electric range in HEVs |

F_{loc} | Location of the nourishment basis |

${I}_{mn,t}^{L}$ | Current flow in line between the buses n and m |

Iter | Iteration number. |

m | Distance passed by HEVs |

$Min{C}_{bat}/Max{C}_{bat}$ | Min/max of battery capacity in HEVs |

N | Number of population |

N_{v} | Number of forecast samples for Charging demand of HEVs |

$P$ | Battery charging power rate |

${P}_{m,t}^{G}$ | Power generation by DG. |

${P}_{m,t}^{M}$ | Amount of power generated by the main grid |

${P}_{m,t}^{HEV}$ | Amount of power charging demand of HEV |

${P}_{m,t}^{G}$ and ${Q}_{m,t}^{G}$ | Amount of active and reactive power generated by m^{th} DG at time t |

${P}_{m,t}^{Ch}$ | Charging power of storage |

${P}_{m,t}^{Disch}$ | Discharging power of the storage |

${P}_{m,t}^{D}$ | Load demand on but m at time t |

${P}_{lm,t}^{L}$ | Active power flow on line connecting buses l to m |

${P}_{m,t}^{D}$ and ${Q}_{m,t}^{D}$ | Active and reactive load demand on bus m |

${P}_{m,t}^{M}$ | Amount of power generated/consumed by the main grid at time t |

s, a, c, f, and e | Weighting factor for the relevant motivation factors |

r | Random value |

$R{U}_{m}^{G}$ and $R{D}_{m}^{G}$ | Ramp up and down limits for m^{th} DG |

${R}_{mn}$ | Resistance connecting buses m and n |

r_{i} | Random number in the range [0,1] |

s | Indicator of modification |

${S}_{\mathrm{modification}}$ | Set of modification |

SoC | Depth of discharge in HEVs |

t_{start} | Starting time for the charging |

t_{D} | Charging length for HEVs |

$U{T}_{m}^{AD}$ | Minimum up time of the load |

$U{T}_{m}^{G}$ | Minimum up time limits for DG |

v_{j} | Speed of the neighbor |

${V}_{m,t}$ | Voltage value of bus m |

${w}_{mn,t}^{L}$ | Line status, 0 or 1 |

${x}_{m,t}^{G}$ | ON/OFF status of the DG |

${\tilde{y}}_{i}/{y}_{i}$ | Forecast/real value of the total charging demand sample of HEV |

${y}_{m,t}^{Ch}$ | Charging status of the battery |

${y}_{m,t}^{Disch}$ | Discharging status of the battery |

${z}_{m,t}^{AD}$ | ON/OFF status of the load demand |

${\theta}_{lm,t}^{L}$ | Fictional current flow of distribution lines |

${\theta}_{m,t}^{M}$ | Fictional current flow of utility grid |

${\theta}_{m,t}^{D}$ | Fictional current flow demand |

${\eta}_{c}$ | Charging efficiency in HEVs |

${\rho}_{m}^{G}$ | Cost of power purchasing from the DG |

${\rho}_{t}^{M}$ | Cost of power purchasing from the main grid |

$\delta $ | Time interval (here 1 h) |

${\theta}_{i}$ | i^{th} solution in the DA |

${\mu}_{{C}_{bat}}$/${\sigma}_{{C}_{bat}}$ | Mean/standard deviation of battery in HEVs |

${\mathsf{\Omega}}^{BG}$ | Set of buses with installed DG |

${\mathsf{\Omega}}^{T}$ | Set of operation time horizon (here 24 h) |

${\mathsf{\Omega}}^{L}$ | Set of lines |

${\eta}_{m}^{Ch}$ | Charging efficiency |

${\eta}_{m}^{Disch}$ | Discharging efficiency |

## Appendix A

Branch No. | From Bus | To Bus | R (ohm) | X (ohm) |
---|---|---|---|---|

1 | 1 | 2 | 0.0005 | 0.0012 |

2 | 2 | 3 | 0.0005 | 0.0012 |

3 | 3 | 4 | 0.0015 | 0.0036 |

4 | 4 | 5 | 0.0251 | 0.0294 |

5 | 5 | 6 | 0.366 | 0.1864 |

6 | 6 | 7 | 0.3811 | 0.1941 |

7 | 7 | 8 | 0.0922 | 0.0470 |

8 | 8 | 9 | 0.0493 | 0.0251 |

9 | 9 | 10 | 0.8190 | 0.2707 |

10 | 10 | 11 | 0.1872 | 0.0691 |

11 | 11 | 12 | 0.7114 | 0.2351 |

12 | 12 | 13 | 1.0300 | 0.3400 |

13 | 13 | 14 | 1.0440 | 0.3450 |

14 | 14 | 15 | 1.0580 | 0.3496 |

15 | 15 | 16 | 0.1966 | 0.0650 |

16 | 16 | 17 | 0.3744 | 0.1238 |

17 | 17 | 18 | 0.0047 | 0.0016 |

18 | 18 | 19 | 0.3276 | 0.1083 |

19 | 19 | 20 | 0.2106 | 0.0696 |

20 | 20 | 21 | 0.3416 | 0.1129 |

21 | 21 | 22 | 0.0140 | 0.0046 |

22 | 22 | 23 | 0.1591 | 0.0526 |

23 | 23 | 24 | 0.3463 | 0.1145 |

24 | 24 | 25 | 0.7488 | 0.2745 |

25 | 25 | 26 | 0.3089 | 0.1021 |

26 | 26 | 27 | 0.2732 | 0.0572 |

27 | 3 | 28 | 0.0044 | 0.0108 |

28 | 28 | 29 | 0.0640 | 0.1565 |

29 | 29 | 30 | 0.3978 | 0.1315 |

30 | 30 | 31 | 0.0702 | 0.0232 |

31 | 31 | 32 | 0.3510 | 0.1160 |

32 | 32 | 33 | 0.839 | 0.2816 |

33 | 33 | 34 | 1.7080 | 0.5646 |

34 | 34 | 35 | 1.474 | 0.4673 |

35 | 3 | 36 | 0.0044 | 0.0108 |

36 | 36 | 37 | 0.0640 | 0.1565 |

37 | 37 | 38 | 0.1053 | 0.1430 |

38 | 38 | 39 | 0.0304 | 0.0355 |

39 | 39 | 40 | 0.0018 | 0.0021 |

40 | 40 | 41 | 0.7283 | 0.8509 |

41 | 41 | 42 | 0.310 | 0.3623 |

42 | 42 | 43 | 0.0410 | 0.0478 |

43 | 43 | 44 | 0.0092 | 0.0116 |

44 | 44 | 45 | 0.1089 | 0.1373 |

45 | 45 | 46 | 0.0009 | 0.0012 |

46 | 4 | 47 | 0.0034 | 0.0084 |

47 | 47 | 48 | 0.0851 | 0.2083 |

48 | 48 | 49 | 0.2898 | 0.7091 |

49 | 49 | 50 | 0.0822 | 0.2011 |

50 | 8 | 51 | 0.0928 | 0.0473 |

51 | 51 | 52 | 0.3319 | 0.1114 |

52 | 9 | 53 | 0.1740 | 0.0886 |

53 | 53 | 54 | 0.2030 | 0.1034 |

54 | 54 | 55 | 0.2842 | 0.1447 |

55 | 55 | 56 | 0.2813 | 0.1433 |

56 | 56 | 57 | 1.5900 | 0.5337 |

57 | 57 | 58 | 0.7837 | 0.2630 |

58 | 58 | 59 | 0.3042 | 0.1006 |

59 | 59 | 60 | 0.3861 | 0.1172 |

60 | 60 | 61 | 0.5075 | 0.2585 |

61 | 61 | 62 | 0.0974 | 0.0496 |

62 | 62 | 63 | 0.1450 | 0.0738 |

63 | 63 | 64 | 0.7105 | 0.3619 |

64 | 64 | 65 | 1.0410 | 0.5302 |

65 | 11 | 66 | 0.2012 | 0.0611 |

66 | 66 | 67 | 0.0047 | 0.0014 |

67 | 12 | 68 | 0.7394 | 0.2444 |

68 | 38 | 69 | 0.0047 | 0.0016 |

Bus No. | Active Power (kW) | Reactive Power (kVar) |
---|---|---|

1 | 0 | 0 |

2 | 0 | 0 |

3 | 0 | 0 |

4 | 0 | 0 |

5 | 0 | 0 |

6 | 2.60 | 2.2 |

7 | 40.4 | 30 |

8 | 75 | 54 |

9 | 30 | 22 |

10 | 28 | 19 |

11 | 145 | 104 |

12 | 145 | 104 |

13 | 8 | 5.50 |

14 | 8 | 5.50 |

15 | 0 | 0 |

16 | 45.50 | 30 |

17 | 60 | 35 |

18 | 60 | 35 |

19 | 0 | 0 |

20 | 1 | 0.60 |

21 | 114 | 81 |

22 | 5.30 | 3.50 |

23 | 0 | 0 |

24 | 28 | 20 |

25 | 0 | 0 |

26 | 14 | 10 |

27 | 14 | 10 |

28 | 26 | 18.6 |

29 | 26 | 18.6 |

30 | 0 | 0 |

31 | 0 | 0 |

32 | 0 | 0 |

33 | 14 | 10 |

34 | 19.5 | 14 |

35 | 6 | 4 |

36 | 26 | 18.55 |

37 | 26 | 18.55 |

38 | 0 | 0 |

39 | 24 | 17 |

40 | 24 | 17 |

41 | 1.2 | 1 |

42 | 0 | 0 |

43 | 6 | 4.3 |

44 | 0 | 0 |

45 | 39.22 | 26.3 |

46 | 39.22 | 26.3 |

47 | 0 | 0 |

48 | 79 | 56.4 |

49 | 384.7 | 274.5 |

50 | 384.7 | 274.5 |

51 | 40.5 | 28.3 |

52 | 3.6 | 2.7 |

53 | 4.35 | 3.5 |

54 | 26.4 | 19 |

55 | 24 | 17.2 |

56 | 0 | 0 |

57 | 0 | 0 |

58 | 0 | 0 |

59 | 100 | 72 |

60 | 0 | 0 |

61 | 1244 | 888 |

62 | 32 | 23 |

63 | 0 | 0 |

64 | 227 | 162 |

65 | 59 | 42 |

66 | 18 | 13 |

67 | 18 | 13 |

68 | 28 | 20 |

69 | 28 | 20 |

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**Figure 7.**Total energy consumption value in the microgrid in the base case (ignoring HEVs’ demand and DGs).

**Figure 9.**Comparison of the convergence curves of different optimization methods for optimizing the total cost function value.

**Figure 11.**Comparative operation cost of the microgrid in the coordinated and intelligent charging patterns.

**Table 1.**Charger type in HEVs [36].

Charger Type | Input Voltage | Maximum Power (kW) |
---|---|---|

Level 1 | 120 VAC | 1.44 |

Level 2 | 208–240 VAC | 11.5 |

Level 3 | 208–240 VAC | 96 |

Level 3 (DC) | 208–600 VDC | 240 |

**Table 2.**HEV classes [36].

Class | Market Share | Min C_{bat} (kWh) | Max C_{bat} (kWh) |
---|---|---|---|

Micro Car | 0.2 | 8 | 12 |

Economy Car | 0.3 | 10 | 14 |

Mid-Size Car | 0.3 | 14 | 18 |

SUV | 0.3 | 19 | 23 |

**Table 3.**Historical raw data of the total energy charging demand of HEVs over a month in the microgrid.

Time (Day) | 1–8 | |||||||
---|---|---|---|---|---|---|---|---|

Charging demand (MWh) | 2.3928 | 2.1667 | 2.2810 | 2.3690 | 2.4564 | 2.4876 | 2.2998 | 2.1136 |

Time (day) | 9–16 | |||||||

Charging demand (MWh) | 2.1185 | 2.1678 | 2.4336 | 2.1663 | 2.4215 | 2.1614 | 2.4739 | 2.2099 |

Time (day) | 17–24 | |||||||

Charging demand (MWh) | 2.1401 | 2.1649 | 2.3312 | 2.2661 | 2.2107 | 2.4290 | 2.3172 | 2.3010 |

Time (day) | 25–30 | |||||||

Charging demand (MWh) | 2.4684 | 2.1807 | 2.3955 | 2.3939 | 2.2238 | 2.2783 |

Storage | Bus | Capacity (kWh) | Min-Max Charging/Discharging Power (kW) | Min Charging/Discharging Time (h) |
---|---|---|---|---|

DES | 15 | 1500 | 50–250 | 4 |

Load | Type | Bus | Min-Max Capacity (kW) | Required Energy (kWh) | Initial Start/End Time (h) | Min Up Time (h) |
---|---|---|---|---|---|---|

L1 | S | 28 | 0–60 | 240 | 11–14 | 1 |

L2 | S | 56 | 0–60 | 240 | 15–19 | 1 |

L3 | S | 18 | 20–60 | 240 | 16–19 | 1 |

L4 | C | 35 | 10–40 | 200 | 1–24 | 24 |

L5 | C | 59 | 20–60 | 300 | 13–24 | 12 |

Method | MAPE (%) | MARPE | RMSE |
---|---|---|---|

ARMA | 2.08753 | 6.25845 | 2.63238 |

ANN | 2.26421 | 6.56019 | 2.86453 |

SVR | 1.36508 | 3.40989 | 1.45778 |

SVR-DA | 1.16523 | 3.11277 | 1.44568 |

Proposed SVR-MDA Method | 0.97752 | 1.88198 | 1.04481 |

**Table 7.**Robustness and searching features of different optimization algorithms: minimizing total microgrid cost over 20 trails for 24 h of operation.

Method | Cost ($) (×10^{5}) | ||||
---|---|---|---|---|---|

Average | Worst | Best | Standard Deviation | CPU Time (s) | |

GA | 6.6927 | 7.8865 | 6.5364 | 0.1524 | 17.269 |

PSO | 6.5378 | 7.9124 | 6.4473 | 0.1325 | 15.703 |

Original DA | 6.5085 | 7.5295 | 6.4527 | 0.1358 | 14.275 |

Proposed MDA | 6.4163 | 6.5946 | 6.2653 | 0.1063 | 13.276 |

Wind Turbine 1 (kW) | Wind Turbine 2 (kW) | Solar Panel 1 (kW) | Solar Panel 1 (kW) | HEV Charging Demand (Coordinate) | HEV Charging Demand (Intelligent) |
---|---|---|---|---|---|

16.898 | 29.7500 | 0 | 0 | 233.02 | 275.78 |

16.898 | 29.7500 | 0 | 0 | 106.52 | 289.04 |

16.898 | 29.7500 | 0 | 0 | 77.76 | 227.24 |

16.898 | 29.7500 | 0 | 0 | 72.00 | 210.00 |

16.898 | 29.7500 | 0 | 0 | 60.48 | 159.68 |

8.662 | 15.2500 | 0 | 0 | 50.40 | 136.68 |

16.898 | 29.7500 | 0 | 0 | 40.32 | 84.92 |

12.354 | 21.7500 | 1.6000 | 1.6000 | 28.80 | 53.28 |

16.898 | 29.7500 | 30.000 | 30.000 | 20.16 | 43.20 |

29.252 | 51.5000 | 60.200 | 60.200 | 11.52 | 41.74 |

83.070 | 146.250 | 83.600 | 83.600 | 8.64 | 38.86 |

98.548 | 173.5000 | 95.600 | 95.600 | 2.88 | 23.04 |

37.062 | 65.2500 | 191.20 | 191.20 | 1.44 | 20.16 |

22.436 | 39.5000 | 168.40 | 168.40 | 0 | 12.96 |

16.898 | 29.7500 | 63.000 | 63.000 | 0 | 7.20 |

12.354 | 21.7500 | 33.8000 | 33.8000 | 0 | 2.88 |

16.898 | 29.7500 | 4.4000 | 4.4000 | 0 | 0 |

16.898 | 29.7500 | 0 | 0 | 0 | 4.32 |

12.325 | 21.7000 | 0 | 0 | 0 | 17.26 |

16.898 | 29.7500 | 0 | 0 | 0 | 20.14 |

12.311 | 21.6750 | 0 | 0 | 113.6 | 50.34 |

12.311 | 21.6750 | 0 | 0 | 432.78 | 152.42 |

8.6620 | 15.2500 | 0 | 0 | 578.00 | 240.10 |

5.8220 | 10.2500 | 0 | 0 | 440.02 | 167.10 |

Storage | −1 | −1 | −1 | −1 | −1 | 1 | 1 | 1 | 1 | 1 | −1 | −1 | −1 | −1 | −1 | −1 | 1 | 1 | 1 | 1 | 1 |

L1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |

L2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 |

L3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 |

L4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |

L5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |

Hours | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 |

Sectionalizing Switches | 4 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |

5 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | |

6 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | |

11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | |

13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

18 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | |

20 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | |

23 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

26 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

29 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | |

30 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

33 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | |

36 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

38 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

47 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

49 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

52 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

57 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |

Tie Switches | 69 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 |

70 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | |

71 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |

72 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | |

73 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 |

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**MDPI and ACS Style**

Lan, T.; Jermsittiparsert, K.; T. Alrashood, S.; Rezaei, M.; Al-Ghussain, L.; A. Mohamed, M.
An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand. *Energies* **2021**, *14*, 569.
https://doi.org/10.3390/en14030569

**AMA Style**

Lan T, Jermsittiparsert K, T. Alrashood S, Rezaei M, Al-Ghussain L, A. Mohamed M.
An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand. *Energies*. 2021; 14(3):569.
https://doi.org/10.3390/en14030569

**Chicago/Turabian Style**

Lan, Tianze, Kittisak Jermsittiparsert, Sara T. Alrashood, Mostafa Rezaei, Loiy Al-Ghussain, and Mohamed A. Mohamed.
2021. "An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand" *Energies* 14, no. 3: 569.
https://doi.org/10.3390/en14030569