# Optimal Operation of Microgrids with Demand-Side Management Based on a Combination of Genetic Algorithm and Artificial Bee Colony

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## Abstract

**:**

## 1. Introduction

## 2. Demand-Side Management

## 3. Formulation of the Problem

_{1}and w

_{2}are the weight coefficients of the cost of operating the network and the cost of implementing demand-side management programs, respectively. If these two coefficients are considered as one, the value of operation cost and demand-side management cost are considered the same. But if the goal is to add more value to consumption management programs, the weight factor w

_{2}can be considered larger [30].

_{l}is the number of hours of load I shifting and m is the total number of shiftable loads. Operating costs of generation units include generation costs, starting costs, and maintenance costs. Also, because in the microgrid it is possible to buy or sell energy to the network, the cost of buying and selling energy from the network is included in the operation cost function. Equation (3) shows the CF operation cost function in the optimization problem [31].

_{ci}, and V

_{co}: minimum and maximum allowable wind speed; V

_{r}and V: are the nominal speed and the actual speed of the wind, respectively. The coefficients a, b and c are obtained according to the catalog information of the existing device. The power generated by solar cells depends on the intensity of light and ambient temperature, which is obtained according to Equation (5):

_{STC}: Maximum cell generation power under standard test conditions;${G}_{INC}$: ambient light intensity; ${G}_{STC}$: Radiation intensity under standard test conditions, k: Output power temperature coefficient; T

_{c}: cell temperature, T

_{r}: reference temperature. Renewable sources of WT and PV generate electricity through wind and solar energy instead of fuel. Therefore, the fuel cost for these units will be zero. On the other hand, the investment cost of constructing these units is heavy and should be considered along with the maintenance costs in examining the microgrid status from an economic perspective. Accordingly, the total cost of WT and PV units is calculated using Equation (6):

^{Inv}: ratio of investment cost to generate power of the unit, I

^{M}: unit maintenance cost.

_{FC}: the output power of the fuel cell; ${\mu}_{FC}$: the efficiency of the fuel cell. According to Equation (9), the economic model of a microturbine is similar to a fuel cell, except that the efficiency of the microturbine increases with increasing power.

_{sp}is the power sold to the grid. The cost of repairing and maintaining units is directly related to their power generation. Therefore, the cost of repair and maintenance of unit i at hour t is expressed as Equation (12).

_{l}is the permissible time to shift the load lth.

## 4. Proposed Hybrid Algorithm

## 5. Simulation Results

#### 5.1. Implementation of the Proposed GA-ABC Algorithm

_{1}and w

_{2}are equal to one and the electricity purchase and sale prices of the grid are considered variable (According to Figure 7). In the proposed algorithm, the population is 500 and the number of iterations is 200. Figure 9 shows the convergence process of the proposed algorithm. In this figure, the horizontal axis represents the number of iterations and the vertical axis represents the value of the fitness function, the □ symbol represents the best value and the Δ symbol represents the average value of the fitness function. In Figure 9, “BEST”, represents the best value nomination, which is equal to 3.13547 × 10

^{6}at the end of the generation section and which mixed up with the mean value. However, it is important to mention, that the “FIT” value is the fitness value that evaluates the function of the generation ratio as it is in Figure 9. At the depart, the “FIT” value is around 4.6 × 10

^{6}, and at the end of the generation ratio, the “FIT”, takes the best FIT value, which is the possible minimum value.

#### 5.2. The Effect of Weighting Coefficients

_{2}is zero, load shifting is considered non-cost. It is clear that in this case, the lowest operating cost and the highest amount of load shifting have been created. In a state where both weight coefficients are equal to one; Operating costs have increased and load shifting has decreased compared to the previous case. As the demand-side management weight coefficient increased, the number and hours of shifting load decreased but the operating cost increased. Therefore, due to the importance of reducing load management costs, the appropriate coefficient can be selected for the demand side management costs. In the last row (without DSM) there is a case where the demand side management program is not considered and shifting is considered zero all the load.

#### 5.3. The Effect of PV Panels

#### 5.4. The Effect of Electricity Prices

#### 5.5. Comparison of GA-ABC Algorithm Performance

_{1}, and W

_{2}are assumed to be one, and as a result, the total cost of Equation (1) is $156.8897, and for the case without DSM, the value is $160.8619. Comparing the results of Figure 12, you can see that the proposed algorithm has performed well. In the reference [33], the Fuzzy Self Adaptive PSO algorithm is used to dispatch the management of microgrid production units, the best answer of which is obtained here at $164.9967. Reference [34] presents an improved genetic algorithm for optimal microgrid power-sharing, the best answer obtained here is $163.6199 . In reference [35], for optimal economic dispatch in the microgrid, the improved ABC algorithm is used, where the best answer is 162.3335 $. In reference [36], the combined algorithm differential evolution and harmony search are used for optimal planning of microgrid unit production and cost reduction, and the best answer obtained here is $159.2037. In reference [37], to optimize the operation of the microgrid and reduce the cost, the Adaptive Modified Firefly Algorithm has been used, and the best answer obtained here is 160.4894. Finally, the output of the proposed algorithm including the generation power of microgrid units cost reduction, and DSM results are shown in Figure 13 and Figure 14. Figure 13 shows the microgrid and main grid generation power, and Figure 14 shows the network load profile changes as a result of DSM.

#### 5.6. Implement the Proposed Method on the Standard 33-Bus IEEE Network

- Scenario 1: Without considering DSM and without the presence of CHP units in the microgrid.
- Scenario 2: Without considering DSM and with the presence of CHP units in the microgrid.
- Scenario 3: With considering DSM and without the presence of CHP units in the microgrid.
- Scenario 4: With considering DSM and with the presence of CHP units in the microgrid.

## 6. Conclusions

- Provide shifting load instead of cutting and shedding load and supply of critical load by microgrid.
- Improved 32.01% performance of genetic algorithm based on combination with ABC algorithm.
- Investigating the effect of unit types on cost reduction including PV, WT, MT, FC, MT, BAT and CHP.
- Comparison of GA-ABC algorithm with meta-heuristic algorithms such as PSO, DEHS, AHP and IQPSO.
- Analysis and review of the results of the proposed method during different scenarios with the best performance reduction of 57.01%.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Nomenclature

DSM | demand-side management |

ABC | artificial bee colony |

OPF | optimal power flow |

DG | distributed generation |

PSO | Particle swarm optimization algorithm |

DSM | Demand-side management |

GA | Genetic algorithm |

CHP | Combined heat and power |

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**Figure 22.**OPF results with ABC algorithm, (

**a**) Voltage profile, (

**b**) Line transmission power changes for scenario 4.

Parameters | Description |
---|---|

$\overline{X}$_{m}{(x_{mi}, i = 1,…, d)} | mth of a candidate answer |

D | Number of problem dimensions |

ȳ_{m} | Neighborhood of $\overline{X}$_{m} |

x_{mi} | The value of the variable mth in the ith dimension |

|P| | Population size |

lb_{i} | The lower limit for the ith dimension |

μb_{i} | The upper limit for the ith dimension |

ϕ_{mi} | Random number in the range (−1, 1) |

p_{m} | Probability of selecting the feed source of the employed bee m by the observer bees |

**Table 2.**Diesel Generator Information [32].

Generation Unit | Maximum Power | Minimum Power | Cost Function |
---|---|---|---|

Diesel generator | 4 MW | 0.1 MW | F(p) = 0.02268p^{2} + 15.06p + 817.47 |

**Table 3.**Coefficients of DSM cost function [32].

Load | A | B | C |
---|---|---|---|

Load 1 | 0 | 0.23 | 1 |

Load 2 | 0 | 0.53 | 2 |

Load 3 | 0 | 0.61 | 1 |

Load 4 | 0.032 | 0.96 | 5 |

Load 5 | 0 | 0.52 | 3 |

Load 6 | 0 | 0.11 | 4 |

Load 7 | 0.02 | 0.33 | 5 |

Load 8 | 0 | 0.25 | 3 |

Load 9 | 0 | 0.16 | 2 |

Load 10 | 0 | 0.48 | 3 |

Coefficients | Costs | Time of Loads Shifting in Terms of Hours | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

W_{1} | W_{2} | CF ($) | DC ($) | F | St1 | St2 | St3 | St4 | St5 | St6 | St7 | St8 | St9 | St10 |

1 | 1 | 3,081,652.3354 | 53,820 | 3,135,472.335 | 3 | 0 | 1 | 0 | 0 | 1 | 0 | 8 | 2 | 0 |

Coefficients | Costs | Time of Loads Shifting in Terms of Hours | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

W_{1} | W_{2} | CF ($) | DC ($) | F | St1 | St2 | St3 | St4 | St5 | St6 | St7 | St8 | St9 | St10 |

1 | 0 | 3,016,445.0043 | 0 | 3,016,445.004 | 6 | 0 | 5 | 6 | 17 | 4 | 17 | 9 | 2 | 23 |

1 | 1 | 3,081,652.3354 | 53,820 | 3,135,472.335 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 8 | 2 | 0 |

1 | 2 | 3,174,678.1628 | 18,600 | 3,193,278.162 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |

Without DSM | 3,144,023.2700 | - | 3,144,023.27 | - | - | - | - | - | - | - | - | - | - |

PV | Costs | Time of Loads Shifting in Terms of Hours | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

CF ($) | DC ($) | F | St1 | St2 | St3 | St4 | St5 | St6 | St7 | St8 | St9 | St10 | |

Presence | 3,081,652.3354 | 53,820 | 3,135,472.3354 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 8 | 2 | 0 |

Absence | 5,556,267.2400 | 26,720 | 5,582,987.24 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 4 | 0 | 0 |

Price | Costs | Time of Loads Shifting in Terms of Hours | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

CF ($) | DC ($) | F | St1 | St2 | St3 | St4 | St5 | St6 | St7 | St8 | St9 | St10 | |

Variable | 3,081,652.3354 | 53,820 | 3,135,472.3354 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 8 | 2 | 0 |

Fixed | 3,026,958.2900 | 64,240 | 3,091,198.29 | 3 | 1 | 1 | 0 | 0 | 1 | 0 | 8 | 3 | 1 |

Units | Pmin (kW) | Pmax (kW) |
---|---|---|

PV | 0 | 25 |

WT | 0 | 15 |

MT | 6 | 30 |

FC | 3 | 30 |

BAT | −30 | 30 |

Grid | −30 | 30 |

Bus | Pmin (kW) | Pmax (kW) | C_{DG} ($/kWH) | Rup (kW/H) | Rdn (kW/H) | SUc ($) | SDc ($) |
---|---|---|---|---|---|---|---|

2 | 50 | 400 | 27 | 200 | 100 | 20 | 25 |

7 | 40 | 500 | 45 | 250 | 250 | 20 | 25 |

8 | 20 | 550 | 35 | 250 | 250 | 50 | 25 |

25 | 50 | 700 | 50 | 700 | 700 | 0 | 0 |

Bus | Pmin (kW) | Pmax (kW) | Hmax (kWth) | A | B | C | D | E | F |
---|---|---|---|---|---|---|---|---|---|

8 | 810 | 2470 | 1800 | 0.0435 | 36 | 12.5 | 0.027 | 0.6 | 0.011 |

16 | 400 | 1258 | 1356 | 0.0345 | 14.5 | 26.5 | 0.03 | 4.2 | 0.031 |

Scenarios | Coefficients | Income ($) | Cost ($) | Profit ($) | |
---|---|---|---|---|---|

W_{1} | W_{2} | ||||

Scenario1 | 1 | 0 | 7492 | 5306.29 | 2185.7133 |

Scenario2 | 1 | 0 | 7956.636 | 2349.38 | 5607.2560 |

Scenario3 | 1 | 1 | 7185.814 | 4991.39 | 2194.4243 |

Scenario4 | 1 | 1 | 7898.686 | 2280.98 | 5617.7067 |

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## Share and Cite

**MDPI and ACS Style**

Dashtdar, M.; Flah, A.; Hosseinimoghadam, S.M.S.; Kotb, H.; Jasińska, E.; Gono, R.; Leonowicz, Z.; Jasiński, M.
Optimal Operation of Microgrids with Demand-Side Management Based on a Combination of Genetic Algorithm and Artificial Bee Colony. *Sustainability* **2022**, *14*, 6759.
https://doi.org/10.3390/su14116759

**AMA Style**

Dashtdar M, Flah A, Hosseinimoghadam SMS, Kotb H, Jasińska E, Gono R, Leonowicz Z, Jasiński M.
Optimal Operation of Microgrids with Demand-Side Management Based on a Combination of Genetic Algorithm and Artificial Bee Colony. *Sustainability*. 2022; 14(11):6759.
https://doi.org/10.3390/su14116759

**Chicago/Turabian Style**

Dashtdar, Masoud, Aymen Flah, Seyed Mohammad Sadegh Hosseinimoghadam, Hossam Kotb, Elżbieta Jasińska, Radomir Gono, Zbigniew Leonowicz, and Michał Jasiński.
2022. "Optimal Operation of Microgrids with Demand-Side Management Based on a Combination of Genetic Algorithm and Artificial Bee Colony" *Sustainability* 14, no. 11: 6759.
https://doi.org/10.3390/su14116759