# A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid

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## Abstract

**:**

## 1. Introduction

## 2. Related Work

## 3. Problem Formulation

#### 3.1. Cost Minimization

#### 3.2. UC Maximization

#### 3.3. Multi-Objective Function

## 4. Proposed Solution

#### 4.1. GA, BPSO, WDO and BFOA Algorithms

Algorithm 1: GA algorithm. |

Algorithm 2: BPSO algorithm. |

Algorithm 3: WDO algorithm. |

Algorithm 4: BFOA algorithm. |

#### 4.2. Developing a Hybrid GWD Optimization Algorithm

Algorithm 5: GWD algorithm. |

## 5. Results and Discussion

- Cost: Amount of electricity bills for the total number of units consumed per unit time in cents.
- Energy Consumption: It is calculated as the total energy utilized per unit time in kilowatts per hour.
- PAR: It is defined as the total peak load divided by average load during the whole day.
- UC: It is calculated in terms of minimum cost and minimum appliance delay.

#### 5.1. Single Home

#### 5.2. Fifty Homes

#### 5.3. Performance Trade-Offs in the Proposed Technique

#### 5.4. Statistical Validation of GWD and Counter Part Algorithms Using ANOVA

## 6. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 1.**Working of demand side management (DSM). AMI: Advanced metering infrastructure, HEM: Home Energy Management.

Variables and Subscripts | Description |
---|---|

t | Time Interval |

${E}_{ij}$ | Energy Consumption of an Appliance |

$PR\left(t\right)$ | Electricity Price at time t |

${A}_{i}$ | Set of Appliances |

S | Swarm Size |

${l}_{i}$ | Length of Operation Time Counter |

${x}_{i}$ | Position of Swarms |

X | Appliance ON and OFF Status |

${g}_{best}$ | Global Best Position of Particles |

${p}_{best}$ | Local Best Position of Particles |

P | Population Size |

${x}_{new}$ | New Position of Particles |

${V}_{i}$ | velocity of Particles |

w | Weight of Particles |

$EcostSavings$ | Electricity Cost Savings |

α | Cost Function Variable |

β | Delay Function Variable |

$delay$ | Delay Function Counter |

$EappUtil$ | Appliance Utility |

$RT$ | RT Coefficient |

g | Gravitational Constant |

c | Constant in the Update Equation |

$maxV$ | Maximum Allowed Speed |

H | Number of Homes |

$pop1$, $pop2$ | New Population |

$Max.Cost$ | Maximum Cost |

$Gen.$ | Generation |

$tsize$ | Total Size |

$Maxgen$ | Maximum Generations |

Abbreviations | Definition |
---|---|

ANOVA | Analysis of variation |

AC | Air conditioner |

ACO | Ant colony optimization |

ADA | Activity-dependent appliances |

AMI | Advanced metering infrastructure |

ANN | Artificial neural network |

BPSO | Binary PSO |

BFOA | Bacterial foraging optimization algorithm |

CAC | Central AC |

CPP | Critical peak pricing |

CN | Control node |

CW | Clothes washer |

DSM | Demand side management |

DR | Demand response |

DW | Dish washer |

EMC | Energy management controller |

EP | Energy price |

F | Fan |

FCFS | First come first serve |

FF | Furnace fan |

GA | Genetic algorithm |

HG | Home gateway |

HP | Heat pump |

IHD | In-home display |

IBR | Inclined block rate |

LOT | Length of operation time |

MC | Master controller |

ODA | Occupancy-dependent appliances |

OIA | Occupancy independent appliances |

OPH | Off peak hour |

PSO | Particle swarm optimization |

PAR | Peak to average ratio |

PH | Peak hour |

PB | Priority bit |

RAC | Room AC |

RF | Refrigerator |

RTP | Real-time pricing |

SM | Smart meter |

SH | Space heater |

TOU | Time of use |

UC | User comfort |

WDO | Wind-driven optimization |

WH | Water heater |

WSN | Wireless sensor network |

Techniques | Targeted Area | Objective | Drawbacks |
---|---|---|---|

GA-Based DSM Scheme for SG [8] | Residential, Commercial and Industrial Area | Cost Minimization | Inconsideration of PAR and UC |

Optimal Energy Consumption Scheduling Algorithm [9] | HEMS | Cost Minimization | Compromising the UC and RES |

Residential Load Management in Smart Homes [10] | Residential Energy Load | Cost and PAR Reduction, UC Maximization | Explicit Pressure Values Degrade Performance |

Home Energy Management for Residential Customers [24] | HEMS | Concentrates on UC, Energy Conservation and PAR | Commitments are Required for Effective Maintenance |

Optimal DR Mechanisms [25] | Commercial and Residential Buildings | Considerations on DR Mechanisms | Do not Focus on Randomizing Automatic EMS |

Smart Charging and Appliance Scheduling Approaches [13] | Appliance Scheduling and Storage | Cost Maximization and Maximum Storage Utilization | Inconsideration of Superclustering |

Optimal Residential Appliance Scheduling via HEMDAS [27] | HEM | Cost Minimization and UC Maximization | Inconsideration of the Initial Installation Cost |

Realistic scheduling mechanisms [18] | EMS | UC Maximization | Inconsideration of EC and PAR |

BFOA in Constrained Numerical Optimization [11] | Residential Area | PAR Reduction and Cost Minimization | Inconsideration of Larger Population Size |

Electricity Demand Modeling [30] | Rural Households | Energy Consumption Minimization | Inconsideration of Control Variables for Electric Demand |

Enabling Privacy in a Distributed Game-Theoretical Scheduling Systems [31] | Game-theoretic DSM | Focused on Privacy, Electricity Bills Minimization and PAR Reduction | Inconsideration of Total Bill Reduction |

Information and Communication Infrastructures [32] | ICTs | Energy Efficiency | Inconsideration of UC |

Optimal Residential Load Management [33] | Residential Customers | Energy Efficiency | Inconsideration of Cost |

Queuing-based Energy Consumption Management [34] | Residential SG Networks | Cost Minimization and Delay Reduction | Inconsideration of Parameters Tuning |

Residential Load Scheduling in SG [35] | DSM | Concentrates on Energy | Inconsideration of Cost Minimization |

SG and Smart Home Security [30] | DR | Energy Efficiency | Tradeoff between Demand Limit and UC |

Modifications | Expected Outcomes |
---|---|

Scheduling using PBs | Curtails load |

(refer to Equations (1)–(3)) with constraints | Reduced PAR |

Enhanced UC | |

Use of RTP | Tracks the real-time behavior of system |

steps (10, 11, ..., 19) | Minimizes the cost |

Refinements | Expected Consequences |
---|---|

Addition of PBs for scheduling | Reduce energy consumption |

(refer to Equations (1)–(3)) with the required constraints | Minimizes the PAR |

Boosts up UC | |

Use of RTP | Monitors the real-time behavior of the system |

steps (21, 22, ..., 25) | Minimizes the cost |

Adaptations | Expected Results |
---|---|

Incorporation of the PBs | Minimizes energy consumption |

(refer to Equations (1)–(3)) by considering constraints | Reduces the PAR |

Improves UC | |

Use of RTP | Tracks the real-time behavior of the system |

steps (10, 11, ..., 19) | Minimizes the cost |

Refinements | Expected Achievements |
---|---|

Scheduling using PBs | Reduce energy consumption |

(refer to Equations (1)–(3)) along with their constraints | Minimizes the PAR |

Increases UC | |

Use of RTP | Monitors the real-time behavior of the system |

steps (12, 13, ..., 20) | Reduces the cost |

Modifications | Anticipated Outcomes |
---|---|

Enhancements | Expected Results |

Using PBs for scheduling | Reduce energy consumption |

(refer to Equations (1)–(3)) | Minimizes the PAR |

Increases UC | |

Use of RTP | Tracks the real-time behavior of the system |

steps (10, 11, ..., 20) | Minimizes the cost |

Class Name | Appliance Name | Power Rating | LOT | Deferrable Load |
---|---|---|---|---|

Class B | Space Heater | 1 | 9 | 1 |

Class B | Heat Pump | 0.11 | 4 | 1 |

Class B | Portable Heater | 1.00 | 5 | 1 |

Class B | Water Heater | 4.50 | 8 | 1 |

Class B | Clothes Washer | 0.51 | 9 | 1 |

Class B | Clothes Dryer | 5.00 | 5 | 1 |

Class B | Dishwasher | 1.20 | 11 | 1 |

Class B | First-Refrigerator | 0.50 | 24 | 1 |

Class A | Fan | 0.5 | 11 | 0 |

Class A | Furnace Fan | 0.38 | 8 | 0 |

Class A | Central AC | 2.80 | 12 | 0 |

Class A | Room AC | 0.90 | 5 | 0 |

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

Population Size | 200 |

Selection | Tournament Selection |

Elite Count | 2 |

Crossover | 0.9 |

Mutation | 0.1 |

Stopping Criteria | Max. Generation |

Max. Generation | 1000 |

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

Swarm Size | 20 |

Max. Velocity | 4 ms |

Min. Velocity | 4 ms |

Local Pull | 2 N |

Global Pull | 2 N |

Initial Momentum Weight | 1.0 Ns |

Final Momentum Weight | 0.4 Ns |

Stopping Criteria | Max. iteration |

Max. Iteration | 600 |

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

Swarm Size | 10 |

Max. V | 4 m/s |

RT-Coefficient | 3 |

g | 0.2 |

c | 0.4 |

Dimensions | [−1, +1] |

Stopping Criteria | Max. Iteration |

Max. Iterations | 500 |

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

Population Size | 10 |

Maximum Number of Steps | 30 |

Number of Chemotactic Steps | 5 |

Number of Elimination Steps | 5 |

Number of Reproduction Steps | 25 |

Probability | 0.5 |

Step Size | 0.1 |

Stopping Criteria | Max. Generations |

Max. Generations | 100 |

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

Particle Size | 20 |

Number of Iterations | 500 |

Max. V | 0.4 |

Dimensions | [−1, +1] |

RT-Coefficient | 3.0 |

g | 0.2 |

c | 0.4 |

α | 0.4 |

Crossover Rate | 0.9 |

Mutation Rate | 0.1 |

Technique | Tariff Model | Achievement | Tradeoff |
---|---|---|---|

GA | RTP | Minimizes the cost up to 56% and reduces the PAR to 26% in individual testing and hybrid case cost is minimized up to 30% and PAR is reduced up to 49% | UC is compromised in scheduled case up to 60% in hybrid case while it is improved in individual testing to 90% |

WDO | RTP | Reduces cost up to 67.18% and reduces the PAR to 26% in individual testing and hybrid case cost is minimized up to 30% PAR is 70% reduced | UC is compromised in scheduled case up to 60% in hybrid case and in individual testing to 50% |

GWD | RTP | Reduces cost up to 17.87% and reduces the PAR to 26% in individual testing and hybrid case cost is minimized up to 30% PAR is 17% reduced | UC is compromised in scheduled case up to 60% |

BPSO | RTP | Reduces cost up to 70% and reduces the PAR to 25% | UC is compromised up to 50% |

Technique | Source of Variation | Sum of Squares | df | MS | F | Prob > F |
---|---|---|---|---|---|---|

WDO | Between Groups | 1.4383 | 11 | 0.13075 | 0.48 | 0.9134 |

Within Groups | 29.5488 | 108 | 0.2736 | |||

Total | 30.9871 | 119 | ||||

GA | Between Groups | 3.058 | 11 | 0.27803 | 1.18 | 0.2956 |

Within Groups | 562.86 | 2388 | 0.2357 | |||

Total | 565.918 | 2399 | ||||

GWD | Between Groups | 0.6647 | 11 | 0.06043 | 0.61 | 0.813 |

Within Groups | 10.6203 | 108 | 0.09834 | |||

Total | 11.285 | 119 |

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## Share and Cite

**MDPI and ACS Style**

Javaid, N.; Javaid, S.; Abdul, W.; Ahmed, I.; Almogren, A.; Alamri, A.; Niaz, I.A.
A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid. *Energies* **2017**, *10*, 319.
https://doi.org/10.3390/en10030319

**AMA Style**

Javaid N, Javaid S, Abdul W, Ahmed I, Almogren A, Alamri A, Niaz IA.
A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid. *Energies*. 2017; 10(3):319.
https://doi.org/10.3390/en10030319

**Chicago/Turabian Style**

Javaid, Nadeem, Sakeena Javaid, Wadood Abdul, Imran Ahmed, Ahmad Almogren, Atif Alamri, and Iftikhar Azim Niaz.
2017. "A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid" *Energies* 10, no. 3: 319.
https://doi.org/10.3390/en10030319