# An Efficient Demand Side Management System with a New Optimized Home Energy Management Controller in Smart Grid

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## Abstract

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## 1. Introduction

- We have proposed EHEMC with an objective to minimize electricity cost and average waiting time.
- To address our objective efficiently a hybrid optimization algorithm GHSA is proposed, which is later on compared with the existing algorithms, including WDO, HSA, and GA (Section 3).
- We implement the proposed GHSA algorithm for single home (SH) and multiple homes (MHs) and analyze its performance in the presence of pricing tariffs: RTEP and CPP. We observe that as the number of homes is increased computational time of the system also increases. However, our proposed algorithm GHSA effectively address the problem with less computation time.
- In order to flexibly adjust the energy consumption profile different power ratings and operational time slots are assigned in MHs. Specifically, we consider fifty homes in MHs case.
- The effect of electricity cost, energy consumption, and average waiting time is demonstrated by feasible regions (Section 2.10).
- The PAR is minimized to avoid peak power plants.
- Finally, extensive simulations are conducted to validate the effectiveness of proposed algorithm GHSA in terms of electricity cost, PAR, and average waiting of the appliances.

#### Nomenclature

## 2. System Modeling

#### 2.1. HEMS Architecture

#### 2.2. Energy Consumption Model

#### 2.3. Load Categorization

#### 2.4. Energy Cost and Unit Price

#### 2.5. Problem Formulation

- Assuming ${A}_{n}$ as number of items $\left(N\right)$.
- Each of the items comprises of two attributes i.e., weight and the value. The weight of the items expresses the energy usage of the appliances in time interval $\left(t\right)$. In addition, the value of the items denotes the energy cost of the appliances. However, the weight of the appliances is independent of the time interval.
- We consider $\mathcal{N}$ number of knapsacks in order to limit power consumption of each category of the appliances and also to limit the total power capacity $\left({\mathbf{C}}_{\mathbf{g}}\right)$.

#### 2.6. PAR

#### 2.7. User Comfort

#### 2.8. Objective Function

#### 2.9. Optimization Techniques

#### 2.9.1. GA

#### 2.9.2. WDO

#### 2.9.3. HSA

#### 2.9.4. GHSA

#### 2.10. Feasible Region

#### 2.10.1. Feasible Region for SH

Algorithm 1: GHSA |

#### 2.10.2. Feasible Region for MHs

## 3. Simulation and Discussion

#### 3.1. Load Profile

#### 3.2. Cost Per Hour

#### 3.3. Electricity Cost Per Day

#### 3.4. PAR

#### 3.5. User Comfort

## 4. Conclusions and Future Work

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 2.**(

**a**) Feasible region of energy consumption for SH using RTEP; (

**b**) Feasible region of energy consumption for SH using CPP.

**Figure 3.**(

**a**) Feasible region of average waiting time for SH using RTEP; (

**b**) Feasible region of average waiting time for SH using CPP.

**Figure 4.**(

**a**) Feasible region of energy consumption for MHs using RTEP; (

**b**) Feasible region of energy consumption for MHs using CPP.

**Figure 5.**(

**a**) Feasible region of average waiting time for MHs using RTEP; (

**b**) Feasible region of average waiting time for MHs using CPP.

**Figure 7.**(

**a**) Load profile of SH with RTEP; (

**b**) Load profile of SH with CPP; (

**c**) Load profile of MHs with RTEP; (

**d**) Load profile of MHs with CPP.

**Figure 8.**(

**a**) Cost per hour of SH with RTEP; (

**b**) Cost per hour of SH with CPP; (

**c**) Cost per hour of MHs with RTEP; (

**d**) Cost per hour of MHs with CPP.

Techniques | Aims | Distinctive Attributes | Limitations |
---|---|---|---|

Hybrid technique (GA and PSO) [25] | Minimization of cost and PAR | HEMS model is considered with distributed energy resources and energy storage system | User comfort is ignored |

EA [26] | Cost reduction | Energy optimization in residential, commercial, and industrial area | System complexity is enhanced |

ILP [27] | Cost and PAR minimization | Classification of appliances using day ahead pricing model | PAR is not addressed |

DP [28] | Cost and PAR minimization | Optimizes energy consumption behavior with high penetration of RESs | System complexity is increased and user comfort is ignored |

GA [29] | Cost reduction and user comfort | Cost reduction by optimizing energy consumption and time slots are divided | System deals with large number of appliances in multiple sector which increases system complexity |

GA, BPSO, ACO [7] | Cost and PAR reduction by satisfying user comfort | HEMC schedules the appliances by considering user satisfaction and RESs integration | System complexity and computational time are increased |

GA [8] | Cost reduction and user comfor | Optimizes energy consumption behavior with RESs incorporation | Challenges related to RESs are not addressed and PAR is ignored |

Hybrid technique (LP and BPSO) [9] | Cost reduction and user comfort maximization | Thermostatically and interruptible appliances are considered with day ahead pricing model | PAR is not considered |

FP [10] | Electricity cost reduction | Cost efficient model with distributed energy resources and practical implementation of the model proposed | PAR and user comfort are not taken into account |

GA [11] | Cost and PAR reduction | Proposed model is tested using radial residential electrical network | System complexity and computational time are enhanced |

HSA [12] | Basic concepts of HSA, its structure, and applications | Improved and hybrid HSA with application | Real time implementation is not considered |

Hybrid technique (EDE and HSA) [13] | Startup and generation cost of RESs | Verification is done using IEEE standard bus system | Computational time is increased |

BPSO [15] | Electricity cost minimization considering user preference | Simplicity and robustness of BPSO | Computational time is increased as time slot is divided into sub time slots |

GA [16] | Minimization of electricity cost, PAR, and waiting time | Generic model of DSM with EMC using RTEP | User comfort is not addressed efficiently |

Single knapsack [17] | Energy consumption optimization considering six layer architecture | Comprehensive model for energy management addressing six layer architecture | Complicated architecture in terms of modeling in practical scenario |

Symbols | Description |
---|---|

${E}_{c,TL}$ | Total energy consumption in a day |

${\varsigma}_{{\mathcal{R}}_{a},{T}_{L}}$ | Energy consumption of regularly operated appliances |

${\varsigma}_{{\mathcal{S}}_{a},{T}_{L}}$ | Energy consumption of shift-able operated appliances |

${\varsigma}_{{\mathcal{E}}_{a},{T}_{L}}$ | Energy consumption of elastic operated appliances |

${\varrho}_{{\mathcal{R}}_{a}}^{{T}_{L}}$ | Cost per day of regularly operated appliances |

${\varrho}_{{\mathcal{S}}_{a}}^{{T}_{L}}$ | Cost per day of shift-able appliances |

${\varrho}_{{\mathcal{E}}_{a}}^{{T}_{L}}$ | Cost per day of elastic appliances |

$\epsilon $ | Electricity pricing signal |

$\zeta $ | ON-OFF states of appliances |

${a}_{\alpha}$ | Starting time of appliance |

${b}_{\beta}$ | Ending time of appliance |

$\mathcal{W}$ | Waiting time of appliance |

${O}_{t}$ | Operation time interval |

${V}_{cur}$ | Velocity of air parcel in current iteration |

${V}_{new}$ | Velocity of air parcel in new iteration |

${\varsigma}_{{a}_{i}}$ | Energy consumed by appliance i |

${P}_{cur}$ | Pressure of air parcel in a current location |

${x}_{new}$ | Position of air parcel in the new location |

${x}_{cur}$ | Position of air parcel in the current location |

${x}_{old}$ | Position of air parcel in the previous location |

${X}_{new}$ | Updated value of harmony |

${\tau}_{r}$ | Request time of the appliances |

${\mathcal{T}}_{max}$ | Maximum time of the appliance operation |

${\mathcal{T}}_{mini}$ | Minimum time of the appliance operation |

${{\rm Y}}_{TL}$ | Total electricity cost for fifty homes |

$\mathsf{\Omega}$ | Rotation of the earth |

∇ | Pressure gradient |

$\rho $ | Air parcel density |

$\mu $ | velocity of the wind |

$\delta V$ | Infinite mass and volume |

g | Earth’s gravity |

Appliances Group | Appliances | Power Ratings (kW) | Time of Operation (h) |
---|---|---|---|

Regularly operated appliances | Vacuum pump | 0.6 | 6 |

Water pump | 1.18 | 8 | |

Dish washer | 0.78 | 10 | |

Oven | 1.44 | 18 | |

Shift-able appliances | Washing machine | [3.60 0.5 0.38 ] | [5 4 3] |

Cloth dryer | [4.4 2 0.8] | [4 3 2] | |

Elastic appliances | Refrigerator | [1 0.75 0.5] | [18 16 15] |

AC | [1.5 1.44 1] | [15 13 14] | |

Water heater | [4.45 1.2 1] | [7 5 4] | |

Water dispenser | [1.5 1 0.5] | [11 10 9] |

Parameters | Values |
---|---|

Maximum iteration | 200 |

Population size | 30 |

${P}_{c}$ | 0.9 |

${P}_{m}$ | 0.1 |

Parameters | Values |
---|---|

Maximum iteration | 200 |

Population size | 30 |

${V}_{min}$ | 0.9 |

${V}_{max}$ | 0.1 |

RT | 3 |

$\alpha $ | 0.4 |

DimMax | 5 |

DimMin | −5 |

g | 0.2 |

Parameters | Values |
---|---|

Maximum iteration | 100 |

Population size | 30 |

HMCR | 0.9 |

$P{A}_{min}$ | 0.4 |

$P{A}_{max}$ | 0.9 |

$B{w}_{min}$ | 0.0001 |

$B{w}_{max}$ | 0.1 |

Schemes | Techniques | Computational Time (s) |
---|---|---|

WDO | 2.61 | |

HSA | 2.01 | |

SH | GA | 1.5 |

GHSA | 1.43 | |

WDO | 100.21 | |

HSA | 96.1 | |

MHs | GA | 70.46 |

GHSA | 60.33 |

Schemes | Techniques | |||||
---|---|---|---|---|---|---|

Unscheduled | WDO | HSA | GA | GHSA | ||

SH with RTEP | Load profile (kWh) | 13.84 | 8.66 | 6.01 | 5.01 | 5.80 |

Percentage decrement | — | 37.42% | 56% | 63.80% | 63.29% | |

Improvement | — | 5.18 | 7.83 | 8.83 | 8.76 | |

SH with CPP | Load profile (kWh) | 13.84 | 5.12 | 6.37 | 5.10 | 4.95 |

Percentage decrement | — | 63.21% | 53.97% | 63.15% | 65.12% | |

Improvement | — | 9.27 | 7.47 | 8.74 | 10.12 | |

MHs with RTEP | Load profile (kWh) | 351 | 128.88 | 125.28 | 241.46 | 124.29 |

Percentage decrement | — | 63.28% | 65.92% | 31.20% | 65.72% | |

Improvement | — | 222.12 | 290.72 | 109.54 | 290.71 | |

MHs with CPP | Load profile (kWh) | 351 | 114.49 | 140.38 | 134.68 | 136.01 |

Percentage decrement | — | 67.38% | 60% | 61.6% | 61.25% | |

Improvement | — | 236.51 | 210.62 | 216.32 | 214.99 |

Schemes | Techniques | |||||
---|---|---|---|---|---|---|

Unscheduled | WDO | HSA | GA | GHSA | ||

SH with RTEP | Total cost (cents) | 21.53 | 18.65 | 17.04 | 16.01 | 15.01 |

Percentage decrement | — | 13.37% | 20.58% | 25.63% | 29.86% | |

Improvement | — | 2.88 | 4.49 | 5.52 | 6.43 | |

SH with CPP | Total cost (cents) | 37.5 | 25.68 | 23.98 | 22.63 | 20.21 |

Percentage decrement | — | 31.52% | 36.05% | 39.65% | 46.19% | |

Improvement | — | 11.82 | 13.52 | 14.87 | 17.21 | |

MHs with RTEP | Total cost (cents) | 648.43 | 320.74 | 480.36 | 445.36 | 284.39 |

Percentage decrement | — | 50.54% | 25.91% | 31.31% | 56.06% | |

Improvement | — | 327.69 | 168.07 | 303.7 | 364.04 | |

MHs with CPP | Total cost (cents) | 1087 | 631.02 | 643.18 | 608.18 | 500.21 |

Percentage decrement | — | 41.94% | 40.82% | 44.04% | 54.04% | |

Improvement | — | 445.98 | 443.82 | 478.82 | 587 |

Schemes | Cost (cents) | Techniques | |||
---|---|---|---|---|---|

WDO | HSA | GA | GHSA | ||

SH with RTEP | Maximum cost | 20.26 | 19.78 | 17.39 | 17.02 |

Average cost | 19.24 | 18.41 | 16.70 | 16.02 | |

Minimum cost | 18.65 | 17.04 | 16.01 | 15.01 | |

SH with CPP | Maximum cost | 33.46 | 35.76 | 34.54 | 31.21 |

Average cost | 29.57 | 29.87 | 28.60 | 25.71 | |

Minimum cost | 25.68 | 23.98 | 22.63 | 20.21 | |

MHs with RTEP | Maximum cost | 446.38 | 520.63 | 535.71 | 408.99 |

Average cost | 383.56 | 500.49 | 490.53 | 346.66 | |

Minimum cost | 320.74 | 480.36 | 445.36 | 284.39 | |

MHs with CPP | Maximum cost | 810.87 | 904.41 | 800.70 | 780.08 |

Average cost | 720.94 | 773.79 | 704.44 | 640.14 | |

Minimum cost | 631.02 | 643.18 | 608.18 | 500.21 |

Schemes | Techniques | |||||
---|---|---|---|---|---|---|

Unscheduled | WDO | HSA | GA | GHSA | ||

SH with RTEP | PAR | 5.01 | 4.31 | 3.24 | 3.73 | 3.09 |

Percentage decrement | — | 13.97% | 35.32% | 25.54% | 38.32% | |

Improvement | — | 0.7 | 1.77 | 1.28 | 1.92 | |

SH with CPP | PAR | 5.01 | 4.68 | 3.48 | 3.64 | 3.13 |

Percentage decrement | — | 6.58% | 30.53% | 27.34% | 37.52% | |

Improvement | — | 0.33 | 1.53 | 1.37 | 1.88 | |

MHs with RTEP | PAR | 22.46 | 12.78 | 14.70 | 12.70 | 11.73 |

Percentage decrement | — | 43.09% | 34.55% | 43.45% | 47.77% | |

Improvement | — | 11.78 | 9.84 | 11.48 | 12.78 | |

MHs with CPP | PAR | 24.54 | 13.92 | 12.98 | 14.01 | 12.01 |

Percentage decrement | — | 43.27% | 47.01% | 42.66% | 50.08% | |

Improvement | — | 10.62 | 11.56 | 10.53 | 12.53 |

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## Share and Cite

**MDPI and ACS Style**

Hussain, H.M.; Javaid, N.; Iqbal, S.; Hasan, Q.U.; Aurangzeb, K.; Alhussein, M. An Efficient Demand Side Management System with a New Optimized Home Energy Management Controller in Smart Grid. *Energies* **2018**, *11*, 190.
https://doi.org/10.3390/en11010190

**AMA Style**

Hussain HM, Javaid N, Iqbal S, Hasan QU, Aurangzeb K, Alhussein M. An Efficient Demand Side Management System with a New Optimized Home Energy Management Controller in Smart Grid. *Energies*. 2018; 11(1):190.
https://doi.org/10.3390/en11010190

**Chicago/Turabian Style**

Hussain, Hafiz Majid, Nadeem Javaid, Sohail Iqbal, Qadeer Ul Hasan, Khursheed Aurangzeb, and Musaed Alhussein. 2018. "An Efficient Demand Side Management System with a New Optimized Home Energy Management Controller in Smart Grid" *Energies* 11, no. 1: 190.
https://doi.org/10.3390/en11010190