# An Intelligent Hybrid Heuristic Scheme for Smart Metering based Demand Side Management in Smart Homes

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## Abstract

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## 1. Introduction

## 2. Related Work

- We have proposed a smart HEMS with an objective to minimize both, cost and user discomfort.
- Hybrid heuristic optimization technique to schedule the time-flexible and power-flexible appliances.
- Analyze the effect of different pricing scheme on power consumption pattern.
- Analyze the impact of using smaller and larger time slots on cost, comfort and complexity of the model.
- Minimize PAR and peak power consumption in order to avoid peak power plants.

## 3. Problem Description

## 4. Problem Formulation

- Time-flexible appliances—The appliances included in this category have flexible starting time while their finishing time is fixed eg., Cloth Washer. These appliances must complete their operation before finishing time and their operation can be delayed to another time slot within its scheduling horizon. These appliances operate continuously once scheduled while consuming fix power.$$\begin{array}{c}\hfill {X}_{i}^{t}=\left(\right)open="\{"\; close>\begin{array}{c}1\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\forall \phantom{\rule{0.277778em}{0ex}}{t}_{s}\le t\le {t}_{f}\wedge {\epsilon}_{t}0\hfill \\ 0\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}otherwise\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\hfill \end{array}\end{array}$$$$\begin{array}{c}\text{}\hfill {P}_{i}^{t}={\xi}_{a}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\end{array}$$
- Power-flexible appliances—These appliances have fixed starting and finishing time while their power profile is flexible. These appliances operate with power within the minimum and maximum power range. This category includes lights and air conditioner.$$\begin{array}{c}\hfill {X}_{i}^{t}=\left(\right)open="\{"\; close>\begin{array}{c}1\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\forall \phantom{\rule{0.277778em}{0ex}}{t}_{s}\le t\le {t}_{f}\hfill \\ 0\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}otherwise\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\hfill \end{array}\end{array}$$$$\begin{array}{c}\text{}\hfill {P}_{i}^{t}=\left[min{\xi}_{a}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}max{\xi}_{a}\right]\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\phantom{\rule{0.277778em}{0ex}}\end{array}$$

## 5. System Model

## 6. Proposed Solution

#### 6.1. GA

Algorithm 1: GA |

#### 6.2. TLBO

Algorithm 2: TLBO Algorithm |

#### 6.3. TLGO

Algorithm 3: TLGO Algorithm |

## 7. Simulations and Discussions

#### 7.1. Peak Power Consumption under DAP

#### 7.2. Peak Power Consumption under CPP

#### 7.3. Electricity Consumption Cost under DAP

#### 7.4. Electricity Consumption Cost under CPP

#### 7.5. Discomfort under DAP

#### 7.6. Discomfort under CPP

#### 7.7. PAR

#### 7.8. Feasible Region

#### 7.8.1. Feasible Region for Power Consumption and Cost

#### 7.8.2. Feasible Region for User Discomfort and Cost

#### 7.9. Performance Trade-Off

## 8. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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Technique | Objective | Features | Remarks |
---|---|---|---|

BPSO and ILP [20] | Cost minimization and thermal comfort maximization | Interval number analysis to handel uncertain hot water demand and ambient temperature | System complexity |

MINLP [21] | Minimize cost and maximize user comfort | Considered both thermal and electrical appliances and study seasonal price variations and their effect on cost | PAR is ignored |

MINLP [22] | Consumers’ bill reduction | High prices are applied to restrict the load below the threshold | High computational complexity |

Approximate dynamic programming [23] | Maximize revenue generated by RES | Energy trading among consumers | No mechanism to handle uncertainties caused by integration of RES |

Distributed Optimization algorithm [24] | Minimize consumers’ electricity bill | Integration of RES and PHEV for energy trading | User dissatisfaction |

Quantum EA [25] | Minimize carbon emissions and production cost | Fuzzy logic is used to handel uncertainties caused by integration of RES | User comfort is compromized |

HEMS algorithm with source priority [26] | Minimize cost and peak power demand | Maximize the utilization of solar panel and improve the quality of power by selective harmonic elimination method | Uncertainties caused by integration of RES are not considered |

Multi-objective EA [27] | Minimize cost and waiting time of appliances | Admission control mechanism to manage residential load | Power-flexible appliances are not taken into consideration |

Branch and bound [28] | Cost and carbon emission reduction | Increase the utilization of wind energy and ESS | User comfort is not handeled |

LP [32] | Minimize consumers’ electricity bill | Use of aggregator to coordinate the charging and discharging of batteries | PAR and user satisfaction is totally ignored |

Analytical model [34,35] | Reduce cost and peak power demand | Recursive formulas to determine the distribution of power units in use | Assumption of infinite number of appliances [34] results in overestimation |

GA [36] | Minimize power consumption in residential, commercial and industrial sector | Performance of proposed model is compared with other evolutionary algorithms | PAR is compromised |

GA and MILP [37] | Minimization of consumers’ bill | Integration of RES and ESS and a mechanism is presented to sale surplus energy to gird | Consumers’ frustration |

GA [39] | Minimize cost while maximizing user satisfaction | Manage the load in user defined budget | PAR is ignored |

ILP [40] | Minimize peak hoyrly load | Increasing number of appliances results in balanced load | Users have little incentives to participate in load scheduling |

Fractional programming [41] | Consumers’ bill reduction | Proposed a novel concept of cost efficiency | PAR is compromised |

MILP and Mixed integer quadratic programming [47] | Minimize cost and achieved and balanced load curve | 50 households are taken into consideration | High computational complexity |

MILP [49] | Consumers’ bill reduction | Different energy phases of appliances are discussed | Inscalable |

Appliance Name | Class | Power Rating (kW) | Starting Time | Finishing Time | Length of Operation Time |
---|---|---|---|---|---|

Cloth washer | Time-flexible | 0.7 | 6:00 p.m. | 7:00 a.m. | 2 h |

Lights | Power-flexible | 0.2–0.8 | 6:00 p.m. | 11:00 p.m. | 6 h |

Air conditioner | Power-flexible | 0–1.4 | 8:00 a.m. | 8:00 a.m. | 24 h |

Kettle | Inflexible | 1.2 | 8:00 a.m., 5:00 p.m., 8:00 p.m. | 9:00 a.m., 6:00 p.m., 9:00 p.m. | 15 min |

Toaster | Inflexible | 1.2 | 7:00 a.m. | 8:00 a.m. | 10 min |

Refrigerator | Inflexible | 0.2 | 8:00 a.m. | 8:00 a.m. | 24 h |

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

Population size | 300 |

Number of iterations | 200 |

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

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

Technique Name | Daily Cost | Daily Discomfort | Peak Power Consumption | PAR |
---|---|---|---|---|

GA | 4.85% increase | 13.89% increase | 1.02% increase | 4.93% increase |

TLBO | 3.96% increase | 8.73% increase | 10.5% increase | 9.48% increase |

TLGO | 1.44% increase | 2.98% increase | 2.18% increase | 0.86% increase |

Ref. | Discomfort | Cost Savings |
---|---|---|

Pilloni et al. [31] | 1.65–1.70 (Annoyance rate) | 33% |

Ogunjuyigbe et al. [39] | 9.6 (Dissatisfaction level) | |

20.2 (Dissatisfaction level) | ||

30.9 (Dissatisfaction level) | ||

Y. Peizhong et al. [18] | 7.64 h (Average monthly delay) | 20% |

K. Muralitharan et al. [27] | 73.32 s average delay (By considering 10 appliances) | 38% |

76.28 s average delay (By considering 11 appliances) | ||

86.83 s average delay (By considering 12 appliances) | ||

94.39 s average delay (By considering 13 appliances) | ||

107.58 s average delay (By considering 14 appliances) | ||

K. Ma et al. [50] | 1.76 | 34% |

GA (Proposed) | 2.37 | 31% |

TLBO (Proposed) | 2.14 | 31.5% |

TLGO (Proposed) | 1.83 | 33% |

Appliance | k | $\mathit{\rho}$ | ${\mathit{\omega}}_{\mathit{a}}$ |
---|---|---|---|

Cloth washer | 3 | 0.001 | - |

Lights | - | - | [0.5–1] |

Air conditioner | - | - | [0.4–1] |

Discomfort | Cost ($) |
---|---|

0 | 22.00 |

2 | 13.42 |

2.2 | 13.00 |

4 | 11.85 |

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**MDPI and ACS Style**

Manzoor, A.; Javaid, N.; Ullah, I.; Abdul, W.; Almogren, A.; Alamri, A.
An Intelligent Hybrid Heuristic Scheme for Smart Metering based Demand Side Management in Smart Homes. *Energies* **2017**, *10*, 1258.
https://doi.org/10.3390/en10091258

**AMA Style**

Manzoor A, Javaid N, Ullah I, Abdul W, Almogren A, Alamri A.
An Intelligent Hybrid Heuristic Scheme for Smart Metering based Demand Side Management in Smart Homes. *Energies*. 2017; 10(9):1258.
https://doi.org/10.3390/en10091258

**Chicago/Turabian Style**

Manzoor, Awais, Nadeem Javaid, Ibrar Ullah, Wadood Abdul, Ahmad Almogren, and Atif Alamri.
2017. "An Intelligent Hybrid Heuristic Scheme for Smart Metering based Demand Side Management in Smart Homes" *Energies* 10, no. 9: 1258.
https://doi.org/10.3390/en10091258