# An Efficient Power Scheduling in Smart Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes

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## Abstract

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## 1. Introduction

## 2. Related Work

- Similar schemes used in [26] focus on the supplier side, which minimize the daily fuel cost, production cost and maximizes the sales revenue for grid-connected micro-grid; meanwhile, this paper expands the work carried out in [12] by incorporating the RES using ToU and CPP to schedule household appliances.
- A model is proposed to provide scheduling of appliances within the smallest execution time via the Earliglow optimization algorithm. In addition, it provides a platform that enables a shared RES and ESS.
- Including RES as well as ESS encourages the generation of on-site power which further alleviate the electricity cost and PAR with a minimal user waiting time, simultaneously.
- The proposed model elaborates the individual appliances’ energy consumption behavior, which provides hourly appliances scheduling and operations.

## 3. Problem Statement

#### 3.1. Appliance Specification

#### 3.1.1. Shiftable Appliances

#### 3.1.2. Non Shiftable Appliances

#### 3.2. Electricity Cost

#### 3.3. Energy Consumption

#### 3.4. Load Balancing

#### 3.5. Objective Function

#### 3.6. Electricity Price Models

#### 3.7. RES

## 4. Proposed Schemes

Algorithm 1: Proposed optimal scheduling technique [26] |

#### 4.1. Jaya Algorithm

Algorithm 2: Proposed Jaya based HEMS [12] |

#### 4.2. SBA

Algorithm 3: SBA based HEMS |

#### 4.3. Earliglow Algorithm

#### 4.4. Enhanced Differential Evolution Algorithm (EDE)

## 5. Simulations and Discussions

#### 5.1. Case I (without HEM)

#### 5.2. Case II (with HEM)

#### 5.3. Case III (HEM with RES)

#### 5.4. Performance Trade-Off Made by Optimization Schemes

#### 5.5. Hourly Load Behavior of Household Appliances

#### 5.6. FR for Electricity Cost and Energy Load

- The hourly cost of electricity for each load must fall within the lowest and highest electricity cost without HEM.
- The hourly cost of electricity for each load must be less than the hourly electricity cost without HEM.
- The entire hourly load must fall within the lowest and highest combined energy without HEM.

#### 5.7. FR for Cost and Waiting Time

- The hourly waiting time should not be more than the maximum average waiting cost.
- The cost of hourly waiting time must be within the minimum and maximum waiting time.
- The total hourly waiting time must not exceed the minimum and maximum total waiting time.

## 6. Conclusions and Future Work

## Author Contributions

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 3.**Illustration of a load shifting operation by a household appliance. S is the size of load reduction; K is the load recovery; W is the duration of K, greater or smaller than K; t: time.

**Figure 5.**(

**a**) electricity pricing signals; (

**b**) total electricity cost; (

**c**) hourly electricity cost using critical peak price (CPP); (

**d**) hourly electricity cost using time-of-use (ToU); (

**e**) hourly electricity consumption without renewable energy sources (RES) using CPP; (

**f**) hourly electricity consumption without RES using ToU.

**Figure 6.**(

**a**) hourly electricity cost with RES using CPP; (

**b**) hourly electricity cost with RES using ToU; (

**c**) hourly electricity consumption with RES using CPP; (

**d**) hourly electricity consumption with RES using ToU; (

**e**) PAR; (

**f**) appliances’ waiting time.

**Figure 8.**Hourly electricity consumption for each appliance with HEM. (

**a**) hourly electricity consumption for each appliance with HEM for Jaya using CPP; (

**b**) hourly electricity consumption for each appliance with HEM for SBA using CPP; (

**c**) hourly electricity consumption for each appliance with HEM for Earliglow using CPP; (

**d**) hourly electricity consumption for each appliance with HEM for Jaya using ToU; (

**e**) hourly electricity consumption for each appliance with HEM for SBA using ToU; (

**f**) hourly electricity consumption for each appliance with HEM for Earliglow using ToU.

**Figure 9.**(

**a**) FR for cost and electricity consumption using CPP; (

**b**) FR for cost and electricity consumption using TOU; (

**c**) FR for cost and waiting time using CPP; (

**d**) FR for cost and waiting time using ToU.

Technique(s) | Achievement(s) | Pricing Schemes | Limitation(s) |
---|---|---|---|

BBSA [3] | Reduces the energy consumption, electricity cost and achieves energy savings for week days and weekend | RTP | Consumer comfort and RES are not considered |

MILP, Dijkstra [4] | Minimizes the total cost, trades the computational complexity for lower performance | ToU | Inconsideration of consumer comfort, PAR and RES |

ILM, NILM [5] | Reduces the electricity cost, provides cost saving and minimizes the greenhouse gas emission | - | Ignored consumer comfort and RES |

ADP [6] | Considered V2home and V2grid for energy management, provides cost reduction, peak load shifting and ESS is also considered using electric vehicle battery | RTP | Inconsideration of consumer comfort |

GWD [9] | Minimizes electricity cost and protects user comfort | - | Energy consumption and PAR are ignored |

GHSA [13] | Minimizes electricity cost, PAR and maximizes the consumer comfort | RTEP and CPP | Inconsideration of RES |

IoT [14] | Motivates users to locally monitor and control devices | - | Ignored load scheduling, consumer comfort and electricity cost reduction |

MILP [15] | Minimizes the total electricity cost of multiple households with distributed energy resources while maintaining the consumer’s thermal comfort level | ToU | Inconsideration of PAR |

Stochastic model [16] | Considers limited size of RES to minimize electricity cost | ToU and CPP | Computational complexity |

GA, Cuckoo and BPSO [17] | Reduces electricity cost, PAR and provides energy saving using RES | ToU | Inconsideration of power loss |

MILP [18] | Incorporates a distributed generation, ESS and minimizes the electricity cost | Day-ahead RTP | Computational complexity |

Multi-objective evolutionary algorithm [19] | Addresses load balancing and threshold problem; reduces electricity cost as well the waiting time | ToU | Inconsideration of RES |

Pareto-optimal front [20] | Electricity cost is reduced and operational delay is enhanced | Day-ahead RTP | Ignored user comfort and RES |

BFOA, GA, BPSO, WDO and hybrid (genetic BPSO) [21] | Reduces electricity cost and PAR | RTP | Ignored consumer comfort and RES |

MOA [22] | Reduces electricity cost | RTP-IBR | Inconsideration of RES, ESS and consumer comfort |

Fuzzy controller [23] | Optimal users’ comfort, peak load and electricity bill minimization | Dynamic price | Required high computational time |

NSGA-II [24] | Ensures customers’ convenience and electricity cost minimization | RTP | Inconsideration of RES |

Informatics solution and ANN [25] | Performs consumption forecast, reduces peak consumption, optimizes daily appliances’ operation and lessen main grid burden | ToU | Inconsideration of RES |

**Table 2.**Appliances specification details [12]. h: (hours); LOT: Length of operational time.

Appliance Class | Appliance Name | Power Rating (kW) | Starting Time (h) | Ending Time (h) | LOT (h) |
---|---|---|---|---|---|

Shiftable | Cloth dryer | 1.5 | 06 | 14 | 04 |

Vacuum cleaner | 1 | 06 | 15 | 30 min | |

Refrigerator | 0.125 | 06 | 15 | 24 | |

Air conditioner | 1 | 12 | 24 | 10 | |

Dish washer | 1 | 08 | 22 | 30 min | |

Pool pump | 2 | 12 | 21 | 08 | |

Electric vehicle | 2.5 | 16 | 24 | 2.5 | |

Television | 0.25 | 01 | 16 | 6 h 45 min | |

Iron | 1 | 06 | 16 | 30 min | |

Hair dryer | 1 | 06 | 13 | 1 h 30 min | |

Water heater | 1.5 | 06 | 23 | 03 | |

Other | 1.5 | 06 | 24 | 24 | |

Nonshiftable | Light | 0.5 | 16 | 24 | 6 h 15 min |

Electric stove | 1.5 | 06 | 14 | 05 | |

Personal computer | 0.25 | 08 | 24 | 04 | |

Heater | 1.5 | 03 | 15 | 03 |

Jaya-Parameter | Value | SBA-Parameter | Value | EDE-Parameter | Value |
---|---|---|---|---|---|

Population size | 30 | Population size | 30 | Population size | 30 |

No. decision variables | 16 | No. decision variables | 16 | No. decision variables | 16 |

Min | 0.1 | Min | 0 | Min | 0.1 |

Max | 0.9 | Max | 1 | Max | 0.9 |

No. of runner | 50 | Scaling factor | 0.5 | ||

Length of root | 5 | Crossover probabilities | 0.3, 0.6, 0.9 | ||

Maximum Iteration | 100 | Maximum Iteration | 100 | Maximum Iteration | 100 |

Technique | Electricity Cost | Electricity Cost Savings (%) | Computation Time (s) | |||
---|---|---|---|---|---|---|

Pricing scheme | CPP | ToU | CPP | ToU | CPP | ToU |

without HEM | 3787.00 | 1309.10 | - | - | - | - |

Earliglow without ESS | 2149.00 | 1128.00 | 43.25 | 13.83 | 0.1185 | 0.1202 |

Earliglow with ESS | 1408.60 | 1035.60 | 62.80 | 20.89 | 0.1185 | 0.1202 |

Jaya without ESS | 2541.00 | 1119.90 | 32.90 | 14.45 | 0.0755 | 0.0806 |

Jaya with ESS | 1060.20 | 1032.90 | 72.00 | 21.09 | 0.0755 | 0.0806 |

SBA without ESS | 2261.00 | 1092.20 | 38.07 | 18.21 | 0.8673 | 0.3709 |

SBA with ESS | 2014.20 | 999.79 | 45.41 | 25.35 | 0.8673 | 0.3709 |

EDE without ESS | 2373.00 | 1125.10 | 37.33 | 16.49 | 2.2728 | 0.3803 |

EDE with ESS | 1385.80 | 953.51 | 56.88 | 24.63 | 2.2728 | 0.3803 |

Technique | Energy Consumption | Energy Consumption Savings (%) | Computation Time (s) | |||
---|---|---|---|---|---|---|

Pricing scheme | CPP | ToU | CPP | ToU | CPP | ToU |

without HEM | 105.00 | 105.00 | - | - | - | - |

Earliglow without ESS | 105.00 | 105.00 | - | - | 0.1185 | 0.1202 |

Earliglow with ESS | 98.00 | 98.00 | 6.66 | 6.66 | 0.1185 | 0.1202 |

Jaya without ESS | 105.00 | 105.00 | - | - | 0.0755 | 0.0806 |

Jaya with ESS | 93.00 | 95.00 | 11.42 | 9.52 | 0.0755 | 0.0806 |

SBA without ESS | 105.00 | 105.00 | - | - | 0.8673 | 0.3709 |

SBA with ESS | 103.00 | 98.00 | 1.90 | 6.66 | 0.8673 | 0.3709 |

EDE without ESS | 105.00 | 105.00 | - | - | 2.2728 | 0.3803 |

EDE with ESS | 97.00 | 92.00 | 7.61 | 12.38 | 2.2728 | 0.3803 |

Cases | Load (kW) | Price (Cents) | Cost (Cents) |
---|---|---|---|

Minimum load, minimum price | 1.00 | 11.40 | 11.40 |

Minimum load, maximum price | 1.00 | 894.65 | 894.65 |

Maximum load, minimum price | 13.25 | 11.40 | 151.05 |

Maximum load, maximum price | 13.25 | 894.65 | 11,854.00 |

Cases | Load (kW) | Price (Cents) | Cost (Cents) |
---|---|---|---|

Minimum load, minimum price | 1.00 | 8.70 | 8.70 |

Minimum load, maximum price | 1.00 | 174.90 | 174.90 |

Maximum load, minimum price | 13.25 | 8.70 | 115.27 |

Maximum load, maximum price | 13.25 | 174.90 | 2317.40 |

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## Share and Cite

**MDPI and ACS Style**

Samuel, O.; Javaid, S.; Javaid, N.; Ahmed, S.H.; Afzal, M.K.; Ishmanov, F. An Efficient Power Scheduling in Smart Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes. *Energies* **2018**, *11*, 3155.
https://doi.org/10.3390/en11113155

**AMA Style**

Samuel O, Javaid S, Javaid N, Ahmed SH, Afzal MK, Ishmanov F. An Efficient Power Scheduling in Smart Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes. *Energies*. 2018; 11(11):3155.
https://doi.org/10.3390/en11113155

**Chicago/Turabian Style**

Samuel, Omaji, Sakeena Javaid, Nadeem Javaid, Syed Hassan Ahmed, Muhammad Khalil Afzal, and Farruh Ishmanov. 2018. "An Efficient Power Scheduling in Smart Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes" *Energies* 11, no. 11: 3155.
https://doi.org/10.3390/en11113155