#
Towards Effective and Efficient Energy Management of Single Home and a Smart Community Exploiting Heuristic Optimization Algorithms with Critical Peak and Real-Time Pricing Tariffs in Smart Grids^{ †}

^{1}

^{2}

^{3}

^{4}

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

- Load shifting strategy from high power demanding hours to low power demanding hours.
- Planning constraints in which different incentives are given to the customers by utility for flexible load shifting because of variation in quality of services provided by them.

## 2. Literature Review

## 3. Problem Statement, Objectives and Mathematical Formulation

#### 3.1. Problem Statement

#### 3.2. Objectives

- Scheduling of home appliances,
- EC and PAR reduction,
- Balancing the load,
- Maximizing the UC,
- Trade-off between EC and UC exploited,
- Comparative analysis is also presented.

#### 3.3. Mathematical Modeling

## 4. Proposed Methodology

#### 4.1. System Model

#### 4.2. Appliance Classification

- Shiftable Appliances,
- Controllable Appliances,
- Non-Shiftable Appliances.

#### 4.2.1. Shiftable Appliances

#### 4.2.2. Controllable Appliances

#### 4.2.3. Non-Shiftable Appliances

#### 4.3. Pricing Tariff

#### 4.3.1. CPP

- to send how much consumption has taken place from customers to utilities,
- and to send information to customers from utilities

#### 4.3.2. RTP

#### 4.4. Implemented Techniques

#### 4.4.1. BFOA

#### BFOA Steps

- Chemotaxis: the period of a bacteria’s life is measured by the number of these steps, where the fitness J (i) of the bacteria is measured by contiguity to another bacteria’s new position O (i), then a tumble besides the measured price surfaces one at a time by adding a unit step scope C (i) and it lies between [−1, 1] in the direction of tumble. We generate a vector A (i) for representation of this random direction called as ‘Tumble’.
- Reproduction: where bacteria performed well to move on their generation and the only cells that can perform are those that have done well in their life time.
- Elimination and Dispersal: where cells are discarded and new random cells are inserted having low probability.

#### 4.4.2. FPA

#### Types of Pollination

- Biotic pollination,
- Abiotic pollination.

#### Types of Different Processes in Pollination

- Self-pollination,
- Cross-pollination.

#### FPA Steps

- Biotic cross-pollination is calculated as a process of global-pollination in which pollen vectors transport pollinators by means of Levy flights.
- Abiotic and self-pollination are used for local-pollination.
- Pollinators sustain flower’s uniformity by reproduction probability.
- The transferring of local and global pollination is calculated by a switch probability p, belongs to [0, 1].

#### 4.5. Hybridization

#### 4.5.1. HBFPA

Algorithm 1 Algorithm for HBFPA |

## 5. Simulation Results and Discussion

#### 5.1. For Single Homes

#### 5.1.1. Load Consumption

#### Load Consumption using CPP

#### Load Consumption Using RTP

#### 5.1.2. Electricity Cost

#### Cost Using CPP

#### Cost Using RTP

#### 5.1.3. Peak-to-Average Ratio

#### PAR Using CPP

#### PAR Using RTP

#### 5.1.4. Waiting Time

#### Waiting Time Using CPP

#### Waiting Time Using RTP

#### 5.2. For Multiple Homes

#### 5.2.1. OTI 1 min

#### Load Using CPP

#### EC, PAR and WT Using CPP

#### Load Using RTP

#### EC, WT and PAR Using RTP

#### 5.2.2. OTI 60 min

#### Load Using CPP

#### EC, WT and PAR Using CPP

#### Load Using RTP

#### EC, WT and PAR Using RTP

#### 5.3. Feasible Regions

- Min cost, Min Load,
- Min cost, Max Load,
- Max cost, Min Load,
- Max cost, Max Load.

#### Feasible Region Using CPP

#### Feasible Region Using RTP

#### 5.4. Performance Trade-Off

## 6. Conclusions and Future Work

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Mehreen, G.; Patidar, S. Understanding the energy consumption and occupancy of a multi-purpose academic building. Energy Build.
**2015**, 87, 155–165. [Google Scholar][Green Version] - Today in Energy—U.S. Energy Information Administration (EIA). Available online: https://www.eia.gov/todayinenergy/detail.php?id=12251 (accessed on 3 August 2018).
- Logenthiran, T.; Srinivasan, D.; Shun, T.Z. Demand side management in smart grid using heuristic optimization. IEEE Trans. Smart Grid
**2012**, 3, 1244–1252. [Google Scholar] [CrossRef] - Awais, M.; Javaid, N.; Mateen, A.; Khan, N.; Mohiuddin, A.; Rehman, M.H.A. Meta Heuristic and Nature Inspired Hybrid Approach for Home Energy Management Using Flower Pollination Algorithm and Bacterial Foraging Optimization Technique. In Proceedings of the 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA), Krakow, Poland, 16–18 May 2018; pp. 882–891. [Google Scholar] [CrossRef]
- Tariq, M.; Khalid, A.; Ahmad, I.; Khan, M.; Zaheer, B.; Javaid, N. Load Scheduling in Home Energy Management System Using Meta-Heuristic Techniques and Critical Peak Pricing Tariff. In Proceedings of the International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, Barcelona, Spain, 8–10 November 2017; Springer: Cham, Switzerland, 2017; pp. 50–62. [Google Scholar]
- Rahim, S.; Javaid, N.; Ahmad, A.; Khan, S.A.; Khan, Z.A.; Alrajeh, N.; Qasim, U. Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energy Build.
**2016**, 129, 452–470. [Google Scholar] [CrossRef] - Mahmood, D.; Javaid, N.; Alrajeh, N.; Khan, Z.A.; Qasim, U.; Ahmed, I.; Ilahi, M. Realistic scheduling mechanism for smart homes. Energies
**2016**, 9, 202. [Google Scholar] [CrossRef] - Ahmad, A.; Khan, A.; Javaid, N.; Hussain, H.M.; Abdul, W.; Almogren, A.; Alamri, A.; Niaz, I.A. An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources. Energies
**2017**, 10, 549. [Google Scholar] [CrossRef] - Ma, K.; Yao, T.; Yang, J.; Guan, X. Residential power scheduling for demand response in smart grid. Int. J. Electr. Power Energy Syst.
**2016**, 78, 320–325. [Google Scholar] [CrossRef] - Muralitharan, R.S.; Shi, Y. Multiobjective optimization technique for demand side management with load balancing approach in smart grid. Neurocomputing
**2016**, 177, 110–119. [Google Scholar] [CrossRef] - López, M.A.; Torre, S.; Martín, S.; Aguado, J.A. Demand-side management in smart grid operation considering electric vehicles load shifting and vehicle-to-grid support. Int. J. Electr. Power Energy Syst.
**2015**, 64, 689–698. [Google Scholar] [CrossRef] - Chanda, S.; De, A. A multi-objective solution algorithm for optimum utilization of smart grid infrastructure towards social welfare. Int. J. Electr. Power Energy Syst.
**2014**, 58, 307–318. [Google Scholar] [CrossRef] - Khalid, A.; Javaid, N.; Mateen, A.; Khalid, B.; Khan, Z.A.; Qasim, U. Demand side management using hybrid bacterial foraging and genetic algorithm optimization techniques. In Proceedings of the 2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS), Fukuoka, Japan, 6–8 July 2016; IEEE: Piscataway, NJ, USA, 2016; pp. 494–502. [Google Scholar]
- Vardakas, J.; Zorba, N.; Verikoukis, C.V. A survey on demand response programs in smart grids: Pricing methods and optimization algorithms. IEEE Commun. Surv. Tutor.
**2015**, 17, 152–178. [Google Scholar] [CrossRef] - Aslam, S.; Iqbal, Z.; Javaid, N.; Khan, Z.A.; Aurangzeb, K.; Haider, S.I. Towards efficient energy management of smart buildings exploiting heuristic optimization with real-time and critical peak pricing schemes. Energies
**2017**, 10, 2065. [Google Scholar] [CrossRef] - Gupta, I.; Anandini, G.N.; Gupta, M. An hour wise device scheduling approach for demand side management in smart grid using particle swarm optimization. In Proceedings of the 2016 National Power Systems Conference (NPSC), Bhubaneswar, India, 19–21 December 2016; IEEE: Piscataway, NJ, USA, 2016. [Google Scholar]
- Geem, Z.W.; Yoon, Y. Harmony search optimization of renewable energy charging with energy storage system. Int. J. Electr. Power Energy Syst.
**2017**, 86, 120–126. [Google Scholar] [CrossRef] - Valenzuela, L.; Valdez, F.; Melin, P. Flower pollination algorithm with fuzzy approach for solving optimization problems. In Nature-Inspired Design of Hybrid Intelligent Systems; Springer: Cham, Switzerland, 2017; pp. 357–369. [Google Scholar]
- Dubey, H.M.; Pandit, M.; Panigrahi, B.K. A biologically inspired modified flower pollination algorithm for solving economic dispatch problems in modern power systems. Cognit. Comput.
**2015**, 7, 594–608. [Google Scholar] [CrossRef] - Kakran, S.; Chanana, S. Smart operations of smart grids integrated with distributed generation: A review. Renew. Sustain. Energy Rev.
**2018**, 81, 524–535. [Google Scholar] [CrossRef] - Mary, G.A.; Rajarajeswari, R. Smart grid cost optimization using genetic algorithm. Int. J. Res. Eng. Technol.
**2015**, 3, 282–287. [Google Scholar] - Bharathi, C.; Rekha, D.; Vijayakumar, V. Genetic Algorithm Based Demand Side Management for Smart Grid. Wirel. Pers. Commun.
**2017**, 93, 481–502. [Google Scholar] [CrossRef] - Whitley, D. A genetic algorithm tutorial. Stat. Comput.
**1994**, 4, 65–85. [Google Scholar] [CrossRef] - Hezam, I.; Abdel-Baset, M.; Hassan, B. A hybrid flower pollination algorithm with tabu search for unconstrained optimization problems. Inf. Sci. Lett.
**2016**, 5, 29–34. [Google Scholar] [CrossRef] - Prerna, S.; Kothari, A. Linear antenna array optimization using flower pollination algorithm. SpringerPlus
**2016**, 5, 306. [Google Scholar] - Graditi, G.; di Somma, M.; Siano, P. Optimal Bidding Strategy for a DER aggregator in the Day-Ahead Market in the presence of demand flexibility. IEEE Trans. Ind. Electron.
**2018**, 66, 1509–1519. [Google Scholar] - Graditi, G.; di Silvestre, M.L.; Gallea, R.; Sanseverino, E.R. Heuristic-based shiftable loads optimal management in smart micro-grids. IEEE Trans. Ind. Inf.
**2015**, 11, 271–280. [Google Scholar] [CrossRef] - Ferruzzi, G.; Cervone, G.; Monache, L.D.; Graditi, G.; Jacobone, F. Optimal bidding in a Day-Ahead energy market for Micro Grid under uncertainty in renewable energy production. Energy
**2016**, 106, 194–202. [Google Scholar] [CrossRef] - Iqbal, Z.; Javaid, N.; Iqbal, S.; Aslam, S.; Khan, Z.A.; Abdul, W.; Almogren, A.; Alamri, A. A Domestic Microgrid with Optimized Home Energy Management System. Energies
**2018**, 11, 1002. [Google Scholar] [CrossRef] - El-Hawary, M.E. The smart grid-state-of-the-art and future trends. Electr. Power Compon. Syst.
**2014**, 42, 239–250. [Google Scholar] [CrossRef] - Khan, A.; Javaid, N.; Ahmad, A.; Akbar, M.; Khan, Z.A.; Ilahi, M. A priority-induced demand side management system to mitigate rebound peaks using multiple knapsack. J. Ambient Intell. Hum. Comput.
**2018**, 1–24. [Google Scholar] [CrossRef] - Khan, A.; Javaid, N.; Khan, M.I. Time and device based priority induced comfort management in smart home within the consumer budget limitation. Sustain. Cities Soc.
**2018**, 41, 538–555. [Google Scholar] [CrossRef] - Yang, X.-S. Flower pollination algorithm for global optimization. In Proceedings of the International Conference on Unconventional Computing and Natural Computation, Orléans, France, 3–7 September 2012; Springer: Berlin/Heidelberg, Germany, 2012; pp. 240–249. [Google Scholar]
- Passino, K. Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst.
**2002**, 22, 52–67. [Google Scholar] - Balasubramani, K.; Marcus, K. A study on flower pollination algorithm and its applications. Int. J. Appl. Innov. Eng. Manag.
**2015**, 3, 230–235. [Google Scholar] - Rodrigues, D.; Yang, X.-S.; de Souza, A.N.; Papa, J.P. Binary flower pollination algorithm and its application to feature selection. In Recent Advances in Swarm Intelligence and Evolutionary Computation; Springer: Cham, The Netherlands, 2015; pp. 85–100. [Google Scholar]

**Figure 4.**Load for single home using critical peak pricing. (

**a**) load using operational time interval of 1 min; (

**b**) load using operational time interval of 20 min; (

**c**) load using operational time interval of 30 min; (

**d**) load using operational time interval of 60 min.

**Figure 5.**Load for single home using real time pricing. (

**a**) load using operational time interval of 1 min; (

**b**) load using operational time interval of 20 min; (

**c**) load using operational time interval of 30 min; (

**d**) load using operational time interval of 60 min.

**Figure 6.**Total cost, waiting time and peak to average ratio for a single home using critical peak pricing. (

**a**) Electricity cost using operational time interval of 1, 20, 30, 60 min; (

**b**) Waiting time using operational time interval of 1, 20, 30, 60 min; (

**c**) Peak to average ratio using operational time interval of 1, 20, 30, 60 min.

**Figure 7.**Total cost, waiting time and peak to average ratio for single home using real time pricing. (

**a**) Electricity cost using operational time interval of 1, 20, 30, 60 min; (

**b**) Waiting time using operational time interval of 1, 20, 30, 60 min; (

**c**) Peak to average ratio using operational time interval of 1, 20, 30, 60 min.

**Figure 8.**Load for multiple homes: (10, 30 and 50) using critical peak pricing. (

**a**) load using operational time interval of 1 min for 10 homes; (

**b**) load using operational time interval of 1 min for 30 homes; (

**c**) load using operational time interval of 1 min for 50 homes.

**Figure 9.**Electricity cost, waiting time and peak to average ratio for multiple homes: (10, 30 and 50) using critical peak pricing. (

**a**) Electricity cost using operational time interval of 1 min; (

**b**) Waiting time using operational time interval of 1 min; (

**c**) Peak to average ratio using operational time interval of 1 min.

**Figure 10.**Load for multiple homes: (10, 30 and 50) using Real time pricing. (

**a**) load using operational time interval of 1 min for 10 homes; (

**b**) load using operational time interval of 1 min for 30 homes; (

**c**) load using operational time interval of 1 min for 50 homes.

**Figure 11.**Electricity cost, waiting time and peak to average ratio for multiple homes: (10, 30 and 50) using real time pricing. (

**a**) Electricity cost using operational time interval of 1 min; (

**b**) Waiting time using operational time interval of 1 min; (

**c**) Peak to average ratio using operational time interval of 1 min.

**Figure 12.**Load for multiple homes: (10, 30 and 50) using critical peak pricing. (

**a**) load using operational time interval of 60 min for 10 homes; (

**b**) load using operational time interval of 60 min for 30 homes; (

**c**) load using operational time interval of 60 min for 50 homes.

**Figure 13.**Electricity cost, waiting time and peak to average ratio for multiple homes: (10, 30 and 50) using critical peak pricing. (

**a**) Electricity cost using operational time interval of 60 min; (

**b**) Waiting time using operational time interval of 60 min; (

**c**) Peak to average ratio using operational time interval of 60 min.

**Figure 14.**Load for multiple homes: (10, 30 and 50) using real time pricing. (

**a**) load using operational time interval of 60 min for 10 homes; (

**b**) load using operational time interval of 60 min for 30 homes; (

**c**) load using operational time interval of 60 min for 50 homes.

**Figure 15.**Electricity cost, waiting time and peak to average ratio for multiple homes: (10, 30 and 50) using real time pricing. (

**a**) Electricity cost using operational time interval of 60 min; (

**b**) Waiting time using operational time interval of 60 min; (

**c**) Peak to average ratio using operational time interval of 60 min.

**Figure 16.**Feasible regions for a single home using critical peak pricing. (

**a**) Operational time interval of 1 min; (

**b**) Operational time interval of 20 min; (

**c**) Operational time interval of 30 min; (

**d**) Operational time interval of 60 min.

**Figure 17.**Feasible regions for a single home using real time pricing. (

**a**) Operational time interval of 1 min; (

**b**) Operational time interval of 20 min; (

**c**) Operational time interval of 30 min; (

**d**) Operational time interval of 60 min.

Schemes | Achievement | Limitations |
---|---|---|

GA, BPSO, ACO [6] | Cost, PAR reduction and UC maximization | Computational complexity is not considered |

BFOA, BFOA, GA, BPSO, WDO, GBPSO [7] | Reduces the EC and limits PAR | Trade-off between EC and PAR is not considered |

GA, PSO, WDO, BFO, HGPSO [8] | Minimizes the electricity bill by | EC and PAR reduction are not considered |

scheduling household appliances | ||

BFOA [9] | Reduces the EC with affordable UC | Trade-off among EC and |

UC is not considered | ||

BPSO [10] | Develops efficient scheme to minimize the EC | Privacy of user is not considered |

MOEA [11] | EC minimization and reduction in WT | Consumer’s threshold limit is not focused |

DR programs [13] | Minimize power consumption | Implements the DR program |

peak demand hours not considered | ||

BPSO [16] | Reduces peak hours demands | Peak demand is reduced |

Reduction in the bill | Electric cost is not considered | |

HSA [17] | Reduces operational cost | UC is not considered |

DSM model is presented using GA [18] | Reduces operational cost, PAR | Time complexity is completely ignored |

In-place (PL) generalized algorithm [19] | EC and UC trade-off | Ignores the system complexity |

GA | EC and PAR reduction | System complexity is ignored |

current procedural terminology [20] | ||

GA [21] | EC and PAR is minimized by the proposed scheme | Installation cost is completely ignored |

GA [22] | EC is reduced by using GA | UC is ignored and PAR is also neglected |

GA [23] | EC is minimized with reduction in PAR | UC is neglected |

Hybrid algorithm using FPA and TS [24] | Hybrid version to optimize unconstrained problems | Ignores the optimization problem with multiple constrains |

FPA [25] | Side lobe level minimization and null placement | Ignores interferences in undesired direction |

Terms | Notations |
---|---|

Electric rate per slot (t) | $E{P}_{rate}^{t}$ |

Power rating per appliance (ap) | ${P}_{rate}^{ap}$ |

Maximum population size | ${N}_{p}$ |

Appliance load | ${L}_{oad}$ |

Scheduled load | ${L}_{oad}^{sch}$ |

Unscheduled L | ${L}_{oad}^{unsch}$ |

Domain of electric rate | ${E}_{rate}$ |

Fitness function | ${E}_{F}$ |

Load per slot (t) | ${L}_{oad}^{t}$ |

Appliances | $app$ |

Group | Appliances | PR (kWh) | LOTs |
---|---|---|---|

Controllable Appliances | Oven | 1.30 | 10.0 |

Kettle | 2.00 | 1.00 | |

Coffee Maker | 0.80 | 4.00 | |

Rice Cooker | 0.85 | 2.00 | |

Blender | 0.30 | 2.00 | |

Frying Pan | 1.10 | 3.00 | |

Toaster | 0.90 | 1.00 | |

Fan | 0.20 | 15.0 | |

Shiftable appliances | Washing Machine | 0.50 | 6.00 |

Clothes Dryer | 1.20 | 6.00 | |

Non-Shiftable Appliances | Dish Washer | 0.70 | 8.00 |

Vacuum Cleaner | 0.40 | 8.00 | |

Hair Dryer | 1.50 | 2.00 | |

Iron | 1.00 | 6.00 |

Terms | Notations |
---|---|

OTIs | t |

Total time in hours | T |

Upper bound | $\alpha $ |

Lower bound | $\beta $ |

Appliances | D |

Fitness | ${E}_{F}$ |

Maximum population size | ${N}_{p}$ |

Newly generated population | Xnew |

Old generated population | X |

Techniques | Cost (Cents) Using CPP | |||
---|---|---|---|---|

1 min | 20 min | 30 min | 60 min | |

Unscheduled | 1.5323 × ${10}^{3}$ | 1.1210 × ${10}^{3}$ | 1.2912 × ${10}^{3}$ | 1.1319 × ${10}^{3}$ |

BFOA | 848.3800 | 829.7933 | 753.6100 | 952.7100 |

FPA | 785.6600 | 777.5267 | 804.0100 | 1.0423 × ${10}^{3}$ |

HBFPA | 785.6600 | 725.2600 | 608.0100 | 762.3100 |

Techniques | Cost (Cents) Using RTP | |||
---|---|---|---|---|

1 min | 20 min | 30 min | 60 min | |

Unscheduled | 362.3626 | 269.1267 | 344.2463 | 333.1345 |

BFOA | 280.0945 | 265.0310 | 291.4513 | 275.6915 |

FPA | 286.3116 | 267.5580 | 300.4783 | 276.2255 |

HBFPA | 267.9894 | 235.0647 | 269.3313 | 275.1495 |

Techniques | PAR Using CPP | |||
---|---|---|---|---|

1 min | 20 min | 30 min | 60 min | |

Unscheduled | 7.241 | 4.9 | 3.47 | 3.3784 |

BFOA | 3.4365 | 6.48 | 3.99 | 3.43 |

FPA | 3.5248 | 3.68 | 2.22 | 2.2289 |

HBFPA | 6.2047 | 4.4388 | 3.4657 | 3.2657 |

Techniques | PAR Using RTP | |||
---|---|---|---|---|

1 min | 20 min | 30 min | 60 min | |

Unscheduled | 7.5619 | 6.3920 | 6.4850 | 3.6803 |

BFOA | 4.5242 | 3.6784 | 3.8245 | 3.3175 |

FPA | 5.4937 | 3.6784 | 3.4365 | 2.2289 |

HBFPA | 6.2047 | 4.3417 | 4.2125 | 3.2138 |

Techniques | Waiting Time Using CPP | |||
---|---|---|---|---|

1 min | 20 min | 30 min | 60 min | |

BFOA | 137.3321 | 154.1667 | 86.7857 | 139.2857 |

FPA | 140.0812 | 153.3929 | 102.6786 | 147.8571 |

HBFPA | 214.3473 | 227.5595 | 149.8214 | 135.00 |

Techniques | Waiting Time Using RTP | |||
---|---|---|---|---|

1 min | 20 min | 30 min | 60 min | |

BFOA | 54.9940 | 130.9762 | 80.2500 | 133.5714 |

FPA | 59.7152 | 147.9762 | 90.5357 | 165.00 |

HBFPA | 153.5777 | 228.5714 | 160.1786 | 130.00 |

Appliances | Power Rating 1 | Power Rating 2 | Power Rating 3 | Power Rating 4 |
---|---|---|---|---|

Washing-Machine | 0.50 | 0.70 | 0.90 | 0.40 |

Clothes Dryer | 10.0 | 1.20 | 1.40 | 1.60 |

Dish Washer | 0.38 | 0.50 | 0.70 | 0.80 |

Vacuum Cleaner | 0.80 | 1.00 | 0.20 | 0.50 |

Hair Dryer | 1.50 | 1.20 | 1.40 | 1.70 |

Iron | 1.00 | 1.30 | 1.50 | 1.20 |

Oven | 1.30 | 1.50 | 1.70 | 1.90 |

Kettle | 2.00 | 2.15 | 2.40 | 2.14 |

Coffee Maker | 0.80 | 0.40 | 0.50 | 0.20 |

Rice Cooker | 0.85 | 0.89 | 0.72 | 0.79 |

Blender | 0.30 | 0.47 | 0.40 | 0.70 |

Frying Pan | 1.10 | 1.50 | 1.90 | 2.00 |

Toaster | 0.90 | 1.00 | 0.50 | 0.70 |

Fan | 0.20 | 0.50 | 0.40 | 0.70 |

Techniques | Cost (Cents) Using CPP for OTI 1 min | ||
---|---|---|---|

10 homes | 30 homes | 50 homes | |

Unscheduled | 4.2213 × ${10}^{5}$ | 1.2535 × ${10}^{6}$ | 2.1077 × ${10}^{6}$ |

BFOA | 3.8669 × ${10}^{5}$ | 1.1425 × ${10}^{6}$ | 1.9247 × ${10}^{6}$ |

FPA | 4.4563 × ${10}^{5}$ | 1.2881 × ${10}^{6}$ | 2.1096 × ${10}^{6}$ |

HBFPA | 0.2128 × ${10}^{5}$ | 0.7098 × ${10}^{5}$ | 1.2205 × ${10}^{6}$ |

Techniques | WT Using CPP for OTI 1 min | ||
---|---|---|---|

10 homes | 30 homes | 50 homes | |

BFOA | 544.0428 | 1.7259 × ${10}^{3}$ | 2.8516 × ${10}^{3}$ |

FPA | 722.7521 | 2.0134 × ${10}^{3}$ | 3.6380 × ${10}^{3}$ |

HBFPA | 1.5302 × ${10}^{3}$ | 4.5947 × ${10}^{3}$ | 7.6534 × ${10}^{3}$ |

Techniques | PAR Using CPP for OTI 1 min | ||
---|---|---|---|

10 homes | 30 homes | 50 homes | |

Unscheduled | 75.9236 | 233.9259 | 381.1938 |

BFOA | 46.1947 | 137.6369 | 231.5394 |

FPA | 49.2012 | 150.6242 | 248.5587 |

HBFPA | 61.5829 | 180.9010 | 304.4469 |

Techniques | Cost (Cents) Using RTP for OTI 1 min | ||
---|---|---|---|

10 homes | 30 homes | 50 homes | |

Unscheduled | 2.6912 × ${10}^{5}$ | 7.9680 × ${10}^{5}$ | 1.2990 × ${10}^{4}$ |

BFOA | 2.0976 × ${10}^{5}$ | 6.2206 × ${10}^{5}$ | 1.0124 × ${10}^{6}$ |

FPA | 2.1458 × ${10}^{5}$ | 6.3795 × ${10}^{5}$ | 1.0328 × ${10}^{6}$ |

HBFPA | 2.0176 × ${10}^{5}$ | 5.9593 × ${10}^{5}$ | 9.7160 × ${10}^{5}$ |

Techniques | WT Using RTP for OTI 1 min | ||
---|---|---|---|

10 homes | 30 homes | 50 homes | |

BFOA | 574.6065 | 1.6632 × ${10}^{3}$ | 6.7364 × ${10}^{3}$ |

FPA | 746.8147 | 2.1254 × ${10}^{3}$ | 4.5967 × ${10}^{3}$ |

HBFPA | 2.7175 × ${10}^{3}$ | 3.6963 × ${10}^{3}$ | 7.6552 × ${10}^{3}$ |

Techniques | PAR Using RTP for OTI 1 min | ||
---|---|---|---|

10 homes | 30 homes | 50 homes | |

Unscheduled | 77.0644 | 232.1643 | 394.5380 |

BFOA | 45.6238 | 138.9392 | 234.6092 |

FPA | 50.2105 | 153.5715 | 253.3993 |

HBFPA | 57.1704 | 184.7282 | 302.1077 |

Techniques | Cost (Cents) Using CPP for OTI 60 min | ||
---|---|---|---|

10 homes | 30 homes | 50 homes | |

Unscheduled | 1.5059 × ${10}^{4}$ | 4.2003 × ${10}^{4}$ | 7.1746 × ${10}^{4}$ |

BFOA | 1.0666 × ${10}^{4}$ | 2.9568 × ${10}^{4}$ | 4.9553 × ${10}^{4}$ |

FPA | 1.0836 × ${10}^{4}$ | 2.9822 × ${10}^{4}$ | 5.1076 × ${10}^{4}$ |

HBFPA | 0.83 × ${10}^{5}$ | 2.5075 × ${10}^{4}$ | 4.274 × ${10}^{4}$ |

Techniques | WT Using CPP for OTI 60 min | ||
---|---|---|---|

10 homes | 30 homes | 50 homes | |

BFOA | 1.0043 × ${10}^{3}$ | 3.2093 × ${10}^{3}$ | 5.1507 × ${10}^{3}$ |

FPA | 1.0757 × ${10}^{3}$ | 3.2429 × ${10}^{3}$ | 4.1714 × ${10}^{3}$ |

HBFPA | 1.3836 × ${10}^{3}$ | 4.1714 × ${10}^{3}$ | 6.8836 × ${10}^{3}$ |

Techniques | PAR Using CPP for OTI 60 min | ||
---|---|---|---|

10 homes | 30 homes | 50 homes | |

Unscheduled | 38.7901 | 112.7401 | 189.7837 |

BFOA | 23.5727 | 78.1047 | 120.4385 |

FPA | 24.6797 | 75.6910 | 129.6416 |

HBFPA | 30.3457 | 98.0471 | 167.4858 |

Techniques | Cost (Cents) Using RTP for OTI 60 min | ||
---|---|---|---|

10 homes | 30 homes | 50 homes | |

Unscheduled | 3.9954 × ${10}^{3}$ | 1.2170 × ${10}^{4}$ | 2.0437 × ${10}^{4}$ |

BFOA | 3.1541 × ${10}^{3}$ | 9.6811 × ${10}^{3}$ | 1.6242 × ${10}^{4}$ |

FPA | 3.1884 × ${10}^{3}$ | 9.8798 × ${10}^{3}$ | 1.6251 × ${10}^{4}$ |

HBFPA | 3.1877 × ${10}^{3}$ | 1.6242 × ${10}^{3}$ | 1.6039 × ${10}^{4}$ |

Techniques | WT Using RTP for OTI 60 min | ||
---|---|---|---|

10 homes | 30 homes | 50 homes | |

BFOA | 1.4314 × ${10}^{3}$ | 4.0729 × ${10}^{3}$ | 6.7364 × ${10}^{3}$ |

FPA | 1.4371 × ${10}^{3}$ | 3.8336 × ${10}^{3}$ | 6.869 × ${10}^{3}$ |

HBFPA | 1.3964 × ${10}^{3}$ | 4.1579 × ${10}^{3}$ | 6.9236 × ${10}^{3}$ |

Techniques | PAR Using RTP for OTI 60 min | ||
---|---|---|---|

10 homes | 30 homes | 50 homes | |

Unscheduled | 37.3187 | 115.6367 | 195.2858 |

BFOA | 25.9190 | 74.4312 | 122.2942 |

FPA | 25.8770 | 80.9041 | 129.3206 |

HBFPA | 33.8192 | 102.8952 | 166.9638 |

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Awais, M.; Javaid, N.; Aurangzeb, K.; Haider, S.I.; Khan, Z.A.; Mahmood, D. Towards Effective and Efficient Energy Management of Single Home and a Smart Community Exploiting Heuristic Optimization Algorithms with Critical Peak and Real-Time Pricing Tariffs in Smart Grids. *Energies* **2018**, *11*, 3125.
https://doi.org/10.3390/en11113125

**AMA Style**

Awais M, Javaid N, Aurangzeb K, Haider SI, Khan ZA, Mahmood D. Towards Effective and Efficient Energy Management of Single Home and a Smart Community Exploiting Heuristic Optimization Algorithms with Critical Peak and Real-Time Pricing Tariffs in Smart Grids. *Energies*. 2018; 11(11):3125.
https://doi.org/10.3390/en11113125

**Chicago/Turabian Style**

Awais, Muhammad, Nadeem Javaid, Khursheed Aurangzeb, Syed Irtaza Haider, Zahoor Ali Khan, and Danish Mahmood. 2018. "Towards Effective and Efficient Energy Management of Single Home and a Smart Community Exploiting Heuristic Optimization Algorithms with Critical Peak and Real-Time Pricing Tariffs in Smart Grids" *Energies* 11, no. 11: 3125.
https://doi.org/10.3390/en11113125