# An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources

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## Abstract

**:**

## 1. Introduction

## 2. Related Work

## 3. Problem Statement

## 4. Problem Formulation

#### 4.1. Energy Generation Model of PV System

#### 4.2. Energy Storage Model

#### 4.3. Energy Consumption Model

#### 4.4. PAR

#### 4.5. Energy Pricing Model

#### 4.6. Appliance Scheduling Problem

#### 4.7. Feasible Region

## 5. Proposed System Architecture

## 6. Scheduling Algorithms

#### 6.1. GA

#### 6.2. BPSO

#### 6.3. WDO

#### 6.4. BFO

#### 6.5. HGPO

## 7. Results and Discussions

#### 7.1. Case 1: Integration of RES and ESS

#### 7.1.1. Energy Consumption

#### 7.1.2. Electricity Cost

#### 7.1.3. Total Cost

#### 7.1.4. PAR

#### 7.2. Feasible Region of the Objective Function

#### 7.3. Case 2: OHEMS with Integrated RES and ESS

#### 7.3.1. Energy Consumption

#### 7.3.2. Electricity Cost

#### 7.3.3. Total Cost

#### 7.3.4. PAR

## 8. Conclusions and Future Work

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Nomenclature

Symbol | Description | Symbol | Description |

${E}^{PV}$ | Available energy from PV system | ${X}_{gbest}$ | Global best value |

${\eta}^{PV}$ | Efficiency of PV system | ${X}_{lbest}$ | Local best value |

${A}^{PV}$ | Area of PV generator | ${F}_{pg}$ | Pressure gradient force |

${I}_{r}$ | Solar radiation | ${F}_{C}$ | Coriolis force |

${T}^{a}$ | Ambient temperature | ${F}_{G}$ | Gravitational force |

${\alpha}_{1},{\alpha}_{2}$ | Shape factors | ${\beta}_{1},{\beta}_{2}$ | Scale factors |

$ES$ | Stored energy | ${F}_{F}$ | Friction force |

$\kappa $ | Duration of one time slot | ${F}_{G}$ | Gravitational force |

${\eta}^{ESS}$ | Efficiency of ESS | ${P}_{old}$ | Pressure at current location |

$E{P}^{Ch}$ | Charge rate of ESS at time | ${P}_{max}$ | High pressure point |

$E{P}^{Dch}$ | Discharge rate of ESS at time | $\alpha $ | Constant for update position |

$E{P}_{UB}^{Ch}$ | Upper charge limit of ESS | $\omega $ | Earth rotation |

$E{P}_{LB}^{Dch}$ | Lower discharge limit of ESS | $\Delta $t | Unit step time |

$E{S}^{UB}$ | Upper limit of energy storage | ${\nu}_{new}$ | Updated velocity |

M | Number of controllable appliances | ${\nu}_{old}$ | Current velocity |

N | Number of un-shiftable appliances | R | Universal gas constant |

${E}^{a}$ | Energy consumption of shiftable appliances | $Sig$ | Sigmoid function |

${E}^{b}$ | Energy consumption of non-shiftable appliances | n | Total number of appliances |

${E}^{total}$ | Total energy consumption | ${P}^{DAP}$ | DAP signal |

${E}_{P}^{a}$ | Electricity cost of shiftable appliances energy consumption | ${C}_{i}$ | Step size |

${E}_{P}^{b}$ | Electricity cost price of non-shiftable appliances energy consumption | $\theta $ | Position of bacteria |

${E}_{P}$ | Total bill of energy consumption | $\mathsf{\Omega}$ | Rotation of earth |

${X}_{m\u03f5M}^{a}$ | ON/OFF status of shiftable appliances | $\delta \nu $ | Finite volume of air |

${X}_{n\beta \u03f5N}^{b}$ | ON/OFF status of non-shiftable appliances | w | Inertia factor |

${E}_{grid}$ | Available grid energy | ${r}_{1},{r}_{2}$ | Random numbers |

${E}_{unsch}^{min}$ | Minimum energy consumed in unscheduled senario | ${c}_{1}$ | Local pull |

${\tau}_{0}$ | Lower limit of scheduling horizon | ${c}_{2}$ | Global pull |

${\tau}_{sch}$ | Scheduling time | $dimMax$ | Upper limit of WDO dimensions |

${\tau}_{max}$ | Upper limit of scheduling horizon | $dimMin$ | Lower limit of WDO dimensions |

t | Time slots | $\Delta $ | Pressure gradient |

L | Length of chromosomes | $\mu $ | Velocity vector of wind |

${V}_{i}^{t+1}$ | Particle upcoming velocity | ${V}_{j}^{t}$ | Particle current velocity |

${x}_{i}^{t+1}$ | Particle upcoming position | ${x}_{i}^{t}$ | Particle current position |

${N}_{e}$ | Number of elimination steps | ${P}_{c}$ | Probability of crossover |

${N}_{c}$ | Number of chemotaxis steps | ${P}_{m}$ | Probability of mutation |

${N}_{p}$ | Number of population steps | ${w}_{i}$ | Initial weight constant |

${N}_{s}$ | Number of swimming steps | ${w}_{f}$ | Final weight constant |

${N}_{r}$ | Number of reproduction steps | ${v}_{max}$ | Upper limit of velocity |

${P}_{ed}$ | Probability of elimination-dispersal | ${v}_{min}$ | Lower limit of velocity |

## List of Acronyms

Acronym | Description | Acronym | Description |

$SG$ | Smart grid | $SHs$ | Smart homes |

$SCs$ | Smart cities | $REMS$ | Residential energy management system |

$EMS$ | Energy management system | $HEMS$ | Home energy management system |

$DR$ | Demand response | $DSM$ | Demand side management |

$OHEMS$ | Optimized home energy management system | $UC$ | User comfort |

$RESs$ | Renewable energy sources | $RSERs$ | Renewable and sustainable energy resources |

$DGs$ | Distributed generation | $ICTs$ | Information and communication tecnologies |

$PV$ | Photovoltaic | $ESS$ | Energy storage system |

$SM$ | Smart meter | $AMI$ | Advance metering infrastructure |

$MC$ | Master controller | $DC$ | Direct current |

$AC$ | Alternating current | $SS$ | Smart scheduler |

$GA$ | Genetic algorithm | $PSO$ | Particle swarm optimization |

$BPSO$ | Binary particle swarm optimization | $WDO$ | Wind driven optimization |

$BFO$ | Bacterial foraging optimization | $HGPO$ | Hybrid GA-PSO |

$LSA$ | Lighting search algorithm | $ANN$ | Artificial neural networks |

$OLA$ | Observe, learn and adopt | $LF$ | Load forecasting |

$DP$ | Dynamic programming | $IPSO$ | Improved particle swarm optimization |

$CP$ | Convex programming | $DAP$ | Day ahead pricing |

$DAP$ | Real time pricing | $ToUP$ | Time of use pricing |

$PP$ | Peak pricing | $CPP$ | Critical peak pricing |

$LP$ | Linear programming | $ILP$ | Integer linear programming |

$MILP$ | Mixed integer linear programming | $RES$ | Multiple knapsack problem |

## References

- Guo, Y.; Pan, M.; Fang, Y. Optimal power management of residential customers in the smart grid. IEEE Trans. Parallel Distrib. Syst.
**2012**, 23, 1593–1606. [Google Scholar] [CrossRef] - Agnetis, A.; de Pascale, G.; Detti, P.; Vicino, A. Load scheduling for household energy consumption optimization. IEEE Trans. Smart Grid
**2013**, 4, 2364–2373. [Google Scholar] [CrossRef] - Basso, T.S.; DeBlasio, R. IEEE 1547 series of standards: Interconnection issues. IEEE Trans. Power Electron.
**2004**, 19, 1159–1162. [Google Scholar] [CrossRef] - Maharjan, I.K. Demand Side Management: Load Management, Load Profiling, Load Shifting, Residential and Industrial Consumer, Energy Audit, Reliability, Urban, Semi-Urban and Rural Setting; LAP Lambert Academic Publishing: Saarbrucken, Germany, 2010; pp. 1–116. [Google Scholar]
- Shao, S.; Pipattanasomporn, M.; Rahman, S. Demand response as a load shaping tool in an intelligent grid with electric vehicles. IEEE Trans. Smart Grid
**2011**, 2, 624–631. [Google Scholar] [CrossRef] - Hubert, T.; Grijalva, S. Realizing smart grid benefits requires energy optimization algorithms at residential level. In Proceedings of the IEEE PES Innovative Smart Grid Technologies (ISGT), Anaheim, CA, USA, 17–19 January 2011; pp. 1–8. [Google Scholar]
- Tsui, K.M.; Chan, S.C. Demand response optimization for smart home scheduling under real-time pricing. IEEE Trans. Smart Grid
**2012**, 3, 1812–1821. [Google Scholar] [CrossRef] - Oberdieck, R.; Pistikopoulos, E.N. Multi-objective optimization with convex quadratic cost functions: A multi-parametric programming approach. Comput. Chem. Eng.
**2016**, 85, 36–39. [Google Scholar] [CrossRef] - Beaudin, M.; Zareipour, H. Home energy management systems: A review of modelling and complexity. Renew. Sustain. Energy Rev.
**2015**, 45, 318–335. [Google Scholar] - Zhou, B.; Li, W.; Chan, K.W.; Cao, Y.; Kuang, Y.; Liu, X.; Wang, X. Smart home energy management systems: Concept, configurations, and scheduling strategies. Renew. Sustain. Energy Rev.
**2016**, 61, 30–40. [Google Scholar] [CrossRef] - Calvillo, C.F.; Sánchez-Miralles, A.; Villar, J. Energy management and planning in smart cities. Renew. Sustain. Energy Rev.
**2016**, 55, 273–287. [Google Scholar] [CrossRef] - Ruiz-Romero, S.; Colmenar-Santos, A.; Mur-Pérez, F.; López-Rey, Á. Integration of distributed generation in the power distribution network: The need for smart grid control systems, communication and equipment for a smart city—Use cases. Renew. Sustain. Energy Rev.
**2014**, 38, 223–234. [Google Scholar] [CrossRef] - Lobaccaro, G.; Carlucci, S.; Löfström, E. A review of systems and technologies for smart homes and smart grids. Energies
**2016**, 9, 348. [Google Scholar] [CrossRef] - Wright, C.; Baur, S.; Grantham, K.; Stone, R.B.; Grasman, S.E. Residential energy performance metrics. Energies
**2010**, 3, 1194–1211. [Google Scholar] [CrossRef] - Khan, A.R.; Mahmood, A.; Safdar, A.; Khan, Z.A.; Khan, N.A. Load forecasting, dynamic pricing and DSM in smart grid: A review. Renew. Sustain. Energy Rev.
**2016**, 54, 1311–1322. [Google Scholar] [CrossRef] - Lee, J.Y.; Choi, S.G. Linear programming based hourly peak load shaving method at home area. In Proceedings of the 16th International Conference on Advanced Communication Technology (ICACT), Phoenix Park, PyeonhChang, Korea, 16–19 February 2014; pp. 310–313. [Google Scholar]
- Qayyum, F.A.; Naeem, M.; Khwaja, A.S.; Anpalagan, A.; Guan, L.; Venkatesh, B. Appliance scheduling optimization in smart home networks. IEEE Access
**2015**, 3, 2176–2190. [Google Scholar] [CrossRef] - Yoo, J.; Park, B.; An, K.; Al-Ammar, E.A.; Khan, Y.; Hur, K.; Kim, J.H. Look-ahead energy management of a grid-connected residential PV system with energy storage under time-based rate programs. Energies
**2012**, 5, 1116–1134. [Google Scholar] [CrossRef] - Ahmed, M.S.; Mohamed, A.; Homod, R.Z.; Shareef, H. Hybrid LSA-ANN based home energy management scheduling controller for residential demand response strategy. Energies
**2016**, 9, 716. [Google Scholar] [CrossRef] - Rasheed, M.B.; Javaid, N.; Ahmad, A.; Khan, Z.A.; Qasim, U.; Alrajeh, N. An efficient power scheduling scheme for residential load management in smart homes. Appl. Sci.
**2015**, 5, 1134–1163. [Google Scholar] [CrossRef] - Wen, Z.; O’Neill, D.; Maei, H. Optimal demand response using device-based reinforcement learning. IEEE Trans. Smart Grid
**2015**, 6, 2312–2324. [Google Scholar] [CrossRef] - Adika, C.O.; Wang, L. Smart charging and appliance scheduling approaches to demand side management. Int. J. Electr. Power Energy Syst.
**2014**, 57, 232–240. [Google Scholar] [CrossRef] - Peyvandi, M.; Zafarani, M.; Nasr, E. Comparison of particle swarm optimization and the genetic algorithm in the improvement of power system stability by an SSSC-based controller. J. Electr. Eng. Technol.
**2011**, 6, 182–191. [Google Scholar] [CrossRef] - Deconinck, G.; Decroix, B. Smart metering tariff schemes combined with distributed energy resources. In Proceedings of the Fourth International Conference on Critical Infrastructures (CRIS), Linkoping, Sweden, 27–30 April 2009; pp. 1–8. [Google Scholar]
- Chavali, P.; Yang, P.; Nehorai, A. A distributed algorithm of appliance scheduling for home energy management system. IEEE Trans. Smart Grid
**2014**, 5, 282–290. [Google Scholar] [CrossRef] - Yang, H.T.; Yang, C.T.; Tsai, C.C.; Chen, G.J.; Chen, S.Y. Improved PSO based home energy management systems integrated with demand response in a smart grid. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Sendai, Japan, 25–28 May 2015; pp. 275–282. [Google Scholar]
- Lior, N. Sustainable energy development: The present (2009) situation and possible paths to the future. Energy
**2010**, 35, 3976–3994. [Google Scholar] [CrossRef] - Üçtuğ, F.G.; Yükseltan, E. A linear programming approach to household energy conservation: Efficient allocation of budget. Energy Build.
**2012**, 49, 200–208. [Google Scholar] [CrossRef] - Zhu, Z.; Tang, J.; Lambotharan, S.; Chin, W.H.; Fan, Z. An integer linear programming based optimization for home demand-side management in smart grid. In Proceedings of the Innovative Smart Grid Technologies (ISGT), Columbia, SC, USA, 16–20 January 2012; pp. 1–5. [Google Scholar]
- Sou, K.C.; Weimer, J.; Sandberg, H.; Johansson, K.H. Scheduling smart home appliances using mixed integer linear programming. In Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), Orlando, FL, USA, 12–15 December 2011; pp. 5144–5149. [Google Scholar]
- Tischer, H.; Verbic, G. Towards a smart home energy management system-a dynamic programming approach. In Proceedings of the Innovative Smart Grid Technologies Asia (ISGT), Jeddah, Saudi Arabia, 17–20 December 2011; pp. 1–7. [Google Scholar]
- Mohsenian-Rad, A.H.; Wong, V.W.; Jatskevich, J.; Schober, R. Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid. In Proceedings of the Innovative Smart Grid Technologies (ISGT), Gaithersburg, MD, USA, 19–21 January 2010; pp. 1–6. [Google Scholar]
- Fernandes, F.; Sousa, T.; Silva, M.; Morais, H.; Vale, Z.; Faria, P. Genetic algorithm methodology applied to intelligent house control. In Proceedings of the 2011 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG), Paris, France, 10–16 April 2011; pp. 1–8. [Google Scholar]
- Del Valle, Y.; Venayagamoorthy, G.K.; Mohagheghi, S.; Hernandez, J.C.; Harley, R.G. Particle swarm optimization: Basic concepts, variants and applications in power systems. IEEE Trans. Evol. Comput.
**2008**, 12, 171–195. [Google Scholar] [CrossRef] - Bayraktar, Z.; Komurcu, M.; Werner, D.H. Wind Driven Optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetics. In Proceedings of the Antennas and Propagation Society International Symposium (APS/URSI), Toronto, ON, Canada, 11–17 July 2010; pp. 1–4. [Google Scholar]
- Ali, E.S.; Abd-Elazim, S.M. Bacteria foraging optimization algorithm based load frequency controller for interconnected power system. Int. J. Electr. Power Energy Syst.
**2011**, 33, 633–638. [Google Scholar] [CrossRef] - Solar Energy. Available online: https://en.wikipedia.org/wiki/Solarenergy (accessed on 9 March 2017).
- Shirazi, E.; Jadid, S. Optimal residential appliance scheduling under dynamic pricing scheme via HEMDAS. Energy Build.
**2015**, 93, 40–49. [Google Scholar] [CrossRef] - Häberlin, H. Analysis of Loss Mechanisms in Crystalline Silicon Modules in Outdoor Operation. In Photovoltaics System Design and Practice; John Wiley & Sons: West Sussex, UK, 2012; pp. 538–542. [Google Scholar]
- Javaid, N.; Khan, I.; Ullah, M.N.; Mahmood, A.; Farooq, M.U. A survey of home energy management systems in future smart grid communications. In Proceedings of the Eighth International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA), Compiegne, France, 28–30 October 2013; pp. 459–464. [Google Scholar]
- 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] - VII. Parameters of GA. Available online: http://www.obitko.com/tutorials/genetic-algorithms/parameters.php (accessed on 11 March 2017).
- Soares, J.; Silva, M.; Sousa, T.; Vale, Z.; Morais, H. Distributed energy resource short-term scheduling using Signaled Particle Swarm Optimization. Energy
**2012**, 42, 466–476. [Google Scholar] [CrossRef] - Bayraktar, Z.; Komurcu, M.; Bossard, J.A.; Werner, D.H. The wind driven optimization technique and its application in electromagnetics. IEEE Trans. Antennas Propag.
**2013**, 61, 2745–2757. [Google Scholar] [CrossRef] - Passino, K. M. Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst.
**2012**, 22, 52–67. [Google Scholar] [CrossRef] - Kumar, K.S.; Jayabarathi, T. Power system reconfiguration and loss minimization for a distribution systems using bacterial foraging optimization algorithm. Int. J. Electr. Power Energy Syst.
**2012**, 36, 13–17. [Google Scholar] [CrossRef] - Majhi, R.; Panda, G.; Majhi, B.; Sahoo, G. Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques. Expert Syst. Appl.
**2009**, 36, 10097–10104. [Google Scholar] [CrossRef]

Infrastructure | Traditional Grid | SG |
---|---|---|

Power system | Centralized generation. Uni-directional flow of energy from utility to the consumers. | Decentralized generation. Two-way flow of energy between the utility and the prosumers. |

Power Losses | High power losses due to centralized structure and inadequate storage facilities. | Significantly reduces the power losses due to DG at distribution level (i.e., the DG eliminates the losses of transmission network). |

Information system | Aged metering and monitoring system | Advanced metering and monitoring system: AMI and supervisory control and data acquisition system (SCADA) |

Communication system | Wired technology | Both wired and wireless technologies |

ESSs | Main storage facility is pump-hydro power plants. | Facilitate the distributed ESSs integration |

RSERs | Mainly includes dispatchable RESs (Hydro-power plants) | Provides decentralized control for RSERs (solar, wind, tidal, geo-thermal and biomass energies etc.) |

Self-healing | Reacts to stop further damage. Emphasis is on protection of assets following system faults. | Automatically senses and reacts to actual and emerging contingencies. Focus is on prevention. |

Optimization of assets | Negligible incorporation of limited operational data with assets management processes and technologies. Time based maintenance. | Greatly expanded sensing and measurement of grid conditions, grid technologies deeply integrated with assets management processes to effectively manage assets and costs. Condition based maintenance. |

Consumer engagement | No proper involvement of the consumers in DSM and DR activities (i.e., no mechanism to send the varying electricity prices to the consumers in realtime and forced load shedding is carried out to maintain the balance between demand and supply) | Provides dynamic pricing, net metering and other incentive based schemes. |

Power Quality | Focus only on the reduction of failures and interruptions. | Ensure the quality of electricity for the smooth operations of sensitive electronics devices/equipments. |

Technique | Domain | Desired Objective | Findings | Remarks |
---|---|---|---|---|

LP [16] | REMS | Reduction of electricity bill and PAR | Objectives are achieved via charging ESS from grid in off-peak hours and then discharging it in peak hours | RES has not been utilized |

ILP [17] | Appliances scheduling and integration of RES | Reduction of electricity bill and peak load | A significant reduction bill and peak loads is achieved via RES integration and optimizing energy consumption pattern | UC and ESS is not considered |

MILP [18] | HEMS, and integration as well as grid interconnection of RES | Reduction of cost and PAR via RES utilization | Cost reduction is achieved | Infeasible for small scale residential consumers |

LSA-ANN, and PSO-ANN [19] | HEMS, and appliances scheduling | Cost reduction and comparison of LSA-ANN and PSO-ANN | LNA-ANN outperformed the PSO-ANN, and significantly reduce the electricity bill | UC and PAR reduction is not considered |

PSO, WDO, and K-WDO [20] | Appliances scheduling | Minimization of electricity cost and maximization of UC | A favorable trade-off between the cost and UC is achieved, and K-WDO performs better than other algorithms | RES is not exploited |

RL [21] | Fully-automated EMS | Optimization of appliances operating time, and reduction of PAR | Peaks formation is avoided, and cost reduction is achieved | UC and RES is not considered |

GA [22] | REMS | Electricity cost and PAR reduction | New peaks formation is avoided by dividing the appliances into clusters | UC is compromised, and RES is not considered |

GA, and PSO [23] | Appliances scheduling | Optimization of appliances operating time to pay minimum electricity bill | GA-based scheduling achieves the desired objectives with less computational cost and efforts | UC is compromised at achieving the minimum electricity bill |

GA [24] | HEMS | Reduction of electricity bill and PAR | Hybrid of RTP and ToUP is ued to avoid the peaks formation | UC is not considered |

Greedy iterative algorithm [25] | EMS, and optimization of grid operation | Optimization of consumers energy consumption pattern for grid station stability | RTP signal is used an invisible hand to optimized the energy consumption pattern | UC is compromised |

IPSO [26] | Grid station stability | Reduction of load in peak hours | The desired objective is achieved by rejecting extra load requests in peak hours | Only passive appliances are considered, and UC is compromised. |

Case | Discerption |
---|---|

1 | $Loa{d}_{min}$, $Pric{e}_{min}$ |

2 | $Loa{d}_{min}$, $Pric{e}_{max}$ |

3 | $Loa{d}_{max}$, $Pric{e}_{min}$ |

4 | $Loa{d}_{max}$, $Pric{e}_{max}$ |

Shiftable Loads | Non-Shiftable Loads |
---|---|

Washing machine | Personal computers |

Air conditioner | Security cameras |

Clothes dryer | Microwave oven |

Water heater | Refrigerator |

Dish washer | Television |

ESS | Lights |

Parameters | Value |
---|---|

Number of iterations | 500 |

Populationsize | 200 |

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

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

n | 11 |

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

Number of iterations | 300 |

Swarmsize | 200 |

${v}_{max}$ | 4 |

${v}_{min}$ | −4 |

${w}_{i}$ | 2 |

${w}_{f}$ | 0.4 |

${c}_{1}$ | 2 |

${c}_{2}$ | 2 |

n | 11 |

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

Number of iterations | 500 |

Populationsize | 200 |

dimMin | $-5$ |

dimMax | 5 |

${v}_{min}$ | $-0.3$ |

${v}_{max}$ | 0.3 |

RT | 3 |

n | 11 |

g | 0.2 |

$\alpha $ | 0.4 |

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

Maximungeneration | 500 |

${N}_{e}$ | 24 |

${N}_{r}$ | 5 |

${N}_{c}$ | 5 |

${N}_{p}$ | 30 |

${N}_{s}$ | 2 |

${C}_{i}$ | 0.01 |

${P}_{ed}$ | 0.5 |

$\theta $ | 0.5 |

Algorithm | Computational Time (Sec) |
---|---|

GA | 2.21 |

BPSO | 1.86 |

WDO | 2.84 |

BFO | 3.59 |

HGPO | 1.71 |

Load Type | Appliances | Power Rating (kW) | Daily Usage (Hours) |
---|---|---|---|

Shiftable | ESS | 3 | – |

Washing machine | 0.8 | 5 | |

Air conditioner | 1.3 | 10 | |

Clothes dryer | 0.7 | 4 | |

Water heater | 1 | 8 | |

Dish washer | 0.2 | 3 | |

Un-shiftable | Personal computers | 0.2 | 18 |

Security cameras | 0.1 | 24 | |

Microwave oven | 0.5 | 7 | |

Refrigerator | 0.9 | 20 | |

Television | 0.2 | 8 | |

Lights | 0.1 | 6 |

Load | Range (kW) |
---|---|

High peak | 5 and above |

Peak | 4–4.9 |

Moderate | 2–3.9 |

Minimum | 1–1.9 |

Negligible | 0.1–0.9 |

Scenario | Cost (Cents) | Difference (Cents) | Reduction (%) |
---|---|---|---|

Unscheduled | 862.66 | - | - |

Unscheduled + RES | 713.78 | 148.88 | 17.25% |

Unscheduled + RES + ESS | 690.63 | 172.03 | 19.94% |

Scenario | PAR | Difference | Reduction (%) |
---|---|---|---|

Unscheduled | 3.0482 | - | - |

Unscheduled + RES | 2.5702 | 0.478 | 15.68% |

Unscheduled + RES + ESS | 2.3912 | 0.657 | 21.55% |

Scheduling Technique | Cost (Cents) | Difference (Cents) | Reduction (%) |
---|---|---|---|

Unscheduled + RES + ESS | 690.63 | - | - |

GA scheduled + RES + ESS | 622.97 | 67.66 | 9.80% |

BPSO scheduled + RES + ESS | 555.32 | 135.31 | 19.60% |

WDO scheduled + RES + ESS | 584.24 | 106.39 | 15.40% |

BFO scheduled + RES + ESS | 581.56 | 109.07 | 15.80% |

HGPO scheduled + RES + ESS | 517.15 | 173.48 | 25.12% |

Scheduling Technique | PAR | Difference | Reduction (%) |
---|---|---|---|

Unscheduled + RES + ESS | 2.391 | - | - |

GA scheduled + RES + ESS | 2.054 | 0.337 | 14.09% |

BPSO scheduled + RES + ESS | 2.312 | 0.079 | 3.30% |

WDO scheduled + RES + ESS | 2.350 | 0.041 | 22.10% |

BFO scheduled + RES + ESS | 1.589 | 0.802 | 33.54% |

HGPO scheduled + RES + ESS | 1.796 | 0.595 | 24.88 % |

© 2017 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**

Ahmad, A.; Khan, A.; Javaid, N.; Hussain, H.M.; Abdul, W.; Almogren, A.; Alamri, A.; Azim Niaz, I. An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources. *Energies* **2017**, *10*, 549.
https://doi.org/10.3390/en10040549

**AMA Style**

Ahmad A, Khan A, Javaid N, Hussain HM, Abdul W, Almogren A, Alamri A, Azim Niaz I. An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources. *Energies*. 2017; 10(4):549.
https://doi.org/10.3390/en10040549

**Chicago/Turabian Style**

Ahmad, Adnan, Asif Khan, Nadeem Javaid, Hafiz Majid Hussain, Wadood Abdul, Ahmad Almogren, Atif Alamri, and Iftikhar Azim Niaz. 2017. "An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources" *Energies* 10, no. 4: 549.
https://doi.org/10.3390/en10040549