# 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 |

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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