A Multi-Objective Scheduling Technique for Home Energy Management System

In this paper, we proposed a home energy management system (HEMS) that includes photovoltaic (PV), electric vehicle (EV), and energy storage systems (ESS). The proposed HEMS fully utilizes the PV power in operating domestic appliances and charging EV/ESS. The surplus power is fed back to the grid to achieve economic benefits. A novel charging and discharging scheme of EV/ESS is presented to minimize the energy cost, control the maximum load demand, increase the battery life, and satisfy the user’s-traveling needs. The EV/ESS charges during low pricing periods and discharges in high pricing periods. In the proposed method, a multi-objective problem is formulated, which simultaneously minimizes the energy cost, peak to average ratio (PAR), and customer dissatisfaction. The multi-objective optimization is solved using binary particle swarm optimization (BPSO). The results clearly show that it minimizes the operating cost from 402.89 cents to 191.46 cents, so that a reduction of 52.47% is obtained. Moreover, it reduces the PAR and discomfort index by 15.11% and 16.67%, respectively, in a 24 h time span. Furthermore, the home has home to grid (H2G) capability as it sells the surplus energy, and the total cost is further reduced by 29.41%.

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A Multi-Objective Model for Home Energy Management System Self-Scheduling using the Epsilon-Constraint Method

2020 IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG) ◽

10.1109/cpe-powereng48600.2020.9161526 ◽

2020 ◽

Cited By ~ 1

Author(s):

Mohammad Javadi ◽

Mohamed Lotfi ◽

Gerardo J. Osorio ◽

Abdelrahman Ashraf ◽

Ali Esmaeel Nezhad ◽

...

Keyword(s):

Energy Management ◽

Management System ◽

Energy Management System ◽

Multi Objective ◽

Home Energy Management ◽

Objective Model ◽

Home Energy Management System ◽

Constraint Method

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A multi-objective home energy management system based on internet of things and optimization algorithms

Journal of Building Engineering ◽

10.1016/j.jobe.2020.101603 ◽

2021 ◽

Vol 33 ◽

pp. 101603 ◽

Cited By ~ 1

Author(s):

Xiuwang Wang ◽

Xinna Mao ◽

Hossein Khodaei

Keyword(s):

Internet Of Things ◽

Energy Management ◽

Management System ◽

Optimization Algorithms ◽

Energy Management System ◽

Multi Objective ◽

Home Energy Management ◽

Home Energy Management System

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Economically Effective and Intelligently Responsive Home Energy Management System

Mehran University Research Journal of Engineering and Technology ◽

10.22581/muet1982.1903.06 ◽

2019 ◽

Vol 38 (3) ◽

pp. 581-598

Author(s):

Muhammad Irfan Mushtaq ◽

Asif Siddiq ◽

Muhammad Owais Tariq ◽

Akbar Ali ◽

Anum Zahra

Keyword(s):

Energy Management ◽

Management System ◽

Energy Demand ◽

Peak Time ◽

Energy Management System ◽

Multi Objective Optimization ◽

Multi Objective ◽

Home Energy Management ◽

Diesel Generators ◽

Home Energy Management System

Energy management in home is one of the major issue now-a-days. There are different types of load like shiftable, non-shiftable, seasonal loads and auxiliary loads. In this research article, an energy management system is proposed for home which helps to schedule different loads on the basis of their types and price. It will help to minimize the cost of electricity by shifting load from peak time to off peak time. Emission will be minimized by charging penalty by adopting multi-objective optimization. Each source of energy has its own price of penalty with respect to time. Penalty is charged to minimize the use of sources like commercial supply and diesel generators which emits hazardous gases. In proposed model, user will get electricity from commercial supply, diesel generators and solar panels to provide continuous supply of electricity to fulfill the energy demand. The shiftable loads will be shifted from peak time to off peak time and higher price source to lower price source to minimize the overall price. In this research, we have proposed an EEIR (Economically Effective and Intelligently Responsive) HEMS (Home Energy Management System) by solving multi-objective optimization problem from BILP (Binary Integer Linear Programming) using branch and bound algorithm.

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A multi-objective home energy management system for explicit cost-comfort analysis considering appliance category-based discomfort models and demand response programs

Energy and Buildings ◽

10.1016/j.enbuild.2021.110868 ◽

2021 ◽

Vol 240 ◽

pp. 110868

Author(s):

Oğuzkağan Alıç ◽

Ümmühan Başaran Filik

Keyword(s):

Energy Management ◽

Demand Response ◽

Management System ◽

Energy Management System ◽

Multi Objective ◽

Home Energy Management ◽

Home Energy Management System ◽

Demand Response Programs

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Home Energy Management System Embedded with a Multi-Objective Demand Response Optimization Model to Benefit Customers and Operators

Energies ◽

10.3390/en14020257 ◽

2021 ◽

Vol 14 (2) ◽

pp. 257

Author(s):

Aya Amer ◽

Khaled Shaban ◽

Ahmed Gaouda ◽

Ahmed Massoud

Keyword(s):

Energy Management ◽

Demand Response ◽

Management System ◽

Comfort Level ◽

Energy Resources ◽

Energy Management System ◽

Distribution Transformer ◽

Multi Objective ◽

Home Energy Management ◽

Home Energy Management System

This paper proposes a Home Energy Management System (HEMS) that optimizes the load demand and distributed energy resources. The optimal demand/generation profile is presented while considering utility price signal, customer satisfaction, and distribution transformer condition. The electricity home demand considers electric vehicles (EVs), Battery Energy Storage Systems (BESSs), and all types of non-shiftable, shiftable, and controllable appliances. Furthermore, PV-based renewable energy resources, EVs, and BESSs are utilized as sources of generated power during specific time intervals. In this model, customers can only perform Demand Response (DR) actions with contracts with utility operators. A multi-objective demand/generation response is proposed to optimize the scheduling of various loads/supplies based on the pricing schemes. The customers’ behavior comfort level and a degradation cost that reflects the distribution transformer Loss-of-Life (LoL) are integrated into the multi-objective optimization problem. Simulation results demonstrate the mutual benefits that the proposed HEMS provides to customers and utility operators by minimizing electricity costs while meeting customer comfort needs and minimizing transformer LoL to enhance operators’ assets. The results show that the electricity operation cost and demand peak are reduced by 31% and 18%, respectively, along with transformer LoL % which is reduced by 28% compared with the case when no DR was applied.

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