A Survey of 'User Comfort' in Home Energy Management Systems in Smart Grid

2015 ◽  
Author(s):  
A. Mahmood ◽  
A. Ahmad ◽  
H.T. Javed ◽  
Z. Mehmood ◽  
Z.A. Khan ◽  
...  
Keyword(s):  
Smart Grid ◽  
Energy Management ◽  
Management Systems ◽  
Energy Management Systems ◽  
Home Energy Management ◽  
User Comfort

scholarly journals An Overview of the Building Energy Management System Considering the Demand Response Programs, Smart Strategies and Smart Grid

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3299 ◽  
Author(s):  
Mohammad Shakeri ◽  
Jagadeesh Pasupuleti ◽  
Nowshad Amin ◽  
Md. Rokonuzzaman ◽  
Foo Wah Low ◽  
...  
Keyword(s):  
Smart Grid ◽  
Energy Management ◽  
Demand Response ◽  
Smart Grids ◽  
Management System ◽  
Management Systems ◽  
Energy Management System ◽  
Energy Management Systems ◽  
Home Energy Management ◽  
Demand Response Programs

Electricity demand is increasing, as a result of increasing consumers in the electricity market. By growing smart technologies such as smart grid and smart energy management systems, customers were given a chance to actively participate in demand response programs (DRPs), and reduce their electricity bills as a result. This study overviews the DRPs and their practices, along with home energy management systems (HEMS) and load management techniques. The paper provides brief literature on HEMS technologies and challenges. The paper is organized in a way to provide some technical information about DRPs and HEMS to help the reader understand different concepts about the smart grid, and be able to compare the essential concerns about the smart grid. The article includes a brief discussion about DRPs and their importance for the future of energy management systems. It is followed by brief literature about smart grids and HEMS, and a home energy management system strategy is also discussed in detail. The literature shows that storage devices have a huge impact on the efficiency and performance of energy management system strategies.

An intelligent system architecture in home energy management systems (HEMS) for efficient demand response in smart grid

2017 ◽  
Vol 138 ◽  
pp. 154-164 ◽  
Author(s):  
Mohammad Shakeri ◽  
Mohsen Shayestegan ◽  
Hamza Abunima ◽  
S.M. Salim Reza ◽  
M. Akhtaruzzaman ◽  
...  
Keyword(s):  
Smart Grid ◽  
Energy Management ◽  
Demand Response ◽  
System Architecture ◽  
Intelligent System ◽  
Management Systems ◽  
Energy Management Systems ◽  
Home Energy Management

scholarly journals Home Energy Management System Incorporating Heat Pump Using Real Measured Data

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2937 ◽  
Author(s):  
Cao ◽  
O’Rourke ◽  
Lyons ◽  
Han
Keyword(s):  
Energy Management ◽  
Heat Pump ◽  
Measured Data ◽  
Management Systems ◽  
End User ◽  
Energy Management Systems ◽  
Comfort Levels ◽  
Home Energy Management ◽  
The Cost ◽  
User Comfort

The demand for electricity has been rising significantly over the past years and it is expected to rise further in the coming years due to economic and societal development. Smart grid technology is being developed in order to meet the rising electricity requirement. In order for the smart grid to perform its full functions, the Energy Management Systems (EMSs), especially Home Energy Management Systems (HEMS) are essential. It is necessary to understand the energy demand of the loads and the energy supply either from the national grid or from renewable energy technologies. To facilitate the Demand Side Management (DSM), Heat Pumps (HP) and air conditioning systems are often utilised for heating and cooling in residential houses due to their high-efficiency power output and low CO2 emissions. This paper presents a program for a HEMS using a Particle Swarm Optimisation (PSO) algorithm. A HP is used as the load and the aim of the optimisation program is to minimise the operational cost, i.e., the cost of electricity, while maintaining end-user comfort levels. This paper also details an indoor thermal model for temperature update in the heat pump control program. Real measured data from the UK Government’s Renewable Heat Premium Payment (RHPP) scheme was utilised to generate characteristic curves and equations that can represent the data. This paper compares different PSO variants with standard PSO and the unscheduled case calculated from the data for five winter days in 2019. Among all chosen algorithms, the Crossover Subswarm PSO (CSPSO) achieved an average saving of 25.61% compared with the cost calculated from the measured data with a short search time of 1576 ms for each subswarm. It is clear from this work that there is significant scope to reduce the cost of operating a HP while maintaining end user comfort levels.

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