Preferred attributes of home energy management products for smart grids - results of a design study and related user survey

2018 ◽  
Vol 16 (2) ◽  
pp. 99 ◽  
Author(s):  
Uchechi Obinna ◽  
Peter Joore ◽  
Linda Wauben ◽  
Angèle Reinders
Keyword(s):  
Energy Management ◽  
Smart Grids ◽  
User Survey ◽  
Design Study ◽  
Home Energy Management

scholarly journals A Survey of Communications and Networking Technologies for Energy Management in Buildings and Home Automation

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Aravind Kailas ◽  
Valentina Cecchi ◽  
Arindam Mukherjee
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Energy Management ◽  
Smart Grids ◽  
Energy Demand ◽  
Demand Curve ◽  
Future Research ◽  
Energy Awareness ◽  
Home Energy Management ◽  
Networking Technologies

With the exploding power consumption in private households and increasing environmental and regulatory restraints, the need to improve the overall efficiency of electrical networks has never been greater. That being said, the most efficient way to minimize the power consumption is by voluntary mitigation of home electric energy consumption, based on energy-awareness and automatic or manual reduction of standby power of idling home appliances. Deploying bi-directional smart meters and home energy management (HEM) agents that provision real-time usage monitoring and remote control, will enable HEM in “smart households.” Furthermore, the traditionally inelastic demand curve has began to change, and these emerging HEM technologies enable consumers (industrial to residential) to respond to the energy market behavior to reduce their consumption at peak prices, to supply reserves on a as-needed basis, and to reduce demand on the electric grid. Because the development of smart grid-related activities has resulted in an increased interest in demand response (DR) and demand side management (DSM) programs, this paper presents some popular DR and DSM initiatives that include planning, implementation and evaluation techniques for reducing energy consumption and peak electricity demand. The paper then focuses on reviewing and distinguishing the various state-of-the-art HEM control and networking technologies, and outlines directions for promoting the shift towards a society with low energy demand and low greenhouse gas emissions. The paper also surveys the existing software and hardware tools, platforms, and test beds for evaluating the performance of the information and communications technologies that are at the core of future smart grids. It is envisioned that this paper will inspire future research and design efforts in developing standardized and user-friendly smart energy monitoring systems that are suitable for wide scale deployment in homes.

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.

Development of a mobile application for home energy management in smart grids

2015 ◽  
Author(s):  
Imane L'hadi ◽  
Mohammed Bakr Sikal ◽  
Sarah Lahtani ◽  
Ahmed Khallaayoun ◽  
Rachid Lghoul
Keyword(s):  
Energy Management ◽  
Smart Grids ◽  
Mobile Application ◽  
Home Energy Management

scholarly journals Demand Side Management Techniques for Home Energy Management Systems for Smart Cities

2021 ◽  
Vol 13 (21) ◽  
pp. 11740
Author(s):  
Muhammad Majid Hussain ◽  
Rizwan Akram ◽  
Zulfiqar Ali Memon ◽  
Mian Hammad Nazir ◽  
Waqas Javed ◽  
...  
Keyword(s):  
Energy Management ◽  
Smart Grids ◽  
Management System ◽  
Smart Cities ◽  
Demand Side Management ◽  
Management Systems ◽  
Demand Side ◽  
Pv System ◽  
Energy Management Systems ◽  
Home Energy Management

In this paper, three distinct distributed energy resources (DERs) modules have been built based on demand side management (DSM), and their use in power management of dwelling in future smart cities has been investigated. The investigated modules for DERs system are: incorporation of load shedding, reduction of grid penetration with renewable energy systems (RES), and implementation of home energy management systems (HEMS). The suggested approaches offer new potential for improving demand side efficiency and helping to minimize energy demand during peak hours. The main aim of this work was to investigate and explore how a specific DSM strategy for DER may assist in reducing energy usage while increasing efficiency by utilizing new developing technology. The Electrical Power System Analysis (ETAP) software was used to model and assess the integration of distributed generation, such as RES, in order to use local power storage. An energy management system has been used to evaluate a PV system with an individual household load, which proved beneficial when evaluating its potential to generate about 20–25% of the total domestic load. In this study, we have investigated how smart home appliances’ energy consumption may be minimized and explained why a management system is required to optimally utilize a PV system. Furthermore, the effect of integration of wind turbines to power networks to reduce the load on the main power grid has also been studied. The study revealed that smart grids improve energy efficiency, security, and management whilst creating environmental awareness for consumers with regards to power usage.

scholarly journals State-of-the-Art Artificial Intelligence Techniques for Distributed Smart Grids: A Review

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1030 ◽  
Author(s):  
Syed Saqib Ali ◽  
Bong Jun Choi
Keyword(s):  
Artificial Intelligence ◽  
Smart Grid ◽  
Power System ◽  
Energy Management ◽  
Smart Grids ◽  
State Of The Art ◽  
Energy Resources ◽  
Distributed Energy ◽  
Artificial Intelligence Techniques ◽  
Home Energy Management

The power system worldwide is going through a revolutionary transformation due to the integration with various distributed components, including advanced metering infrastructure, communication infrastructure, distributed energy resources, and electric vehicles, to improve the reliability, energy efficiency, management, and security of the future power system. These components are becoming more tightly integrated with IoT. They are expected to generate a vast amount of data to support various applications in the smart grid, such as distributed energy management, generation forecasting, grid health monitoring, fault detection, home energy management, etc. With these new components and information, artificial intelligence techniques can be applied to automate and further improve the performance of the smart grid. In this paper, we provide a comprehensive review of the state-of-the-art artificial intelligence techniques to support various applications in a distributed smart grid. In particular, we discuss how artificial techniques are applied to support the integration of renewable energy resources, the integration of energy storage systems, demand response, management of the grid and home energy, and security. As the smart grid involves various actors, such as energy produces, markets, and consumers, we also discuss how artificial intelligence and market liberalization can potentially help to increase the overall social welfare of the grid. Finally, we provide further research challenges for large-scale integration and orchestration of automated distributed devices to realize a truly smart grid.

scholarly journals Large-Scale Home Energy Management Using Entropy-Based Collective Multiagent Deep Reinforcement Learning Framework

2019 ◽  
Author(s):  
Yaodong Yang ◽  
Jianye Hao ◽  
Yan Zheng ◽  
Chao Yu
Keyword(s):  
Reinforcement Learning ◽  
Energy Management ◽  
Smart Grids ◽  
Large Scale ◽  
Energy Generation ◽  
Distributed Energy ◽  
Electricity Cost ◽  
Home Energy Management ◽  
Distributed Energy Generation ◽  
And Storage

Smart grids are contributing to the demand-side management by integrating electronic equipment, distributed energy generation and storage and advanced meters and controllers. With the increasing adoption of electric vehicles and distributed energy generation and storage systems, residential energy management is drawing more and more attention, which is regarded as being critical to demand-supply balancing and peak load reduction. In this paper, we focus on a microgrid scenario in which modern homes interact together under a large-scale setting to better optimize their electricity cost. We first make households form a group with an economic stimulus. Then we formulate the energy expense optimization problem of the household community as a multi-agent coordination problem and present an Entropy-Based Collective Multiagent Deep Reinforcement Learning (EB-C-MADRL) framework to address it. Experiments with various real-world data demonstrate that EB-C-MADRL can reduce both the long-term group power consumption cost and daily peak demand effectively compared with existing approaches.

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