Special Issue "Smart Home and Energy Management Systems"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy".

Deadline for manuscript submissions: closed (31 January 2018)

Special Issue Editor

Guest Editor
Prof. Dr. João P. S. Catalão

Faculty of Engineering of the University of Porto, R. Dr. Roberto Frias, 4200-465 Porto, Portugal
Website | E-Mail
Interests: power system operations and planning; hydro and thermal scheduling; wind and price forecasting; distributed renewable generation; demand response and smart grids

Special Issue Information

Dear Colleagues,

We are inviting submissions to the Special Issue on Smart Home and Energy Management Systems.

Energy efficiency is one of the central issues in the development of smart homes. Intelligent energy management systems, encompassing advanced information and communication technologies, automation and control, will enable energy savings without decreasing comfort levels. Real-time and stochastic optimization methods; advanced heuristics, distributed and predictive control; Internet of Energy and other smart solutions will unlock the full potential of smart homes. Techniques to forecast energy consumption; microgeneration and self-consumption management solutions; small-scale energy storage deployment; thermal comfort conditions and environmental quality research; demand-side applications and charging plug-in electric vehicles at home, are attracting more and more interest from the research community.

In this Special Issue, we invite submissions exploring cutting-edge research and recent advances in the fields of Smart Home and Energy Management Systems. Both theoretical and experimental studies are welcome, as well as comprehensive review and survey papers.

Prof. Dr. João P. S. Catalão
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Smart home

  • Energy efficiency

  • Management systems

  • Optimization and control

Published Papers (8 papers)

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Research

Open AccessArticle Machine Learning for Identifying Demand Patterns of Home Energy Management Systems with Dynamic Electricity Pricing
Appl. Sci. 2017, 7(11), 1160; doi:10.3390/app7111160
Received: 16 October 2017 / Revised: 4 November 2017 / Accepted: 6 November 2017 / Published: 12 November 2017
Cited by 1 | PDF Full-text (2255 KB) | HTML Full-text | XML Full-text
Abstract
Energy management plays a crucial role in providing necessary system flexibility to deal with the ongoing integration of volatile and intermittent energy sources. Demand Response (DR) programs enhance demand flexibility by communicating energy market price volatility to the end-consumer. In such environments, home
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Energy management plays a crucial role in providing necessary system flexibility to deal with the ongoing integration of volatile and intermittent energy sources. Demand Response (DR) programs enhance demand flexibility by communicating energy market price volatility to the end-consumer. In such environments, home energy management systems assist the use of flexible end-appliances, based upon the individual consumer’s personal preferences and beliefs. However, with the latter heterogeneously distributed, not all dynamic pricing schemes are equally adequate for the individual needs of households. We conduct one of the first large scale natural experiments, with multiple dynamic pricing schemes for end consumers, allowing us to analyze different demand behavior in relation with household attributes. We apply a spectral relaxation clustering approach to show distinct groups of households within the two most used dynamic pricing schemes: Time-Of-Use and Real-Time Pricing. The results indicate that a more effective design of smart home energy management systems can lead to a better fit between customer and electricity tariff in order to reduce costs, enhance predictability and stability of load and allow for more optimal use of demand flexibility by such systems. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems)
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Open AccessArticle A Robust Optimization Strategy for Domestic Electric Water Heater Load Scheduling under Uncertainties
Appl. Sci. 2017, 7(11), 1136; doi:10.3390/app7111136
Received: 3 October 2017 / Revised: 31 October 2017 / Accepted: 1 November 2017 / Published: 5 November 2017
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Abstract
In this paper, a robust optimization strategy is developed to handle the uncertainties for domestic electric water heater load scheduling. At first, the uncertain parameters, including hot water demand and ambient temperature, are described as the intervals, and are further divided into different
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In this paper, a robust optimization strategy is developed to handle the uncertainties for domestic electric water heater load scheduling. At first, the uncertain parameters, including hot water demand and ambient temperature, are described as the intervals, and are further divided into different robust levels in order to control the degree of the conservatism. Based on this, traditional load scheduling problem is rebuilt by bringing the intervals and robust levels into the constraints, and are thus transformed into the equivalent deterministic optimization problem, which can be solved by existing tools. Simulation results demonstrate that the schedules obtained under different robust levels are of complete robustness. Furthermore, in order to offer users the most optimal robust level, the trade-off between the electricity bill and conservatism degree are also discussed. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems)
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Open AccessArticle Coordinated Control of the Energy Router-Based Smart Home Energy Management System
Appl. Sci. 2017, 7(9), 943; doi:10.3390/app7090943
Received: 2 August 2017 / Revised: 3 September 2017 / Accepted: 12 September 2017 / Published: 13 September 2017
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Abstract
Home area energy networks will be an essential part of the future Energy Internet in terms of energy saving, demand-side management and stability improvement of the distribution network, while an energy router will be the perfect choice to serve as an intelligent and
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Home area energy networks will be an essential part of the future Energy Internet in terms of energy saving, demand-side management and stability improvement of the distribution network, while an energy router will be the perfect choice to serve as an intelligent and multi-functional energy interface between the home area energy network and power grid. This paper elaborates on the design, analysis and implementation of coordinated control of the low-voltage energy router-based smart home energy management system (HEMS). The main contribution of this paper is to develop a novel solution to make the energy router technically feasible and practical for the HEMS to make full use of the renewable energy sources (RESs), while maintaining “operational friendly and beneficial” to the power grid. The behaviors of the energy router-based HEMS in correlation with the power grid are investigated, then the coordinated control scheme composed of a reference voltage and current compensation strategy and a fuzzy logic control-based power management strategy is developed. The system model is built on the MATLAB/Simulink platform, simulation results have demonstrated that the presented control scheme is a strong performer in making full use of the RES generations for the HEMS while maintaining the operational stability of the whole system, as well as in collaboration with the power grid to suppress the impact of RES output fluctuations and load consumption variations. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems)
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Open AccessArticle A Power Processing Circuit for Indoor Wi-Fi Energy Harvesting for Ultra-Low Power Wireless Sensors
Appl. Sci. 2017, 7(8), 827; doi:10.3390/app7080827
Received: 13 July 2017 / Revised: 6 August 2017 / Accepted: 7 August 2017 / Published: 11 August 2017
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Abstract
This article proposes a complete power processing circuit for an indoor 2.45 GHz Wi-Fi energy harvesting system. The proposed power processing circuit works by using power harvested from indoor Wi-Fi transmitters. The overall system of this work is simplified as an equivalent circuit
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This article proposes a complete power processing circuit for an indoor 2.45 GHz Wi-Fi energy harvesting system. The proposed power processing circuit works by using power harvested from indoor Wi-Fi transmitters. The overall system of this work is simplified as an equivalent circuit and analyzed mathematically. A two-port network is analyzed in formulating the relevant equations of the equivalent circuit. The importance of matching the impedance of a harvesting antenna to the rectifier circuit is highlighted by using simulation analysis, and it is shown that the impedance matching for both components has satisfied the conditions for a high sensitivity circuit and radio frequency-to-direct current (RF-to-DC) power conversion. Actual experiments showed that the proposed power processing circuit could operate with an incident power as low as −50 dBm. It has been found that the proposed harvesting system stored 0.11 J in a 200 mF supercapacitor as the storage device in 20 hours of the experimentation periods. Moreover, actual results for the overall energy harvesting system is compared with previous research, and it has been found that the proposed system has advantages over the listed works. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems)
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Open AccessArticle A Real Model of a Micro-Grid to Improve Network Stability
Appl. Sci. 2017, 7(8), 757; doi:10.3390/app7080757
Received: 30 June 2017 / Revised: 14 July 2017 / Accepted: 22 July 2017 / Published: 26 July 2017
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Abstract
This paper discusses the smart energy model of a smart grid using a significant share of renewable energy sources combined with intelligent control that processes information from a smart metering subsystem. An algorithm to manage the microgrid via the demand-response strategy is proposed,
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This paper discusses the smart energy model of a smart grid using a significant share of renewable energy sources combined with intelligent control that processes information from a smart metering subsystem. An algorithm to manage the microgrid via the demand-response strategy is proposed, accentuating the requirement that the total volume of energy produced from renewable sources is consumed. Thus, the system utilizes the maximum of renewable sources to reduce CO2 emissions. Another major benefit provided by the algorithm lies in applying the current weather forecast to predict the amount of energy in the grid; electricity can then be transferred between the local and the main backup batteries within the grid, and this option enables the control elements to prepare for a condition yet to occur. Individual parts of the grid are described in this research report together with the results provided by the relevant algorithm. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems)
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Open AccessArticle Modified Chaos Particle Swarm Optimization-Based Optimized Operation Model for Stand-Alone CCHP Microgrid
Appl. Sci. 2017, 7(8), 754; doi:10.3390/app7080754
Received: 21 June 2017 / Revised: 17 July 2017 / Accepted: 19 July 2017 / Published: 25 July 2017
Cited by 1 | PDF Full-text (2400 KB) | HTML Full-text | XML Full-text
Abstract
The optimized dispatch of different distributed generations (DGs) in stand-alone microgrid (MG) is of great significance to the operation’s reliability and economy, especially for energy crisis and environmental pollution. Based on controllable load (CL) and combined cooling-heating-power (CCHP) model of micro-gas turbine (MT),
[...] Read more.
The optimized dispatch of different distributed generations (DGs) in stand-alone microgrid (MG) is of great significance to the operation’s reliability and economy, especially for energy crisis and environmental pollution. Based on controllable load (CL) and combined cooling-heating-power (CCHP) model of micro-gas turbine (MT), a multi-objective optimization model with relevant constraints to optimize the generation cost, load cut compensation and environmental benefit is proposed in this paper. The MG studied in this paper consists of photovoltaic (PV), wind turbine (WT), fuel cell (FC), diesel engine (DE), MT and energy storage (ES). Four typical scenarios were designed according to different day types (work day or weekend) and weather conditions (sunny or rainy) in view of the uncertainty of renewable energy in variable situations and load fluctuation. A modified dispatch strategy for CCHP is presented to further improve the operation economy without reducing the consumers’ comfort feeling. Chaotic optimization and elite retention strategy are introduced into basic particle swarm optimization (PSO) to propose modified chaos particle swarm optimization (MCPSO) whose search capability and convergence speed are improved greatly. Simulation results validate the correctness of the proposed model and the effectiveness of MCPSO algorithm in the optimized operation application of stand-alone MG. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems)
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Open AccessArticle Design and Implementation of an Interactive Interface for Demand Response and Home Energy Management Applications
Appl. Sci. 2017, 7(6), 641; doi:10.3390/app7060641
Received: 24 April 2017 / Revised: 1 June 2017 / Accepted: 16 June 2017 / Published: 21 June 2017
Cited by 2 | PDF Full-text (6972 KB) | HTML Full-text | XML Full-text
Abstract
Demand response (DR) implementations have recently found wide application areas in the context of smart grids. The effectiveness of these implementations is primarily based on the willingness of end-users to be involved in such programs. In this paper, an interactive and user-friendly interface
[...] Read more.
Demand response (DR) implementations have recently found wide application areas in the context of smart grids. The effectiveness of these implementations is primarily based on the willingness of end-users to be involved in such programs. In this paper, an interactive and user-friendly interface is presented in order to facilitate and accordingly to increase the participation of end-users in DR programs. The proposed interface has the capability of providing the targeted information about the DR events to end-users and system operators, as well as allowing end-users to interactively monitor and control the progress of their appliances. In addition to its benefits to system operators and thus to the improved operation of power systems, the proposed interface particularly aims to exploit the potential energy-related cost savings by providing the required information and resources to end-users via mobile phone. A separate interface apart from the mentioned end-user oriented interface has also been developed for the system operator to more effectively check the status of DR applications in detail. The capabilities of the proposed concept are evaluated in a real smart home in terms of various aspects. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems)
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Open AccessArticle Combined Operation of Electrical Loads, Air Conditioning and Photovoltaic-Battery Systems in Smart Houses
Appl. Sci. 2017, 7(5), 525; doi:10.3390/app7050525
Received: 11 April 2017 / Revised: 9 May 2017 / Accepted: 11 May 2017 / Published: 18 May 2017
Cited by 1 | PDF Full-text (4745 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, a novel Energy Management System (EMS) is proposed for a hybrid energy system with photovoltaic (PV) and energy storage system for a smart house. The EMS is designed to control the shiftable loads, the air conditioning and the electric storage
[...] Read more.
In this paper, a novel Energy Management System (EMS) is proposed for a hybrid energy system with photovoltaic (PV) and energy storage system for a smart house. The EMS is designed to control the shiftable loads, the air conditioning and the electric storage system. The aim is to reduce the electrical energy consumption cost without compromising the end-user comfort. Monte Carlo Simulation (MCS) is used to estimate the optimal size of the hybrid system considering energy saving and investment costs. Simulations results confirm the effectiveness of the proposed EMS in decreasing the electrical energy consumption and costs. The proposed method for the sizing of the hybrid system is also able to select the best size of the PV-battery system in smart houses. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems)
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