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 May 2018)

Special Issue Editor

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

FEUP and INESC TEC, 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 (17 papers)

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Research

Open AccessArticle Double-Consensus Based Distributed Optimal Energy Management for Multiple Energy Hubs
Appl. Sci. 2018, 8(9), 1412; https://doi.org/10.3390/app8091412
Received: 21 July 2018 / Revised: 15 August 2018 / Accepted: 17 August 2018 / Published: 21 August 2018
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Abstract
This paper presents a novel distributed double-consensus algorithm to solve the optimal energy management problem for multiple energy hubs interconnected with each other. The objective is achieved by establishing two interactive and paralleled consensus procedures modified by their corresponding feedback terms. Meanwhile, a
[...] Read more.
This paper presents a novel distributed double-consensus algorithm to solve the optimal energy management problem for multiple energy hubs interconnected with each other. The objective is achieved by establishing two interactive and paralleled consensus procedures modified by their corresponding feedback terms. Meanwhile, a novel projection operation method is proposed to map the infeasible values into the feasible operating region. The proposed algorithm can effectively handle the coupled variables problem existing in the objective function and constraint limits. Moreover, the optimality and convergence analysis are performed strictly under strong connectivity conditions only. Simulations performed on standard test cases are provided to illustrate the effectiveness of the proposed distributed algorithm. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems)
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Open AccessArticle A Stackelberg Game Approach for Price Response Coordination of Thermostatically Controlled Loads
Appl. Sci. 2018, 8(8), 1370; https://doi.org/10.3390/app8081370
Received: 27 May 2018 / Revised: 26 July 2018 / Accepted: 1 August 2018 / Published: 15 August 2018
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Abstract
In this paper, we study the demand response of the thermostatically controlled loads (TCLs) to control their set-point temperatures by considering the tradeoff between the electricity payment and TCL user’s comfort preference. Based upon the dynamics of the TCLs, we set up the
[...] Read more.
In this paper, we study the demand response of the thermostatically controlled loads (TCLs) to control their set-point temperatures by considering the tradeoff between the electricity payment and TCL user’s comfort preference. Based upon the dynamics of the TCLs, we set up the relationship between the set-point temperature and the energy demand. Then, we define a discomfort function with respect to the associated energy demand which represents the discomfort level of the set-point temperature. More specifically, the system is equipped with a coordinator named electric energy control center (EECC) which can buy energy resources from the electricity market and sell them to TCL users. Due to the interaction between EECC and TCL users, we formulate the specific energy trading process as a one-leader multiple-follower Stackelberg game. As the main contributions of this work, we show the existence and uniqueness of the equilibrium for the underlying Stackelberg games, and develop a DR algorithm based on the so-called Backward Induction to achieve the equilibrium. Several numerical simulations are presented to verify the developed results in this work. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems)
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Open AccessArticle Energy Management Strategy for the Hybrid Energy Storage System of Pure Electric Vehicle Considering Traffic Information
Appl. Sci. 2018, 8(8), 1266; https://doi.org/10.3390/app8081266
Received: 8 July 2018 / Revised: 24 July 2018 / Accepted: 28 July 2018 / Published: 31 July 2018
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Abstract
The main challenge for the pure electric vehicles (PEVs) with a hybrid energy storage system (HESS), consisting of a battery pack and an ultra-capacitor pack, is to develop a real-time controller that can achieve a significant adaptability to the real road. In this
[...] Read more.
The main challenge for the pure electric vehicles (PEVs) with a hybrid energy storage system (HESS), consisting of a battery pack and an ultra-capacitor pack, is to develop a real-time controller that can achieve a significant adaptability to the real road. In this paper, a comprehensive controller considering the traffic information is proposed, which is composed of an adaptive rule-based controller (main controller) and a fuzzy logic controller (auxiliary controller). Through analyzing the dynamic programming (DP) based power allocation of HESS, a general law for the power allocation of HESS is acquired and an adaptive rule-based controller is established. Then, to further enhance the real-time performance of the adaptive rule-based controller, traffic information, which consists of the traffic condition and road grade, is considered, and a novel method combining a K-means clustering algorithm and traffic condition is proposed to predict the future trend of vehicle speed. On the basis of the obtained traffic information, a fuzzy logic controller is constructed to provide the correction for the power allocation in the adaptive rule-based controller. Ultimately, the comparative simulations among the traditional rule-based controller, the adaptive rule-based controller, and the comprehensive controller are conducted, and the results indicate that the proposed adaptive rule-based controller reduces battery life loss by 3.76% and the state of change (SOC) consumption by 3.55% in comparison with the traditional rule-based controller. Furthermore, the comprehensive controller possesses the most excellent performance and reduces the battery life loss by 2.98% and the SOC consumption of the battery by 1.88%, when compared to the adaptive rule-based controller. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems)
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Open AccessArticle Analysis of the Effect of Parameter Variation on a Dynamic Cost Function for Distributed Energy Resources: A DER-CAM Case Study
Appl. Sci. 2018, 8(6), 884; https://doi.org/10.3390/app8060884
Received: 25 April 2018 / Revised: 24 May 2018 / Accepted: 25 May 2018 / Published: 28 May 2018
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Abstract
This paper investigates the effect of selected strategies of distributed energy resources (DER) on an energy cost function that optimizes the distribution of distributed energy resources for a mid-rise apartment building. This is achieved through comparing parameter optimization results for both a high-level
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This paper investigates the effect of selected strategies of distributed energy resources (DER) on an energy cost function that optimizes the distribution of distributed energy resources for a mid-rise apartment building. This is achieved through comparing parameter optimization results for both a high-level and low-level optimizer, respectively. The optimization process is carried out using the following approach: (1) a two-objective function is constructed with one objective function similar to that of the high-level optimizer (DER-CAM); (2) an evolutionary algorithm (EA) with modified selection capability is used to optimize the two-objective function problem in (1) for four selected cases of DER utilization that were previously optimized in DER-CAM; and (3) the optimization results of the low-level optimizer are compared with the outcome of DER-CAM optimization for the four selected cases. This is done to establish the capability of DER-CAM as an effective tool for optimal distributed energy resource allocation. Results obtained reveal the effect of load shifting and solar photovoltaic (PV) panels with power exporting capability on the optimization of the cost function. The Pareto-based MOEA approach has also proved to be effective in observing the interactions between objective function parameters. Mean inverted generational distance (MIGD) values obtained over 10 runs for each of the four cases that were considered show that a DER combination of PV panel, battery storage, heat pump, and load shifting outperforms the other strategies in 70% of the total simulation runs. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems)
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Open AccessArticle Robust Optimization for Household Load Scheduling with Uncertain Parameters
Appl. Sci. 2018, 8(4), 575; https://doi.org/10.3390/app8040575
Received: 23 March 2018 / Revised: 31 March 2018 / Accepted: 2 April 2018 / Published: 7 April 2018
Cited by 2 | PDF Full-text (1451 KB) | HTML Full-text | XML Full-text
Abstract
Home energy management systems (HEMS) face many challenges of uncertainty, which have a great impact on the scheduling of home appliances. To handle the uncertain parameters in the household load scheduling problem, this paper uses a robust optimization method to rebuild the household
[...] Read more.
Home energy management systems (HEMS) face many challenges of uncertainty, which have a great impact on the scheduling of home appliances. To handle the uncertain parameters in the household load scheduling problem, this paper uses a robust optimization method to rebuild the household load scheduling model for home energy management. The model proposed in this paper can provide the complete robust schedules for customers while considering the disturbance of uncertain parameters. The complete robust schedules can not only guarantee the customers’ comfort constraints but also cooperatively schedule the electric devices for cost minimization and load shifting. Moreover, it is available for customers to obtain multiple schedules through setting different robust levels while considering the trade-off between the comfort and economy. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems)
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Open AccessArticle Development of a High-Fidelity Model for an Electrically Driven Energy Storage Flywheel Suitable for Small Scale Residential Applications
Appl. Sci. 2018, 8(3), 453; https://doi.org/10.3390/app8030453
Received: 13 November 2017 / Revised: 8 March 2018 / Accepted: 8 March 2018 / Published: 16 March 2018
Cited by 4 | PDF Full-text (16527 KB) | HTML Full-text | XML Full-text
Abstract
Energy storage systems (ESS) are key elements that can be used to improve electrical system efficiency by contributing to balance of supply and demand. They provide a means for enhancing the power quality and stability of electrical systems. They can enhance electrical system
[...] Read more.
Energy storage systems (ESS) are key elements that can be used to improve electrical system efficiency by contributing to balance of supply and demand. They provide a means for enhancing the power quality and stability of electrical systems. They can enhance electrical system flexibility by mitigating supply intermittency, which has recently become problematic, due to the increased penetration of renewable generation. Flywheel energy storage systems (FESS) are a technology in which there is gathering interest due to a number of advantages offered over other storage solutions. These technical qualities attributed to flywheels include high power density, low environmental impact, long operational life, high round-trip efficiency and high cycle life. Furthermore, when configured in banks, they can store MJ levels of energy without any upper limit. Flywheels configured for grid connected operation are systems comprising of a mechanical part, the flywheel rotor, bearings and casings, and the electric drive part, inclusive of motor-generator (MG) and power electronics. This contribution focusses on the modelling and simulation of a high inertia FESS for energy storage applications which has the potential for use in the residential sector in more challenging situations, a subject area in which there are few publications. The type of electrical machine employed is a permanent magnet synchronous motor (PMSM) and this, along with the power electronics drive, is simulated in the MATLAB/Simulink environment. A brief description of the flywheel structure and applications are given as a means of providing context for the electrical modelling and simulation reported. The simulated results show that the system run-down losses are 5% per hour, with overall roundtrip efficiency of 88%. The flywheel speed and energy storage pattern comply with the torque variations, whilst the DC-bus voltage remains constant and stable within ±3% of the rated voltage, regardless of load fluctuations. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems)
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Open AccessFeature PaperArticle Model Predictive Control Home Energy Management and Optimization Strategy with Demand Response
Appl. Sci. 2018, 8(3), 408; https://doi.org/10.3390/app8030408
Received: 9 February 2018 / Revised: 23 February 2018 / Accepted: 27 February 2018 / Published: 9 March 2018
Cited by 9 | PDF Full-text (4198 KB) | HTML Full-text | XML Full-text
Abstract
The growing demand for electricity is a challenge for the electricity sector as it not only involves the search for new sources of energy, but also the increase of generation capacity of the existing electrical infrastructure and the need to upgrade the existing
[...] Read more.
The growing demand for electricity is a challenge for the electricity sector as it not only involves the search for new sources of energy, but also the increase of generation capacity of the existing electrical infrastructure and the need to upgrade the existing grid. Therefore, new ways to reduce the consumption of energy are necessary to be implemented. When comparing an average house with an energy efficient house, it is possible to reduce annual energy bills up to 40%. Homeowners and tenants should consider developing an energy conservation plan in their homes. This is both an ecological and economically rational action. With this goal in mind, the need for the energy optimization arises. However, this has to be made by ensuring a fair level of comfort in the household, which in turn spawns a few control challenges. In this paper, the ON/OFF, proportional-integral-derivative (PID) and Model Predictive Control (MPC) control methods of an air conditioning (AC) of a room are compared. The model of the house of this study has a PV domestic generation. The recorded climacteric data for this case study are for Évora, a pilot Portuguese city in an ongoing demand response (DR) project. Six Time-of-Use (ToU) electricity rates are studied and compared during a whole week of summer, typically with very high temperatures for this period of the year. The overall weekly expense of each studied tariff option is compared for every control method and in the end the optimal solution is reached. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems)
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Open AccessArticle Wireless Sensors and IoT Platform for Intelligent HVAC Control
Appl. Sci. 2018, 8(3), 370; https://doi.org/10.3390/app8030370
Received: 28 January 2018 / Revised: 14 February 2018 / Accepted: 27 February 2018 / Published: 3 March 2018
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Abstract
Energy consumption of buildings (residential and non-residential) represents approximately 40% of total world electricity consumption, with half of this energy consumed by HVAC systems. Model-Based Predictive Control (MBPC) is perhaps the technique most often proposed for HVAC control, since it offers an enormous
[...] Read more.
Energy consumption of buildings (residential and non-residential) represents approximately 40% of total world electricity consumption, with half of this energy consumed by HVAC systems. Model-Based Predictive Control (MBPC) is perhaps the technique most often proposed for HVAC control, since it offers an enormous potential for energy savings. Despite the large number of papers on this topic during the last few years, there are only a few reported applications of the use of MBPC for existing buildings, under normal occupancy conditions and, to the best of our knowledge, no commercial solution yet. A marketable solution has been recently presented by the authors, coined the IMBPC HVAC system. This paper describes the design, prototyping and validation of two components of this integrated system, the Self-Powered Wireless Sensors and the IOT platform developed. Results for the use of IMBPC in a real building under normal occupation demonstrate savings in the electricity bill while maintaining thermal comfort during the whole occupation schedule. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems)
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Open AccessArticle Smart Control System to Optimize Time of Use in a Solar-Assisted Air-Conditioning by Ejector for Residential Sector
Appl. Sci. 2018, 8(3), 350; https://doi.org/10.3390/app8030350
Received: 25 January 2018 / Revised: 23 February 2018 / Accepted: 23 February 2018 / Published: 28 February 2018
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Abstract
The present work provides a series of theoretical improvements of a control strategy in order to optimize the time of use of solar air-conditioning by an ejector distributed in multiple solar collectors of vacuum tubes for the residential sector, which will allow us
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The present work provides a series of theoretical improvements of a control strategy in order to optimize the time of use of solar air-conditioning by an ejector distributed in multiple solar collectors of vacuum tubes for the residential sector, which will allow us to reduce carbon-dioxide emissions, costs and electrical energy consumption. In a solar ejector cooling system, the instability of the solar source of energy causes an operational conflict between the solar thermal system and ejector cooling cycle. A fuzzy control structure for the supervisory ejector cycle and multi-collector control system is developed: the first control is applied to control the mass flow of the generator and the evaporator for different cooling capacities (3, 3.5, 4, 4.5 and 5 kW) and set a temperature reference according to the operating conditions; the second is applied to keep a constant temperature power source that feeds the low-grade ejector cooling cycle using R134aas refrigerant. For the present work, the temperature of the generator oscillates between 65 °C and 90 °C, a condenser temperature of 30 °C and an evaporator temperature of 10 °C. For the purpose of optimization, there are different levels of performance for time of use: the Mode 0 (economic) gives a performance of 17.55 h, Mode 5 (maximum cooling power) 14.86 h and variable mode (variable mode of capacities) 16.25 h, on average. Simulations are done in MATLAB-Simulink applying fuzzy logic for a mathematical model of the thermal balance. They are compared with two different types of solar radiation: real radiation and disturbed radiation. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems)
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Open AccessArticle Machine Learning for Identifying Demand Patterns of Home Energy Management Systems with Dynamic Electricity Pricing
Appl. Sci. 2017, 7(11), 1160; https://doi.org/10.3390/app7111160
Received: 16 October 2017 / Revised: 4 November 2017 / Accepted: 6 November 2017 / Published: 12 November 2017
Cited by 2 | 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
[...] Read more.
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; https://doi.org/10.3390/app7111136
Received: 3 October 2017 / Revised: 31 October 2017 / Accepted: 1 November 2017 / Published: 5 November 2017
Cited by 2 | PDF Full-text (4254 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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; https://doi.org/10.3390/app7090943
Received: 2 August 2017 / Revised: 3 September 2017 / Accepted: 12 September 2017 / Published: 13 September 2017
Cited by 2 | PDF Full-text (5032 KB) | HTML Full-text | XML Full-text
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
[...] Read more.
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; https://doi.org/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; https://doi.org/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; https://doi.org/10.3390/app7080754
Received: 21 June 2017 / Revised: 17 July 2017 / Accepted: 19 July 2017 / Published: 25 July 2017
Cited by 5 | 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; https://doi.org/10.3390/app7060641
Received: 24 April 2017 / Revised: 1 June 2017 / Accepted: 16 June 2017 / Published: 21 June 2017
Cited by 3 | 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; https://doi.org/10.3390/app7050525
Received: 11 April 2017 / Revised: 9 May 2017 / Accepted: 11 May 2017 / Published: 18 May 2017
Cited by 2 | 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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