Special Issue "Smart Home and Energy Management Systems 2019"

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

Deadline for manuscript submissions: closed (30 June 2019).

Special Issue Editor

Prof. Dr. João P. S. Catalão
Website
Guest Editor
Faculty of Engineering, University of Porto, Porto, Portugal
Interests: power system operations and planning; hydrothermal scheduling and wind/price forecasting; power system economics and electricity markets; risk analysis, uncertainty, and stochastic programming; renewable energies and demand-side management
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Special Issue Information

Dear Colleagues,

 “Smart Home and Energy Management Systems 2019” is a continuation of the previous and successful Special Issue, “Smart Home and Energy Management Systems”.

We are inviting submissions to this Applied Sciences Special Issue on “Smart Home and Energy Management Systems 2019”.

This is the second Special Issue, which will focus on energy efficiency and 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 are 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 semimonthly 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 1800 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 (19 papers)

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Open AccessArticle
Optimal Scheduling Method of Controllable Loads in Smart Home Considering Re-Forecast and Re-Plan for Uncertainties
Appl. Sci. 2019, 9(19), 4064; https://doi.org/10.3390/app9194064 - 29 Sep 2019
Cited by 1
Abstract
Renewable energies (REs) such as photovoltaic generation (PV) have been gaining attention in distribution systems. Recently, houses with PV and battery systems, as well as electric vehicles (EV) are expected to contribute to not only the suppression of global warming but also reducing [...] Read more.
Renewable energies (REs) such as photovoltaic generation (PV) have been gaining attention in distribution systems. Recently, houses with PV and battery systems, as well as electric vehicles (EV) are expected to contribute to not only the suppression of global warming but also reducing electricity bill on the consumer side. However, there are numerous challenges with the introduction of REs at the demand side such as the actual output of REs often deviating from the forecasted output, which causes fluctuation of the power flow and this is challenging for the distribution or transmission system operator. For this challenge, it is expected that smart grid technology using controllable loads such as a fixed battery or EV battery, can suppress fluctuation of power flow. This paper presents a decision method of optimal scheduling of controllable loads to suppress the fluctuation of power flow by PV output in the smart home. An optimization method to cope with uncertainties such as variability of PV power and effective forecasting methods are considered in the proposed scheme. In order to decrease the expected operational cost and to validate the robustness for the uncertainty’s optimization approach, statistical analysis is executed for the optimal scheduling scheme. From the optimization results, the proposed methodology suppressed the fluctuation of power flow in the smart home and also minimized the consumer operational cost. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems 2019)
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Open AccessFeature PaperArticle
A New Approach for Grid-Connected Hybrid Renewable Energy System Sizing Considering Harmonic Contents of Smart Home Appliances
Appl. Sci. 2019, 9(18), 3941; https://doi.org/10.3390/app9183941 - 19 Sep 2019
Abstract
Even renewable energy sources provide several advantages, especially from an environmental point of view, where the world has faced great challenges in the last few decades; several negative issues also exist regarding the integration of renewable resources-based power production units in electric power [...] Read more.
Even renewable energy sources provide several advantages, especially from an environmental point of view, where the world has faced great challenges in the last few decades; several negative issues also exist regarding the integration of renewable resources-based power production units in electric power systems. One of the main problems related to pivotal renewable energy resources such as solar, wind, etc., is their stochastic and uncontrollable nature in terms of power production. Therefore, this stochasticity in the supply side of the power system may pose many challenges for system operators. This issue is also problematic for smaller applications where the stochastic production by a main resource, such as a roof-top photovoltaic system, and load demand may not match perfectly at each time instant and therefore should be compensated by additional resources such as battery-based energy storage systems. Herein, the economic considerations to ensure minimum costs for such a hybrid system design are vital so as to increase the penetration of such systems. Therefore, the optimal sizing and planning of hybrid systems have recently gained increasing importance to enhance power system operation in the context of the smart grid paradigm. From a different perspective, harmonics are one of the most important power quality problems in system operations caused by widespread integration of power electronic loads with non-linear characteristics that should be considered. Thus, a new approach for grid-connected hybrid renewable energy system sizing is provided. In order to determine optimal capacities for photovoltaic (PV) and energy storage system (ESS) units for covering residential consumer demand, a mixed integer linear programming (MILP)-based formulation is presented. The main objective is minimizing total costs of the system consisting of investment, capital and maintenance cost functions. A daily power curve is created accurately with real measurements of non-linear loads considering harmonic contents of smart home appliances in Yildiz Technical University, Istanbul, Turkey. In addition, real radiation and temperature values are used in PV production as well as dynamic pricing schemes for realistic evaluations. Moreover, optimal sizing results are compared for both the harmonic-based power curve and rated power curve in terms of satisfying objective function. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems 2019)
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Open AccessArticle
A Multi-Communication-Based Demand Response Implementation Structure and Control Strategy
Appl. Sci. 2019, 9(16), 3218; https://doi.org/10.3390/app9163218 - 07 Aug 2019
Cited by 1
Abstract
Demand response (DR) is widely accepted as a feasible and potential solution to improve the operation of the power system. In this paper, an economical and practical DR system architecture based on internet and Internet of things (IoT) communication technologies is discussed to [...] Read more.
Demand response (DR) is widely accepted as a feasible and potential solution to improve the operation of the power system. In this paper, an economical and practical DR system architecture based on internet and Internet of things (IoT) communication technologies is discussed to achieve wide-area DR control without using an expensive metering infrastructure. Multi agents are introduced with respective control strategies to implement multi-time-scale control in a power system. In order to support quick DR strategies, a novel smart terminal design for the proposed DR system is described with functions of local parameter detection and action. The practicality of the proposed system was validated on a developed hardware-in-loop co-simulation platform. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems 2019)
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Open AccessArticle
An Evaluation Framework to Support Optimisation of Scenarios for Energy Efficient Retrofitting of Buildings at the District Level
Appl. Sci. 2019, 9(12), 2448; https://doi.org/10.3390/app9122448 - 14 Jun 2019
Cited by 3
Abstract
Energy-efficient retrofitting of buildings has become essential to achieve the environmental objectives of the European Union’s (EU) strategies towards reducing carbon emissions and energy dependency on fossil fuels. When tackling retrofitting projects, the issue of scale becomes essential as sometimes this can determine [...] Read more.
Energy-efficient retrofitting of buildings has become essential to achieve the environmental objectives of the European Union’s (EU) strategies towards reducing carbon emissions and energy dependency on fossil fuels. When tackling retrofitting projects, the issue of scale becomes essential as sometimes this can determine the sustainability of the project. Therefore, a comprehensive approach is essential to ensure effective decision-making. A platform has been designed within the EU funded OptEEmAL project to support stakeholders in this process, providing functionalities that can automatically model and evaluate candidate retrofitting alternatives considering their priorities, targets and boundary conditions. A core element of this platform is the evaluation framework deployed which implements a multi-criteria decision-making approach to transform the priorities of stakeholders into quantifiable weights used to compare the alternatives. As a result, more informed decisions can be made by the stakeholders through a comprehensive evaluation of the candidate retrofitting scenarios. This paper presents the approach followed to develop and integrate this evaluation framework within the platform as well as its validation in a controlled environment to ensure its effectiveness. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems 2019)
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Open AccessArticle
Underfloor Heating Using Ceramic Thermal Panels and Solar Thermal Panels in Public Buildings in the Mediterranean: Energy Savings and Healthy Indoor Environment
Appl. Sci. 2019, 9(10), 2089; https://doi.org/10.3390/app9102089 - 21 May 2019
Cited by 4
Abstract
Radiant floor air conditioning systems based on capillary tube mats, in addition to offering high comfort standards, generate significant energy savings. They allow the use of renewable energies such as thermal solar panels and combine them with solar cooling systems based on lithium [...] Read more.
Radiant floor air conditioning systems based on capillary tube mats, in addition to offering high comfort standards, generate significant energy savings. They allow the use of renewable energies such as thermal solar panels and combine them with solar cooling systems based on lithium chloride or absorption systems with lithium bromide in summer, cooling water down to 15–16 °C through solar thermal panel energy collection. Thus, in addition to energy savings from the transport of low water flows, annual energy demand is also reduced. This research analyses the application of thermal ceramic panels (TCP)—containing polypropylene (PPR) tube capillary mats—to public buildings in the Spanish Mediterranean. A case study of the Museum of the University of Alicante (MUA) is presented. Water was distributed individually from a split system heat pump inside the building combined with a thermal solar panel system on the roof. The MUA’s annual energy demand was quantified using thermal simulation tools and was monitored during the entire one-year cycle. Simulations were conducted both for the radiant floor system and an all-air conventional convective system, as well as with solar thermal panel applications. The reduction in annual energy demand was 24.91% when TCP panels are used on the floor. This is a considerable value, but lower than others results obtained in Central Europe due to the higher values of humidity. When solar thermal panels are installed on the rooftop the energy savings can increase to 60.70%. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems 2019)
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Open AccessArticle
Real-Time Voltage Stability Assessment Method for the Korean Power System Based on Estimation of Thévenin Equivalent Impedance
Appl. Sci. 2019, 9(8), 1671; https://doi.org/10.3390/app9081671 - 23 Apr 2019
Abstract
This study aims to develop a real-time assessing methodology for a power systems voltage stability. The proposed algorithm is based on the Thévenin equivalent (TE) impedance estimation method, which applies the phasor measurement unit technology practically. To present an accurate analysis of the [...] Read more.
This study aims to develop a real-time assessing methodology for a power systems voltage stability. The proposed algorithm is based on the Thévenin equivalent (TE) impedance estimation method, which applies the phasor measurement unit technology practically. To present an accurate analysis of the real-time situations of a power system, the developed voltage stability index can be used as useful information for system operators to establish appropriate countermeasures. Moreover, by considering the results of voltage stability margin calculation within the Korean power system, the effect of voltage stability on the dynamic behavior of the system is presented. Furthermore, to increase the accuracy, load model parameter estimation is introduced in this algorithm. The load model might be used for calculating the stability margin more accurately. The power-voltage curve is drawn in theory using the TE voltages and impedances. To validate the case study of the proposed method, simulations were executed using the Matlab software. The simulations demonstrated the effectiveness of the proposed method and detected voltage stability/instability under severe contingency scenarios. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems 2019)
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Open AccessArticle
An Intelligent Hybrid Energy Management System for a Smart House Considering Bidirectional Power Flow and Various EV Charging Techniques
Appl. Sci. 2019, 9(8), 1658; https://doi.org/10.3390/app9081658 - 22 Apr 2019
Cited by 6
Abstract
Compelled by environmental and economic reasons and facilitated by modern technological advancements, the share of hybrid energy systems (HES) is increasing at modern smart house (SH) level. This work proposes an intelligent hybrid energy management system (IHEMS) for an SH connected to a [...] Read more.
Compelled by environmental and economic reasons and facilitated by modern technological advancements, the share of hybrid energy systems (HES) is increasing at modern smart house (SH) level. This work proposes an intelligent hybrid energy management system (IHEMS) for an SH connected to a power network that allows a bidirectional power flow. The SH has electrical and thermal power loops, and its main components include renewable energy from wind and photovoltaics, electric vehicle (EV), battery energy storage system, a fuel cell which serves as a micro-combined heat and power system, and a boiler. The proposed IHEMS models the components of the SH, defines their constraints, and develops an optimization model based on the real coded genetic algorithm. The key features of the developed IHEMS are highlighted under six simulation cases considering different configurations of the SH components. Moreover, the standard EV charging techniques are compared, and it is observed that the charging method which is flexible in timing and power injection to the EV is best suited for the economic operation of the SH. The simulation results reveal that the proposed IHEMS minimizes the 24-hour operational cost of the SH by optimally scheduling the energy resources and loads. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems 2019)
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Open AccessArticle
Parameterized Modeling and Planning of Distributed Energy Storage in Active Distribution Networks
Appl. Sci. 2019, 9(8), 1643; https://doi.org/10.3390/app9081643 - 20 Apr 2019
Cited by 1
Abstract
In recent years, distributed energy storage (DES) has experienced rapid growth and has been widely applied in active distribution networks (ADNs). Owing to the close correlation between the characteristics and the application scenarios, DES modeling needs to be parameterized separately for various application [...] Read more.
In recent years, distributed energy storage (DES) has experienced rapid growth and has been widely applied in active distribution networks (ADNs). Owing to the close correlation between the characteristics and the application scenarios, DES modeling needs to be parameterized separately for various application demands. In this paper, a parameterized model for optimal DES planning in ADNs is proposed. The typical scenarios for DES planning are generated by the clustering technique, containing the patterns of load demand, wind turbine output and photovoltaic output. Secondly, an optimal planning model of DES considering parameterized characteristics is established, which is essentially a mixed integer non-linear optimization problem. Then, the model is converted to a mixed-integer second-order cone programming model, which can be solved efficiently by available commercial software. Finally, case studies on the modified IEEE 33-node system and IEEE 123-node system verify the efficiency of the proposed method, and the effects of DES planning are validated by two evaluation indexes. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems 2019)
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Open AccessArticle
Virtual Inertia and Mechanical Power-Based Control Strategy to Provide Stable Grid Operation under High Renewables Penetration
Appl. Sci. 2019, 9(6), 1043; https://doi.org/10.3390/app9061043 - 13 Mar 2019
Cited by 5
Abstract
This paper presents a virtual inertia and mechanical power-based control strategy to provide a stable operation of the power grid under high penetration of renewable energy sources (RESs). The proposed control technique is based on a new active and reactive power-based dynamic model [...] Read more.
This paper presents a virtual inertia and mechanical power-based control strategy to provide a stable operation of the power grid under high penetration of renewable energy sources (RESs). The proposed control technique is based on a new active and reactive power-based dynamic model with the permanent magnet synchronous generator (PMSG) swing equation, in which all PMSG features i.e., inertia and mechanical power are embedded within the controller as the main contribution of this paper. To present an accurate analysis of the virtual PMSG-based parameters, the desired zero dynamics of the grid angular frequency are considered to evaluate the effects of virtual mechanical power (VMP) on the active and reactive power sharing, as well as the investigation of virtual inertia variations for the grid angular frequency responses. Moreover, by considering various active power errors and virtual inertia, the impacts of active power error on reactive power in the proposed control technique, are precisely assessed. Simulation results are employed in Matlab/Simulink software to verify the stabilizing abilities of the proposed control technique. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems 2019)
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Open AccessArticle
Time-Constrained Nature-Inspired Optimization Algorithms for an Efficient Energy Management System in Smart Homes and Buildings
Appl. Sci. 2019, 9(4), 792; https://doi.org/10.3390/app9040792 - 23 Feb 2019
Cited by 10
Abstract
This paper proposes two bio-inspired heuristic algorithms, the Moth-Flame Optimization (MFO) algorithm and Genetic Algorithm (GA), for an Energy Management System (EMS) in smart homes and buildings. Their performance in terms of energy cost reduction, minimization of the Peak to Average power Ratio [...] Read more.
This paper proposes two bio-inspired heuristic algorithms, the Moth-Flame Optimization (MFO) algorithm and Genetic Algorithm (GA), for an Energy Management System (EMS) in smart homes and buildings. Their performance in terms of energy cost reduction, minimization of the Peak to Average power Ratio (PAR) and end-user discomfort minimization are analysed and discussed. Then, a hybrid version of GA and MFO, named TG-MFO (Time-constrained Genetic-Moth Flame Optimization), is proposed for achieving the aforementioned objectives. TG-MFO not only hybridizes GA and MFO, but also incorporates time constraints for each appliance to achieve maximum end-user comfort. Different algorithms have been proposed in the literature for energy optimization. However, they have increased end-user frustration in terms of increased waiting time for home appliances to be switched ON. The proposed TG-MFO algorithm is specially designed for nearly-zero end-user discomfort due to scheduling of appliances, keeping in view the timespan of individual appliances. Renewable energy sources and battery storage units are also integrated for achieving maximum end-user benefits. For comparison, five bio-inspired heuristic algorithms, i.e., Genetic Algorithm (GA), Ant Colony Optimization (ACO), Cuckoo Search Algorithm (CSA), Firefly Algorithm (FA) and Moth-Flame Optimization (MFO), are used to achieve the aforementioned objectives in the residential sector in comparison with TG-MFO. The simulations through MATLAB show that our proposed algorithm has reduced the energy cost up to 32.25% for a single user and 49.96% for thirty users in a residential sector compared to unscheduled load. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems 2019)
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Open AccessArticle
Optimization Design and Analysis for a Single Motor Hybrid Powertrain Configuration with Dual Planetary Gears
Appl. Sci. 2019, 9(4), 707; https://doi.org/10.3390/app9040707 - 18 Feb 2019
Cited by 3
Abstract
To further improve the comprehensive operating performance of the single motor hybrid electric vehicle, a single motor hybrid powertrain configuration with dual planetary gears (SMHPC-2PG) design is proposed in this paper. By adopting a topology design method that characterizes the constraint relationship between [...] Read more.
To further improve the comprehensive operating performance of the single motor hybrid electric vehicle, a single motor hybrid powertrain configuration with dual planetary gears (SMHPC-2PG) design is proposed in this paper. By adopting a topology design method that characterizes the constraint relationship between power resource components and planetary gear (PG) nodes, all feasible configuration candidates based on the basic configuration scheme are systematically explored, and dynamic models of configuration candidates are automatically generated. The optimal fuel economy and dynamic performance for configuration candidates are simulated by applying the global optimal control strategy based on dynamic programming (DP). Results of this study demonstrate that SMHPC-2PG with excellent operating performance can be screened out by this method. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems 2019)
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Open AccessArticle
Bayesian Game-Theoretic Bidding Optimization for Aggregators Considering the Breach of Demand Response Resource
Appl. Sci. 2019, 9(3), 576; https://doi.org/10.3390/app9030576 - 10 Feb 2019
Cited by 2
Abstract
Demand response (DR) aggregator controlling and aggregating flexible resource of residential users to participate in DR market will contribute the performance of DR project. However, DR aggregator has to face the risk that users may break the contract signed with aggregator and refuse [...] Read more.
Demand response (DR) aggregator controlling and aggregating flexible resource of residential users to participate in DR market will contribute the performance of DR project. However, DR aggregator has to face the risk that users may break the contract signed with aggregator and refuse to be controlled by aggregator due to the uncertainty factors of electricity consumption. Therefore, in this paper, community operator (i.e., DR aggregator) is proposed to equip auxiliary equipment, such as energy storage and gas boiler, to compensate for power shortage caused by users’ breach behavior. DR aggregated resource with different auxiliary equipment will have different characteristics, such as breach rate of DR resource. In the proposed DR framework, for selling the aggregated resource, community operator has to compete the market share with other operators in day-ahead DR market. In the competition, each operator will try its best to make the optimal bidding strategy by knowing as much information of its opponents as possible. But, some information of community operator (e.g., DR resource’s characteristic) belongs to privacy information, which is unknown to other operators. Accordingly, this paper focuses on the application of incomplete information game-theoretic framework to model the competition among community operators in DR bidding market. To optimize bidding strategy for the high profit with incomplete information, a Bayesian game approach is formulated. And, an effective iterative algorithm is also presented to search the equilibrium for the proposed Bayesian game model. Finally, a case study is performed to show the effectiveness of the proposed framework and Bayesian game approach. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems 2019)
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Open AccessArticle
Optimal Management of an Energy Storage Unit in a PV-Based Microgrid Integrating Uncertainty and Risk
Appl. Sci. 2019, 9(1), 169; https://doi.org/10.3390/app9010169 - 04 Jan 2019
Cited by 10
Abstract
This paper addresses an optimized management of a storage energy battery which is part of a microgrid with a connection to the main grid and is supplied by a photovoltaic (PV) power plant. The main contribution of this paper is to consider uncertainty [...] Read more.
This paper addresses an optimized management of a storage energy battery which is part of a microgrid with a connection to the main grid and is supplied by a photovoltaic (PV) power plant. The main contribution of this paper is to consider uncertainty in electricity price while managing the battery storage. The forecasted value for demand and PV unit are predicted by a seasonal autoregressive integrated moving average model (SARIMA)—capable of accurately characterizing both seasonality effects and tail fatness. The optimal operation of the battery is determined by resolving a linear optimization program in which the objective function comprises the conditional value at risk (CVaR). Using CVaR ensures that the demand is fully supplied while minimizing the risk and operational cost. The cost function is the difference between power sold and bought subject to the charging and discharging rates for the battery and defining upper and lower bounds for the level of battery charge. The simulation results confirm that the risk consideration has a significant effect on the optimized management of a storage energy battery in a photovoltaic grid-connected microgrid. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems 2019)
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Open AccessArticle
Design and Implementation of an IoT-Oriented Energy Management System Based on Non-Intrusive and Self-Organizing Neuro-Fuzzy Classification as an Electrical Energy Audit in Smart Homes
Appl. Sci. 2018, 8(12), 2337; https://doi.org/10.3390/app8122337 - 22 Nov 2018
Cited by 11
Abstract
Smart cities are built to help people address issues like air pollution, traffic optimization, and energy efficiency. Electrical energy efficiency has become a central research issue in the energy field. Smart houses and buildings, which lower electricity costs, form an integral part of [...] Read more.
Smart cities are built to help people address issues like air pollution, traffic optimization, and energy efficiency. Electrical energy efficiency has become a central research issue in the energy field. Smart houses and buildings, which lower electricity costs, form an integral part of a smart city in a smart grid. This article presents an Internet of Things (IoT)-oriented smart Home Energy Management System (HEMS) that identifies electrical home appliances based on a novel hybrid Unsupervised Automatic Clustering-Integrated Neural-Fuzzy Classification (UAC-NFC) model. The smart HEMS designed and implemented in this article is composed of (1) a set of IoT-empowered smart e-meters, called smart sockets, installed as a benchmark in a realistic domestic environment with uncertainties and deployed against non-intrusive load monitoring; (2) a central Advanced Reduced Instruction Set Computing machine-based home gateway configured with a ZigBee wireless communication network; and (3) a cloud-centered analytical platform constructed to the hybrid UAC-NFC model for Demand-Side Management (DSM)/home energy management as a load classification task. The novel hybrid UAC-NFC model proposed in DSM and presented in this article is used to overcome the difficulties in distinguishing electrical appliances operated under similar electrical features and classified as unsupervised and self-organized. The smart HEMS developed with the proposed novel hybrid UAC-NFC model for DSM was able to identify electrical household appliances with an acceptable average and generalized classification rate of 95.73%. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems 2019)
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Open AccessArticle
An Optimal Energy Management Method for the Multi-Energy System with Various Multi-Energy Applications
Appl. Sci. 2018, 8(11), 2273; https://doi.org/10.3390/app8112273 - 16 Nov 2018
Cited by 4
Abstract
As the development of the multi-energy system (MES), various ME applications are deployed. ME applications not only bring advanced functionalities to the MES, but also show great potentials in promoting the operation performance of the MES, especially improving the accommodation of renewable energy [...] Read more.
As the development of the multi-energy system (MES), various ME applications are deployed. ME applications not only bring advanced functionalities to the MES, but also show great potentials in promoting the operation performance of the MES, especially improving the accommodation of renewable energy sources (RES). However, the realization of these potentials largely relies on the energy management, which shall facilitate the effective function of each ME application and the coordinated collaboration of all the ME applications. Without a comprehensive energy management methodology, ME applications may mutually interfere, which not only hinder the RES utilization, but also may harm the MES operation performance. In this premise, this paper integrates the energy management model of the combined cooling, heat and power plants, power-to-hydrogen/gas-to-power plants, and demand side management model of the EV charging loads into the energy management model of the MES, and proposes an comprehensive optimal day-ahead energy management framework to simultaneously improve the profit, RES utilization rate, and energy saving performance of the MES. To address the proposed optimization model, Elitist Non-dominated Sorting Genetic algorithm II algorithm is employed to heuristically find the Pareto-optimal results. Finally, case studies prove the effectiveness of the proposed methodology. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems 2019)
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Open AccessArticle
Investment Incentives in Competitive Electricity Markets
Appl. Sci. 2018, 8(10), 1978; https://doi.org/10.3390/app8101978 - 18 Oct 2018
Cited by 26
Abstract
This paper presents the analysis of a novel framework of study and the impact of different market design criterion for the generation expansion planning (GEP) in competitive electricity market incentives, under variable uncertainties in a single year horizon. As investment incentives conventionally consist [...] Read more.
This paper presents the analysis of a novel framework of study and the impact of different market design criterion for the generation expansion planning (GEP) in competitive electricity market incentives, under variable uncertainties in a single year horizon. As investment incentives conventionally consist of firm contracts and capacity payments, in this study, the electricity generation investment problem is considered from a strategic generation company (GENCO) s perspective, modelled as a bi-level optimization method. The first-level includes decision steps related to investment incentives to maximize the total profit in the planning horizon. The second-level includes optimization steps focusing on maximizing social welfare when the electricity market is regulated for the current horizon. In addition, variable uncertainties, on offering and investment, are modelled using set of different scenarios. The bi-level optimization problem is then converted to a single-level problem and then represented as a mixed integer linear program (MILP) after linearization. The efficiency of the proposed framework is assessed on the MAZANDARAN regional electric company (MREC) transmission network, integral to IRAN interconnected power system for both elastic and inelastic demands. Simulations show the significance of optimizing the firm contract and the capacity payment that encourages the generation investment for peak technology and improves long-term stability of electricity markets. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems 2019)
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Open AccessArticle
Evaluation of Energy Saving and Emission Reduction Effects for Electricity Retailers in China Based on Fuzzy Combination Weighting Method
Appl. Sci. 2018, 8(9), 1564; https://doi.org/10.3390/app8091564 - 05 Sep 2018
Cited by 3
Abstract
China’s electricity market is in the environment for a round of new electric power reform, energy planning and transformation and the carbon market construction. The current market players are in urgent need of implementing their own energy saving and emission reduction actions. Relatively [...] Read more.
China’s electricity market is in the environment for a round of new electric power reform, energy planning and transformation and the carbon market construction. The current market players are in urgent need of implementing their own energy saving and emission reduction actions. Relatively extensive and systematic researches on the assessment of the energy saving and emission reduction effects for the power plants, power grid companies, and technical equipment have been carried out at home and abroad. However, there are still vacancies in the researches on those for electricity retailers emerged on the sales side. Based on the carding and analysis of related policies and guidance, in this paper, relevant indicators are considered to build the evaluation indicator system of the energy saving and emission reduction effects for electricity retailers. The combination weights are gained by means of analytic hierarchy process and entropy weight method. Then, after the combined empowerment of indicators, the multi-level fuzzy comprehensive evaluation of energy saving and emission reduction effects for electricity retailers is conducted. Finally, choosing 10 electricity retailers (numbered from A to J) as evaluation objects, this model is used for obtaining the evaluation results and ranking of energy saving and emission reduction effects of electricity retailers, which provides reasonable ideas for the construction of evaluation indicator system and effective comprehensive evaluation methods of energy saving and emission reduction effects for market players in the electricity sales side. The results of example analysis show that, from a single dimension, the best electricity retailers in market transactions, technical means, integrated energy services, management system, and social responsibilities are followed by B, J, D, G, C, or I. However, from a global perspective, the sorted evaluation results are D, J, I, A, H, G, E, B, F, and C, which reflects the overall energy saving and emission reduction effects of electricity retailers through the two-level fuzzy comprehensive evaluation. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems 2019)
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Review

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Open AccessFeature PaperReview
Energy Management for Smart Homes—State of the Art
Appl. Sci. 2019, 9(17), 3459; https://doi.org/10.3390/app9173459 - 21 Aug 2019
Cited by 1
Abstract
Smart home is a concept that aims to enhance the comfort of residents and facilitate household activities. The smart home is an application of ubiquitous computing which can provide the user with context-aware automated or assistive services in the form of ambient intelligence, [...] Read more.
Smart home is a concept that aims to enhance the comfort of residents and facilitate household activities. The smart home is an application of ubiquitous computing which can provide the user with context-aware automated or assistive services in the form of ambient intelligence, remote control of home appliances, or automation. Smart homes attempt to integrate smartness into homes to guarantee the residents’ convenience, safety, and security, while conserving the energy. The capabilities of a smart home in the context of different applications, have been scrutinized for this investigation. Different proposed architectures, protocols, and infrastructures have been taken into consideration. As the data management process is a vital part of a smart home system, many procedures of data collection, storage, and analysis have been surveyed. Methods of data acquisition has also been discussed. Existing challenges, pros, and cons of proposed schemes along with future perspectives of smart homes are identified in this report, which is intended to promote future research directions. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems 2019)
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Open AccessReview
Towards a Smarter Energy Management System for Hybrid Vehicles: A Comprehensive Review of Control Strategies
Appl. Sci. 2019, 9(10), 2026; https://doi.org/10.3390/app9102026 - 16 May 2019
Cited by 4
Abstract
This paper presents a comprehensive review of energy management control strategies utilized in hybrid electric vehicles (HEVs). These can be categorized as rule-based strategies and optimization-based strategies. Rule-based strategies, as the most basic strategy, are widely used due to their simplicity and practical [...] Read more.
This paper presents a comprehensive review of energy management control strategies utilized in hybrid electric vehicles (HEVs). These can be categorized as rule-based strategies and optimization-based strategies. Rule-based strategies, as the most basic strategy, are widely used due to their simplicity and practical application. The focus of rule-based strategies is to determine and optimize the optimal threshold for mode switching; however, they fall into a local optimal solutions. To have better performance in energy management, optimization-based strategies were developed. The categories of the existing optimization-based strategies are identified from the latest literature, and a brief study of each strategy is discussed, which consists of the main research ideas, the research focus, advantages, disadvantages and improvements to ameliorate optimality and real-time performance. Deterministic dynamic programming strategy is regarded as a benchmark. Based on neural network and the large data processing technology, data-driven strategies are put forward due to their approximate optimality and high computational efficiency. Finally, the comprehensive performance of each control strategy is analyzed with respect to five aspects. This paper not only provides a comprehensive analysis of energy management control strategies for HEVs, but also presents the emphasis in the future. Full article
(This article belongs to the Special Issue Smart Home and Energy Management Systems 2019)
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