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Demand Response in Smart Homes
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Demand Response in Smart Homes

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "G: Energy and Buildings".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 18035

Special Issue Editors

Prof. Dr. Raman B. Paranjape

E-Mail Website
Guest Editor
Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada
Interests: complex system modeling and simulation; software agent system modeling; optimization in home energy systems; renewable energy integration in home energy systems; impacts of EVs on energy distribution; smart meters for residential demand response
Dr. Zhanle Wang

E-Mail Website
Guest Editor
Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada
Interests: computational methods for smart grids; demand response; simulation and modeling optimal control of electricity usage; vehicle-to-grid systems (V2G); renewable energy integration; solar/wind power; optimization theory and applications; multi-agent systems

Special Issue Information

Dear Colleagues,

We are excited to invite full paper submissions for a Special Issue on “Demand Response in Smart Homes”. As you are aware, demand response has been one of the most important mechanisms instigated by power utilities for controlling peak usage and generation costs. While the concepts of demand response remain the same for residential users, there are a number of key characteristics that make this problem domain unique, including the integration of multiple households into cohesive energy user blocks, user needs being driven by both inflexible and flexible demands, the advent of new power sinks such as electric vehicles and home battery banks, and the incorporation of new home-based energy sources such as solar, wind, and geothermal. With all these disparate forces to manage, a comprehensive home demand response methodology is called for. We request researchers worldwide to come together in this Special Issue with their best ideas to help formulate this new paradigm.  

The topics of interest for publication include, but are not limited to:

  • Demand response and demand side management;
  • Smart home technologies;
  • Electric vehicle charging/discharging control;
  • Vehicle-to-grid applications;
  • Home energy storage;
  • Geothermal energy in smart homes;
  • Machine learning for load disaggregation and classification;
  • Smart load aggregation/control algorithms;
  • Roof-top solar energy prediction;
  • Hybrid systems for the integration of solar energy, energy storage, thermal energy, demand flexibility, and electric vehicles;
  • Dynamic electricity tariffs and incentive mechanisms for demand response programs;
  • Mechanisms for smart homes or aggregators to participate in electricity markets.

Prof. Dr. Raman B. Paranjape
Dr. Zhanle Wang
Guest Editors

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 submissions that pass pre-check are 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. Energies 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 2600 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

  • Demand response
  • Demand side management with smart meters
  • Smart homes and buildings
  • Smart grid
  • Active consumers
  • Prosumers
  • Elastic demand
  • Load flexibility, forecasting, aggregation and disaggregation
  • Distributed energy resources
  • Battery systems
  • Energy storage systems
  • Electricity market
  • Electric vehicles
  • Renewable energy
  • Home islanding
  • Transactive energy
  • Energy management and tariffs

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Published Papers (5 papers)

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Research

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21 pages, 1561 KiB  
Article
Electric Vehicle Charging Model in the Urban Residential Sector
by Mohamed El-Hendawi, Zhanle Wang, Raman Paranjape, Shea Pederson, Darcy Kozoriz and James Fick
Energies 2022, 15(13), 4901; https://doi.org/10.3390/en15134901 - 4 Jul 2022
Cited by 14 | Viewed by 3364
Abstract
Electric vehicles (EVs) have become increasingly popular because they are highly efficient and sustainable. However, EVs have intensive electric loads. Their penetrations into the power system pose significant challenges to the operation and control of the power distribution system, such as a voltage [...] Read more.
Electric vehicles (EVs) have become increasingly popular because they are highly efficient and sustainable. However, EVs have intensive electric loads. Their penetrations into the power system pose significant challenges to the operation and control of the power distribution system, such as a voltage drop or transformer overloading. Therefore, grid operators need to prepare for high-level EV penetration into the power system. This study proposes data-driven, parameterized, individual, and aggregated EV charging models to predict EV charging loads in the urban residential sector. Actual EV charging profiles in Saskatchewan, Canada, were analyzed to understand the characteristics of EV charging. A location-based algorithm was developed to identify residential EV charging from raw data. The residential EV charging data were then used to tune the EV charging model parameters, including battery capacity, charging power level, start charging time, daily EV charging energy, and the initial state of charge (SOC). These parameters were modeled by random variables using statistic methods, such as the Burr distribution, the uniform distribution, and the inverse transformation methods. The Monte Carlo method was used for EV charging aggregation. The simulation results show that the proposed models are valid, accurate, and robust. The EV charging models can predict the EV charging loads in various future scenarios, such as different EV numbers, initial SOC, charging levels, and EV types (e.g., electric trucks). The EV charging models can be embedded into load flow studies to evaluate the impact of EV penetration on the power distribution systems, e.g., sustained under voltage, line loss, and transformer overloading. Although the proposed EV charging models are based on Saskatchewan’s situation, the model parameters can be tuned using other actual data so that the proposed model can be widely applied in different cities or countries. Full article
(This article belongs to the Special Issue Demand Response in Smart Homes)
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14 pages, 2465 KiB  
Article
Statistical Analysis of Baseline Load Models for Residential Buildings in the Context of Winter Demand Response
by Alain Poulin, Marie-Andrée Leduc and Michaël Fournier
Energies 2022, 15(12), 4441; https://doi.org/10.3390/en15124441 - 18 Jun 2022
Cited by 2 | Viewed by 2233
Abstract
By reducing electricity consumption during peak times, peak shaving could reduce the need for carbon intensive resources and defer capacity related investments. Households, where they use electricity for space or water heating, are major contributors to the winter peak demand and promising candidates [...] Read more.
By reducing electricity consumption during peak times, peak shaving could reduce the need for carbon intensive resources and defer capacity related investments. Households, where they use electricity for space or water heating, are major contributors to the winter peak demand and promising candidates for related demand response (DR) initiatives. The impact of such initiatives is determined by comparing the actual consumption during a DR event to a baseline, i.e., the estimated consumption that would have occurred in the absence of an event. This paper explores the challenges associated with modeling a baseline in the context of residential winter DR programs with individual performance-based incentives. A sample of more than a thousand residential load profiles was used in this study to provide a statistical comparison of performance metrics for different baseline load models. Arithmetic, regression based, and matching-day models were considered. Results show that adjusted arithmetic models achieve similar performances to the more complex regression model without the need for weather data. These simpler models were also found to be less sensitive to the number of events called during the season. Performing individual adjustments for each of the two daily peak periods also provides better accuracy. Full article
(This article belongs to the Special Issue Demand Response in Smart Homes)
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21 pages, 5043 KiB  
Article
A Robust Kalman Filter-Based Approach for SoC Estimation of Lithium-Ion Batteries in Smart Homes
by Omid Rezaei, Reza Habibifar and Zhanle Wang
Energies 2022, 15(10), 3768; https://doi.org/10.3390/en15103768 - 20 May 2022
Cited by 15 | Viewed by 2955
Abstract
Battery energy systems are playing significant roles in smart homes, e.g., absorbing the uncertainty of solar energy from root-top photovoltaic, supplying energy during a power outage, and responding to dynamic electricity prices. For the safe and economic operation of batteries, an optimal battery-management [...] Read more.
Battery energy systems are playing significant roles in smart homes, e.g., absorbing the uncertainty of solar energy from root-top photovoltaic, supplying energy during a power outage, and responding to dynamic electricity prices. For the safe and economic operation of batteries, an optimal battery-management system (BMS) is required. One of the most important features of a BMS is state-of-charge (SoC) estimation. This article presents a robust central-difference Kalman filter (CDKF) method for the SoC estimation of on-site lithium-ion batteries in smart homes. The state-space equations of the battery are derived based on the equivalent circuit model. The battery model includes two RC subnetworks to represent the fast and slow transient responses of the terminal voltage. Moreover, the model includes the nonlinear relationship between the open-circuit voltage (OCV) and SoC. The proposed robust CDKF method can accurately estimate the SoC in the presence of the time-varying model uncertainties and measurement noises. Being able to cope with model uncertainties and measurement noises is essential, since they can lead to inaccurate SoC estimations. An experiment test bench is developed, and various experiments are conducted to extract the battery model parameters. The experimental results show that the proposed method can more accurately estimate SoC compared with other Kalman filter-based methods. The proposed method can be used in optimal BMSs to promote battery performance and decrease battery operational costs in smart homes. Full article
(This article belongs to the Special Issue Demand Response in Smart Homes)
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17 pages, 4963 KiB  
Article
How Can Floor Covering Influence Buildings’ Demand Flexibility?
by Hyeunguk Ahn, Jingjing Liu, Donghun Kim, Rongxin Yin, Tianzhen Hong and Mary Ann Piette
Energies 2021, 14(12), 3658; https://doi.org/10.3390/en14123658 - 19 Jun 2021
Cited by 3 | Viewed by 2646
Abstract
Although the thermal mass of floors in buildings has been demonstrated to help shift cooling load, there is still a lack of information about how floor covering can influence the floor’s load shifting capability and buildings’ demand flexibility. To fill this gap, we [...] Read more.
Although the thermal mass of floors in buildings has been demonstrated to help shift cooling load, there is still a lack of information about how floor covering can influence the floor’s load shifting capability and buildings’ demand flexibility. To fill this gap, we estimated demand flexibility based on the daily peak cooling load reduction for different floor configurations and regions, using EnergyPlus simulations. As a demand response strategy, we used precooling and global temperature adjustment. The result demonstrated an adverse impact of floor covering on the building’s demand flexibility. Specifically, under the same demand response strategy, the daily peak cooling load reductions were up to 20–34% for a concrete floor whereas they were only 17–29% for a carpet-covered concrete floor. This is because floor covering hinders convective coupling between the concrete floor surface and the zone air and reduces radiative heat transfer between the concrete floor surface and the surrounding environment. In hot climates such as Phoenix, floor covering almost negated the concrete floor’s load shifting capability and yielded low demand flexibility as a wood floor, representing low thermal mass. Sensitivity analyses showed that floor covering’s effects can be more profound with a larger carpet-covered area, a greater temperature adjustment depth, or a higher radiant heat gain. With this effect ignored for a given building, its demand flexibility would be overestimated, which could prevent grid operators from obtaining sufficient demand flexibility to maintain a grid. Our findings also imply that for more efficient grid-interactive buildings, a traditional standard for floor design could be modified with increasing renewable penetration. Full article
(This article belongs to the Special Issue Demand Response in Smart Homes)
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Review

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34 pages, 758 KiB  
Review
A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes
by Amit Shewale, Anil Mokhade, Nitesh Funde and Neeraj Dhanraj Bokde
Energies 2022, 15(8), 2863; https://doi.org/10.3390/en15082863 - 14 Apr 2022
Cited by 36 | Viewed by 5593
Abstract
The residential sector is a major contributor to the global energy demand. The energy demand for the residential sector is expected to increase substantially in the next few decades. As the residential sector is responsible for almost 40% of overall electricity consumption, the [...] Read more.
The residential sector is a major contributor to the global energy demand. The energy demand for the residential sector is expected to increase substantially in the next few decades. As the residential sector is responsible for almost 40% of overall electricity consumption, the demand response solution is considered the most effective and reliable solution to meet the growing energy demands. Home energy management systems (HEMSs) help manage the electricity demand to optimize energy consumption without compromising consumer comfort. HEMSs operate according to multiple criteria, including electricity cost, peak load reduction, consumer comfort, social welfare, environmental factors, etc. The residential appliance scheduling problem (RASP) is defined as the problem of scheduling household appliances in an efficient manner at appropriate periods with respect to dynamic pricing schemes and incentives provided by utilities. The objectives of RASP are to minimize electricity cost and peak load, maximize local energy generation and improve consumer comfort. To increase the effectiveness of demand response programs for smart homes, various demand-side management strategies are used to enable consumers to optimally manage their loads. This study lists out DSM techniques used in the literature for appliance scheduling. Most of these techniques aim at energy management in residential sectors to encourage users to schedule their power consumption in an effective manner. However, the performance of these techniques is rarely analyzed. Additionally, various factors, such as consumer comfort and dynamic pricing constraints, need to be incorporated. This work surveys most recent literature on residential household energy management, especially holistic solutions, and proposes new viewpoints on residential appliance scheduling in smart homes. The paper concludes with key observations and future research directions. Full article
(This article belongs to the Special Issue Demand Response in Smart Homes)
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