Energy Management in Smart Houses with Batteries, Renewable Energy and Context Awareness
Baris Aksanli, Jagannathan Venkatesh, Tajana Simunic Rosing

Citation
Baris Aksanli, Jagannathan Venkatesh, Tajana Simunic Rosing. " Energy Management in Smart Houses with Batteries, Renewable Energy and Context Awareness". Talk or presentation, 29, September, 2013; Poster from the First International Workshop on the Swarm at the Edge of the Cloud (SEC'13 @ ESWeek), Montreal.

Abstract
Residential energy consumption accounts for around 40% of the overall energy consumption in the US, with tens of millions of individual consumers. This large scale consumption creates a promising opportunity to save energy, and in turn, reduce the total cost of energy for consumers. In this work, we focus on how residences can automate and control their energy consumption in an efficient way. This automation is achieved through monitoring and management, which are enabled by advances in smart grid technologies such as smart metering, sensor deployment, and communication methods. In addition, in this study, we consider smart battery usage and leverage context awareness along with renewable energy integration. Using batteries is a well-known method to take advantage of the diurnal patterns of the energy consumption profile of a house and variable electricity prices. These devices can store energy when it is cheap and provide the stored energy when the price is higher. Several studies have previously addressed this idea and formulated optimization problems to solve for the best battery characteristics to minimize the total cost of energy. Although these studies account for battery properties in their formulations, they do not model the non-linear behavior of the batteries. These properties, including the depth and rate of battery discharge, are affected highly by the battery type. We first define a battery configuration study and then formulate the problem of selecting the best battery configuration in order to minimize the total cost of energy. This study leverages our previous work, where we introduce an accurate battery lifetime model and validate the model against real measurements, with an average error less than 5%. We begin our analysis by investigating the case of two-tier time of use electricity pricing, which consists of a high and low energy price. For this case, we assume that there is no renewable energy integration. We formulate the problem with electricity pricing dynamics and non-linear battery behavior, and obtain a feasibility control inequality that can test if a battery configuration is profitable. The inequality we obtain is simple and easy to apply. We verify the outcomes of this inequality with a simulation study using real house energy consumption profiles from MIT REDD dataset. We use multiple battery types for our analysis and show that up to 43% energy cost savings is possible with battery usage. However, without specific battery configurations, these savings can be reduced or even eliminated altogether. Our next steps in this topic include the analysis of different pricing dynamics, other than two-level pricing, and the investigation of how the results and the framework would change with renewable energy integration and its variable nature. More specifically, we want to answer the following questions: 1) which pricing mechanism makes the battery usage more profitable? 2) how does the renewable energy integration and its variable nature have an impact on the best battery configuration? We further consider the impact of context awareness in the home. The advent of smart metering, non-intrusive load monitoring (NILM), and the prevalence of mobile and stationary sensing provides the ability to determine energy consumption in the home and occupant behavior. This data can, in turn, be used to modify the behavior of energy consumers in the home. We investigate these scenarios through a series of case studies involving both internal and external sensed data, demonstrating up to a 50% reduction in grid consumption. Internal variables such as user occupancy and appliance output data enables prediction of future energy consumption, which in turn can enable home automation. External variables such as temperature and solar irradiance can be used to control the behavior of heating and cooling (HVAC) appliances. Both can be used to better understand, predict, and react to user and environmental behavior in the home, and consequently, control energy consumption and cost.

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Citation formats  
  • HTML
    Baris Aksanli, Jagannathan Venkatesh, Tajana Simunic Rosing.
    <a
    href="http://www.terraswarm.org/pubs/133.html"><i>
    Energy Management in Smart Houses with Batteries, Renewable
    Energy and Context Awareness</i></a>, Talk or
    presentation,  29, September, 2013; Poster from the <a
    href="http://www.terraswarm.org/conferences/13/swarm/index.htm"
    >First International Workshop on the Swarm at the Edge of
    the Cloud (SEC'13 @ ESWeek)</a>, Montreal.
  • Plain text
    Baris Aksanli, Jagannathan Venkatesh, Tajana Simunic Rosing.
    " Energy Management in Smart Houses with Batteries,
    Renewable Energy and Context Awareness". Talk or
    presentation,  29, September, 2013; Poster from the <a
    href="http://www.terraswarm.org/conferences/13/swarm/index.htm"
    >First International Workshop on the Swarm at the Edge of
    the Cloud (SEC'13 @ ESWeek)</a>, Montreal.
  • BibTeX
    @presentation{AksanliVenkateshRosing13_EnergyManagementInSmartHousesWithBatteriesRenewable,
        author = {Baris Aksanli and Jagannathan Venkatesh and Tajana
                  Simunic Rosing},
        title = { Energy Management in Smart Houses with Batteries,
                  Renewable Energy and Context Awareness},
        day = {29},
        month = {September},
        year = {2013},
        note = {Poster from the <a
                  href="http://www.terraswarm.org/conferences/13/swarm/index.htm"
                  >First International Workshop on the Swarm at the
                  Edge of the Cloud (SEC'13 @ ESWeek)</a>, Montreal.},
        abstract = {Residential energy consumption accounts for around
                  40% of the overall energy consumption in the US,
                  with tens of millions of individual consumers.
                  This large scale consumption creates a promising
                  opportunity to save energy, and in turn, reduce
                  the total cost of energy for consumers. In this
                  work, we focus on how residences can automate and
                  control their energy consumption in an efficient
                  way. This automation is achieved through
                  monitoring and management, which are enabled by
                  advances in smart grid technologies such as smart
                  metering, sensor deployment, and communication
                  methods. In addition, in this study, we consider
                  smart battery usage and leverage context awareness
                  along with renewable energy integration. Using
                  batteries is a well-known method to take advantage
                  of the diurnal patterns of the energy consumption
                  profile of a house and variable electricity
                  prices. These devices can store energy when it is
                  cheap and provide the stored energy when the price
                  is higher. Several studies have previously
                  addressed this idea and formulated optimization
                  problems to solve for the best battery
                  characteristics to minimize the total cost of
                  energy. Although these studies account for battery
                  properties in their formulations, they do not
                  model the non-linear behavior of the batteries.
                  These properties, including the depth and rate of
                  battery discharge, are affected highly by the
                  battery type. We first define a battery
                  configuration study and then formulate the problem
                  of selecting the best battery configuration in
                  order to minimize the total cost of energy. This
                  study leverages our previous work, where we
                  introduce an accurate battery lifetime model and
                  validate the model against real measurements, with
                  an average error less than 5%. We begin our
                  analysis by investigating the case of two-tier
                  time of use electricity pricing, which consists of
                  a high and low energy price. For this case, we
                  assume that there is no renewable energy
                  integration. We formulate the problem with
                  electricity pricing dynamics and non-linear
                  battery behavior, and obtain a feasibility control
                  inequality that can test if a battery
                  configuration is profitable. The inequality we
                  obtain is simple and easy to apply. We verify the
                  outcomes of this inequality with a simulation
                  study using real house energy consumption profiles
                  from MIT REDD dataset. We use multiple battery
                  types for our analysis and show that up to 43%
                  energy cost savings is possible with battery
                  usage. However, without specific battery
                  configurations, these savings can be reduced or
                  even eliminated altogether. Our next steps in this
                  topic include the analysis of different pricing
                  dynamics, other than two-level pricing, and the
                  investigation of how the results and the framework
                  would change with renewable energy integration and
                  its variable nature. More specifically, we want to
                  answer the following questions: 1) which pricing
                  mechanism makes the battery usage more profitable?
                  2) how does the renewable energy integration and
                  its variable nature have an impact on the best
                  battery configuration? We further consider the
                  impact of context awareness in the home. The
                  advent of smart metering, non-intrusive load
                  monitoring (NILM), and the prevalence of mobile
                  and stationary sensing provides the ability to
                  determine energy consumption in the home and
                  occupant behavior. This data can, in turn, be used
                  to modify the behavior of energy consumers in the
                  home. We investigate these scenarios through a
                  series of case studies involving both internal and
                  external sensed data, demonstrating up to a 50%
                  reduction in grid consumption. Internal variables
                  such as user occupancy and appliance output data
                  enables prediction of future energy consumption,
                  which in turn can enable home automation. External
                  variables such as temperature and solar irradiance
                  can be used to control the behavior of heating and
                  cooling (HVAC) appliances. Both can be used to
                  better understand, predict, and react to user and
                  environmental behavior in the home, and
                  consequently, control energy consumption and cost.},
        URL = {http://terraswarm.org/pubs/133.html}
    }
    

Posted by Christopher Brooks on 1 Oct 2013.

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