Custom Writing Service http://StudyHub.vip/A-Modular-Framework-To-Enable-Rapid-Eva

1. Energy Syst
DOI 10.1007/s12667-014-0121-9
ORIGINAL PAPER
A modular framework to enable rapid evaluation
and exploration of energy management methods in
smart home platforms
Xiao Qin · Lin Lin · Susan Lysecky ·
Janet Roveda · Young-Jun Son ·
Jonathan Sprinkle
Received: 25 May 2013 / Accepted: 26 March 2014
© Springer-Verlag Berlin Heidelberg 2014
Abstract Numerous efforts focus on developing smart grid and smart home plat-
forms to provide monitoring, management, and optimization solutions. In order to
more effectively manage energy resources, a holistic view is needed; however the
involved platforms are complex and require integration of a multitude of parameters
such as the end-user behavior, underlying hardware components, environment, etc.,
many of which operate on varying time scale at various levels of detail. A general and
modular framework is presented to enable designers to focus on modeling, simulating,
analyzing, or optimizing specific sub-components without requiring a detailed imple-
mentation across all levels. We incorporate two case studies in which the proposed
framework is utilized to help an end user evaluate platform configurations given an
energy usage model, as well as integrate an energy optimization module to investigate
rescheduling of appliance usage times in an effort to lower cost.
Keywords Smart grid · Transaction level modeling · Simulation · Optimization
1 Introduction
The rising cost and demand of conventional fossil fuels are driving large initiatives in
not only developing methods to efficiently harvest energy from renewable resources,
This work is supported by the Air Force Office of Scientific Research (#FA9550-091-0519), the National
Science Foundation award (CNS-0930919), and the Arizona Research Institute for Solar Energy
(AzRISE).
X. Qin (B) · L. Lin · S. Lysecky · J. Roveda · J. Sprinkle
Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ 85721, USA
e-mail: seanqinxiao@gmail.com
Y.-J. Son
Department of System and Industrial Engineering, University of Arizona, Tucson, AZ 85721, USA
123

2. X. Qin et al.
but in the development of effective monitoring and management mechanisms of the
underlying computational and physical resources. The development of a smart grid
comprises a broad range of technology solutions to improve reliability, security, effi-
ciency, and robustness of these energy infrastructures. Simulation tools are valuable
resources for developers to evaluate new optimization and management mechanisms,
test complex systems before integration, visualize and observe system behaviors, etc.
However, the complexity, diversity, and interdependency of these systems can be a
roadblock in enabling end-users to obtain a holistic system-level view.
For example, a developer may want to study how to effectively manage hybrid
energy generation resources (e.g. solar, wind, battery, and grid). In addition to inves-
tigating the management methodologies themselves, the developer must additionally
consider solar and battery models, weather models based on region, energy usage mod-
els of the underlying platform, among a variety of additional details that can detract
from the focus of their work. A general and modular framework would provide many
advantages, allowing designers to focus on the sub-component of interest without
requiring a detailed implementation across all levels.
A general and modular framework can similarly benefit end users, such as a home-
owner. From a consumer perspective, energy consumption is invisible and abstract,
leading to a poor understanding of how much energy appliances consume, as well as
misconceptions in how to conserve energy [1]. Providing monitoring statistics is not
sufficient for consumers to understand how each behavior affects energy consumption
and cost [2]. A general and modular framework can provide these users with the ability
to visualize the behavior of platform sub-components as well as the long-term impacts
of their energy decisions, whether these changes are physical or behavioral changes.
Moreover, studies show that feedback systems often take an increasingly passive role
over time leading to decreased energy savings [3]. Thus the platform can be extended
(Fig. 1) to provide a closed-loop system to enable the platform to dynamically adapt
to the ever-changing environment
A number of tools are currently available to help designers plan and evaluate the
resulting energy cost of various structures. Autodesk green building studio [4] is a
web service that analyzes a building model and provides a baseline report on the
proposed buildings carbon output from the consumption of resources such as fuel,
electricity, or water. From an architectural viewpoint, an updated carbon-footprint
analysis is provided, enabling designs to consider various building configurations that
integrate photovoltaic solar panels, automated lighting controls, window glazing, and
so on. ENERGY-10TM [5] similarly is a software tool developed by the National
Renewable Energy Laboratory’s (NREL) Center for Building and Thermal Systems
that help architects, builders, and engineers quickly identify the most cost-effective,
energy-saving measures to take in designing a low-energy building. The simulation
software is limited however for examining small commercial and residential buildings
characterized by one or two thermal zones.
A suite of simulation tools is also available such as the EnergyPlus simulation
program [6], built on top of DOE-2 [7], to calculate hourly energy cost for a variety of
commercial and residential buildings. Parameters such as the buildings construction
andclimateareconsidered,andintegratelow-levelHVACandductlossmodels,among
others. VisualDOE [8] is a Windows interface to the DOE-2.1 energy simulation
123

3. Rapid evaluation and exploration of energy management framework
Fig. 1 An integrated platform is needed at the house, neighborhood, and community levels to monitor and
respond to changes within the environment as well as interact across different levels
program, where end users can construct a model of the building’s geometry through
built-in drawing tool or importing model files. A library of constructions, fenestrations,
systems and operating schedules is also included, along with integration of custom
elements.
The alternative energy product suite (AEPS) system planning tool [9] is a software
application that focuses on the design, modeling, and simulation of electrical energy
systems with an emphasis on renewable energy sources (solar, wind, and hydro).
These tools calculate energy generation, consumption, and storage for modeled sys-
tems. Energy and cost data can be analyzed to optimize the modeled system based on
user objectives and priorities. REM/DesignTM similarly calculates heating, cooling,
domestic hot water, lighting and appliance loads, consumption, and costs based on a
description of the home’s design and construction features as well as local climate and
energy cost data [10].
TRNSYS [11] is an energy simulation program taking a modular system approach
that utilizes a system description language to enable a user to specify platform com-
ponents and the manner in which they are connected. Due to its modular approach,
TRNSYS is extremely flexible for modeling a variety of energy systems in differ-
ing levels of complexity. However, no assumptions about the building or system are
made (although default information is provided) and it is up to the end user to provide
detailed information about the building and sub-systems.
Although numerous platforms exist, these platforms are optimized for their respec-
tive purposes, and focus on the physical attributes of a given system. Lack of flexibility
on subsystem configurations is also a common drawback of these systems because of
their subsystem encapsulation nature. Moreover, these tools are targeted for developers
who must specify the impact of low-level parameters within a given design. However,
123

4. X. Qin et al.
behavioral factors also impact the energy consumption of building [12] and must be
considered to obtain a holistic system view. This work aims to develop a general and
modular framework based on transaction-level modeling paradigm to enable users to
quickly and easily design, evaluate, optimize, and control their respective applica-
tions within a holistic environment. With TLM approach, this framework permits the
concurrent consideration of design alternatives which can currently only be individu-
ally explored in stovepiped analysis tools, therefore greatly reduce the complexity of
subsystem integration and configuration.
TLM has been extensively utilized in the field of computer engineering [13–15]
where energy of system-on-chip (SoC) platforms were studied and modeled. There
are also a few reports on TLM used in electrical systems, such as [16] where the
transmission model was built with TLM. The scope of this paper however, aims at
developing an energy modeling, simulation and optimization framework for a smart
home platform, which may comprise various energy generation, consumption and
storage profiles, therefore providing users a holistic view of the platform’s perfor-
mance. Moreover, by utilizing a TLM based framework the computation and com-
munication between modules are separated, enabling developers to refine individual
models and capture behavior at a variety of levels implementation while interacting
with existing models. To demonstrate the flexibility of the proposed TLM framework,
several experiments are highlighted including various energy usage, generation, and
storage platform evaluations, as well as the integration and evaluation of scheduling
algorithms given various platform configurations. To illustrate framework usage, we
construct several platform scenarios and evaluate their corresponding tasks.
2 Simulation framework
Transaction-level modeling (TLM) is a design abstraction that has grown in popularity
over the last decade [17,18]. Utilized to combat the increasing design complexity
in the digital and embedded system domain [19,20,22,21], TLM has served as a
design abstraction that permits rapid assembly of software and hardware subsystems.
At the core, this methodology enables system level by separating the specification
of computation and communication mechanisms. Thus, TLM captures methods for
implementingelementsatdifferentlevelsofabstractiontoenabledeveloperstobalance
the speed and accuracy of the underlying system simulation [23,24]. These design
abstractions can similarly benefit researchers and developers within the smart grid
and smart home domain, as increasing complexity and interconnection of numerous
subsystems pose many challenges. Thus, within the proposed platform we seek to
illustrate how transferring TLM concepts can similarly be beneficial.
Therefore TLM is transferred from the digital design domain to take advantage of
a framework with modular components and subsystems capable of specifying varying
levels of detail and accuracy. The flexibility of the TLM abstraction aids developers
in tackling system complexity as well as reducing designer effort. The platform can
be captured holistically while utilizing models of various levels of abstractions. In
this manner, developers can observe the interaction of various components, as well as
observe system level performance, while relieving developers from having to capture
123

5. Rapid evaluation and exploration of energy management framework
(a) (b)
Fig. 2 A transaction-level modeling example capturing. a A simple computational and peripheral compo-
nents connected through a communication channel and b an increasingly complex system using hierarchy
every aspect of the platform in great detail. As developers refine individual compo-
nents, integration is simple as long as interfaces are maintained. Thus developers are
able to rapidly capture, evaluate, and verify the application of interest. Then if the
underlying details of a model are needed, or if higher accuracy is required, models
can be updated to satisfy these system requirements. Moreover, these features grant
maximum flexibility for the developers to decide at what level accuracy is required as
compared to the speed of simulation.
In the following subsections we will introduce in details the construction of the
simulation framework using TLM. We will mainly focus on the concept of components
constructions without introducing the detailed programming language and code, users
are encouraged to refer to specific programming language such as SystemC [25] and
SpecC [26] for more details.
Figure 2a provides a generalized example utilizing TLM to capture a basic platform
configuration. A TLM model typically consists of components, channels, interfaces,
ports, and connections. In this example, processing and peripheral components can
interact with one another through a channel. The computational element (CE_1) may
represent a processor that reads from a temperature sensor (PE_1) and looks for abnor-
mal readings. In this example the sensor may define a read interface, specifying the
expected interactions. The processor would implement a read port, adhering to that
specification. As long as the interface and port definitions do not change, the underly-
ing implementation of the components (e.g. untimed, approximately timed, register-
transfer, cycle accurate) can be updated or refined without impacting the system level
compatibility. Moreover, these expected interactions referred to as transactions sep-
arate the underlying communication details from the implementation details. Figure
2b additionally illustrates the ability to manage complexity of larger systems. Com-
ponents can be defined hierarchically (CE_4), composed of sequential or concurrent
processes (CE_6/CE_7). In addition, developers are also able to integrate communi-
cation details as needed, such as bus arbitration or timing. The modular representation
of components and component interactions enables developers to easily integrate and
interchange components, defined in various levels of abstraction, to create a variety
of customized platform configurations.
123

6. X. Qin et al.
Fig. 3 Overview of component available in the simulation framework
To further illustrate the workflow of TLM design, we take a closer look at the exam-
ple sketched in Fig. 2a. Suppose we are trying to model a simple electrical supplier-
consumer system where the consumer is a home appliance (say TV) and the supplier
is the electrical grid. The appliance has a list of attributes specifying its characteris-
tics, in this simple example we assume it’s the power consumption only. Moreover,
we assume the appliance’s behavior (On/Off) is governed by a time-variant function
b = f (t), when the appliance is On, it will consume energy from the grid specified by
its power consumption. On the other hand, the grid could provide whatever amount
of energy the appliance is requesting (assuming no blackouts nor brownouts), and
need calculate how much energy the appliance consumed. For this scenario we simply
model the grid as the computational element CE_1 and the appliance as peripheral
element PE_1. Then the port and interface between these two components would be
a simple supply-consume relationship. On each clock edge, the appliance request a
certain amount of energy defined by f (t) from the grid through the interface, and
the grid accumulates the total amount of energy consumed by the appliance as the
computation conducted by the computational elements. Because of this design, this
simple system could easily expanded by adding more appliances (peripheral elements)
connecting to the grid, without rewriting the existing elements, or knowing the details
of these elements.
Figure 3 provides an overview of components available within the simulation frame-
work. Component interfaces and ports have been omitted to improve readability. Basic
categoriesofcomponentsareenergygenerationmodels,energystoragemodels,energy
usage models, and energy management and optimization methods. In the following
sections each of these categories is expanded.
2.1 Energy usage models
The energy usage model reflects the energy load profile of the platform under consider-
ation (e.g. single detached home, commercial building, neighborhood). In this frame-
work, as we want the ability to optimize usage patterns, we take a similar approach
to Yao and Steemers [12], which considers using patterns based on both behavioral
123

7. Rapid evaluation and exploration of energy management framework
Wh
Time Time
0:000
12:00
23:00
18:00
06:00
09:00
15:00
03:00
21:00
0:000
12:00
23:00
18:00
06:00
09:00
15:00
03:00
21:00
Time Time
0:000
12:00
23:00
18:00
06:00
09:00
15:00
03:00
21:00
0:000
12:00
23:00
18:00
06:00
09:00
15:00
03:00
21:00
(a)
12000
10000
8000
6000
4000
2000
0
Wh
(b)
12000
10000
8000
6000
4000
2000
0
Wh
(c)
Wh
(d)
12000
10000
8000
6000
4000
2000
0
12000
10000
8000
6000
4000
2000
0
Fig. 4 Sample energy usage models aggregated to illustrate various energy load profiles—a UP_All Day,
b UP_Evening, c UP_Morning/Evening, and d UP_Afternoon/Evening
and physical determinants. Behavioral determinants are strongly tied to human fac-
tors such as household composition, climate, and cultural background, dictating the
frequency an appliance is utilized, and considered as flexible decisions. Physical deter-
minants, on the other hand, are fixed decisions and tied to the energy consumption of
a particular subsystem or the buildings size.
Within the simulation framework, four usage patterns are defined based on a field
study in [27]. Each of the usage patterns contains the same set of appliances, such as the
air conditioner, refrigerator, television, microwave, dishwasher, dryer, and water heater
corresponding to physical determinants. However, the duration and time at which
these appliances are utilized vary and are based on behavioral determinates. Figure 4
illustrates the aggregated energy load over a 24-h period for four different energy usage
models (UP_All Day, UP_Evening, UP_Morning/Evening, UP_Afternoon/Evening),
where the prefix UP stands for user pattern, and the following phrase indicates the
when most of the appliances are running in that user pattern.
The appliance level models are aggregated together to form these energy usage
models. To aid the management and optimization mechanisms developed, each of
the tasks within the energy usage model (i.e. appliance usage) are annotated with
additional properties such as Priority, Deadline, and Mobility. These properties are
optional and enable optimization mechanisms described later to better meet user goals.
The Priority provides the user with the ability to specify the relative importance of a
task. The task Deadline is used to specify the upper time bound in which the given
task must be completed by. Lastly, the task Mobility enables a user to specify whether
a task is static or flexible. Static tasks must adhere to the usage schedule provided
123

8. X. Qin et al.
and cannot be moved to execute at another time. Alternatively, flexible tasks can be
delayed to a later time slot; however these tasks must still adhere to the user specified
Deadline.
2.2 Energy generation and storage models
While energy usage models outline how energy is consumed within the platform,
energy generation models define possible sources of energy available within the plat-
form. Hybrid energy systems, which consist of a combination of one or several renew-
able energy sources in addition to the standard electricity grid, provide a promising
avenue to address our demand for energy [28–30]. As such, the proposed frame-
work similarly considers three resources solar, battery, and grid. Within each type
of energy resource, different models are available facilitating a number of platform
permutations.
2.2.1 Solar generation patterns
As harvesting and efficient use of solar energy is a topic of great interest, a number
of resources are available which provide the data of daily energy generation for dif-
ferent types of solar photovoltaic configurations. The surface meteorology and solar
energy website [31] provides more than 20 years of data tables from over 1,000 loca-
tions. The national solar radiation data similarly provides hourly readings of solar
radiation and other meteorological elements for use within simulation environments
to gauge the performance of new designs in typical conditions [5]. Users can eas-
ily create a number of solar generation models to reflect a wide variety of platform
scenarios.
We take advantage of local resources and utilize field data measurements taken
from the Tucson electric power (TEP) solar test yard [32]. Currently more than 600
PV modules from 20 different manufacturers are deployed, with AC power, DC power,
irradiance, and temperature readings logged every five minutes. Four solar generation
models (SG_1, SG_2, SG_3, SG_4) were integrated within the proposed platform
and are based on physical measurements. The SG_1 solar generation model captures
historical readings from a combination of PV cells within a larger field study from
the TEP test yard. The SG_2 solar generation model reflects readings corresponding
to eight Sanyo HIP-J54BA2 solar panels between May and October, 2012. Solar
generation model SG_3 and SG_4 include physical measurements obtained between
November and April utilizing Sanyo HIP-J54BA2 solar panels, with eight versus
sixteen panels, respectively.
2.2.2 Grid
Energy from the electricity grid is the most common energy resource and is assumed to
be available any time the end user requires energy. However, pricing differs greatly by
location, vendor, time of day, type of facility, and so on. A number of pricing schemes
have been proposed and explored. The current framework supports two residential
123

9. Rapid evaluation and exploration of energy management framework
Table 1 Residential pricing plan R-01 based on season and energy consumption
Summer (May–October) Winter (November–April)
Base power supply charge (per KWh) $ 0.033198 $ 0.025698
Deliver charge (per KWh)
First 500 KWh $ 0.046925 $ 0.047369
Next 3,000 KWh $ 0.068960 $ 0.067309
3,501 KWh and above $ 0.088960 $ 0.087309
pricing models based the local utility [33]. In the first model (EG_1) the cost per
KWh is based on the season, summer versus winter, as well as the total amount of
energy utilized during the billing cycle, as shown in Table 1. The second model (EG_2)
similarlyaccountsfortheseasonandamountofenergyutilized,butadditionallyfactors
in a time of use cost based on-peak, and off-peak usage. Summer months additionally
include a shoulder-peak time.
2.2.3 Energy storage models
The storage module is an optional but important component of the renewable energy
framework. Our framework currently includes two simple battery models. The first
model is based on a compressed air energy storage system [34] that can be used in
both utility and personal applications. The CAES model (CS_1) contains 50 KWh
capacity and a 70 % efficiency rate [35]. The second storage model (BB_1) is based on
a battery bank of 12-volt PVX 2580L solar batteries [36], typically used for off grid
and grid tied systems. The model currently limits the average discharge to 50 %, but
the end user can configure the desired discharge level. In addition, the model assumes
the inverter operates at 90 % efficiency, and connects eight batteries to create a battery
bank able to support 48 VDC, and 516 Ah over a 24-h period. Additionally, the battery
model incorporates a self-discharge rate of 1 % per month.
Unlike the physical measurements utilized to capture the solar generation model, a
higher level of abstraction is utilized to model the battery storage model. Specifically,
the battery models are simply a set of time-variant functions. For example the self-
discharge rate of a battery is modeled as a function in below
P = P × (1 − SD) (1)
where P is the current energy stored in the battery and SD the self-discharge rate of this
battery. This function gets executed on every clock within the simulation framework. A
code snippet is given in Listing 1 to further illustrate the modeling of a battery model,
where the self-discharge and charging function are shown. As stated previously, by
utilizing a TLM framework developers can capture different components within the
framework using varying levels of granularity. In one instance the focus may be on the
refinement and optimization of efficient solar panel designs, thus a detailed view of the
energy storage system is not required, but the interaction between these components
123

10. X. Qin et al.
are important. However, as the focus of the work changes developers are able expand
the energy storage models to register-transfer or circuit level descriptions if needed,
while keeping the same simulation framework.
void battery ::main()
{
. . .
while (1) {
wait(tiktok) ; /∗wait for each clock pulse∗/
/∗self discharge at each clock pulse∗/
current=current∗(1−leakage_rate) ;
}
. . .
}
. . .
int battery ::charge(double ener)
{
/∗charge battery with ener amount of energy∗/
current=current+ener∗battery_eff ;
/∗check if battery is saturated∗/
if (current>bt_size) {
current=bt_size ;
}
return 1;
}
Listing 1 Code snippet of battery model
2.3 Energy management and optimization strategies
The proposed framework additionally incorporates an energy management and opti-
mization (EMO) component to enable developers to investigate how intelligence can
be incorporated at various levels within the platform. For example, an EMO com-
ponent can be integrated with the energy usage model and investigate strategies to
modify the tasks execution times to bias time slices where renewable resource or
off-peak grid pricing are available. From an end user perspective, this module can be
utilized to obtain a static schedule that can be enacted by the end user or as a dynamic
scheduling mechanism that automatically modifies platform based on environmental
stimuli. Alternatively, an EMO component can be integrated at the energy generation
and storage level to gauge strategies in determining which resource (solar, battery,
grid) should be utilized to support a given task. The EMO component can also be
integrated at the appliance level to observe long-term behavior or detect anomalies. In
Sect. 3 we provide several case studies that highlight the use of the EMO component.
3 Case studies
The goal of the simulation framework is to provide developers with a holistic and
modular view of the desired platform, enabling developers to effectively analyze and
evaluate the impact of platform changes, optimizations, or policies. In the following
123

11. Rapid evaluation and exploration of energy management framework
Fig. 5 Platform configurations considered (solar, storage, grid) to evaluate
sections, we present several case studies that demonstrate the frameworks ability to
quickly evaluate a variety of platform energy storage configurations as well as evaluate
the effectiveness of several optimization strategies.
3.1 Evaluation of platform configurations
In the first case study a homeowner may want to evaluate grid pricing options or
integrating solar and battery resources. Figure 5a illustrates a platform configuration
that strictly evaluates various electrical grid pricing plans (EG_x), Fig. 5b extends
the platform to consider a grid-tied solar system that includes a various solar energy
generation models (SG_x), and Fig. 5c considers a grid-tied solar system with either
a CAES storage system (CS_1) or battery bank (BB_1).
Figure 6 illustrates the resulting cost from one month of simulation time given two
energy usage models UP_AllDay and UP_Evening, which repeat themselves every
day, under a variety of grid, solar, and storage configurations. If users only have
access to the electrical grid, they can evaluate which of the two pricing plans yield
the lowest cost, as depicted by EG_1 and EG_2. As shown in Fig. 6a the UP_AllDay
model yields a small difference in cost, with $ 40.31 and $ 53.16 for each of the pricing
plans, respectively. However, for the UP_Evening model (Fig. 6b), selection of the
first pricing plan yields a monthly cost of $ 41.61 whereas selection of the second
pricing plans yield a monthly cost of $ 58.44. The variation in cost stems from the fact
that tasks are concentrated in the evening, corresponding to on-peak hours that incur
higher prices.
Both energy usage models benefit from the integration of solar and battery resources
to help to further reduce reliance on the electricity grid energy, thereby reducing the
resulting monthly cost. It is not surprising that platform configurations utilizing the
SG_4 solar generation model typically result in lower costs, as this pattern considers
the largest number of solar panels. One artifact here can be observed is that the SG_4
solar generation coupled with a CAES model (EG_x, SG_4, CS_1) outperforms the
same configuration but a battery bank storage option (EG_x, SG_4, BB_1). This can be
explained by the larger capacity of the CAES model than its battery back counterpart.
In a situation that solar generation could provide abundant(often larger than a battery
bank could store) energy production as SG_4, an energy storage model with small
capacity could get saturated therefore waste the amount of energy that is beyond its
capacity, while a larger capacity model could still be able to store this energy, thus it
has better performance than a battery bank configuration. On the other hand, when the
123

12. X. Qin et al.
(a)
(b)
Fig. 6 Estimated cost for a variety of platform configurations corresponding to usage pattern, a UP_AllDay
and b UP_Evening
solar generation would not surpass the capacity limit, the higher efficiency of a battery
bank makes it more suitable than a low-efficient but large-capacity CAES solution.
A user can easily and quickly interact with the simulation framework to consider a
variety of platform scenarios to gain insight into how platform changes (as compared
to behavioral changes) such as electrical pricing policies, solar generation, or storage
impact the resulting energy cost. Users are able to get an idea of how changes in the
physical platform, such as integration of solar or storage impact cost. In addition,
users can evaluate how much savings is achieved based on how much solar or storage
is added, explicitly weighting the tradeoffs (i.e. initial cost vs. long term savings).
Alternatively, users can consider modifications the platform configuration, such as
different pricing plans from the utility company, to see the impact on cost given the
same energy usage pattern.
123

13. Rapid evaluation and exploration of energy management framework
3.2 Evaluation of task scheduling methods
In addition to analyzing a variety of platform scenarios, we also seek to understand
the role that optimization can play in helping to reduce cost and utilizing energy more
efficiently. The impact of various scheduling strategies is evaluated in the context of
a grid-tied solar system, combined with various energy usage and energy generation
models. In this context scheduling is utilized as a mechanism to determine when to
best execute tasks within the energy usage pattern (e.g. when to run the dishwasher).
By enabling the flexibility of task execution, the platform will strive to take advantage
of reduced grid pricing or availability of solar energy while meeting user specified
constraints (e.g. task priority, task execution deadline). This case study illustrates
how slight changes in behavior can yield large savings in monetary cost. Moreover,
a number of perspectives are also considered in addition to cost, such as peak power
and adaptability.
3.2.1 Scheduling overview
Taking inspiration from the domain of computing systems and manufacturing systems
we have integrated several well-established scheduling algorithms [37,38] within the
proposed simulation framework including earliest deadline first (EDF), shortest job
first (SJF), least power first (LPF), highest power first (HPF), and least laxity first
(LLF) where laxity indicates the amount of time left before the tasks deadline.
However, unlike traditional real-time system scheduling, our problem has several
key differences. First, the tasks provided within the energy usage model are non-
preemptive, once a task begins it cannot be interrupted. Secondly, there are no under-
lying physical resources to limit the number of tasks executed assuming the electrical
grid can provide sufficient energy when needed.
Two main processes are executed as part of the simulation phase. The first process
requires a sorting of the individual tasks within the energy usage model based on
the scheduling algorithm employed as well as user specified priorities. The second
process decides when to add tasks based on certain execution criteria (e.g. energy
availability or deadline requirement). Users can configure the scheduling to adhere
to a soft deadline setup, in which tasks are processed only when the non-grid energy
is available even if the user-defined deadline is violated, however in the experiments
a hard deadline setup is utilized in which a task relies on grid energy to meet user
specified deadlines.
3.2.2 Scheduling evaluation
Figure 7 shows the platform configuration utilized to evaluate the various sched-
ule methodologies, consisting of a variety of energy usage models (UP_AllDay,
UP_Evening, UP_Morning/Evening, UP_Afternoon/Evening), a variety of solar gen-
eration models (SG_1, SG_2, SG_3, SG_4), and a fixed electrical grid model (EG_2).
In this case study the platform does not include a storage model. Different scheduling
algorithms SCH_x are then evaluated on this configuration. The prefix SCH stands
123

14. X. Qin et al.
Fig. 7 Platform configurations utilized to evaluate various energy management and optimization schemes
related to rescheduling of tasks within energy usage model
for the word scheduling while x can be any one of the aforementioned scheduling
algorithm outlined in Sect. 3.2.1.
Figure 8 illustrates the impact of utilizing different scheduling algorithms for the
UP_AllDay energy usage model. For brevity the prefix SCH of each scheduling algo-
rithm is removed in the figure axis. To determine the impact of the scheduling algo-
rithms, we utilize a non-optimized base case denoted as ORIG (which stands for the
original schedule). On average rescheduling can reduce the cost by 38, 41, 40, and
45 % for the SG_1, SG_2, SG_3, and SG_4 solar generation patterns, respectively. No
one scheduling methodology results in the lowest cost, rather energy usage models and
platform configurations must be evaluated on a case-by-case basis to determine the best
optimization methodology. For example, the SJF algorithm achieved the lowest cost
for the SG_1 and SG_2 solar generation models, whereas the HPF and LPF algorithms
yielded the lowest costs for the SG_3 and SG_4 solar generation models, respectively.
A number of parameters impact the selection of the scheduling algorithm that yields
the lowest cost. For example, assuming the EG_2, SG_4 platform configuration, the
LPF, SJF, and EDF scheduling algorithm each yield the lowest cost given the par-
ticular energy usage model employed. As the underlying platform changes, resulting
from changes due to weather or the underlying infrastructure, a dynamically adaptable
system can similarly help users not only evaluate potential savings, but adapt to the
environment over time. Across all scheduling methodologies and platform configura-
tions, scheduling yielded savings of 2–55 % compared to the no scheduling option,
with an average savings of 33 %.
3.2.3 Peak power
One drawback of the current suite of scheduling algorithms is the possibility of increas-
ing the peak usage. Because tasks are scheduled based on predicted load at the corre-
sponding time slice, there exists a possibility that there will not be sufficient resources
to complete all of the running tasks, or tasks whose deadlines are approaching and
must be started. Large increases in peak power occur when a scarcity of resources is
present, as in the SG_1 and SG_2 solar generation models, the scheduling mechanisms
continue to wait to release tasks to execute as adequate solar energy is not available.
123

15. Rapid evaluation and exploration of energy management framework
(a)
(b)
Fig. 8 Estimated cost for a variety of platform configurations corresponding to usage pattern, a UP_AllDay
and b UP_Afternnon/Evening
At some point, deadlines of these tasks approach and may lead to a large number
of tasks running concurrently to meet the user defined deadline. In some platform
configurations, the UP_AllDay energy usage model yielded increases of 50 and 94 %
peak power as compared to the original energy usage model. In configurations with
higher solar energy generation, such as SG_3, the peak power is increased on average
by a modest 14 %, or in the case of SG_4, decreases on average by 18 %, as there is
sufficient solar energy to execute the desired tasks before their deadlines.
Thus we have integrated two additional scheduling mechanisms, FF1 and FF2, is
derived from a first come, first served scheduling methodology which sorts tasks and
assign task execution times in a single process. In the FF1 approach tasks are sorted
in order of decreasing energy then assigned to the first available time slice that can
support that task. The FF2 approach similarly sorts tasks in order of decreasing energy
but assigns tasks to the latest time slice that can support a given task. By assigning
all tasks in a single step, the scheduling algorithm can avoid assigning a large number
of overlapping tasks to ensure large peak power is not incurred. However, in order
to facilitate these algorithms, the framework must have a view of the anticipated
energy generation, storage, and usage models ahead of time. Numerous efforts have
considered methods for learning and prediction within smart grid and smart home
applications [39–43] and can be integrated within the framework.
123

16. X. Qin et al.
While these two algorithms did not necessarily outperform the original suite of
scheduling algorithms in terms of lowest monetary cost, on average the peak power
is decreased by 28 %. In terms of cost, these methods yielded savings of 12–57 %,
with an average savings of 39 % compared to the no scheduling option. A number
of tradeoffs must be considered, thus a user requires tools to understand the data and
guide the optimization to meet their needs.
3.2.4 Disturbances
We additionally seek to understand the benefit of dynamically adapting the underlying
platform. In this case study, additional tasks that are not originally part of the energy
usage model (i.e. disturbances) are injected. These tasks are static and must be executed
at the time they are introduced into the system. As each disturbance is injected, the
EMO component executes the scheduling algorithm to re-assign execution times to
the remaining flexible tasks. The platform configuration shown in Fig. 7 is utilized,
however the grid pricing model is fixed to EG_1 and the solar generation model is
fixed to SG_4.
In the first scenario a single disturbance is injected at different times of the day -
morning, afternoon, or evening. The number of disturbances and the time at which a
disturbance occur impacts the additional cost incurred by the platform. For example, a
task injected in an off-peak time will incur lower costs. In the worst case if the grid is
utilized to execute this task, the off peak pricing would be lower than peak pricing in
the evening. Thus, to gauge the impact of these disturbances the difference in cost of
the original and modified schedule was determined for each algorithm, and normalized
to the corresponding no scheduling cost. It is not surprising to see that disturbances
injected in the afternoon (Fig. 9b) on average yielded a lower normalized cost of
0.42, whereas the morning and evening tasks (Fig. 9a, c) yielded a normalized cost
of 0.54 and 0.60, respectively. Afternoon disturbances typically incur lower costs,
as renewable resources are abundant, and in most cases can be utilized to execute
these tasks. The morning and evening disturbances must rely, at least in part, on the
grid. Moreover, the evening tasks leave little flexibility for the EMO component to
determine alternative time slots to execute remaining tasks. However, in each of these
scenarios the addition of the EMO component is able to reduce the impact of these
disturbances as compared to their no schedule counterparts.
We additionally considered three scenarios where two disturbances are injected in
the morning/afternoon, afternoon/evening, or morning/evening (Fig. 10a–c), yielding
a normalized cost of 0.47, 0.44, 0.55, respectively. Again, the scheduling algorithms
are able to consistently minimize the cost of these disturbances as compared to their
no schedule counterparts. Interestingly, the morning/afternoon incur higher costs than
the afternoon/evening scenarios. In the previous example, disturbances in the morning
incurred lower costs as compared to disturbances in the evening as the scheduling
algorithms had more flexibility in finding alternative time slots for the remaining
tasks. In the two disturbance scenario, the morning disturbance (in conjunction with
the afternoon disturbance) incurred a higher cost than a task injected in the evening.
As the number of disturbances increases much of the task mobility is lost, and instead
becomes driven by the need to meet task deadlines.
123

17. Rapid evaluation and exploration of energy management framework
(a)
(b)
(c)
Fig. 9 Normalized cost of one disturbance in a evening, b afternoon, c morning for various energy usage
models
4 Conclusion
A flexible and modular simulation framework has been presented that integrates a
variety of energy usage, generation, and storage models. And an initial platform had
123

18. X. Qin et al.
(a)
(b)
(c)
Fig. 10 Normalized cost of two disturbance in a morning/afternoon, b afternoon/evening, c morn-
ing/evening for various energy usage models
been developed to demonstrate the concept of TLM, its easy-to-use and flexibility
features. Utilizing the proposed framework users can focus on the development of
the sub-system of interest while gauging the impact of these changes at the system
level. Several case studies were provided to illustrate use of the proposed simulation
123

19. Rapid evaluation and exploration of energy management framework
framework from a variety of perspectives. Consumers are able to evaluate the impact
of various grid pricing options, as well as cost savings from integration of renew-
able resources such as solar. Developers can easily integrate new models within the
simulation framework, such as the energy management and optimization component.
From these case studies we were able to see the impact of platform parameters such as
energy usage and storage models on the underlying optimization algorithms, as well
as impact on additional constraints such as peak power.
The proposed simulation framework provides developers with a modular platform
which enables developers to investigate a variety of topics related to energy systems
research. The TLM paradigm utilized within this framework enables easy integration
of additional models, enabling researchers from the community to integrate and reuse
models, thereby providing a richer set of resources to support numerous application
scenarios.
While the focus of this paper is on introducing and applying TLM methodology
into the smart grid and domain through heterogeneous energy system modeling and
simulation, expansions such as additional usage patterns can further demonstrate the
feasibility of the framework, for example integrating additional usage patterns that
span months to years to enable long-term analysis, solar generation models from
diverse locations, as well as configurable battery models to support a wider range of
characteristics.
However, showing the framework works for short dataset is a necessary condition
because users will not need to wait for months for the framework to be able to use. In
fact, the proposed framework is able to run simulations and generate the corresponding
outputs regardless of the size of the dataset. If a user wanted to evaluate a simulation
over a horizon larger than the available dataset, one can always perform extrapolations
on the current dataset to get a larger one, or simply repeat the available dataset as
discussed in Sect. 3.1, and/or perturb current dataset to simulate different patterns, thus
generate a synthesized simulation output. On the other hand, when a larger dataset
of sample readings is available, the user would just need to feed in this dataset to
the framework and get corresponding simulations. For example, the authors have
developed an HVAC prediction and control system using this concept, within a real-
time deployment environment, based on approximately 18-month sampled data [44].
The same process holds for additional solar generation models as well.
Lastly, as optimization and management of smart home components is a multi-
dimensional problem that diverse based on user and application scenario, we also
plan to integrate a user interface to users to better understand platform data, simplify
platform configuration, and specify user constraints.
References
1. Froehlich, J.: Promoting energy efficient behaviors in the home through feedback: The role of human-
computer interaction. In: HCIC 2009 Winter, Workshop (2009)
2. Roth, K., Brodrick, J.: Home energy displays. ASHRAE J. 50(7), 136–137 (2008)
3. Hargreaves, T., Nye, M., Burgess, J.: Making energy visible: a qualitative field study of how house-
holders interact with feedback from smart energy monitors. Energy Policy 38(10), 6111–6119 (2010).
doi:10.1016/j.enpol.2010.05.068
123

20. X. Qin et al.
4. Autodesk Inc.: Green building studio. http://usa.autodesk.com/green-building-studio (2010)
5. National Renewable Energy Lab: Energy-10. http://www.nrel.gov/buildings/energy10.html (2010)
6. Crawley, D.B., Lawrie, L., Pedersen, C., Winkelmann, F.: Energyplus: energy simulation program.
ASHRAE J. 42(4), 49–56 (2000)
7. Los Alamos National Laboratory: Doe-2 reference manual. http://doe2.com/doe2 (1980)
8. Architectural Energy Corp.: Visual doe. http://www.archenergy.com/products/visualdoe (2012)
9. Alternative Software Concept: Aeps. http://www.alteps.com (2012)
10. Architectural Energy Corp.: Rem/design. http://www.archenergy.com/products/remdesign (2012)
11. University of Wisconsin Madison: Trnsys: a transient systems simulation program. http://sel.me.wisc.
edu/trnsys/index.html (2012)
12. Yao, R., Steemers, K.: A method of formulating energy load profile for domestic buildings in the UK.
Energy Build. 37(6), 663–671 (2005)
13. Beltrame, G., Sciuto, D., Silvano, C.: Multi-accuracy power and performance transaction-level model-
ing. Computer Aided Design Integr. Circ. Syst. IEEE Trans. 26(10), 1830–1842 (2007). doi:10.1109/
TCAD.2007.895790
14. Dinh-Duc, A.V., Vivet, P., Clouard, A.: A transaction level modeling of network-on-chip architecture
for energy estimation. In: Research, innovation and vision for the future, 2007 IEEE International
Conference on, pp. 58–64 (2007). doi:10.1109/RIVF.2007.369136
15. Mbarek, O., Pegatoquet, A., Auguin, M.: Power domain management interface: flexible protocol inter-
face for transaction-level power domain management. Computers Digital Tech. IET 7(4), 155–166
(2013). doi:10.1049/iet-cdt.2012.0107
16. Kavicky, J., Shahidehpour, S.M.: A subarea-level transaction simulation framework supporting parallel
paths and energy tagging. Power Syst. IEEE Trans. 15(2), 873–878 (2000). doi:10.1109/59.867187
17. Cai, L., Gajski, D.: Transaction level modeling: an overview. In: Hardware/software codesign and
system synthesis, 2003. First IEEE/ACM/IFIP International Conference on, pp. 19–24 (2003). doi:10.
1109/CODESS.2003.1275250
18. Donlin, A.: Transaction level modeling: flows and use models. In: Hardware/software codesign and
system synthesis, 2004. CODES + ISSS 2004. International Conference on, pp. 75–80 (2004). doi:10.
1109/CODESS.2004.240821
19. Calazans, N., Moreno, E., Hessel, F., Rosa, V., Moraes, F., Carara, E.: From VHDL register transfer
level to systemc transaction level modeling: a comparative case study. In: Integrated circuits and
systems design, 2003. SBCCI 2003. Proceedings. 16th Symposium on, pp. 355–360 (2003). doi:10.
1109/SBCCI.2003.1232853
20. Caldari,M.,Conti,M.,Coppola,M.,Curaba,S.,Pieralisi,L.,Turchetti,C.:Transaction-levelmodelsfor
AMBA bus architecture using systemc 2.0. In: Proceedings of the conference on design, automation and
test in Europe: designers’ Forum—vol. 2, DATE ’03, pp. 20026. IEEE Computer Society, Washington,
DC, USA. http://dl.acm.org/citation.cfm?id=1022685.1022921 (2003)
21. Pasricha, S., Dutt, N., Ben-Romdhane, M.: Extending the transaction level modeling approach for
fast communication architecture exploration. In: Proceedings of the 41st annual Design Automation
Conference, DAC ’04, pp. 113–118. ACM, New York, NY, USA (2004). doi:10.1145/996566.996603
22. Klingauf, W.: Systematic transaction level modeling of embedded systems with systemc. In: Proceed-
ings of the conference on design, automation and ttest in Europe—vol. 1, DATE ’05, pp. 566–567.
IEEE Computer Society, Washington, DC, USA (2005). doi:10.1109/DATE.2005.293
23. Cai, L., Gajski, D.: Transaction level modeling in system level design. Center for Embedded Computer
Systems (2003)
24. Rose, A., Swan, S., Pierce, J., Fernandez, J.M., et al.: Transaction level modeling in systemc. Open
SystemC Initiative 1(1.297). http://www.systemc.org (2005)
25. Open, SystemC, Initiative: SystemC language. http://www.systemc.org (2010)
26. Gajski, D., Zhu, J., Dömer, R., Gerstlauer, A., Zhao, S.: SpecC: Specification Lauguage and Design
Methodology. Kluwer Academic Publishers, Massachusetts (2000)
27. Jenny, A., Lpez, J.R.D., Mosler, H.J.: Household energy use patterns and social organisation for optimal
energy management in a multi-user solar energy system. Progress Photovolt.: Res. Appl. 14(4), 353–
362 (2006). doi:10.1002/pip.672
28. Ashok, S.: Optimized model for community-based hybrid energy system. Renew. Energy 32, 1155–
1164 (2007)
123

21. Rapid evaluation and exploration of energy management framework
29. Carrasco, J., Franquelo, L., Bialasiewicz, J., Galvan, E., Guisado, R., Prats, M., Leon, J., Moreno-
Alfonso, N.: Power-electronic systems for the grid integration of renewable energy sources: a survey.
Ind. Electron. IEEE Trans. 53(4), 1002–1016 (2006). doi:10.1109/TIE.2006.878356
30. Schmid, J., Jimenez, M., Chadjivassiliadis, J.: Integration of renewable energies and distributed gen-
erators into electricity grids. In: Photovoltaic energy conversion, 2003. Proceedings of 3rd World
Conference on, vol. 3, pp. 2821–2826. IEEE (2003)
31. Stackhouse, P., Whitlock, C., Kusterer, J.: Surface meteorology and solar energy. A renewable energy
resource web site. http://eosweb.larc.nasa.gov/sse/ (2010)
32. Cronin, A.: Tep photovoltaic test yard data and related information. http://www.physics.arizona.edu/
cronin/Solar/TEPweb (2010)
33. Tucson, Electric, Power, Company: Rates and tariff—pricing plans. https://www.tep.com/Customer/
Rates/Pricing (2012)
34. Taylor, P., Johnson, L., Reichart, K., Dipietro, P., Philip, J., Butler, P.: A summary of the state of
the art of superconducting magnetic energy storage systems, flywheel energy storage systems, and
compressed air energy storage systems. Technical, Report SAND99-1854 (1999)
35. Doty, Energy: Compressed air energy storage(caes)-utilities and cars. http://www.dotyenergy.com/
Markets/CAES.html (2010)
36. Sun, Xtender: Sun xtender solar batteries-photovoltaic battery. http://www.sunxtender.com/
solarbattery.php?id=1 (2012)
37. Li, W., Kavi, K., Akl, R.: An efficient non-preemptive real-time scheduling. In: Internaltional Confer-
ence on parallel and distributed computing systems, pp. 154–160 (2005)
38. Salmani, V., Naghibzadeh, M., Habibi, A., Deldari, H.: Quantitative comparison of job-level dynamic
schedulingpoliciesinparallelreal-timesystems.In:TENCON2006.2006IEEERegion10Conference,
pp. 1–4. IEEE (2006)
39. Chen,C.,Das,B.,Cook,D.J.:Energypredictionbasedonresident’sactivity.In:InternationalWorkshop
on knowledge discover from sensor data (sensor-KDD) (2010)
40. Tso, G.K., Yau, K.K.: Predicting electricity energy consumption: a comparison of regression analysis,
decision tree and neural networks. Energy 32(9), 1761–1768 (2007)
41. Aula, F., Lee, S.: Grid power optimization based on adapting load forecasting and weather forecasting
for system which involves wind power systems. Smart Grid Renew. Energy 3, 112–118 (2012)
42. Suehrcke, H., McCormick, P.: A performance prediction method for solar energy systems. Solar Energy
48(3), 169–175 (1992)
43. Kramer, O., Satzger, B., Lssig, J.: Power prediction in smart grids with evolutionary local kernel
regression. Hybrid Artif. Intell. Syst. 6076, 262–269 (2010). doi:10.1007/978-3-642-13769-3_32
44. Qin, X.: A data-driven approach for system approximation and set point optimization, with a focus in
HVAC systems. Ph.D. Dissertation, The University of Arizona (2014)
123