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Design, Modeling, and Capacity Planning for Micro-Solar Power Sensor Networks Design, Modeling, and Capacity Planning for Micro-Solar Power Sensor
Networks
Jay Taneja, Jaein Jeong, David Culler
Computer Science Division, UC Berkeley
Berkeley, CA 94720, USA
{taneja,jaein,culler}@cs.berkeley.edu
Abstract
This paper describes a systematic approach to building
micro-solar power subsystems for wireless sensor network
nodes. Our approach composes models of the basic pieces
- solar panels, regulators, energy storage elements, and ap-
plication loads - to appropriately select and size the com-
ponents. We demonstrate our approach in the context of
a microclimate monitoring project through the design of
the node, micro-solar subsystem, and network, which is de-
ployed in a challenging, deep forest setting. We evaluate
our deployment by analyzing the effects of the range of so-
lar proles experienced across the network.
1
Introduction
The purpose of this paper is to provide a framework for
the design of micro-solar subsystems in wireless sensor net-
works. Its motivation is simple; we were designing a micro-
climate network for studies of hydrological cycles in forest
watersheds and needed a systematic means of engineering,
sizing, and analyzing the power subsystem. Many tools and
calculators are available for macro-solar installations in res-
idential and commercial applications, but only anecdotal,
point designs are represented in the sensor network litera-
ture for in situ micro-solar power. The basic components
are obvious and well documented [13] solar panels, reg-
ulators, and batteries but the selection, sizing, and com-
position of the components is not. The problem is rather
different from the macro-solar setting because of the very
small power transfers involved microwatts to milliwatts
rather than kilowatts to megawatts. Micro-solar operates at
very different efciencies and every bit of power condition-
ing or monitoring impacts the overall performance. We do
not have the luxury of putting the panels on a convenient
rooftop with ample exposure, it needs to be where the mea-
surements are to be taken, regardless of how shaded that
may be. At the same time, new degrees of design freedom
are presented by the tiny magnitude of the energy require-
ments.
As a preliminary framework, we begin by formulating a
general model of micro-solar systems that is sufcient for
constructing a capacity planning calculator to guide the
sizing of the various elements. We then ground the study
in a concrete design developed for the HydroWatch appli-
cation. It is a well-engineered climate monitoring node and
network with a exible power subsystem that can support
various specic design points and provides visibility into
the solar performance in real application settings. Putting
the model and empirical vehicle together, we study the de-
sign choices in each element of the solar subsystem to arrive
at a deployment candidate. We then utilize this to collect de-
tailed empirical data from the on-going deployment to drive
what is expected to be an iterative renement cycle.
2
Micro-Solar Planning Model
There have been several micro-solar power designs in the
literature. [6, 8, 9, 17, 19, 24] We aim to generalize the de-
sign space using the basic micro-solar model as illustrated
in Figure 1. Ultimately, the demand side is determined by
the power requirements of the wireless sensor node and its
associated protocols. It has been well established that this
load is bimodal [16, 18] with standby current in the neigh-
borhood of 10 uA and active current in the neighborhood of
10 mA. Thus, the duty cycle determines the average power
requirement,
P
mote
, as a weighted sum of these two ele-
ments that are separated by three orders of magnitude. For
example, a 1% duty cycle places the load in the neighbor-
hood of 110 uA, or .33 mW at 3 volts.
The supply side is dictated by the incident solar energy,
which is a function of the latitude, day of the year, panel ori-
entation, and angle of inclination. Rules of thumb for vari-
ous locations are widely available. To obtain greater insight
into the trade-offs, we incorporated the basic astronomical
1 P
sol
Power generated from the solar panel
P
bat
chg
Power input to charge the energy storage
P
bat
dis
Power discharged from the energy storage
P
mote
Power consumed by the load
P
shunted
Power being shunted when in excess
Eff
reg
in
Power efciency of the input regulator
Eff
reg
out
Power efciency of the output regulator
Eff
bat
Charge-discharge efciency of the energy storage
Figure 1. Micro-solar system architecture and
related parameters.
calculations directly in the computational model, [7].
The portion of incident solar energy that is available
at the panel is determined by a variety of environmental
factors. The absorption by the atmosphere is well under-
stood, and we all recognize the spectrum of weather fac-
tors, clouds, fog, and so on. In addition, any particular point
of installation will have various obstructions and shadows.
This critical attenuation factor can only be characterized
empirically. Experience with many deployments in differ-
ent settings can provide statistical models. Care in choosing
sites can potentially improve the expected availability. As a
rough starting point in this study, we used a guideline that a
half hour of sunlight per day should be sufcient to sustain
operation. Below, we re-examine this planning guideline
in light of specic model parameters and experience in the
forest. (It proved to be very optimistic.)
The panel transforms available incident solar radiation
to electrical power. A given panel is characterized by its IV
curve and, in particular, three points: open-circuit voltage
(
V
oc
), short-circuit current (
I
sc
), and its maximum power
point (MPP). Internally, these are determined by the serial
and parallel composition of the solar cells and the total area
of the panel. Increasing temperature depresses the IV curve
somewhat, reducing the power output. For the large, expen-
sive panels used in macro-solar installations these factors
are accurately characterized in data sheets and well vali-
dated. For the small, inexpensive panels used in micro-solar
applications, empirical characterization is often required.
More importantly, the operating point of the IV curve is
determined by the load experienced at the panel, which is
determined by the input regulator or the storage facility and
downstream load in the absence of a regulator. For most
panels, the IV curve is nearly at for voltages less than that
of the MPP, so power increases nearly linearly with V.
0
5
10
15
20
25
50
0
50
100
150
200
250
Hour
mW
Power Flow
Panel Output
Load
Net Battery
D
T
R
S
T
D
0
5
10
15
20
25
0.88
0.9
0.92
0.94
0.96
0.98
1
Hour
Rate over Total Capacity
Battery Capacity
Battery Capacity
D
T
R
S
T
D
Discharge
P
sol
= 0, P
bat
chg
= 0, P
bat
dis
> 0, P
mote
= const
P
mote
= P
bat
dis
� Eff
reg
out
Transition
P
sol
> 0, P
bat
chg
= 0, P
bat
dis
> 0, P
mote
= const
P
mote
= (P
sol
� Eff
reg
in
+ P
bat
dis
) � Eff
reg
out
Recharge
P
sol
> 0, P
bat
chg
> 0, P
bat
dis
= 0, P
mote
= const
P
sol
� Eff
reg
in
= P
bat
chg
+ P
mote
/Eff
reg
out
Saturation
P
sol
> 0, P
bat
chg
= 0, P
bat
dis
= 0, P
mote
= const
P
sol
� Eff
reg
in
= P
shunted
+ P
mote
/Eff
reg
out
Figure 2. Energy ow and daily phases in our
micro-solar model.
The input regulator conditions the output of the panel to
meet the operational constraints of the particular battery, in-
cluding voltage limits, current limits, and charge duration.
Whereas macro-solar inverters operate in the neighborhood
of 95% efciency, in the sub-watt range, regulator efcien-
cies of 70-80% and below are more typical. The product
of such low efciencies translates into a signicant overall
supply:demand ratio.
A wide range of battery organizations and chemistries
are available for storing charge, as well as supercapacitors.
They have differing operating voltages, charge algorithms,
and complexities. From a system design perspective, it is
desirable for the power subsystem to be able to charge a
fully discharged battery without software in the loop, so that
when placed in sunlight the device is guaranteed to eventu-
ally become active.
The portion of energy transferred into the battery dur-
ing the day and discharged during the night incurs an ad-
ditional transfer efciency, Eff
bat
, about 66% for NiMH
chemistries. The capacity of the battery determines the po-
tential lifetime in darkness, but also how much energy can
be harvested while the sun shines, as discussed below.
The output regulator matches the battery characteristics
to the requirements of the mote. It too is characterized by
its efciency, Eff
regout
, and in particular its efciency at
two very different operating points: 10s of microwatts most of the time and 10s of milliwatts during short active periods.
For a typical bimodal
P
mote
, effective efciency of 50% or
less is expected. This determines the load experienced by
the supply and storage components of the power subsystem.
In general, the daily power cycle has ve phases, as il-
lustrated in Figure 2. From sundown to sun up, the bat-
tery discharges, supplying the device load. As the panel
is initially illuminated, a transition period occurs during
which the battery provides only a portion of the device load.
With sufcient illu