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Poster Paper: Maximizing Renewable Energy Usage in
Buildings using Smart Energy Switching Platform
Qasim Khalid, Naveed Arshad
Department of Computer Science
SBASSE, LUMS, Pakistan
{qasim.khalid,
naveedarshad}@lums.edu.pk
Jahangir Ikram
Department of Electrical Engineering
SBASSE, LUMS, Pakistan
{jikram}@lums.edu.pk
Categories and Subject Descriptors
H.4 [Information Systems Applications]: Miscellaneous;
J.7 [Computer Applications]: Computer in Other Sys-
tems—Command and control
General Terms
Optimization, Algorithm, Hybrid Solar PV System
Keywords
solar PV optimization, hybrid renewable energy system, fine-
grained clustering, smart switch
1. INTRODUCTION
Solar energy is slated to be an important energy source for
reducing dependence in fossil fuels. Past few years have seen
unprecedented deployments of solar energy in many coun-
tries of the developed world. However, solar energy uptake
in developing countries is rather slow. This is particularly
true for solar energy installations on buildings. Since build-
ings consume more than 40% of energy, it is important that
greener buildings are encouraged through on-site production
of renewable energy [2]. However, limited possibility of en-
ergy buyback programs in developing countries is one of the
reason for less solar deployments. Also, in some countries
the electricity infrastructure is so fragile that energy buy-
back programs at smaller scale are not feasible. Therefore,
if the building owners like to go green then huge battery
banks are needed to make the best use of solar energy. But
battery banks add quite a bit of cost to the overall solar
energy infrastructure in buildings. This extra upfont and
maintenance cost is one of the reasons that hampers the
growth of solar on buildings.
Hybrid solar energy systems are used when energy buy-
back programs are available [1]. In this work we make a case
that traditional hybrid solar energy deployments are not fea-
sible for places without energy buyback programs. Instead
we propose an idea of a solar energy system for buildings
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SenSys’15, November 1–4, 2015, Seoul, Republic of Korea
ACM 978-1-4503-3631-4/15/11.
http://dx.doi.org/10.1145/2809695.2817884.
that is somewhere between hybrid and off-grid solar energy
systems.
Hybrid Solar Energy Systems make the coupling of the
solar energy and grid energy at the main electricity distri-
bution point of a building. This model of energy distribution
works best when the grid is stable and energy buyback pro-
grams are available. In absence of the option of putting
the energy back on the grid one would like the buildings
to maximally utilize the solar energy. But the hybrid solar
energy systems, while costing a fortune, do not utilize the
maximum available solar energy. This is because the hy-
brid inverters charge the batteries from grid energy at night
and when the batteries are fully charged in the morning the
solar energy available is only utilized to run the needs of
the buildings during daytime. Thus, the batteries are not
charged from the solar energy, hence, maximum solar energy
is not utilized. Also, the energy needs of buildings are not
consistent with the solar insolation curve and about 30-50%
solar energy is not utilized. Furthermore, there are other
problems with the inverters that make them less efficient for
places without energy buybacks. To this end in this work,
we present a Smart Energy Switching Platform (SESP) that
takes the coupling of the solar power and energy from the
grid at device level for more fine grained utilization of solar
energy. Our initial results show a reduction of about 42%
in upfront and maintenance cost of SESP when compared
with hybrid solar energy systems for the same building.
2. APPROACH AND ARCHITECTURE
Figure 1 shows the architecture of SESP. Selected devices
in the building are connected with two sources of energy
i.e. solar and grid using a Smart Switch. Smart Switch is
placed with a single electrical device or a cluster of devices to
control the source of energy. Smart Switches sense the live
energy usage of devices. This live energy information is com-
municated to a Central Coordinator using Zigbee communi-
cation module. The central controller also receives live infor-
mation on real-time solar energy production. State of charge
in the batteries is also reported to the central controller. En-
ergy information from the respective smart switches, infor-
mation on solar energy production and the state of charge
in the batteries are used for making the switch of a cluster
between solar or grid energy based using user-defined pref-
erences. User may set priorities on the state of charge in the
batteries, or priorities on running clusters on solar or grid
energy. Based on these priorities, SESP tries to maximize
the usage of solar energy amongst the device clusters. SESP
also has a weather forecaster that predicts the solar insola-
401
tion of the next day and the next few hours using publicly
available weather information[3].
Figure 1: Smart Energy Switching Platform. (Diamond
symbols are Smart Switches where energy from solar and
energy from the grid is coupled)
In its default state, the SESP works in the following man-
ner: At sunrise the system starts sensing the solar energy
production. Central controller keeps a sorted list energy us-
age information from all clusters. As soon as the solar energy
production is enough to switch the least energy consuming
cluster its energy source is shifted to solar. The Central Co-
ordinator sends a control signal to a Smart Switch to change
the source of energy for the cluster. Based on a user-defined
time interval the central controller checks the solar energy
production and tries to match with a combination of clus-
ters to maximally utilize the solar energy. Also the central
controller keeps enough buffer in the batteries to meet any
shortfall solar energy production for a brief time period.
3. PRELIMINARY RESULTS
Figure 2a shows the result of SESP on a sunny day in a
building with six clusters of varying profiles. In this figure,
one can note that as soon as the solar energy is available
at sunrise one or more clusters are shifted to solar energy.
After a given time interval a new set of clusters are shifted
to solar energy. This process continues until sunset. The
solar energy usage is almost exactly the same as the solar
insolation curve. Thus, at the end of the day the efficiency
of solar energy production in this particular building is more
than 97% of the available solar insolation. Figure 2b shows
the same building with patchy sunlight. On this particular
day the solar energy efficiency is more than 94%.
To summarize the SESP is able to bring the cost of the
system down by about 42% even after adding the cost of
Smart Switches and related electric wiring. Further the pay-
back period of a solar energy system is down from nine to
to seven years. This reduction in cost and payback period
is possible by reducing the number of batteries required in
the system which in turn reduces the maintenance cost of
the system. Also, using SESP one does not need expen-
sive MPPT Hybrid Inverters and can use the PWM Off-grid
inverters which are available in half the price.
(a) Sunny
(b) Both Sunny Cloudy
Figure 2: SESP Solar Energy Utilization on a sunny day
and in a day with partial overcast
4. CONCLUSION AND FUTURE WORK
SESP provides an alternate model for solar energy uti-
lization at places where energy buyback programs such as
net-metering or feed-in tariffs are not available. The design
of SESP is deliberately kept simple to encourage its usage in
developing countries. Moreover, one can install SESP with
only two Smart Switches. By doing this, cost of the system
will be reduce even further.
One possible future direction is to extend this system at
neighborhood scale. Since some buildings have minimal en-
ergy needs during weekends and holidays the energy could
be utilized in neighborhood buildings by making an energy
co-op. Presently the system uses a simple bin-packing al-
gorithm. However, for scalability this needs to be updated
with a more sophisticated and scalable algorithm.
Another possible direction is to improve the algorithm to
cater for planning power load shedding during the daytime.
The battery needs to have enough storage to keep the high
priority clusters energized during such outages. Another
more challenging improvement in the algorithm is to plan
for unplanned outages of varying durations.
Yet another direction is to cater varying power profiles of
the clusters. Presently we are assuming the power profiles
of the clusters to be constant. However, in reality power
profiles vary all the time.
5. REFERENCES
[1] O. Erdinc and M. Uzunoglu. Optimum design of hybrid
renewable energy systems: Overview of different approaches.
Renewable and Sustainable Energy Reviews, 16(3):1412 – 1425,
2012.
[2] L. Perez-Lombard, J. Ortiz, and C. Pout. A review on buildings
energy consumption information. Energy and Buildings,
40(3):394 – 398, 2008.
[3] N. Sharma, J. Gummeson, D. Irwin, and P. Shenoy. Cloudy
computing: Leveraging weather forecasts in energy harvesting
sensor systems. In Sensor Mesh and Ad Hoc Communications
and Networks (SECON), 2010 7th Annual IEEE
Communications Society Conference on, pages 1–9. IEEE,
2010.
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