Community Solar Self-Consumption Feasibility Study

Manon DIRAND, Marine LECLERC, Patrick LEGUILLETTE December 6th
2018
P.1
Community Solar Self-Consumption
15 min
Community
Solar self-
consumption

2018
P.2
Winner in 2018 of the 3rd edition of the DataCity
contest led by NUMA in partnership with the City-
Council of Paris, BeeBryte has put its energy
intelligence expertise at the service of a feasibility
study of a community solar self-consumption project.
The study focused on the Mac-Donald district, in the
19th
arrondissement of Paris, with the valuable
support of the project partners Sopra Steria and BNP
Paribas Real Estate.
DEFINITION OF COMMUNITY SOLAR SELF-
CONSUMPTION
It refers to a community of producers and consumers
collectively consuming the electricity it produces.
In France, the legal framework was set by Ordinance
No. 2016-1019 of July 27, 2016, ratified by Law No.
2017-227 of February 24, 2017, supplemented by
Decree No. 2017- 676 of April 28, 2017.
In this case, the producer(s) and the consumer(s)
involved must create a common legal entity (the
"Organizing Legal Entity") and have consumption and
injection points located downstream of the same Low
Voltage transformer.
THE ORGANIZING ENTITY
• Signs a contract with the power grid manager
(e.g. ENEDIS in France)
• Decides on the ‘allocation keys’ to be used to
break down the energy produced between the
various participating consumers. Otherwise a pro-
rated rule is applied based on each participant’s
respective energy consumption. ENEDIS then
takes this key into account in order to correct the
metering records that it sends to the electricity
retailer servicing each consumer.
• Sends an invoice to the participants for their share
of renewable energy consumed in order to pay
the producer(s). Thanks to the tax exemption (~
30% of the utility bill) on the self-consumed
energy, savings can be generated with solar
energy even if its price is higher than the net price
of grid electricity.
Note: other financial support and / or tax exemptions
may exist such as premium for self-consumption,
investment grants ...
DATA CITY –MAC-DONALD DISTRICT STUDY
In concrete terms, it was a question of modeling
throughout the year the energy footprint of the
district according to the consumption of each
participant, then to simulate the production in case of
installation of solar panels on the rooftop and:
• Estimate how much of the district's consumption
this production would cover,
• Identify which users would benefit the most,
• Study different allocation keys according to the
specificities of each participant.

2018
P.3
RECONSTRUCTION OF THE DISTRICT’S ENERGY
FOOTPRINT
The first step of the study consisted in reconstructing
the overall consumption profile of the district
throughout the year, considering seasonal factors and
with a time-step adapted to the synchronous
distribution of solar production.
For this, the consumption profiles of each type of
energy end-user (offices, trade, residential, nursery,
parking, ...) have been reconstructed at an hourly
rate. These profiles were then assembled to get the
profile of the district.
The main difficulty laid in the fact that each user had
consumption data in various formats and resolutions
ranging from 10 minute steps to just monthly billing
data.
Therefore some assumptions were made.
This first study showed that the main electricity
consumers in the district are businesses (30%), the
banking branch (24%) and residential buildings (22%).
However, by comparing the consumption with the
surface of the premises, it appeared that the users
who consume the most are the offices and shops with
about 125 kWh / m² / year. The car park and the
delivery road that consume only for lighting and
ventilation have a consumption of 14 kWh / m² / year
i.e. 9 times less, knowing that in this district all the
thermal uses (heating and air conditioning) are served
by a district heating/cooling network and not by the
electricity network.
The main consumers (bank, residential, cargo and
shops) have consumption profiles whose peaks occur
at different times during the day:
• Around 12PM for the bank branch and the Cargo
• About 8PM for residential
Consumption is however continuous throughout the
day for businesses.
Thanks to the bulking effect, the profile of the district
remains homogeneous throughout the year despite
the differences in buildings consumption between
seasons.
There is also a good match between the aggregate
consumption profile and the solar production profile,
with a maximum consumption in the middle of the
day.

2018
P.4
ALLOCATION KEYS - METHODOLOGY
Assuming that a third-party invests in photovoltaic
panels installed on the rooftop and sells the energy to
the users, different allocation keys were simulated,
with the double objective of optimizing the electricity
bill savings for users but also the return on investment
for the owner of the solar panels.
Thus, the higher the sale price of solar energy, the
more the project is profitable for the investor but the
less users save on their utility bill.
Conversely, reducing the selling price of solar energy
increases the savings for users but can drastically
reduce the profitability of the solar investment.
Two different strategies were explored in this study:
Strategy #1 : Proportional distribution of energy
In this strategy, a single purchase price is set for solar
energy and solar energy is distributed every hour in
proportion to the share of consumption of each actor.
Profitability of the investment
The study logically confirmed the increase in the
profitability of the project with the sale price of solar
energy. Its Internal Rate of Return (IRR) is
independent of the solar power installed for small
installations and then decreases when the solar power
increases.
Indeed, for smaller solar installations, there is no
overproduction, and the financial gain is directly
proportional to the solar power. When the amount of
solar energy produced becomes too important, some
of the energy is “lost” and injected into the network
and the project loses profitability.
It is also observed that the IRR decreases if one
decides to add a battery to store the solar energy
surpluses, especially in the case of a low installed
capacity: the extra cost due to the purchase of the
storage system is not compensated by the additional
incomes generated. Here, the profitability of a solar
project with battery increases up to 3 MWp (the
bigger the solar installed capacity, the more
overproduction the battery can store and monetize),
but beyond 3 MWp, the battery can no longer absorb
overproduction and profitability decreases again.
Evolution over the years to come
The figures also show the various IRR projections 3
years and 5 years down the road considering a
decrease in the cost of solar panels and storage
systems, coupled with an increase in the price of
electricity from the grid. In each case, we note that
the profitability of solar projects with or without
battery increases. In 3 years, the cost decreases from
1.3 € / Wc to 1.07 € / Wc (-17%) and in 5 years, the
cost decreases to 0.95 € / Wc (-27%).
Thus, in the case of a 3 MWp solar project, with a
resale price of 6ct € / kWh the IRR increases from
4.7% to 9.5%, hence a profitability multiplied by two
in 5 years! Similarly, with a resale price of 9ct € / kWh,
the IRR increases from 8.4% to 14.9% between 2018
and 2023.

2018
P.5
Note: the 3MWp solar system mentioned here is
theoretical, based on an estimate of the available
rooftop surface of the buildings in the district. It must
be taken as a maximum, the actual surface being
more likely smaller.
Benefit for users
Unsurprisingly, the more expensive the solar energy,
the lower the savings. If the price of solar is higher
than the average purchase price from the grid, i.e.
8.64ct € / kWh, there is no savings generated at the
district level.
The reduction of the carbon footprint and the rate of
self-production (or solar penetration rate) increases
with the installed solar power.
As long as the size of the solar installation does not
generate overproduction, the battery does not reduce
the carbon footprint or the self-production rate.
On the other hand, in the case of a larger solar
installation, the storage system makes it possible to
absorb the overproduction and to improve these
indicators.
Example
If we observe the self-production rate and carbon
savings for each user, we see a rather homogeneous
distribution since the energy is distributed
proportionally to consumption.
On the other hand, the financial savings generated are
very uneven according to the consumers. Indeed, they
depend on the ratio between the electricity purchase
price from the grid and the price of solar energy.
The more expansive the electricity is for the
consumer, the more advantageous is the price
arbitrage between the grid and solar production,
leading to higher savings.
Residential consumers in particular pay electricity
around 10 cts € / kWh and save 10.7% while the
nursery that pays its electricity at 4.3 cts € / kWh
makes only 1.6% savings.

2018
P.6
Example of battery control strategy
Explanations:
1) Charge the storage system when the price of
electricity and the carbon cost are low
2) and Discharge when price and carbon cost*
are high
3) Absorption of overproduction
*Note : the carbon cost of solar energy was set at 0. The carbon cost of
energy from the grid is calculated according to the energy mix of the
French grid, hour by hour.
Strategy #2 : Equal distribution of savings
We saw that the previous allocation key led to uneven
savings for the different participants. Another strategy
was therefore proposed, in which the objective set is
that of an identical percentage of savings between
users, with the calculation of the purchase price of
solar energy making it possible to achieve this
objective. The distribution is done in two stages:
1. A first pass uses solar energy to shave the power
peaks of users who have a power subscription,
2. A second pass distributes the remaining solar
energy so that the energy allocated to each
consumer during the day is proportional to his
daily consumption-share in the district (for the
first strategy, energy was distributed equitably
every hour).
Benefit for the users
In this strategy, the selling price is determined
according to the common objective (%) of desired
savings. We saw earlier that the savings generated
were logically higher when the solar price is low,
which explains why the curve corresponding to the 9%
savings target (yellow curve) is lower than the 3%
curve (purple curve).
If one is interested in the influence of installed solar
power, and considering that each kWh of solar energy
to be provided to a user represents an arbitrage
opportunity between the grid and the solar system, its
potential gain is the difference between the price of
the grid and the price of solar energy.
The savings are increasing with:
• The number of opportunities, i.e. the number of
kWh,
• The potential gain, i.e. when the price of solar is
low compared to the grid.
If we set the savings objective to achieve, then when
the solar installation is small, the amount of available
energy is low, so we set a very low solar price (or even
negative) to compensate for the lack of opportunities
by the gain at every opportunity.
Conversely, a large solar power generates a large
amount of energy, hence a large number of
opportunities and the price of solar increases.

2018
P.7
Profitability of the investment
As a direct result of the above, a significant
percentage of savings naturally implies a low solar
price, and therefore less profitability for an investor.
In addition, a savings target being set, we saw that a
higher sales price could be set when production was
higher, i.e. for high installed power. However, we
observe that beyond a certain size of the solar
installation, overproduction and profitability
decrease.
As for the previous allocation key, the profitability of
the solar project is expected to increase in the coming
years, with the decrease in the cost of solar panels
and storage systems, coupled with an increase in the
price of energy.
Case-study
Renewable energy penetration rate and carbon
savings remain relatively consistent from one user to
another.
Even if it is distributed differently, the energy
distributed to each user remains proportional to his
share of consumption in the district.
Unlike the previous distribution strategy, we have
here a distribution of savings fairer to all users!
Indeed, what differs in this case is the purchase price
of solar for each user. Since we are targeting the same
amount of savings for everyone, a user who pays
more for his electricity form the grid, will also pay
more for his solar energy than his neighbors, but at
the end it remains more profitable than buying all his
electricity from the grid.
CONCLUSION
At the scale of a district where residential and various
business activities are mixed, allowing a bulking effect
in consumption profiles, a community solar self-
consumption project is not only possible technically
and legally, but also financially viable for investors
today. It is also and above all economically interesting
for the various participants, according to varying
degrees that can be adjusted (allocation keys)
depending on the operating context.
In the case of the Mac-Donald district, the installation
of photovoltaic panels on the available rooftop
surface could cover up to 20% of the district's
electrical needs with green and local energy. It also
means about 20% less CO2 emissions at the district
level, together with a reduction of the electricity bill
for users.
An infinity of allocation keys does exist, and only
simulations make it possible to identify those
corresponding most to the desired balance between
redistribution of solar benefits to those who consume
it and profitability for the investor.

2018
P.8
Typically, the 2 keys studied in our case are answerinf
to 2 different objectives (while preserving an
acceptable profitability for the investor):
• Strategy #1, which is based on an identical solar
kWh price for all participants, clearly favors
residential consumers, who benefit from a
significant decrease (almost 11%) in their energy
bill, because they are charged a higher price for
each kWh from the grid. This strategy may, for
example, be particularly interesting in a social
housing context, promoting a social and solidarity
dimension of the approach for other users.
• Strategy #2 is more equitable between the
players in terms of relative bill reduction, but
results in a much more expansive solar kWh for
residential consumers. This strategy will typically
attract businesses by reducing their expenses. In
addition, the promotion of the project is easier
with an identical offer (the same percentage
discount) presented to all users, so treatment at
first sight is more equitable, although we now
know that it has a cost of unequal electrical
energy between the various categories of users,
typically between residential and professional.
If you are developing a community solar project, the
tools and expertise developed by BeeBryte can
support you throughout your process, from the
upstream technical and economic analysis of your
project to its operation optimized by a patented
software technology, including the definition and
management of the allocation key system that is best
suited to the objectives of the various stakeholders.
Don’t hesitate to contact us!
BeeBryte is using Artificial Intelligence, IoT &
BlockChain to get commercial buildings, factories, EV
charging stations or entire eco-suburbs to consume
electricity in a smarter, more efficient and cheaper
way while reducing carbon footprint!
BeeBryte is based in France & Singapore, and is
accelerated by Intel & TechFounders. One of its
shareholders is Compagnie Nationale du Rhone (CNR),
the largest French Renewable Energy producer.
Since its creation in 2015, BeeBryte's solutions have
received many awards including EDF Pulse, DENA
Start-up Energy Transition award & Hello Tomorrow
Challenge.
contact@beebryte.com
www.twitter.com/BeeBryteGroup
www.beebryte.com

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