Por dentro do modelo 4MD - Como a EPE projeta a micro e minigeração no Brasil

Empresa de Pesquisa Energética
Ministério de Minas e Energia
Gabriel Konzen
Por dentro do modelo 4MD
Entendendo como a EPE projeta a micro e
minigeração distribuída no Brasil
Rio de Janeiro, 30 de Janeiro de 2018

Why is it important to forecast the DG adoption?
High
Adoption
Low
Adoption
High
Adoption
Low
Adoption
High
Adoption
Low
Adoption
OK
Supply issues
Overinvestment
OK
DG
Forecasting
Expansion
Result
Reality
 Adjust investments in centralized generation;
✓
✓
 Foresee the impacts of different policy designs
 Impacts on DG adoption, electricity rates, job creation, tax collection, avoided
emissions, etc.
Avaiable in portuguese
and english
Icon made by smashicons from www.flaticon.com

Source: COX and ALM, 2008
 Usually, it follows an “S” shaped curve.
What do we know so far about the diffusion of new
technologies?

Why does it happen?
Icons made by smalllikeart, mynamepong, freepik from www.flaticon.com
Innovators Imitators
Source: Rogers, 2003

Who are the adopters?
Source: Sigrin et al., 2015

What factors matter?
Source: Sigrin et al., 2015

“Upgraded” Bass Models
How can we model that?
 Easy to implement
 Rely only on historical data ->
trend extrapolation
 Do not consider market
saturation aspects
 Results are not reliable
Time Series
Models
Agent-Based Models
(ABM)
“Pure” Bass Models
 Bottom-up
 Modeled as a collection of
autonomous decision-making
entities
 Decision of each agent to
invest in a PV system happens
when the utility of the system
exceeds a certain threshold
 Data intensive
 Generally applied in smaller
universes
 Top-down
 Require little input data
 Based on the innovation and
imitation components
 Do not allow scenario
analysis
X  Add a bottom-up model to
estimate the market size
 Allow scenario analysis
✓

The Bass Model principle:
𝑓(𝑡) = 𝑝(1 − 𝐹 𝑡 ) + 𝑞 ∙ 𝐹 𝑡 )(1 − 𝐹 𝑡 )
Innovation Imitation
0
0,01
0,02
0,03
0,04
0,05
0,06
0,07
0,08
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Change-f(t)
Cumulative-F(t)
Time
Imitation
Innovation
Cumulative
f(t) – the market portion that adopts at time t
F(t) – the market portion that have adopted by
time t
p – coefficient of innovation
q – coefficient of imitation

The Bass Model
𝑁 𝑡 = 𝑚 ∙ 𝐹 𝑡 = 𝑚 ∙
𝑖=0
𝑡
𝑓(𝑖)
1st Step: estimate the potential market
Number of
adopters
2nd Step: define the adoption rate

Brazil’s Regulatory Framework
for DG

Regulatory framework for distributed generation in Brazil
Net Metering Model
was approved in Brazil
in 2012
In 2015 there was a
review of the regulation,
which brought several
improvements, as:
• higher power limit
• new business models
• reduced deadlines
• standardization of
procedures
Valid for any
renewable and
cogeneration source
Up to 5 MW

• Regulation allows generation and consumption in different places;
• It also allows the virtual net metering (multiple homeowners participating in the
same metering system).
Community Solar
Regulatory framework for distributed generation in
Brazil

4MD Model Overview

Sources Included
Sectors
Geographic resolution
General Information
 Residential
 Others (low voltage)
 Medium voltage
 PV
 Wind
 Thermal
 Small Hydro*
 56 Utilities
 6 Subsystems
*Modeled separately (more details at the end of the presentation)
Time-frame
 Until 2050 – yearly steps

Niche Market
Payback
time
Maximum
Market Fraction
Potential
market (m)
Demographics/
Number of
customers
1st Step: Estimate the potential market (m)
Residential Sector
Other sectors
(analogous)+
Technical
aspects
1
Geolocated Data

1st Step (b): Defining the niche market for non residential
sectors
Dwellings which
income > 3
minimum wages
Medium Voltage
Non-residential
customers
Low voltage Non-
residential
customers Others (low voltage)
Medium Voltage
Low voltage Non-
residential niche
market
Niche/total
dwellings
Medium Voltage
niche market
Niche
%
Niche Market
Residential

Potential
market (m)
Innovation (p)
and imitation (q)
coefficients
Residential Sector
Other sectors
(analogous)+
Historical
Data
S-curve
Number of
adopters –
N(t)
2nd Step: define the adoption rate – F(t)
 Least Squares Regression
Method to find p and q for each
sector
 Parameters are the same for the
whole country
-
5.000
10.000
15.000
20.000
2013 2014 2015 2016 2017
Adopters
Observed Regression
Example for the residential sector
𝐹 𝑡 =
1 − 𝑒− 𝑝+𝑞 𝑡
1 +
𝑞
𝑝
𝑒− 𝑝+𝑞 𝑡

3rd Step: distribute the adopters between the sources
 In the previous steps, the model
forecasted the number of
adopters of a generic technology
 Here we distribute the adopters of
each sector between the different
technologies, according to:
 Historical data
 Resources potential in each
subsystem (subjective).
PV ThermalWind
Adopters
Subsystem MAD MAN N NE S SE
PV 97% 97% 97% 93% 95% 92%
Wind 0% 0% 0% 5% 2% 1%
Thermal 3% 3% 3% 2% 3% 7%
Residential Sector

Side note: DG market for non-PV technologies
PV; 112
Thermal; 14
Wind; 10
Small Hydro;
46
Capacity [MW] - Remote Generation
PV; 441
Thermal; 24
Wind; 0 Small Hydro; 14
Capacity [MW] - Local Generation
Note: January, 2019 data.
Source: CanalEnergia, April 11, 2018

Model Summary Niche Market
Payback
time
Maximum
Market Fraction
S-curve
Number of
Adopters
Potential
Market
Demographics/
Number of
customers
Innovation (p)
and imitation
(q) coefficients
Adopters per
technology
Capacity and
Generation
Distribution
between
technologies
Capacity Factor and
average installed capacity
per technology
Historical
Data
1st Step: Estimate the potential market (m)
2ndStep: define the adoption rate – F(t)
3rdStep: distribute the adopters
4thStep: calculate capacity and generation

Model Overview
The approach for small hydros
 Simplified, top-down,
methodology, based on the
remaining avaiable projects
Remaining hydro potential (< 5 MW)
Remaining Potential (MW)
Initial Market
S-curve
Remaining
Projects
Innovation (p)
and imitation
(q) coefficients
Installed Capacity
and Energy
Capacity Factor
Utilization factor
Potential
Market

Should we move to a fixed charge for distributed
generators?
Results – Installed capacity under two scenarios
-43%
0
5.000
10.000
15.000
20.000
25.000
2018 2019 2020 2021 2022 2023 2024 2025 2026 2027
InstalledCapacity(MW)
Binomial Tariffs Current Rules - Full Net Metering
12GW
21GW

PV
82%
Wind
5%
Thermal
7%
Hydro
6%
Installed Capacity in 2027
PV
55%
Wind
10%
Thermal
22%
Hydro
13%
Generation in 2027
Results – The share for each source in 2027
Note: relative to the binomial tariffs scenario

Final Remarks (1)
 Model documentation available
online
Source: Dong et al., 2016
Avaiable in portuguese
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 Lack of detailed data and national surveys

Final Remarks (2)
 Novel methodologies under
review (System Dynamics)
Paper to be submitted by the end of the month
Potential
Adopters
Adopters
Adoption
Rate
New Potential
Adopters
Final Potential
Market
Adoption
by Imitation
Imitation
q
Adoption by
Innovation
Innovation
p
+
+
+
+
+
+ +
+
-
-
+
-
-
Source: Cole et al., 2016
Example of iteration between centralized expansion model and
distributed generation diffusion model
 Should we model DG in an integrated
fashion (endogenously) in the Energy
Expansion Models?
 Intermediate alternatives

Avenida Rio Branco, 1 - 11o andar
20090-003 - Centro - Rio de Janeiro
http://www.epe.gov.br/
Gabriel Konzen
E-mail: gabriel.konzen@epe.gov.br
Phone: + 55 (21) 3512-3242
Twitter: @EPE_Brasil
Facebook: EPE.Brasil

Por dentro do modelo 4MD - Como a EPE projeta a micro e minigeração no Brasil

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