Utilities Policy 81 (2023) 101513
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Utilities Policy
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Full-length article
Analysis of hourly price granularity implementation in the Brazilian
deregulated electricity contracting environment
Ciniro Aparecido Leite Nametala a,b ,∗, Wandry Rodrigues Faria a , Guilherme Guimarães Lage c ,
Benvindo Rodrigues Pereira Jr a
a
São Carlos School of Engineering, University of São Paulo, Brazil
Department of Engineering and Computation, Federal Institute of Minas Gerais, Brazil
c Departamento de Engenharia Elétrica, Centro de Ciências Exatas e de Tecnologia, Universidade Federal de São Carlos, Brazil
b
ARTICLE
INFO
Keywords:
Brazilian electricity market
Deregulated electricity market
Electricity hourly price
Power system economics
Hydrological crisis
ABSTRACT
Brazil’s electricity market is the largest in Latin America and the ninth largest in the world. It has been
implemented as a mixed market in which regulated and deregulated contracting environments coexist. The
volume of transactions in the deregulated market has experienced steep growth over the last few years and is
expected to surpass the regulated market. Different programs to diversify the country’s energy matrix have
been devised, especially by integrating intermittent renewable sources to address the deregulated market
expansion. Consequently, such an energy policy path has prompted the need to increase the granularity of
the Brazilian deregulated market’s spot price, namely the Difference Settlement Price (DSP). The DSP had
been weekly defined accounting for three loading levels and four submarkets, and, as of 2021, it has been
hourly defined accounting for four submarkets; the weekly DSP is inefficient in actually signaling prices based
on ex-ante marginal cost of operation of the interconnected Brazilian power system. Besides such granularity
alteration, Brazil has also undergone a severe hydrological crisis in 2021 that led to significantly lower water
inflows into major hydrographic watersheds and, as a result, most hydroelectric power plant reservoirs hit
a 91-year low. The described scenario is relevant in utility policies and energy economics since it depicts a
significant paradigm shift experience in such a large electricity market. This study presents the first hourly
DSP behavior analysis since its implementation in the Brazilian electricity market and explores its statistical
characteristics and relationships with exogenous variables throughout 2021. Additionally, we discuss the hourly
DSP’s volatility observed in the year 2021 and how it has resulted in price spikes. At last, we compare the
behavior of the Brazilian hourly DSP with the energy prices of five other countries’ electricity markets. Despite
being a significant market improvement, the DSP granularity increase per se could not accurately represent
the actual marginal cost of operation over the year 2021 since, besides instabilities observed in the hourly
DSP, market intervention mechanisms had to be applied by Brazilian regulatory agencies to minimize the
hydrological crisis’ impacts.
1. Introduction
The first attempt to implement a deregulated electricity market
occurred in Chile in 1982 (Chile, 1992). New Zealand followed the
example in 1987, and many other countries pioneered an economic liberalization move in the sector. By the year 2000, all the countries that
integrated the Organization for Economic Co-operation and Development (OECD) had been somewhat through reforms that granted specific
classes of consumers (mainly industrial) the possibility of choosing their
energy supplier; in some cases, this freedom was even extended to residential consumers (Nery, 2012). Brazil integrated this movement and
restructured its electricity sector by implementing the first mechanisms
that would allow the celebration of bilateral contracts for buying and
selling electricity (Brazil, 1995). The Brazilian electricity market framework has undergone several redesigns over the last 25 years (Dutra and
Menezes, 2022). Nowadays, Brazil adopts a wholesale electricity market divided into two contracting environments: Regulated Contracting
Environment (RCE) and Deregulated Contracting Environment (DCE).
The RCE aggregates the captive consumers that must buy energy from
the distribution company holding the regional monopoly, whereas the
costumers whose load demand is equal to or higher than 500 kW may
choose to migrate to the DCE, thus, being able to choose their electricity
supplier (Brazil, 2018, 2019).
∗ Corresponding author.
E-mail addresses: ciniro@usp.br (C.A.L. Nametala), wandry@usp.br (W.R. Faria), glage@ufscar.br (G.G. Lage), brpjunior@usp.br (B.R. Pereira Jr).
https://doi.org/10.1016/j.jup.2023.101513
Received 4 October 2022; Received in revised form 31 January 2023; Accepted 31 January 2023
Available online 7 February 2023
0957-1787/© 2023 Elsevier Ltd. All rights reserved.
Utilities Policy 81 (2023) 101513
C.A.L. Nametala et al.
Nomenclature
List of Acronyms
ANA
ARM
BB
BRL
BRL/MWh
BUIX
CCEE
CMOD
CMOP
DCE
DISL
DSP
DTW
EARM
ENAL
ENAM
EXCX
FUOS
GASP
GHYD
GNUC
GRSP
GSOL
GTHE
GWIN
HCA
IBGE
IBOV
IGPX
INNS
INSS
INTR
IPCA
IPE
1
LOAD
LPSL
MCO
MME
MSC
NE
NO
OECD
- National Water and Sanitation Agency
- Average Reserve Margin
- Bollinger Bands
- Brazilian Real
- electric energy price in BRL
- Building Cost Index
- Chamber of Electric Energy Commercialization
- Marginal Cost of Operation – Day-Ahead
Planning
- Marginal Cost of Operation – Week-Ahead
Planning
- Deregulated Contracting Environment
- Diesel sales
- Difference Settlement Price
- Dynamic Time Warping
- Reservoir stored energy
- Long-term spilled-water-hydraulic potential moving average
- Spilled-water-hydraulic potential
- USD-BRL exchange rate
- Fuel oil sales
- Natural gas production
- Hydraulic generation
- Nuclear generation
- Brazilian Gross Domestic Product (GDP)
- Solar generation
- Thermal generation
- Wind generation
- Hierarchical Clustering Algorithm
- Brazilian Institute of Geography and
Statistics
- B3 stock exchange Ibovespa or Bovespa
Index
- General Price Index – Internal Availability
(IGP-DI)
- Power exchange between NO and SECW
subsystems
- Power exchange between SECW and SO
subsystems
- SELIC base interest rate
ONS
PEPC
PEPD
RCE
RM
S
SECW
SSC
SST
STM
UNCX
USD
WHO
- Extended National Consumer Price Index
- Spilled-Water-Hydraulic Potential Forecast
Indicator
- Load
- Liquefied petroleum gas sales
- Marginal Cost of Operation
- Ministry of Mines and Energy
- Maximum Structural Ceiling
- Northeastern
- Northern
- Organization for Economic Co-operation
and Development
- National Power System Operator
- Brent crude oil price
- Oil production
- Regulated Contracting Environment
- Reserve Margin
- Southern
- Southeastern and Central-Western
- System Service Charges
- Singular Spectrum Transformation
- Short-Term Market
- Economic Uncertainty Indicator Brazil
(EUI-Br)
- U.S. Dollar
- World Health Organization
the transaction of BRL 133 billion (approximately USD 25.60 billion)
through energy auctions and BRL 44 billion (approximately USD 8.47
billion) in the Short-Term Market (STM) (CCEE, 2022c). The admission
of new consumers into the Brazilian deregulated market environment is
a successful case, as the number of agents in the DCE experiences steep
growth annually (CCEE, 2022a). This result from financial benefits to
the customer and the various incentives for the DCE migration, such
as a programmed gradual removal of entry barriers of non-industrial
consumers (Brazil, 2018, 2019). According to agencies responsible for
the Brazilian energy trading rules, such as the ABRACEEL (the Brazilian
Association of Energy Traders) and the Brazilian Chamber of Electric Energy Commercialization (Câmara de Comercialização de Energia
Elétrica, ‘‘CCEE’’), there is an ongoing study on the migration of the socalled A and B tariff groups, whose load demand is lower than 500 kW,
to the DCE. These groups currently correspond to 19% of the RCE,
an unprecedented moment for energy economics in Brazil because,
when this migration is accomplished, the DCE will aggregate 59% of
the entire market, surpassing the RCE in total demand for the first
time (Brazil, 2019; ABRACEEL, 2022). The gradual removal of entry
barriers for new customers and the integration of multiple electricity
markets are not observed exclusively in Brazil. Such a tendency may be
observed in Europe (Ioannidis et al., 2021a) in the form of new policies
and negotiation systems, such as the NordPool, which is currently
composed of 16 countries (Nord Pool Group, 2022).
The most expressive consumer category in the expansion of the
DCE has been due to the so-called ‘‘special consumers’’ (CCEE, 2022a).
Special consumers, whose load demand is between 500 kW and 1 MW,
must purchase energy exclusively from small hydroelectric power
plants or other renewable sources incentivized by the government,
such as wind, solar, and biomass (Brazil, 2018). The incentives are
aligned with the Brazilian energy policy path for diversification of
the energy matrix in development in the last few years. The country’s
installed generation capacity is 174 GW, and the annual electric energy
consumption in 2019 was 546 TWh, 74.29% of which was supplied by
The Brazilian electricity market is the largest in Latin America
and the ninth largest in the world (Hochberg and Poudineh, 2021).
In March of 2022, the negotiated volume in the DCE accounted for
34.63% of the country’s total demand averaging about 71,304 MW.
This amount represents a 7.5% increase compared with the same month
in 2021, whereas the RCE observed a 0.9% growth. Moreover, in March
of 2022, the number of agents in the DCE increased by 12.6% compared with 2021 (CCEE, 2022b).In 2021, the DCE was responsible for
1
Acronyms in italics refer to their respective initials in Brazilian Portuguese.
This terminology was adopted because this is how the query should be
performed when searching for any information regarding these acronyms.
Throughout the text, these specific situations are explicitly informed and
generally refer to names of agencies, companies, or governmental institutions.
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C.A.L. Nametala et al.
the decoupling between the system’s MCO and the actual DSP practiced
in the DCE (Hunt. et al., 2018; de Souza Moreira et al., 2021; Getirana
et al., 2021). In this context, after a testing period between the years
of 2018 and 2020, in which the weekly DSP was published along
with the hourly DSP during the so-called ‘‘shadow operation’’ (Evelina
et al., 2018), in January 2021, the Brazilian Ministry of Mines and
Energy (Ministério de Minas e Energia, ‘‘MME’’), jointly with the CCEE
and the ONS, adopted an hourly granularity for the DSP calculation
with a day-ahead horizon. Thus, 2021 was a challenging adapting year
for the market agents and institutions that manage electric energy
commercialization. In addition to the DSP granularity alterations and
the expectation of economic recovery as a result of the end of the
quarantine situation due to the COVID-19 pandemic, between July and
September of 2021, the country faced the worst recorded hydrological
crisis, which culminated in hydroelectric power plant reservoirs hitting
a 91-year low as informed by reports from the government (ONS,
2022a), independent monitoring agencies (Bangolin et al., 2021; Naumann et al., 2021), and publicly available data on the ONS and the
Brazilian National Water and Sanitation Agency (Agência Nacional de
Águas e Saneamento Básico, ‘‘ANA’’) portals (ONS, 2022b; ANA, 2022).
renewable sources, 9.87% from wind power plants, and 1.88% from
solar power plants. In March of 2022, wind power plants’ energy supply
participation peaked at 11.88%, while solar power plants’ participation
increased to 2.72% (Hochberg and Poudineh, 2021; ANEEL, 2022).
Despite the government’s efforts to diversify the energy matrix, the
Brazilian electric energy sector is still centered on a hydro-thermal system composed of 56.25% hydroelectric power plants and 24.56% thermoelectric power plants (ANEEL, 2022). The generation system is coordinated by the Brazilian National Power System Operator (Operador
Nacional do Sistema Elétrico, ‘‘ONS’’), which adopts a tight pool model
(based on hydro-thermal coordination and thermal unit commitment)
to minimize the dispatch costs (Hunt, 2002) and determines the merit
order to supply load demand fully. The power dispatch is carried out
in conjunction with the CCEE. Dispatch must be centralized, given that
most of the Brazilian hydroelectric power plants belong to multiple
owners and, in some cases, share the same river basin/hydrographic
watershed. This power dispatch is also time-coupled as some of its
decision variables consist of using the water currently available in the
reservoirs or its storage for future power generation (da Silva, 2012;
Tolmasquim, 2015). The MCO of each power plant, and, consequently,
its position in the merit order, is calculated by a set of optimization
programs (NEWAVE, DECOMP, and DESSEM) that take into account
multiple time horizons and water inflow probabilistic scenarios in
the long-, mid- and short-term by means of dynamic dual stochastic
programming (Santos et al., 2020).
The MCO is the key parameter used in calculating the DSP, which
is the base price for CCEE’s ex-post power balance compensation; the
DSP is also adopted as a reference for negotiating bilateral energy contracts in the DCE. Electricity contracts may be divided into categories
according to the time interval between the contract celebration and
the energy physical delivery date. Generally, the energy negotiated
in electricity auctions or capacity expansion programs involves supply
contracts for 3 to 7 years ahead. Contracts celebrated in the STM may
be physically delivered one year ahead, up to 1 month ahead in the spot
market, or even eight days after physical delivery, which is within the
regularization stage (CCEE, 2021b, 2022e). The consolidated contracts
in the STM are valued at the DSP. In March of 2022, 18.1% of the DCE
transactions were carried out in the STM (CCEE, 2022b).
Given that the DSP is fundamentally based on the MCO, obtained
by solving dynamic dual stochastic programming problems, the DSP
incorporates volatile characteristics not exclusively related to economic
and market conditions. Fuel prices and the macroeconomic scenario
may affect the stakeholders’ behaviors and, indirectly, the dispatch’s
merit order. Furthermore, the DSP itself is also affected by hydroelectric
power plants’ reservoir levels, water inflows forecasts, specific characteristics of power plants, load demand forecasts, power exchange limits
between different subsystems, transmission capacities (MVA ratings)
of power lines and transformers, transmission security constraints,
forecasts of installed capacity increase or transmission system expansion, among others (da Silva, 2012; Mayo, 2012; Santos et al., 2020).
The variables related to the power system add a spatial component
to the problem due to the physical elements’ geographical location.
In this sense, the Brazilian electricity market used to practice different DSP values accounting for three loading levels for each of its
four submarkets: light, medium and heavy loading for the Northern
(N), Northeastern (NE), Southeastern and Central-Western (SECW) and
Southern (S) submarkets, respectively.
Despite the ex-ante 14-day semi-hourly-based MCO calculation as
of 2000, the weekly DSP had been practiced for over 20 years (Santos
et al., 2020). However, recent investments in wind and solar power
plants contributed to increasing MCO volatility due to the intermittency/uncertainties related to these sources’ physical delivery of energy (Nunes et al., 2021). Furthermore, the hydrological crises in
Brazil over the last few years that have led to significantly lower
water inflows into major hydrographic watersheds and changes in the
traditional load profile due to the COVID-19 pandemic have aggravated
2. Literature review
Although the Brazilian electricity market in the year 2021 has been
a timely topic in utility policies and energy economics, scientific production addressing such a scenario and analyzing its influence factors
in the behavior of the hourly DSP is still scarce. To the best of our
knowledge, only a few papers have separately addressed parts of this
topic. Few and far between studies have been conducted by Brazilian
universities whose primary focus has been the promotion of initial
research at the undergraduate level by analyzing the impact of the
hourly DSP on the modularization of contracts. The study presented
in Marchetti and Rego (2022), published in a scientific journal, carries
out an analysis of the impact of both DSP granularities in the wind
and solar power plants in the STM; the authors compare weekly and
hourly tariffs and, between both scenarios, conclude that the hourly
DSP has negatively affected wind power plants and positively affected
solar power plants depending on its geographical location. The work
of Silveira Gontijo et al. (2021) employed time series scanning techniques to search for repetitive patterns in the Brazilian energy price
data between 2006 and 2019 and reported their method’s predictive
superiority. Other studies before 2021 carried out prospective analyses
and discussed possible new regulatory needs in the commercialization
environment (Munhoz, 2017; Abreu et al., 2020; Ramos et al., 2020).
Regarding other applications, such as price forecasts in deregulated
markets, bibliographic reviews have discussed hundreds of works (Ziel
and Weron, 2018; Nowotarski and Weron, 2018; Lago et al., 2018;
Narajewski and Ziel, 2020; Lago et al., 2021); however, none has been
specifically addressed to the Brazilian electricity market.
Analysis of the electricity price behavior may lead to a more profound understanding of the reasons that cause this financial time series
to react to exogenous factors (Hong et al., 2015, 2020). Research
on the analysis of electricity prices has already been conducted on
markets from North America (Ferrer et al., 2018; Shaikh, 2022), Europe (Norouzi et al., 2021; Härtel and Korpås, 2021; García et al.,
2021), Asia (Yang et al., 2021; Abbasi et al., 2021), and other regions (Mayer and Trück, 2018; Shinde and Amelin, 2019; Soto et al.,
2021) with various focuses and objectives. Some works have also
analyzed periods with significant events, such as (Ioannidis et al.,
2021a), which addresses the transitional period of the Target Model
implementation in the Greek wholesale market. Nonetheless, there has
not been any evidence of specific research addressing the Brazilian
electricity market while considering the hourly DSP. In this sense, we
aim to bridge this gap by reporting the first price behavior statistical
analysis for the Brazilian electricity market after the price’s hourly
granularity implementation. Statistical methods are utilized to find
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Utilities Policy 81 (2023) 101513
C.A.L. Nametala et al.
patterns and relationships among the hourly DSP, exogenous power
system, and macroeconomic variables in this period. Additionally, we
apply switching detection techniques to the price series seeking to
characterize the 2021 hydrological crisis. We also developed a process
to identify the occurrence of outliers in the hourly DSP, providing an
index that might be used to predict price spikes. Finally, we conduct
a discussion on the Brazilian deregulated market’s seasonal behavior
of load and price in comparison to the following markets: Queensland
(Australia), IEX (India), PJM (USA), IESO (Canada), and EEX (Germany). The data and other supplementary materials used in this work
can be consulted in the project’s public repository (Nametala et al.,
2022a). Therefore, the main contributions of this work are:
product to increase by 4.6% in the second semester (FGV, 2022).
This reaction makes the Brazilian government expect an economic
recovery (Brazil, 2021b). This expectation was observed in the electric
energy consumption, which rose 4.1% compared to 2020 and returned
to the baseline registered before the beginning of the COVID-19 pandemic (CCEE, 2021a). However, from the ONS standpoint, this increase
in the power demand was a source of concern, given that the water
inflow expectation for the Brazilian hydrographic watersheds was not
met (ONS, 2022b; ANA, 2022). Thus, most of the hydroelectric power
plants’ reservoirs hit a 91-year low (ONS, 2022b). In this scenario,
thermoelectric power plants were dispatched without following the
merit order to avoid compulsory rationing or even load shed. These
dispatch operations were funded through the DSP raise for the DCE
and increase in the tariffs for the RCE. In this period, the so-called
hydrological scarcity flag was created; this flag was an additional
mechanism adopted by the ONS to raise the DCE tariff prices beyond
the previously allowed limits (ONS, 2022a). According to the Brazilian
Institute of Geography and Statistics (Instituto Brasileiro de Geografia e
Estatística, ‘‘IBGE’’), the average price of electricity in 2021 was 21.21%
higher than in 2020. This value accounted for 10.65% of the IPCA
variation in the period (IBGE, 2022b,a); therefore, the electricity price
was directly responsible for the inflation increase.
Other ONS actions regarding the hydrological crisis generated a
14% gain in reservoir-stored energy in 2021. Among these, one can
highlight those listed in ONS’s Annual Administration Report (ONS,
2022a) and Flexibility Protocol of the National Integrated System (ONS,
2021):
• Such analyses may serve as a reference for developing regulatory
policies in deregulated electricity markets, especially for electricity markets based on hydro-thermal energy systems such as the
Brazilian one.
• Assessing the impact of internal and exogenous variables in the
electricity markets may help develop novel forecasting and price
modeling methods.
• The numerical characterization of the water crisis is a reference
for adopting price protection mechanisms in potential future
crises.
• A novel index that helps traders protect themselves against future
price spikes.
• The understanding that underlying load-related models affect
seasonal prices as observed in distinct energy markets in other
electricity markets.
The remainder of this study is organized as follows. Section 3
briefly describes the macroeconomic scenario observed in the year
2021, focusing on the effects of the hydrological crisis and the COVID19 pandemic. Section 4 characterizes data and discusses details of the
methodology regarding the experimental protocol. The analyses and
discussions are carried out in Section 5. Finally, a summary of the
main conclusions drawn from such statistical analysis is provided in
Section 6.
• Awareness campaigns promoting the rational use of electricity.
• Thermal-based energy dispatch and import models flexibilization.
• Flexibilization of reservoir operating levels at Itaipu, Furnas,
Jupiá and Porto Primavera hydroelectric power plants, the largest
ones in the country.
• Flexibilization of operational security and reliability criteria from
N − 2 (which guarantees power system operation within operational limits after the most impactful simultaneous loss of two
transmission lines) to N − 1 (which guarantees power system
operation within operational limits after the most impactful loss
of a single transmission line).
• Implementation of a voluntary demand reduction program.
• The creation of task forces to improve the computational models
that calculate the MCO.
3. The case of Brazil
An inflation rise mainly characterized the Brazilian macroeconomic
scenario in 2021. In Brazil, inflation is measured by the Extended
National Consumer Price Index (Índice Nacional de Preços ao Consumidor
Amplo, ‘‘IPCA’’) and reached 10.06% by the end of 2021 (FGV, 2022).
This value corresponds to almost twice the target stipulated by the
Brazilian Central Bank (5.25%) and more than twice the value registered for the year 2020 (4.52%) (Banco Central, 2022b). The rise in
commodity prices, especially oil, directly impacted the IPCA as they
are part of its formation process. Consequently, fuel prices increased,
raising the average cost of refueling vehicles by 51% and liquefied
petroleum gas (cooking gas) by 40% (IPTL, 2022). The Brazilian Central
Bank initiated a systematic process to increase the base interest rate
practiced in the country as a means to control both inflation and
devaluation of the Brazilian Real (BRL) against the U.S. Dollar (USD)
(−7.94%). As a result, the benchmark SELIC base interest rate was
raised from 2% to 9.25% in 2021 (Banco Central, 2022a). Regarding the
COVID-19 pandemic, South America was declared the new pandemic
epicenter by the World Health Organization (WHO) in 2021 (Urban and
Nakada, 2021). In 2021, Brazil recorded 22 million infected people and
619,056 deaths and was ranked the third most affected country in the
world (Dong et al., 2022).
In this context of economic fragility, states and cities implemented
social distancing policies to combat the COVID-19 pandemic, which
involved temporary business close. After registering an economic recess
in the first semester, the results of mass vaccination (public policy as
of the first semester of 2021) allowed the Brazilian gross domestic
Regarding the influences of the global economy in Brazil, it should
be mentioned that despite being the 15th economy in the world, the
Brazilian gross domestic product growth was 4.6% in 2021, while
the world average was 5.7% (Austing Bank, 2022). Brazil grew more
than its BRICS peers (Russia, India, China, and South Africa), with an
average growth of 3.3%. However, Russia was already suffering from
the expectation of war against Ukraine during this period. Also, due
to their geographical proximity, India and China were more affected
than Brazil. In Europe, this influence was clear, especially on electricity
prices after the start of the war in 2022, mainly due to the energy
matrix based on natural gas in Germany and adjacent countries that
make use of Russian reserves (Boungou and Yatié, 2022; Jagtap et al.,
2022).
4. Data set and methodology
This section presents the data characterization and discusses the
details of the experimental protocol used to analyze the hourly DSP
behavior and its influence factors in the DCE of the Brazilian electricity
market in the year 2021.
4
Utilities Policy 81 (2023) 101513
C.A.L. Nametala et al.
Table 1
Power system variables according to ONS (2022b,d,c).
Acronym
Description
Unit
Frequency
LOAD
CMOP
CMOD
GHYD
GTHE
GWIN
GSOL
GNUC
EARM
ENAM
ENAL
INNS
INSS
Load
Marginal Cost of Operation – Week-Ahead Planning
Marginal Cost of Operation – Day-Ahead Planning
Hydraulic generation
Thermal generation
Wind generation
Solar generation
Nuclear generation
Reservoir stored energy
Spilled-water-hydraulic potential
Long-term spilled-water-hydraulic potential moving average
Power exchange between N and SECW subsystems
Power exchange between SECW and S subsystems (INSS)
MWh/h
BRL/MWh
BRL/MWh
Average MW
Average MW
Average MW
Average MW
Average MW
GWh
Average MW
%
Average MW
Average MW
Hourly
Weekly
Hourly
Hourly
Hourly
Hourly
Hourly
Hourly
Daily
Daily
Daily
Hourly
Hourly
Table 2
Macroeconomic variables according to FGV (2022).
Acronym
Description
Unit
Frequency
FUOS
LPSL
DISL
PEPD
GASP
PEPC
IBOV
IGPX
BUIX
UNCX
GRSP
INTR
EXCX
Fuel oil sales
Liquefied petroleum gas sales
Diesel sales
Oil production
Natural gas production
Brent crude oil price
B3 stock exchange Ibovespa or Bovespa Index
General Price Index – Internal Availability (IGP-DI)
Building Cost Index
Economic Uncertainty Indicator – Brazil (EUI-Br)
Brazilian Gross Domestic Product (GDP)
SELIC base interest rate
USD-BRL exchange rate
BRL
BRL
BRL
Barrel
m3
USD/barrel
Points
Points
Points
Points
BRL
%
BRL
Monthly
Monthly
Monthly
Monthly
Monthly
Daily
Daily
Monthly
Monthly
Monthly
Monthly
Monthly
Monthly
4.1. Data characterization
4.2. Experimental protocol
The year 2021 is a period of 8760 hourly values between 00:00 h
January 1st, 2021, and 23:00 h December 31st, 2021. The DSP hourly
series for this interval was obtained from CCEE’s price panel portal (CCEE, 2022d). The power system’s variables were obtained from
ONS’s operation history (ONS, 2022b) and energy tomorrow (ONS,
2022d) portals and are listed in Table 1. The macroeconomic variables
shown in Table 2 were obtained from the Getúlio Vargas Foundation’s economic data portal (FGV, 2022). The sources of prices and
loads for international electricity market comparison may be consulted
in Nametala et al. (2022a). All data and referenced portals are public.
Hereafter, the variables will be referred to by the acronym presented in
the first column of Tables 1 and 2. An exploratory analysis of the data
contained in these tables may be consulted in Nametala et al. (2022a).
However, some of the time series are not originally granulated
hourly; in these cases, we repeated the daily, weekly, or monthly values
24, 168, or 720 times, respectively. We employed this strategy because
this is how the information is available to the market agents since
there is no special access to smaller granularities, even if there are
eventual insiders. All time series were previously analyzed for integrity
issues. The IBOV and PEPC series had no records for holidays and
weekends because the stock exchange was closed on these days; the
missing data has been replaced with the last known value. Additionally,
we have identified the absence of two CMOD and four LOAD values
and replaced them with the last known values of the same time and
day from the previous week. None of the other series presented any
integrity problems.
The DSP, LOAD, CMOP, CMOD, EARM, ENAM, and ENAL have
specific values for each respective subsystem/submarket (N, NE, SECW
and S). In this case, a simple arithmetic average has been adopted
for the DSP, CMOP, and CMOD involving the four submarkets when
a country-wide analysis was needed. As for LOAD, EARM, ENAM, and
ENAL, the values of each region were added. The other variables are
originally applicable nationwide.
Several works have been dedicated to the study techniques that
allow specific electricity price analyses for electricity prices (Weron,
2014; Lago et al., 2018; Gontijo et al., 2020; Lago et al., 2021). The
analysis, whose scope aims to characterize the hourly DSP behavior
during the year 2021, is carried out under four perspectives. The
methods adopted for characterizing each aspect are presented in this
subsection.
4.2.1. Regime switch
The regime switch deals with identifying possible clusters of values
with individual patterns for a time series’ given interval. This study
aims to observe the effects of operative decisions made by the ONS
and CCEE in the hourly DSP, especially during the hydrological crisis
peak between June and September of 2021. The identification of regime
switches is carried out by employing two methods:
(a) Linear and generalized Markov-switching autoregressive models
using the expectation–maximization algorithm made available in
the MSwM package for the R language by Perlin (2015): two
possible regimes are defined, and stochastic variables indicate
which regime prevails in each time interval.
(b) SST is available in the Banpei library for the Python language
(Tsuruta and Feng, 2022): the SST analyses each time interval
and generates values ranging from 0 to 1. Whenever the SST
value crosses the threshold of 0.01, there is an indication of a
change in the DSP behavior pattern. The pattern remains unchanged whenever the SST is kept below or above this threshold.
This threshold was defined by using two standard deviations
above and below the average based on the distribution of price
returns with an approximately normal distribution with zero
mean and unity standard deviation.
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wherein 𝑑 is the number of standard deviations 𝑠𝑡𝑑𝑒𝑣(𝑅) from the
median 𝑚𝑒𝑑𝑖𝑎𝑛(𝑅). In this study, we adopt 𝑑 = 2 because a series
of returns generally present stationarity due to the different applications (Moretin and Toloi, 2006). Considering an approximately uniform
price distribution, 95.4% of the prices are not considered spikes.
Then, once the price spikes are determined, the spikes’ behavior analysis is conducted based on two parameters: (i) Reserve Margin (RM) or surplus generation behavior; and (ii) generation sources
availability.
The RM (Mayo, 2012) is defined as the ratio between demand and
generation capacity (Weron, 2014; Tafakori et al., 2018; Grossi and
Nan, 2019) and is one of the fundamental indicators for electricity price
spikes. The work presented in Cartea et al. (2009) was the first proposal
to consistently analyze the price spike occurrence and its relationship
with the RM for long periods. In their paper, the authors demonstrated
that the RM explains the price spike probability 𝜌:
4.2.2. Exogenous variables influence
This analysis considers 13 variables from the Brazilian power system
and 13 macroeconomic variables, detailed in Section 4.1. The analysis
focuses on two aspects, the first related to the similarities between the
DSP and another series, and the second regarding the instantaneous
influence that a change in magnitude in an exogenous series may have
on the DSP.
There are many techniques to evaluate the similarity between time
series (Gontijo et al., 2020). In this paper, the similarity determination
between two time series is carried out by employing two methods:
(a) Pearson’s pairwise correlation test: evaluates the similarity between the values of both series for the same time value. The
test statistic values are arranged in a correlogram wherein statistically significant pairs at 0.05 or greater are marked. The
significance test follows a T-distribution with an asymptotic
confidence interval based on the Fisher Z-transformation. The
analysis is implemented using the corrplot package to generate
the correlogram and the cor.test package for the significance test,
both for the R language (Friendly, 2002; Hahsler et al., 2008).
(b) Hierarchical Clustering using DTW: this method evaluates the
similarity between the intervals of different time series regardless of their time shift and determines the best alignment considering a matrix of distances. Next, a search for the shortest
path that connects the initial (0, 0) and final (𝑛, 𝑚) points in the
matrix is conducted (Yu et al., 2019). In Gontijo et al. (2020),
different similarity metrics for time series were evaluated. We
adopt 168-hour windows for every pair of time series, including
the DSP. This specific window size is chosen because it describes
a complete cycle in the load behavior pattern. Once the distances
are calculated, the most similar pairs are grouped using the
HCA. The DTW is calculated using the package dtwclust available for the R language (Sardá-Espinosa, 2017) and the HCA
is implemented as presented in Voorhees (1986). The HCA was
employed using Euclidean distance and grouping by average.
𝜌(𝑡1 , 𝑡2 ) =
(4)
wherein 𝑑 and 𝑐 are the demand and generation capacity in two
instants, 𝑡1 and 𝑡2 .
The authors of Weron (2014) reproduced the study presented in
Cartea et al. (2009) for the English electricity market and observed
that the same relationship between price and RM. In this study, we
replicate the analysis proposed in Cartea et al. (2009), Weron (2014)
for the Brazilian electricity market for the first time. The RM calculation
adopted in this approach for each instance 𝑖 is given by:
RM𝑖 =
LOAD𝑖
GHYD𝑖 + GTHE𝑖 + GWIN𝑖 + GSOL𝑖 + GNUC𝑖
(5)
whose variables are shown in Table 1.
The proposed analysis aims to verify a pattern in the RM values
prior to a spike in the DSP series. We employ windows of 48, 72, 168,
and 336 h prior to the occurrence of each price spike determined by the
previously described method. Next, we calculate the RM arithmetic average for each window, which we refer to as an ARM in the remainder
of this work. Then, a data set containing (𝑅𝑖 , 𝐴𝑅𝑀(𝑖−𝑗) ) pairs is created,
wherein 𝑅𝑖 is the 𝑖th spike’s magnitude calculated as in (1) and 𝑗 is the
window size. Furthermore, this data set is clustered using the K-means
algorithm as presented in Bock (2007), set with Euclidean distance and
adopting the Elbow method to determine the value of 𝑘 as in Nametala
et al. (2020, 2022b). At last, we evaluate the spike density for each
ARM interval.
The K-means algorithm employment is an improvement to the
analysis protocol compared to the work presented in Weron (2014).
We adopt the same window sizes employed by the latter to facilitate
the comparison; we also add a 72-hour window size in our analysis.
Regarding the effects of generation sources’ availability on the price,
the authors in Hong et al. (2020) proposed a valid relationship for
the German electricity market. We reproduce the same analysis for the
Brazilian electricity market in this study. Thus, we elaborate an index
to identify the moments that precede the price spike occurrence. The
proposed index is based on the following:
The impact evaluation is carried out employing quantile regression,
which allows quantifying the magnitude of a particular exogenous time
series’ influence on the DSP for specific intervals of the distribution
determined by the quantiles (Koenker et al., 2017).
The following quantiles are considered: 0.1, 0.2, 0.3, 0.4, 0.5,
0.6, 0.7, 0.8, and 0.9. The adjustment is conducted by minimizing
the weighted sum of absolute residuals, which is modeled as a linear programming problem. The conditional quantile function estimate
is calculated for the nine quantiles considering the DSP series as a
reference and the other series as covariates. The standard errors are
estimated by adopting the bootstrap technique with 30 replications. The
test statistic is calculated using the Barrodale and Roberts algorithm as
described in Koenker and D’Orey (1987) and implemented using the
quantreg package available for the R language (Koenker, 2005; Koenker
et al., 2017). After calculating the quantile regression, the impact’s
magnitude coefficients are arranged in a heat map. Additionally, the
HCA is used to group the quantiles and covariates hierarchically.
(a) Return series: we apply a transformation on the series as shown
in:
√
𝑑𝑖 = 𝑅2𝑖
(6)
4.2.3. Price spikes analysis
The first step of the process to detect price spikes in the DSP series
is subtracting two consecutive values in the price series 𝑃 to obtain the
series of returns 𝑅, as per:
𝑅𝑖 = 𝑃(𝑖+1) − 𝑃𝑖
𝑑(𝑡1 , 𝑡2 )
𝑐(𝑡1 , 𝑡2 )
wherein 𝑅 is the series value at instant 𝑖, given by (1).
(b) BB: this is an index adopted in technical, financial analysis which
generates lower and upper bounds for each instant of the series
as per:
(1)
The returns are considered spikes if their values exceed either the
inferior (𝐿𝑖 ) or the superior (𝐿𝑠 ) limits, as in:
(7)
𝐿𝑖 = 𝑚𝑒𝑑𝑖𝑎𝑛(𝑅) − 𝑑 ⋅ 𝑠𝑡𝑑𝑒𝑣(𝑅)
(2)
𝐵𝑎𝑛𝑑𝑠𝑖 = 𝜇[𝑡(𝑖−𝑗) ,𝑡𝑖 ] ± 𝑑 ⋅ 𝜎[𝑡(𝑖−𝑗) ,𝑡𝑖 ]
𝐿𝑠 = 𝑚𝑒𝑑𝑖𝑎𝑛(𝑅) + 𝑑 ⋅ 𝑠𝑡𝑑𝑒𝑣(𝑅)
(3)
wherein 𝑗 is the window size adopted to calculate the moving
average 𝜇 and the standard deviation 𝜎 between instants 𝑡(𝑖−𝑗)
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Fig. 1. DSP series (BRL/MWh) switching behavior in 2021. The upper part illustrates the probability of occurrence of the two Markov regimes, whereas, in the lower part, the
solid blue line indicates the SST variation throughout the year. The regime switches are indicated by the windows highlighted in blue and red. A new window is created whenever
the SST variation crosses the threshold 0.01 indicated by the green dashed line.
and 𝑡𝑖 ; 𝑑 represents how many standard deviation from the mean
value are considered to generate the band values at instant 𝑖.
The equation returns two values represented by 𝐵𝑎𝑛𝑑𝑠𝑖 for each
instant 𝑖. The BB presents an abnormal variation when the price
exceeds the threshold given by any of the bands.
5.1. Regime switch
We implemented two Markov regimes, namely, 1 and 2. The upper
part of Fig. 1 shows the first regime’s probability of occurrence in the
DSP series. This analysis indicates that regime 1 ceased to prevail in
the DSP exactly when the hydrological crisis peaked. After a period of
high values and instability for the DSP between early May (hour 2900)
and late July (hour 5100), the CCEE began to practice the so-called
Maximum Structural Ceiling (MSC) price, valued at BRL 583.88/MWh.
The MSC was practiced for 57 days, between June 26th (hour 4240)
and August 23rd (hour 5622). This analysis is complemented by the
SST variation which can be observed in the lower part of Fig. 1.
According to the SST variation (solid blue line), the year is divided
into seven windows characterized by the cross of the 0.01 SST threshold
(dashed green line). In the first window, one can observe a sequence
of DSP reduction at the beginning of the year, followed by a series of
increments observed between May and July (second window). Next, the
hydrological crisis period was divided into three windows. In the fourth
window, the MSC prevailed. In the third and fifth windows, one can
observe a volatile DSP with values close to the MSC, which indicates a
probable hesitation by the CCEE to establish the use of the ceiling price
at the beginning of the hydrological crisis and to remove it in the end.
The inflow forecasts indicated a rainy season confirmed only in October
(hour 6560), which caused the DSP value to drop until it reached the
minimum value observed in the first semester (BRL 49.77/MWh). This
behavior can be noticed in the sixth window. Finally, the last behavior
pattern (seventh window) was observed between October 19th (hour
6995) and December 31st (hour 8760) and presented an average value
of BRL 90.63/MWh.
4.2.4. International comparison
This analysis aims to evaluate and enumerate similarities among
prices practiced in different electricity markets. The markets’ price
values were converted according to daily USD quotations for their local
currencies. Next, we conduct a descriptive statistic summary of these
price series. At last, we verify the correlation between load and price
in each electricity market. We adopt the average load and the price’s
seasonal component, extracted from the series’ additive decomposition,
to conduct the correlation analysis.
The statistical summary contemplates the median, average, maximum and minimum values, standard deviation, skewness, and kurtosis.
The series decomposition, is carried out as follows:
𝑍𝑡 = 𝑇𝑡 + 𝑆𝑡 + 𝛼𝑡 ,
(8)
may be used to separate the series 𝑍𝑡 into three components: the trend
component 𝑇𝑡 , the seasonal component 𝑆𝑡 and the random variation
component 𝛼𝑡 (Hamilton, 2020). In this study, we adopt the additive
decomposition described in Kendall and Stuart (1983) and available
in the package stats for the R language. A 24-hour frequency was
employed to obtain only the 𝑆𝑡 component’s daily cycle. This block is
then compared with the average load for every Monday of 2021 (we
adopt Mondays as a reference because this was the weekday with the
lowest incidence of holidays throughout the year, presenting greater
load profile consistency).
5.2. Influence of exogenous variables in the hourly DSP
5. Analysis and discussions
In this subsection, we discuss the influences of the power system
and macroeconomic variables on the hourly DSP value oscillations.
The hourly DSP behavior is analyzed in the following subsections
according to the four perspectives discussed in Section 4.2. The source
code was written in the R (version 4.1.2) and Python 3 languages,
running Pop!_OS (version 22.04) operating system. The code ran in
an Intel i9-10900, GPU GeForce RTX 2080 Super, and 32 GB of RAM.
The source code and the complementary analyses are available in this
project’s online repository (Nametala et al., 2022a).
5.2.1. Power system variables
ENAM determined GHYD in 2021, as one may observe in Fig. 2.
Once the reservoirs started to deplete during the first quarter of the
year, followed by the peak of the hydrological crisis in the second quarter, the ONS had to increase GTHE and GNUC, while GWIN and GSOL
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Fig. 2. DSP variation (BRL/MWh – solid red line) relationship with energy physical delivery and ENAM. The dashed lines correspond to the third-degree polynomial regression
estimation of the GHYD in blue, GTHE in gray and the sum of the other sources (GWIN + GSOL + GNUC) in light green. Further detail regarding the interval between the black
dashed vertical lines, ranging from May 1st (hour 2881) to June 4th (hour 3720), is presented below in Fig. 9.
Table 3
Relative monthly participation of each generation source in 2021.
Month
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
TOTAL
GHYD
(%)
65.96
73.84
76.09
72.27
68.10
58.70
52.99
48.74
50.30
52.08
58.65
66.44
62.01
GTHE
(%)
18.71
13.92
12.51
14.93
17.88
25.63
27.89
28.23
26.64
27.28
23.95
17.33
21.24
GWIN
(%)
10.38
7.40
7.65
9.42
11.00
12.49
15.10
16.61
15.85
14.69
11.68
11.56
11.99
GSOL
(%)
0.90
0.78
1.04
1.12
1.14
1.22
1.23
1.23
1.57
1.29
1.55
1.46
1.21
GNUC
(%)
2.64
2.62
2.56
2.27
2.19
1.22
1.63
2.97
2.86
2.83
2.60
2.85
2.44
Table 4
Monthly average DSP and average CMOP and their difference.
GWIN +
GSOL +
GNUC
(%)
13.92
10.80
11.24
12.82
14.33
14.93
17.96
20.81
20.27
18.81
15.83
15.88
15.63
Month
DSP
(BRL/MWh)
CMOP
(BRL/MWh)
CMOP - DSP
(BRL/MWh)
CMOP - DSP
(%)
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
240.63
163.89
88.22
108.84
205.82
334.61
583.88
583.88
575.62
249.27
88.10
66.53
252.55
166.26
89.80
124.06
185.47
524.19
1122.02
2516.44
854.78
239.72
81.14
62.33
11.93
2.36
1.58
15.21
−20.35
189.57
538.14
1932.56
279.15
−9.54
−6.95
−4.19
4.96
1.44
1.79
13.98
−9.89
56.65
92.17
330.99
48.50
−3.83
−7.89
−6.30
5.2.2. Macroeconomic variables
The B3 is the Brazilian stock exchange and the fifth largest in the
world (Chaves and Silva, 2018; B3, 2022a). The main B3 benchmark
index is the Ibovespa (IBOV), which is composed of a hypothetical
portfolio representing 70% of all the stock value traded and regards
the companies with the greatest market shares, including those in
the energy, oil, and gas segments such as the Petróleo Brasileiro S.A.
(Petrobras) (Filho and Rocha, 2020). Petrobras has the Brazilian government as its largest shareholder (Pundrich et al., 2021) and was the
oil company with the highest net profit in the world in the first quarter
of 2021 (Petrobrás, 2022). In March 2022, 15.82% of IBOV was due to
Petrobras shares (B3, 2022b). The company was responsible for 80% of
all Brazilian domestic fuel market supply in 2021 (Brazil, 2022).
It can be noticed in Fig. 4 that the total production (PEPD + GASP)
and sale (FUOS + LPSL + DISL) of the primary fuels traded in Brazil in
2021 were preceded by an oscillation IBOV. It should be mentioned
that the production and sales of the primary fuels traded in Brazil
follow the same trend presented by IBOV. Between 2011 and 2020,
16.94% of Petrobras’ production was exported (Brazil, 2022); therefore,
the USD-BRL exchange rate (EXCX) affects fuel sales in the domestic
market. At the beginning of the DSP rise in 2021, EXCX was BRL 4.39.
As the hydrological crisis aggravated, EXCX also increased. IBOV, fuel
production and sales, and the DSP inversely followed this rise.
increased their power injection given their primary natural source
availability in the period. This maneuver began in May and the monthly
participation of each generation source is detailed in Table 3.
The dispatch of costly thermal power plants and the lack of water inflow increase for the foreseeable future caused CMOP to reach
BRL 2516.44/MWh in August, even with the increase of other sources
(GWIN, GSOL and GNUC), as it may be seen in Fig. 3 and Table 4. The
highest value recorded by the ONS for CMOP was BRL 3044.45/MWh,
and it was practiced for 22 days between July 30th at 00:00 h (hour
5401) and August 20th at 23:00 h (hour 5568). The highest DSP values
registered occurred during the two most volatile periods before and
after the MSC adoption. The maximum average DSP (average of the
four submarkets) reached BRL 765.11/MWh on July 4th at 18:00 h
(hour 4435). Additionally, on September 12th at 00:00 h (hour 6097),
the DSP reached its highest historical value when the NE submarket
traded energy at BRL 1128.72/MWh, corresponding to 193% of the
MSC value. During the period when the MSC was employed, the
difference between CMOP and the average DSP reached 330%, which
was the greatest decoupling ever recorded since the DSP’s adoption.
The difference between CMOP and the DSP is shared equally and
paid by the DCE agents in the form of an additional charge, namely
System Service Charges (SSC). The SSC’ creditors are power plants
(especially thermal) dispatched for emergency load supply, disregarding the merit order (CCEE, 2022f; ONS, 2022a). The SSC’ financial
impact on the electricity market in 2021 generated a programmed
cost of BRL 14 billion per month until 2025 (CCEE, 2022g,f; association of small hydroelectric plants and hydroelectric generating plants
ABRAPCH, 2022). This event has led the CCEE to reevaluate the MSC
from 2022 onward as BRL 646.58/MWh (CCEE, 2022e).
5.2.3. Similarity analysis
Fig. 5 presents the correlograms among the DSP and power system
variables from Table 1 and macroeconomic variables from Table 2.
Fig. 5(a) shows that power system variables most closely related
to the DSP are CMOP (0.78) and CMOD (0.78), which are published
by the ONS, followed by GTHE (0.67) and GWIN (0.5). The most
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Fig. 3. DSP variation (BRL/MWh – solid red line) and MCO variation (BRL/MWh – solid brown line) relationship with variables that indicate the reservoir level (values scaled in the
range [0,1]). The long-term spilled-water-hydraulic potential moving average (ENAL) was multiplied by the Spilled-Water-Hydraulic Potential Forecast Indicator (IPE) (%/month).
The ONS employs this calculation to forecast water inflows (ONS, 2022c).
Fig. 4. DSP variation (BRL/MWh – solid red line) relationship with variables that indicate the Brazilian macroeconomic scenario in 2021 (values scaled in the range [0,1]).
Fig. 5. Correlograms among (a) the DSP and power system variables and (b) the DSP and macroeconomic variables; a crossed correlation value in both correlograms indicates
statistical non-significance, as described in Section 4.2.2.
significant inverse correlations are observed for spilled-water-hydraulic
potential ENAM (−0.75) and ENAL (−0.49), power exchange between
subsystems INNS (−0.5) and INSS (−0.56), and GHYD (−0.48). In
this study, LOAD presented a correlation with the DSP close to the
statistical non-significance (0.07). Nonetheless, the correlation between
LOAD and GHYD is the second greatest amongst all the variable pairs
(0.79), surpassed only by the correlation between CMOP and CMOD.
In this sense, it must be considered that, although there is no causality
indication, there is evidence indicating that the DSP indirectly followed
LOAD. The inverse correlation between the generation pairs GHYD
and GTHE, and GHYD and GWIN, illustrated in Fig. 2, was statistically confirmed with correlation values of −0.55 and −0.56, respectively. The pair GWIN and GTHE also present a significant correlation
(0.53).
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Fig. 6. DTW-generated dendrogram among (a) the DSP and power system variables and (b) the DSP and macroeconomic variables (b).
between LOAD and ENAM, which was only 0.2 in Fig. 5(a), but DTW
found some similarities between the two time series.
The HCA divided the macroeconomic variables into three subsets,
as one may observe in Fig. 6(b): (i) subset one contains the grouping of
DSP and IBOV the grouping of ‘‘Fuel Production’’ and ‘‘Fuel Sales’’; (ii)
subset two first grouped the price (IGPX) and building (BUIX) indexes,
then GRSP, then PEPC, and then INTR; (iii) subset three aggregates
UNCX and EXCX. The variables clustered in subsets 1 and 2 correspond
to the correlations presented in Fig. 5(b). The only difference is that
EXCX was not included in subset one even though its correlation with
the DSP is 0.7. In the dendrogram analysis, EXCX presented high
similarity with UNCX, which showed a low correlation with the DSP
(0.3) in Fig. 5(b).
Finally, considering only variables with high similarity (correlation
higher than 0.4), most of them had similar behavior to the DSP during 2021. The correlogram results indicate that ENAM and EXCX are
the most influential exogenous variables, whereas the DTW analysis
emphasizes CMOP, CMOD, GTHE, IBOV, ‘‘Other Generations’’, ‘‘Fuel
Production’’ and ‘‘Fuel Sales’’.
Regarding the correlations among the DSP and macroeconomic
variables in Fig. 5(b), IBOV and fuel production (PEPD and GASP)
and sale (FUOS, LPSL and DISL) indexes are the most closely related
variables to the DSP. FUOS presents the most significant positive correlation with the DSP (0.7). Only two variables presented an inverse
correlation with the DSP; the most significant is the EXCX (−0.7). The
indexes representing prices and inflation, such as IGPX and BUIX, do
not present high correlation values (0.27 and 0.25, respectively). It may
be observed that IGPX and BUIX have a high correlation (higher than
0.8) with PEPC and GRSP. However, PEPC and GRSP are the variables
least correlated with the DSP (0.04 and 0.01, respectively). Thus, one
can conclude that the DSP did not follow either trade price indexes in
2021 or the base interest rate (INTR), but those series related to the
fuel industry and IBOV, which, in turn, is also influenced by the fuel
industry as explained in Section 5.2.2.
The correlation analysis is complemented by dendrograms shown in
Fig. 6, which were estimated using DTW and clustered by the HCA. Regarding the power system variables, Fig. 6(a) presents GWIN, GSOL and
GNUC grouped under ‘‘Other Generations’’ due to their low individual
impact; ENAL was removed because it is a long-term moving average,
not an accurate measure. Additionally, the variables indicating power
exchange between subsystems (INNS and INSS) were removed from
this study as these variables mainly regard their respective submarkets.
As for the macroeconomic variables in Fig. 6(b), the production and
sales series were added under ‘‘Fuel Production’’ and ‘‘Fuel Sales’’,
respectively.
The power system variables and the DSP were divided into three
subsets, as one may observe in Fig. 6(a): (i) subset 1 (upper part of
Fig. 6(a)) aggregates CMOD and CMOP forecasts with the DSP; (ii)
subset 2 (middle part of Fig. 6(a)) aggregates GTHE and ‘‘Other Generations’’; and (iii) subset 3 (lower part of Fig. 6(a)) groups GHYD and
LOAD, then includes ENAM, and finally EARM. The HCA, then, groups
the first two subsets into the same cluster. This clusterization makes
sense since subset one is composed of price series, which strongly
correlate with the most expensive power sources. Hence, it is fair
to assume a strong correlation between subsets 1 and 2. Subset 3 is
composed of variables related to water use which positively correlate
with LOAD, as illustrated in Fig. 5(a). This is analysis shows that even
though ENAM is the most correlated variable with the DSP, they are
not the most similar when considering the time series ‘‘profile’’, as
described by DTW. This analysis inversely applies to the correlation
5.2.4. Impact analysis
The set of variables considered in this analysis is the same as
adopted to generate the dendrograms in the previous subsection. The
first thing to notice in the heatmaps shown in Fig. 7 is that the DSP
distribution’s central quantiles (0.4, 0.5 and 0.6) are the most affected
by the considered variables as the highest coefficients are observed
in this region, i.e., every variable had some effect on the average
DSP values. Considering the central quantile 0.5 in Fig. 7(a), one may
notice that every variable displays coefficients greater than zero. EARM
presented coefficient values between 0 and 0.3; between 0.3 and 0.7
coefficient range are GHYD, LOAD, ‘‘Other Generations’’ and GTHE;
and, at last, ENAM, CMOD and CMOP display coefficients between
0.7 and 1. Hence, this analysis confirms the correlation study, which
indicated that ENAM has the most significant impact on the DSP central
quantile.
Another interesting point is that CMOP has a 13.15% higher impact
on the DSP than CMOD, although both variables have shown the
exact correlation (0.78) with the DSP and are virtually coupled (0.99
correlation). Additionally, despite being one of the least similar series
compared to the DSP, LOAD is one of the variables with the greatest
impact in the DSP central quantile (0.5), surpassing the effect of GTHE.
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Fig. 7. Quantiles and covariables clustering via HCA based on the quantile regression results: (a) DSP and power system variables as covariables; and (b) DSP and macroeconomic
variables as covariables.
Table 5
List of variables with a quantile regression coefficient greater than 0.4 per
quantile; the related variables had the greatest influence on the DSP in 2021.
This phenomenon is not observed for quantiles 0.4 and 0.6; most of
the variables, including the CMOP and CMOD, have a lower, or even
zero, impact on the DSP for these quantiles. The variables with the
greatest influence in the DSP for quantiles 0.4 and 0.6 are GTHE and
‘‘Other Generations’’ in this order. This result is explained by the DSP
deviation from the mean value associated with the dispatch of more
costly power plants and power injection of intermittent power sources,
as shown in Fig. 2. As for quantiles 0.1 and 0.2, no variable presents
a detectable impact. The same may be stated for the quantiles 0.8 and
0.9, except for CMOP and CMOD. This finding indicates an inelastic
demand prevalence, i.e., the load profile is maintained even if the price
is lower or higher than the average.
Regarding the impact of macroeconomic variables in the DSP, illustrated in Fig. 7(b), the variables with higher impacts are IGPX,
IBOV, ‘‘Fuel Production’’ and PEPC. This information is useful given
that highly correlated variables such as EXCX and ‘‘Fuel Sales’’, despite
showing similar behavior (positive or inverse), do not significantly
affect the DSP. On the one hand, EXCX and ‘‘Fuel Sales’’ present impact
in the DSP only for the central quantile and show the lowest influence
amongst the analyzed variables (0.12). On the other hand, IGPX, IBOV,
‘‘Fuel Production’’ and PEPC demonstrate the greatest influence in the
DSP for the 0.5 quantile. These four variables are the most influential
for other quantiles as well. In the DSP distribution’s quantiles 0.6 and
0.7, IGPX and IBOV are the most significant variables, whereas for
quantile 0.3, ‘‘Fuel Production’’ and PEPC are dominant. None of the
variables present much effect in the DSP for the 0.2 and 0.8 quantiles.
Finally, for quantile 0.9, the variables with the highest impact in the
DSP are IBOV and PEPC, and for quantile 0.1, the variables are IBOV
and GRSP.
Table 5 summarizes the most impactful variables per quantile (only
coefficients higher than 0.4 are considered). As one may notice, there is
a transition interval (quantiles 0.2 and 0.8) between the central and the
respective extreme quantiles wherein no variable significantly affects
the DSP. We highlight that 12 out of the 18 variables considered in
this study appear in Table 5: IBOV and IGPX appear four times, CMOP,
GTHE, ‘‘Fuel Production’’ and PEPC appear three times, CMOD was
mentioned twice, and the other variables appear only once. Amongst
the power system variables, only GTHE is significant for more than one
quantile, and amongst the macroeconomic variables, IBOV and IGPX
presented the highest impacts in the DSP.
Quantile
Variables
0.1
0.2
0.3
0.4
IBOV, GRSP
–
‘‘Fuel Production’’
CMOP, GTHE, PEPC, ‘‘Fuel Production’’, IGPX
0.5
GTHE, CMOP, CMOD, ‘‘Other Generations’’, ENAM,
LOAD, GHYD, PEPC, ‘‘Fuel Production’’, IBOV, IGPX
0.6
0.7
0.8
0.9
GTHE, ‘‘Other Generations’’, IGPX, IBOV
IGPX
–
CMOP, CMOD, IBOV, PEPC
5.3. Price spikes
The DSP spikes were analyzed considering two aspects: (i) the
relationship between the spike and the ARM; and (ii) the relationship
between the spike and the availability of generation sources.
5.3.1. Average reserve margin
The DSP’s spikes (positive or negative) and the correspondent ARM
are shown in Fig. 8 for each of the considered time windows. The dots
were colored in gray and black to represent the clustering resulting
from the K-means algorithm application (the white circles represent the
centroids). The dashed blue lines define the ARM values: 0.985, 0.995,
1.005, 1.015, 1.025, 1.035, and 1.045. The region highlighted in green
presented the least occurrence of price spikes.
It should be mentioned that the optimized 𝑘 value for the K-means
algorithm indicated the existence of two groups for every plot shown
in Fig. 8. In this sense, one may state that there are two behaviors
relating to price spikes and the ARM regardless of the time window.
Noteworthy, this result is not a consequence of the number of prices
practiced in these intervals, given that the K-means detected a single
group for non-spike prices.1
The point density per interval analysis shows that price spikes may
occur even when the load is entirely supplied by the generators (ARM
1
11
Such study may be found in Nametala et al. (2022a).
Utilities Policy 81 (2023) 101513
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Fig. 8. DSP magnitude spikes (positive and negative) and their corresponding ARM for 48, 72, 168, and 336 h prior to the price occurrence. The green-shaded ARM interval
(delimited by the dashed blue lines) has the least point density. DSP presents fewer spikes for the time window when the ARM is within the interval [1.005, 1.015].
Fig. 9. Price spikes observed between May 1st (hour 2881) and June 4th (hour 3720).
close to 1). However, the amount of spikes observed is lower when the
ARM ranges from 1.015 to 1.025, which characterizes a prospective
scenario of demand and supply unbalance. This behavior is numerically
featured in Table 6, which shows the number of price spikes contained
in each ARM interval. The last column presents the ratio between the
number of price spikes and the total occurrence of each ARM interval.
One may notice that the probability of spikes is lower for the ARM
between 1.015 and 1.025 regardless of the time window, even though
this interval does not present the least frequent of non-spike values.
Observe, for instance, that few price values were recorded for the ARM
between 0.985 and 0.995; however, the proportion in which price
spikes occurred for this ARM interval is expressive, especially for the
48- and 72-hour windows. There was no record of price spikes when
the ARM was lower than 0.985, there were seven non-spike values, and
no price spike was recorded when the ARM was higher than 1.045.
The authors of Cartea et al. (2009) and Weron (2014) have also
found a specific ARM interval for every time window wherein the
proportion of price spikes was low, and the number of price spike occurrences on the adjacent intervals was high. Furthermore, the authors
of Weron (2014) mention that ‘‘it is as if, once the demand-to-capacity
ratio exceeds a certain, very high level, the supply (and perhaps the
generation) side(s) of the market do everything they can to prevent
spikes’’. Based on the study presented in this section, one can affirm
that the same behavior prevailed in the Brazilian electricity market in
2021 after the hourly DSP began being practiced. Finally, we highlight that patterns related to price spikes were also found in other
markets and using other techniques (Ioannidis et al., 2021b), which
corroborates these results.
5.3.2. Availability of generation sources
Assuming that the RM affects the DSP value and knowing that
the RM is calculated based on the total generation capacity, then a
study that separately investigates the availability of each power source
may provide details regarding the characterization of the moments that
precede a price spike. Fig. 9 illustrates the price spikes between May
1st (hour 2881) and June 4t (hour 3720). The DSP, ENAM, LOAD,
GHYD, GTHE, ‘‘Other Generations’’ and ‘‘Total Generation’’ (GHYD +
GTHE + GWIN + GSOL + GNUC) are shown in Fig. 9(a); the dashed
lines are third-degree polynomial regressions for GHYD (light blue),
GTHE (gray) and ‘‘Other Generations’’(light green). The DSP, CMOP,
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Table 6
Price spike frequency of occurrence considering the ARM observed in the historical windows of 48, 72, 168, and
336 hours – in all cases, there is a lower percentage of spikes for ARMs between 1.015 and 1.025 (highlighted in
bold blue in the last column).
Average Reserve
Margin Interval
48-Hour-Behind Window
Start
End
Spike (−)
Spike (+ )
0.985
0.995
1.005
1.015
1.025
1.035
0.995
1.005
1.015
1.025
1.035
1.045
9
50
26
7
31
15
7
53
21
3
35
14
Average Reserve
Margin Interval
72-Hour-Behind Window
Start
End
Spike (−)
Spike (+ )
0.985
0.995
1.005
1.015
1.025
1.035
0.995
1.005
1.015
1.025
1.035
1.045
8
51
26
9
35
9
7
53
21
9
35
8
Average Reserve
Margin Interval
168-Hour-Behind Window
Start
End
Spike (−)
Spike (+ )
0.985
0.995
1.005
1.015
1.025
1.035
0.995
1.005
1.015
1.025
1.035
1.045
2
55
38
6
35
2
3
46
34
14
34
2
5
101
72
20
69
4
336-Hour-Behind Window
Start
End
Spike (−)
Spike (+ )
Spikes
0.985
0.995
1.005
1.015
1.025
1.035
0.995
1.005
1.015
1.025
1.035
1.045
0
51
43
6
38
0
0
45
44
6
38
0
0
96
87
12
76
0
2
Probability (%)
25.40
3.00
2.08
0.89
5.67
7.90
47
3327
2216
1119
1098
338
Non-spikes
Probability (%)
19.23
3.10
2.13
1.37
5.99
5.84
63
3254
2162
1300
1099
274
Spikes
Average Reserve
Margin Interval
(𝑝𝑖 − 𝑝𝑚𝑖𝑛 )
(𝑝𝑚𝑎𝑥 − 𝑝𝑚𝑖𝑛 )
Non-spikes
Spikes
15
104
47
18
70
17
EARM, ENAM and ENAL are shown in Fig. 9(b); the last three variables
were multiplied by the IPE and scaled in the range [0, 1].
The third-degree regressions presented in Fig. 9(a) provide the
general behavior of the considered power sources and, therefore, may
not be used to carry out a short-term analysis of the impact of the
generation sources’ availability in the DSP spikes. However, when analyzing the sources’ hourly injection, one observes that the price spikes
are preceded by an increase in thermal generation and a reduction in
‘‘Other generations’’, which is followed by an inversion in this sequence
(reduction of GTHE and increase of ‘‘Other Generations’’), and then the
price spike occurs. The DSP spikes are unrelated to CMOP, as Fig. 9(b)
shows detachment between these variables. Also, neither EARM nor
ENAM and ENAL variations exerted observable influence in the DSP.
This behavior was also noticed for spikes in intervals other than the
ones presented in Fig. 9.2
Considering that the percentage of GTHE drastically changes before
the price spike occurrence, one can use this ratio to create an index
capable of identifying possible moments preceding a DSP spike. First,
we employed the scaling function 𝑠𝑐𝑎𝑙𝑒():
=
𝑝𝑠𝑐𝑎𝑙𝑒𝑑
𝑖
Spikes
16
103
47
10
66
29
Non-spikes
Probability (%)
7.25
3.19
3.48
1.01
6.91
2.74
64
3070
1994
1952
930
142
Non-spikes
Probability (%)
–
3.48
3.28
0.63
6.82
–
0
2666
2569
1878
1039
0
Then, the ratio between the ‘‘Total Generation’’ (TG) and any generation source is converted into a 𝑧 index, calculated as:
(
)
TG𝑖
𝑠𝑐𝑎𝑙𝑒 GWIN
𝑖
𝑧𝑖 =
(10)
(
)
TG𝑖
𝑠𝑐𝑎𝑙𝑒 GHYD +GTHE
𝑖
𝑖
Finally, we apply BB in (7) in the 𝑧 time series.
The results of applying the methodology mentioned above for creating a price spike predictor index 𝑧 are illustrated in Fig. 10. Observe
in Figs. 10(a.2) and 10(b.2) that whenever the index surpasses the
BB upper threshold, the DSP returns (𝑅) follows the pattern hours
later, characterizing a price spike. Table 7 evaluates the index’s overall
capacity of correctly detecting a price spike in the 48, 72, 168, and
336 h following the moment the index value crossed the BB upper
limit. The method’s number of successful predictions is similar for every
time window, presenting an average 66.5% hit rate. We highlight that
the elaboration of an index based on machine learning techniques or
advanced statistical forecasting is not within the scope of this study. In
this sense, we suggest using the proposed index only in a supervised
context.
(9)
Thus, considering an isolated assessment of the generation sources’
availability relationship with price spikes, one can affirm that the
behavior observed by Hong et al. (2020) for the German market also
occurred in the Brazilian market in 2021.
Such study may be found in Nametala et al. (2022a).
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C.A.L. Nametala et al.
Fig. 10. Use of BB for the DSP returns (a.1 and b.1) and the generation source availability index 𝑧 (a.2 and b.2), with price spikes in May (prior to the hydrological crisis) (a.1
and a2.) and price spikes in June (during the hydrological crisis) (b.1 and b.2).
Table 7
Detection of price spikes by the index based on BB for 48, 72, 168, and 336 hours
ahead.
Measure
48 h
72 h
168 h
336 h
Correct
Wrong
Ratio (miss to one hit)
Hit rate (%)
480
158
0.3292
67.08
735
251
0.3415
65.85
1731
580
0.3351
66.49
3441
1151
0.3345
66.55
is practiced in the Canadian market. It should be considered that,
despite practicing a similar price to the American market, the Brazilian
minimum wage per month in 2021 was USD 229.22 (Brazil, 2021a).
The average wage in Canada was USD 1688.81 (Canada, 2020); for
Germany, the value reached USD 1698.97 (Germany, 2022) and the
USA have a USD 1160.00 national wage floor (USA, 2022). In light of
this information, one concludes that the Brazilian energy price is the
most expensive amongst the considered markets since the value per
MWh represents 16.18% of the minimum wage, whereas the percentage
observed in Canada is 1.13%, 5.28% for Germany and 2.76% for the
USA.
Another comparative analysis is presented in Table 9, wherein the
correlation (by quarters and annual) between the energy prices and
a total load of each market is presented. As one may observe, the
correlation between the variables is positive for every electricity market
except for the Brazilian one. The higher annual correlation values were
observed for the Indian and American markets in this order. In some
quarters, these markets presented correlations between price and load
higher than 0.7.
Although the Brazilian electricity market presented an annual correlation between price and load close to zero, we carried out another
study aiming to filter the price series’ trend (𝑇𝑡 ) and random variation
(𝛼𝑡 ) components according to (8). We applied an additive decomposition to meet this end and selected the repeated pattern from the
seasonal component (𝑆𝑡 ). This pattern was compared to the average
load observed for Mondays in each market in 2021. The results are illustrated in Fig. 11, which shows that once interferences were removed,
every market presented a positive correlation between the energy price
and the system load. Even the Brazilian market, which presented the
worst correlation values for direct analysis, presented the second higher
correlation between price and load (0.94), only lower than the value
observed for the American market (0.95). The correlations calculated
for the Australian, Indian, Canadian and German markets were, respectively, 0.81, 0.38, 0.94, and 0.72. This result indicates that seasonality
may play a role in price formation as an underlying model. In this sense,
even though the prices show different characteristics for each market,
as evidenced in Table 8, there are also common features as described
in this analysis.
5.4. International comparison
The prices (converted to USD) practiced during 2021 by the SECW
Brazilian electricity submarket, referred to from now on as the Brazilian
market for the sake of simplicity, are compared to the ones practiced
by Queensland (Australia), IEX (India), PJM (USA), IESO (Canada) and
EEX (Germany) markets in Table 8. Other charts on energy prices
practiced in these markets may be consulted in Nametala et al. (2022a).
Observe that each market has different price distributions with
specific skewness and kurtosis. The Brazilian market presents the lowest
skewness and kurtosis amongst the analyzed markets. Additionally, it
can be extracted from the minimum price columns that the Australian,
German and Canadian markets allow the practice of negative prices.
This event occurs when an energy supplier must liquidate their positions due to previously celebrated energy contracts. Such stakeholders
sometimes prefer to pay for their power injection to be consumed than
to expose themselves to the spot price. This scenario cannot happen
in the Brazilian market. It can also be noticed that, in the Brazilian,
Australian, and German markets, the mean is 43.18%, 83.30%, and
26.18% higher than the median, respectively.
Furthermore, the standard deviations are significant compared to
the mean for every market. The standard deviations for the Brazilian,
Indian, American, and German markets correspond to, respectively,
72.87%, 67.6%, 48.79%, and 73.33% of the mean; the most impressive findings are observed for the Canadian (116.77%) and Australian
(400.06%) markets. These values indicate that all the analyzed markets are susceptible to variations resulting in price spikes. Finally,
by analyzing the mean, one can conclude that the most expensive
energy price is observed in the German market, whereas the cheapest
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Table 8
Price statistical summary by market. A version of this table that shows the relative values to the average wage of each country can be consulted
in Nametala et al. (2022a).
Market
Median
Mean
Min.
Max.
St. Dev.
Skewness
Kurtosis
SECW (Brazil)
Queensland (Australia)
IEX (India)
PJM (USA)
IESO (Canada)
EEX (Germany)
37.1
35.71
43.52
32.12
19.1
89.73
53.12
65.46
53.34
37.69
21.29
113.23
8.72
−740.74
8.2
14.8
−3.26
−84.15
151.51
11627.91
271.16
164.65
1318.1
696.63
38.71
261.88
36.06
18.39
24.86
83.03
0.65
25.38
3.36
1.8
23.66
1.9
1.83
904.9
16.54
8.12
1070.54
7.97
Fig. 11. Comparison between the seasonal blocks extracted from the price series via additive decomposition (solid red lines) and the average load (solid black lines) of every
Monday in 2021 for the Brazilian market and the Queensland (Australia), IEX (India), PJM (USA), IESO (Canada) and EEX (Germany) markets.
Table 9
Correlation between price and load by quarter for each market.
Market
JAN–MAR
APR–JUN
JUL–SEP
OCT–DEC
Annual
SECW (Brazil)
Queensland (Australia)
IEX (India)
PJM (USA)
IESO (Canada)
EEX (Germany)
0.05
0.24
0.54
0.52
0.22
0.48
0.11
0.2
0.47
0.78
0.53
0.28
0.18
0.35
0.41
0.75
0.6
0.35
−0.03
0.27
0.74
0.45
0.26
0.51
−0.08
0.18
0.53
0.46
0.36
0.29
and IGPX as the most relevant variables. Among these variables,
in this order, two are related to the ONS price forecasts, one
regards the hydrological conditions, one concerns the thermoelectric power generation, the stock exchange provides one to
indicate the financial market’s health, the Brazilian Central Bank
controls another, and the last one is published by an independent
foundation to assess the final consumer prices. This analysis has
shown that the set of variables influencing the DSP may be more
diverse and broad than the ones regarding only the power system
operating conditions.
• Price spikes: Specific patterns in the reserve margin and availability of generation source behaviors preceded the DSP spikes in
2021. The reserve margin pattern observed in this investigation
corroborates the results presented in Weron (2014) and Hong
et al. (2020), which conducted studies for the English and German
electricity markets. The availability of generation sources was
used to create a simple index that could correctly indicate most
of the price spikes before their occurrence. Given our results and
those presented in Weron (2014) and Hong et al. (2020), we speculate that this behavior may be recurrent in other energy markets
that adopt at least an hourly granularity. This hypothesis may be
used as an opportunity for investigation in future research.
• International comparison: The statistical summary for the prices
practiced in six electricity markets indicated the many differences between these time series. The correlation between prices
and their respective power system load differs for each market.
However, once the trend and random variation components were
removed from the price series, all the markets presented a significant correlation between the price and an average weekday load
(we have adopted Mondays in this study). This analysis shows
that subjacent models are embedded in the price formation of
these six electricity markets and that such models may also exist
in other unaccounted markets.
6. Conclusion
In this study, we analyzed the DSP behavior in the DCE of the
Brazilian electricity market in 2021, when the hourly price granularity
was adopted. Concurrently, Brazil faced the worst hydrological crisis
while strongly dependent on hydroelectric power generation. This investigation has focused on the DSP time series’ statistical aspect, its
relationship with exogenous variables, and a comparison with electricity markets from other countries. We addressed four perspectives from
which we draw the following main conclusions:
• Regime switching: The regime switch analysis is consistent with
the time of the hydrological crisis since the two methods employed could capture two different behaviors in the price series
throughout the year 2021. In this sense, one may state that the
hydrological crisis period was numerically described given that a
cluster was created specifically for the prices practiced during the
hydrological crisis. Thus, a more profound analysis of this period
may be conducted especially comparing this event with previous
hydrological shortage periods.
• Exogenous variables influence: Considering both the similarity
and the impact of the exogenous variables’ time series simultaneously, one can list CMOP, CMOD, ENAM, GTHE, IBOV, EXCX
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Utilities Policy 81 (2023) 101513
C.A.L. Nametala et al.
One of the interests stated at the enactment of MME ordinance
#33/2017 was that the hourly DSP adoption would allow more coupling between the system’s physical operation cost and the practiced
energy price. Thus, the hourly DSP was designed to be a reliable
economic signaling mechanism, overcoming deficiencies in the weekly
DSP, which, historically, was unable to follow the Brazilian energy
matrix diversification and, therefore, created anomalies and generated
financial losses, especially during peak consumption scenarios. Since
all the costs due to the decoupling between MCO and DSP are shared
between market agents in the form of charges, one may conclude
that the hourly granularity implementation has not been sufficient for
dealing with this issue in the event of a hydrological crisis. The decoupling between the MCO and the average DSP reached 330% in some
moments, regardless of the intervention of regulatory agencies. This
phenomenon encourages the celebration of long-term contracts since
the risks and exposure to DSP are reduced. It should also be mentioned
that long-term contracts diminish the STM’s relevance, thus ignoring
consumer preferences. This situation undermines the establishment of
a sustainable market, as it indicates the existence of artificiality in the
price. Artificiality, in turn, distorts the agents’ analysis for decisionmaking both in the short-term to minimize costs, for example, and in
the long-term, for instance, in the context of capacity expansion investments. In this sense, we conclude that the hourly DSP adoption is an
advance for the Brazilian energy pricing regulatory policies. However,
if the intention is indeed to establish a sustainable deregulated market,
the price formation process must change to reduce inconsistencies
between the system’s physical operation and the DSP. If this objective
is achieved, the requests made to the Brazilian government by market
agents during the so-called ‘‘shadow operation’’ may finally be met,
which implies changes favoring transparency, liquidity, tax reduction,
risk management and the emergence of new businesses, products, and
services in the future.
Finally, we highlight that the implementation of the hourly DSP
creates a new dynamic related to the natural evolution of deregulated markets. The approximation between the price practiced in the
market and the entire system’s cost curve allows the appearance of
new business models. According to international experience, the new
environment leads to the establishment of increasingly transparent
mechanisms. So far, what has been seen is the adoption of policies
that meet the new demands with the implementation of exchanges
wherein energy trading becomes anonymous. Brazil has never had a
mandatory energy trading environment wherein energy is negotiated
like other commodities. Creating an energy exchange would profoundly
change the current structure of the Brazilian energy sector, requiring
elaborating a new utility policy for the sector. In this sense, market
agents and regulatory agencies should prepare themselves to consider
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Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to
influence the work reported in this paper.
Data availability
The data are publicly available in the project’s online repository
reported in Nametala et al. (2022a) in the manuscript.
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