Short-term electricity price in Brazil: current limitations and the benefits of a

transition to a market price formation model

Andr Luiz Zanette*

Overview

Short-term electricity price is an important economic indicator for the use of resources

for electricity production and electricity use. Short-term price also represents a reference

for medium and long-term price, which are the main indicators for investment in new

capacity. The existence of an efficient mechanism for electricity price formation can

result in important incentives for agents responses that result in more efficient

decisions on investment, operation and electricity use [1]. The primary function of an

organized spot market for electricity is to maximize cost efficiency by supplying the

demand for power from the most economic source available. It is difficult to achieve

such optimization without a continuous price setting mechanism producing a

transparent equilibrium price [2].

In the Brazilian electricity market, the price that better represents a spot price is the

Settlement Price for the Differences (PLD), which is the price at which the differences

between the actual amount of energy produced or consumed and contracts are settled at

the Electric Energy Trading Chamber CCEE. This price, however, is not defined by

the market, but calculated through centralized models, which are the same models used

by the Brazilian Electric System Operator (ONS) for operation planning and system

dispatch. As every model, these also represent an approximation of the reality. Although

these models simulate the system operation considering the expectations of electricity

supply and demand, the prices they calculate are often substantially different from the

marginal cost of electricity production, thus resulting in an inefficient signal to

producers and customers.

In this context, the objective of this work is to analyze the main limitations of the

current model for short-term electricity pricing in Brazil, propose a transition to a

market price formation model and evaluate the main benefits of this transition.

Methods

The first part of the work analyzes the main limitations of the artificially calculated

short-term electricity price in Brazil, that are related to the quality of the data used by

* Energy Planning Program COPPE/UFRJ

the models and the differences between the operation planned by the models and the

real operation, and its impacts on the operation and expansion of the electricity system

and on electricity trading. The differences between the prices and the real operational

costs are also evaluated.

The second part proposes a transition to a market price formation model, considering

the specific characteristics of the Brazilian electricity system. To compare the price

formation in Brazil with a market price formation model, the work also presents some

comparisons with the electricity market in the Nordic countries. Finally, the expected

results and potential benefits of this transition in terms of efficient operation and

investments are evaluated.

Results

Limitations of the short-term electricity price formation model in Brazil

Short-term electricity price in Brazil is calculated by the same modes used by the

Brazilian Electric System Operator (ONS) for operation planning and system dispatch.

The basic objective of the operation planning for a hydrothermal system is to determine,

for each stage of the planning period, the dispatch for each plant that meets the demand

at the lowest cost during the entire period. This cost comprises the variable cost of

thermal plants and the cost of supply interruptions [3]. According to hydrological

conditions, electricity demand, fuel prices, deficit costs, the existing generation and

transmission capacity and the schedule of new generation and transmission projects, the

models calculate the optimal dispatch. The process for calculating the Settlement Price

for the Differences (PLD) comprises the use of the models Newave and Decomp that

calculate the marginal cost of operation. PLD is calculated weekly for each demand

level and region based on the marginal cost of operation, limited by a minimum and a

maximum price1. The models consider three demand levels and four regions, according

to transmission constraints among them (Southeast/Middle-West, South, Northeast and

North). In 2012, the minimum and maximum prices are respectively USD 6 and

360/MWh. The main difference between the model simulations used by ONS is that for

PLD calculation the transmission constraints internal to the regions are not considered,

1 The medium-term operation model Newave represents the hydropower plants in an aggregated form and the operation policy is based in stochastic dual dynamic programming. The short-term model Decomp represents the hydropower plants individually and uses the future cost functions calculated by Newave model to determine the operation policy and the marginal costs of operation.

so the electricity traded is assumed as equally available in the entire region and,

consequently, there is only one price inside each region.

Although the models try to represent the actual conditions of electricity supply and

demand to calculate electricity prices, the data used by them have several limitations.

As discussed below, the demand and supply projections considered in the models may

be significantly different from the reality. Moreover, the system operator tends to use

several mechanisms to increase the security of electricity supply that are not considered

in the models, which may result in an operation that may be considerably different than

the indicated by the models.

As it represents the official expectations, the demand projected is frequently higher than

the observed. Figure 1 compares the demand projections used in the medium-term

model and the actual demand. As can be noted, the demand projected for 2009 was

considerably higher than the actual, as a result of the international economic crisis.

After a few revisions, the difference was reduced, but it increased again in 2011. These

demand projections can indicate the need of a higher thermal generation, which may not

be necessary. On the other hand, a demand higher than the projected during the summer

months, as observed in recent years, can result in an additional thermal generation while

the prices are low, thus resulting in an unsuitable signal to the market. These differences

between projected and realized demand are also significant in the short-term model. As

Figure 2 shows, the demand during peak hours in summer days can be substantially

higher than the considered in the model, thus requiring the dispatch of expensive

thermal plants, which is not represented in the electricity prices.

Figure 1 Differences between the demand projections considered in the medium-term

model and the actual demand.

Source: Based on data from ONS-EPE [4].

Figure 2 Demand considered in the short-term models and the actual demand in a

typical summer day.

Source: Based on data from ONS [5].

Demand is also assumed to be price-inelastic, despite an approximation of a demand

curve is considered in the models in the form of four deficit costs. As shown in Figure

3, the medium-term model considers that at an electricity production cost of about USD

600/MWh a 5% supply deficit is acceptable, at about USD 1200/MWh a 10% deficit,

and so on. Although this mechanism is similar to a demand curve, it is not possible to

ensure that it effectively represents the real propensity of the customers to reduce their

demand, once this deficit costs are not defined by the market, but by the official

institutions. Moreover, the deficit costs are only used in the medium-term model and are

not considered in the short-term model, which assumes an inelastic demand.

As a comparison, Figure 4 shows the aggregated demand curve built considering all the

customers bids for a one hour period at the electricity market in Nordic countries (Nord

Pool Spot) for the following day. As can be noted, even with the orders posted with

only one day of anticipation, customers accept to significantly reduce their demand at

prices much lower than the deficit costs considered in the medium-term model in Brazil.

Figure 3 Deficit costs used in the medium-term model for electricity price formation

in Brazil.

Source: Based on data from EPE [6].

Figure 4 Demand curve for a period of one hour in Nord Pool Spot.

Source: Based on data from Nord Pool Spot [7].

Moreover, the official supply projections used by the models are frequently

overestimated. This is particularly true when considering new capacity additions to the

system, which is especially relevant in the Brazilian system, where demand is expect to

grow at a 4.5% rate in the next decade [8]. The frequent revisions of capacity expansion

projections by the Brazilian Electricity Regulatory Agency (ANEEL) to represent a

more realistic supply in the models have led to sharp increases in prices and in thermal

generation. As an example, Figure 5 shows the differences between the power

generation from alternative renewable sources (small hydro, biomass and wind power)

considered in the models and the actual generation, which resulted in a revision of the

availability of these power plants in 2011 that represented a 3,000 MWa reduction in

power generation. These reviews in supply projections are also frequent with respect to

thermal power generation. In 2010, ANEEL reviewed the schedule of new projects to

consider a 2,000 MW delay in new thermal power capacity. A new revision occurred in

2012 to consider a 2,500 MW reduction in the future capacity of fuel oil fired thermal

plants.

Although these revisions contribute to improve the quality of the data used by the

models to operation planning and price formation, they are frequently abrupt and may

result in a significant increase in price and thermal generation volatility. In a market

price formation model, these revisions could occur gradually, representing the market

expectations related to electricity supply, in the same manner as it would occur with

demand projections if they represented market expectations.

Figure 5 Differences between the actual alternative renewable power generation and

the considered in the models.

Source: ANEEL [9].

Given the importance of hydropower in Brazil, which represents about 90% of total

electricity production, water inflow forecasting is particularly important for system

operation planning and electricity price formation. Despite the continuous improvement

of the official models used to forecast hydropower water inflow, the differences

between the inflows projected and the actual inflows are frequently significant. As a

comparison, in a market model the offers from hydropower producers may reflect better

their expectations of electricity supply.

The models also do not consider the need of spinning reserve for the system, i.e., an

additional capacity that is kept available to generate when necessary, of about 5% of the

total demand. This means that the models consider that the capacity available for power

generation is higher than the actual available capacity. With respect to this limitation,

ANEEL is planning to discuss the representation of the spinning reserve in the models

in the regulatory agenda for 2013-2014 [10].

Another limitation of using these models to calculate prices is that the real operation

may be significantly different from the operation calculated by the models. As the

system operator tends to adopt a more conservative operation, given its aversion to risk,

it may determine additional thermal generation to increase energy security, which is not

considered in price formation.

Besides the thermal generation to meet peak demand, which is not included in the

models used to calculate the PLD, the system operator also applies the so-called short-

term operational procedures, which define a target for a minimum level of water storage

in the reservoirs for the month of November (the end of the dry season), to guarantee

the supply in the next year in the case of a severe dry [11]. To achieve this target, the

operator can call thermal plants for an additional power generation during the dry

season, which is not represented in the models that calculate short-term prices. It means

that the prices can be maintained lower than the real operational costs during long

periods, without an efficient signaling to the market, given that the costs of this

additional generation are divided among all customers and, in the case of the regulated

customers, this will represent an increase in the tariff only for the following year.

Figure 6 presents the differences between the thermal generation calculated by the

models and the total thermal generation including the additional thermal generation and

the differences between the short-term prices and the real operational costs. As can be

noted, these differences were particularly significant during the dry seasons in 2010 and

2012 (2011 was a year with water inflows much higher than historical averages, thus

not requiring almost any thermal generation) and tend to continue if the criteria

considered for system operation are kept different from the used by the models for price

formation.

Figure 6 - Additional generation and differences between short-term prices and average

costs for additional thermal generation.

Source: Based on data from CCEE [12, 13, 14].

Once the same models used to operation planning are also used to project the system

expansion, the economic signaling to an efficient expansion has the same limitations as

those represented by the artificial prices to system operation. Given that the models

used for expansion planning and for the auctions for new power generation projects also

do not consider the risk-aversion curves2 used in the medium-term model and the short-

term operational procedures discussed above, the benefits of flexible thermal generation

are underestimated in these auctions, thus affecting the competitivity of these projects

[15]. This is also true for wind and biomass projects that are complementary to

hydropower generation, whose benefits of producing more energy during the dry season

are not well represented.

Proposal for a market price formation model and its potential benefits

Considering the limitations of these centrally calculated prices, this work presents a

proposal for a transition to a market price formation model for electricity in Brazil and

evaluates the potential benefits of this model. It is proposed that the transition occurs in

two stages. In the first stage, the system operator continues to determine the generation

of hydro and thermal plants, including the additional dispatch for energy security, but

2 The risk-aversion curves are used in the medium-term model to represent an additional cost in the case of depleting the hydro reservoirs to under certain monthly levels.

Additional generation for energy security

0

25

50

75

100

125

150

175m

ai/1

0

jun/

10

jul/1

0

ago/

10

set/1

0

out/1

0

nov/

10

dez/

10

jan/

11

fev/

11

mar

/11

abr/1

1

mai

/11

jun/

11

jul/1

1

ago/

11

set/1

1

out/1

1

nov/

11

dez/

11

jan/

12

fev/

12

mar

/12

abr/1

2

mai

/12

jun/

12

jul/1

2

ago/

12

USD

/MW

h

0

500

1000

1500

2000

2500

3000

3500

Add

ition

al g

ener

atio

n (M

Wa)

Average cost of additional generation (USD/MWh) PLD (USD/MWh) Additional generation (MWa)

the thermal generation represents only a reference to the market. After that, producers

and customers post their orders to buy and sell electricity for the different periods of the

next day. The bids and offers will form the aggregated supply and demand curves. The

market price and the electricity production and use for each agent result from the

balance of supply and demand. At this stage, thermal generation is settled by the market

and customers may buy and sell electricity according to the prices, which is not allowed

under the current model, although the possibility of customers to sell the excess

amounts of electricity they purchased is being studied [16]. Also, there was an

experience of allowing customers to sell electricity during the supply shortage in 2001,

when the energy certificates were created [17].

At this stage, thermal generators will be able to buy electricity from customers or other

generators to meet their contracts when prices are lower than their production costs or

produce additional amounts of electricity when the prices are higher. Figure 7-a shows

an example of a bid from a thermal generator with a production cost of USD 50/MWh.

In this example, the generator makes a bid for prices lower than this value and offers an

additional amount of electricity when the prices are higher. Electricity customers will be

able to buy additional amounts of electricity or sell part of the energy they purchased

according to market prices, thus becoming active participants in the process of price

formation, as shown in Figure 7-b.

Figure 7 Examples of bids and offers from thermal generator (a) and customer (b).

Source: Own work.

Another difference is that the secondary energy, i.e., the additional amount of

hydropower generation that is distributed among hydropower producers is traded at

market prices. Many benefits of market price formation can already be verified at this

(a) (b)

stage. An important signaling to energy efficiency arises when thermal generation is

defined by the market and consumers can increase or reduce the amount of energy they

use according to market prices.

In the second stage of the proposed model, the system operator becomes an independent

operator that coordinates the generation and transmission system, and hydropower is

also included in market price formation, completing the transition to a market price

formation model. From this stage on, the bids and offers from all producers and

customers will determine the system dispatch and market prices.

The main characteristics of the market model and the transition period proposed, as well

as the current model for price formation, are presented in Table 1. At this stage the

supervision of market regulators to avoid the exercise of market power is crucial. There

are several benefits of this transition to a market price model, as discussed below.

Table 1 Main characteristics of the current model for price formation and the proposed

transition period and market model.

Current model Transition Period Market Model

ONS defines hydro and thermal generation

ONS defines hydro generation and the reference thermal generation

ONS coordinates operation and evaluates the security of supply

Prices are calculated by computational models

Price resulting from producers and customers bids and offers

Price resulting from producers and customers bids and offers

Additional dispatch for security of supply (peak demand and short-term operational procedures) are not included in price formation

Additional dispatch are included in price formation

Hydro and thermal generation defined according to market prices

Customers not allowed to sell electricity

Customers allowed to sell electricity

Customers allowed to sell electricity

Thermal plants only allowed to substitute other plants dispatch

Thermal plants can buy electricity from customers to meet the demand and their contracts

Thermal plants can buy electricity from customers and hydro producers to meet the demand and their contracts

Hydro producers are paid PLD for additional generation

Hydro producers sell additional generation at market prices

Hydro producers free to negotiate electricity among them and with other agents

Inefficient signaling for energy efficiency and system expansion, especially for flexible thermal generation, wind and biomass

More efficient economical signaling to energy efficiency and system expansion, especially for flexible thermal generation, wind and biomass

More efficient economical signaling to energy efficiency, hydropower generation and system expansion, especially for flexible thermal generation, wind and biomass

Source: Own work.

This model of market price formation can contribute to a more efficient use of energy

resources. A short-term price that represents the real operational costs allows the market

to decide the better combination of flexible generation and energy efficiency measures

to meet the peak demand and to compensate the lower hydropower availability during

the dry season. As discussed before, in the current model the system operator defines

the thermal generation to meet peak demand and to increase the security of supply and

transfer these costs to all customers, thus not allowing producers and customers to

choose a more efficient alternative.

The transition to a market price formation would allow a more effective customer

response, especially with the introduction of smart metering and distributed generation

systems, both recently regulated by ANEEL [18, 19]. Although only the customers with

demand higher than 0,5 MW are allowed to choose their electricity supplier and thus

would be able to sell the amount of electricity that exceeds their demand, due to a lower

level of activity or energy efficiency measures, distribution companies or retailers

would be able to develop and offer customized products to customers that accept to

reduce their demand during peak hours or in the dry season, for example. The

introduction of the electricity retailer trader, which will represent smaller customers in

the CCEE [20] and the white tariff for electricity distribution companies, which results

in higher prices during peak hours and smaller prices during hours with low demand for

regulated customers [21], are two good examples of measures that can have their

benefits enhanced with the transition to a market price formation model.

In the long-term, the maintenance of long periods of higher prices during peak time

would represent an important signal to evaluate the need and viability of investments to

increase flexible thermal generation, the power generation capacity in existing

hydropower, energy efficiency projects or even wind and power or solar photovoltaic,

whose peak in electricity production is close to the peak of demand. Figure 8-a shows

an example of an hourly price signal in the Nordic countries market.

In the Brazilian system, with increasing hydro production in plants without reservoirs

that generate most of their energy during the wet season, this may also represent an

important incentive to investment in flexible thermal plants and wind and biomass

plants that are complementary to hydroelectric plants and produce more electricity

during the dry season, as shown in Figure 9. Also, the existence of an efficient market

price and the possibility of customers to sell electricity would represent an incentive to

energy efficiency projects, probably as effective as energy efficiency auctions. Fig 8-b

illustrates the seasonal price signal in the Nordic market, where the higher demand and

lower availability of hydropower results in higher prices in the winter months.

Figure 8 Electricity prices in the Nord Pool Spot for one day (a) and in the forward

and futures Nordic electricity market (b).

Source: Based on data from Nord Pool Spot [7] and Nasdaq OMX Commodities [22].

Figure 9 Seasonal complementarity between hydropower and biomass and wind

power.

Source: ONS [23].

(a) (b)

The existence of a short-term market price would also contribute to the development of

financial markets for electricity, with products like futures, forwards and options, thus

also stimulating long-term contracts in the deregulated market, which would incentive

the investment in new power generation projects especially to this market.

An efficient price signal, which represents the differences between the marginal costs in

each region, is also important to indicate the real benefits of investments to increase the

transmission capacity between the regions and in the increase of generation capacity or

energy efficiency in each region. Moreover, this price signal is important to evaluate the

viability of the projects for energy integration that are being developed in South

America, which include new transmission capacity between the countries and binational

hydropower.

Finally, the participation of all players in market price formation may result in a

significant evolution from the centrally calculated prices. The bids and offers of all

agents represent the expectations of the whole market, which contains a much larger

amount of information than the currently used to calculate prices and can thus lead to

more efficient and representative prices. The bids and offers also reflect the risk profile

of the agents, which is probably a better representation than the risk criteria centrally

defined. Although the models are being constantly improved, it is difficult to imagine

that the centralized models will be able to keep pace with developments of the industry,

especially with the introduction of smart grids, intermittent renewables and distributed

generation.

Conclusions

The main conclusions of this work are that the short-term electricity price in Brazil has

several limitations that may lead to an inefficient operation and investment. These

limitations arise both from the quality of the information used by the computational

models to calculate the prices as from the differences between the planned operation

and real-time operation, and can result in substantial differences between the short-term

prices and the real operational costs. These differences are becoming especially relevant

when considering the increasingly role of more expensive thermal generation in Brazil

and the lower participation of hydropower in new capacity addition. Also, it is

important to note that the economic signal to investments in projects that aim to meet

peak demand and compensate the lower availability of hydropower during the dry

season is inefficient, given that the models used for expansion planning are the same

used for operation planning and thus have the same limitations of these.

Considering the importance of an adequate price signaling to the efficient system

operation and expansion, the work proposes a transition to a market price formation

model for electricity in Brazil.

The transition to a market price model is proposed in this work to occur in two stages.

In the first stage, the Brazilian Electric System Operator keeps its current function of

determining the hydrothermal dispatch, but short-term price is defined by the balance

between market supply and demand. The main change in this first stage is the effective

participation of customers in the price formation process, which would result in a more

efficient signaling to the entire market. Moreover, the dispatch to meet peak demand

and for security reasons due to the lower availability of hydro power during the dry

season are included in the price formation mechanism.

In the second stage, the transition to a market model would be completed with the

effective inclusion of hydropower producers in the price formation and system dispatch.

The system operator would cease to determine the dispatch and the price formation

would involve the effective participation of all producers and customers. In this stage,

the role of regulators is of special importance.

The main benefit of this transition to a market price formation model is the increase in

the efficiency of the electricity industry, which would benefit both electricity customers

and producers. In the market model, shot-term prices would provide an economic

signaling to a more efficient electricity production and use. In addition to the benefits to

system operation efficiency, the short-term market prices, associated with an electricity

exchange with futures, forwards and options traded in a transparent way, would also

contribute to more efficient decisions of investment in capacity expansion, resulting in

an important incentive to energy efficiency and the investment in a more efficient

system expansion, including flexible thermal generation and wind, biomass and solar

power.

References [1] IEA, 2011. Empowering Customer Choice in Electricity Markets. OECD/IEA. [2] Nord Pool Spot, 2012. Price formation in Nord Pool Spot. Disponvel em: [3] CCEE, 2010. Viso Geral das Operaes na CCEE. Disponvel em:

[4] ONS-EPE, 2012. Previso da Carga dos Sistemas Interligados. Disponvel em: [5] ONS, 2010. Avaliao das Condies de Atendimento demanda horria do SIN. NT 160/2010. [6] EPE, 2011. Atualizao do valor para patamar nico de custo de dficit - 2011. N EPE-DEE-RE-021 /2011. [7] Nord Pool Spot, 2012. Elspot Prices. Disponvel em: [8] EPE, 2012. Plano Decenal de Energia 2012-2021. Empresa de Pesquisa Energtica. [9] ANEEL, 2011. Nota Tcnica n 023/2011 - SRG/ANEEL. [10] ANEEL, 2012. Nota Tcnica Conjunta n 001/2012 -SPG/SCG/SRE/SEM/SRG/SRT/SRD/SRC/SFF/SMA/CEL/ANEEL. [11] ONS, 2008. Procedimentos operativos de curto prazo para aumento da segurana energtica do sistema interligado nacional. NT 059/2008. [12] CCEE, 2011. Relatrio de Informaes ao Pblico Anlise Anual 2010. Disponvel em: [13] CCEE, 2012. Relatrio de Informaes ao Pblico Anlise Anual 2011. Disponvel em: [14] CCEE, 2012. InfoMercado CCEE n 62 Outubro/2012. Disponvel em: [15] FERREIRA, L. S., ZANETTE, A. L., 2012. Impacto da incorporao dos critrios de segurana utilizados no planejamento da operao sobre a competitividade das usinas termeltricas nos leiles de energia nova. Anais do XII SEPOPE 2012. [16] MME, 2010. Portaria n. 73, de 1 de maro de 2010. Ministrio de Minas e Energia. [17] GCE, 2001. Resoluo n. 13, de 1 de junho de 2001. Cmara de Gesto da Crise de Energia Eltrica. [18] ANEEL, 2012. Resoluo Normativa n. 502, de 7 de agosto de 2012. Agncia Nacional de Energia Eltrica. [19] ANEEL, 2012. Resoluo Normativa n. 482, de 17 de abril de 2012. Agncia Nacional de Energia Eltrica. [20] ANEEL, 2012. Nota Tcnica n 55/2012-SEM/ANEEL. [21] ANEEL, 2010. Estrutura Tarifria para o Servio de Distribuio de Energia Eltrica Sinal Econmico para a Baixa Tenso. Nota Tcnica n 362/2010SRE-SRD/ANEEL. [22] NASDAQ OMX COMMODITIES, 2012. Market Prices. Disponvel em: [23] ONS, 2011. Plano anual da operao energtica PEN 2011. Vol. I Relatrio Executivo. RE 3/116/2011.