REGULATORY SCHEMES FOR THE BRAZILIAN MARKET OF
ELECTRICITY TRANSMISSION
Maria da Conceição Sampaio de Sousa
Universidade de Brasilia: [email protected]
Eduardo Serrato
ANEEL: [email protected]
MOTIVATION
Regulatory schemes should be designed to stimulate competition and improve efficiency in the transmission and distribution segment, in
the electrical sector organized as a natural monopoly.
Yet, in Brazil, the Regulatory Agency for Electricity (ANEEL) uses an ad-hoc scheme to remunerate the firms engaged on electricity
transmission. In spite of the huge amounts involved in those reimbursements there is no evaluation of this scheme.
The presence of sub-additivity of costs, congestion, vertical scale economies with power generation and externalities brought about by
the existence of loop flow among transmissions networks make difficult the regulatory design in this sector.
To account for these elements, different regulatory schemes have been proposed; they include long-term financial-transmission-right as
well as those that incorporate incentive systems which permit to tackle with asymmetric information and strategic behavior. Our paper is
inserted in this debate.
OBJECTIF
This paper evaluates the regulatory process in the electricity transmission sector in Brazil by combining contract theory
and Data Envelopment Analysis. We use a multi-product, multi-input regulatory cost-based framework to assess the
performance of 14 firms operating in this segment during the period 2004-2007. We compute C-MDEA (Multiple Data
Envelopment Analysis) efficiency and Malmquist productivity indexes to derive financial transfers to the firms and
compare them with the ones actually received.
METHODOLOGY
 The Dynamic Yardstick Competition Model (Agrell, Bogetoft and Tind (1995, 1997))
Consider a principal that delegates the production of p outputs to an agent I that transform the input vectori
produce the output y  Rp . Input and output prices are given, respectively by
x
w i  Rqx and p i  R y ;
p
x  Rq x to
t = 1,...,T. The dynamic
regulation model is a blueprint incentive model linked to a conditional revenue cap. The DEA-Yardstick is given by:
b  c    [C ( y | w )  c ] t  1, ..., T
i
i
t
bti
is a committed revenue cap,
CtDEA cost
t
cti
i
DEA
i
i
i
t
t
t
is DMU’s actual cost and
i
[1]
is an incentive parameter. The regulator estimates a
function for the firms and uses this information to determine revenues. To avoid the ratchet effect, the DEA
cost function is computed for the utility peers thus excluding the DMU analyzed. Hence, the cost efficiency is
C
t
DEA  i
( y | w) .
i
We compute historic efficiency scores E 0 , for each firm as:
C (y | w )
E 
w x
DEA
i
0
i
i
0
0
i
i
0
0
[3]
The cost function CDEA is generated taking into account the whole set of information. Assuming that the proportion of
the initial inefficiency that the firm should suppress each year is δ, the cost norm becomes:
(1   (1  E ) )
E 
E
i
i
0
0
i
0
t
[4]
 is defined to assure that the suppression total of the inefficiency along the regulatory period do not exceed the initial
inefficiency for the i-th firm. Inserting [4] into [1] we have the dynamic yardstick revenue cap, R Yi, that will be used for
the analysis of the regulatory schemes in the Brazilian market of electricity transmission in Brazil:
DEA  i i i


C
(
y
|
w
)


Yi
i
i
t
i
t
t
t
R  c    (1    (1  E )) 
 c  t  1, ..., T
t
t
0
t
Ei

0


[5]
 M-DEA Method (Stosic and Fittipaldi (2007))
The approach MDEA computes efficiency indexes for different combinations of inputs and outputs. This procedure
gives efficiency spectra (frequency distributions) for each DMU, from which efficiency ranking can be extracted,
together with confidence intervals. The method identifies the largest sets of Q inputs and P outputs as follows.
1. Choose, sequentially, different subsets of inputs and outputs such as
q  {1,..., Q} e
p  {1,..., P} .

Q
possibilities to choose a subset containing q inputs (from a total of Q), there are

P
p 1
 P
   2 P  1
 p
q 1
As we have
Q
   2 Q  1
q 
possible
Q
 
q 
choices.
Similarly, there are
output choices.
Q
P
2. Compute DEA   ( 2  1)(2  1) scores for all combination of inputs and outputs, each one corresponding to a
specific input output set.
3. Define the final DEA efficiency score, for a given DMU, as the average on ω (1,...,Ω) from the computed scores:
C
MDEA

  C
 1
MDEA
/
Data
Table 1: – Inputs and Outputs – Transmission System – 2004-2007
Input/Outputs
Inputs
Length of the lines
# Substations
Total Controllable Costs
Financial Costs
Outputs
Capacity of Energy Transportation (Lines)
Capacity of Energy Transformation (Substations)
Network Density
Dimension
Km
(R$x106)
(R$x106)
V2xKm
MVA
[(Km) /(Km2x103)]
RESULTS
Efficiency Scores and Productivity Changes
 There is a differentiated pattern of productivity ameliorations, with several power transmission utilities
experiencing a productivity decline for the period 2005/2007. This fact illustrates the difficulties in implementing
regulatory schemes based on ad-hoc fixation of the productivity parameters.
 Moreover, for the new utilities, efficiency gains (EC) were much lower; some of them show a productivity decline
during the period. Frontier shifts are consistent with the entry of new utilities in the power transmission.
 Finally, remark that starting from low efficiency levels, ELETROSUL, CEMIG and ELETRONORTE show
impressive productive improvements by means of both, the catch up effect and significant switches of the
technological frontier.
Table 2: Malmquist Indexes and its Components for Power Transmissions Firms – 2005/2007
Firms
Publics
CEEE
CEMIG
CHESF
COPEL
ELETRONORTE
ELETROSUL
FURNAS
Mean
CTEEP
Privates
EATE
ECTE
ETEO
ETEP
EXPANSION
TSN
Mean
Global Mean
2007/2005
Efficiency Changes
(EC)
E0
Malmquist Index
(M)
0,573
0,526
0,485
0,520
0,527
0,386
0,822
0,549
0,650
0,881
1,273
0,768
1,121
1,193
1,394
1,083
1,102
1,010
1,019
1,240
0,750
1,069
1,155
1,354
1,011
1,085
1,084
0,864
1,027
1,024
1,049
1,033
1,029
1,071
1,014
0,931
0,598
0,833
0,637
0,662
0,769
0,514
0,669
0,607
0,914
1,022
1,148
0,975
0,931
0,994
1,010
1,051
0,945
1,005
1,064
0,992
0,977
0,994
1,084
1,047
0,967
1,016
1,079
0,984
0,953
1,000
0,931
1,002
Technological Changes
(TC)
Cost and Reimbursement Norms
Figure 2: Costs and Reimbursements – MDEA-Yardstick (RYd) and RAP – Public Utilities
5,000
R$x106
4,000
3,000
2,000
Public Utilities
Σ RAP
1,000
Σ RYd
Σ Actual Cost (cit)
0
2004
2005
2006
2007
800
700
R$x106
600
500
400
300
Private Utilities
Σ RAP
200
Σ Ryd
100
Σ Actual Cost (cit)
0
2004
2005
2006
2007
Figure 3: Costs and Reimbursements – MDEA-Yardstick (RYd) and RAP – Private Utilities
Results 3: Information Rents and Yardstick Bonuses
 Information rents are lower and less erratic in the MDEA-Yardstick regime because this scheme, not only
distributes in a better way the flow of revenues, also take into account the specific productivity conditions for
each utility.
 For the new firms, in both regulatory regimes, the information gains increase monotonically during the period
2004/2007, contrasting with the variability shown by their public counterparts.
 For the more inefficient public utilities, under the yardstick norm, reimbursement would be inferior to their costs.
The reason is that with ρ = 0.5, they would have the receipts reduced by an amount equivalent to half its excess
costs (Ct*i - cit), for each year. Such a penalty would attain mainly the CHESF, ELETRONORTE and FURNAS,
because these firms did not reduce their costs in the same proportion as their “reference firm”.
Table 3: Information Rents Extracted by the Power Transmission Firms under the RAP and the MDEA-Yardstick
Firms
Publics
CEEE
CEMIG
CHESF
COPEL
ELETRONORTE
ELETROSUL
FURNAS
Total l
CTEEP
Privates
EATE
ECTE
ETEO
ETEP
EXPANSION
TSN
Total
Total
2004
Information Rents - R$ millions - june/2004
Current Rents = RAP – Costs
Yardstick Rents = RYd – Costs
2005
2006
2007
2004
2005
2006
2007
57,3
59,0
45,1
70,7
-5,1
74,9
627,6
929,4
184,3
75,6
32,5
141,3
184,2
-27,0
121,0
675,0
1202,7
282,7
82,0
-16,4
247,8
152,5
-244,0
127,6
752,0
1101,6
-29,4
112,2
114,8
140,2
204,4
-18,9
193,2
608,5
1354,4
573,1
4,1
5,6
17,6
3,7
12,0
10,5
7,3
60,8
12,8
36,8
10,7
106,8
110,3
31,8
65,2
102,5
464,0
206,8
57,4
-1,4
243,8
111,4
-15,7
107,7
205,1
708,3
112,5
69,6
56,4
196,4
131,2
100,9
148,0
51,5
753,9
432,0
41,0
23,0
53,0
11,2
21,3
43,4
192,9
88,3
25,9
53,7
23,6
37,9
53,8
283,1
92,1
27,7
53,9
25,0
60,2
109,4
368,4
107,3
28,5
53,7
26,7
50,0
101,9
368,1
3,1
0,2
0,5
0,6
0,8
4,5
9,6
33,1
1,9
2,5
8,7
11,7
14,6
72,5
38,9
3,9
4,4
10,5
25,4
46,8
129,8
43,3
3,6
3,0
10,7
18,8
39,4
118,7
1306,6
1768,5
1440,6
2295,6
83,2
743,3
950,6
1304,6
Table 3: Yardstick Incentive Bonuses - 2004 a 2007
Firms
Public Utilities
Yardstick Incentive Bonuses = ρ (Ct*i - cit )
2005
2006
2004
2007
COPEL
CEMIG
CEEE
ELETROSUL
FURNAS
CHESF
ELETRONORTE
Total
CTEEP
Private Utilities
EATE
TSN
EXPANSION
ETEP
ECTE
ETEO
Total
-3,67
-5,60
-4,11
-10,54
-7,35
-17,55
-12,01
-60,83
-12,78
3,21
-37,11
-18,52
-19,12
-54,99
-10,58
-53,66
-190,77
-108,45
-29,51
-73,99
-32,81
-55,02
-80,56
-41,04
-223,23
-536,16
-326,17
2,51
-0,65
-14,70
-29,66
-70,58
-101,28
-114,61
-328,97
-43,16
-3,08
-4,46
-0,77
-0,58
-0,17
-0,51
-9,57
14,26
-4,28
4,82
3,75
0,99
-1,78
17,76
12,34
19,16
13,51
3,26
0,85
-3,46
45,66
23,04
19,07
9,87
4,83
1,88
-2,23
56,46
Total
Obs.: C = ((1-δ(1- E0))tCMDEA-i /E0);
-83,18
-281,46
-816,67
-315,67
*i
Results 4: Sensibility Analysis
 Notice that more efficient firms would prefer contract with a higher ρ because they could extract larger revenues
(higher RYd). On the other hand, the under-performing utilities would like to have less powered contract,
characterized by lower values of ρ.
Figure 4: Reimbursement Norms for Transmission Utilities -Public, Privates Utilities for different values of de ρ –
2005.
5,000
R$x106
4,000
3,000
2,000
Public Utilities
Σ RAP
1,000
Σ Ryd
Σ Actual Cost (cit)
0
0
0.1
0.2
0.3
0.4
0.5
Rho
0.6
0.7
0.8
0.9
1
800
R$x106
600
400
Private Utilities
Σ RAP
200
Σ Ryd
Σ Actual Cost (cit)
0
0
0.1
0.2
0.3
0.4
0.5
Rho
0.6
0.7
0.8
0.9
1
Conclusions
 Our results suggest that the new private utilities are more efficient when compared to their public counterparts.
Furthermore, they kept this productivity differential across the period analyzed.
 Decomposition of the Malmquist index indicates that for all firms productivity increases derived, mainly, from
improvements in technical efficiency.
 Our results also suggest that, for 2004/2005, revenues actually paid by the ANEEL, when compared to the ones
implied by the yardstick competition, led to higher profits suggesting that productivity gains were captured by the
agents.
 Besides, for the new and private utilities, these informational rents increase monotonically during the period
analyzed in both, the actual regulatory regime and the MDEA Yardstick scheme.
 Finally, our simulations suggest that, by reducing informational rents and coping with the ratchet effect, the
proposed regulatory scheme led to lower costs and higher efficiency in the electricity transmission sector.