Alexandre Pais

Carlos Alberto Santos

D A Silva

José Ribeiro

Lina Afonso

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 C I R E D 20th International Conference on Electricity Distribution Prague, 8-11 June 2009 
 
Paper 0548 
 
 
CIRED2009 Session 5 Paper No  0548      
QUALITY OF SUPPLY DRIVEN INVESTMENT PLANNING AND REGULATORY 
SUPPORT USING REPRESENTATIVE NETWORKS  
 
 
                     Alexandre PAIS DA SILVA              José RIBEIRO DA SILVA                     Nina CLEMENTE 
         EDP-Distribuição – Portugal           EDP-Distribuição – Portugal              EDP-Distribuição – Portugal 
  alexandremiguel.silva@edp.pt             jose.ribeirodasilva@edp.pt                  nina.clemente@edp.pt 
   
                                                           Lina AFONSO                        Carlos  ALBERTO SANTOS 
                                               EDP-Distribuição – Portugal                EDP-Distribuição – Portugal 
               Lina.afonso@edp.pt                     carlosalberto.santos@edp.pt                                   
 
ABSTRACT 
The regulation of electricity distribution network 
monopolies has been shifting from asset-based to 
performance-based regulation and therefore is becoming 
more dependent on the quality of customer service: EDP 
Distribuição (EDPD) is exposed to a quality of supply 
incentive mechanism which is adjusted on a yearly basis. 
This regulatory framework has the potential to create 
opportunities that may generate value for both business and 
customers provided adequate information supports the 
setting of performance targets and of the financial 
incentives.  
Following significant performance improvements that have 
been attained in recent years, EDP-Distribuição has 
identified the need for further, technically more demanding 
quality of supply planning. The conventional rationale for 
planning quality of supply driven investment has been 
challenged and novel approaches have been developed 
accordingly. 
EDP-Distribuição has been implementing a quality of 
supply representative network (RN) based approach to 
analyse its MV networks. This enabled to appreciate and 
explain the differences in performance across urban, semi-
urban and rural networks. EDP-Distribuição is addressing 
the need to quantify the impact that historical design and 
operation practices, different topology and population 
characteristics may have on network performance. This is 
perceived as an important contribution towards 
understanding the costs associated with setting adequate 
quality of supply performance targets.  
INTRODUCTION 
EDPD is a company of EDP-Energias de Portugal. It 
operates the majority of the distribution system in 
continental Portugal (high, medium and low voltages) 
connecting over 6.6 million customers.  
Due to the intrinsically monopolistic nature of the 
distribution business, the revenue recovered by EDPD is 
closely regulated by ERSE (Portuguese energy services 
regulator) in order to protect the customers and deliver 
economic efficiency.  
ERSE has maintained on behalf of the customer’s interests a 
quality of supply regulation based on the monitoring of both 
individual and overall standards of performance. Failure to 
comply with the former entitles customers to receive 
compensation payments from EDPD. Failure to comply with 
the latter may require EDPD to plan, submit and seek 
approval for specific investment programs to address 
difficulties. 
Performance Based Regulation (PBR) 
 
In addition to the quality of supply regulation aspects 
already mentioned, the performance of distribution systems 
is now becoming customer driven. This means that the logic 
of weighting the investment on the network against the loss 
of kWhs not supplied has been changed. This leads to the 
need to include an assessment of the customer’s worth of 
supply and the benefit they derive by system investment.   
This is the underlying concept of what is called 
performance-based regulation. Limited aspects of this form 
of regulation have been introduced in Portugal through the 
quality of supply incentive mechanism. This was set up to 
strengthen the incentives with respect to delivering the 
quality of output. As this mechanism is based on financial 
penalties and rewards, the overall distribution revenue is a 
function not only of the operating and capital costs incurred 
by the network owner in providing the service, but also of 
the quality of customer service [1].  
The parameters of the quality of supply incentive 
mechanism enforced by ERSE are reviewed on a yearly 
basis. Currently, it determines that the maximum reward or 
penalty incurred by EDPD is 5M€ annually (Fig.1) with 
reference to the performance observed two years before. A 
reference value of energy not distributed (END) was set up 
as a percentage of the total annual distributed energy and 
has been decreased every year. This reference value 
represents the underlying assumption of the quality of 
supply TIEPI (Tempo de Interrupção Equivalente da 
Potência Instalada) target with no costs for the customer. A 
dead band (flat area of the chart depicted in Fig.1) around 
this target has been set to account for data imprecision [3]. 
The mechanism has been set to value the non-distributed 
kWh at 1.5€. 
 
 C I R E D 20th International Conference on Electricity Distribution Prague, 8-11 June 2009 
 
Paper 0548 
 
 
CIRED2009 Session 5 Paper No  0548      
 
Incentive curve (2006) END – 2006 (MWh) 
Fig.1 – Quality of supply incentive scheme for 2006 [2] 
 
TIEPI is a system reliability index that measures the severity 
of interruptions in terms of the amount of time that the 
curtailed installed rated power (MV/LV distribution 
substations) is out of supply in relation to the whole network 
installed power: 
∑
∑ ∑
=
=
=
×
=
k
j
j
j
k
j
NI
i
ij
PI
PIDI
TIEPI
j
1
1
1
 
 
DIij - duration of the interruption I of the MV load point j 
PIj – Installed rated power of the load point j 
k – total number of MV load points 
NIj – number of interruptions of load point j originated 
upstream 
 
This index is used to monitor the quality of supply of MV 
networks which have the greatest influence on the annual 
outage costs as experience shows. SAIFI and SAIDI indices 
are also closely monitored in these networks but have had 
no impact on the quality of supply mechanism so far. 
EDP-Distribuição quality of supply track record 
Quality of supply has long been a major priority for EDPD. 
Fig.2 gives an overview of the performance improvements 
achieved in the past years. It should be pointed out that the 
incentive mechanism currently in place began to influence 
the company’s revenue in 2005 based on targets and quality 
of supply data from 2003 which did not consider previous 
investment efforts.  
The data in Fig.2 has been compiled by geographical areas. 
 “Zona A” refers to predominantly urban areas (over 25000 
inhabitants) and district capitals.  “Zona B” refers to areas 
with more than 2500 and less than 25000 inhabitants. “Zona 
C” covers the remaining territory.   
Previous investment programmes that progressively targeted 
the most problematic parts of the network have considerably 
increased the level of automation and remote control of HV 
and MV systems. These programs have also addressed the 
construction of new infrastructure and the refurbishment of 
existing assets. 
0
60
120
180
240
300
360
420
480
540
2001 2002 2003 2004 2005 2006 2007
m
in
u
to
s
Zona A Zona B Zona C
 
Fig 2 – Annual TIEPI performance (2001-2007)  
 
Given that a significant proportion of the potential gains 
have already been attained and that the regulatory targets 
are becoming even more demanding, EDP-Distribuição has 
identified the need for further and technically more 
demanding quality of supply planning.  . 
 
Quality of supply investment planning using 
Representative Networks  
 
The enormous diversity of topologies, customer densities 
and protection levels of MV feeders in a real distribution 
system has been a major obstacle for the strategic planning 
activity. It would be easier to plan a distribution system if 
the circuits were identical or if they could be grouped by 
similar characteristics and studied accordingly.  Previous 
research [4][5] has defined Representative networks (RN) 
as typical feeders: each RN is supposed to be the best fit to 
a specific group of real feeders. Building on these concepts, 
a RN based planning approach and performance comparison 
framework was proposed and validated [6] [7].   
The work described in this paper goes one step further 
towards the implementation of a reference network based 
approach that enables an effective reliability driven network 
planning and a network performance comparison framework 
to be established that adequately supports PBR. 
This paper builds on that knowledge base and covers a few 
key points: 
• how the disaggregation of distribution networks  
can facilitate an efficient investment policy  whilst 
relating to the regulatory framework in place; 
• how EDPD tailored network development 
scenarios can be measured both in terms of 
expected performance gains and related costs; 
• how EDPD can manage a portfolio of network 
development scenarios by comparing them and 
using them to establish its own reference networks, 
namely for each disaggregated group. 
 
 C I R E D 20th International Conference on Electricity Distribution Prague, 8-11 June 2009 
 
Paper 0548 
 
 
CIRED2009 Session 5 Paper No  0548      
CASE STUDY 
The present regulation does not propose any network based 
disaggregation in its quality of supply monitoring nor does 
it establish performance benchmarks for particular groups of 
feeders. It sets, however, standards of performance for three 
zone categories (A,B,C as described previously) which 
cover the whole country. Therefore, a first step to address 
the performance requirements in each zone was to 
disaggregate MV feeders on the basis of the predominant 
zone which they supplied.  3 groups of sample MV feeders 
(approximately 120 in total) were set up (urban, semi-urban 
and rural). The number of feeders to include in the analysis 
has no impact on its complexity given the methodology 
employed. Further disaggregation in each group may be 
exploited for the benefit of more refined planning studies 
where needed (for instance, differentiating between feeders 
with on-feeder remote control and without it). 
An array of network development scenarios were simulated 
which enabled the measurement of the expected quality of 
supply benefits. This was used to establish correspondences 
between expected TIEPI, SAIDI and SAIFI gains which are 
both network and scenario specific and must be handled 
accordingly. Therefore, the results included in this section 
have been collated for methodological demonstration 
purposes and do not necessarily represent accurate values 
that apply to the whole distribution system. 
 
Fig.3 – Expected performance gains resulting from  
investment in automation in rural feeders with  pre-existing 
remotely controlled switchgear  
 
Results such as those depicted in Fig.3 allow the 
quantification of potential performance improvements that 
may be within reach in the group of feeders identified, 
provided sufficient investment in automation is carried out. 
Because TIEPI is directly related with the incentive 
mechanism, an implicit economic valuation of the SAIFI 
and SAIDI benefits derived may be obtained as well (there 
are currently no explicit incentive mechanisms concerning 
these indices). 
It is also possible to acquire information regarding the local 
and company-wide impacts of network development 
scenarios. For instance, Table 1 displays sensitivity data 
concerning the variation of the average duration of repair 
operations once the network has been reconfigured to re-
supply as many customers as possible, following a fault. 
  
Table 1: Average repair time sensitivity analysis 
Repair time sensitivity 
analysis 
 
Feeder group 
group 
TIEPI 
(min)/h 
national  
TIEPI  
(min) /h 
Rural (no remote control) 28.7 3 x 10
-3 
Rural (with remote control) 26.7 3.4 x 10
-3
 
Semi-urban 2.3 5.6 x 10
-2
 
The results shown in Table 1 take into account the 
topologies of the feeders in each group and, therefore, the 
average impact of the duration of repair operations whether 
these occur in underground or overhead networks. Semi-
urban networks typically have more remotely controlled 
switching devices and switching capability given their 
relatively higher loads. This is why an hour of waiting time 
for the completion of repair operations has a lower impact 
in these networks (locally) and still produces a higher 
impact (more than 10 times higher) on company-wide 
performance. These results may help optimizing the location 
of repair response teams, emergency generators and spare 
gear. 
Having built a portfolio of network development scenarios 
for each network group, the assessment of their relative 
benefits remains to be accomplished. Designing a reference 
network for each group requires scenarios to be measured, 
and the corresponding costs to be quantified and compared. 
Furthermore, designing an investment strategy demands that 
investment scenarios are either rejected or selected and 
subsequently prioritized.  
An example is shown of such a case concerning the 
particular scenario of investing in automation schemes. The 
benefits are accounted for in euros, based on the economic 
signal provided by the incentive scheme.  In this way it is 
possible to compare the merits of the scenario for groups of 
feeders of different characteristics. 
 
 
Fig. 4 – Investing in automation: typical B/C ratios obtained 
for various groups of feeders   
 C I R E D 20th International Conference on Electricity Distribution Prague, 8-11 June 2009 
 
Paper 0548 
 
 
CIRED2009 Session 5 Paper No  0548      
 
The data depicted in Fig.4 helps to determine which 
scenarios may be of economic interest (B/C >1) when 
applied to the groups of feeders considered in the analysis. 
Load growth has been taken into account as far as it can be 
estimated. Investment costs were diluted over a 30 year 
period using a 10% interest rate. It is clear that under the 
present regulatory framework it is not cost-effective to 
improve the quality of supply of these urban feeders by 
investing in automation. Alternatively, this scenario seems 
to be of interest in the case of the rural networks analysed, 
particularly for those without remote control which 
represent a small proportion of MV feeders already.  
Results such as these may also be compared on similar 
grounds with other network development scenarios which 
may be part of the portfolio: undergrounding overhead 
networks, refurbishing or re-enforcing existing networks, 
building more substations and shortening the average feeder 
length, etc.  
Depending on the volume of investment involved these 
scenarios may be better characterized in order to facilitate 
their costing and consequently the definition of the global 
investment strategy to pursue. 
 
Fig. 5 – Investing in automation: rural feeders without 
remote control  
 
Fig.5 relates B/C ratios with the volume of investment in 
this particular group of feeders, assuming a growing number 
of installed devices, on average, per feeder .  
CONCLUSIONS 
A RN based approach has been employed to design 
investment strategies by enabling the measurement (in terms 
of several quality of supply indices), the costing and the 
comparison of network development scenarios. These 
scenarios have been simulated for groups of real feeders 
taking into account their specific characteristics (lengths, 
failure rates, topologies, customer densities, etc). The 
making of these groups took into account the specific 
regulatory environment in which EDPD operates.  This 
approach has also enabled the assessment of the strength of 
the incentive scheme currently in place by enabling the 
establishment of a direct link between quality of supply 
related investments and the corresponding benefits that may 
be expected, both locally and at the company level. Within 
this context, this framework effectively supports the 
development of a reference network in a manageable 
manner, for particular groups of feeders. This facilitates the 
characterization of the optimum set of network development 
scenarios for each case. Simultaneously, this framework 
enables an efficient prioritization of investment scenarios at 
the company level. 
The methodology can be employed to create or to specify 
benchmark networks. These are conceptually similar to 
reference networks and can be used to compare companies 
against a network of known performance. Benchmark 
networks may be the result of the average set of variables 
across companies, across distribution areas of similar 
characteristics or may be set by the regulator on the basis of 
some other process. The framework has the capability to 
compare the performances between companies whilst 
assessing the reasons for any differences. 
 
ACKNOWLEDGMENTS 
The authors would like to acknowledge the contribution of 
the colleagues from the network planning, network dispatch 
and network maintenance units who provided invaluable 
inputs concerning the collection of data and critical analysis. 
REFERENCES 
[1] ERSE: “Relatório da Qualidade de Serviço – 2006 
Transporte e Distribuição de Energia Eléctrica em Portugal 
Volume I –Sumário Executivo”, Novembro 2007 
[2] ERSE: “Relatório da Qualidade de Serviço – 2006 
Transporte e Distribuição de Energia Eléctrica em Portugal 
Volume II –Análise da Informação”, Novembro 2007 
[3] Falcão, A. “Incentivo à melhoria da qualidade de serviço 
na actividade de distribuição de energia eléctrica”, 
Nº12/2008, ERSE, 2006 
[4] Strbac, G. and Allan, R.:”Performance regulation of 
distribution systems using reference networks”, Power 
Engineering Journal, December 2001 
[5] Levi, V., Strbac, G. and Allan, R.: “Assessment of 
performance-driven investment strategies of distribution 
systems using reference networks”, IEE Proc.- Generation, 
Transmission and Distribution Vol 152, No.1, January 2005  
[6] Silva, A., Mohamed, S., Dragovic, J., Djapic, P., Strbac, 
G., Allan, R. “Reliability evaluation of distribution networks 
and performance comparison using representative 
networks”, CIRED 2007, Vienna, Austria, May 2007 
[7] Silva, A. “Framework for analyzing performance driven 
distribution network investment strategies”, PhD Thesis, 
Imperial College London, 2008 
[8]  Billinton, R. and Allan, R.N.,”Reliability Evaluation of 
Power Systems”, Pitman Books, New York and London, 
1984 
 


QUALITY OF SUPPLY DRIVEN INVESTMENT PLANNING AND REGULATORY SUPPORT USING REPRESENTATIVE NETWORKS

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QUALITY OF SUPPLY DRIVEN INVESTMENT PLANNING AND REGULATORY SUPPORT USING REPRESENTATIVE NETWORKS

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