Faculdade de Economia da Universidade de Coimbra
Grupo de Estudos Monetários e Financeiros (GEMF)
Av. Dias da Silva, 165 – 3004-512 COIMBRA, PORTUGAL
NUNO CARVALHO FIGUEIREDO, PATRÍCIA PEREIRA DA SILVA & PEDRO CERQUEIRA
The Renewables Influence on Market Splitting: the Iberian Spot
ESTUDOS DO GEMF
N.º 14 2014
Este trabalho é financiado por Fundos Nacionais através da
a EFS Initiative, University of Coimbra, Sustainable Energy Systems – MIT‐P, Coimbra, Portugal
b INESCC, Coimbra, Portugal
c Faculty of Economics, University of Coimbra, Coimbra, Portugal
d GEMF, Grupo de Estudos Monetários e Financeiros,Coimbra, Portugal
The Renewables Influence on Market Splitting: the Iberian Spot Electricity Market
Nuno Carvalho Figueiredo a,b, Patrícia Pereira da Silva b,c, Pedro Cerqueira c,d
This paper aims to assess the influence of wind power generation on the market splitting behaviour of the Iberian electricity spot markets.
We use logit models to express the probability response for market splitting of day‐
ahead spot electricity prices together with explanatory variables like, wind speed, available transmission capacity and electricity demand.
The results show that the probability of market splitting increases with the increase of wind power generation. The European interconnection capacity target of 10% of the peak demand of the smallest interconnected market has to be reconsidered, in order to keep electricity market integration a reality.
As demonstrated, investment in interconnection capacity has to follow the investment and deployment of further wind power capacity, so coordination policies governing both interconnection development and renewable incentives should be designed.
Keywords ‐ Electricity Market Integration, Market Splitting, Renewable Energy
JEL: C10, C35, D47
In Iberia, both the Portuguese and the Spanish wind power sectors were developed with extreme success (Batlle 2011; Moreno and Martínez‐Val 2011; Gelabert et al. 2011;
Ruiz Romero et al. 2012), following the European targets for the promotion of Renewable Energy Source electricity (RES‐E), aiming to reduce dependency on imported fossil fuels and allowing for the reduction in Green House Gas emissions (European Union 2001;
European Union 2009a). Comparing the hourly demand profile with the wind power installed capacity evolution in Iberia (Figure 1) we can observe that currently with the adequate climate conditions, wind power can supply a big share of electricity to the system.
The large deployment of RES‐E generation and in particular of wind power in Europe was achieved by strong financial support mechanisms, like feed‐in tariffs, fiscal incentives, tax exemptions and other (Meyer 2003; Jager et al. 2011; Amorim et al. 2013). This poses new challenges, both in the technical sense and in the market design. Electricity systems
have to be restructured to accommodate RES‐E intermittency (Lynch et al. 2012) and policy design has to reflect required market integration of these technologies (Batlle et al. 2012;
Benatia et al. 2013).
Figure 1 ‐ Hourly demand and wind power installed capacity in Iberia
The impact of high penetration of RES‐E on electricity as been discussed throughout a number of scientific papers and reports. In particular some of the issues discussed related with the high level growth of wind power installed capacity reported are: the importance of adequate interconnections and transmission capacities to transport excess production, electrical system fault endurance, available and flexible standby generating capacity to accommodate load variability and effective control or curtailment of wind power production (Söder et al. 2007; Franco and Salza 2011; Benatia et al. 2013). Also wind forecasting is fundamental to allow wind power load management and electrical system balancing (Milligan et al. 2009).
Due to the almost inexistent marginal costs of RES‐E generation, they will come first in the merit order of power plant dispatch. Therefore, by displacing higher marginal cost electricity generation, one could expect some level of decrease on the electricity spot market prices. This fact is reported by several authors (Moreno and López; Sensfuß et al.
2008; Sáenz de Miera et al. 2008; Klessmann et al. 2008; Mauritzen 2010; Jónsson et al.
2010; Amorim et al. 2010; Cruz et al. 2011; Cutler et al. 2011; Gelabert et al. 2011; Mulder and Scholtens 2013).
Several authors have studied electricity market integration, as it is one of the main objectives in Europe. Studies were made concerning different geographic areas: De Vany &
Walls (1999), Park, Mjelde, & Bessler (2006) in the US regional markets; Worthington, Kay‐
Spratley & Higgs (2005) and Higgs (2009) in Australia; Bower (2002), Boisseleau (2004), Armstrong and Galli (2005), Zachmann (2008), Bosco et al. (2010), Bunn and Gianfreda (2010), Pellini (2012) and Figueiredo and Silva (2012 and 2013) in Europe. Nonetheless, literature lacks on the assessment of the impacts that the high penetration of RES‐E generation have on interconnected market behaviour. The market coupling/splitting mechanism and its behaviour considering the influence of high wind power penetration is therefore herein offered.
In the following section a summary of the electricity market reform and an overview of the market coupling/splitting mechanisms, in place in the Iberian spot electricity markets, is given. The interconnections available in Iberia are described in Section 3 and the data used in this study is presented in Section 4. The estimated models are described in Section 5, followed by the analysis and discussion of results in Section 6. Conclusions can be found in Section 7.
2. REGIONAL ELECTRICITY MARKETS
Successive European Directives (European Union 1997; European Union 2003a;
European Union 2009b) where issued with the objective to established common rules for the various electricity markets in Europe, based on the liberalisation of the sector, without prejudice of the public service required and the access by the generators and consumers to the transmission and distribution grids (Jamasb and Pollitt 2005). These requirements are guaranteed by regulating authorities established in each country (Silva 2007). Member‐
States would take necessary measures to facilitate transit of electricity between transmission grids in accordance with the conditions laid down (European Union 2003b;
European Union 2009c) in order to achieve an adequate integration of national electricity transmission grids and associated increase of electricity cross‐border transfers, ensuring the optimization of the production infrastructure (Jacottet 2012). However, with this set up, back in 2006 market integration in Europe was still far from being achieved (Coppens and Vivet 2006), which led to the creation of Regional Electricity Markets, to provide an intermediate step for the consolidated European Electricity Market (ERGEG 2006).
The interaction between electricity markets within a region can occur through high voltage interconnections with limited capacity. This limited interconnection capacity might have additional constraints due to the electricity physical behavior, originating unidentified flows not necessarily related with cross‐border trading or contracts. A consumer that contracted with one generator across the border will probably receive electricity from a different generator causing the so called Loop Flow Problem (Coppens and Vivet 2006).
One of the preferred methods and currently used to link interconnected spot markets is through a market coupling mechanism, where markets with lower prices export electricity to markets with higher prices through interconnections (Meeus et al. 2009). If the interconnection capacity is large enough to accommodate the exported electricity flows
(without congestion) then the price is the same in both markets, otherwise market splitting occurs and two regional market prices are cleared (EPEX et al. 2010). The basis of this mechanism is the calculation of the Available Transfer Capacity (ATC), which is made by the Transmission System Operators (TSO) taking into account the safety and reliability of the electrical system, together with an allowable safety margin. Therefore import and export ATC can have different values depending on loop flows and technical constraints (Luna and Martínez 2011).
Figure 2 – Market coupling without congestion (EPEX
et al. 2010)
Figure 3 – Market coupling with congestion – market splitting (EPEX et al. 2010)
3. THE IBERIAN INTERCONNECTIONS
The Portugal‐Spain electrical interconnection currently consists of eight HV lines. A new interconnection line between Tavira and P. Guzman is being constructed (to be concluded by REE on the Spanish side) and another new line is forecasted to be in service by 2015 between V. Fria and O Covelo, which with several other internal line reinforcements will allow the completion of the interconnection capacity between Portugal and Spain of 3000 MW, essential for the joint Iberian electricity market MIBEL (REN 2013).
Cross‐border interconnections offer numerous advantages under normal operating conditions, such as optimal power station daily production, increasing opportunities for operation with renewable energies, the promotion of competition and enhancement of supply security (Figueiredo and Silva 2012). However, interconnectors are limited and have constraints due to the physical behaviour of electricity, which flows through the easiest path. Therefore, high voltage grids interconnected through many interconnectors placed in different geographic positions originate unidentified flows not necessarily related with cross‐border contracts causing congestion of transmission lines and interconnectors, the so called Loop Flow Problem (Coppens and Vivet 2006).
It is the TSO responsibility to manage the above mentioned constraints (Turvey 2006) and to deal specifically with cross‐border exchanges in electricity, which in turn have to comply with European Community Regulation 1228/2003/EC of 26 June 2003 and also later with European Community Regulation 714/2009 of 13 July 2009. These regulations establish a set of rules for cross‐border exchanges in electricity, in order to enhance competition, establish a compensation mechanism for cross‐border flows of electricity, setting principles on cross‐border transmission charges and allocating available capacities of interconnections (European Union 2003c; European Union 2009c). With the third legislative package the creation of the European Network of Transmission System
Operators (ENTSO) was established, aiming to prepare network codes to guarantee an efficient transmission network management, together with allowing trade and supply of electricity across borders. The Agency for the Cooperation of Energy Regulators (ACER) requested ENTSO to draft the so called network codes, laying down common rules applicable to the energy sector (ENTSO‐E 2014a). As part of these, the Network Code on Capacity Allocation and Congestion Management (CACM) will establish the interconnection capacity allocation methods to be applied in the European markets. ACER has recommended the adoption of this code by the European Commission on the 26th of May 2014 (ACER 2014).
Hourly price data and ATC were extracted from the OMIE data download web page (OMIE 2013) for the day‐ahead spot electricity prices in €/MWh, for Portugal and Spain.
Demand data was extracted from the European Network of Transmission System Operators for Electricity data portal (ENTSO‐E 2014b). Additionally, wind power installed capacities in both Iberian countries were obtained from Eurostat statistics bulk data download web page (Eurostat 2013) and wind speed data extracted from Weather Underground web page (Weather Wunderground 2013). Wind speed data was then validated and averaged over each hour and geographically, considering the areas with higher concentration of wind parks installed in each country (The Wind Power 2013).
In Figure 4 the spot electricity prices for the both Iberian countries are plotted, together with the corresponding differences. Market integration in Iberia was demonstrated in other studies (Figueiredo and Silva 2013a; Figueiredo and Silva 2013b), however we can still observe many market splitting occurrences.
Concerning the ATC (Figure 5), the increase in interconnection capacity between the two Iberian countries is visible in the plot, with the last improvement being observed in mid 2012. It is also to note that ATC limitations are more often observed in the direction Spain to Portugal.
Figure 4 – Spot electricity prices and corresponding differences in Iberia
In Figure 6 the average wind speed in both Iberian countries is plotted and the reader can observe the big variability created in wind generation. However, we cannot see a clear seasonality in the wind speed pattern, which means that wind generation might be present throughout all seasons of the year.
Summary statistics for the time series are presented in Table 1. Skewness and kurtosis values indicate that all price time‐series have non‐normal distribution, which is confirmed by Jarque‐Bera statistic rejection of the null for normal distribution testing.
Figure 5 – Available Transfer Capacities in Iberia
Figure 6 – Wind speed in Iberia
Time series summary statistics
Wind Speed Spain
Wind Speed Portugal
[€/MWh] [€/MWh] [€/MWh] [MW] [MW] [MW] [MW] [km/h] [km/h]
Mean 46.71161 45.60964 1.101976 1548.897 1566.429 29143.02 5752.907 5.663591 6.519888
Median 46.5 46 0 1500 1500 29279 5729 5.003364 5.823533
Maximum 180.3 145 136.3 2400 2400 44258 9397 26.35932 28.37574
Minimum 0 0 ‐47.46 50 150 1067 3310 0.892408 0.605483
Std. Dev. 15.58027 15.21566 4.233299 439.352 514.4117 5286.531 1084.979 2.863928 3.569009 Skewness 0.003219 ‐0.14001 4.166229 ‐0.178521 0.095002 0.050637 0.263738 0.863859 0.915203 Kurtosis 4.111249 4.009405 70.77425 2.624141 2.029153 2.432735 2.289435 3.620756 3.818572
Jarque‐Bera 1984.458 1763.332 7492891 431.868 1572.642 533.5838 1258.464 5416.005 6460.184
Probability 0 0 0 0 0 0 0 0 0
Observations 38567 38567 38567 38567 38567 38567 38567 38567 38564
5. MODEL ESTIMATION
The estimated models aim to provide indications about the behaviour of the market splitting mechanism in the Iberian spot electricity markets, considering the wind power penetration in these same markets. We have modelled the probability of market splitting as a function of relevant explanatory variables related with wind power generation and market splitting mechanism. To model the split probability we can describe it in terms of a non‐observable latent variable (Split*) such that:
1| ∗ , 1 ∗ 0 (1)
,where Split is the binary variable of occurring market splitting conditional to X explanatory variables and e is the error term.
In order to overcome some limitations of the linear probability models, namely the fact that we can obtain probabilities below zero or above one (Davidson and Mackinnon 2004), through a transformation function we have used models of the form:
1| , ∗ , 1 ∗ 0 (2)
,where G is a cumulative distribution function (cdf), which in the case of the probit model is the normal distribution cdf, where e has a standard normal distribution, and in the case of the logit is the logistic distribution cdf, where e has a standard logistic distribution.
We have opted to use the logit model as our binary response model, due to the fact that the probit model latent error does not follow a normal distribution (Wooldridge 2003).
1| ∗ 0| 0| (3)
,where X is a matrix of explanatory variables and e is the error term that is an independently distributed variable following the standard logistic distribution, from which we can obtain,
| 1 Λ Λ , where Λ (4)
In this study the wind speed is the main explanatory variable used in our models, expressing the amount of electricity generated from wind energy sources. The two other explanatory variables used are related with demand of each country and the ATC between both spot electricity markets. The former expresses the ability of the country to consume the electricity produced by wind energy sources and the latter expresses the ability to export the electricity generated at lower marginal costs.
Wind power curves are well described by a third order polynomial in the wind speed (Joensen et al. 1999; Stathopoulos et al. 2013). Therefore we modelled the probability of market splitting as a function of the ratio of the third order polynomial in wind speed and the demand in each spot electricity market, following the concept of wind power penetration level (Jónsson et al. 2010). Another concept introduced in the model is the ratio of ATC to demand expressing the size of interconnection capacity compared with the demand of each market.
The estimated model associated to the latent variable is:
∗ ∙ ∙ ∙ ∙ ∙
∙ ∙ ∙
,where Split is the binary variable of occurring market splitting, WSPT and WSES are the average wind speeds in Portugal and Spain respectively, ATCPT‐ES and ATCES‐PT the available transmission capacity for both directions of the interconnections and DPT and DES the demand in Portugal and Spain respectively.
In the estimated models (Table 2) all coefficients are significant to 0.1%, having a value for Percent Correctly Predicted of 79.69%, in spite of the rather low McFadden pseudo R‐
square. The “Neglected Heterogeneity” specification issue might have an influence on the coefficient estimates causing an underestimation of the effects, however relative effects of the explanatory variables can be extracted (Mood 2009; Wooldridge 2010).
OMIE market splitting model 1 – No Heteroskedasticity correction
Dependent variable: Split
Estimate Std. Error z value Pr(>|z|)
0 ‐1.3617 0.0686 ‐19.85 <2e‐16 ***
1 2178.3855 147.5561 14.76 <2e‐16 ***
2 ‐261.8331 17.2916 ‐15.14 <2e‐16 ***
3 7.9904 0.5881 13.59 <2e‐16 ***
4 20604.4118 903.0495 22.82 <2e‐16 ***
5 ‐2611.7491 135.4113 ‐19.29 <2e‐16 ***
6 98.4730 6.0111 16.38 <2e‐16 ***
7 ‐2.1370 0.1515 ‐14.10 <2e‐16 ***
8 ‐35.0798 0.9519 ‐36.85 <2e‐16 ***
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Log‐likelihood: ‐1.784e+04 on 9 Df
Studentized Breusch‐Pagan test
BP = 4199.087, df = 8, p‐value < 2.2e‐16
Percent correctly predicted: 79.69%
McFadden pseudo R‐squared:
'log Lik.' 0.07897156 (df=9)
Another present specification issue is the heteroskedasticity of the error term, which is observed in the estimated model. In order to overcome this, an additional model was estimated attempting to correct this issue,
1| Λ , ∗ , ~Λ 0, exp 2 (5)
1| Λ , ∗ , ~Λ 0, exp 2 (6)
,where X’ is a matrix of explanatory variables belonging to the original information set and a vector of parameters to be estimated (Davidson and Mackinnon 2004). The market splitting probability is then not only dependent on the original regression function, but also on the skedastic function. The correction attempted was done by considering the ratio
average wind speed over demand, both in Portugal and Spain. These variables were initially chosen due to to high variability expected in the wind speed, thus in the wind power generated. The inclusion of further variables in the correction term made the model not to converge and are not herein presented.
OMIE market splitting model 2 – WS/D Heteroskedasticity correction
Dependent variable: Split
Coefficients (binomial model with logit link):
Estimate Std. Error z value Pr(>|z|)
0 ‐2.290e+00 1.883e‐01 ‐12.161 <2e‐16 ***
1 4.118e+03 4.004e+02 10.283 <2e‐16 ***
2 ‐5.364e+02 5.432e+01 ‐9.874 <2e‐16 ***
3 1.752e+01 1.974e+00 8.873 <2e‐16 ***
4 1.041e+05 5.361e+03 19.416 <2e‐16 ***
5 ‐2.102e+04 1.104e+03 ‐19.047 <2e‐16 ***
6 1.003e+03 5.428e+01 18.473 <2e‐16 ***
7 ‐7.024e+00 4.773e‐01 ‐14.716 <2e‐16 ***
8 ‐9.421e+01 4.399e+00 ‐21.414 <2e‐16 ***
Latent scale model coefficients (with log link):
Estimate Std. Error z value Pr(>|z|)
1 11.90 29.45 0.404 0.686
2 5577.54 227.25 24.543 <2e‐16 ***
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Log‐likelihood: ‐1.758e+04 on 11 Df
LR test for homoskedasticity: 524.5 on 2 Df, p‐value: < 2.2e‐16 Dispersion: 1
Number of iterations in nlminb optimization: 26
Percent correctly predicted: 79.41%
McFadden pseudo R‐squared:
'log Lik.' 0.09251255 (df=11)
The logit model coefficients cannot be interpreted directly so predicted probabilities were calculated for some created data sets as shown and analysed in the next section.
6. ANALYSIS AND DISCUSSION OF RESULTS
Prediction results from the models express that market splitting probability increases when the wind speed is higher, consequently with higher wind power generation (Figure 7). This can be explained by having low marginal cost electricity available to flow across the
border. It is to note the steep inflexion of the surface around a certain average wind speed (10 km/h for Spain in model 2 and 20 km/h for Portugal in both models), meaning that market splitting probability as a dramatic increase above a determined wind speed. Also to note is the loss of symetry when introducing the heroskedasticity correction, which may actually make sense due to the different sizes of the Portuguese and the Spanish electricity markets. With low average wind speeds in Spain the market splitting probability responds quite drastically to an increase in the Portuguese wind speed, whilst this effect looses its influence when the average wind speed increases in Spain. Model 2 in this respect has a more consistent behaviour than model 1 as the different sizes of the electrical systems play a role in the asymmetric behaviour of the market splitting probability.
Further predictions were then made at different average wind speeds to evaluate probability response to ATC and demand.
Figure 7 –OMIE – Predicted probability response to average wind speed
Market splitting probability decreases with increasing ATC (allowing higher flows of electricity between markets), as one could expect by the concept definition of market splitting (Figure 8). Considering the mean of the average wind speed and the existing interconnection capacity between both spot electricity markets we can conclude that the existing interconnection level is adequate, bearing in mind that it is actually higher than the European Union recommendation of 10% of the peak demand of the smaller interconnected market (Amorim et al. 2014). The same tendency of decreasing probability response to increasing ATC can be seen in both models estimated, however different shapes for the probability response surfaces can be observed with increasing average wind speed (Figure 10 and Figure 11 of Appendix A). Moreover, in order to have a reasonable market splitting probability level and maintain both spot electricity markets integrated, the
requirements for interconnection capacity should increase with increasing available wind power.
Notwidstanding the specification issues of the estimated models and considering the double of the wind power generation level available in the system (corresponding to an average wind speed level of 10 km/h), we estimate that in order to maintain the current market splitting occurrence probability (around 5% with an ATC of 2500 MW and at mean average wind speed), the interconnection capacity should be increased to 3700 MW (Table 4).
Table 4 – OMIE market splitting probability estimates
WS/D heteroskedasticity correction
ATC level [MW] Mean of the average wind speed
Average wind speed of 10 km/h
2500 5% 11%
2700 4% 10%
2900 3% 9%
3100 2% 8%
3300 2% 7%
3500 1% 6%
3700 1% 5%
Considering an average wind speed of 20 km/h, as observed in Figure 11, the market splitting probability response to ATC does not decrease significantly, demonstrating that there is a limit for wind power generation from where investment in increasing interconnection capacity is not capable of improving electricity spot market integration.
Figure 8 –OMIE – Predicted probability response to ATC at mean average wind speed
Figure 9 –OMIE – Predicted probability response to demand at mean average wind speed
The probability response predictions to demand for the mean average wind speed in the estimated models are plotted in Figure 9. Here we can observe a different behaviour between the two models and an asymmetry between the Portuguese and Spanish
probability response behaviour to demand. By increasing demand in only one of the markets, generation with higher marginal costs in this market would set the clearing price, causing lower marginal cost electricity to be imported through the interconnection and leading eventually to a high probability of market splitting occurrence. This behaviour could explain model 1 at mean average wind speed for the Spanish demand response, but not the Portuguese demand response, maybe due to their relative sizes. This, of course, would only happen if there is enough lower marginal cost electricity in the other market, in this case in Portugal, otherwise an increasing demand would cause a decrease of the market splitting probability, as the available low marginal cost electricity would be consumed internally. This behaviour is actually consistent with the observed in model 2 at mean average wind speed. Also when the wind speed increases, having more low marginal cost electricity available in the system, the market splitting probability increases even with the increasing demand due to the available excess of low marginal cost electricity and inverting the normal behaviour of the system (Figure 13).
One of the benefits of the integration of spot electricity markets is the optimization of RES‐E generation. The influence of high penetration of wind energy source electricity generation and its integration, together with the requirement to achieve an Iberian integrated electricity spot market is herein studied. Market splitting behaviour was modelled through logit models estimating the probabilities of its occurrence.
It is shown that when the wind speed is higher, consequently having higher wind power generation available, market splitting probability increases. To maintain the same market splitting probability level with increasing wind speed, consequently with increasing available low marginal cost electricity in the system, the requirements for interconnection capacity have to increase.
As demonstrated, investment in interconnection capacity has to follow the investment and deployment of further wind power capacity, so coordination policies governing both interconnection development and renewable incentives should be designed. Also as an indicative requirement, additional interconnection capacity to reach 3700 MW is required if Iberia doubles its available wind power generation, or any other low marginal cost generation, in the system.
In order to accomplish the aimed future decarbonisation of the European Union’s power sector, renewables expansion and in particular wind power generation will have to increase. Moreover, the reality of electricity market integration at a European level can only be aimed if the interconnection capacity target of 10% of the peak demand of the smallest interconnected market is re‐evaluated and increased. Availability of large amounts of wind power generation will have to be managed in order to optimize infrastructure.
Regulation should be adjusted and coordinated to allow different mechanisms for optimization, deployment of effective energy storage facilities, wind power production curtailment and transmission system expansion.
Future work is required to explore other type of models, in order to overcome the limitations and specification issues of the logit models described in this paper.
This Work has been framed under the Energy for Sustainability Initiative of the University of Coimbra, supported by the R&D Project EMSURE, Energy and Mobility for Sustainable Regions (CENTRO 07 0224 FEDER 002004) and also partially supported by the Portuguese Foundation for Science and Technology under project grant[s] PEst‐OE/
This paper was written while the third author was a visiting scholar at the Economics Department of the University of Massachussets, Amherst with support of the FCT grant
We would also like to recognise the kind assistance provided by the Portuguese TSO REN for making data available.
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Figure 10 – OMIE – No correction for heteroskedasticity ‐ ATC predicted probability response with 10, 15, 20 and 25 km/h average wind speed
Figure 11 – OMIE – WS/D correction for heteroskedasticity ‐ ATC predicted probability response with 10, 15,
20, and 25 km/h average wind speed
Figure 12 – OMIE – No correction for heteroskedasticity ‐ Demand predicted probability response with 10, 15, 20, and 25 km/h average wind speed
Figure 13 – OMIE – WS/D correction for heteroskedasticity ‐ Demand predicted probability response with 10, 15, 20, and 25 km/h average wind speed
(Available on-line at http://www.uc.pt/feuc/gemf )
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