Although the detailed statistics is not reported in this paper, the capacities of transmission lines are relatively constant over the years. SARD-CSUD is the only transmission line whose capacity has been reinforced in 2011, thanks to the installation of new submarine cables that started to operate in March 2011. CSUD-SUD connection has the biggest transmission line capacity, while SARD-CSUD and SICI-SUD are the most limited lines.

Introduction and literature review

The interest in alternative energy has sparked in Europe as the climate change problem emerged. The 2009 Climate and Energy package has motivated European governments to stimulate renewable energy penetration through supporting schemes in order to meet the target of a 20% share of EU energy consumption produced from renewable sources by 2020. According to the more recent figures from Eurostat, the share of renewables in gross final energy consumption has reached 14.95% in the EU-28 in 2013. The economic literature has emphasized the likely reductions of wholesale prices entailed by increasing renewable supply and originated from the displacement of higher variable cost production in the merit order ranking. This phenomenon is referred to as "merit order effect". A larger renewable production has also determined an increase in wholesale price variance as a consequence of technological dependency on exogenous variables. Evidences of these effects have been empirically analyzed for instance in Australia (Cutler et al., 2011), Austria (Wurzburg at al., 2013), Denmark (Jónsson et al., 2010), Germany (Wurzburg at al., 2013; Ketterer, 2014), Israel (Milstein and Tishler, 2011), Ireland (O'Mahoney and Denny, 2011), Italy (Clo et al., 2015), Spain (Gelabert et al., 2011).

Besides the effects on prices, renewable supply has raised some concerns regarding network functioning and congestion management. Some geographical locations seem particularly well suited for the installation of new capacity due to the abundance of natural resources (e.g. the North for wind and the South for solar in both Germany and Spain). Nevertheless, the existing networks may not be adequately developed to guarantee a constant and smooth flowing of more efficient RES production3 toward consumption sites. When production and consumption sites do not coincide and are, on the contrary, very far from each other, increasing renewable output may put an additional stress on the infrastructure, amplifying transportation needs and multiplying congestion occurrence. The opposite happens if renewable supply relieves deficits of production in historical importing regions. Hence, depending on the location of supply and demand, a larger renewable production may have a positive or negative effect on congestion occurrence and, as a consequence, on congestion cost.

The impact of renewable on network congestion may be explicitly investigated in national electricity markets organized as two or more inter-connected sub-markets (or bidding zones) where transmission rights are assigned through implicit auctions. [START_REF] Gelabert | An ex-post analysis of the effect of renewables and cogeneration on Spanish electricity prices[END_REF] A sub-market or bidding zone is defined as the largest geographical area within which market participants can offer and buy energy in the intra-day, day-ahead and longer time frame markets; its boundaries are generally settled based on physical transmission limits in order to achieve an efficient use of the infrastructure. In the absence of transmission constraints, prices are equal across zones; when inter-zonal constraints are binding, zonal market prices diverge. With an implicit auctioning for transmission rights, transmission capacity is (implicitly) included in the auctions of electricity. In other words, the resulting electricity prices per area reflect both the cost of energy in each internal bidding area and the cost of congestion. Implicit auctions ensure that power flows from the surplus areas (low price areas) towards the deficit areas (high price areas). The analysis of the links between renewables and congestion results to be extremely relevant in the path toward the implementation of the European Electricity Target Model which envisages the creation of bidding zones (defined or not by national borders) within a single EU market. Because of heterogeneous generation mix, geographical conditions, RES support schemes and national network configura-tions across EU Members, the European Target Model may face at a larger scale the same challenges of those Countries with bidding areas having experienced a significant renewable penetration.

This article aims at contributing to the scant literature on the effect of increasing renewable power production on congestion frequency and cost. Schröeder et al. (2013) have provided a technical and economic analysis of congestion as a consequence of integration of renewables in Germany, concluding that actual transmission development plans are insufficient to cope with increasing penetration and the installation of a new transmission facility seems to be welfare-improving. Woo et al. (2011) study the effect of increasing wind generation on zonal price differences in Texas ERCOT power market with an ordered logit model for the occurrence of congestion and an OLS model for the level of paired-price differences. The analysis stems from the observation that wind generation is mostly concentrated in the West zone, which is scarcely populated, whereas generation capacity in Houston zone falls short of its zonal load. The authors show that rising wind supply, nuclear generation, load from non-West zones and gas price increases the likelihood and the size of strictly positive paired-price differences between the West and the other zones;5 increasing the load in the West zone has exactly the opposite effect since it reduces exporting needs.

In order to assess the impact of increasing renewable output on congestion frequency and cost, we use Italian electricity market as a case study. For its particular features, Italy serves well our research purpose. Firstly, the Italian Power Exchange is composed of six regional sub-markets, which aggregate in macro-zones all administrative regions. Since each of the zones has its own specific generation mix, they provide heterogeneity in our samples. Secondly, the ambitious support policies for the development of renewable power sources have generated a significant amount of new investments in solar and wind power plants. According to the latest available data, [START_REF] Hemdan | Integration of superconducting cables in distribution networks with high penetration of renewable energy resources: Techno-economic analysis[END_REF] the supply from these power plants has covered 15.89% of the electricity purchased in the day-ahead market in 2014. Solar and wind production sold through the day-ahead market have registered an increase of 267.2% from 2010 to 2014. Southern regions have shown the highest growth rate due to the favorable weather conditions. This rapid growth is an essential characteristic for studying RES impact on congestion. Thirdly, the inter-zonal transmission capacities are not equally distributed in the Italian electricity system. In particular, transmission lines that connect the islands to the Italian peninsula have limited capabilities. With high renewable penetration in some regions and transmission limitations, Italy has the ideal conditions for a case study.

To empirically test the effect of renewables on congestion in Italy, we have built a unique database collecting and matching data with hourly frequency for a five year period (2010-2014) from two sources: GME, the market operator, which publishes the hourly offers in the day-ahead market together with equilibrium prices, quantities and inter-zonal transits; REF-E, a consulting group, who has created a list of Italian power plants classified by technology and geographical location. We have estimated then two econometric models performed on five zonal pairings: a multinomial logit model, whose dependent variable has three discrete values capturing both the occurrence of congestion and its direction, and a 3 stage least square model which seeks to quantify the effects of renewable production on implicit and explicit congestion costs. [START_REF] Jónsson | On the market impact of wind energy forecasts[END_REF] Up to our knowledge, Sapio (2014) is the only author testing the effect of larger solar and wind generation on congestion between Sicily and Southern Italy using a binary dynamic logit model and a vector autoregressive model on 2012 hourly data. The likelihood of congestion tout-court seems to increase with the demand in Sicily and the supply of solar in the rest of Italy and to decrease with all other regressors (demand in the rest of Italy, solar and wind supplies in Sicily, wind supply in the rest of Italy). When directional congestion is analyzed the author finds that a rise in the demand in Sicily and in the supply of solar in the rest of Italy decreases the likelihood of congestion from Sicily, while a rise in the load in the rest of the peninsula, in the supply of wind and solar in Sicily and in the indicators on market power have the opposite effect; the opposite pattern is found for congestion to Sicily. With the VAR model the author validates logit results.

This article originally contributes to the literature in several ways. First, we enlarge the scope of the analysis by considering all Italian neighbouring zones in order to verify the consistency of the empirical models beyond the specificities of each pair. Second, we employ a multinomial logit model, instead of a binary model, in order to separately capture the effect of increasing renewable production on the probability of both directional congestions (to and from) compared to the benchmark situation of no congestion. Third, we consider zonal figures on production and demand instead of aggregated figures to isolate the contribution of each zone to the occurrence of congestion. Fourth, we estimate the impact of renewable output not only on congestion frequency but also on congestion cost, something that has never be done before in the literature. Fifth, the 3 stage least square model allows us to solve endogeneity issues concerning hydroelectric production and to consider the national electricity system as a whole with correlated equations.

Our analysis suggests that the effect of a larger local wind and solar supply is to decrease the probability of suffering congestion in entry and to increase the probability of causing a congestion in exit compared to no congestion case. Increasing hydroelectric production has a similar effect. A rise in local demand on the contrary increases the probability of congestion in entry (due to larger import) and decreases the probability of congestion in exit. These results hold for both importing and exporting regions, but importing regions are much less likely to cause congestion in exit, therefore the installation of new RES capacity in these zones may have a positive effects in terms of flow balance between regions. The estimations on congestion cost reveal that, due to the merit order effect, local larger renewable tend to push the congestion cost towards negative value as it decrease the marginal cost for balancing the system. Much bigger shock of renewable quantity consequently could reduce saturated line and merge the zone (congestion cost = 0) or could change the direction of the flow into the opposite direction (congestion cost< 0) because of excessive supply. This is true for all the zone in the case of explicit congestion cost, but it is only applied in importing regions when we look at the implicit congestion cost. [START_REF] Ketterer | The impact of wind power generation on the electricity price in Germany[END_REF] Therefore, increase of renewable should be promoted in the importing zones, but the overall growth should be controlled in order to avoid congestion to the opposite direction.

The remainder of the paper is organized as follows. Next section briefly describes Italian electricity market and the rules for congestion management. The third section provides an overview of the day-ahead market transactions in terms of generation mix, interzonal transits and price differences between neighboring zones. The forth section is dedicated to the econometric analysis. The last section concludes.

Italian electricity market

The Italian Power Exchange (IPEX) is composed by a spot market (MPE), a forward market (MTE) and a platform for the physical delivery of contracts concluded on the financial derivatives segment of the Italian Stock Exchange (CDE). The spot market is composed by three sub-markets: the day-ahead (MGP), the intra-day and the ancillary services markets. GME (Gestore dei Mercati Elettrici) manages the IPEX together with the OTC Registration Platform for forward electricity contracts that have been concluded off the bidding system. Our analysis focuses solely on the MGP (Mercato del Giorno Prima), the day-ahead market. The Italian geographical market consist of 7 foreign virtual zones, 6 geographical zones and 5 poles of limited production (national virtual zones). A stylized representation of the geographical market with the most relevant links between zones is reported in Figure 1. The 20 administrative regions composing the Italian territory are aggregated in the 6 geographical zones (Fig. 2). The poles of limited production are coupled with the closest geographical zone: Monfalcone (MFTV) is associated to NORD, Brindisi (BRNN), Foggia (FOGN) and Rossano (ROSN) to SUD and Priolo (PRGP) to SICI. In this market, transactions take place between the ninth day before the day of physical delivery and the day before the day of delivery. The sellers submit hourly offers for each generating unit specifying the quantity and the minimum price at which they are willing to trade their power. The aggregated supply curve is built according to the merit order in an ascending order of price. In a symmetrical way, the market demand curve is generated through the aggregation of single bids in a descending order of price. [START_REF] Milstein | Intermittently renewable energy, optimal capacity mix and prices in a deregulated electricity market[END_REF] The hourly market price is determined by the intersection of the demand, and the supply curves, following an iterative procedure. Firstly, the geographical market is considered as unique: if the day-ahead production/consumption plan respects all network constraints across zones (no congestion), a single price for the whole country emerges. [START_REF] O'mahoney | The merit-order effect of wind generation in the Irish electricity market[END_REF] On the contrary, if a network constraint is saturated, then the geographical market is divided into two sub-markets, each one aggregating all the zones above and below the saturated constraint. The market demand and supply curves are rebuilt for the two sub-markets (taking into account the quantity that can flows between zones up to the transmission limit), and two zonal prices result. The hourly auction is a uniform price auction which means that all accepted units are entitled to receive the system marginal price (or prices when de-zoning arises because of transmission congestion). Figures 3 and4 In the permanence of network saturation, the process of sub-setting the market continues until all constraints are satisfied (Fig. 5). While producers receive the zonal prices in the occurrence of congestion, the buyers pay the National Single Price (PUN) for the electricity bought in the pool: the PUN is an average of zonal prices weighted for the zonal purchases. [START_REF] Sapio | Renewable flows and congestion in the Italian power grid: Binary time series and vector autoregressions[END_REF] To better gauge the relevance of congestion phenomenon in Italy, we have reported in Table 1 its frequency and the average number of zonal divisions for the five year [START_REF] Sapio | Renewable flows and congestion in the Italian power grid: Binary time series and vector autoregressions[END_REF] The purchased quantity should be netted of purchases from pumped-storage units and from foreign zones. In the example reported in Fig. 4 the PUN would be equal to 32.35A C/Mwh: 3 Day-ahead market: generation mix, transits and zonal price differences

PUN = P i Q i Q i = (
This section is devoted to the description of Italian production mix, interzonal transits and zonal price differences as they result from the day-ahead ex-post market data.

From 2010 to 2014, the contribution of Italy's main source of electricity, gas, has gradually decreased with the 2014 quantity (75.1 Twh) representing almost the half of 2010 figure. RES supply has surpassed total CCGT production for the first time in 2014 (100.9 Twh versus 75.1 Twh). In this year, renewable production has exceeded the target established in the National Renewable Action Plan (NREAP) to produce 100 TWh of renewable energy by 2020. Even with high renewable penetration, Italy is still a net importer. The statistics suggests that on average 15.9% of the quantity accepted in the day-ahead market is supplied by neighboring countries. [START_REF] Schröeder | The Integration of Renewable Energies into the German Transmission Grid: A Scenario Comparison[END_REF] A detailed figure of the quantity sold in the day-ahead market by production source for the period 2010-2014 is reported in the Appendix (Figure 9). The breakdown of renewable supply by technology (Figure 6) reveals that wind and solar have experienced the strongest growth. Solar production registers the highest increase as the supply of 2014 is 4.5 times the one of 2010. As a result, in 2014 the solar accounts for 29.9% of total renewable supply and 10.7% of the total production mix. Wind supply in 2014 is 2.6 time the production in 2010 reaching 14.6% of total renewable supply. Hydro production has decreased instead from 2010 to 2012 to rise again afterwards. In 2014, it represents half of total renewable production. Physical exchanges resulting from the day-ahead auction have experienced some changes over the years. Figure 7 shows the average net electricity flows on Italian main lines. [START_REF] Woo | Wind generation and zonal-market price divergence: Evidence from Texas[END_REF] CNOR, SICI and SARD are net importers, while CSUD and SUD act as a hub in the center and southern part of Italy, as play the role of both importer and exporter. NORD and ROSN are the main exporting regions that deliver electricity to CNOR, SUD and SICI. However, ROSN is a virtual generation zone used for balancing the system, thus the regions do not have a withdrawal point (buyer). In By studying the series of zonal prices, we expect to detect a lasting price difference between importing and exporting neighbouring regions. We report the series of pairedprice differences for the period 2010-2014 in Figure 8 for the following pairs: CNOR-NORD; CNOR-CSUD; SARD-CSUD; CSUD-SUD; SICI-SUD. It is worthy to note that during the considered period the zonal prices of SUD and ROSN have differed for less than the 2% of the time, while the zonal price differences between SICI-SUD and SICI-ROSN have followed very similar patterns. This result allows us to consider SICI-SUD pair, which are formally non contiguous zones, instead of the two pairs SICI-ROSN and SUD-ROSN. For the pair CNOR and NORD we observe a substantial increase in the number of hours with negative price difference starting from 2012. This result seems to confirm that after a period characterized by a strong reliance on import from NORD, CNOR has reduced its importing needs. In CNOR-CSUD pair, CNOR has been an importer for most of the time, with rising frequency of positive price differences overtime. The graph also suggests that SARD generally imports from CSUD while the frequency of congestion between these two regions has decreased at the end of 2012 as shown by many hours of identical price. In CSUD-SUD, where the first zone is always importing, we may detect a slightly decrease in the value of positive price differences. The series of price differences between SICI and SUD reveals that the negative price differences have decreased overtime while the positive have substantially remained constant.

SICI-SUD

The first zone of the pair is generally an importing region.

Multinomial logit model

For each zonal pair (ZONE1-ZONE2) the dependent variable in the multinomial logit model, y, may assume three values:14

• y = -1 when the zonal price in ZONE1 is lower than the zonal price in ZONE2: in this case we say that there is "congestion from" ZONE1 (which is exporting power) or a "negative price difference";

• y = 0 when the zonal prices in ZONE1 and ZONE2 are equal: in this case we say that there is "no congestion" (the flows between the two zones respect the transmission constraint) and hence no price difference;

• y = 1 when the zonal price in ZONE1 exceeds the zonal price in ZONE2: in this case we say that there is "congestion to" ZONE1 (which is importing power) or a "positive price difference".

On average, the zonal prices of the neighboring zones paired for the five year period differ about 27% of the time; however, this figure hides a large heterogeneity (see Table 15 in Appendix B). If the link CNOR-NORD has been uncongested for 92% of the time, the zones SICI and SUD have registered the same price only 18% of the total hours. Quite surprisingly, congestion coming from CNOR have been more frequent that those coming from NORD, indicating a change in flows direction between these two zones. In CNOR-CSUD, CNOR confirms to be an importer with congestion to this region accounting for 7% of the hours, while most of the time the two zone have experienced no congestion (91% of the time). The lines SARD-CSUD and CSUD-SUD have followed similar patterns with congestion to the first zone occurring 15% and 16% of the time, respectively. The frequencies of no congestion have been also similar (83% and 85% of the time respectively). Is is worthy to note that while congestion from SARD to SUD has occurred, although quite rarely (1% of the hours), CSUD has never exported to SUD. Finally flows to SICI have congested the line SICI-SUD 75% of the time while flows from SICI have done so for 7% of the hours.

Fo each zonal pair we are going to estimate the following two equations:

log P r(y = -1) P r(y = 0) = α 1 + 4 i=1 η i Y i + 4 r=1 β r X r + (1a) log P r(y = 1) P r(y = 0) = α 2 + 4 i=1 η i Y i + 4 r=1 β r X r + (1b)
where:

• α 1,2 are the constants

• Y i is the matrix of yearly dummies
• X r is the matrix of regressors and includes:

-Hydro generation in the pairing zones (ZONE Hydro)

-Wind generation in the pairing zones (ZONE Wind)

-Photovoltaic generation in the pairing zones (ZONE PV tot)

-Demand in the pairing zones (ZONE D)

Multinomial logit results

Estimation results are shown in Tables from 3 to 7. 15 The second and the third columns report the results in terms of log-odds and marginal effects respectively when the congestion is from ZONE1 (y = -1). The fourth and the fifth columns present the results in terms of log-odds and marginal effects when the congestion is to ZONE1 (y = 1). When congestion is coming from ZONE1 (second and third columns) we observe that:16 1) In ZONE1:

• Rising wind production increases the probability of congestion in all pairs;

• Rising solar production increases the probability of congestion in CNOR-NORD and SICI-SUD pairs (the coefficient is not significant in CNOR-CSUD and SARD-CSUD pairs);

• Rising hydro production increases the probability of congestion in all pairs with the exception of SARD-CSUD (probably due to the scarce hydro production in SARD)

• Rising the demand decreases likelihood of congestion (with the exception of SARD where the coefficient is not significant)

2) In ZONE2:

• Rising wind production decreases the probability of congestion in all pairs except in CNOR-NORD (where wind production in NORD seems to increase congestion);

• Rising solar production decreases the probability of congestion in CNOR-CSUD and in SICI-SUD pairs (while it increases congestion in CNOR-NORD and SARD-CSUD);

• Rising hydro production decreases the probability of congestion in all pairs;

• Rising the demand in ZONE2 increases the probability of congestion in all pairs.

These results indicate that a larger renewable generation in ZONE1 is associated with an increase in the relative log odds of a congestion coming from ZONE1 with respect to no congestion due to improved export possibilities. A larger RES production in ZONE2 on the contrary decreases the likelihood of congestion from ZONE1 since less import are needed. An opposite reasoning works for the demand: when the demand is larger in ZONE1 there is less export, therefore less probability of congestion from ZONE1. The reverse is true when the demand rises in ZONE2 since more import are needed and hence the probability of congestion from ZONE1 increases.

The results in terms of marginal effects allow to directly quantify the impact of each regressor on the probability of congestion. Marginal effect coefficients indicate how the probability of an outcome increases when the regressor increases by a megawatt hours, all the other regressors kept at their average. For example, in CNOR-NORD pair the value of the coefficient associated to wind generation in CNOR indicates that a Mwh increase in generation raises by 0.024% the probability of a congestion from CNOR (ZONE1) to NORD (ZONE2).

We expect to observe results of opposite sign when we study congestion to ZONE1 (fourth and fifth columns). In this case the estimations unveil that: 1) In ZONE1:

• Rising wind production decreases the probability of congestion in all pairs with the exception of CNOR-CSUD (where the coefficient on wind is not significant) and of CSUD-SUD where a larger wind supply in ZONE1 seems to increase the congestion in entry;

• Rising solar production decreases the probability of congestion in CNOR-NORD, SARD-CSUD and SICI-SUD pairs (the coefficient is not significant in CNOR-CSUD and it is again positive in CSUD-SUD);

• Rising hydro production decreases the probability of congestion in CNOR-NORD and SICI-SUD pairs (in CNOR-CSUD the coefficient is not significant and its is positive in SARD-CSUD and CSUD-SUD);

• Rising the demand increases likelihood of congestion in all pairs;

2) In ZONE2:

• Rising wind production increases the probability of congestion in all pairs except in CNOR-NORD (where wind production in NORD seems to decrease congestion and in SARD-CSUD where the regressor is not significant);

• Rising solar production increases the probability of congestion in all pairs with the exception of SARD-CSUD;

• Rising hydro production increases the probability of congestion in all pairs;

• Rising the demand in ZONE2 decreases the probability of congestion in all pairs.

The results for congestion to ZONE1 seem to validate the conclusions drawn in the case of congestion from ZONE1. When ZONE1 is importing power, a larger renewable generation in ZONE1 reduces importing needs and thus the relative log odds of a congestion to ZONE1 with respect to no congestion. A larger RES production in ZONE2 on the contrary increases the likelihood of congestion to ZONE1 due to the improved export possibilities. An opposite pattern is again followed by the demand: when the demand is larger in ZONE1 there is more need to import, therefore a higher probability of congestion to ZONE1. Finally, when the demand rises in ZONE2 less production can be exported and as a consequence the probability of causing a congestion to ZONE1 decreases. In terms of marginal effect, we observe for instance that in CNOR-NORD pair the value of the coefficient associated to wind generation in CNOR indicates that a Mwh increase in generation decreases by 0.0065% the probability of a congestion to CNOR (ZONE1).

Thanks to the symmetry, the results can be easily summarized (Table 8). Increasing renewable production in a zone increases the likelihood of causing a congestion to the neighboring zone, due to larger export possibilities. At the same time, a larger local supply reduces import needs thus decreasing the likelihood of suffering congestion in entry. Increasing local demand has an opposite effect: it lowers export possibilities, thus decreasing the probability of causing congestion in exit, and it raises import needs, therefore increasing the probability of suffering congestion in entry. It is worthy to note that these results hold for both importing and exporting regions. However, the importing regions are less likely to produce congestion in exit and more likely to suffer congestion in entry. Therefore a larger RES production in these regions is expected to bring more balances in flows between regions, while a larger RES production in exporting zones may exacerbate the problem of congestion.

3 Stage least square model

Having understood the impact of RES production and demand on the probability of congestion, we extend our research to capture their effect on congestion cost. Congestion cost is paid by both IPEX participants and producers with bilateral contracts.

In the day-ahead market, all participants pay an implicit congestion cost (ICC) per Mwh of net electricity flow through GME17 , which is calculated based on the price difference

ZON E1 ZON E2 = P ZON E1 -P ZON E2 (2)
P ZON E1 is the zonal price of the importing zone and P ZON E2 is the zonal price of the exporting zone. The larger the difference between the zonal prices of the neighboring regions, the larger the implicit congestion cost is. Producers with bilateral contract pay instead an explicit congestion cost called Corrispettivo per l'assegnazione dei diritti di utilizzo della capacità di trasporto (CCT). The explicit congestion cost (CCT) per MWh is calculated as:

CCT zone = P zone -P U N ( 3
)
where PUN is the the National Single Price. If the bilateral producer is in an exporting region, the CCT is negative (P Zone < P U N ) meaning that the producer should pay the congestion cost. On the contrary, if the CCT is positive it is the network operator who pays the fee to the bilateral producer. The larger the difference between the zonal price and the PUN, the larger the CCT is.

Both CCT and ICC appear to be non-normally distributed based on Jarque-Bera test. In terms of level, CCT in SICI displays the highest positive mean followed by CCT in SARD. The positive mean values of CCT indicate that SICI and SARD are net importers since their zonal price is frequently higher than the PUN. It also shows that less-efficient production units are mainly utilized in these regions. CCT in SUD, on the other hand, registers the lowest mean followed by CSUD, CNOR and NORD. Hence, these zones have the most efficient and least-cost productions. In the case of the ICC, the means in SICI-SUD display the highest value with SARD-CSUD quite far behind. Hence, both transmission lines can be considered as the two most expensive lines in terms of congestion cost. They are frequently congested and only a small portion of efficient supply in the importing zone can be used for balancing the system. Transmission lines in CSUD-SUD, CNOR-NORD and CNOR-CSUD are ranked third, fourth and fifth from the most expensive transmission line, respectively.

Unlike in Woo et al. (2011), in order to to capture the Italian power exchange specificities we use 3SLS method instead of only OLS. In the first stage of 3SLS, we attempt to attack endogeneity problems of hydro in order to avoid bias in the estimation. Unlike renewable-energy supply, hydro production can be adjusted depending on the weather and portfolio optimization since it can be stored. In run-of-river hydro, a poundage is generally present for short term reserve whereas hydro with pumping technology is operated fully on the price arbitrage. 18 Hence, their output cannot be considered as fully exogenous varoanles. In this study, we use lagged hydro production as the instrument variables for hydro production. 19 We select t -1, t -24 and t -168 since the hydro production has daily seasonality, weekly seasonality and depends on the production of the hour before. Hence, our first-stage regression equation can be formulated as follows.

Ĥt = θ + η 1 H t-1 + η 2 H t-24 + η 3 H t-168
Where, Ĥt is the fitted value of hydro production at time t and θ is a constant.

Seemingly Unrelated Regressions (SUR) in the latter stage of our 3SLS is aimed to capture the impact of renewable while capturing the co-movement in multiple congestion costs. For the case of CCT, there are six congestion cost regressions, which represent each zone in Italy. In the case of ICC, there are five congestion cost regressions representing the connection between zones. For both CCT and ICC, each regression has the same general equation as follows.

y = α + i=23 i=1 δ i h i + τ T + β(D -Ĥ -R) + κ ĤR + e (4)
Where, y can be CCT or ICC, α is a constant, h is hourly dummy variables, T is the time trend, R, H, and D are vectors of renewable production, fitted hydro supply, and demand respectively. Then, β is a vector of coefficients of the difference between demand and renewables and κ is a vector of coefficients of the interaction between renewable and hydro. In the case of CCT, the vectors consist of production/consumption from the zone and the sum on rest of Italy. As for the ICC, the vectors consist only variables from ZONE1 (generally importing zone) and ZONE2 (generally exporting zone). 20 Statistics estimations of high frequency data such as hourly electricity price require extra care from researchers as heteroskedsticity in the regression could provide bias in error estimation. Therefore, the estimation of the 3SLS is done under Feasible Generalized Least Square (FGLS) method since it can optimize the coefficient estimations of SUR whose true covariance matrix is unknown. This method is going to be used for treating unknown form of heteroskdasticity and autocorrelation that may present in the SUR disturbances (see Greene (2012)).

3SLS Result

Before going into details of each estimations, we report the results the correlation errors matrix as a result of SUR estimation (see Table 9). First, let us look into the location effect on the two extreme, NORD and SICI. We may observe more negative correlation on the south direction (from top to bottom). Therefore, whenever CCT decreases in the NORD the CCT will increase in the south. This result can be explained as NORD is generally the main exporter and SICI is generally the main importer. Excessive supply from the North provides congestion, which subsequently creates lower zonal price and lower CCT. This excessive supply is transferred across Italy towards south. As a result, it increases the zonal price of the Southern region, which subsequently produces higher CCT in these zones. Second, big positive correlation is shown in the main connections especially, CNOR-CSUD and SARD-CSUD. This positive value indicates the same direction of increase/decrease in the CCT. This can only be explained if these zones are often converging into only one zone.

In the case of the ICC, the SUR estimation shows that the variables only have very small correlations as all of the values are close to zero (see table 10). Hence, the variables and regressions are close to perfectly independent or unrelated. However, residuals from CNORD-NORD and CSUD-SUD seems to have small correlation (> 0.1). In addition, the estimation results through OLS show inconsistent numerical coefficient compared to SUR. Therefore, the regressions can still be considered as one system, but with very small correlation between each other. • Coefficients of DHR ZON E suggest that increasing demand or lower renewable or hydro increase the congestion cost;

• SARD, SICI and SUD have the highest impact as it can increase the congestion cost for 0.0063 A C, 0.0062 A Cand 0.002 A Cper Mwh increase in demand (or decrease in renewable) ;

• HR ZON E imply that larger hydro coupled with larger renewable production could decrease the congestion cost much further towards negative value (NORD is the only exception);

• SICI, SARD and SUD have the highest impact as it can further decrease the congestion cost for 0.0001 A C, 0.0000064 A Cand 0.0000052 A Cper Mwh increase in renewable coupled with hydro;

2) outside the given zone:

• Coefficients of DHR Italy ZON E show that increasing demand or lower renewable or hydro decrease the congestion cost;

• Case of CSUD, NORD and SARD have the highest impact as it can decrease the congestion cost for 0.00044A C, 0.00036 A Cand 0.0003 A Cper Mwh increase in demand (or decrease in renewable) ;

• HR Italy ZON E suggest that larger hydro coupled with larger renewable in NORD, SICI and SARD could increase the congestion cost much further towards positive value (SUD, CSUD and CNOR display decreasing impact on congestion cost);

• Case of SICI and SARD have the highest impact as it can further increase the congestion cost for 0.000095 A Cand 0.000023 per 1000 Mwh increase in renewable and hydro. Our findings indicate that rising renewable or hydro production in the given zone will push CCT towards negative value.This is due to merit order effects that shift the supply function in the zonal market thus producing lower congestion cost. Then, pushing renewable penetration much further could dissipate the congestion , (CCT = 0), or continue to decrease the price, (CCT < 0), due to abundance efficient supply that saturate the lines. In the latter case, the given zone shift into an exporting zone (P ZON E < P U N ) and all producers in this zone are penalized from contributing to the congestion. The same output can be captured in the lower demand at the given zone. On the contrary, increasing renewable from outside the zone (rest of Italy) will trigger maximization of the transmission line, which subsequently increases the PUN price on the market equilibrium. As for the load, lower value from outside of the zone (rest of Italy) will shift the equilibrium thus producing lower PUN price. Consequently, it provides advantages for power producers as they may benefit higher rewards (P ZON E > P U N ) or lower congestion cost (P ZON E < P U N ))

In the case of ICC, we follow the intuition from Woo et al.(2011) analysis on the Texas electricity market where rising demand in non-west zones (fewer wind resource) and high wind output from west increases price difference. Hence, the results should provide a positive value on DHR ZON E1 while negative value DHR ZON E2. Our hypotheses are proven in our estimation displayed in table 12.

1) In importing zone (ZONE1):

• Coefficients of DHR ZON E suggest that increasing demand or lower renewable or hydro increase the congestion cost; • Importing zones of SICI-SUD , SARD-CSUD and CSUD-SUD have the highest impact as it can increase the congestion cost for 0.0351 A C, 0.013 A Cand 0.0029 A Cper Mwh increase in demand (or decrease in renewable); • HR ZON E imply that larger hydro coupled with larger renewable production could decrease the congestion cost much further towards negative value (Positive value is found in the case of CSUD-SUD and SARD-CSUD);

• Improting zones of SICI-SUD and CNOR-NORD have the highest impact as it can further decrease the congestion cost for 0.0001 A Cand 0.0000069 A Cper Mwh increase in renewable coupled with hydro;

2) In exporting zone (ZONE2):

• Coefficients of DHR ZON E show that increasing demand or lower renewable or hydro decrease the congestion cost (with the exception of SARD-CSUD);

• Exporting zones of SICI-SUD , CSUD-SUD and CNOR-CSUD have the highest impact as it can decrease the congestion cost for 0.005 A C, 0.0022 A Cand 0.0015 A Cper Mwh increase in demand (or decrease in renewable);

• HR ZON E suggest that larger hydro coupled with larger renewable in the exporting zone of CSUD-SUD, SICI-SUD and CNOR-CSUD could increase the congestion cost much further towards positive value (opposite behavior can be found on SARD-CSUD and CNOR-NORD); In comparison to Woo et al. (2011) and the case of CCT, identical mechanism can be implied from our estimations. Lower demand or higher renewable supply in the importing zone may push lower zonal equilibrium prices, which impact the congestion cost towards negative value. As in CCT, higher growth of renewable production could change the net flow condition, which can be either a less-saturated line resulted in the merge of both zones (ICC = 0) or change the directions of electricity (ICC < 0). The same idea can be preserved in low growth (or simply smaller) demand in the importing zone. The changes or shock towards negative value will result in excessive efficient supply or less saturated congestion. Therefore, the congestion cost could be negative (in the case of excessive supply) and zero (in the case of less saturation). The opposite behavior can be applied in the exporting zone. Larger renewable or hydro will occupy the transmission capacity and create a new zone, an importing zone. As a consequence, low efficiency units are called in the importing zone for balancing the system and the congestion cost (ICC) increases. We may sum and generalize our finding as the table 13. Due to the merit order effect, rising renewable (hydro or lower demand) decrease the price equilibrium and subsequently push the congestion cost towards negative value. Additional impact on the congestion cost may occur if larger renewable is coupled with larger hydro. This conclusion is applied for supplies from the importing zones (ZONE1), in the case of ICC, and both importing and exporting zones (ZONE), in the case of CCT. However, it is important to be noted that, continuous reduction of congestion cost will subsequently merge the zone (congestion cost = 0) since the transmission line is less saturated from the import. Hence, bigger shock may change the direction of the flow (congestion cost < 0) since there is excessive efficient supply needs to be transferred. On their respective counter flow zones (ZONE2 and Rest of Italy), an opposite behavior is observed as the merit order effect shift price equilibrium of ZONE2 and PUN. Moreover, additional impact is captured if big renewable is coupled with large hydro. Therefore, from the point of view of Terna, increase of renewable should be promoted in the importing zones as they tend to reduce the congestion cost in both CCT and ICC or create less saturated line (CCT = ICC = 0). Then, excessive growth in all the zones should be avoided in order to avoid excessive efficient supply that cause congestion.

Conclusion

Our empirical analysis has shown that demand and renewable supply have different impacts on the congestion occurrence and cost. The results of the multinomial logit model suggests that the effect of a larger local renewable supply is to decrease the probability of suffering congestion in entry and to increase the probability of causing a congestion in exit compared to no congestion case. Increasing hydroelectric production has a similar effect. A rise in local demand on the contrary increases the probability of congestion in entry (due to larger import) and decreases the probability of congestion in exit. This results holds for both importing and exporting regions. However, the importing regions are less likely to produce congestion in exit. Therefore a larger RES production in these regions is expected to bring more balances in flows between neighboring regions, while a larger RES production in exporting zones may exacerbate the problem of congestion. On the other hand, estimation on congestion cost suggest that lower demand and larger renewable shift the congestion cost towards negative. Then, additional decrease in congestion cost will be found if larger renewable is coupled with larger hydro. Therefore, continuous increase of renewable (negative shock in demand) may consolidate two neighboring zones into one or may change the direction of the electricity. This is particularly true in all the case of CCT and all importing zones on ICC. In their respective opposite direction of the counterflow (Rest of Italy and exporting zone), exact opposite impacts are displayed.

Both of our estimations allow us to draw some conclusion for policy construction.

• Additional incentive in the importing regions. Increase of renewable in the importing zones provides a more balanced system since it less likely to produce congestion in exit and reduces the odds for congestion in entry. In addition, both CCT and ICC could be reduced or dissipated as they shift the zonal price equilibrium towards negative value. Therefore, in the point of view of TSO and policy maker, further promotion of renewable growth in importing regions is recommended. In the current state, operators would prefer rising renewable in the exporting zones since they could profit from the high zonal price and congestion cost.

• Growth of intermittent supply should be controlled.

Although it is true that larger renewable decrease the congestion cost and reduce the frequency, bigger shock may provide an opposite effect. Rising renewable increase the odds for congestion in exit regardless of the zones and the estimation in congestion cost validate this phenomenon as continuous increase may change the net flow direction (congestion cost < 0). Hence, excessive growth will worsen the congestion problems.

• Identical behavior will occur in the large scale. If a larger scale market (e.g Europe) is done under the same algorithm and bidding zone system, similar behavior should be seen. For instance, high demand in importing countries (zones) will stimulate exports of efficient supply from the neighboring countries (zones), thus increasing the odds for congestion in entry and increase its cost. However, the market would require well-organised transmission management and detailed research on bidding zones since several TSO are involved.

There are several directions that can be pursued in order to capture the full picture of the electricity market. It is important to be noticed that there are few econometric studies in this line of research, thus future extensive studies may be needed to obtain better views on RES and congestion. Our paper assumes non-inference bids, which allow us to simplify the problem. Therefore, more research can be directed towards strategical bidding in the electricity market. With more renewable supply in the market, it is important to understand renewable impact on the thermal units' bids.

Appendix A Additional figures

Figure 1 :

 1 Figure 1: A stylized representation Source: Terna

Figure 2 :

 2 Figure 2: Italian geographical zones Source: Authors' elaboration on GME

Figure 4 :

 4 Figure 4: Pricing with dezoning

Figure 5 :

 5 Figure 5: Multiple congestion

Figure 7 :

 7 Figure 7: Average physical exchanges between the zones Source: Authors' elaboration on GME

Figure 8 :

 8 Figure 8: Series of zonal price differences, 2010-2014 Source: Authors' elaboration on GME

illustrate through an example how the inter zonal price mechanisms work under uniform auction rule without and with congestion respectively. Import 50 Mwh Plant 1 550 Mwh 20€/Mwh Plant 2 150 Mwh 30€/Mwh Zone A Demand 650 Mwh Transit 50 Mw Plant 3 200 Mwh 40€/Mwh Transmission capacity 100 Mwh Total dispatched supply Zone A 700 Mwh Zone B Demand 200 Mwh Total dispatched supply Zone B 150 Mwh PA=PB=40€/Mwh Export 50 Mwh Figure

  3: Pricing without dezoning[START_REF] O'mahoney | The merit-order effect of wind generation in the Irish electricity market[END_REF] The price will be in correspondence of the intersection of national demand and supply curves.

	Plant 1	Plant 2
	700 Mwh	150 Mwh
	20€/Mwh	30€/Mwh
		Import
		100 Mwh

Zone A Demand 650 Mwh Transit 100 Mw Plant 3 200 Mwh 40€/Mwh Transmission capacity 100 Mwh Total dispatched supply Zone A 850 Mwh Zone B Demand 200 Mwh Total dispatched supply Zone B 100 Mwh PA=30€/Mwh < PB=40€/Mwh Export 200 Mwh

Table 1 :

 1 We observe that congestion frequency has never gone below 82%, reaching a peak in 2013 with the network congested 93.6% of the time. The average number of sub-markets has slowly decreased from 2.416 to 2.28 between 2010 to 2012 to rise again in in 2013 and 2014. Congestion frequency

		Frequency Percentage Zonal divisions (Mean)	Std	N
	2010	7210	82.3%	2.416	0.809 8760
	2011	7403	84.5%	2.307	0.686 8760
	2012	7921	90.1%	2.240	0.531 8784
	2013	8205	93.6%	2.278	0.518 8760
	2014	8044	91.8%	2.284	0.543 8760

30 × 650) + (40 × 200) 650 + 200 = 32.35 period 2010-2014.

Source: Authors' elaboration on GME

Table 2 :

 2 Database summary

		Number of bids Number of units
	2010	6 975 701	976
	2011	7 149 431	1 257
	2012	7 090 579	1 281
	2013	7 737 633	1 152
	2014	8 086 282	1 189
		Source: Authors' elaboration on GME
	1. CNOR-NORD
	2. CNOR-CSUD
	3. SARD-CSUD
	4. CSUD-SUD

Table 3 :

 3 Estimations for CNOR-NORD

	VARIABLES	Congestion from CNOR	mfx	Congestion to CNOR	mfx
	CNOR Hydro	0.00221***	1.13e-05***	0.000395*	1.83e-05*
		-0.00046	-2.43E-06	-0.00021	-9.82E-06
	CNOR Wind	0.0287***	0.000148***	-0.00225	-0.00012
		-0.00511	-2.73E-05	-0.00204	-9.77E-05
	CNOR PV Tot	-0.00042	-2.10E-06	-0.00016	-7.29E-06
		-0.0012	-6.18E-06	-0.00025	-1.20E-05
	CNOR D	-0.00148***	-8.02e-06***	0.00160***	7.68e-05***
		-0.00016	-1.01E-06	-8.71E-05	-4.00E-06
	CSUD Hydro	-0.00199***	-1.05e-05***	0.000799***	3.87e-05***
		-0.00054	-2.84E-06	-0.00024	-1.16E-05
	CSUD Wind	-0.00479***	-2.56e-05***	0.00355***	0.000171***
		-0.00048	-2.52E-06	-0.00011	-5.18E-06
	CSUD PV Tot	-0.00186*	-9.80e-06*	0.000900***	4.36e-05***
		-0.00099	-5.11E-06	-0.00021	-1.01E-05
	CSUD D	0.00144***	7.78e-06***	-0.00150***	-7.21e-05***
		-0.00012	-7.98E-07	-6.52E-05	-2.93E-06
	Year2	-2.444***	-0.00759***	-0.636***	-0.0256***
		-0.248	-0.00062	-0.0724	-0.0025
	Year3	-0.338***	-0.00154***	-0.183**	-0.00827***
		-0.13	-0.00057	-0.0711	-0.00309
	Year4	0.0498	0.000375	-0.485***	-0.0205***
		-0.125	-0.00067	-0.0766	-0.00283
	Year5	-0.660***	-0.00262***	-1.531***	-0.0518***
		-0.169	-0.00065	-0.0904	-0.00224
	Constant	-5.299***		-1.382***
		-0.261		-0.125
	Observations	43,824	43,824	43,824	43,824
	Log-Lik Intercept Only:	-14607.4		Log-Lik Full Model:	-12640.4
	D(43798):	25280.78		LR(24):	3934.012
				Prob > LR:	0
	McFadden's R2:	0.135		McFadden's Adj R2:	0.133
	ML (Cox-Snell) R2:	0.086		Cragg-Uhler(Nagelkerke)	R2: 0.176
	Count R2:	0.913		Adj Count R2:	-0.008
	AIC:	0.578		AIC*n:	25332.78
	BIC:	-442829		BIC':	-3677.5
	BIC used by Stata:	25558.66		AIC used by Stata:	25332.78
	*** p<0.01
	** p<0.05
	* p<0.1

Table 4 :

 4 Estimations for CNOR-CSUD

	VARIABLES	Congestion from SARD	mfx	Congestion to SARD	mfx
	SARD Hydro	0.00309	1.50E-06	0.00215***	0.000121***
		-0.00268	-1.40E-06	-0.00057	-3.18E-05
	SARD Wind	0.00863***	4.66e-06***	-0.00999***	-0.000563***
		-0.0004	-9.63E-07	-0.00027	-1.39E-05
	SARD PV Tot	-0.00379	-1.82E-06	-0.00308***	-0.000173***
		-0.00283	-1.48E-06	-0.00109	-6.10E-05
	SARD D	-0.00016	-2.04E-07	0.00411***	0.000231***
		-0.0004	-2.13E-07	-0.00012	-6.80E-06
	CSUD Hydro	-0.00819***	-4.16e-06***	0.000441***	2.51e-05***
		-0.00058	-8.90E-07	-0.00015	-8.48E-06
	CSUD Wind	-0.00100**	-5.04e-07**	-0.0001	-5.80E-06
		-0.0004	-2.31E-07	-0.00017	-9.31E-06
	CSUD PV Tot	0.00296***	1.52e-06***	-0.000909***	-5.13e-05***
		-0.00066	-4.48E-07	-0.00024	-1.36E-05
	CSUD D	-0.000180**	-8.72e-08**	-0.000126***	-7.12e-06***
		-7.36E-05	-4.01E-08	-2.11E-05	-1.17E-06
	Year2	-4.913***	-0.00133***	-0.775***	-0.0360***
		-0.44	-0.00026	-0.0445	-0.00183
	Year3	-4.018***	-0.00108***	-1.897***	-0.0714***
		-0.208	-0.00022	-0.0655	-0.00223
	Year4	-8.216***	-0.00254***	-1.873***	-0.0706***
		-0.647	-0.00042	-0.0753	-0.00226
	Year5	-7.746***	-0.00235***	-0.745***	-0.0348***
		-0.654	-0.0004	-0.0629	-0.00254
	Constant	-0.635		-5.095***
		-0.433		-0.124
	Observations	43,824	43,824	43,824	43,824
	Log-Lik Intercept Only:	-21426.2		Log-Lik Full Model:	-16034.4
	D(43798):	32068.87		LR(24):	10783.6
				Prob > LR:	0
	McFadden's R2:	0.252		McFadden's Adj R2:	0.25
	ML (Cox-Snell) R2:	0.218		Cragg-Uhler(Nagelkerke) R2:	0.350
	Count R2:	0.845		Adj Count R2:	0.065
	AIC:	0.733		AIC*n:	32120.87
	BIC:	-436041		BIC':	-10527.1
	BIC used by Stata:	32346.76		AIC used by Stata:	32120.87
	*** p<0.01
	** p<0.05
	* p<0.1

Table 5 :

 5 Estimations for SARD-CSUD

	VARIABLES	Congestion from CSUD	mfx	Congestion to CSUD	mfx
	CSUD Hydro			0.00143***	0.000103***
				-0.00015	-1.05E-05
	CSUD Wind			0.00114***	8.23e-05***
				-0.00019	-1.36E-05
	CSUD PV Tot			0.000270**	1.94e-05**
				-0.00013	-9.20E-06
	CSUD D			0.00128***	9.20e-05***
				-3.32E-05	-2.43E-06
	SUD Hydro			0.000601***	4.33e-05***
				-0.00012	-8.85E-06
	SUD Wind			0.000706***	5.09e-05***
				-8.88E-05	-6.42E-06
	SUD PV Tot			0.00126***	9.09e-05***
				-7.93E-05	-5.79E-06
	SUD D			-0.00109***	-7.88e-05***
				-6.55E-05	-4.79E-06
	Year2			-0.558***	-0.0351***
				-0.0451	-0.00251
	Year3			-1.043***	-0.0591***
				-0.055	-0.00248
	Year4			-1.910***	-0.0925***
				-0.0697	-0.00248
	Year5			-1.405***	-0.0742***
				-0.077	-0.00305
	Constant			-6.927***
				-0.119
	Observations	0	0	43,824	43,824
	Log-Lik Intercept Only:	-18641.31		Log-Lik Full Model:	-14165.7
	D(43811):	28331.303		LR(12):	8951.316
				Prob > LR:	0
	McFadden's R2:	0.24		McFadden's Adj R2:	0.239
	ML (Cox-Snell) R2:	0.185		Cragg-Uhler(Nagelkerke)	R2: 0.322
	Count R2:	0.859		Adj Count R2:	0.067
	AIC:	0.647		AIC*n:	28357.3
	BIC:	-439917.9		BIC':	-8823.06
	BIC used by Stata:	28470.246		AIC used by Stata:	28357.3
	*** p<0.01
	** p<0.05
	* p<0.1

Table 6 :

 6 Estimations for CSUD-SUD

	VARIABLES	Congestion from SICI	mfx	Congestion to SICI	mfx
	SICI Hydro	0.0109***	0.000402***	-0.0118***	-0.00146***
		-0.0019	-3.68E-05	-1.31E-03	-0.000136
	SICI Wind	0.00198***	0.000133***	-0.00570***	-0.000652***
		-0.000126	-4.53E-06	-9.30E-05	-1.05E-05
	SICI PV Tot	0.000965**	0.000111***	-0.00553***	-0.000616***
		-0.000484	-9.54E-06	-2.83E-04	-2.98E-05
	SICI D	-0.00169***	-0.000114***	0.00487***	0.000557***
		-0.000181	-4.68E-06	-1.16E-04	-1.25E-05
	SUD Hydro	-0.00132***	-3.81e-05***	0.000793***	0.000108***
		-0.000193	-3.64E-06	-1.16E-04	-1.20E-05
	SUD Wind	-0.000754***	-2.17e-05***	0.000447***	6.11e-05***
		-8.59E-05	-1.71E-06	-5.08E-05	-5.34E-06
	SUD PV Tot	-0.000643***	-3.00e-05***	0.00106***	0.000126***
		-0.000173	-3.32E-06	-9.74E-05	-1.02E-05
	SUD D	0.000722***	2.03e-05***	-0.000397***	-5.52e-05***
		-0.000127	-2.43E-06	-7.89E-05	-8.17E-06
	Year2	0.0642	-0.00661***	0.503***	0.0479***
		-0.0605	-0.000968	-0.0461	-0.00385
	Year3	0.195***	-0.0205***	1.942***	0.142***
		-0.0747	-0.000951	-0.0552	-0.003
	Year4	-0.772***	-0.0359***	3.014***	0.198***
		-0.105	-0.00135	-0.0649	-0.0032
	Year5	-0.708***	-0.0399***	3.700***	0.226***
		-0.105	-0.0015	-0.0709	-0.00346
	Constant	0.333**		-7.747***
		-0.152		-0.118
	Observations	43,824	43,824	43,824	43,824
	Log-Lik Intercept Only:	-31133.193		Log-Lik Full Model:	-20880.1
	D(43798):	41760.267		LR(24):	20506.12
				Prob > LR:	0
	McFadden's R2:	0.329		McFadden's Adj R2:	0.328
	ML (Cox-Snell) R2:	0.374		Cragg-Uhler(Nagelkerke)R2:	0.493
	Count R2:	0.812		Adj Count R2:	0.253
	AIC:	0.954		AIC*n:	41812.27
	BIC:	-426349.993		BIC':	-20249.6
	BIC used by Stata:	42038.154		AIC used by Stata:	41812.27
	*** p<0.01
	** p<0.05
	* p<0.1

Table 7 :

 7 Estimation for SICI-SUD

Table 8 :

 8 Multinomial logit result summary

		Wind	PV	Hydro	Demand
	Congestion from ZONE1	ZONE1: ↑ ZONE2: ↓	ZONE1: ↑ ZONE2: ↓	ZONE1: ↑ ZONE2: ↓	ZONE1: ↓ ZONE2: ↑
	Congestion to ZONE1	ZONE1: ↓ ZONE2: ↑	ZONE1: ↓ ZONE2: ↑	ZONE1: ↓ ZONE2: ↑	ZONE1: ↑ ZONE2: ↓

Table 9 :

 9 Error correlation matrix CCT

		NORD	CNOR	CSUD	SARD	SUD	SICI
		1	0.081	-0.371	-0.375	-0.356	-0.601	NORD
			1	0.414	-0.175	0.04	-0.449	CNOR
				1	-0.002	0.412	-0.298	CSUD
					1	-0.067	-0.05	SARD
						1	-0.094	SUD
							1	SICI
	CNOR NORD	CNOR CSUD	SARD CSUD	CSUD SUD	SICI SUD
	1		0.056		-0.042	0.166		-0.048	CNOR NORD
			1		0.022	-0.089		0.038	CNOR CSUD
					1	-0.012		0.052	SARD CSUD
						1		0.077	CSUD SUD
								1	SICI SUD

Table 10 :

 10 Error correlation matrix of ICC Tthe estimation of CCT shown in table 11 below. The coefficients of DHR ZON E and HR ZON E provide us further insight into the market mechanism.

	1) In a given zone:

Table 11 :

 11 Estimation result of explicit congestion cost (CCT)

•

  Exporting zones of SICI-SUD and CNOR-CSUD have the highest impact as it can increase the congestion cost for 0.0000057 A Cand 0.0000026 A Cper Mwh increase in renewable coupled with hydro.

	Dependent	variable:	CNOR NORD	Dependent	variable:	CSUD SUD
		Coefficient	p-value		Coefficient	p-value
	DHRhat CNOR	0.00260924	***	DHRhat CSUD	0.00297075	***
	DHRhat NORD	-0.00040112	***	DHRhat SUD	-0.00220462	***
	HRhat CNOR	-6.93E-06	***	HRhat CSUD	6.60E-06	***
	HRhat NORD	-2.63E-08	***	HRhat SUD	1.08E-06	***
	SSR	1600184		SSR	2668576
	S.E of regression	6.054287		S.E of regression	7.818399
	R-squared	0.097813		R-squared	0.125821
	Dependent	variable:	CNOR CSUD	Dependent	variable:	SICI SUD
		Coefficient	p-value		Coefficient	p-value
	DHRhat CNOR	0.00161601	***	DHRhat SICI	0.0351641	***
	DHRhat CSUD	-0.0015499	***	DHRhat SUD	-0.00562983	***
	HRhat CNOR	-3.73E-06	***	HRhat SICI	-0.00010442	***
	HRhat CSUD	2.60E-06	***	HRhat SUD	5.70E-06	***
	SSR	1525946		SSR	37992233
	S.E of regression	5.91218		S.E of regression	29.50023
	R-squared	0.037288		R-squared	0.312282
	Dependent	variable:	SARD CSUD
		Coefficient	p-value
	DHRhat SARD	0.0136536	***
	DHRhat CSUD	0.00111795	***
	HRhat SARD	0.00017197	***
	HRhat CSUD	-4.75E-06	***
	SSR	26731158
	S.E of regression	24.74497
	R-squared	0.061857
	*P-value < 10%
	**P-value < 5%
	***P-value < 1%

Table 12 :

 12 Estimation result of implicit congestion cost

Table 13 :

 13 3SLS result summary

Table 15 :

 15 Accepted quantity in the day-ahed market by source (Twh), 2010-2014 Network status in neigbouring regions, 2010-2014 Congestion from = first region has lower price No congestion = Equal prices Congestion to= first region has higher price

	100%	5.8	4.1			3.0	3.3	3.6
	80% 90%	59.5	59.6		74.1	91.4	100.9
		30.6	30.1
	60% 70%	24.4	29.3		29 32.3	29.3	30.5
	50%						26.2	25
	30% 40%	149.6	138.5		113.8	92.5	75.1
	20%
	10%	48.8	49.9		46.5	46.5	46.9
	0%	2010	2011			2012	2013	2014
		Import	CCGT	Coal	Other	Renewable resource	Pumping
	Figure 9: Source: Authors' elaboration on GME

In terms of marginal cost.

The same analysis can be applied to market coupling.

A strictly positive paired-price difference occurs when the West price is lower than the price in the other zones and vice-versa, meaning that the congestion is "coming from the West".

GME, Annual Report 2014.

The difference between these two type of congestion costs will be described in the following sections.

We will explain the difference between explicit and implicit congestion cost in the section 4.

For each day and each offer/bid point, a maximum of 24 bids/offers may be submitted. Three types of offer/bid exist: simple, consisting of a pair of values indicating the volume of electricity offered/bid in the market by a market participant and the price for a given hour; multiple, consisting of the division of an overall volume offered/bid in the market by the identicle market participant for the same hour; pre-defined, consisting of simple or multiple offers/bids, which are daily submitted to GME (GME).

CSUD-SUD pair is characterized by the occurrence of only two outcomes; in this case we estimate a logit model with a binary dependent variable.

Standard errors are reported below the coefficients.

Note that in CSUD-SUD pair there are never negative price differences.

Terna receives congestion cost payment from the offset of purchase and sales value of GME. Hence, they implicitly charge operational cost of managing transmission line to all stakeholders in IPEX.

Note that pumping hydro is not considered renewable as run-of-river hydro.

Due to lower frequency in weather data, we avoid using weather in our instrument variables.

Constant, time trend, and hourly dummy variables are deterministic trend with a purpose of avoiding spurious regression. We run stationarity test on the regressors in order to determine these deterministic trend. The stationarity test can be found in Appendix D.

Acknowledgments

Silvia Concettini gratefully acknowledges the financial support from the ANR -Investissements d'avenir (ANR-11-IDEX-0003-02) and from the chair Energy and Prosperity at Fondation du Risque (ILB).

Stationary test is required in order to avoid spurious regression. In addition, the test is also necessary to identify the deterministic trend in our data and builds our regression equation. We use augmented Dickey fuller (ADF) test in this study. The result of our test can be seen in the table 17 and 18. The initial test uses the equation below for the first identification.

Where, z t is the value of variable z at time t, z t-1 is the value of variable z at time t -1, Γ is a constant, T is the time trend. The null hypothesis is that λ = 1, and the data can be concluded as non-stationary under constant and trend. The results show that most of our data was already statistically stationary in constant and trend. Demand from all the zones and all dependent variables are stationary under this setting. Unfortunately, hydro productions and renewable supply are still not stationary. Therefore, seasonality trends are needed to be added in the regression. Another test is applied with a regression as follows.

It can be observed that we add hourly dummies for capturing the seasonality trend since we have the data under this frequency. We also omitted dummies for the hour 24 in order to avoid multicollinearity in regression. The results show that our data are stationary under constant, time and seasonal trend for both dependent variables and regressors. Hence, these three important features need to be integrated in our regression equations in order to de-trend the data.


Nadia Achour Renault

Fodil Meraghni
email: fodil.meraghni@ensam.eu

Laurent Peltier

Joseph Fitoussi

MICROSTRUCTURAL AND EXPERIMENTAL ANALYSIS OF STRAIN RATE EFFECT FOR SHORT GLASS FIBER REINFORCED POLYPROPYLENE

The scope of this work is to study glass ber reinforced polypropylene composites in low and high speed loading test in tension, interrupted tension, bi tension and shear. This kind of composites are commonly used in automotive industry, for structural components, in particular due to their capability to absorb energy. This study investigates the strain rate eect and the damage

evolution on glass ber reinforced PP. The glass ber content of the material varies in the range between 0 and 60wt%. High speed loadings have been performed from quasi static to 300s -1 strain rate. To minimize the amplitude of measurement perturbation, the specimen dimension geometry has been optimized by experimental-computational procedure using nite element modeling developed for the four load case. High speed tests are conducted on a servo-hydraulic machine. A high speed camera allows to follow the deformation of the surface of specimens. Strain evolutions are determined by contact-less measurements (Digital Image Correlation and labels following).

Damage evolution is analyzed at microscopic and macroscopic scale: the inuence of micro-structure and strain rate on the damage mechanisms is studied. The material micro-structure specic to the injection molding process and damage mechanisms are analyzed by both SEM and X-ray tomography observations.Moreover, they are correlated with the macroscopic response of the composites submitted to high speed loadings mentioned above.

introduction

Polymeric composite are more and more used in automotive industry [START_REF] Brooks | Composites in automotive application design[END_REF] .One of the main challenges for these materials is to increase the energy absorption properties of the structural components to ensure the integrity of the cockpit and passenger safety [START_REF] Ramakrishna | Microstructural design of composite materials for crashworthy applications[END_REF]. It is in this context of innovation that the study of the mechanical behavior of composite materials and structures under moderate dynamic loads becomes essential. Indeed, there is an increased need for constitutive laws that can integrate physical degradation kinetics dynamics and variability of the micro-structure of these materials. This work, wich is the rst step of the development of a micromechanical model based on Mori Tanaka scheme, is a detailed analysis of the micro-structure and the experimental behavior of the composite both under static and moderated dynamic loading cases. Thus this manuscript is structured as follows : rst the material and it's micro-structure analyzed by X-ray Tomography will be presented. Then, an overview of the tensile tests at low and high speed loading results is presented and the last part demonstrates the equi bi tensile test at low speed.

Micro-structural analysis

The material studied here is an injection molded composite composed by polypropylene and short bers. The composite glass transition temperature is Tg=0 • C. The particular structureof 3 layershas been obtained by the injection process and it is shown by X-ray tomography on gure 2.In order to analyze the eect of ber concentration in the micro-structure,experimental study is performed in polypropylene with 0% (PPGF00), 30% (PPGF30), 40%(PPGF40) and 60% (PPGF60) of glass ber. The X ray tomography allowed us to evaluate the spatial disposition of glass ber in the composite. As we can see in the gure 1 for the PPGF40 the bers are more concentrated in the middle layer. The size of this layer is dependent on the total ber concentration in the material : for the PPGF60 the middle layer size corresponds to 35%of the total size while the middle layer of PPGF40 corresponds to 20% of the total size. geometry is optimized by experimental-computational procedure using nite element modeling presented here after.

Specimen optimization

The aim of this optimization is to obtain the specimen dimensions which allow to reach a strain rate of 150 to 200s -1 and minimize the amplitude of measurement perturbation. The methodology used here, developed by [START_REF] Fitoussi | Experimental methodology for high strain-rates tensile behaviour analysis of polymer matrix composites[END_REF], [START_REF] Jendli | Multi-scales modelling of dynamic behaviour for discontinuous bre SMC composites[END_REF], consist in both using a damping joint to limit the shock waves and simulating the experimental tests by varying the specimen geometry in order to reach a uniform deformation of 200s -1 in the specimen central area.

High speed tensile tests

High speed tensile tests are conducted on a servo-hydraulic machine. A high speed camera allows to follow the deformation of the surface of speci- As we can see on the example of σε diagram from the gure 3, the overall behavior of the glass ber reinforced polypropylene vary signicantly compared to low strain rate loading. The young modulus is calculated by linear regression on the linear part of the curves. Strain rate has an important inuence on the composite behavior, in particular on the composite Young Figure 3: PPGF40 behavior at dierent strain rates on ow direction modulus, the maximum stress and the threshold stress between the linear and non linear regions. In the case of the pure matrix ( PPGF00) the strain rate increase involves an increase of the yielding and a decrease of the ultimate strain. The ultimate stress is also expanded but the Young modulus is not aected by the strain rate.

Figure 4 shows the evolution of Young modulus for glass ber reinforced polypropylene at 40% . One can observe that when the strain rate increases, the Young modulus increases. The total response is characterized by two steps : a low increase until a strain rate deformation of 80s -1 and a high one above the a strain rate of 80s -1 .

Figure 5 shows the constraints thresholds evolution for glass ber reinforced polypropylene at 40% . One can observe that the strain rate has the same eect as in the case of the Young modulus : the constraint thresholds increase with the strain rate and this increase is more signicant for strain rates above 80s -1 .

Experimental bi-tensile test

Bi-tensile test have been conduced on the glass ber reinforced polypropylene at 30%, 40%, and 60%. Tests have been driven in displacement. a stretching of 0.1mm.s -1 is imposed in the two directions. the deformation eld evolution is presented in gure 6. evolution is even more pronounced when the strain rate exceeds 80s -1 . Finally, bi-tensile tests have been conduced on composite at two strain rates to evaluate the material behavior in a multidirectional case. These results will allow us to develop, identify and validate a micro-mechanical model based on Mori-Tanaka homogenization scheme.

Figure 1 :

 1 Figure 1: ber concentration in volume through the thickness

Figure 2 :

 2 Figure 2: micro-structure of PPGF40 observed on X-ray tomography

Figure 4 :Figure 5 :

 45 Figure 4: evolution of the Young modulus with the strain rate for the PPGF40 on the ow direction

Figure 6 :

 6 Figure 6: evolution of the deformation eld in a biaxial tensile test for the PPGF40