Rodica Loisel
email: rodica.loisel@univ-nantes.fr

David Shropshire

Christian Thiel

Arnaud Mercier

Flexibility assessment in nuclear energy dominated systems with increased wind energy shares

Keywords: power plants dispatching, flexibility, wind, nuclear, ramping rates

This study analyses the system integration of wind energy in terms of load balancing and power plants scheduling. The case study is the French power system, which relies on high rates of nuclear power, representing 78% in the total generation (2008). The study evaluates the ability of nuclear reactors to follow the load under several configurations of power plants in 2030 with at least 28 GW wind power representing 11% in the total generation. A dynamic optimization dispatching model is built with a detailed discretetime formulation under the nuclear power ramp up and down constraints. Results show that operating the French power system with high infeed of wind power by 2030 seems technically feasible but relies heavily on the capacity of nuclear reactors to follow variations, on energy storage to insure flexibility and on the market capacity to allow generators to adapt continuously to the demand. Simulations show that balancing the wind power variation is less a matter of installing more flexible capacities, as load factors might decrease and reduce the investors' interest when prices are relatively low. Balancing becomes more an issue of ramping rates and unit scheduling, power market regulation and real-time market interactions with the day-ahead and intra-day markets.

Introduction

The deployment of renewable energy in the European Union has led to substantial wind power generation, but has also increased the concern over the reliability of power systems [START_REF]Offshore Wind Energy: Action needed to deliver on the Energy Policy Objectives for 2020 and beyond[END_REF]. According to estimations of the Global Wind Energy Council, France has the second largest wind potential among Member States. The installed wind capacity is planned to increase from the current 7 GW (2012) 1 to about 25 GW by 2020 [START_REF] Nrap | National action plan for the promotion of renewable energies[END_REF], out of which the on-shore wind potential is documented to reach 19 GW by 2020 at 24% load factor, and off-shore wind power 6 GW at 34% load factor.

One of the issues raised by the wind power integration concerns the flexibility requirements [START_REF] Gross | The Costs and Impacts of Intermittency: An assessment of the evidence on the costs and impacts of intermittent generation on the British electricity network[END_REF][START_REF] Purvins | Challenges and options for a large wind power uptake by the European electricity system[END_REF]. The literature is rich with computer models simulating power systems with increased wind power levels ( [START_REF] Traber | Gone with the wind? -Electricity market prices and incentives to invest in thermal power plants under increasing wind energy supply[END_REF]- [START_REF] Pandzic | Virtual power plant mid-term dispatch optimization[END_REF]). This study gives an original appreciation of the flexibility in systems with a large composition of must-run power plants by analysing the system capability to involve quick-starting and long-lasting capacities on three market segments: the day-ahead market, the positive and the negative regulation markets.

The literature documents different approaches to define reserve requirements. Holttinen [START_REF] Holttinen | Impact of hourly wind power variations on the system operation in the Nordic countries[END_REF] shows that wind power variations require additional reserves similarly to the demand variation. Goransson and Johnsson [START_REF] Goransson | Dispatch modeling of a regional power generation system -Integrating wind power[END_REF] take into account the forecast errors for wind power only when determining the size of the secondary reserve. While Maurer et al. [START_REF] Maurer | Dimensioning of secondary and tertiary control reserve by probabilistic methods[END_REF] suggests that all variable renewable forecast errors could be balanced through tertiary reserves, because the wind power is forecasted at the same time frame as the tertiary control can be activated. In this study, it is assumed that the balancing of wind fluctuations occurs mostly on the day-ahead market. The issue raised is how the variability of increased wind power will interact with the nuclear power generation, considering the high share in the French electricity generation (78% in 2008), and projections for increased rigidity due to fewer reactors and projected higher utilization rates in the long-term levels [START_REF]EU Energy Trends to[END_REF].

Despite the March 2011 Fukushima accident and adverse public perception of nuclear safety, globally today the nuclear power is back on the policy agendas of many countries, with projections for new nuclear power plants [START_REF] Linares | The economics of new nuclear power plants in liberalized electricity markets[END_REF]. The main drivers are an increasing energy demand, the concerns over the climate change, the security of supply and the dependence on imports of fossil fuels. Most of the studies show that the nuclear energy is the most cost-effective over the base-load technologies [START_REF] Shropshire | Advanced Fuel Cycle Cost Basis[END_REF], while some others show that in liberalized markets, the cost competitiveness of nuclear power is questionable [START_REF] Linares | The economics of new nuclear power plants in liberalized electricity markets[END_REF].

This study analyses the nuclear power from a different perspective, and estimates the flexibility capability of nuclear power reactors installed within the French power system. Their capacity to follow the load variations is tested in a system built by 2030 with a short-term perspective from intra-year to an intra-hour timeline. A cost minimization linear programming model has been developed to this aim. Section 2 describes the study case and the methodological approach. Section 3 presents the model results with focus on the dispatching of power plants. Section 4 concludes on the possible solutions to balance the wind variability.

Case study framework

Case definition

The French power system is the second largest in Europe, after the German system. The technological mix in 2008 is mainly composed of nuclear power, providing about 78% of the electricity generation, hydropower (10%), coal (4%), gas (4%), oil (1.1%), wind (1%), and biomass (less than 1%). 2 The share of renewables attains 12% of the French electricity generation in 2008 and is planned to increase to 23% by 2020 [START_REF] Nrap | National action plan for the promotion of renewable energies[END_REF]. France imports nearly all of its oil, gas and coal, but has a good diversification of its import sources [12]. France benefits from a strategic geographical location in Western Europe and from significant cross-border connections with neighboring countries (15.75 GW).

The perspective of future trends in the French market are closely related to the European power market evolution, such as the decentralization in generation, the reduction of overcapacity and the ageing infrastructure. Particular challenges are related to structural imbalances for meeting the peak demand. The country exports mostly baseload electricity and imports mainly during peak periods. The French government intends to enhance the flexibility of the power grids and to boost investments in peaking capacity.

The nuclear power capacity will decline between 2020 and 2030 due to ageing, with some possibilities to extend the lifetime for some of the reactors. New projects are planned or under construction for European Pressurized Water Reactors (EPRs) and for generation IV reactors beyond 2040 [12]. The nuclear reactors currently used in France are the Pressurized Water Reactor type, which is flexible to some extent and has good technical capacities for load-following [13]. The interaction with higher wind power generation will depend however on technical constraints in providing more flexibility but also on the market design to schedule power plants.

Model description

A power plant dispatching model is designed to describe the power generators in the French system. The model, called Dispa-SET, is developed within Joint Research Centre in the context of activities undertaken under the Strategic Energy Technology Plan.

Two scenarios are built for 2030 around the issue: how much gas-fired capacities would be installed as back-up for the wind variability? The first scenario is based on installed capacity data from EC [START_REF]EU Energy Trends to[END_REF] and a second one assumes lower gas-fired capacities such as the utilization rate would result in higher profits and increased interest for investors.

A description of the model can be found in Loisel [START_REF] Loisel | Power system flexibility with electricity storage technologies: a technical-economic assessment of Compressed Air Energy Storage[END_REF], with an application to the energy storage business model, for compressed air energy storage applications. The model is based on linear programming, and it is implemented in GAMS. 3 The program minimizes the annual system costs of operating power generators. The model aggregates major generator types into representative technologies (see Table 1). For a given amount of installed capacity, the model determines the most cost-efficient combination of technologies that meets the demand under the system constraints.

Fixed demands accounting for the national supply and the power export flows are addressed on the spot market and regulated markets. Imports are instead adjusted by the model, as a function of fixed import power prices and system needs to fill the gap between supply and demand. Dynamic principles describe the system over one year, with (8784×2) time slices, according to the French balancing mechanism setting imbalance prices and volumes every half-hour. Note. The figures highlighted show the differences between the two scenarios built for 2030.

The objective function is the sum of variable costs only, such as the fuel costs, the carbon price, the variable O&M costs and the import price. Technical constraints are minimum operational loads, the ramping restrictions and the maximum load factors. Minimum operational levels are introduced to avoid high start-up and shut-down costs for nuclear and coal-fired power plants; they can also define power purchase obligations (biomass) and the lack of heat demand for CHP.

Ramping rates define the output variation between two periods, from one half-hour to the next one. They are defined for nuclear and coal-fired plants only. Maximum load factors define the maximum use of a technology due to decreased demand, load-following, limited natural resource inflow or the power plant unavailability during maintenance. The maximum load factors reported in Table 1 are calibrated against the data on the capacity installed and the power generation by technology [START_REF]EU Energy Trends to[END_REF]. These rates increase by 2030 due to technological progress for both wind power and solar power. The values for annual load factors shown in Table 2 are instead the outputs of the model, simulated under the constraint of the above maximum load factor rates.

The model baselines the power system in the reference year 2008. The database is composed of real half-hour figures for power demand and of hourly data for exportimport flows and hydro power generation. 4 Technology description is documented by Eurostat 5 and EC [START_REF]EU Energy Trends to[END_REF] for installed capacities, and by SETIS databases for operational parameters 6 . Calibration of ramping rates for nuclear power is based on the real data 7 on hourly generation in 2008, with a maximum ramp-up of the generation of 6% and a rampdown of -6% per half-hour. Despite this wide range, more than 90% of the number of events of nuclear power variation concentrates within the range of variation of -1% and +1% of the power recorded in the previous half-hour. For 2030, the EC scenario is using higher load factors (90%) for lower installed capacity than in 2008 (57.7 GW against 63.4 GW); thus, a decrease in ramping capability is assumed, i.e. ± 1% in 2030. The storage capacity installed stands for pumped hydropower storage applications, thereby having an efficiency rate as high as 81% [START_REF]European Commission, Electricity Storage in the Power Sector in Technology Map of the SET-Plan[END_REF].

Scenarios description

Three scenarios are built, one for 2008 and two for 2030.

The reference scenario, EC 2030, is documented by EC projections (the scenario called Reference [START_REF]EU Energy Trends to[END_REF]). The model assumes a demand increase from 490 TWh in 2008 to 598 TWh in 2030, with equivalent load spreads between the two periods. The EC projection does not integrate any demand side control measures nor the development of electrical vehicles which could change the demand curve [START_REF] Thiel | Cost and CO2 aspects of future vehicle options in Europe under new energy policy scenarios[END_REF]. France exports mostly baseload electricity, but with the decrease in the nuclear power in the future, exports will be reduced and imports will become more significant. Additional cross-border capacities are planned to enhance trade with Spain, Italy and Belgium, and to alleviate also transmission congestion. The model assumes an increase in capacities from the current 15750 MW to 20000 MW by 2030. The model assumes that exports decrease by 20% in 2030. Imports are adjusted by the model, driven by the gap between demand and supply, and by price difference between countries.

Most
The alternative case, GAS 2030, assumes a lower amount of installed capacity for combined-cycle gas turbines (CCGT) and natural gas gas-turbines (NGGT). The generation by technology remains unchanged compared to the scenario EC 2030, and the only difference is in the use of the gas-fired units, which results in higher load factors and increased profits for investors. The case GAS 2030 assumes an operation of 3500 hours for CCGT and 1700 hours for NGGT, which improves the economic attractiveness. In the scenario EC 2030, the load factors of gas-fired plants are about 10% for CCGT and 0.7% for NGGT (see Table 1), corresponding roughly to respectively 880 and 60 full hours of operation. The scenario GAS 2030 is representing the case of decentralization in generation and reduced installed capacities.

The model has a perfect forecast of the wind profile, substantiated by further progress in wind forecast techniques [START_REF] Ewea | Large scale integration of wind energy in the European power supply: analysis, issues and recommendations[END_REF]. Since the wind power fluctuations are perfectly foreseeable, balancing of wind fluctuations occurs on the day-ahead market.

Sensitivity scenarios test the system flexibility by assuming forecast errors, and alternatively, a lack of wind during one week.

Results and discussion

Power plants dispatching in 2030

Table 2 presents the main results, here aggregated, such as the annual generation and annual load factors. The technology energy mix obtained for 2030 is composed of similar shares in the total power generation between the two scenarios EC 2030 and GAS 2030 for nuclear 70%, hydro 9%, coal 1%, oil 0.1%, wind 11%, solar 2% and biomass 1%.

Harder constraints set on gas-fired capacity in the scenario GAS 2030 enhance a different dispatching of power plants, which results in less gas and more CHP-based power generation, as well as in additional storage discharge. More power is generated in the GAS 2030 case, in order to fuel the storage plants and to offer the missing flexibility due to lower gas capacity. Consequently, the load factor of storage technologies increases by 13% between the two cases.

In both scenarios, the wind power generation amounts to 70 TWh in 2030 for an installed capacity of 28 GW, and represents 11% of the total power generation. Wind curtailment occurs occasionally when ramping rates and minimum operational levels constrain the system, but attains low levels over the year (1.3 GWh in the EC 2030 case and 4.9 GWh in GAS 2030 case). In the case GAS 2030, the wind curtailment occurs eight times during the year and enhances a decrease in the spot price down to zero during those events. The low number of curtailments implies that the system is flexible enough to integrate the wind power, due mainly to the nuclear power capacity to follow the load and to the flexibility of hydro and gas-fired technologies. The scenario EC 2030 records low load factor rates of gas-fired units, which would decrease the profit rate of investors and their incentives to keep operational the entire capacity. To reverse this trend, the scenario Gas 2030 assumes a lower installed gas-fired capacity, which would improve the use and the economics of gas-based units. In support to the scenario EC 2030, it is to be noticed that by 2016, France will develop a market that would compensate utilities for maintaining spare capacity; this would prevent the retirement of unprofitable power stations with low load factors due to increased renewable installed capacities [25].

Assessment of system flexibility in 2030

The French nuclear power, with an annual load factor of ~78%, is low when compared with other countries † , due to the French specificity to use the nuclear power for baseload, semi-base load and regulation provision. Some 46 nuclear power units are endowed with additional control rods for load-following [START_REF] Pouret | Is nuclear power inflexible?[END_REF] which allow nuclear to play a more active role in providing regulation [13].

Figure 1 illustrates the dispatching of power plants during the 48 half-hour time slices of a weekday (Friday) with a significant wind power infeed. The favourable position of wind power in the merit order curve makes the system dispatch the wind-based technology in substitution to other alternatives. The nuclear power is crowded out during low demand at the beginning of the day with a pace within the range ±1% between two half-hour time slices. With the demand increase during that day, the nuclear power is triggered up. Until it gets full power, other options are activated, such as gas-fired units and storage discharge. At noon, PV power generation replaces these peak technologies. In Table 3, real data recorded for nuclear power in 2008 shows that the average is of 172 MWh/2 for the downward variation and of 190 MWh/2 for the upward variation. These levels increase by 2030 in EC 2030 case to 468 MWh/2 and to 461 MWh/2 respectively. For ramping rates higher than the level chosen for these simulations (1%), the wind power variability would increase the speed of up-and down-ward variations as shown in the Annexe 1. For instance, if ramp rates are allowed to be lifted toward ± 6%, the number of ramping events becomes more distributed over the range of ramp rates (i.e., 0-1%, 1-2%, etc.). This implies that a more flexible nuclear system would be more effective at reducing the variable supply. By 2030, the load factor increase from 78% in 2008 to 90% which enhances a less flexible nuclear power system. Figure 2 shows the system reaction during the same day as in Fig

Model

In theory, an EPR can load-follow within the range of 25-100% of nominal full power and is designed to change load at most twice per day. A ramping rate of 5% per minute going from 25 to 100% of capacity can be achieved, but would be limited to 100 cycles a year [START_REF] Areva | Preconstruction Safety Report[END_REF].

In practice, data might be different at a system level compared to individual technology parameters. If some equipment can increase their power very quickly, in practice, grid codes and economic costs induce lower ramp rates than the technical potential. In this model, ramping operations are not monetized, while in practice additional monitoring and mechanical constraints would be expected to increase plant costs [START_REF] Pouret | Is nuclear power inflexible?[END_REF]. Technical aspects add to factors reducing the load factor, since the load-following induces more frequent maintenance and reduces the availability of nuclear power plants. This induces a cost in the system, due to accumulated damages from erosion, fatigue and corrosion, and to additional operations such as the treatment and the discharge of the water. Therefore the ramping rates in this model are fixed at ±1% by 2030, taking into account the statistics of nuclear power in the base year, the results simulations for different ramping values and the high load factor recorded in 2030.

From a market perspective, the power price is set at the equilibrium between supply and demand and is the marginal cost of producing the last unit of electricity. Table 4 shows the prices obtained with the model on spot and reserve power markets. The GAS 2030 scenario shows a lower price on average (23 €/MWh in GAS 2030 against 29 €/MWh in EC 2030 case) due to lower gas consumption (-28%). The power price is based on variable operational cost and on fuel costs, therefore, when it is activated, the gas-fired technology is the marginal plant setting the power price. The peak prices are higher in GAS 2030 case than in EC 2030, due to the market tensions created by lower installed capacities.

EC 2030 GAS 2030

Note: PR, SR and TR represent primary, secondary and tertiary reserves respectively while +/correspond to the positive and negative regulations.

The balancing market price could be underestimated by the assumption that the operators can continuously make the trade-off between the day-ahead market and the balancing/ real-time market. Within this design, the generator can make a choice to allocate its supply as an ancillary service or as energy as in [START_REF] Farahmand | Balancing market integration in the Northern European continent[END_REF]. If the arbitrage between markets was not possible, a higher margin of reserve would be necessary to ensure the liquidity on both markets at each moment and would result in lower levels of capacity factors.

The comparison between the EC and GAS scenarios is similar to the trade-off between more flexibility and more efficiency. Several areas are exploited currently in France, at the demand level within smart grid and green city projects; on the supply side, with energy storage applications; and in the trade design, through interconnections and by moving the adjustment mechanism closer to the real-time [12].

Sensitivity tests

Two sensitivity tests were conducted: a forecast error test (3.3.1) and no wind case (3.3.2). Since the GAS 2030 scenario has lower market liquidity, this scenario is taken further to understand the sensitivity to different wind power profiles. The model tests the system capability to provide positive regulation only. For negative forecast errors, wind power curtailment can occur, since this wind excess is considered to be imputable to the wind power operator and not to the market itself.

3.3.1

The forecast error test analyses the flexibility of the system for short-term wind power variations. Assuming forecast errors during a 6 hour period, the test simulates the system reaction to a lower wind power level, by 5% than predicted. This downward variation is balanced differently by the market according to the duration. During the first half-hour, primary and secondary reserves are activated, the next half-hour the secondary reserve only is still active, the next hour tertiary reserves substitute the secondary reserve, and finally, for the next four hours, balancing needs are provided by the spot market. The test selects a day with high power demand (11 th of January, during a weekday-Friday) and high wind potential to evaluate the forecast errors from 16:00 to 22:00.

Table 5 shows the variation by technology for each market segment between the error test and the reference case. The test results in additional regulation requirements of 905 MW on the primary market, and in average 770 MW for secondary adjustment, 850 MW on the tertiary market and 900 MW on the spot market. The reference hour listed in the table is the beginning of the 30 minute period (e.g., 21:30 to 22:00). The price difference between the sensitivity test and the reference case corresponds to 9 €/MWh on the primary regulation market, and is negligible on the secondary market. The price difference on the tertiary reserve market corresponds to the equilibrium price itself, because the demand for tertiary services is zero during this period in the reference case. On the spot market, the price difference is higher during the first half-hour since the decrease in wind power potential enhances the rescheduling of power plants, and exerts a tension on the market equilibrium. At the end of the test period, the shock is completely absorbed on the spot market, as the price differential becomes zero.

By technology, it has to be noticed the activation of power plants which were already operational -nuclear, coal, CCGT and CHP; and that only NGGT, hydro and storage act as reserves. Solar power is also used for tertiary regulation during an evening time simulation, and corresponds to reliable provision based on concentrated solar power provision. Storage and NGGT are also bidding in the spot market due to price increase which makes transactions attractive. Imports are not modelled as an option for real-time balancing purpose in order to estimate the capability of the generation system itself to balance the wind forecast errors.

The system has proved the robustness to balance wind forecast errors during 6 hours. The way this capability could be aggregated over the entire year depends on the frequency of the event. The balancing need could be satisfied if the event would have by assumption a daily basis frequency. Limits are however to be highlighted, for nuclear power with concern to the load factor in particular, since it attains already a high level (90%); and for hydro-power, since its generation is constrained by resource availability. Alternatives such as CCGT, NGGT and storage by contrast would still have a large capacity to increase their use over the entire year. For pumped hydro storage technology, the potential of high number of cycles allows for increased capacity factors which could moreover increase the storage investment profitability [START_REF] Loisel | Valuation Framework for Large Scale Electricity Storage in a Case with Wind Curtailment[END_REF].

3.3.2

No wind case tests the capacity of the system to react during one week without wind from 26 th of February to 4 th of March. This amounts to a loss of wind power of 3.2 TWh over the entire week between the reference case, GAS 2030, and the No Wind Test.

Results show that the system offsets the wind power by using following technologies with their respective shares in the total compensated generation: nuclear (47%), coal (4%), oil (1.5%), CCGT (8%), NGGT (3%), biomass (8%), PVS (5%), CHP (8%), imports (10.5%), and other renewables (5%).

Figure 3 shows the spot price difference between a day with significant wind power generation and the same day without wind at all. Results highlight a price difference in range of 1-21 €/MWh during the study day. If extended for a week then the difference ranges from 1-26 €/MWh with an average of 9.6 Euros between the two cases. This is in line with experiences in other European countries with similar rates of wind penetration such as assumed here, showing a price decrease in range of 3-23 €/MWh [22; 23]. The scenario GAS 2030 proves the system flexibility to balance forecast errors and wind power variations. These effects should be analyzed in relationship with the relatively low level of variable renewables, 13%. Wind and solar decrease the use of thermal generators but they could still remain synchronized to the grid and activated according to the resources variability, which avoids additional investments in back-up capacities.

Conclusions

The study shows by means of a dynamic optimization model that operating the French power system with 28 GW of wind power (or 11% of the total generation) by 2030 seems technically feasible for the infrastructure documented in EC [START_REF]EU Energy Trends to[END_REF]. But successful wind integration relies heavily on three factors: 1) the capacity of nuclear reactors to follow variations, 2) storage applications to insure flexibility and 3) market capacity to allow generators to adapt continuously to the demand. Simulations show that balancing the wind power variations is an issue of ramping rates and of unit scheduling. This study shows that the nuclear power operating at higher load factors (90%) by 2030 as stated in EC [START_REF]EU Energy Trends to[END_REF] is not conflicting with the variable nature of wind power. Given the French fleet of nuclear reactors capability to follow the load, the wind power is almost entirely integrated by the system, as only a negligible share of wind power is curtailed (0.002% of the wind power generated). Further research would be necessary to estimate the value of the flexibility provided with the nuclear power in the French power system; this would improve the economics of nuclear power and the profitability of using the nuclear power to balance the wind power variation.

The market design by 2030 would influence the cost and the mechanism of balancing the wind power fluctuations. Today, intra-day mechanisms that allow rescheduling power plants still remain inflexible to an extent. Market regulation is changing by extending the balancing areas and by opening the market to more operators. Balancing wind fluctuations result in extra costs on real-time and day-ahead markets. To date, the major French generation operator, Electricité de France, EdF, is not paid for the provision of balancing services due to wind power variations, since these costs remain very low. This could become an issue at higher wind penetration rates, requiring in the future the internalisation of balancing costs into the power price. Concluding however on the way the wind power impacts the system cost should at the same time account for the influence that the wind power would have on the nuclear power industry in terms of ramping costs.

The current liberalization of the electricity markets raises challenges for the market organization. If more centralized markets concentrate trades and increase market liquidity, the decentralization instead would decrease the ability to optimize the use of capacities. The choice of the optimal technology mix will be based on the trade-off between more flexible systems and more efficient ones. The system will need a different market design such as the ongoing capacity market development, and new technologies and solutions such as smart grids, demand-side management, interconnections, distributed generation and power storage applications.

Fig. 1 .

 1 Fig. 1. Power plants dispatching during a windy day, scenario EC 2030

Fig. 3 .Spot

 3 Fig.3. Spot price difference in cases with and without wind, in the scenario GAS 2030

Table 1 .

 1 Main inputs in the model for the years 2008 and 2030

					2008						2030
	Technology	Installed capacity	Efficiency	Fuel Cost	CO2 emissi	Max Loads	O&M costs	Ramp	Installed capacity, MW	Efficiency	Fuel Cost	Max Loads	O&M costs	Ramp
		MW		€/MWh	kg/kW h	%/year €/MWh	%/half-hour	EC 2030 GAS 2030		€/MWh %/year €/MWh	%/half-hour
	Nuclear Old	63360	33%	2.7		75%	1.75	6%	40458	40458	34%	3.01	93%	1.75	1%
	Nuclear New								17339	17339	36%	3.01	92%	0.61	2%
	Coal	10406	36%	7.7 0.34	27%	1.69	14%	1251	1251	40%	9.13	55%	1	15%
	Hydro RoR	10315	100%	0		29%	2.5 100%	21363	21363	100%	0	30%	2.48 100%
	Hydro Lake	13824	100%	0		29%	2.5 100%			100%	0	30%	2.48 100%
	Oil steam turbine	436	39%	15.4 0.28	9%	0.86	30%	0	0		17.65
	Oil gas turbine	7373	33%			9%	2.1 100%	5826	5826	39%		0.01%	0.86 100%
	CCGT	883	57%	20.3 0.20	76%	0.86	50%	16832	2530	65% 24.90	10%	0.4 100%
	NGGT	410	39%			76%	0.86 100%	7821	1175	45%		10%	0.4 100%
	Biomass	446	27%	23.3 0.36	17%	2.1 100%	2547	2547	30% 37.69	60%	2 100%
	CHP	6600	35%	20.3 0.25	42%	8 100%	7377	7377	40%	0.25	50%	6 100%
	Wind On-shore	3422	100%	0		19%	0 100%	18939	18939	100%	0	26%	0 100%
	Wind Off-shore								9000	9000	100%	0	34%	0 100%
	Solar	13	17%	0		8%	0 100%	11090	11090	17%	0	14%	0.01 100%
	Other RES								1731	1731	100%	0	38%	2 100%
	Storage	4302	81%	0				100%	4000	4000	81%	0			100%
	Imports	15570				8%	38.9	80%	20000	20000			25%	22.7	80%
	Total Capacity MW	121791							165575	144627
	Demand, GWh	490483							598214	598214
	Export, GWh	58689							46951	46951
	Loss, GWh	10848							12756	12756

Table 2 .

 2 Results of simulations for the years 2008 and 2030

						EC 2030		GAS 2030
		Genera	Annual	Share	Generat	Annual	Share	Genera	Annual	Share
	Technology	tion	Load	in the total	ion	Load	in the total	tion	Load	in the total
				Generat			Generat		Generat
		GWh	%	ion	GWh	%	ion	GWh	%	ion
	Nuclear Old	432592	78%	78% 319845	90%	49% 319845	90%	49%
	Nuclear New				137075	90%	21% 137075	90%	21%
	Coal	24680	27% 4.5%	5824	53%	1% 5824	53%	1%
	Hydro RoR Hydro Lake	26860 35999	30% 4.8% 30% 6.5%	57398	31%	9%	57379	31%	9%
	Oil steam turbine	5829	9% 1.1%	0			0
	Oil gas turbine	345	9% 0.1%	461	0.8% 0.1%	461 0.9% 0.1%
	CCGT	5895	76%	1% 14782	10%	2% 8787	40% 1.3%
	NGGT	2739	76% 0.5%	470	0.7% 0.1% 1984	19% 0.3%
	Biomass	196	5% 0.04%	6712	30% 1.0% 6712	30% 1.0%
	CHP	13493	23% 2.4% 19507	30% 3.0% 23605	36% 3.6%
	Wind On-shore	5727	19%	1% 43608	26% 6.7% 43608	26% 6.7%
	Wind Off-shore				27045	34% 4.1% 27044	34% 4.1%
	Solar	9	8%	0% 13638	14% 2.1% 13638	14% 2.1%
	Other RES				5807	38% 0.9% 5820	38% 0.9%
	Total Generation,
	GWh	554362			652170			651782
	Storage	2467	7%		3638	10%		4776	0%
	Imports	9135	7%	2%	8000	5%	1% 8000	1%	1%
	Export, GWh (input)	58689		11% 46951		7% 46951		7%
	Net imports, GWh	-49554			-38951			-38951

2008

Note. Hydro power units are aggregated in one technology in 2030, due to memory constraints.

Table 3 .

 3 Nuclear power statistics from real data and from model simulations: the number # of fluctuations in the range ±6%; and average variations (MWh/2)

	1, but considering no ramping constraints on

Table 4 .

 4 Prices on the day-ahead and balancing markets

	Generation / Prices	Day-ahead	PR+	PR-	SR+	SR-	TR+	TR-	Day-ahead	PR+	PR-	SR+	SR-	TR+ TR-
	Total Volume, GWh	652,202 1,082 1,796	187	232	201	1.1 651,801	1,082 1,796	187	232	201	1
	Peak Load, MW	107,489 2,219 2,727	782	655 1,195	200 109,229	2,219 2,727	782	655 1,195	200
	Average Price, €/MWh	29	30	24	30	29	30	22	23	25	20	25	29	25	29
	Maximum Price, €/MWh	38	38	37	37	37	38	22	48	42	37	42	37	39	37
	Minimum Price, €/MWh	0.00

Table 5 .

 5 Results difference in MW between Error test and Reference case, GAS 2030

	Hour	16:00	16:00	16:30	17:

00 17:30 18:00 18:30 19:00 19:30 20:00 20:30 21:00 21:30

	Technology/
	Market PR+	SR+	SR+	TR+	TR+ Spot Spot Spot Spot Spot Spot Spot Spot
	Nuclear Old	81	204	255	238	289	0	0	0	0	0	0	0	0
	Nuclear New	35	173	157	173	173	-18	0	0	0	0	0	-3	0
	Coal

† See IEA statistics at www.iaea.org (website last accessed at 17/10/2012).


Jawhar Elgueder

Florent Cochennec

Lionel Roucoules
email: lionel.roucoules@utt.fr

Emmanuelle Rouhaud

Product-process interface for manufacturing data management as a support for DFM and virtual manufacturing

Keywords: product-process interface, DFM, virtual engineering, manufacturing process selection, manufacturing simulation

In order to tackle a continuous improvement of virtual engineering, product modelling has to integrate always more knowledge that refer to every decision taken during the product development process. Those decisions have to be related to the assessment of the whole product lifecycle. This paper particularly addresses the domain of product's industrialisation that aims at selecting the manufacturing processes. This selection must currently be done as soon as possible and has to be strongly linked with product definition and CAD 1 modelling. This paper presents first some new results concerning a product-process interface to integrate manufacturing information in the product model and how it leads the definition of the CAD model. Secondly this interface, that also manages specific information coming from the manufacturing process (tolerances, stresses gradient…), is used to improve the whole manufacturing process plan simulation. This process plan has, indeed, to track every material transformation issued from each manufacturing operation.

For almost 30 years CAD systems have been developed and improved to currently reach very powerful features to support product's forms modelling. Nevertheless they are actually presented and used as one of the central systems that make the design process a geometric centric approach. This approach has shown its great interest in industry to tackle the problem of digitizing hand-done drawing or to improve the CAD-CAM 2 links and to enhance the process plan activity. Nowadays, the CAD model also finds an interest to improve the digital mockup used during a decision making process for instance.

1 Computer Aided Design 2 Computer Aided Manufacturing However current CAD systems are not able to manage all the information related to the product definition. This information as mentioned in [START_REF] Krause | Product Modelling[END_REF] has to be related to the whole lifecycle (from requirement specifications to dismantling information). The product, and its CAD model, is then defined, as far as possible, taken into account "X" constraints as assumed in a DFX3 approach. One of the domains that have to be integrated in design is manufacturing (i.e. DFM). That means that manufacturing activities have to be assessed concurrently to the product development and the CAD modelling activity.

Once the CAD done, manufacturing processes can be detailed. As far manufacturing simulation is concerned, CAD model is seen as input and software tools have to simulate the behaviour of the materials flow during each manufacturing operation (ex : forging, casting, machining….).

The main issue of that design approach remains in the fact that:

-The CAD model is almost never defined taking into account manufacturing information. -The manufacturing simulations do not take into account the history of the whole process planning. The input CAD is very often seen as virgin of any previous manufacturing operation.

This paper gives some results to manage the whole manufacturing process plan information and to integrate those data (i.e. knowledge synthesis approach) in the CAD model that is, then, constructed with respect to a more adequate DFM approach.

The second part introduces the design approach and the main concepts used to breakdown the product and its CAD model. It also gives the product-process interface concepts used to tackle the information synthesis.

The third part gives the first ideas and results to manage the manufacturing information of the global process in order to use it during the whole manufacturing simulation process.

Finally the conclusion and the perspectives for further work are enounced.

2-Objectives, context and concepts of the DFM approach

The fundaments of authors' DFM approach are the integration of manufacturing information4 constraints and data at the earliest stage of design. The developed model of integration (i.e. product-process interface model) is based on the research work done by Roucoules and Skander [START_REF] Roucoules | Manufacturing process selection and integration in product design. Analysis and synthesis approaches[END_REF]. They showed that taking manufacturing information into account as soon as possible in the design process is of great interest for manufacturing process selection. That indeed supports the emergence of product geometry [START_REF] Roucoules | Knowledge intensive approach towards multiple product modelling and geometry emergence to foster cooperative design[END_REF] and goes towards a limited number of iterations between design and manufacturing decisions; the term of "right the first time" is used for such approaches versus the approaches of "do until right".

Considering that the manufacturing domain is extended to other product lifecycle phases (e.g. assembly, recycling, dismantling, etc.), the assumption is that the design process should then be centred on multiple-views product modelling and expert analyses instead of being CAD centric. One of the main issues of that CAD centric approach remains in the unique product breakdown that does not reflect the design intends of every expert designers involved in the design group.

Figure 1 The general context of authors' research work lies on the multiple views product breakdown concepts proposed in [T2]. As presented in [START_REF] Roucoules | CoDE: a Co-operative Design Environment. A new generation of CAD systems[END_REF], the first design step consists in the definition of functional surfaces to achieve design requirements. These functional surfaces can emerge from specific "Function-Structure" analysis that describes every product specifications as energetic flows in the product structure. One example based on FBS [G1] and bond-graph concepts [T1] is given in [START_REF] Meyer | Case study of a MEMS switch supported by a FBS and DFM framework[END_REF]. The second steps aims at adding (i.e. integrating) lifecycle information to this first product description. This approach is often called "design by least commitment".

Skander et al. [S1] treat the activity of "manufacturing processes selection" (i.e. manufacturing expertise on figure 2) and then proposed to apply the Design For Manufacturing approach as soon as the first functional surface is defined. They thus propose a specific product model based on an adaptation of the skin and skeleton concepts [MW1, TB1] to allow the "X" constraints integration (see figure 2), and specifically the manufacturing constraints integration [R1 and S1].

This specific product model can be seen as an "interface model" used to specify, vulgarize the product information issued from different activities (i.e. expertises) (e.g. "technological components selection" or "manufacturing processes selection"). These interface models (e.g. product-process interface) are translated into a collaborative multiple views definition of the product.

The central "product modelling" concepts, and specifically the "relation" concept, are then used to link and/or propagate data from different expertises.

Product Model Interface model

Manufacturing skin

2.2-Objectives of the DFM approach

Once the first functional surfaces are specified, the design actor in charge of the industrialisation should wonder about which manufacturing processes would be eligible for generating these surfaces. Many industrial and research studies have been done to characterise product-process relationships (e.g. [BD1]). Skander et al. proposed to translate these product-process relationships in specific skin and skeleton attributes in order to analyse the correlation between product specifications and the process-resulting product characteristics. Then, the translation of the energetic flows definition in specific skin and skeleton attributes will lead to the creation of a technological interface model (see figure 2) and the translation of the product-process relationships in a same way will lead to the creation of manufacturing interface model corresponding to the product alternatives resulting from the analysis of all available manufacturing processes capabilities. Checking the consistency of the data contained in these two interface models will then imply the acceptance of some product-process alternatives and the reject of some others. The acceptance criteria are based on the fact that the data obtained during the product-process constraints identification must be sufficiently pertinent to define the process capabilities.

The DFM activity is detailed in figure 3. The first task (A1) aims at analysing the requirements specification using energetic flows and specific technological interface model as presented on figure 2. Once this task achieved, designers have to find product-process alternatives in which the manufacturing constraints are integrated (A2). The DFM output is then a list of products with respect to available manufacturing plans. The selection of the final product-process alternatives is not treated in the presented approach. Indeed, such a choice is led by economic criterions and depends on many external factors as the factory production capabilities, the lead-time of the production… The authors are nevertheless convinced that the proposition of product-process alternatives in which manufacturing constraints have been integrated brings solid arguments to the process selection activity.

2.3-Product-process interface modelling

As mentioned above the integration of manufacturing information is based on a specific product-process interface.

That model comes from the assumption that every manufacturing operation is based on a material flow. Those flows (cf. Figure 4) are then defined with:

-Sections defining the initial and final surfaces through which the material is going (i.e. transversal surfaces). -A trajectory on which the material is formed.

-An envelope section which is generated. Based on that flow (called manufacturing skeleton) the material can be added (ex: injection), removed (ex: machining) or deformed (ex: forging) to obtain the final part surfaces (called manufacturing skin). Those surfaces are in the added and removed processes categories equal to the envelope surface.

Beyond very good results presented in [START_REF] Skander | Design and manufacturing interface modelling for manufacturing processes selection and knowledge synthesis in design[END_REF] that concerns the current results of that approach for nominal aspects, figure 8 gives the novelties of that paper. The new results concern the capabilities of that product-process interface: -To manage product tolerances coming from manufacturing operations. Each level of tolerancing features (dimensional tolerances, form tolerances and roughness) is concerned. Figure 8 shows how those features are integrated in the product-process interface (i.e. manufacturing skeleton) characteristics.

-To manage material heterogeneity coming from manufacturing operations. It is also obvious that material flows (cf. above assumption) generate some gradients inside the manufactured product. Those gradients (called in the following "heterogeneities") can, for instance, come from (cf. Figure 8): o Thermal phenomena in the skeleton's sections that come from a cooling phase which is not always homogeneous during casting operations. o Mechanical stresses gradient on the skeleton's trajectory coming from high deformation in forging operations.

Another example of that heterogeneity (i.e. residual stresses) is given on the following section. It is based on peen forming process. More details can nevertheless be found in [CR1].

2.4-Application of product-process interface to the peen-forming process

The peen-forming process is a cold-work forming process mainly used in the aeronautical and aerospace industry to form large metallic panels (cf. Figure 5). The concept is to project balls on the part in order create some local plastic deformation. The global elastic equilibrium then generates geometrical deformation. It presents many advantages for this kind of application: none spring-back problems are encountered; the parts can be formed at ambient temperature, the process induces little metallurgical modifications and none dilatational dispersions; the residual stresses states are partially mastered; a good reproducibility can be achieved [START_REF] Ramati | Single piece wing skin utilization via advanced peen forming technologies[END_REF]. Being used for more than fifty years, this process is still under industrial and research development.

Many analytical and numerical models are proposed in the literature for predicting the geometrical distortions induced [G2], [START_REF] Grasty | Shot peen forming sheet metal: finite element prediction of deformed shape[END_REF], [START_REF] Homer | Aircaft wing skin contouring by shot-peening[END_REF] and [START_REF] Han | Combined finite/discrete element and explicit/implicit simulations of peen forming process[END_REF]. These models are based on the numerical introduction of equivalent plastic strains as a boundary condition of a finite element problem, which implies that the plastic strain fields induced by the treatment must be known. Some models have been proposed to predict the residual stress fields induced by known peening parameters [START_REF] Rouhaud | Handbook on Residual Stress[END_REF] but these models are still to be developed in order to complete the state of knowledge of the process. These studies are indeed depending on the treated materials and on the peening parameters retained for the treatment. The actual state of knowledge makes thus difficult to plan the forming phases and trials and tests are still a needed way to achieve a specific geometry. This section treats the use of mechanical analysis to identify the product-process interface (i.e. material flow as presented in 2.3) as presented in [S1] in order to integrate, as soon as possible, peen forming information in the product definition following the general design approach presented in 2.2.

The Peen Forming process specificity lies on the fact that the material flow induces an elastic response of the sheet blank which generates the global distortion. Indeed, contrary to classical forming processes as stamping for example, only gentle curved shapes can be obtained due to the fact that the forming mechanism is based on elastic deformations and not chiefly on plastic ones. Then, the forming origin is the incompatible plastic strain field induced by the shot impacts while the forming mechanism involved lies on the elastic strains resulting from the material compatibility condition.

The authors decided as a first assumption to model the material flow taking only into account the plastic strains induced by the treatment, this data being the starting point of the study of the distortions induced. Three basic curving attributes must be defined to cover the process capabilities: cylindrical, spherical and saddle shaped, the combination of these three attributes for the description of a large sheet metal being of course thinkable. Let us concentrate on the spherical form attribute, which is the simplest one. An illustration of a manufacturing skeleton and its corresponding manufacturing skin is given in figure 6.

2.5-Illustration of the product-process interface in the DFM approach

Keeping in mind the CAD model presented on figure 1 and taken into account the previously presented product-process interface, the manufacturing product breakdown would be the following (cf. Figure 7): -An extrusion operation as primary process. Tolerances are integrated in the section of the extrusion skeleton. (Step 1) -A profiling machining operation as secondary process. (Step 2) -Four machining operations as secondary processes.

(

Step 3)

Step 1

Step 2

Step 3

Step 1

Step 2

Step 3 The CAD model is then created according to manufacturing information (i.e. manufacturing skeleton) that leads the CAD breakdown and all the information related to product tolerances (as presented on Figure 8).

3-Managing manufacturing information for manufacturing process simulation

So far we have presented how product-process interface is used in a DFM approach. The second goal is to take into account this new information of material heterogeneity (cf. figure 8) to better simulate each manufacturing operation. Every simulation can then, indeed, integrate an initial state with respect to the history of previous operations of the process plan. It is then compulsory to model every gradient of information (ex: stresses coming from forging, casting…) coming from this history.

3.1-Manufacturing Data management

Figure 9 gives an overview of a KBE5 application developed to manage the global process plan with respect to the previously presented product-process interface.

That application proposes via its Graphic User Interface to manage both process and product information. The main functions are: -To select manufacturing process that could respect the requirements specification coming from the first step of the design approach (cf. The final structure breakdown therefore gives every product alternatives according to manufacturing process plan alternatives (cf. breakdown tree on Figure 9) chosen by the user. It is important to note that each manufacturing alternative provides a CAD alternative and different material heterogeneity. The evolution of the CAD after each manufacturing operation with respect to that heterogeneity and to the simulation is then also different for each alternative. That why it is nowadays important to manage all the manufacturing information.

The data model of the KBE application is currently implemented using OCAF6 package encapsulated in MFC 7 objects and Open CASCADE 3D viewer.

Figure 2 :

 2 Figure 2: Product modelling for "X" constraints integration.

Figure 3 :

 3 Figure 3: The DFM activity schematisation [RS1].

Figure 4 :

 4 Figure 4: Material flow definition for product-process interface.

Figure 5 :

 5 Figure 5: Illustration of the peen forming process.

[Figure 6 :

 6 Figure 6: Illustration of manufacturing skeleton concepts in a peen formed product case.

Figure 7 :

 7 Figure 7: Illustration of the proposed DFM approach.

Figure 8 :

 8 Figure 8: Example of product information issued from manufacturing process and managed by the product-process interface.

  2.). -To define every manufacturing operation parameters. This is, so far, done manually by the user according to his experience and the final part he wants to create. -To define, via a database, product features based on manufacturing skeleton. That includes: o The emergence of the product CAD model integrating all the manufacturing variability. o The tolerances on the product coming from manufacturing capability. o The product's material behaviour (ex: stresses gradient) coming from material flows.

Technological features of CAD system that does not have any sense for manufacturing experts Technological features of CAD system that does not have any sense for manufacturing experts Figure 1: Incoherency between CAD model breakdown and manufacturing breakdown.

	2.1-Design context: CE, DFM and product
	modelling
	Integrated design aims at linking all mechanical expertises
	taking part in the design of a new product from functional
	specifications to the product's industrialisation and
	dismantling. Since this design concept appeared (more or
	less since two decades), many research investigations have
	been done to propose design methods, information
	management methods and models supporting the
	collaborative activities [S2] [AH1]. It is not the issue of this
	paper to detail all those works.
	shows the features breakdown used to obtain the CAD
	model. Obviously, this breakdown does not represent what
	should or could be the real manufacturing process plan. It does
	not have any sense for the engineers in charge of the
	manufacturing activities.

Design For X: design approach able to take into account activity information (e.g. manufacturing, assembly…) during the product development.

Information is used in this work as both "new data" that complete product or process definition or "constraints" that is used to reduce the range of value of an existing data. Some details can be found in[START_REF] Roucoules | Knowledge intensive approach towards multiple product modelling and geometry emergence to foster cooperative design[END_REF].

KBE: Knowledge Based Engineering. Software developed in order to link CAD systems and Knowledge database

Open CASCADE Application Framework

http://www.le-webmag.com/article.php3?id_article=2&lang=

How to define a CAD model taking into account manufacturing information

Product-process database

3.2-Manufacturing data management and simulation

Based on this KBE application it is then possible to know what is the exact initial state of the product before each manufacturing operation simulation. This initial state obviously encapsulates the product behaviour issued from previous manufacturing operations. Indeed each manufacturing interface (i.e. manufacturing skeleton) of the data structure gives that information.

As presented in the figure 10, the difficulty currently remains in transferring each gradient from the KBE data management structure to the initial model of the simulation (most often Finite Element Simulation). Manufacturing skeletons are, indeed, not based on meshing and the gradient of information have then to be linked to topological parameters that have a strong meaning for manufacturing experts. That is not the case of any meshes that are only dedicated to specific simulation models.

Keeping the link between manufacturing parameters and product information is very useful to notify every change concerning product definition that can therefore be quickly propagated to manufacturing information without processing any new FEA.

The proposed solution based on the presented product-process interface is to link information gradient to each manufacturing skeleton which is represented by topological features and linked to manufacturing parameters (cf. Figure 10); each skeleton being adequate for each material flow of the given manufacturing operation. In very complicated cases for which information gradient cannot be explicit, a specific mesh could be associated to skeleton features; each mesh being also adequate to the specific material flow of the manufacturing operation.

4-Conclusion and recommendations for future work

This paper presents a product-process interface model for design for manufacturing (DFM) approach.

This model based on material flow modelling with respect to skeleton and skin concepts is first used to integrate manufacturing information as soon as possible in the product design process (i.e. "by least commitments design approach"). This integration strongly leads the CAD modelling and by the way focuses the design process on expert designers' knowledge and not on CAD model any more.

The second objective of that interface model is to manage manufacturing information linked to product characteristics (ex: topology, tolerances, material behaviour…). It is then easy to use that link to simulate manufacturing processes taking into account the evolution of product characteristics with respect to the manufacturing plan. The whole history of each manufacturing operation is then linked to the product definition that is not currently the case in CAD centric design approach.

The main perspectives for future work concern:

-The achievement of the KBE application in order to test more complicated cases. The current developments are related to the implementation of a skeleton library and the coupling with a productprocess database. -The implementation of field transfer mechanisms to support the whole management of the manufacturing process simulation.

5-Acknowledgment

This research work is partly supported by SNECMA enterprise. It makes part of the MAIA 8 project.

6-References

[AH1] Andreasen M., Hein L., "Integrated product development", Springer-Verlag, London, 1987.

[BD1] Boothroyd G. et al. Product design for manufacture and assembly. Marcel Dekker, ISBN 0-82479-176-2, 1994.

[CR1] Cochennec F., Roucoules L., Rouhaud E., Mechanical Analysis to identify knowledge for a DFM approach.