1
Integration of Renewable Energy in the
European Power Grid: Market Mechanism for
Congestion Management
A. Vergnol1,a, V. Rious2, J. Sprooten1, B. Robyns1, J. Deuse3
1
Laboratory of Electrotechnics and Power Electronics of Lille (L2EP) of the University of Lille North of France,
Ecole des Hautes Etudes d’Ingénieur (HEI), 13 rue de Toul, F-59046 Lille, France
2
Supelec, Power Systems and Energy Department
3 rue Joliot-Curie, 91192 Gif-sur-Yvette CEDEX, France
3
Suez – Tractebel,
avenue Arianne 7, B-1700 Brussels, Belgium
Abstract:
With the increased use of wind energy several Transmission System Operators (TSO) have
increasing difficulties for congestion forecasting due to the unpredictable nature of the energy
source. To maintain the state of the system within acceptable and secure operating conditions, the
TSOs require the curtailment of the production of generators to avoid local congestion on the
power grid. These actions reduce the revenue of renewable producers and limit the development
of green energy. This is because renewable producers with support schemes bear the inherent
cost of congestion when they are re-dispatched. This paper proposes to use of storage system and
two types of market mechanism that solve the above mentioned problems in case of local
congestion. The first mechanism consists in a compensation between renewable producers to
limit the amount of redispatched generation in situations when the local congested power grid
incorporates only renewable production. If both renewable and conventional productions are
connected close to each other, a second mechanism will be used to incentivize competition
among power adjustment offers.
Keywords:
Congestion management, Market mechanism, Renewable intermittent energy, Wind power
generation.
This work was partly supported by a financing from the regional Council Nord-Pas de Calais and from the European Regional Development Fund
(ERDF).
a
Corresponding author. E-mail address: [email protected]. Tel.: +33 328 384 858; Fax: +33 328 384 804.
2
I. INTRODUCTION
For several years, global warming has become a world priority. One of the solutions to solve this problem is the
increased use of renewable energy. However, the integration of such a production in the present grid is not easy as
this grid was not originally designed to accept this kind of production. In many regions, the Transmission System
Operators (TSO) expect an increase of line congestion in rural areas due to the important increase of wind
generation [1]. To maintain the state of the system within acceptable and secure operating conditions, operators must
apply preventive actions or emergency/correctives actions. These can either be actions on topology1 and/or on
generation. In this paper, as very limited actions on topology are available in weak networks, the focus will be put on
actions on generation to modify the power flow of an overloaded line. However, modifying the power production of
a wind power plant reduces its revenue because its remuneration is linked to the generated energy. In this paper, a
mechanism is proposed to solve this problem of financial compensation for wind producers in case of local
congestion.
Congestion Management (CM) problems were analyzed in the literature. A method classically used by TSOs is to
manage congestion in day-ahead planning by curtailment or disconnection of generation using technical and
economic criteria [2]. The difficulty of the day-ahead method is to accurately predict magnitude and duration of the
congestion. Thus, the main consequence of this approach is a limitation of generation that can be more important
than necessary, as a precise day-ahead prediction of wind power is impossible. In the literature, other methods are
dedicated to CM. Sensitivity-based optimum generation rescheduling and/or load shedding schemes to alleviate
overloading of transmission lines are proposed in [3] and [4]. These methods, based on the computation of an
Optimal Power Flow (OPF), are precise approaches for CM as long as generation and transmission capacities are
well known. Other approaches are market-based methods for congestion elimination [5]–[7] and are also very
efficient as long as two price areas, delimited by the congested elements, can be identified. Furthermore, marketbased methods are affected by errors in load and generation prediction caused by element outage or random
generation such as wind generation. To avoid forecasting errors, a method using a real time supervisor is proposed in
[8]. This method avoids the line congestion while reducing the production constraints to the minimum thanks to the
use of automatic corrective actions. Nevertheless, part of the wind generation is lost.
1
The actions on topology range from bus couplers switching and transformer tap changing to the adjustment of phase shifters. However, in
weak networks there are very limited available possibilities.
3
However, these methods do not take into consideration the nature of the source (renewable or conventional) and
its impact on the associated loss of revenue. Indeed, wind producers lose money in case of lower production as their
revenue is linked to the generated energy and not to the sold energy in advanced. The goal of this paper is to propose
a market mechanism that solves the lose revenue problems for wind producers in case of local congestion.
This paper is divided as follows. Section II presents an overview of the CM with renewable energy used by
European TSOs. In section III, the influence of loss of renewable generation on the congestion cost is studied. The
use of storage system and non-discriminatory market mechanism are presented in section IV for the CM with
renewable energy and finally conclusions are drawn in the last section.
II. REVIEW OF CM USING RENEWABLE GENERATORS IN EUROPE
This section proposes a comparative review of the German, Spanish and French managements of congestions
which are partly due to the presence of wind generators. These three countries are representative of the range of CM
methods used in Europe. The regulations for wind energy in the three countries is shown in TABLE I.
As shown in TABLE I, the support scheme for wind energy in Germany, Spain and France is based on feed-in
tariffs. The most relevant feature of the feed-in tariff scheme is that it secures a certain income during a fixed time
horizon. However, in Spain, power producers using renewable energies can choose between two support schemes, a
feed-in tariff system or a market option system (Market price and premium). In 2008, 87% of Spanish wind power
TABLE I
COMPARISON OF WIND ENERGY REGULATIONS [9]–[16]
When is
Financial
curtailment
compensation for
allowable?
curtailed energy
If included in the
connection network
No
contract between
No
wind farm owner
and TSO
When connection
For curtailment in real
Connection
nodes have
time operation: 15% of
voltage levels can
temporary capacity
the market price.
vary depending
restrictions due to
For planned
on project
system security
curtailment: no
capacity
problems.
financial compensation.
Connection
If included in the
voltage levels can connection network
vary depending
contract between
No
on project
wind farm owner
capacity
and TSO
Influence of project capacity
Support
scheme
Total payment level
for wind power
Germany
Feed-in
tariff
83.6€/MWh for first 5
y., then feed-in tariff
can vary depending on
yield
Feed-in tariff can vary
depending on project
capacity
Spain
Feed-in
tariff or
market
option
Feed-in tariff:
73.23€/MWh for first 20
y., then 61.2€/MWh.
Market option: Premium
plus price market
Feed-in tariff can vary
depending on project
capacity
France
Feed-in
tariff
82€/MWh for first 10 y.,
then feed-in tariff can
vary depending on yield
No
On support scheme
On grid
connection
4
producers chose the market option [13], thanks to the low associated risks introduced by a minimum guaranteed
price 2€/MWh lower than the feed-in tariff value when market price is low, and 11.6€/MWh above feed-in tariff
value for medium market price and equal to market price when the latter is high [11]. Germany and France have also
established different criteria in order to limit the subsidy level to wind power. Germany has defined for each location
a reference production model. After 5 years, each installation is compared to a reference model and in case the
production has reached more than 150% of the reference production, the feed-in tariff for this installation is
decreased by 30%. In France, wind turbines producing less than 2400MWh per installed MW and per year are paid
at a high feed-in tariff. For annual production above 2400MWh this feed-in tariff decreases by 17% at 2800MWh
and by 66% at 3600MWh [16].
TABLE I also shows capacity limits in the regulations for wind energy. Capacity limits are either used to define
connection voltage levels (Spain and France) or to differentiate the feed-in tariff (Germany and Spain). In Germany,
the basic principle regulation about renewable energy is to give priority connection to plants generating electricity
from renewable energy sources and to guarantee priority purchase and transmission of all electricity from renewable
energy sources. But in France, wind farms must be connected in specific areas to be eligible to the feed-in tariff [15].
These areas are determined taking into account the wind potential of the area, the accommodation capacity of the
power grid and Environmental Protection. Based on these criteria, a geographical area, the minimum and maximum
power of all facilities (existing and/or future) based in these areas are defined.
These different measures may lead to the emergence of new congestion on the network. In Spain and in Germany,
in case of capacity restrictions and for the security of the system, renewable production is defined as priority and
conventional power generation must modify their production. Thus, in Germany, renewable energy generation
should not be affected by any network congestions, independent of the actual location of the renewable energy
sources. However, as network upgrades can take years, the additional connection of new renewable energy sources
has been put on hold in some areas, as the existing network capacity is not large enough to guarantee priority
production of new renewable energy resources. Therefore, renewable energy generators can agree with the TSO that
they can be curtailed in situations where all transmission capacity is already used up by other renewable energy
sources. This means that such an agreement allows to connect new renewable generation systems prior to network
reinforcement, but, such new units may be curtailed without any financial compensation [10]. In Spain an agreement
between TSO and producers for curtailment is not needed; renewable generation can be curtailed as a last resort
5
Fig. 1. Test system configuration. 4-bus power system with congestion problem in presence of WFs.
option. Nevertheless in case of curtailment in real time operation, the renewable producer is partially financially
compensated for its lost production at 15% of the electricity market price. For planned curtailment, the renewable
producers receive not financial compensation [14]. In France, the situation is different because renewable energy has
no priority compared to other production and in case of congestion these may be reduced following a rule based on
“lasts-connected are firsts-cut”. Nevertheless, the curtailment duration may not exceed the number of hours defined
in their network connection contract. The producers are not compensated for their curtailment.
The three studied countries have different methods to determine which generation source will be curtailed when
physical restrictions of power flow in the grid. However in Spain and in Germany, the outcome is the same, i.e.
renewable energy sources are typically the last generation source to be curtailed. In France, the result is reversed
because of the rule “lasts-connected are firsts-cut” as renewable energy sources are often the last installed. However,
in the three countries, renewable energy producers are not financially compensated for curtailments (except in Spain
for curtailment in real time operation). This greatly impacts winds producers as they are paid based on their
produced energy (except in Spain), and, contrary to conventional production they lose their primary energy source
during congestion. Therefore, wind power producers are doubly impacted in case of congestion. As it will be shown
in Section III, renewable producers bear the associated congestion cost contrary to classical producers.
III. COST OF CONGESTION AND LOST RENEWABLE ENERGY
To illustrate the present regulation on the congestion cost for renewable producers, the 4-bus test system
described in Fig. 1 will be used. The test system represents a part of a regional network. Gen 1 represents a
decentralized generator and is connected to node 1. Connected at node 4, Gen4 represents the rest of network. This
node is the slack bus. Wind Farms (WF) are connected to nodes 2 and 3. The nominal power and Power Transfer
6
TABLE II
GENERAL INFORMATION OF THE 4-BUS POWER SYSTEM
Pn [MW]
PTDF on line 1-2
Gen 1
20
0%
WF 2
50
80%
WF 3
50
60%
Gen 4
Slack Bus
40%
TABLE III
POWER MODIFICATION COMPARISON OF THE 4-BUS POWER SYSTEM
Cases
CM when WF 2 was the first
connected
CM when WF 3 was the first
connected
Gen 1
[MW]
WF 2
[MW]
WF 3
[MW]
Gen 4
[MW]
+ 20
-5
- 50
+ 35
+ 20
- 30
0
+ 10
Distribution Factor (PTDF) of Gen 1, WF 2, WF 3 and Gen 4 are summarized in TABLE II. The PTDFs of a given
line represent the ratio between the power that transits in this line and power exchanged between two nodes. TABLE
II, PTDF on line 1-2 for power exchange with node 1 are provided. The total system load is 120MW (100MW at
node 1 and 20MW at node 4). It is assumed that in this simple market, Gen 1 will offer 60 €/MWh, Gen 4 will offer
40 €/MWh and the two wind farms are subject to a feed-in tariff set at 80 €/MWh. In the example, WFs have
priority, but in the case of curtailment of wind generation, the choice of the reduced wind farm will be the rule
“lasts-connected are firsts-cut”. Moreover, we consider that the price charged by conventional generators, to reduce
or to increase their production in case of congestion, is equal to their price offer on the market. Fig. 1 represents the
DC load-flow operating point when high wind speed conditions are present. The power flow in line 1-2 can be
estimated using DC load-flow approach by (1).
P
= PTDF
P
+ PTDF
P
line 1 - 2
WF 2 WF 2
WF 3 WF 3
(1)
Here, the power flow in line 1-2 is 70 MW thus the line 1-2 is overloaded because limit capacity of in the line is
50 MW. To solve this problem, different solutions are possible. First, as stated by the rule “lasts-connected are firstscut” the choice of one solution will depend on the order of connection of WF to the grid. TABLE III shows the
power modification asks to production unit to avoid congestion.
In case WF 2 was connected before WF3, WF 3 is completely curtailment. In case WF 3 was the first connected,
WF 2 is only reduced by 20 MW. The difference is explained by comparing the PTDF value (TABLE II) of WF 2
(80%) and that of the WF 3 (60%). Indeed, it is more efficient to reduce line 1-2 constraint using a generator at node
2 than a generator at node 1. To illustrate the priority of WF on the CM, the same test system is used but WF are
7
Fig. 2. Test system configuration. 4-bus power system with congestion problem.
replaced by conventional generators. This is shown in Fig. 2. Gen 2, Gen 3 produce 50MW and Gen 4 produces
20MW. The marginal cost pricing on the market is 40€/MWh. The offer of Gen 2 and Gen 3 is 30€/MWh causing a
congestion on line 1-2. To solve this problem, a re-dispatching will be performed while minimizing congestion cost.
The solution is to increase the Gen 4 to 50 MW and reduce Gen 2 to 50 MW. TABLE IV shows the cost comparison
for different cases. The column “energy sold” corresponds to the energy sold at marginal cost in the day-ahead
market for conventional generators and the energy produced for WFs. TABLE IV shows in the case of conventional
generators, that the energy cost is unchanged in case of congestion, but the congestion cost increases to 500€/h.
However, in the case with WFs and congestion, congestion cost can be increased to 2600€/h this is due to the
priority given to wind power which requires the increase of the most expensive group (Gen 1). Moreover, energy
sold is lower than in the case with WFs without congestion because curtailments are then applied to wind farms
TABLE IV
COST COMPARISON OF THE 4-BUS POWER SYSTEM
Cases
With four
Without
Gens
considering
limits on line With two
1-2
WFs
With four
Gens
When WF
2 was the
Considering
first
limits on line
connected
1-2
When WF
3 was the
first
connected
Gen 1
Gen 2 or WF 2
Gen 3 or WF 3
Gen 4
Energy
Financial
Financial
Financial
Financial
Congestion
Energy
Energy
Energy
Energy
cost
compensation
compensation
compensation
compensation
cost [€/h]
sold
sold
sold
sold
[€/h]
for CM
for CM
for CM
for CM
[€/h]
[€/h]
[€/h]
[€/h]
[€/h]
[€/h]
[€/h]
[€/h]
0
0
2000
0
2000
0
800
0
4800
0
0
0
4000
0
4000
0
800
0
8800
0
0
0
2000
- 1500
2000
0
800
+ 2000
4800
500
0
+ 1200
3600
0
0
0
800
+ 1400
4400
2600
0
+ 1200
1600
0
4000
0
800
+ 400
5600
1600
8
(TABLE III), thus, the energy cost can be reduced by 5600€/h. Therefore in presence of WF, for the entire system it
is preferable that congestion appears in order to reduce the total costs (Energy cost and congestion cost), because the
feed-in tariff for wind energy is higher than the price of conventional generation. Moreover, when the conventional
producers do not produce, they store their fuel while wind producers cannot. Thus, wind producers are penalized in
case of capacity restrictions of the system. One solution is to compensate wind producers for their loss of primary
energy source. The difficulty is to define the price of compensation that takes into account both the loss of primary
energy source and to maintain reasonable congestion cost.
The next section will propose the use of storage system in order to reduce the wind energy lost and two
mechanisms in order to identify a price for loss of primary energy source. First, the influence of a storage system is
studied and secondly, a simple method will reduce the loss of renewable production taking into account the priority
of WF. Finally, a market mechanism for CM is proposed allowing the renewable producers to set their own price for
curtailment.
IV.
COMPENSATION FOR RENEWABLE PRODUCERS
A. Energy storage
In case of congestion, a solution to reduce the financial loss of the wind producers is to store the wind energy and
produce it later. This solution is done in [17] with the use of medium term storage (water reservoir, compressed air
storage systems…). However, three scenarios can be considered if the storage is either managed by an independent
producer, either managed by one or several wind producers or managed by the TSO.
In the case of an independent producer, storage will be managed to maximize its profit, so when the market price
is low the producer will stock energy and will sell it when the price is high. In case of congestion, its price offer for
power reduction or power increase will be compared to offers of the other candidate for CM. The solution offering
the lower congestion cost will be selected. Thus, if the storage is less expensive than the WF and if it is better
located regarding the congestion, the energy loss of the WFs will be reduced as it is illustrated in [18].
Secondly, in the case of an association of storage systems with WF, ancillary services such as wind power
smoothing or reduction of wind power losses in case of congestion can be provided [17]. The case presented in Fig.
1 is considered with an additional storage system of 30MW connected at node 2. This storage system allows storing
9
the energy of WF 2 in case of congestion. Moreover, the congestion cost would remain unchanged for the rest of the
system but the wind producer does not lose energy. Furthermore, stored energy will be sold later and will reduce the
use of classical production. However, it will be necessary to consider that the energy produced by the storage system
is renewable and should therefore benefit from wind power feed in tariff.
Finally, the use of storage system by the TSO is linked to an economic study between congestion cost, investment
cost for grid reinforcement and investment cost of storage system. However, storage may provide additional
ancillary services to the grid that have to be taken into consideration.
B. Partial compensation of the renewable producers
In case of congestion, renewable producers could be interested to exchange their place in the order of curtailment.
This could be the case if this new order allows less power to be curtailed. In this case, the latest installed producer
will financially compensate the curtailed producer such that every actor gets benefits compared to the application of
the rule “lasts-connected are firsts-cut”. The scenario presented in Fig. 1, is reconsidered with this approach.
TABLE V shows the impact of the partial compensation of the renewable producers on the WF revenue. Therefore,
to limit the total amount of redispatched power, production of WF 2 was reduced prior to WF 3. If WF 2 was the
first connected, it will be financially compensated by WF 3. The payment of 2400€/h represents the loss of primary
energy source of WF 2. Moreover, the comparison of TABLE IV and TABLE V in case WF 2 was the first
connected, shows that WF 3 receives 1600€/h against 0€/h. Furthermore, this method allows to minimize the
congestion cost to 1600€/h. However, even if the loss of renewable production is reduced, WF 3 is yet penalized.
The next section will present a second mechanism to incentivize competition among power adjustment offers.
Cases
Compensation between
renewable producers when WF
2 was the first connected
Compensation between
renewable producers when WF
3 was the first connected
TABLE V
PARTIAL COMPENSATION OF THE RENEWABLE PRODUCERS
WF 2
WF 3
Energy
Financial
compensation
Energy
Financial
compensation
sold
compensation between renewable sold
compensation between renewable
[€/h]
for CM [€/h]
producer [€/h]
[€/h] for CM [€/h]
producers [€/h]
Congestion
cost [€/h]
1600
0
+ 2400
4000
0
- 2400
1600
1600
0
0
4000
0
0
1600
10
5000
Congestion cost [€/h]
4500
4000
3500
3000
2500
2000
1500
-100
-80
-60
-40
WF 2 price [€/MWh]
-20
0
Fig. 3. Congestion cost versus WF 2 price for curtailment.
C. Market mechanism for CM
This mechanism provides an incentive for producers to reduce their production in case of congestion through
competition among producers in order to limit the amount of re-dispatched renewable power and so to reduce the
associated congestion cost. Under a market mechanism for CM, renewable producers will set their own price to
reduce their production. Two scenarios are considered. In the first, renewable producers benefit from a fixed feed-in
tariff related to energy produced and this production type has priority. Secondly, all renewable producers sell their
energy on a market and this production type has no priority.
1) Fixed feed-in tariff scenario
The scenario presented in Fig. 1, is reconsidered with this approach. In case WF 3 was the first connected,
TABLE III shows the power modification asks to production unit to avoid congestion. If the Gen 1 and Gen 4 offer
price are fixed, the congestion cost will depend only on the price for curtailment requested by WF 2. This linear
dependence is shown in Fig. 3. The price offer for the WF 2 can only be negative as wind power producers are paid
according to the produced energy. Thus, when its price is 0€/MWh, the congestion cost is equivalent to that of
TABLE IV (i.e., 1600€/h). As shown in Fig. 3, for to limit the congestion cost, it is necessary to cap the price
demanded by WF 2. In case when WF 2 was the first connected, the congestion cost will depend on the price for
curtailment requested by both WF.
Thus, the congestion cost depends on the order of connection and not on the price for curtailment requested by
WF. In conclusion, competition between actors is not possible in the present feed-in tariff rules. Moreover, it’s
11
Congestion cost [€/h]
4500
0
4000
WF 2 price [€/MWh]
-20
WF 2
3500
-40
3000
-60
2500
-80
2000
WF 3
-100
-100
-80
-60
-40
-20
WF 3 price [€/MWh]
0
Fig. 4. Congestion cost following WF 2 price and WF 3 price offer for power curtailment. (Gen 1 price : 40€/MWh and Gen 2
price: 60€/MWh)
necessary to cap the price requested by WF, for example, the price may be limited to feed-in tariff.
The next section will present the same mechanism when all renewable producers sell their energy on a market and
when no priority is given to renewable energy.
2) Energy market scenario
In the future, the development of wind energy will be such that specific rules applied to wind power will be
obsolete. Thus, the wind producers will have to participate in the market and they will have no priority. This section
presents this evolution. The case presented in Fig. 1 is considered but with a price for wind producers of 0€/MWh
that is their marginal cost (we assume perfect competition). Assuming the prices charged by Gen 1 and Gen 4 are
equal to the ones of Fig. 1, in case of congestion, a re-dispatching will be performed while minimizing congestion
cost. Fig. 4 shows the congestion cost versus WF 2 and WF 3 price offer for power curtailment.
If both wind producers agree on prices, the congestion cost may be very high, thus a minimum price will be
necessary to limit the congestion cost. However, this mechanism creates a competition between the two wind
producers as shown in Fig. 4 with the black line delimiting the price region where WF 2 is chosen for power
curtailment and where WF 3 is chosen. The area dedicated to the WF 2 is bigger as this production unit benefits
from a higher PTDF regarding line 1-2 (TABLE II). Moreover, as renewable producers have no priority, the choice
of the generator which increases is defined according to the minimal congestion cost, after Gen 1 was always used
(TABLE III). This creates a dependency between the price requested by Gen 1 and the price requested by both WF.
Indeed, Gen 4 represents the external network and therefore its price offer is not influenced by the local congestion.
12
Congestion cost [€/h]
6000
0
WF 2 price [€/MWh]
-20
-40
Gen 1
&
Gen 4
Only
Gen 4
5500
5000
4500
4000
3500
-60
3000
-80
-100
70
2500
2000
80
90 100 110 120 130 140
Gen 1 price [€/MWh]
Fig. 5. Congestion cost following WF 2 price and Gen 1 offer for power curtailment and augmentation. (Gen 4 price : 40€/MWh
and WF 3 price : -40€/MWh)
In this scenario, it will be kept at 40€/MWh. The dependence between the price offer of WF 2 and of Gen 1 is shown
in Fig. 5. The price offer of WF 3 for power curtailment is fixed at -40€/MWh thus WF 2 will be used if its offer is
upper than -100 €/MWh (Fig. 4). Fig. 5 also illustrates that if the offer of Gen 1 is too high it might not be used. The
black line delimits the price region where only Gen 4 is chosen for power curtailment and where Gen 1 and Gen 4
are chosen. Moreover, the influence of the storage system connected at node 2 managed by an independent producer
can be analyzed.
In conclusion, in presence of WFs on the market, it is possible to introduce a competition even between Gen 1 and
Gen 4 in order to reduce the congestion cost. Nevertheless, a risk exist that local producers agree on their price offer
and it is therefore necessary to regulate the market mechanism in case of local congestions.
V. CONCLUSION
The impact of renewable producers on CM is discussed in this paper. A comparative analysis of various rules
implemented to CM is presented. The need to compensate renewable producers has been discussed. The influence of
storage system and two methods of compensation have been analyzed. First, the use of storage system allows to
reduce financial loss of the wind producers. Secondly, a partial compensation of the renewable producers allows to
reduce the loss of renewable production and to minimize the congestion cost. Finally, a local market mechanism for
CM is presented. However, for this last approach to be efficient, present regulations on renewable production
priority or incentive feed-in tariff should be abandoned. Furthermore, the proposed market mechanisms, applied to
13
local congestions, must be regulated to limit the congestion cost due to the low number of players.
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[13] OMEL, “Electricity Market 2008”, [Online]. Available: http://www.omel.es.
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[15] Act 2005-781 of July 13, 2005, Program setting guidelines for energy policy. [Online]. Available:
http://www.legifrance.gouv.fr.
[16] Orders of November 17, 2008, Program setting the conditions for purchase of electricity installations by using the
mechanical energy of wind. [Online]. Available: http://www.legifrance.gouv.fr.
[17] B. Robyns, J. Sprooten and A. Vergnol, “Supervision of renewable energy for its integration in the electrical system”, IEEE
PES General Meeting, PESGM 2010, Minneapolis, Minnesota, USA, Jul. 25-29, 2010.
[18] P. Monjean, J. Sprooten and B. Robyns, “Influence of technico-economic energy context for the management of wind farms
and storage systems in a constrained network”, IEEE International Symposium on Industrial Electronics, ISIE 2010, Bari,
Italy, Jul. 4-7, 2010.
VII. BIOGRAPHIES
Arnaud Vergnol received the “Master de Recherche en Energie Electrique et Développement Durable” degree from The
Université des Sciences et Technologies de Lille (USTL), France, in 2007. Since October 2007, he is working toward the Ph.D.
degree in electrical engineering in the Laboratory of Electrotechnics and Power Electronics of Lille (L2EP), Ecole des Hautes
Etudes d’Ingénieur (HEI), Lille, France. His research interests include integration of dispersed renewable energy sources,
congestion management and power system.
Vincent Rious received the M.S. degree in electrical engineering from SUPELEC in France (2004) and the PhD degree in
Economics from the University Paris-Sud XI (2007). He is currently associate professor at the Power and Energy Systems
Department in the High School of Electricity SUPELEC. He is the co-director of two masters at SUPELEC, a first one in initial
education for Economics and Management in the Network Industries in collaboration with the University Paris-Sud XI and a
second one in continuing education for Energy Managers. He published several articles and conference papers on grid operation
and investment in the liberalized power system with an increasing integration of renewable resource. He has successively done
these research works with the French TSO RTE and the French regulator CRE.
Jonathan Sprooten received the M.S. degree in electrical engineering and the Ph.D. degree in electrical engineering from the
Université Libre de Bruxelles (ULB) in 2001 and 2007 respectively. From 2001 to 2007, he was research and teaching assistant
in the electrical engineering dept. of ULB. Since then he is lecturer at the Hautes Etudes d'Ingenieur (HEI) and researcher of the
Laboratory of Electrotechnics and Power Electronics of Lille (L2EP). His current fields of interest include distributed generation,
congestion management, energy management and optimization.
14
Benoît Robyns received the “Ingénieur Civil Electricien” and the “Docteur en Sciences Appliquées” degrees from the
Université Catholique de Louvain (UCL), Louvain-la-Neuve, Belgium, in 1987 and 1993, respectively. He received the
“Habilitation à Diriger des Recherches” degrees from the Université des Sciences et Technologies de Lille (USTL), Lille, France,
in 2000. From 1988 to 1995, he was with the Laboratory of Electrotechnics and Instrumentation (LEI) of the Faculty of Applied
Sciences of the Catholic University of Louvain as an Assistant. Since 1995, he has been with the Electrotechnical Department of
the Ecole des Hautes Etudes d’Ingénieur (HEI), Lille, and he is currently the Director of Research of HEI. Since 1998, he has
been with the Laboratory of Electrotechnics and Power Electronics of Lille (L2EP) as a Researcher, and he is currently the head
of the “Electrical Network and Energetic Systems” research team. He is the author or coauthor of over 120 papers and one book
in the fields of digital control of electrical machines, renewable energies, and distributed generation. Prof. Robyns is a member of
the “Société Française des Electriciens et des Electroniciens” (SEE), of the “Société Royale Belge des Electriciens” (SRBE), and
of the “European Power Electronics Association” (EPE).
Jacques Deuse received the Electrical and Mechanical Engineer degree and the Ph.D. degree in electrical engineering from
the University of Liège, Liège, Belgium, in 1972 and 1976, respectively. He began working in the Belgian electric supply
industry in 1977 for developing software programes and methodologies for special studies. He is an expert in dynamic behavior
of electrical power systems; he participated in the development of EUROSTAG software. Since 1989, he has been working with
Tractebel Engineering—Suez as power system expert in the “Power System Consulting” Service of the “Energy and Industrial
Solutions” Department. He participated in a large number of studies: large international interconnection projects, security of
supply of auxiliaries of nuclear power plants, defence plans, analysis of blackouts, restoration plans, etc. He is presently
Technical Director of the European Project EU-DEEP about decentralized generation.