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Damien Ernst – University of Li`ege 
Email: dernst@ulg.ac.be 
ATRIAS 
September 2013 
1
From fit and forget to active network management 
Distribution networks traditionnally operated according to the 
fit and forget doctrine. 
Fit and forget. Network planning is made with respect to a set of 
critical scenarios so as to assure that sufficient operational margins 
are always ensured (i.e., no over/under voltage problems, overloads) 
without any control over the loads or the generation sources. 
Shortcomings. With rapid growth of distributed generation 
resources, the preservation of such conservative margins comes at 
continuously increasing network reinforcement costs. 
2
The buzzwords for avoiding prohibitively reinforcement costs: active 
network management. 
Active network management. Smart modulation of generation 
sources and loads (demand side management) so as to operate the 
electrical network in a safe way, without having to rely on significant 
investments in infrastructure. 
GREDOR project. Redesigning in an integrated way the whole 
decision chain used for managing distribution networks so as to do 
active network management in an optimal way (i.e., maximisation of 
social welfare). 
3
Decision chain 
The four stages of the decision chain for managing distribution 
networks: 
1. Models of interaction 
2. Investments 
3. Operational planning 
4. Real-time control 
4
1. Models of interaction 
A model of interaction defines the flows of information, services and 
money between the different actors. 
Defined (at least partially) in the regulation. 
2. Investments 
Decisions to build new cables, investing in telemeasurements, etc. 
5
3. Operational planning 
Decisions taken few minutes to a few days before real-time. 
Decisions that may interfere with energy markets. Example: decision 
to buy the day-ahead load flexibility to solve overload problems. 
Important: new investments may significantly affect the cost of 
operational strategies. 
4. Real-time control 
Almost real-time decisions. Typical examples of such decisions: 
modifying the reactive power injected by wind farms into the 
network, changing the tap setting of transformers. In the normal 
mode (no emergency situation caused by “unfortunate event”), 
these decisions should not modify production/consumption over a 
market period. 
6
GREDOR as an optimisation problem 
M : set of possible models of interaction 
I : set of possible investment strategies 
O : set of possible operational planning strategies 
R : set of possible real-time control strategies 
Solve: 
argmax 
social welfare(m, i, o, r) 
(m,i,o,r)2M×I×O×R 
7
A simple example 
M: reduced to one single element. 
Model of interaction mainly defined by these two components: 
1. Distribution Network Operator (DNO) can buy the day-ahead 
load flexibility service. 
2. Between the Uncertain beginning variables !of every market ... 
1 period, !T 
it can decide to 
curtail generation for the next market period or activate the load 
... 
flexibility service. System Curtailment state x1 decisions x2 have a x T-cost. 1 
xT Note: No 
uncertainty when taking these curtailment decisions. 
... 
u1 uT 
x0 
0h00 24h00 
Control actions 
Time 
u0 
Period 1 2 ... T 
Stage 0 ... 
1 2 T 
Day-ahead Intra-day 
8
More about the load modulation service 
 Any load offering a flexibility service must follow a baseline 
demand profile unless otherwise instructed by the DNO. 
 The loads specify upward and downward demand modification 
limits per market period. 
 Any modulation instructions by the DNO should not modify the 
net energy volume consumed by a flexible load over the market 
day, with respect to the baseline profile. 
 In the day-ahead, the DNO analyzes the different offers and 
selects the flexible loads it will use for the next market day. 
9
I: made of two elements. Either to invest in an asset A or not to 
invest in it. 
O: the set of operational strategies is the set of all algorithms that 
(i)in the day-ahead process information available to the DNO to 
decide which flexible loads to buy (ii) process before every market 
period this information to decide (a) how to modulate the flexible 
loads (b) how to curtail generation. 
R: empty set. No real-time control implemented. 
social welfare(m, i, o, r): the (expected) costs for the DNO. 
10
The optimal operational strategy 
Let o be an optimal operational strategy. Such a strategy has the 
following characteristics: 
1. for every market period, it leads to a safe operating point of the 
network (no overloads, no voltage problems). 
2. there are no strategies in O leading to a safe operating point of 
the network and a smaller(expected) total cost than o. The total 
expected cost is defined as the cost of buying flexiblity plus the 
costs for curtailing generation. 
It can be shown that the optimal operation strategy can be written 
as a stochastic sequential optimisation problem. 
Solving this problem is challenging. Getting even a good suboptimal 
solution may be particularly difficult for large distribution networks 
and/or when there are the day-ahead strong uncertainty on the 
power injected/withdrawn at the different nodes of the distribution 
network. 
11
The benchmark problem 
HV 
MV Bus 1 
Bus 3 
Bus 2 Bus 4 Bus 5 
12/05/13 16:23 
file:///Users/Quentin/Downloads/generator.svg Page 1 sur 1 
12/05/13 16:23 
file:///Users/Quentin/Downloads/generator.svg Page 1 sur 1 
Solar 
aggregated 
Wind 
Residential 
aggregated Industrial 
When Distributed Generation 
(DG) sources produce a lot of 
power: (I) overvoltage problem at 
Bus 4 (II) overload problem on the 
MV/HV transformer. 
Two flexible loads; only three market periods; possibility to curtail 
the two DG sources before every market period (at a cost). 
12
Information available to the DNO on the day-ahead 
The flexible load offers: 
0.0 1.5 3.0 4.5 6.0 7.5 
Periods 
Load in MW 
1 2 3 
0.0 1.5 3.0 4.5 6.0 7.5 Periods 
Load in MW 
1 2 3 
Left: residential aggregated. Price pr. 
Right: Industrial. Price pi. 
Scenario tree for representing 
uncertainty: 
6 
increase the dimensions of the problem. In the following 
sections, we describe results obtained on a small test system, 
with a short time horizon and a moderate number of scenarios. 
In the concluding section, we discuss pitfalls and avenues for 
solving realistic scale instances. 
Case Study 
We analyze issues arising at the MV level in some Belgian 
distribution systems (Figure 5). Often, wind-farms are directly 
connected to the HV/MV transformer, as modeled in our test 
system by the generator connected to bus 2. Power off-takes 
and injections induced by residential consumers are aggregated 
at bus 4 by a load and a generator representing the total 
production of PV panels. Finally, the load connected to bus 5 
represents an industrial consumer. 
HV 
MV Bus 1 
Bus 3 
Bus 2 Bus 4 Bus 5 
D-A 
W = 80% 
S = 20% 
W = 70% 
S = 40% 
W = 80% 
S = 70% 
W = 65% 
S = 50% 
0.5 
0.5 
W = 40% 
S = 30% 
W = 60% 
S = 60% 
W = 45% 
S = 40% 
0.6 
0.4 
0.3 
0.7 
W = 60% 
S = 10% 
W = 35% 
S = 30% 
W = 35% 
S = 60% 
W = 25% 
S = 40% 
0.5 
0.5 
W = 20% 
S = 20% 
W = 20% 
S = 50% 
W = 10% 
S = 30% 
0.6 
0.4 
0.6 
0.4 
0.3 
0.7 
(a) Scenario tree used for the case study. The nodes show the values of 
the random variables and the label on the edges define the transition 
probabilities between nodes. 
● 
● 
● 
● 
2 4 6 8 10 12 14 
Total generation of DG units in MW 
● 
● 
● 
● 
● 
● 
● 
● 
● 
W = Wind; S = Sun. 
Additional information: a load-flow model of the network; the price 
(per MWh) for curtailing generation. 
13
Decisions output by o 
The day-ahead: To buy flexibility offer from the residential 
aggregated load. 
Before every market period: 
We report results when gen-eration 
follows this scenario: 
7 
0.0 1.5 3.0 4.5 6.0 7.5 
Periods 
Load in MW 
1 2 3 
0.0 1.5 3.0 4.5 6.0 7.5 
Periods 
Load in MW 
1 2 3 
Fig. 7: Flexible loads, with P, P and P represented respec-tively 
by dashed, continuous and dotted lines. 
D-A 
W = 80% 
S = 20% 
W = 70% 
S = 40% 
W = 80% 
S = 70% 
W = 65% 
S = 50% 
0.5 
0.5 
W = 40% 
S = 30% 
W = 60% 
S = 60% 
W = 45% 
S = 40% 
0.6 
0.4 
0.3 
0.7 
W = 60% 
S = 10% 
W = 35% 
S = 30% 
W = 35% 
S = 60% 
W = 25% 
S = 40% 
0.5 
0.5 
W = 20% 
S = 20% 
W = 20% 
S = 50% 
W = 10% 
S = 30% 
0.6 
0.4 
0.6 
0.4 
0.3 
0.7 
Fig. 8: Selected scenario 
Scenario analysis 
Interest of the stochastic model 
The first thing to notice from the results presented in Table ?? 
Results: Generation never cur-tailed. 
Load modulated as follows: 
0.0 1.5 3.0 4.5 6.0 7.5 
0 50 100 150 200 250 
0 1 2 3 4 
Availability price of flexibility 
Curtailed power 
50 100 150 200 250 300 
Objective 
Curtailed Power 
Objective 
2 flexible 
loads 
1 flexible load 
0 flexible 
load 
Fig. 9: Flexibility cost analysis. 
Load in MW 
scenarios. 
In the considered case study, the mean scenario technique 
make the decision not to buy flexibility while the tree-based 
approach only buy the flexibility of the residential load. 
Interest of loads’ flexibility 
Highlight ability to detect 1 whether 2 DSM is function of bid prices vs. issues in the system. 
3 
interesting as 
Periods 
cf. Figure ?? !! To update with new case study !! 
14 
Implementation and algorithmic details 
It is not easy to find a solver that can manage this MINLP, even
On the importance of managing well uncertainty 
E{cost} max cost min cost std dev. 
o 46$ 379$ 30$ 72$ 
MSA 73$ 770$ 0$ 174$ 
where MSA stands for Mean Scenario Strategy. 
Observations: Managing well uncertainty leads to smaller expected 
costs than working along a mean scenario. 
More results in: 
“Active network management: planning under uncertainty for exploiting load 
modulation” Q. Gemine, E. Karangelos, D. Ernst and B. Corn´elusse. Proceedings 
of the 2013 IREP Symposium-Bulk Power System Dynamics and Control -IX 
(IREP), August 25-30, 2013, Rethymnon, Greece, 9 pages. 
15
The optimal investment strategy 
Remember that we had to choose between doing investment A or not. Let AP be 
the amortization period and cost A the cost of investment A. The optimal 
investment strategy can be defined as follows: 
1. Simulate using operational strategy o the distribution network 
with element A several times over a period of AP years. Extract 
from the simulations the expected cost of using o during AP 
years. Let cost o with A be this cost. 
2. Simulate using operational strategy o the distribution network 
without element A several times over a period of AP years. 
Extract from the simulations the expected cost of using o during 
AP years. Let cost o without A be this cost. 
3. If cost A+cost o with A  cost o without A, do investment A. 
Otherwise not. 
16
GREDOR project: four main challenges 
Data: Difficulties for DNOs to gather the right data for building the 
decision models (especially for real-time control). 
Computational challenges: Many of the optimisation problems in 
GREDOR are out of reach of state-of-the-art techniques. 
Definition of social welfare(·, ·, ·, ·) function: Difficulties to reach 
a consensus on what is social welfare, especially given that actors in 
the electrical sector have conflicting interests. 
Human factor: Engineers from distribution companies have to break 
away from their traditional practices. They need incentives for 
changing their working habits. 
17
Acknowledgements 
1. To all the partners of the GREDOR project: EDF-LUMINUS, 
ELIA, ORES, ULG, UMONS, TECTEO RESA, TRACTEBEL 
ENGINEERING. 
2. To the Public Service of Wallonia - Department of Energy and 
Sustainable Building for funding this research. 
18
Website: www.gredor.be 
19

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GREDOR Presentation

  • 1. Damien Ernst – University of Li`ege Email: dernst@ulg.ac.be ATRIAS September 2013 1
  • 2. From fit and forget to active network management Distribution networks traditionnally operated according to the fit and forget doctrine. Fit and forget. Network planning is made with respect to a set of critical scenarios so as to assure that sufficient operational margins are always ensured (i.e., no over/under voltage problems, overloads) without any control over the loads or the generation sources. Shortcomings. With rapid growth of distributed generation resources, the preservation of such conservative margins comes at continuously increasing network reinforcement costs. 2
  • 3. The buzzwords for avoiding prohibitively reinforcement costs: active network management. Active network management. Smart modulation of generation sources and loads (demand side management) so as to operate the electrical network in a safe way, without having to rely on significant investments in infrastructure. GREDOR project. Redesigning in an integrated way the whole decision chain used for managing distribution networks so as to do active network management in an optimal way (i.e., maximisation of social welfare). 3
  • 4. Decision chain The four stages of the decision chain for managing distribution networks: 1. Models of interaction 2. Investments 3. Operational planning 4. Real-time control 4
  • 5. 1. Models of interaction A model of interaction defines the flows of information, services and money between the different actors. Defined (at least partially) in the regulation. 2. Investments Decisions to build new cables, investing in telemeasurements, etc. 5
  • 6. 3. Operational planning Decisions taken few minutes to a few days before real-time. Decisions that may interfere with energy markets. Example: decision to buy the day-ahead load flexibility to solve overload problems. Important: new investments may significantly affect the cost of operational strategies. 4. Real-time control Almost real-time decisions. Typical examples of such decisions: modifying the reactive power injected by wind farms into the network, changing the tap setting of transformers. In the normal mode (no emergency situation caused by “unfortunate event”), these decisions should not modify production/consumption over a market period. 6
  • 7. GREDOR as an optimisation problem M : set of possible models of interaction I : set of possible investment strategies O : set of possible operational planning strategies R : set of possible real-time control strategies Solve: argmax social welfare(m, i, o, r) (m,i,o,r)2M×I×O×R 7
  • 8. A simple example M: reduced to one single element. Model of interaction mainly defined by these two components: 1. Distribution Network Operator (DNO) can buy the day-ahead load flexibility service. 2. Between the Uncertain beginning variables !of every market ... 1 period, !T it can decide to curtail generation for the next market period or activate the load ... flexibility service. System Curtailment state x1 decisions x2 have a x T-cost. 1 xT Note: No uncertainty when taking these curtailment decisions. ... u1 uT x0 0h00 24h00 Control actions Time u0 Period 1 2 ... T Stage 0 ... 1 2 T Day-ahead Intra-day 8
  • 9. More about the load modulation service Any load offering a flexibility service must follow a baseline demand profile unless otherwise instructed by the DNO. The loads specify upward and downward demand modification limits per market period. Any modulation instructions by the DNO should not modify the net energy volume consumed by a flexible load over the market day, with respect to the baseline profile. In the day-ahead, the DNO analyzes the different offers and selects the flexible loads it will use for the next market day. 9
  • 10. I: made of two elements. Either to invest in an asset A or not to invest in it. O: the set of operational strategies is the set of all algorithms that (i)in the day-ahead process information available to the DNO to decide which flexible loads to buy (ii) process before every market period this information to decide (a) how to modulate the flexible loads (b) how to curtail generation. R: empty set. No real-time control implemented. social welfare(m, i, o, r): the (expected) costs for the DNO. 10
  • 11. The optimal operational strategy Let o be an optimal operational strategy. Such a strategy has the following characteristics: 1. for every market period, it leads to a safe operating point of the network (no overloads, no voltage problems). 2. there are no strategies in O leading to a safe operating point of the network and a smaller(expected) total cost than o. The total expected cost is defined as the cost of buying flexiblity plus the costs for curtailing generation. It can be shown that the optimal operation strategy can be written as a stochastic sequential optimisation problem. Solving this problem is challenging. Getting even a good suboptimal solution may be particularly difficult for large distribution networks and/or when there are the day-ahead strong uncertainty on the power injected/withdrawn at the different nodes of the distribution network. 11
  • 12. The benchmark problem HV MV Bus 1 Bus 3 Bus 2 Bus 4 Bus 5 12/05/13 16:23 file:///Users/Quentin/Downloads/generator.svg Page 1 sur 1 12/05/13 16:23 file:///Users/Quentin/Downloads/generator.svg Page 1 sur 1 Solar aggregated Wind Residential aggregated Industrial When Distributed Generation (DG) sources produce a lot of power: (I) overvoltage problem at Bus 4 (II) overload problem on the MV/HV transformer. Two flexible loads; only three market periods; possibility to curtail the two DG sources before every market period (at a cost). 12
  • 13. Information available to the DNO on the day-ahead The flexible load offers: 0.0 1.5 3.0 4.5 6.0 7.5 Periods Load in MW 1 2 3 0.0 1.5 3.0 4.5 6.0 7.5 Periods Load in MW 1 2 3 Left: residential aggregated. Price pr. Right: Industrial. Price pi. Scenario tree for representing uncertainty: 6 increase the dimensions of the problem. In the following sections, we describe results obtained on a small test system, with a short time horizon and a moderate number of scenarios. In the concluding section, we discuss pitfalls and avenues for solving realistic scale instances. Case Study We analyze issues arising at the MV level in some Belgian distribution systems (Figure 5). Often, wind-farms are directly connected to the HV/MV transformer, as modeled in our test system by the generator connected to bus 2. Power off-takes and injections induced by residential consumers are aggregated at bus 4 by a load and a generator representing the total production of PV panels. Finally, the load connected to bus 5 represents an industrial consumer. HV MV Bus 1 Bus 3 Bus 2 Bus 4 Bus 5 D-A W = 80% S = 20% W = 70% S = 40% W = 80% S = 70% W = 65% S = 50% 0.5 0.5 W = 40% S = 30% W = 60% S = 60% W = 45% S = 40% 0.6 0.4 0.3 0.7 W = 60% S = 10% W = 35% S = 30% W = 35% S = 60% W = 25% S = 40% 0.5 0.5 W = 20% S = 20% W = 20% S = 50% W = 10% S = 30% 0.6 0.4 0.6 0.4 0.3 0.7 (a) Scenario tree used for the case study. The nodes show the values of the random variables and the label on the edges define the transition probabilities between nodes. ● ● ● ● 2 4 6 8 10 12 14 Total generation of DG units in MW ● ● ● ● ● ● ● ● ● W = Wind; S = Sun. Additional information: a load-flow model of the network; the price (per MWh) for curtailing generation. 13
  • 14. Decisions output by o The day-ahead: To buy flexibility offer from the residential aggregated load. Before every market period: We report results when gen-eration follows this scenario: 7 0.0 1.5 3.0 4.5 6.0 7.5 Periods Load in MW 1 2 3 0.0 1.5 3.0 4.5 6.0 7.5 Periods Load in MW 1 2 3 Fig. 7: Flexible loads, with P, P and P represented respec-tively by dashed, continuous and dotted lines. D-A W = 80% S = 20% W = 70% S = 40% W = 80% S = 70% W = 65% S = 50% 0.5 0.5 W = 40% S = 30% W = 60% S = 60% W = 45% S = 40% 0.6 0.4 0.3 0.7 W = 60% S = 10% W = 35% S = 30% W = 35% S = 60% W = 25% S = 40% 0.5 0.5 W = 20% S = 20% W = 20% S = 50% W = 10% S = 30% 0.6 0.4 0.6 0.4 0.3 0.7 Fig. 8: Selected scenario Scenario analysis Interest of the stochastic model The first thing to notice from the results presented in Table ?? Results: Generation never cur-tailed. Load modulated as follows: 0.0 1.5 3.0 4.5 6.0 7.5 0 50 100 150 200 250 0 1 2 3 4 Availability price of flexibility Curtailed power 50 100 150 200 250 300 Objective Curtailed Power Objective 2 flexible loads 1 flexible load 0 flexible load Fig. 9: Flexibility cost analysis. Load in MW scenarios. In the considered case study, the mean scenario technique make the decision not to buy flexibility while the tree-based approach only buy the flexibility of the residential load. Interest of loads’ flexibility Highlight ability to detect 1 whether 2 DSM is function of bid prices vs. issues in the system. 3 interesting as Periods cf. Figure ?? !! To update with new case study !! 14 Implementation and algorithmic details It is not easy to find a solver that can manage this MINLP, even
  • 15. On the importance of managing well uncertainty E{cost} max cost min cost std dev. o 46$ 379$ 30$ 72$ MSA 73$ 770$ 0$ 174$ where MSA stands for Mean Scenario Strategy. Observations: Managing well uncertainty leads to smaller expected costs than working along a mean scenario. More results in: “Active network management: planning under uncertainty for exploiting load modulation” Q. Gemine, E. Karangelos, D. Ernst and B. Corn´elusse. Proceedings of the 2013 IREP Symposium-Bulk Power System Dynamics and Control -IX (IREP), August 25-30, 2013, Rethymnon, Greece, 9 pages. 15
  • 16. The optimal investment strategy Remember that we had to choose between doing investment A or not. Let AP be the amortization period and cost A the cost of investment A. The optimal investment strategy can be defined as follows: 1. Simulate using operational strategy o the distribution network with element A several times over a period of AP years. Extract from the simulations the expected cost of using o during AP years. Let cost o with A be this cost. 2. Simulate using operational strategy o the distribution network without element A several times over a period of AP years. Extract from the simulations the expected cost of using o during AP years. Let cost o without A be this cost. 3. If cost A+cost o with A cost o without A, do investment A. Otherwise not. 16
  • 17. GREDOR project: four main challenges Data: Difficulties for DNOs to gather the right data for building the decision models (especially for real-time control). Computational challenges: Many of the optimisation problems in GREDOR are out of reach of state-of-the-art techniques. Definition of social welfare(·, ·, ·, ·) function: Difficulties to reach a consensus on what is social welfare, especially given that actors in the electrical sector have conflicting interests. Human factor: Engineers from distribution companies have to break away from their traditional practices. They need incentives for changing their working habits. 17
  • 18. Acknowledgements 1. To all the partners of the GREDOR project: EDF-LUMINUS, ELIA, ORES, ULG, UMONS, TECTEO RESA, TRACTEBEL ENGINEERING. 2. To the Public Service of Wallonia - Department of Energy and Sustainable Building for funding this research. 18