Nowadays, with the increasing volatility of electricity prices and the growing importance of Demand Response (DR) programs, there are more and more incentives for energy-intensive industrial sites to be flexible in terms of electricity consumption and generation. However, the complexity of these large industrial plants, having to deal with many interconnected processes, multiple energy flows, integrated CHP and renewable electricity generation, as well as many technical constraints is often a barrier to fully leverage the different energy flexibility levers of the plant. In this presentation, we propose an advanced analytics approach to help industrial sites capture the full value of their energy flexibility. Thanks to integrated mathematical models of the full energy ecosystem of the plant which will include the constraints, the flexibility of the different processes and their complex interdependencies; it is now possible to predict the total cost impact of different energy management decisions and also to optimize these decisions with respect to electricity price and demand response incentives. Based on this optimization model, key decisions can be taken over different time horizons: Long term: investment in energy flexibility assets and choice of energy contract Mid term: choice of flexibility products Day-ahead: scheduling of electricity load and internal production Real-time: imbalance and activation management This approach will be illustrated on steel, cement and pulp and paper industries.

1. Advanced Analytics to Capture the Full Value
of Demand Response and Energy Flexibility in
Industrial Sites
ESGI 2016
May 2016, Avignon
Sébastien Mouthuy, N-SIDE

2. Why and how to leverage flexibility
from energy-intensive processes ?
2
Increasing complexity of the energy
structure of the industries
Increasing need for Demand Response
services
Increasing amount of data to be
leveraged
Increasing electricity price volatility
0.00
20.00
40.00
60.00
80.00
100.00
120.00
EUR/MW
h
S S M T W T F
Advanced Analytics
to transform this
complexity into
opportunities

3. 1. Demand Response: Opportunities and challenges for
industrial sites
A. The markets: Where to value my flexibility ?
B. The processes: Where to find and how to manage
my flexibility ?
2. Advanced Analytics to make the most out of energy
flexibility

4. 1. Demand Response: Opportunities and challenges for
industrial sites
A. The markets: Where to value my flexibility ?
B. The processes: Where to find and how to manage
my flexibility ?
2. Advanced Analytics to make the most out of energy
flexibility

5. How to design an optimal energy flexibility strategy ?
5
Process Aspects:
Where to find and
how to manage my
flexibility ?
Markets
Where to value my
flexibility ?
Processes
Where to find and how
to manage my flexibility
?

6. 6
Strong Incentive to
optimize planning
with respect to
variable electricity
price
The Concept:
Flexible process planning based on electricity prices
Markets
Where to value my
flexibility ?
Processes
Where to find and how
to manage my flexibility
?
• TOU (time of use)
• Spot-based contracts
• Balancing price impacted
• …
• Oversized processes allowing
to shift load
• Multi-product processes with
difference of electricity
consumptions
Variable electricity price
Electricity-Intensive
process with flexibility



7. 7
Years / Months in advance 1 to 7 days in advance Real-time
Day-ahead market
Spot-price based
contracts
Intraday and balancing
markets
Deviation penalties
Price
based
Forward contracts (OTC)
Fixed contracts
Reserve
Reserve participation
(e.g. France, Belgium)
Contract with an
aggregator
Activation from TSO
Activation from
aggregator
Reserve participation
(e.g. Germany, Austria)
The market challenge:
Where to value my flexibility ?
Direct Market Access
Indirect Market Access

8. Strategic
Optimization
Reserve
Optimization
Scheduling
Optimization
Real-time
Optimization
… on the different key timeframes
8
• Optimal
electricity
contract
• Optimal
investment in
flexibility assets
• Optimal choice of
flexibility products
and volumes
• Optimal power and
energy price
• Optimal
scheduling of
electricity load
• Optimal
planning of CHP
unit
• Optimal
imbalance
minimization
• Optimal
activation
management

9. Limestone Crushers Storage Raw Mill Storage
Preheater Tower
Cement KilnClinker CoolerStorageGypsum
Cement Mill Storage Shipping
The Concept:
Cement Industry Example
9
Integrated Cement Plant
• 2.5 Million Tons of cement
• Electricity Consumption: 300 GWh/Year
Kiln Feed Preparation
Clinker Production
Clinker Grinding
Continuous Process – ON/OFF

10. Limestone Crushers Storage Raw Mill Storage
Preheater Tower
Cement KilnClinker CoolerStorageGypsum
Cement Mill Storage Shipping
The Concept:
Cement Industry Example
10
Clinker Production:
• Main Process in the Cement
production
• Continuous process
• No possibility to stop
• Load : 10 MW
Clinker Production
 Mostly not flexible
Continuous Process – ON/OFF

11. Limestone Crushers Storage Raw Mill Storage
Preheater Tower
Cement KilnClinker CoolerStorageGypsum
Cement Mill Storage Shipping
The Concept:
Cement Industry Example
11
Continuous Process – ON/OFF
Raw Mill:
• Overcapacity: 40%
• Max Load: 10 MW
• ON-OFF principle
• No possibility of
modulation

12. Limestone Crushers Storage Raw Mill Storage
Preheater Tower
Cement KilnClinker CoolerStorageGypsum
Cement Mill Storage Shipping
The Concept:
Cement Industry Example
12
Continuous Process – ON/OFF
Flexibility Lever 1: Optimization of the raw
mill planning based on the electricity price
by leveraging the overcapacity and the
up/downstream storage
Raw Mill:
• Overcapacity: 40%
• Max Load: 10 MW
• ON-OFF principle
• No possibility of
modulation

13. Limestone Crushers Storage Raw Mill Storage
Preheater Tower
Cement KilnClinker CoolerStorageGypsum
Cement Mill Storage Shipping
The Concept:
Cement Industry Example
13
Continuous Process – ON/OFF
Cement Mill:
• Overcapacity: 40 %
• 3 Cement Mills: 100T/h
• Max Load: 15 MW
• ON/OFF principle
• No possibility of
modulation

14. Limestone Crushers Storage Raw Mill Storage
Preheater Tower
Cement KilnClinker CoolerStorageGypsum
Cement Mill Storage Shipping
The Concept:
Cement Industry Example
14
Continuous Process – ON/OFF
Flexibility Lever 2: Optimization of the cement
mill planning based on the electricity price by
leveraging the overcapacity and the
up/downstream storage
Cement Mill:
• Overcapacity: 40 %
• 3 Cement Mills: 100T/h
• Max Load: 15 MW
• ON/OFF principle
• No possibility of
modulation

15. The Concept:
Paper Industry Example
15
Paper machine
• Power: 15MW
• Light overcapacity: 10%
• Modulation possibility
• Multiple papers
• Consumption/ Paper type
• Up/downstream stock
• No ON/OFF
• Paperweight: Light
• Load: 13 MW
• 1/3 Production
• Paperweight: Heavy
• Load: 17 MW
• 1/3 Production
• Paperweight: Medium
• Load: 15 MW
• 1/3 Production
Continuous Process – Modulation

16. The Concept:
Paper Industry Example
16
• Paperweight: Light
• Load: 13 MW
• 1/3 Production
• Paperweight: Heavy
• Load: 17 MW
• 1/3 Production
• Paperweight: Medium
• Load: 15 MW
• 1/3 Production
Continuous Process – Modulation
Flexibility Lever 1: Modulate the load
depending on electricity price
Paper machine
• Power: 15MW
• Light overcapacity: 10%
• Modulation possibility
• Multiple papers
• Consumption/ Paper type
• Up/downstream stock
• No ON/OFF

17. The Concept:
Paper Industry Example
17
• Paperweight: Light
• Load: 13 MW
• 1/3 Production
• Paperweight: Heavy
• Load: 17 MW
• 1/3 Production
• Paperweight: Medium
• Load: 15 MW
• 1/3 Production
Continuous Process – Modulation
Flexibility Lever 2: Schedule the
different products depending on
electricity prices
Paper machine
• Power: 15MW
• Light overcapacity: 10%
• Modulation possibility
• Multiple papers
• Consumption/ Paper type
• Up/downstream stock
• No ON/OFF

18. The Concept:
Different types of flexible planning…
18
Batch – ON/OFF Process
Monday Tuesday Wednesday Thursday Friday Saterday Sunday
0
10
20
30
40
50
Continuous – ON/OFF Process
0
2
4
6
8
Monday Tuesday Wednesday Thursday Friday Saterday Sunday
Continuous – Modulation Process
0
5
10
15
20
Monday Tuesday Wednesday Thursday Friday Saterday Sunday
Monday Tuesday Wednesday Thursday Friday Saterday Sunday
Multi-Product Process
Monday Tuesday Wednesday Thursday Friday Saterday Sunday

19. The Concept:
… with opportunities in various energy intensive industries
19
Continuous – ON/OFF Process
• Mechanical pulp production
• Compressors in Air separation
• Extrusion in chemical industry
• Electrolysis
Batch – ON/OFF Process
• EAF in steel production
• Pulp mixer in non-integrated paper mill
Continuous – Modulation Process
• Paper machine
• Compressors in Air separation
• Extrusion in chemical industry
Multi-Product Process
• Paper machine Schedule of
different products
• EAF schedule of different products

20. The process challenge:
Where to find and how to manage my flexibilities ?
20
Produce electricity at optimal moment
Electricity
Generation
Electricity
Consumption
Consume electricity at optimal moment
Load Shifting
Load Scheduling
Load Shedding
By-product
Optimization
2
1
3
6
CHP Modulation5
Fuel Switching4

21. 1. Demand Response: Opportunities and challenges for
industrial sites
A. The market aspect: Where to value my flexibility ?
B. The process aspect: Where to find and how to
manage my flexibility ?
2. Advanced Analytics to make the most out of energy
flexibility

22. A mathematical model is key
for considering all the factors…
22
Industrial processes
• All input and output flows
• Ramping up and down
• Min-max capacities
• ON-Off procedure
• Operating rates
Product Demand
• Quantities and delivery dates
• Must / May serve
Grid and market
interaction
• Different electricity
contracts (OTC,
spot based)
• Capacity
constraints
Storage facilities
• Min-max capacities
• Storage target
Economics
• RM, NG and electricity costs
• Revenue from sales
• Opportunity costs
• Fix and variable operating
costs
• Incentive from DR programs
CHP Unit
(boilers, turbines, …)
• Min-max capacities
• Operating rates
• Ramping up and down
• ON-OFF procedure
• Different steam pressure
levels
• Fuel mix constraints
• Maintenance planning

23. …in an integrated way…
23
Electricity Generation
Electricity Consumption

24. Modelling industrial processes
24
Electricity
consumption
Blend
x
%
y
%
z %
Bounds
Yield
Steady
ShutdownStartup
Profile
Ramping

25. Mixed Integer Programming (MIP)
for industrial processes
Modeling an industrial process
• Mostly continuous variables to model quantities (e.g. flows between processes)
• Some binary variables, to capture the discrete nature of some decisions (e.g. on-
off status)
• Usually linear(isable) constraints (e.g. piecewise linear representation of the yield
of the machines)
25
Finding the optimal set of decisions
• Difficult problems: non-convex, no polynomial-
time algorithm
• In practice, with a good solver, global optimum is
found within a few minutes
• Widely used branch-and-bound algorithm:
recursive tree search of binary options
Linear
relaxations

26. Some technical challenges arising from MIP
• Solving time may rise exponentially with the number of
binary variables and the addition of coupling constraints
• Ill-conditioning and numerical difficulties are likely to arise
with data of poor quality
• Some processes are better represented by non-linear
constraints and integer variables (i.e. batch processes).
26

27. Modeling batch processes
27
pack
pack
pack
pack
pac
k
pack
urgent shipping
urgent shipping
to stock
Order 2241
Order 2242
Order 2243
Order 2244
Order 2245
Order 2246
to stock
to stock
to stock
Only one
packing
machine
Precedence Complete
urgent
orders first
MWh

28. Constraint Programming (CP)
for batch production
Modeling a production unit of a batch process
• Only discrete variables, to model machines performing activities as well as start
and end time of activities
• Need for disjunctive constraints: at most one activity scheduled on a given
machine at any time, stock evolution, setup times,…
• Complex precedence and transition constraints
28
Finding the optimal set of decisions
• Difficult problems: highly non-linear, no known
polynomial-time algorithm
• In practice, with a good solver, very good solution
is found within a few minutes
• Widely used propagation algorithms: do not
explore decisions leading to a dead-end
Propagation

29. Some Technical challenges arising from CP
• Algorithmic efficiency strongly depends on how production has been
modelled using CP constraints
– Several models are possible
– Good model may be millions of time faster than bad ones
– Requires expertise
• Constraint programming enables to exploit knowledge humans have of the
production process
– Advantage: leads to more efficient algorithms
– Disadvantage: no automatic configuration with no brain efforts
– Advange by far worth the effort
29

30. The resulting integrated optimization problem
is solved using decomposition techniques
30
Production Scheduling
Electricity Management
• Start of batch production
• Machine usage
• HR planning Compute electricity
demand
Identify mismatch
production <-> electricity cost • Cogen management
• When to turn on/off
• Needs of by-products
• Total energy cost
Optimality

31. The model should take into account
multiple markets and time frames
31
Scheduling
Optimization
Real-time
Optimization
Choose which processes to
stop in response to an
activation
Day-
ahead
Market
Reserve
Market
Bid interruptible
consumptions on the
reserve market
Nominations on DAM Respect balance

32. Real-time activation is uncertain
when committing a schedule
32
Scheduling
Optimization
Real-time
Optimization
Day-
ahead
Market
Reserve
Market

33. Multi-Stage Stochastic Programming (MSSP)
for multi-market optimization
Multi-market modelling
• Multiple real-time scenarios should be considered
• Non-anticipativity constraints: scheduling decisions should be taken
commonly for all real-time scenarios
33
Finding the optimal set of decisions
• Difficult problems: non-convex, no polynomial-
time algorithm
• In practice, with a good solver, global optimum is
found within a few minutes
• Widely used decomposition algorithm: solve a
reduced problem and add only the violated
constraints
Reduced
problems

34. Technical challenges arising from MSSP
• Problem size rises exponentially with the number of
scenarios.
• Generating the minimal number of relevant scenarios is
crucial
34

35. Helping industries get the full value of their energy
flexibility thanks to advanced analytics
35
Produce electricity at
optimal moment
Optimal Planning of
energy production
Optimal Scheduling of
energy consumption
Consume energy at
optimal moment
IntegratedOptimization

36. Thank you !