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Advanced Analytics to Capture the Full Value of Demand Response and Energy Flexibility in
Industrial SitesESGI 2016
May 2016, Avignon
Sébastien Mouthuy, N-SIDE
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Why and how to leverage flexibility from energy-intensive processes ?
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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
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80.00
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EUR/MWh
S S M T W T F
Advanced Analytics to transform this complexity into opportunities
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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
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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
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How to design an optimal energy flexibility strategy ?
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Process Aspects:Where to find and how to manage my flexibility ?
MarketsWhere to value my
flexibility ?
ProcessesWhere to find and how to manage my flexibility
?
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Strong Incentive to optimize planning
with respect to variable electricity
price
The Concept:Flexible process planning based on electricity prices
MarketsWhere to value my
flexibility ?
ProcessesWhere 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
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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 AccessIndirect Market Access
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Strategic Optimization
Reserve Optimization
Scheduling Optimization
Real-time Optimization
… on the different key timeframes
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• 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
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The Concept:Cement Industry Example
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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
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The Concept:Cement Industry Example
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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
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The Concept:Cement Industry Example
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Continuous Process – ON/OFF
Raw Mill:• Overcapacity:
40%• Max Load:
10 MW• ON-OFF principle• No possibility of modulation
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The Concept:Cement Industry Example
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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
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The Concept:Cement Industry Example
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Continuous Process – ON/OFF
Cement Mill:• Overcapacity: 40 %• 3 Cement Mills: 100T/h• Max Load:
15 MW• ON/OFF principle• No possibility of modulation
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The Concept:Cement Industry Example
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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
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The Concept:Paper Industry Example
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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
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The Concept:Paper Industry Example
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• 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
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The Concept:Paper Industry Example
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• 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
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The Concept:Different types of flexible planning…
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Batch – ON/OFF Process
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Continuous – ON/OFF Process
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Monday Tuesday Wednesday Thursday Friday Saterday Sunday
Continuous – Modulation Process
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1012141618
Monday Tuesday Wednesday Thursday Friday Saterday Sunday
Monday Tuesday Wednesday Thursday Friday Saterday Sunday
Multi-Product Process
Monday Tuesday Wednesday Thursday Friday Saterday Sunday
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The Concept:… with opportunities in various energy intensive industries
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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
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The process challenge: Where to find and how to manage my flexibilities ?
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Produce electricity at optimal moment
ElectricityGeneration
Electricity Consumption
Consume electricity at optimal moment
Load Shifting
Load Scheduling
Load SheddingBy-product Optimization
2
1
3 6
CHP Modulation5
Fuel Switching4
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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
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A mathematical model is key for considering all the factors…
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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
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…in an integrated way…
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Electricity Generation
Electricity Consumption
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Modelling industrial processes
Electricity consumption
Blend
x %
y %
z %
Bounds
Yield
Steady
ShutdownStartup
Profile Ramping
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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)
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
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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).
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Modeling batch processes
pack
pack
pack
pack
pack
pack
urgent shipping
urgent shipping
to stock
Order 2241
Order 2242
Order 2243
Order 2244
Order 2245
Order 2246
to stock
to stockto stock
Only one packing machine
Precedence Complete urgent orders first
MWh
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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
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
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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
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The resulting integrated optimization problem is solved using decomposition techniques
Production Scheduling
Electricity Management
• Start of batch production• Machine usage• HR planning Compute electricity
demand
Identify mismatchproduction <-> electricity cost • Cogen management
• When to turn on/off• Needs of by-products
• Total energy cost
Optimality
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The model should take into account multiple markets and time frames
Scheduling Optimization
Real-time Optimization
Choose which processes to stop in response to an activation
Day-ahead Market
ReserveMarket
Bid interruptible consumptions on the reserve market
Nominations on DAM Respect balance
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Real-time activation is uncertain when committing a schedule
Scheduling Optimization
Real-time Optimization
Day-ahead Market
ReserveMarket
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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
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
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Technical challenges arising from MSSP
• Problem size rises exponentially with the number of scenarios.
• Generating the minimal number of relevant scenarios is crucial
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Helping industries get the full value of their energy flexibility thanks to advanced analytics
Produce electricity at optimal moment
Optimal Planning of energy production
Optimal Scheduling of energy consumption
Consume energy at optimal moment
Inte
grat
ed O
ptim
izatio
n
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Thank you !