Advanced Analytics to Capture the Full Value of Demand Response and Energy Flexibility in Industrial Sites

Transcription

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? Increasing electricity price volatility EUR/MW h S S M T W T F Increasing complexity of the energy structure of the industries Advanced Analytics to transform this complexity into opportunities Increasing need for Demand Response services Increasing amount of data to be leveraged 2

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? Markets Where to value my flexibility? Process Aspects: Where to find and how to manage my flexibility? Processes Where to find and how to manage my flexibility? 5

6 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? Variable electricity price TOU (time of use) Spot-based contracts Balancing price impacted Electricity-Intensive process with flexibility Oversized processes allowing to shift load Multi-product processes with difference of electricity consumptions ü ü Strong Incentive to optimize planning with respect to variable electricity price 6

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

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

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

10 The Concept: Cement Industry Example Continuous Process ON/OFF Limestone) Crushers) Storage) Raw)Mill) Storage) Clinker Production Preheater)Tower) Gypsum) Storage) Clinker)Cooler) Cement)Kiln) Cement)Mill) Storage) Shipping) è Mostly not flexible Clinker Production: Main Process in the Cement production Continuous process No possibility to stop Load : 10 MW 10

11 The Concept: Cement Industry Example Continuous Process ON/OFF Limestone) Crushers) Storage) Raw)Mill) Storage) Preheater)Tower) Gypsum) Storage) Clinker)Cooler) Cement)Kiln) Cement)Mill) Storage) Shipping) Raw Mill: Overcapacity: 40% Max Load: 10 MW ON-OFF principle No possibility of modulation 11

12 The Concept: Cement Industry Example Continuous Process ON/OFF Limestone) Crushers) Storage) Raw)Mill) Storage) Preheater)Tower) Gypsum) Storage) Clinker)Cooler) Cement)Kiln) Cement)Mill) Storage) Shipping) 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 12

13 The Concept: Cement Industry Example Continuous Process ON/OFF Limestone) Crushers) Storage) Raw)Mill) Storage) Gypsum) Storage) Clinker)Cooler) Cement)Kiln) Cement)Mill) Storage) Shipping) Preheater)Tower) Cement Mill: Overcapacity: 40 % 3 Cement Mills: 100T/h Max Load: 15 MW ON/OFF principle No possibility of modulation 13

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

15 The Concept: Paper Industry Example Continuous Process Modulation 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: Medium Load: 15 MW 1/3 Production Paperweight: Heavy Load: 17 MW 1/3 Production 15

16 The Concept: Paper Industry Example Continuous Process Modulation 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: Medium Load: 15 MW 1/3 Production Flexibility Lever 1: Modulate the load depending on electricity price Paperweight: Heavy Load: 17 MW 1/3 Production 16

17 The Concept: Paper Industry Example Continuous Process Modulation 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: Medium Load: 15 MW 1/3 Production Paperweight: Heavy Load: 17 MW 1/3 Production Flexibility Lever 2: Schedule the different products depending on electricity prices 17

18 The Concept: Different types of flexible planning Continuous ON/OFF Process Monday Tuesday Wednesday Thursday Friday Saterday Sunday Batch ON/OFF Process 0 Monday Tuesday Wednesday Thursday Friday Saterday Sunday Continuous Modulation Process Multi-Product Process Monday Tuesday Wednesday Thursday Friday Saterday Sunday 0 Monday Tuesday Wednesday Thursday Friday Saterday Sunday 18

19 The Concept: with opportunities in various energy intensive industries 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 19

20 The process challenge: Where to find and how to manage my flexibilities? Electricity Consumption Consume electricity at optimal moment Electricity Generation Produce electricity at optimal moment 1 Load Shifting 5 CHP Modulation 2 Load Scheduling 3 Load Shedding 6 By-product Optimization 4 Fuel Switching 20

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 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 Economics RM, NG and electricity costs Revenue from sales Opportunity costs Fix and variable operating costs Incentive from DR programs Storage facilities Min-max capacities Storage target 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 Grid and market interaction Different electricity contracts (OTC, spot based) Capacity constraints 22

23 in an integrated way Electricity Consumption Electricity Generation 23

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

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. onoff status) Usually linear(isable) constraints (e.g. piecewise linear representation of the yield of the machines) Linear relaxations Finding the optimal set of decisions Difficult problems: non-convex, no polynomialtime 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 25

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

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 Propagation 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 28

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 Start of batch production Machine usage HR planning Production Scheduling Compute electricity demand Optimality Electricity Management Identify mismatch production <-> electricity cost Cogen management When to turn on/off Needs of by-products Total energy cost 30

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

32 Real-time activation is uncertain when committing a schedule Scheduling Optimization Real-time Optimization Dayahead Market Reserve Market 32

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 Reduced problems Finding the optimal set of decisions Difficult problems: non-convex, no polynomialtime 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 33

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 Optimal Scheduling of energy consumption Consume energy at optimal moment Integrated Optimization Optimal Planning of energy production Produce electricity at optimal moment 35

36 Thank you!

37 37 Predictive analytics to forecast electricity price Prices History Weather Forecasts Price forecast Wind and Solar Power Forecasts Statistical Model Network Information