Industrial Demand Response as a Source for Operational Flexibility

Transcription

1 Industrial Demand Response as a Source for Operational Flexibility Demand Response Workshop, Lausanne, Prof. Gabriela Hug, ghug@ethz.ch Xiao Zhang (CMU), Zico Kolter (CMU), Iiro Harjunkoski (ABB) 1

2 Outline Introduction Manufacturing Processes Steel plant Aluminum smelter Cement plant Conclusion 2

3 Introduction Motivation Industrial consumers use large amounts of electric energy and therefore potentially have large incentive to adjust consumption to reduce costs Industrial processes may inherently have storage capability, e.g. thermal processes, batch processes, etc. Key Questions Which processes have the potential to participate? Which markets should they participate in (energy, regulation, reserve)? What models are needed to enable the participation? 3

4 Steel Plant Overview Production Process Electric arc furnaces (EAF) Argon oxygen decarburization units (AOD) Ladle furnaces (LF) Continuous casters (CC) Metal Scrap Slabs (processed in heats) Source: P. M. Castro, L. Sun, and I. Harjunkoski, Resourcetask network formulations for industrial demand side management of a steel plant, Industrial & Engineering Chemistry Research, vol. 52, no. 36, pp ,

5 Steel Plant Modeling Resource Task Network (RTN) Resources Equipment: EAF, AOD, LF, CC Heat: inlet, oulet Electricity: energy, power Task Melt (EAF) Decarb (AOD) Refine (LF) Cast (CC) Transfers Network 5

6 Steel Plant Modeling Flexibility of EAFs Assumptions Power adjustable via transformer taps: within acceptable bounds Total energy required for every heat is fixed processing time: not fixed, within bounds Consequence End of melting task uncertain need extra variables to denote ending of task 6

7 Steel Plant Modeling Arbitrary Flexible Melting Melting power may change during melting process (within operational bounds) Multiple Melting Modes Once the melting started power is fixed Choose modes such that process fully spans time slots Spinning Reserve Provision Account during each hour that plant potentially has to provide spinning reserve 7

8 Simulation Setup Parameters Two units of each equipment: 2 EAFs, 2 AODs, 2 LFs, 2 CCs Heats / groups Nominal power and processing times EAF melting bounds set to of nominal Hourly Prices 8

9 Simulation Results daily production profiles Linux 64 Matlab CPLEX 0.8% 0.4% 1.3% 2.3% 3.5% 3.8% problem size Flexibility increases computation time, but reduces electricity cost. 9

10 Aluminum Smelter Process Description Electrolytic process which transforms alumina to aluminum Current passes through pots which are connected in series to form potline Consumption of potline can be hundreds of MWs Flexibility Power consumption of potline manipulated by adjusting voltage at output of rectifier that supports the required DC current to potline Possibility to adjust power consumption by about 1MW within seconds Opportunity to provide regulation services 10

11 Aluminum Smelter Modeling Question Determine optimal regulation capacity provision given price Constraints Limits on rectifier tap changer Limits on regulation capacity = summation of available capacities over all production lines (needs to be the same for entire hour) Plant production targets (production is assumed to be proportional to electricity consumption) Objective Maximize profit Income from aluminum production cost of electric energy + regulation revenue cost of tap moving penalty on deployment error Formulate stochastic optimization problem with AGC signal as stochastic variable 11

12 Simulation Setup Parameters Two potlines and ten different AGC scenarios Minimum production set to 95% of base production (= production at tap position zero) Per unit profit, tap changing cost, deployment penalty, regulation price Generate bidding curve carrying out simulations for a variety of regulation prices 12

13 Cement Plant Process Modeling Discrete number of crushers which consume energy only on or off state Additional storage for continuous and granular adjustment of power consumption MPC Optimization Prediction of AGC signal based on ARMA model Objective function: Minimize regulation deviation switching of crushers will be extended 13

14 Simulation Setup Parameters 4 cement crushers each consuming 2MW ARMA Model Prediction 2s resolution, 100s horizon 1MWh / 3MW energy storage 14

15 Simulation Results 15

16 Conclusion Industrial plants have incentive and possibility to provide demand response Different industries have different capabilities Depending on industry, various challenges arise: Account for uncertainty Overcome complexity of process Handle granularity restrictions 16

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18 Simulation Results Providing spinning reserve brings benefits. Flexibility increases spinning reserve revenues and reduces energy cost. 18