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DECREASING THE CARBON FOOTPRINT OF OIL PIPELINES BY MINIMIZING PUMPING COSTS USING MILP

DECREASING THE CARBON FOOTPRINT OF OIL PIPELINES BY MINIMIZING PUMPING COSTS USING MILP. Ehsan Abbasi, Vahid Garousi Software Quality Engineering Research Group (SoftQual) www.softqual.ucalgary.ca Department of Electrical and Computer Engineering, University of Calgary

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DECREASING THE CARBON FOOTPRINT OF OIL PIPELINES BY MINIMIZING PUMPING COSTS USING MILP

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  1. DECREASING THE CARBON FOOTPRINT OF OIL PIPELINES BY MINIMIZING PUMPING COSTS USING MILP Ehsan Abbasi, Vahid Garousi Software Quality Engineering Research Group (SoftQual)www.softqual.ucalgary.ca Department of Electrical and Computer Engineering, University of Calgary 2500 University Drive NW, Calgary, AB Canada T2N 1N4

  2. INTRODUCTION 2 • Expenses due to electricity usage surpass 50% of operating cost in case of many fluid distributors   • More than 95% of the electricity consumed is associated with pumping • By the recent multi-tariff electricity charges, pipeline operators have the opportunity to lower their expenses • Less or off-peak consumption of electricity leads to less emission of gases that cause environmental threats www.softqual.ucalgary.ca

  3. PROJECT CONTEXT • The work reported in this talk is conducted as a part of a major project in Alberta, Canada with the collaboration of universities and the petroleum industry. • Project title: Engineering Intelligent Software Systems for Improving the Operational Efficiency of Oil Pipeline Networks • Our focus is on: • Developing optimization algorithms (MILP, GA, etc.) • Developing control/decision support software systems to enable interoperability with existing databases, GIS, SCADA software, etc. • More info: • http://www.ucalgary.ca/~vgarousi/project-sw-energy.html www.softqual.ucalgary.ca

  4. PROBLEM VS. CHALLENGES • Optimal control of fluid distribution systems for a specific period of time is defined as: • selecting pumps operation schedule • and valve settings schedule • that provide the least operational cost • while satisfying the system constraints. • The major types of products to be transported in a pipeline: • Oil • Gas • Water • Pipeline operation problem initially deals with • binary variables (e.g., for turning pumps on or off) • non-linear terms in mathematical relationship between variables due to non-linearity nature of the hydraulic models (i.e., pressure versus flow relationship of the pumps or head loss versus flow relationship of the pipeline).   • This problem is a dynamicmixed-integernon-linear type and with the large number of variables, it is a challenging one. www.softqual.ucalgary.ca

  5. To proceed with MILP (the current work). But we are going to experiment with heuristics-based optimization techniques as well in near future, e.g., GAs. OUR CHOICE www.softqual.ucalgary.ca

  6. Linearization OUR APPROACH • Mixed Integer Linear Programming • Guarantees convergence to global optimum in a finite number of steps • Efficient MILP techniques, such as the branch-and-cut (a.k.a., branch-and-bound) algorithms have been developed, and commercial solvers with large-scale capabilities are currently available • Drawbacks • Execution time for MILP model grows dramatically with the number of integer variables included in the model • The problem has been modeled with the least possible number of binaries. • Nonlinear terms should be linearized prior to modeling which can be a source of error • The nonlinearities have been approximated by linear equations in the limited operation range of variables. www.softqual.ucalgary.ca

  7. OBJECTIVE FUNCTION • Minimizing the cost of operation Cost of each pump = (Electricity Rate1 X Power Charged by Rate1) + (Electricity Rate2 X Power Charged by Rate2) Total Power = Power Charged by Rate1 + Power Charged by Rate2 Power Charged by Rate1 is limited by binary variable X change rate value Power Charged by Rate2 is limited by one minus same binary variable multiplied by its limits Total power should be equal to a linear function of speed. The binary variable sets the constant value to zero when the pump is off www.softqual.ucalgary.ca

  8. CONSTRAINTS • Hydraulics Model • Conservation of mass: • Conservation of momentum: www.softqual.ucalgary.ca

  9. CONSTRAINTS Cont’d • Operational Constraints • Limitation on pressures at the nodes • Limitation on the segments flow rates • Delivery contract • Minimum up time and Minimum down time constraints www.softqual.ucalgary.ca

  10. CASE STUDY • To evaluate our optimization technique, we are working with a Western Canadian oil pipeline operator to apply our optimization technique to its pipeline network. However, extraction of actual parameters to be able to execute the algorithm has not been completed yet. 6 • In the mean-time, a hypothetical generic test system has been designed to test the model. • By coding the formulation in GAMS and using the MILP solver (CPLEX version 11.0), the total optimum cost of $5,850.50 for a 24-hour time frame is obtained. 1 4 6 4 5 5 3 2 1 3 2 7 11 8 10 10 9 8 7 9 www.softqual.ucalgary.ca

  11. SENSITIVITY ANALYSIS 1 • Sensitivity analysis of total number of pumps operation versus increasing the delivery volume contract • Sensitivity analysis of the total pumps operation hours versus increase in the friction factor www.softqual.ucalgary.ca

  12. SENSITIVITY ANALYSIS 2 • Sensitivity analysis of total system cost versus increase in the friction factor • Sensitivity analysis for total cost versus change in flow rate www.softqual.ucalgary.ca

  13. SCALABILITY ANALYSIS • Model and execution parameters associated with each time horizons • Sensitivity analysis for execution time versus change in time horizon www.softqual.ucalgary.ca

  14. SUPPORT SOFTWARE SYSTEMS A pipeline flow and cost simulator. Has novel features.

  15. ONGOING & FUTURE WORKS The proposed model was recently applied (Jan.-Feb.) to a Western Canadian oil pipeline with 800km in length and 6 pump stations. The results are very promising. Results are being prepared for a journal publication. Pipeline operation optimization is a challenging optimization problem for which, we are intending to adopt GA as an alternative optimization technique of optimization to compare it’s results and the efficiency with the current work. www.softqual.ucalgary.ca

  16. Thanks for your attention! www.softqual.ucalgary.ca

  17. CONCLUSION • Optimal scheduling of oil distribution systems is a dynamic mixed-integer problem and its solution is hindered by the difficulties faced in dealing with the large number of discrete and continuous variables. • MILP is an efficient and powerful method for solving practical scale pipeline scheduling problems. • The sensitivity analysis results indicate the applicability of method in finding the optimal solution. • The scalability analysis indicates the efficiency of the method for large practical problems, in sense of execution time as well as memory usage. www.softqual.ucalgary.ca

  18. CASE STUDY Cont’d 18 • If the two constraints of minimum up time and down time are relaxed, then the optimal solution of $4,226.00 is achieved  .   www.softqual.ucalgary.ca

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