Power Optimization

Power optimization refers to short-term power minimization for current pipeline op- erations and off-line optimization for future operation planning. It is mainly concerned with system-wide optimum operations of facilities such as pump stations and pressure reducing stations. The results of a short-term power optimization are typically treated as recommendations and are not used for a closed-loop control.

For a large pipeline system, a mathematical model is used to obtain system-wide optimum solutions. The power optimization model deals with power consumption and DRA usage for liquid pipelines. It determines an optimum pump station selection and unit line-up as well as pressure set points at the on-line stations so as to minimize power/DRA cost. It may compare the DRA cost against the power cost for the given flow rates. The model may adjust flow rates to take advantage of lower energy costs during off-peak hours.

An optimization model can provide information regarding the following: Pump stations and units to be brought on-line

·

Optimum pump station suction or discharge pressure set points, pump unit on/ ·

off switching schedules, and minimum power cost for a specified time period.

Pump unit line-up and operating point, considering that a station may consist of ·

different pump units and that the units can be combined in various modes. The operating points, overlaid on the pump performance curve, can be displayed on the host SCADA screen.

Calculation of the overall pumping costs. When drag reducing agent (DRA) ·

is injected for a liquid pipeline operation, the cost without DRA is compared against the cost with DRA.

In addition, some optimization systems may provide the following information for analysis to help improve pipeline operation efficiency performed by operation staff:

Key optimization results and historical records ·

Flow rate vs. suction/discharge pressure trends with set point change records ·

Flow rate vs. number of pump units brought on-line and power consumption ·

Cumulative pump operating records ·

Pump efficiency trends ·

The model employs the following data in addition to the pipeline configuration and facility data:

Pipeline hydraulics and equipment such as pumps ·

Pipeline and facility availability data ·

Power contract data ·

DRA cost, if the DRA injection systems are installed ·

Line fill and batch schedule data and injection and delivery flow rates for ·

batched liquid pipelines

The primary criterion for an optimization model is to minimize power costs. A secondary criterion is to balance pump unit operating hours, avoiding frequent unit start-ups and shut-downs. The solution from the optimization model should not vio- late any pipeline and facility constraints. These constraints may include maximum and minimum pressures and flows at certain points in the pipeline network such as minimum delivery flow, maximum and minimum pump flows and compression ratio, maximum power, etc.

Optimization models are difficult to apply on complex network configuration and pump stations. However, it was reported that certain mathematical techniques were successfully implemented for liquid pipeline energy optimization [28, 29].

A power optimization system can be implemented as a part of the host SCADA system, and connected via an interface with the SCADA system. Through the interface, the SCADA system sends the current pipeline states required for an optimization run, controls its execution with the data, and receives the optimization results along with alarm and event messages such as new batch lifting and station startup or shutdown. The current states may include the following data:

Lifting and delivery flow rates ·

Pump stations and units which are on-line and off-line ·

Batch and DRA tracking data for liquid pipelines ·

Pipe roughness or efficiency to improve hydraulic calculation accuracy ·

Unit utilization data and maintenance schedule ·

If it is implemented on the SCADA system, the accuracy of the batch/DRA track- ing data and friction factor need to be improved in order to calculate the hydraulic

profiles accurately. In order to calculate the pipeline hydraulics accurately, accurate pipe roughness or pipe efficiency along the pipeline may be required. A real-time batch tracking capability can provide a more accurate hydraulic calculation. Some optimiza- tion models can re-rate pump performance curves by analyzing recent data automati- cally to determine actual pump performance and efficiency.

A power optimization system is typically configured to run at regular intervals as well as on demand by the operator. Running the system at regular intervals ensures that the system will notify the operator of any system changes required due to changes in the pipeline line fill (e.g., batched operation, etc.). When there is a need for flow rate change, the operator will enter the new parameters and obtain new system changes.

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