EP1846660B1 - Method for optimizing the functioning of a plurality of compressor units and corresponding device - Google Patents

Abstract

In a method for controlling a compression installation (1), the installation has at least two compressor units (i=1, , N) that can be separately turned on or off, a plurality of devices for modifying the output of the compressor units and a control device (10). Known methods and devices do not function optimally in terms of the power consumption of the entire compression installation. The power consumption (EG) for the operation of a plurality of compressor units (i=1, , N) of a compression installation (1) can be optimized by calculating a novel circuit configuration (Si, t) and automatically adjusting the novel circuit configuration (Si, t) by a control device (10).

Description

The invention relates to a method for controlling a compression system with at least two separately zu- and / or turn-off compressor units, with a plurality of devices for changing the performance of the compressor units and with a control device.

Furthermore, the invention relates to a control device for controlling a compression system with at least two separately zu- and / or turn off compressor units and with a plurality of devices for changing the performance of the compressor units.

Compaction plants, for example natural gas compression plants, for gas transport and / or gas storage are essential facilities in the sense of national and international energy supply. A system for gas transport consists of a plurality of compression systems, which can each consist of several compressor units. Here the task of the compressor units is to add a sufficient amount of mechanical energy to a pumped medium in order to compensate for friction losses and to ensure the required operating pressures or flows. Compressor units often have very different drives and wheels, as they are designed for example for a base load or a peak load operation. A compressor unit includes e.g. at least one drive and at least one compressor.

Plant automation is especially important for cost-effective driving. The ability of plant automation to guide the process, and the Optimizing the compaction plant within production constraints provides decisive economic benefits.

Often, the compressors of a compression plant are driven by turbines that cover their fuel needs directly from a pipeline. Alternatively, compressors are driven by electric motors. Cost-effective driving means to minimize the energy consumption of the turbines and the electric drives for a given compression capacity, flow rate, delivery capacity and / or given volume flow.

A useful operating range of compressors is limited by adverse effects of internal flow processes. This results in operating limits, such as a temperature limit, exceeding the local speed of sound (compression shock, sip limit), the circumferential tearing off of the flow at the impeller or the surge line.

The automation of a compression system has primarily the task of a dispatching center predetermined setpoints, such as either a flow through the station or a final pressure on the output side to realize as actual values. Specified limits for the suction pressures at the inlet side, the final pressures at the outlet side and the final temperature at the system outlet must not be exceeded.

Out WO 03/036096 A1 a method for optimizing the operation of multiple compressor units of a natural gas compression station is known. In this method, after the start of a second or another compressor unit, the speeds of the running compressor units are run in a fixed speed ratio with respect to stored for each compressor unit map data. In order to realize a first reduction of the energy consumption, the speeds of all are in operation after starting an additional compressor units are changed by an equal percentage flow adjustment until, if possible, all pump preventive valves of the system are closed. Only after all the pump preventive valves are closed, operating points of the compressor unit are pushed in their maps as close as possible to a line of maximum efficiency.

According to EP 0 769 624 B1 For example, a method is known for balancing the load between multiple compressors and manipulating the performance of the compressors to maintain a predetermined relationship between all the compressors when the operating points of all the compressors are farther than a specified value from the surge line.

With EP 0 576 238 B1 For example, a method and apparatus for load sharing is known. With a designated as a guide compressor compressor, a control signal is generated, which is used as a reference for the non-leading compressor.

The methods described above can not satisfactorily reduce the energy consumption of the entire compacting plant.

The invention has for its object to provide a method and apparatus for further optimization of energy consumption for operation of multiple compressor units of a compaction system.

This object is inventively achieved in that, for presetting of new setpoints or change of the current state of the compressor plant by means of an optimization calculation from a current switching configuration of the compressor units with regard to an optimized total energy demand of the compressor plant, a new switching configuration is calculated, and that the new switching configuration is set automatically via the control device.

An advantage of the invention is that, in the optimization of all compressor units available or operable on the respective compacting system, it can be assumed that they are independent of their respective operating or switching state. In particular, the invention allows - in contrast to known controls for compression systems - as a result of the optimization of an automatic connection of a previously out of service compressor unit or a complete shutdown of a compressor unit.

In this case, automatically means "online" in particular, meaning that it can automatically mean, for example, that the switching configuration is used by the operating staff of the compacting system without manual intervention, preferably in real time. Real time means that the result of a calculation is guaranteed within a certain period of time, that is, before a certain time limit has been reached. In this case, the optimization calculation can take place on a separate data processing system, which automatically forwards its calculation data to the control device.

The invention is based on the known sequential concept, ie after the start of an externally specified additional unit, first to close the pump-preventive valves and then to optimize the operating points of the compressor units with regard to their efficiency. According to the invention, the entire compaction plant is preferably considered during each optimization calculation and the switching configuration of the compaction plant, ie the specification of a switching state of the individual compressor units, calculated. The closing of the or all of the pump preventive valves can be ensured by a minimum flow through the compressor units in the optimization. Even a first start of the compression system can already with a favorable with regard to an optimized total energy demand switching configuration done.

Under the, preferably electrically manipulable, switching configuration of a compression system to understand a lot of the respective switching states of the individual compressor units. The switching configuration is represented by the switching states "0" for Off or "1" for On, which is stored bit by bit, for example, in an integer variable.

By switching operation is meant the change from one, in particular electrical, switching state to another.

Advantageously, a prognosis is determined by means of the optimization calculation for at least one, preferably several, future time (s). Since the method allows for predictions up to a given time, it is possible to know about a normal driving style of the station, i. e.g. a conventional load curve to use to minimize the switching frequency of compressor units.

It is expedient that compressor unit-specific data records and / or compressor unit-specific maps evaluated and determined for the individual compressor units operating points, which depend on predetermined or changed values of mass flow and a specific production work, the operating points are set such that the total energy demand the compaction plant is optimized.

Advantageously, the data sets and / or maps are specified as a function of a mass flow and a specific production work of the individual compression units.

Advantageously, in the optimization calculation, in addition to the switching configuration, a load distribution, ie a speed ratio, calculated between the compressor units and changed if necessary.

Another significant advantage is that constraints on the optimization, e.g. The pumping limit can not be violated, even with an optimal efficiency calculation of the speed setpoints for the individual compressor stations can be considered.

It is expedient that the optimization calculation is carried out with a control cycle, in particular self-triggering.

Advantageously, speed setpoint values and / or the new switching configuration for the control device are provided as output variables of the optimization calculation with each control cycle.

It is expedient that for the duration of the control cycle, which is in particular a multiple of a cycle time of a control of the control device, the speed setpoints and / or the switching configuration are kept constant.

In a particular embodiment of the invention, the speed setpoints are scaled with a common factor and used as a setpoint for a compressor unit controller.

A further increase in the effectiveness of the system operation is achieved by the control device with the new switching configuration triggers a warm-up phase of the compressor units for the subsequent connection of a previously out of service compressor unit already before the end of the control cycle.

In a particular embodiment, a load readiness for the next control cycle is communicated with the end of the warm-up phase of the control device. If, for example, the speed of an approaching compressor unit is sufficient is high and the warm-up phase of the turbine is completed, a signal "load ready" is set. This means that the compressor unit participates in the load sharing procedure and is included in the optimization calculation for the best load distribution between those in service.

• ein Modell der einzelnen Verdichtungsaggregate und/oder
• eine Modellbibliothek der gesamten Verdichtungsanlage und/oder
• eine aktuelle spezifische Förderarbeit der einzelnen Verdichtungsaggregate und/oder
• eine aktuelle spezifische Förderarbeit der Verdichtungsanlage und/oder
• einen aktuellen Massenfluss durch das einzelne Verdichtungsaggregat, insbesondere durch einen einzelnen Verdichter und/oder
• einen aktuellen Massenfluss durch die Verdichtungsanlage und/oder
• die aktuelle Schaltkonfiguration und/oder
• ein Saugdruck an der Eingangsseite der Verdichtungsanlage und/oder
• ein Saugdruck an der Eingangsseite des einzelnen Verdichtungsaggregates und/oder
• ein Enddruck an der Ausgangsseite der Verdichtungsanlage und/oder
• ein Enddruck an der Ausgangsseite des einzelnen Verdichtungsaggregates und/oder
• eine Temperatur an der Ausgangsgangsseite der Verdichtungsanlage und/oder
• eine Temperatur an der Eingangsseite der Verdichtungsanlage und/oder
• eine Temperatur an der Ausgangsgangsseite der einzelnen Verdichtungsaggregate und/oder
• eine Temperatur an der Eingangsseite der einzelnen Verdichtungsaggregate und/oder
• die aktuellen Drehzahlen der einzelnen Verdichteraggregate ausgewertet.

In a further preferred embodiment, the input for the optimization calculation

• a model of the individual compression units and / or
• a model library of the entire compaction plant and / or
• an actual specific conveying work of the individual compaction aggregates and / or
• a current specific work of the compaction plant and / or
• a current mass flow through the single compression unit, in particular by a single compressor and / or
• a current mass flow through the compression plant and / or
• the current switching configuration and / or
• a suction pressure on the input side of the compression system and / or
• a suction pressure on the input side of the individual compression unit and / or
• a final pressure at the outlet side of the compressor and / or
• a final pressure on the output side of the individual compression unit and / or
• a temperature at the output side of the compression plant and / or
• a temperature at the input side of the compression system and / or
• a temperature at the output side of the individual compression units and / or
• a temperature at the input side of the individual compression units and / or
• evaluated the current speeds of the individual compressor units.

Conveniently, the optimization calculation according to the principle of model-predictive control by means of forecast calculations minimizes the total energy demand expected up to a later point in time.

In a further preferred embodiment, an energy consumption of a switching operation is taken into account in the optimization calculation.

Conveniently, the energy consumption of the switching process from the data sets and / or the maps of the compressor units is calculated. The knowledge of a proportionate energy consumption for the switching process allows an even more accurate determination of the minimum total energy consumption of the compression plant.

An advantageous variant of the invention is that the specific conveying work of the compressor system for the control cycle is assumed to be constant, in particular in a parallel connection of the compressor units.

An alternative advantageous variant of the invention is that the mass flow of the compressor system for the control cycle is assumed to be constant, in particular in a series circuit of the compressor units.

Appropriately, an active compressor unit is operated at least with a predetermined or predetermined minimum flow.

Advantageously, the optimization calculation is carried out by means of a branch-and-bound algorithm.

In a further advantageous manner, a limit to the branch-and-bound algorithm is determined by solving a relaxed problem using sequential quadratic programming.

A further increase in the efficiency of the calculation method is achieved in that the optimization calculation solves partial problems by means of dynamic programming, in particular in a series connection.

The device-related task is based on the above-mentioned control device solved by an optimization module with the new setpoints or change the current state of the compression system by means of an optimization calculation of a current switching configuration of the compressor units with respect to an optimized total energy demand of the compression plant a new switching configuration is calculated, and by a control module, with which the new switching configuration is automatically adjustable.

The optimization module for optimizing the energy consumption is in particular designed to distribute in combination with the control device and / or the dispatching center the predetermined total load on the individual compressor units so that the station setpoints with the lowest possible energy consumption, i. with maximum overall efficiency, be realized. This includes, for example, both the decision which compressor units are active and which are switched inactive, as well as the specification of how much each of the active units should contribute to the overall performance, so the specification of the load distribution.

In a particular embodiment of the invention, the optimization module is arranged at a spatial distance, in particular several km, to the control device.

According to an expedient embodiment, the optimization module is prepared for the consideration of an energy consumption of a switching operation.

Another embodiment is that the optimization module for optimization calculation for a plurality of control devices of several compression systems is prepared.

The invention also includes a computer program product containing software for carrying out a method according to one of claims 1 to 21. With a machine-readable program code on a data carrier, it is possible to prepare DV systems for an optimization module.

FIG 1

ein Blockschaltbild eines Verfahrens zur Optimierung des Betriebs einer Verdichtungsanlage,

FIG 2

ein verdichter-spezifisches Kennfeld eines Verdich- teraggregats,

FIG 3

eine Steuerungseinrichtung zur Steuerung einer Ver- dichtungsanlage und in

FIG 4

ein Ablaufdiagramm der Verfahrensschritte

gezeigt ist.In the following the invention will be explained in more detail with reference to an embodiment, wherein in

FIG. 1

a block diagram of a method for optimizing the operation of a compression plant

FIG. 2

a compressor-specific characteristic diagram of a compressor unit,

FIG. 3

a control device for controlling a compression system and in

FIG. 4

a flowchart of the method steps

is shown.

beschrieben, wobei

R

eine spezifische Gaskonstante,

κ

ein Isentropenexponent,

Z

ein Realgasfaktor,

c_E,c_A

eine Geschwindigkeit am Eingang bzw. Ausgang des Ver- dichteraggregats,

z_A, z_E

ein Höhenunterschied,

p_E

ein Saugdruck,

p_A

ein Enddruck, und

T_E

eine Eingangstemperatur ist.

The behavior of a single compression unit 3, 4, 5 is modeled by a map 20, the map 20 describes its efficiency and its speed as a function of its operating point 22. The operating point 22 by means of a state variable ṁ , which describes a mass flow through the compression unit, and a specific funding work determinable with equation 1 $$y=\frac{\kappa }{\kappa -1}R{T}_{e}Z\left[{\left(p_A/p_e\right)}^{\frac{\kappa }{\kappa -1}}-1\right]+\frac{c_A^2-c_e^2}{2}+G\cdot \left(z_A-z_e\right)$$

described, wherein

R

a specific gas constant,

κ

an isentropic exponent,

Z

a real gas factor,

c _E , c _A

a speed at the inlet or outlet of the compressor unit,

z _A , z _E

a height difference,

p _E

a suction pressure,

p _A

a final pressure, and

T _E

an input temperature is.

The maps 20 are not provided by a closed formula. From a measurement, a delivery characteristic 21 and an efficiency curve 23 are determined. At a constant speed, the dependence on the conveying work and an efficiency η _i on the volume flow V̇ _i or mass flow ṁ at support points is determined.

In order to model the behavior of a compressor unit 3, 4, 5, in addition, the operating limits, such as a surge line 36, which are caused by the occurrence of certain flow phenomena in the compressor, must be recorded as a function of the speed. From these interpolation points and the associated values for different speeds, the maps 20 can be constructed as a function of mass flow ṁ _i and specific conveying work y _i and their domain of definition by means of suitable approaches, such as piecewise polynomial interpolation or B-splines.

When connected in series compressor units 3, 4, 5, the entire conveying work on the individual compressor units 3, 4, 5 distributed energy optimal, the mass flow is assumed to be the same by the compressors. For a formulation of a minimization problem, in particular in a series connection, equation 2 applies: $$\min ={\displaystyle \underset{t\ge 0}{\Sigma }\underset{i=1}{\overset{N}{\Sigma }}{s}_{i.t}}\frac{y_{i.t}{\dot{m}}_{G.t}}{{\eta }_i\left({\dot{m}}_{G.t},y_{i.t}\right)}+\delta {\displaystyle \underset{t>0}{\Sigma }}{\left(s_{i.t}-s_{i.t-1}\right)}^2$$

• Die Serienschaltung resultiert darin, dass die Summe der spezifischen Förderarbeiten der Verdichter jeder Zeit gleich der Förderarbeit der Station sein muss:

$$y_{g,t}={\displaystyle \sum _{i=1}^N}{y}_{i,t},s_{i,t}{y}_{i,t}^{\min }\left({\dot{m}}_{g,t}\right)\le y_{i,t}\le s_{i,t}{y}_{i,t}^{\max }\left({\dot{m}}_{g,t}\right)$$

To apply mathematical programming, Equation 3 is considered as an equation constraint:

• The series connection results in the fact that the sum of the specific conveying operations of the compressors must be equal to the conveying work of the station at all times:

$$y_{G.t}={\displaystyle \underset{i=1}{\overset{N}{\Sigma }}}{y}_{i.t}.s_{i.t}{y}_{i.t}^{\min }\left({\dot{m}}_{G.t}\right)\le y_{i.t}\le s_{i.t}{y}_{i.t}^{\mathrm{Max}}\left({\dot{m}}_{G.t}\right)$$

In compressors connected in parallel, the total flow to the individual compressor units 3, 4, 5 to distribute, with the specific production work of the compressor for an optimization cycle R is set as given. For a formulation of a minimization problem, in particular in a series connection, equation 4 applies: $$\min ={\displaystyle \underset{t\ge 0}{\Sigma }\underset{i=1}{\overset{N}{\Sigma }}{s}_{i.t}}\frac{y_{i.t}{\dot{m}}_{G.t}}{{\eta }_i\left({\dot{m}}_{G.t},y_{i.t}\right)}+\delta {\displaystyle \underset{t>0}{\Sigma }}{\left(s_{i.t}-s_{i.t-1}\right)}^2$$

• Im Falle der Parallelschaltung muss die Summe der Einzelflüsse zu jedem Zeitpunkt gleich dem geforderten Gesamtfluss sein:

$${\dot{m}}_{g,t}={\displaystyle \sum _{i=1}^N}{\dot{m}}_{i,t},s_{i,t}{\dot{m}}_{i,t}^{\min }\left(y_{g,t}\right)\le {\dot{m}}_{i,t}\le s_{i,t}{\dot{m}}_{i,t}^{\max }\left(y_{g,t}\right)$$

To apply mathematical programming, Equation 5 is considered as an equation constraint:

• In the case of parallel connection, the sum of the individual flows at all times must be equal to the required total flow:

$${\dot{m}}_{G.t}={\displaystyle \underset{i=1}{\overset{N}{\Sigma }}}{\dot{m}}_{i.t}.s_{i.t}{\dot{m}}_{i.t}^{\min }\left(y_{G.t}\right)\le {\dot{m}}_{i.t}\le s_{i.t}{\dot{m}}_{i.t}^{\mathrm{Max}}\left(y_{G.t}\right)$$

Since the total energy consumption is to be minimized, the minimization problem results as the sum of the consumption of all compressor units 3, 4, 5.

Another term is additively linked to the minimization problem, which is an objective function. The costs of switching, ie the energy consumption of a switching operation, are thereby taken into account. At a given suction pressure P _S , a final pressure P _E , a temperature T and the mass flow ṁ can be a proportionate energy consumption for a switching operation of a compressor unit 3, 4, 5 calculated from the maps.

• Ein aktives Verdichtungsaggregat muss, um die Pumpgrenze nicht zu verletzen, einen minimalen Durchfluss, insbesondere einem minimalen Massenfluss ${\dot{m}}_{i,t}^{\min },$
  
  einhalten. Dieser minimale Durchfluss ist abhängig von der augenblicklichen Förderarbeit der Verdichtungsanlage. Ebenso muss der Massenfluss unter einem maximal zulässigen Wert ${\dot{m}}_{i,t}^{\max }$
  
  bleiben.
• Ganz analog zum Massenfluss gelten im Fall von in Serie geschalteten Verdichtern obere und untere Grenzen für die spezifische Förderarbeit $y_{i,t}^{\min }$
  
  und $y_{i,t}^{\max }\mathrm{.}$
  

When optimizing the objective function, the following inequality constraints are met:

• An active compaction unit must, in order not to violate the surge line, a minimum flow, in particular a minimum mass flow ${\dot{m}}_{i.t}^{\min }.$
  
  comply. This minimum flow rate depends on the current production work of the compaction plant. Similarly, the mass flow must be below a maximum allowable value ${\dot{m}}_{i.t}^{\mathrm{Max}}$
  
  stay.
• Analogous to the mass flow, in the case of compressors connected in series, upper and lower limits apply to the specific conveying work $y_{i.t}^{\min }$
  
  and $y_{i.t}^{\mathrm{Max}}\mathrm{,}$
  

The treatment of compression systems with parallel and serially connected aggregates is implemented uniformly and does not require completely different formulations of the minimization problem. A solution results directly from the mathematical formulation as an optimization problem.

FIG. 1 shows a block diagram of a method for optimizing the operation of a compression plant. The compression plant is equipped with three compressor units 3, 4 and 5 shown in a very schematic way. For an interconnection of the compressor units 3, 4 and 5, a parallel connection is assumed. The compressor units 3, 4 and 5 are controlled and regulated by a control device 10. The control device 10 comprises a controller of the control device 12, a first compressor unit controller 13, a second compressor unit controller 14 and a third compressor unit controller 15. An optimization module 11 is in bidirectional communication with the controller 10. By means of the optimization module 11, a nonlinear mixed-integer optimization problem is solved , A mathematical formulation of the optimization problem is implemented in the optimization module 11. Using Gl.4 with a number N = 3 of the compressor units 3, 4 and 5 and a number of input variables 33, the optimization module 11 will provide optimized outputs 32 of the control device 12 for optimized total energy consumption. The input variables 33 are composed of a model library 26, with a model 24a, 24b, 24c for each compressor unit 3, 4, 5 and process variables of the compactor.

• einer aktuellen Temperatur T_g,A an der Ausgangsseite der Verdichtungsanlage,
• einer aktuellen Temperatur T_g,E an der Eingangsseite der Verdichtungsanlage,
• einem aktuellen Enddruck P_g,A an der Ausgangsseite der Verdichtungsanlage,
• einem aktuellen Saugdruck P_g,E an der Eingangsseite der Verdichtungsanlage,
• einem aktuellen Volumenstrom V̇_i für I = 1...3 mit je einer aktuellen Temperatur für Eingang T_i,E und Ausgang eines Verdichteraggregates T_i,_A,
• einem aktuellen Druck P_i,E und P_i,A ,

der einzelnen Verdichteraggregate 3, 4 und 5, als Istwerte 30 versorgt.About actual values 30 and set points 31, the control of the control device 12 with

• a current temperature T _{g, A} at the outlet side of the compression plant,
• a current temperature T _{g, E} at the input side of the compression plant,
• a current final pressure P _{g, A} at the outlet side of the compression plant,
• a current suction pressure P _{g, E} at the input side of the compression plant,
• a current volume flow V̇ _i for I = 1 ... 3, each with a current temperature for input T _{i, E} and output of a compressor unit T _i , _A ,
• a current pressure P _{i, E} and P _{i, A} ,

the individual compressor units 3, 4 and 5, supplied as actual values 30.

The setpoint values or limit values 31 for the control of the control device 12 are made up of a maximum temperature T _{g, A, max of} a pressure P _{g, A (Should)} and a volume flow V̇ _{g (Should)} on the output side of the compression system and a maximum suction pressure P _{g , E (max)} and P _{g, A (max)} on the input side and the output side of the compression system together.

With the actual values 30 as process variables and the basic equation G1.1, the input variables 33 for the optimization module 11 are completed.

In the optimization module 11, a minimum total energy requirement is now calculated. For the compressor units 3, 4 and 5 arranged in parallel, the minimization problem is solved by means of a branch-and-bound algorithm (FIG. LA Wolsey, Integer Programming, John Wiley & Sons, New York, 1998 ), which runs discrete variables in a binary tree. In order not to have to evaluate all branches of the binary search tree, a lower limit G for the minimum is achieved by solving a relaxed problem by means of sequential quadratic programming (FIG. PE Gill, W. Murray, MH Wright, "Practical Optimization", Academic Press, London, 1995 ) certainly.

In the optimization module 11 further special problem classes and adapted problem formulations and efficient algorithms are implemented, as in the following literature T. Jenicek, J. Kralik, "Optimized Control of Generalized Compressor Station ";
S. Wright, M. Somani, C. Ditzel, "Compressor Station Optimization", Pipeline Simulation Interest Group, Denver, Colorado, 1998 ;
K. Ehrhardt, MC Steinbach, "Nonlinear Optimization in Gas Networks", ZIB Report 03-46, Berlin, 2003 and
RG Carter, "Compressor Station Optimization: Computational Accuracy and Speed", 1996 , are to be found.

Based on a continuous operation of the compression system operating points 22 in maps 20, see FIG. 2 , the compressor units 3, 4 and 5 kept in their optimum range.

When changing the volumetric flow V̇ _{g (Should)} the compression system is calculated by means of the optimization calculation in the optimization module 11 from a current switching configuration S _{i, t-1 of} the compressor units 3, 4 and 5 with respect to an optimized total energy demand of the compression system, a new switching configuration S _{i, t} .

A reduction in the volume flow V̇ _{g (Should)} the compression plant by half results in an optimization calculation result, which specifies the following new switching configuration: The compressor unit 5 is shut down by the default S ₅ , _t = 0. Since the required volume flow of the compression system can now be achieved with two out of three compressor units, the compressor unit 5 is inactive. All compressor units 3 and 4 in operation are now driven continuously until the change in the volume flow or a deviation from the setpoint values again results in an optimization calculation with a changed switching configuration. Continuous operation means that the compressor units in operation are operated with an optimized load distribution and with an optimized setting of their operating points 22 in the maps 20. The output variables 32 of the optimization module 11 thus also contain, in addition to the switching states of the compressor units currently to be set, a speed setpoint input λ _i for the individual compressor units 3, 4 and 5.

From the subordinate station control, which runs higher than the optimization cycle, the speed setpoints λ _i , before being applied to the compressor aggregate controllers, scaled by a common factor α to regulate the setpoints. The optimization calculation is executed with a control cycle R in the optimization module 11 itself triggering. In the optimization calculation _, the load distribution between the compressor units, ie the efficiency of optimum speed setpoint values λ _i for the individual compressor units 3, 4 and 5 are cyclically executed in addition to the calculation of a possible switching configuration S _{i, t} . For the duration of the control cycle R, the desired speed values λ _i and the shift configuration S _{i, t-1} are kept constant. Now doubled the volume flow V̇ _{g (Should)} the overall system due to load changes, the optimization calculation with the next control cycle R will specify a new switching configuration S _{i, t} , a new load distribution, and a new location of the efficiency-optimal operating points 22.

The new switching configuration is now operated by three out of three compressor units. Since the result of the optimization calculation is known before the end of the control cycle, a warm-up phase is started for the third compressor unit 5 to be approached. Upon completion of the control cycle R, the new values of the control device 10 and in particular the compressor unit regulators 13, 14, 15 are provided. The previously prepared with a warm-up compressor unit 5 can now be seamlessly connected to the new control cycle R and the optimal total energy consumption for the required flow rate or the required flow rate V̇ _{g (Should)} is given again.

FIG. 2 shows a compressor-specific map 20 of a compressor unit 3. The compressor map 20 shows the speed-dependent delivery characteristics 21 and the efficiency curves of the compressor 23 in dependence of the plotted on the x-axis flow rate V̇ _{3, E} at the input of the compressor and the plotted on the y-axis specific Conveying work γ _{3 of} the compressor ( V̇ = ṁ / δ, δ = density ).

In addition, a surge limit 36 is entered. Efficiency optimal operating points 22 are close to the pumping limit 36 on an efficiency curve 23 with a high efficiency η _{3, max} .For that with FIG. 1 described methods are the maps 20 given as a mathematical function of a mass flow (or the volume flow) and a specific production work of the individual compression units. The mathematical formulation of the maps 20 as a calculation function is part of the optimization module 11 or the optimization calculation.

FIG. 3 shows a control device 10 for controlling a compression system 1. The determined by the optimization module 11 optimal speed setpoints λ _i and the new switching configuration S _{i, t} are, in cooperation with the controller 10, via an adjusting module S to the compressor units 3, 4 and 5 set and / or regulated.

As a controlled variable for a control of the control device 10, in particular that variable of flow, suction pressure, discharge pressure and end temperature, which has the smallest positive control deviation, is used. The control of the control device 10 supplies as output together with the optimization module, the setpoint values for a single compressor unit controller 13, 14, 15 see Fig. 2 ,

FIG. 4 shows a flowchart of the method steps 40, 42, 44 and 46. Starting from a first method step 40, the optimization process is initiated cyclically. With a second method step 42, the current state of the compressor station 1 is determined. The following values are recorded: actual values 30, setpoints 31, limit values and boundary conditions 37 and models 24a, 24b, and 24c from the model library 26. In addition, according to the invention, the current switching state S _i , _{t-1 of} the compression plant 1 is determined. A third method step 44 represents a decision point. The decision is made with the third method step 44 perform an optimization calculation 46 in a fourth method step or end the method 48. On the basis of the present actual values 30 and set values 31, it can be decided whether an optimization calculation is necessary. In the event that the third method step yields a yes decision Y, the method will continue with the fourth method step 46. In the fourth method step 46, the mixed-integer optimization problem is solved. Input variables for the fourth method step 46 are again actual values 30, setpoint values 31, limit values and boundary conditions 37 and the models from a model library 26. As a result of the fourth method step 46, speed setpoint values λ _i and new switching states S _{i, t are} output. The method is ended 48. With the cyclical initiation from the first method step 40, the method is run through again.

Claims (26)

1. Method for controlling a compressor plant (1) having at least two compressor units (i=1, ..., N) which can be connected and/or disconnected separately, having a plurality of devices for changing the operating output of the compressor units (i=1, ..., N) and having a control device (10), characterized in that, in the event that new set points are predefined or there is a change in the current state of the compressor plant (1), by means of an optimization calculation, a new switching configuration (S_i,t) is calculated from a current switching configuration (S_i,t-1 ) of the compressor units (i=1, ..., N), with regard to an optimized total energy demand (EG) of the compressor plant (1), and in that the new switching configuration (S_i,t) is set automatically via the control device (10).
2. Method according to Claim 1, characterized in that a forecast for at least one future time (t), preferably a plurality of future times (t), is determined by means of the optimization calculation.
3. Method according to Claim 1 or 2, characterized in that compressor unit-specific data sets and/or compressor unit-specific characteristic maps (20) are evaluated and, for the individual compressor units (i=1, ..., N), working points (22) are determined, which depend on predefined or changed values of the mass flow ṁ and a specific delivery work (y), the working points (22) being set in such a way that the total energy demand (E_G) of the compressor plant (1) is optimized.
4. Method according to Claim 3, characterized in that the data sets and/or characteristic maps (20) are provided as a function of a mass flow (ṁ_i) or a corresponding volume flow (V̇_i ) and a specific delivery work (λ _i ) of the individual compressor units (i=1, ..., N).
5. Method according to one of Claims 1 to 4, characterized in that, during the optimization calculation, in addition to the switching configuration (S_i,t), a load distribution between the compressor units (i=1, ..., N) is calculated and is changed if necessary.
6. Method according to one of Claims 1 to 5, characterized in that the optimization calculation is carried out with a control cycle (R), in particular in a self-triggering manner.
7. Method according to Claim 6, in which, with each control cycle (R), rotational speed set points (λ _i ) and/or the new switching configuration (S_i,t ) for the control device are provided as output variables (32) from the optimization calculation.
8. Method according to Claim 7, characterized in that, for the duration of the control cycle (R), which, in particular, is a multiple of a cycle time (Z) of the control action (12) of the control device (10), the rotational speed set points (λ _i ) and/or the switching configuration (S_i,t ) are kept constant.
9. Method according to either of Claims 7 and 8, in which the rotational speed set points (λ _i ) are scaled with a common factor (α) and used as a set point for a compressor unit controller (13, 14, 15).
10. Method according to one of Claims 1 to 9, in which the control device (10), using the new switching configuration (S_i,t =1), triggers a warm-up phase of the compressor units (i=1, ..., N) for the subsequent connection of a compressor unit (S _i,t-1=0) that was previously out of operation, even before the end of the control cycle (R).
11. Method according to Claim 10, characterized in that, with the end of the warm-up phase, a readiness to be loaded for the next control cycle (R) is communicated to the control device (10).
12. Method according to one of Claims 1 to 11, in which the following are evaluated as an input (23) for the optimization calculation

    - a model (24) of the individual compressor units (i=1, ..., N) and/or

    - a model library (26) of the entire compressor plant (1) , and/or

    - a current specific delivery work (y _i,t-1) of the individual compressor units (i=1, ..., N) and/or

    - a current specific delivery work (y _g,t-1) of the compressor plant (1) and/or

    - a current mass flow (ṁ _i,t-1 ) through the individual compressor unit (i=1, ..., N), in particular through an individual compressor, and/or

    - a current mass flow (ṁ _g,t-1) through the compressor plant (1) and/or

    - the current switching configuration (S_i,t-1 ) and/or

    - an intake pressure (P_g,E ) on the inlet side (E) of the compressor plant (1) and/or

    - an intake pressure (p_i,E) on the inlet side of the individual compressor unit and/or

    - an end pressure (P_g,A ) on the outlet side (A) of the compressor plant (1) and/or

    - an end pressure (P_i,A ) on the outlet side of the individual compressor unit (i=1, ..., N) and/or

    - a temperature (T_g,A ) on the outlet side (A) of the compressor plant (1) and/or

    - a temperature (T_g,E ) on the inlet side (E) of the compressor plant (1) and/or

    - a temperature (T_i,A ) on the outlet side of the individual compressor units (i=1, ..., N) and/or

    - a temperature (T_i,E ) on the inlet side of the individual compressor units (i=1, ..., N) and/or

    - the current rotational speeds of the compressor units.
13. Method according to one of Claims 1 to 12, in which the optimization calculation minimizes the total energy demand expected at a later time (t) by means of forecast calculations in accordance with the principle of model-predictive control.
14. Method according to one of Claims 1 to 13, characterized in that an energy consumption (E_S) of a switching operation is taken into account during the optimization calculation.
15. Method according to Claim 14, characterized in that the energy consumption (E_S) of the switching operation is calculated from the data sets and/or the characteristic maps (20) of the compressor units (i=1, ..., N).
16. Method according to one of Claims 1 to 15, characterized in that the specific delivery work (y_g ) of the compressor plant (1) is assumed to be constant for the control cycle (R), in particular when the compressor units (i=1, ..., N) are connected in parallel.
17. Method according to one of Claims 1 to 15, characterized in that the mass flow (ṁ_g ) of the compressor plant (1) is assumed to be constant for the control cycle (R), in particular when the compressor units (i=1,..., N) are connected in series.
18. Method according to one of Claims 1 to 17, in which an active compressor unit (S_i =1) is operated at least with a predefinable or predefined minimum flow (ṁ _{i min}).
19. Method according to one of Claims 1 to 18, in which the optimization calculation is carried out by means of a branch and bound algorithm.
20. Method according to Claim 19, in which a limit (G) for the branch and bound algorithm is determined by solving a relaxed problem by means of sequential quadratic programming.
21. Method according to one of Claims 1 to 20, in which the optimization calculation solves partial problems by means of dynamic programming, in particular in the case of series connection.
22. Control device (10) for controlling a compressor plant (1) having at least two compressor units (i=1, ..., N) which can be connected and/or disconnected separately and having a plurality of devices for changing the operating output of the compressor units (i=1, ..., N), characterized by an

    - optimization module (11), with which, in the event that new set points are predefined or there is a change in the current state of the compressor plant, by means of an optimization calculation, a new switching configuration (S_i,t ) can be calculated from a current switching configuration (S _i,t-1) of the compressor units (i=1, ..., N), with regard to an optimized total energy demand (E_G) of the compressor plant (1), and

    - by an actuating module (S), with which the new switching configuration (S_i,t ) can be set automatically.
23. Control device (10) according to Claim 22, characterized in that the optimization module (11) is arranged at a physical distance, in particular a plurality of km, from the control device (10).
24. Control device according to one of Claims 22 to 23, characterized in that the optimization module is set up to take into account an energy consumption (E_S) of a switching operation.
25. Control device according to one of Claims 22 to 24, characterized in that the optimization module (11) is set up for the optimization calculation for a plurality of control devices of a plurality of compressor plants.
26. Computer program product containing software for carrying out a method according to one of Claims 1 to 21.

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