Method and device for controlling a fluid network

Abstract

Method and device for controlling a fluid network The present description relates to a method for estimating, by a calculation device, parameters for controlling a fluid network, from at least one source station to at least one destination station and via at least one pipeline, the network comprising at least one valve and / or at least one pressure reducing station, the method comprising: determining a configuration of the at least one valve and / or of the at least one pressure reducing station and / or of the at least one source station; initializing coefficients associated with the at least one pipeline; a first estimation of the parameters, on the basis of the configuration, updating the coefficients on the basis of the first estimation; searching for a new configuration on the basis of the updated coefficients; a second estimation; and providing the second estimation to a control station. Figure for abstract: Fig. 3

Description

Title of the invention: Method and device for controlling a fluid network Technical field

[0001] The present description relates generally to the improved control and / or adjustment of fluid networks, such as gas networks and / or heat networks. Prior art

[0002] A fluid network, transporting for example gas or heat, generally comprises a set of pipes and nodes. Each node corresponds, among other things, to: - a production unit, such as, for example, a biomethane or synthesis gas injection station or a transport station in the case of a gas distribution network or such as a gas power station or a waste or fatal heat incineration plant in the case of a heat network; - a distribution device, such as an expansion and compression station, in the case of a gas distribution network or a compressor and an expansion device such as a control valve, in the case of a heat network; - a connecting organ; - a cut-off device, such as a valve or tap, in the case of a gas distribution network or a heat network: - a consumer body, such as a tertiary customer or a residential customer in the case of a gas distribution network, or such as a substation in the case of a heat network.

[0003] In the case of gas distribution networks, transmission stations connect a national gas transmission network to a gas distribution network. The transmission station recovers very high-pressure gas from the national transmission network and expands it to then inject it into the distribution network. For example, national transmission networks and distribution networks are not managed by the same entities.

[0004] The control of such a network consists of seeking an adjustment, for example of the pressures to be exerted at the level of the nodes concerned, allowing the transport of the fluid from source stations to destination stations. In particular, the control is sought so as to satisfy operating and consumption constraints while meeting one or more criteria, such as business criteria improving the operation of the network, for example a drop in network pressure, an increase in a share of a renewable product, economic cost criteria and / or environmental, etc.

[0005] However, the search for a setting of such a network is generally carried out by a systematic approach, in particular by following expert rules and, for example, by imposing maximum pressures for most of the stations. In addition, in certain complex cases, for example when biomethane or synthesis gas injection stations are put in competition in the network, simulation tools are used by the person skilled in the art in order to determine a setting respecting the different constraints. The simulation tools used generally implement several successive calculation iterations.

[0006] Furthermore, the increasing complexity of injection parks for a given network, and in particular the increase in the number of biomethane injecting stations, makes the previously cited approaches too complex and too long to implement for the person skilled in the art.

[0007] Furthermore, the complexity of the aforementioned approaches is partly due to the size of the networks. Existing methods do not allow for optimally finding a setting for these large networks. There is a need to improve the methods and processes for developing improved control for given criteria. Summary of the invention

[0008] One embodiment provides for estimation, by a calculation device, of control and / or adjustment parameters of a network configured to transport a fluid, from at least one source station of the network to at least one destination station of the network and via at least one pipeline, the network comprising at least one tap and / or at least one expansion and / or compression station, the method comprising: a) determining an initial configuration of the binary states of the at least one valve and / or of the at least one expansion and / or compression station and / or of the at least one source station; b) the initialization of pressure loss coefficients associated with at least one pipeline; c) an initial estimate of the parameters, based on the initial configuration, d) the subsequent updating of the pressure loss coefficients, by the calculation device, based on the first estimate of the parameters; e) searching for a new configuration of binary states, based on the updated pressure drop coefficients; (f) a second estimation of the parameters, based on the new configuration; and (g) providing the second estimate of the parameters to a cockpit of the network.

[0009] According to one embodiment, the first estimation is carried out by solving a non-linear programming problem, by the calculation device, on the basis of the initial configuration and a pipeline model describing the network.

[0010] According to one embodiment, the initial configuration corresponds to a configuration in which the at least one tap and / or the at least one expansion and / or compression station and / or the at least one source station are in a fixed state, for example in an operating state, or in a non-operating state.

[0011] According to one embodiment, the above method further comprises, following step d), a new performance of step c) resulting in a new first estimation of the parameters, on the basis of the initial configuration.

[0012] According to one embodiment, the above method further comprises, following the performance of step d): - the calculation, by the calculation device, of a first error value associated with the first estimation of the parameters; - comparing the first error value with a first threshold value; and - if the first error value is greater than the first threshold value, a new performance of step c).

[0013] According to one embodiment, the search for the new configuration is carried out by solving a non-linear mixed integer programming problem, by the calculation device, on the basis of the updated pressure drop coefficients.

[0014] According to one embodiment, the above method further comprises, following the performance of step e): - the calculation, by the calculation device, of an objective value associated with the new configuration; - comparing the target value with a target value associated with the initial configuration; and - if the objective value associated with the new configuration is better than the objective value associated with the initial configuration, resuming the process in a new implementation of step c), with the new configuration as the initial configuration.

[0015] According to one embodiment, the objective values ​​are calculated, by the calculation device, on the basis of an objective function associated with the mixed integer nonlinear programming problem.

[0016] According to one embodiment, the above method further comprises, following step e), a new update of the pressure loss coefficients, on the basis of the new configuration.

[0017] According to one embodiment, the network is a gas network comprising, for example, at least 500 pipelines.

[0018] According to one embodiment, the network is a heat network comprising, for example, at least 500 pipes.

[0019] According to one embodiment, the parameters correspond to pressure and / or flow rate values ​​at the level of at least one source station and pressure values ​​at the level of at least one expansion and / or compression station.

[0020] One embodiment provides a computing device comprising: - a non-volatile memory configured to store instructions implementing the method for estimating control and / or adjustment parameters of a network according to the above method; - a processor configured to execute the instructions; - an interface configured to provide the estimated parameters to a network control device. Brief description of the drawings

[0021] These characteristics and advantages, as well as others, will be explained in detail in the following description of particular embodiments given without limitation in relation to the attached figures among which:

[0022] [Fig.l] represents, in a very schematic manner, an example of a network configured to transport a fluid;

[0023] [Fig.2] is a block diagram illustrating a computing device, according to an embodiment of the present description;

[0024] [Fig.3] is a flowchart illustrating steps of a control method, according to an embodiment of the present description;

[0025] [Fig.4] is a flowchart illustrating steps of a first level in the control method, according to an embodiment of the present description; and

[0026] [Fig. 5] is a flowchart illustrating steps of a second level in the control method, according to an embodiment of the present description. Description of the embodiments

[0027] The same elements have been designated by the same references in the different figures. In particular, the structural and / or functional elements common to the different embodiments may have the same references and may have identical structural, dimensional and material properties.

[0028] For the sake of clarity, only the steps and elements useful for understanding the described embodiments have been shown and are detailed. In particular, the problem-solving methods of the NLP (Non Linear Programming) type or of the MINLP (Mixed Integer Non Linear Programming) type are known to those skilled in the art and are not described in detail.

[0029] Unless otherwise specified, when referring to two elements connected between them, it means directly connected without intermediate elements other than conductors, and when we refer to two elements connected (in English "coupled") between them, it means that these two elements can be connected or be linked by means of one or more other elements.

[0030] In the following description, when reference is made to absolute position qualifiers, such as the terms "front", "back", "top", "bottom", "left", "right", etc., or relative position qualifiers, such as the terms "above", "below", "upper", "lower", etc., or to orientation qualifiers, such as the terms "horizontal", "vertical", etc., reference is made unless otherwise specified to the orientation of the figures.

[0031] Unless otherwise specified, the expressions "about", "approximately", "substantially", and "of the order of" mean to within 10%, preferably to within 5%.

[0032] [Fig. 1] represents, in a very schematic and simplified manner, an example of a network 100 configured to transport a fluid. In certain examples, the network 100 is a mesh network. The network 100 comprises, for example, at least one pipe 102. For the sake of clarity, the illustrated network 100 comprises a limited number of pipes 102. However, in general, the network 100 is relatively large in size and comprises, for example, at least 500 pipes 102. By way of example, the network 100 comprises at least 1000 or 1500 pipes 102.

[0033] The network 100 further comprises nodes, each node being connected to one or more other nodes via one or more pipes. Each node corresponds, for example, to a type of functionality of the network. Some nodes are, for example, valves 104, or even taps, configured to allow or not the passage of the fluid between the pipes 102 that they serve. Other nodes are source stations 106. For example, the source stations 106 are production stations or transport stations conveying for example gas, coming from a transport network, to a distribution network. In the case of a gas distribution network, a source station 106 is a biomethane station producing “green” gas or a transport station. In particular, the source stations 106 comprise a subset of the biomethane stations and a subset of the transport stations.In some cases, the network does not include, for example, biomethane stations. In the case of a heat network, a source station 106 corresponds, for example, to a waste incineration plant, a gas power station and / or a fatal heat source.

[0034] Certain other nodes are, for example, consumption stations, or supply / delivery points, 108. For example, a destination station corresponds to an arrival of the fluid at a customer, such as an individual or a community.

[0035] Other nodes correspond for example to expansion and / or compression stations 110 configured to expand and / or compress the gas.

[0036] Generally, each node of the network 100 is either in an operating or non-operating state, or in a closed or open position in the case of the valves 104. In other words, the state of each node is described by a binary value. A configuration of the network then corresponds to the description of the state of each node of the tap type 104, source station 106 and distribution station 110 of the network 100. In the case of a network 100 comprising a number N of such nodes, there are therefore 2N possible configurations of the network 100.

[0037] Control of the network 100 consists of routing the fluid from the source station(s) 106 to one or more stations 108 via distribution stations 110 and / or taps 104 while respecting a flow rate and pressure demand required by the consumption station(s) 110. In one example, the network 100 corresponds to a reverse station and the gas is injected from the distribution network to the transport network via a distribution station 110 of the compressor station type. This example applies in the event of overproduction from the biomethane stations, for example in summer, when the network demand is low and the network operator has made a commitment to the managers of the biomethane stations on a contractual supply guarantee.

[0038] Furthermore, it is desirable for the network control 100 to meet certain criteria, such as, for example, criteria relating to the economic cost of operating the network 100 and / or environmental criteria. For example, in the case where the network 100 is a gas network, an environmental criterion corresponds to the greening of the network by promoting the so-called “green” injection of biomethane, by production stations such as biomethane stations, into the network. The criterion then corresponds, for example, to a rate of injected biomethane. In another example, the criterion consists of satisfying a set of business objectives and constraints on the adjustment of the network 100, such as, for example, lowering the pressure of the network, and / or maximizing the number of “non-green” production stations on standby, i.e. minimizing the number of “non-green” stations in operating condition, etc.

[0039] Generally, these criteria are described by a multi-objective function of the network 100. Improved control of the network 100 then consists, for example, in finding a configuration, among the 2N possible configurations and the pressures and flow rates to be applied at the source stations 106 and the pressures to be applied at the expansion and / or compression stations 110 while meeting the criteria as much as possible.

[0040] The complexity of the search for improved control comes on the one hand from the large number of possible configurations, when the number N is large, for example greater than 10 and, on the other hand, from the pressure drop phenomenon occurring during the transport of the fluid in the network 100. The pressure drop phenomenon is caused by the friction experienced by the fluid against the walls of the pipes. This friction, associated with turbulence phenomena in the fluid, cause a loss of pressure, called pressure loss. Models describing the pressure loss phenomenon are models describing highly non-linear relationships between variables physical and therefore make the search for improved piloting difficult.

[0041] [Fig.2] is a block diagram illustrating a computing device 200, according to an embodiment of the present description.

[0042] The computing device 200 comprises, for example, a non-volatile memory 202 (NV MEM) configured to store, for example, instructions 204 (INSTRUCTIONS). For example, the instructions 204 are instructions for determining a control of the network 100. In other words, the instructions 204 make it possible to determine a configuration and pressure and flow rate values ​​for the stations 106 and 110. In particular, the instructions 204 are adapted to the topology of the network 100 and consequently integrate a topological model of the network 100 as well as a pressure drop model.

[0043] The pressure loss model, integrated into the instructions 204, then describes the pressure losses due to friction between the fluid and the walls of the pipes 102 as well as those due to variations in altitude in the network 100.

[0044] For example, each node of the network is labeled by an index, for example an integer value. In the remainder of the description, cl^ denotes the diameter, in mm, of the pipe connecting nodes i and j; l,j its length, in km; its internal roughness, in microns. For example, the absolute roughness of a new polyethylene pipe is represented by a value of 7 microns and 12 microns for steel pipes. These values ​​in microns are given as an example and are only a modeling choice. The person skilled in the art will be able to adapt these values ​​to models of thinner or wider pipes. In addition, the quantity Pq representing the average pressure in bar, circulating in the pipe connecting nodes i and j and the quantity Qj ■ representing the volumetric flow rate, in Nm3 / h, circulating in this pipe are introduced.

[0045] In the example where network 100 is a gas network, the pressure drop between nodes i and j, noted pj _ pj and expressed in bar2 follows the extended Bass law, derived from the classical Darcy-Weisbach equation and is defined by the following equation: [Math 1] 3600710¼¾^ thread atj where the co fixed efficiencies are: - ^ij being a gas compressibility factor, considered to be constant and for example fixed at 0.0097; - Pa representing the density of air in kg / m3 under normal temperature and pressure conditions, for example taken at 1,013 bars and 273.15 kelvins; - d0 representing the density, in kg / m3, of the gas circulating in the pipeline; - Tm representing the temperature, in kelvins, of the gas circulating in the pipeline; - To representing a reference temperature value, for example 273.15 kelvins; - 2o being a compressibility factor, for example set to the value 1; - Pq being the reference pressure, for example set at 1 bar; - 8 representing the acceleration of gravity, taken at 9.81 m / S-; - △s representing the overall altitude variation on the pipeline; - P representing the density of the gas circulating in the pipeline; and - p being the dynamic viscosity of the gas circulating in the pipeline, for example fixed, for methane under normal conditions of temperature and pressure, at

[0046] The variables involved in the previous equation are then the quantities: - describing the average pressure, in bar, in the pipeline; - ^ij describing a regular load friction term in the pipeline; and - Qj j the volumetric flow rate described previously. In particular, the variable is expressed by a standard explicit approximation of the Swamee-Jain equation and the Colebrook-White equation.

[0047] For example, in turbulent conditions, is such that: [Math 2] X- • = X-^ =----------------- °P cst 'c dimensionless Reynolds number at w lJ (' I oe ( —— + — ) r ' l 3700(7,-,- rJO / ) the pipeline, either:

[0048]

[0049] [Math 3] = °where v is a variable describing the natural speed of the gas in the channel lization. In a case of laminar regime, and no longer turbulent, is such that: [Math 4] . — Xlam = — For example, in a laminar transition zone- ■J ÿ Re' turbulent, the value Xÿ is calculated by interpolation between the values ​​and X-“”- As examples, in instructions 204, the previous model is separated into two distinct models: a model for pipes and a head loss model. For example, a variable ce has a value / .. corresponding to the coefficients of pressure losses in pipes are, for example, described by the pressure loss model and are such that: [Math 5] ^-10½¾ 360071() / ¾^) aij And

[0050]

[0051]

[0052]

[0053] [Math 6] / .. = 2 The variable CiJ and the value / .. are then introduced into the cana-J 'J model lization. The pressure loss pj _ p? is then such that: [Math 7] cQ,^ In another example, network 100 is a heat network. The pressure drop between nodes i and j, denoted / — Pj and expressed in bar, is defined, keeping the same notations as in the previous example of a gas network, by the following equation: [Math 8] p. _ p — 2 -Q | Q | + pg^z where the factor j is non-linear and carries, in this example, the physics of pressure loss in the pipeline. The expression of dynamics in pipes also involves describing the evolution of temperature in the pipes. In particular, by denoting 0, the temperature at node / and the temperature at node j, the equation relating to the conservation of energy in the pipe between nodes i and j is written: [Math 9] - Cp is the specific heat capacity of water, and - Ui j represents the main term for thermal losses in the pipeline and is of the form: [Math 10] _ bi(4h / (rdij)) lf^r) where: 'J 2 / rÀg Zttà.j ' - h is the burial depth of the pipeline, - r is the ratio between the external diameter of the pipe insulation and the diameter of the pipe, - is the thermal conductivity of the insulation, and - is the thermal conductivity of the soil. The computing device 200 further comprises, for example, a processor 206 (CPU) connected to non-volatile memory 202 and to a volatile memory 208 (RAM) by via a bus 210. The processor 206 is, for example, configured to execute the instructions 204.

[0054] The computing device 200 further comprises, for example, an interface 212 (INTERFACE). For example, the interface 212 is configured to transmit the control, or the setting, determined, during the execution of the instructions 204, to a controller (not illustrated in [Fig. 2]) configured to control the network 100, on the basis of the determined control. For example, the controller is a control circuit connected to the computing circuit 200 via the interface 212. For example, the computing device 200 and the control circuit are part of the same electronic device and the interface 212 takes, for example, the form of a bus. In another example, the controller is a device external to the computing device 200 and the interface 212 is for example a USB (Universal Serial Bus) interface, UART (Universal Asynchronous Receiver-Transmitter), etc.

[0055] [Fig. 3] is a flowchart illustrating steps of a control method, according to an embodiment of the present description. In particular, the steps described in relation to [Fig. 3] are, for example in part, executed by the processor 206 during the execution of the instructions 204.

[0056] A step 300 (TOPOLOGY MODEL) comprises, for example, the modeling of the topology of the network 100. For example, the modeling of the network is carried out by means of modeling software, and for example by means of a computer. For example, the topological model is integrated into the instructions 204.

[0057] In a step 302 (INITIAL ASSUMPTION), for each pipeline connecting a pair of nodes i and j of the network 100, the pressure drop coefficients C'J and f.. of the pipeline model are initialized. For example, the initialization of the pressure drop coefficients is done by predetermined values, for example following a study, a simulation or via default values. For example, the default values ​​are inaccurate values, not corresponding to reality. When performing step 302, the configuration of the network is also initialized. For example, this initial configuration corresponds to a configuration in which all the valves are in the open position and in which all the stations 106 to 110 are also in an operating state.

[0058] In a step 304 (LEVEL 1), subsequent to the completion of steps 300 and 302, a first level of calculations is carried out by the calculation device 200. The first level of calculation corresponds to the resolution of a problem of the NLP (Non Linear Programming) type.

[0059] According to one embodiment, the NLP type problem is then solved on the basis of the pipeline model described above and pressure loss coefficients. The pressure loss coefficients are then updated a posteriori based on the calculated flow and pressure values. When an iterative algorithm describing the resolution of an NLP problem is first launched, assumptions about the pressure loss coefficients are made. These assumptions may, for example, be inaccurate and not correspond to reality. For example, initial values ​​for the pressure loss coefficients, favoring flow with very low pressure losses, for example of the order of 10'10, are chosen. The values ​​are then corrected and updated at each iteration of the algorithm. The correction and updating of the values ​​are carried out based on the results for a previous iteration and are used for the next iteration in the pipeline model.

[0060] In particular, the NLP problem is then solved on the basis of the pipe and pressure loss models described in relation to [Fig. 2] and on the pressure loss coefficients fixed and the initial configuration determined during step 302. The resolution of the NLP problem then aims to estimate parameters for controlling the network 100, that is to say to estimate values ​​of the pressures and flow rates to be applied at the source stations 106 and the pressures to be applied at the expansion and / or compression stations 110. The resolution of the NLP problem is therefore carried out on the basis of the initializations carried out during step 302 and therefore does not include an estimation of the pressure loss coefficients, nor the determination of a new configuration.

[0061] Generally, the pipeline model and the pressure drop model are used in solving the NLP problem. The so-called Darcy-Weisbach equation is then directly used in the NLP problem. The NLP problem is then solved in a single iteration. However, since the Darcy-Weisbach equation is non-linear and non-convex, solving the NLP problem is then very expensive or even impossible for dealing with large networks.

[0062] Calculating the load loss coefficients a posteriori with an iterative process, thus separating the pipeline model from the load loss model, then makes it possible to overcome the complexity of solving the NLP problem integrating the pipeline model and the load loss model.

[0063] In a step 306 (LEVEL 1 RESULTS), the results, i.e. the pressure and / or flow rate estimated by solving the NLP problem, are used to, for example, re-evaluate, in post-processing, the pressure loss coefficients. The re-evaluated pressure loss coefficients are then used to update the pipeline model. For example, the control of this update is included in the instructions 204.

[0064] For example, the first level of calculation is iterative, that is to say that following the performing step 306, the method resumes for example at step 304 in order to refine the estimates. For example, the method loops between steps 304 and 306 until convergence of the first level of calculation is obtained.

[0065] Following the performance of step 306, the method continues in a performance of a step 308 (LEVEL 2). Step 308 consists of a questioning of the initial configuration chosen during the performance of step 302. During the performance of step 308, a second level of calculation is executed by the calculation device 308. The second level of calculation then consists of the resolution of a MINLP type problem (from the English “Mixed Integer Non Linear Problem”) on the basis of the pressure drop coefficients re-evaluated during the performance of step 306, in order to verify, or question, the existence of another configuration describing a network closer to the criteria and constraints set. For example, the MINLP problem is the same as the NLP problem except that so-called binary variables relating to the operating state of the valve components and expansion / compression stations are activated.In solving the NLP problem, these binary variables are, for example, set to 1, corresponding to the operating state. The MINLP problem is also solved on the basis of an objective function. For example, an objective function is a function describing an objective in the desired control. For example, in the case of a gas network, the objective is, for example, to maximize the injection of biomethane into the network 100.

[0066] In a step 310 (CONFIGURATION OUTPUT), an objective value of the new configuration is calculated, by the calculation device 200, by applying the objective function. The objective value is for example compared to the objective value associated with the configuration used during step 304.

[0067] For example, when performing step 310, the pressure loss coefficients are again re-evaluated, taking into account the new configuration.

[0068] For example, if the objective value of the new configuration is better than that of the configuration used when performing step 304, the method continues in a step 312 (UPDATE), in which the configuration previously used for the pipeline model in when performing step 304 is replaced by the new configuration. The pressure loss coefficients of the pipeline model are, for example, updated with the coefficients reevaluated when performing step 310.

[0069] Following step 312, the method resumes, for example, in a new embodiment of step 304, in which the pressures and / or flow rates of stations 106 to 114 are again estimated on the basis of the new configuration and the re-evaluated pressure loss coefficients.

[0070] In another example, when the objective value of the new configuration is not not improved compared to the objective value of the configuration from which the previous step 304 was carried out, the method ends. In this example, the pressure loss coefficients are not re-evaluated. In this case, the determined improved control is characterized by the flow rates and / or pressures to be applied to the stations 106 to 110 obtained during the last performance of step 306 and by the configuration used during the last performance of step 306. By way of example, the calculation device 200 then transmits the control obtained to, for example, a network control device 100 in order to control the transport of the fluid from the source stations 106 to the destination stations 108 concerned.

[0071] [Fig.4] is a flowchart illustrating steps of the first calculation level in the control method described in relation to [Fig.3]. In particular, [Fig.4] illustrates in more detail steps 304 and 306 according to an exemplary embodiment.

[0072] In a step 400, corresponding to a portion of step 302, an initial configuration is established. For example, the initial configuration defines a configuration in which all valves 104 are open and in which all stations 106 to 110 are in operating condition.

[0073] The initial configuration is then provided to the pipeline model, initialized in a step 402. Step 402 takes up, for example, elements of steps 300 and 302.

[0074] Step 402 comprises, for example, the integration of the pipeline and pressure drop models into the instructions 204 during a sub-step 404 (MODELS).

[0075] In a sub-step 408 (PRESSURE DROP COEFFICIENTS), the pressure drop coefficients, for each of the pipes, are initialized as described in relation to step 302.

[0076] In a sub-step 410 (OBJECTIVE), an objective function for the NLP problem is determined and is integrated into the instructions 204. For example, in the case of a gas network, the objective function maximizes the injection of biomethane into the network 100.

[0077] Steps 400 and 402 are, for example, carried out at the time of programming the instructions 204. In other words, steps 400 and 402 are carried out independently of the estimation of the control parameters and upstream of the execution of the instructions 204 by the calculation device 200.

[0078] Following the initialization steps 400 and 402, the first level of calculation continues in an implementation of a step 412 (NLP RESOLUTION). The NLP problem then consists of estimating the pressures and flow rates to be applied to the stations 104 to 110, from the initial configuration fixed in step 400 and the parameters initialized during the implementation of step 402.

[0079] Carrying out step 412 further comprises a new estimation, or re-evaluation, of the pressure loss coefficients for each pipeline. This new estimation or re-evaluation is, for example, carried out in post-processing of the NLP problem resolution. The re-evaluated pressure loss coefficients are, for example, updated in the pipeline model when performing a step 414 (UPDATE).

[0080] In a step 416 (Err < Eth?), an error value associated with the control parameters is calculated by the calculation device. For example, the error value is a maximum error value in average flow rate and pressure on the pipes between the parameters calculated during the performance of the previous step 412 and the initial parameters. The error value is then compared to a threshold error value, set upstream. For example, the threshold error value is between 0.0005 and 0.005 bar, for example equal to 0.001 bar.

[0081] In the case where the error value is less than the threshold error value (Y branch), the first level of calculation ends in a performance of a step 418 (PERFORM LEVEE 2). For example, the performance of step 418 corresponds to the execution of an instruction causing the performance of step 308.

[0082] In the case where the error value is greater than the threshold error value (branch N), the method resumes at step 412 in a new resolution of the NLP problem. This new resolution of the NLP problem is carried out this time from the pressure loss coefficients re-evaluated and updated in the pipeline model during the previous step 414. The pressures and flow rate to be applied to the stations 104 to 110 are then determined again and the pressure loss coefficients are then re-evaluated and then updated in the pipeline model. For example, the pressures and flow rates to be applied to stations 104 to 110 are then estimated on the basis of the pressures and flow rates estimated during the last performance of step 412. The comparison then carried out, during a new performance of step 416, is made between the error values ​​calculated from the pressures and flow rates to be applied obtained during the last two performance of step 412.

[0083] The first level of calculation is therefore for example an iterative calculation method, the pressures and flow rate to be applied to the stations 104 to 110 and the pressure loss coefficients being re-evaluated on the basis of the results of the previous iteration until the method converges, that is to say until the error values ​​between two successive iterations differ by at most the threshold error value.

[0084] [Fig. 5] is a flowchart illustrating steps of the second level in the control method, according to an embodiment of the present description. In particular, the second level is carried out following the performance of step 418.

[0085] A step 500 comprises the initialization of the second calculation level. In particular, the initialization comprises, for example, the integration of the pipeline and pressure drop models in the instructions 204. By way of example, the models are the same as those implemented for the completion of step 404.

[0086] An objective function, for example the same function as that determined in step 410, is integrated into the instructions 204 implementing the second level of calculation.

[0087] The initialization step 500 further comprises a sub-step 502 (LEVEL 1 DE-TERMINED PRESSURE DROP COEFFICIENTS), in which the pressure drop coefficients, for each of the pipes, are initialized to the pressure drop coefficients resulting from the outcome of the first calculation level. The pressures and flow rates to be applied to stations 106, 108 and HO are further initialized to the values ​​resulting from the outcome of the first calculation level.

[0088] The MINLP problem 504 (MINLP PROBLEM) is identical to the NLP problem, except that the hypothesis on the configuration of the first level of calculation is called into question. So-called binary variables relating to the operating state of the valves 106 and the expansion / compression stations 110 are activated in order to define an initial configuration for solving the MINLP problem. Indeed, for solving the NLP problem, these binary variables were, for example, set to 1, reflecting an operating state. As an example, a reference objective value is calculated, by the calculation device, on the basis of the objective function and the initial configuration of the MINLP problem 504.

[0089] In a step 506 (MINLP RESOLUTION), the MINLP problem of determining a new configuration of the states of the valves 104 and stations 106 to 110, from all the values, parameters and coefficients initialized during steps 500 and 504, is solved.

[0090] In a step 508 (BEST CONFIG?) an objective value is calculated. For example, the objective value is calculated from the objective function of the new configuration obtained during step 506. A configuration will be considered better than another if, in a context of objective function to be minimized, respectively maximized, its objective value is lower, respectively higher than the value associated with the other configuration.

[0091] In the case where the new configuration is better than configuration 504 (Y branch), the load loss coefficients, as well as the configuration, are updated for the NLP problem. If the result of the objective function of the MINLP problem is better than that calculated for the NLP problem, the load loss coefficients are replaced by those calculated during step 510 and the configuration of the NLP problem is replaced by the configuration found by solving the MINLP problem. The estimation of the control parameters then continues in a step 510 (UPDATE LEVEL 1) in which the new configuration is provided as the initial configuration at the first level of calculation. The iterative method described in relation to [Fig.4] is carried out once again, but this time, from the new configuration, determined by solving the MINLP problem. For example, the pressure loss coefficients are further re-evaluated, in post-processing of step 506 and on the basis of the new configuration and are also used during the initialization of the pressure loss coefficients described in relation to step 408. The new implementation of the method described in relation to [Fig.4] then provides, for the new configuration, new flow rates and pressures to be applied and new re-evaluated pressure loss coefficients. These coefficients and parameters will then be used to carry out the second level of calculation again in order to determine, if possible and by solving a MINLP problem, yet another new configuration.During this second realization of the second level of calculation, the configuration 504 will then be the configuration previously determined and used during the last realization of the first level of calculation.

[0092] In the case where the new configuration is not better (branch N at the output of block 508), the second calculation level ends for example in a step 512 (END). The control parameters retained are then those obtained at the end of the last execution of the first calculation level and correspond to the current configuration 504 and to the pressures and flow rates estimated during the last execution of the first calculation level. Indeed, in this case, the current configuration 504 was determined to be better than that obtained following the resolution of the MINLP problem. For example, the control parameters are transmitted to the network control circuit 100 in order to transfer the fluid from the source stations 106 to the destination stations 108.

[0093] The second level of calculation makes it possible to verify the relevance of the solution found during the first level of calculation. The second level of calculation is omitted if, for example, the positions of the taps 104 are known by a network manager. In another example, the second level of calculation is omitted in order to speed up the estimation of the control parameters.

[0094] An advantage of the described embodiments is that the configuration of the network is estimated separately from the pressure and flow rate values ​​to be applied to the different stations. Thus, this makes it possible to estimate the pressures and flow rates by solving an NLP type problem, which is much less complex and costly than solving a MINLP type problem aimed at estimating the pressures and flow rates and a configuration.

[0095] Another advantage of the described embodiments is that the MINLP type problem, aimed at estimating a configuration, is not iterative. Thus, the iterations making it possible to verify the convergence of the model are carried out on the basis of the NLP type problem.

[0096] Another advantage of the described embodiments is that they allow the displacement calculating the load loss coefficients of the NLP optimization model by performing them a posteriori outside the NLP optimization model. Solving the NLP problem is then easier and the iterative process allows the NLP model to be glued to the physics of the network. In other words, the NLP problem is solved in a single iteration. Thus, a processing device such as a resource-limited machine is capable of solving the NLP problem.

[0097] Various embodiments and variants have been described. The person skilled in the art will understand that certain characteristics of these various embodiments and variants could be combined, and other variants will appear to the person skilled in the art. In particular, the type of network to which the method for estimating the control parameter applies may vary. Indeed, the network considered may, among other things, be a gas, hydrogen, heat, water, etc. network. In addition, the resolution in two calculation levels can also be applied to the search for adjustment for improved management of water networks with, for example, economic, ecological, etc. criteria. In general, the implementation of the method can be carried out on any problems of adjustment and / or control of fluid energy networks, and the resolution is based on a MINLP type problem.

[0098] Finally, the practical implementation of the embodiments and variants described is within the reach of those skilled in the art from the functional indications given above.

Claims

Claims

1. Method for estimating, by a calculation device (200), control and / or adjustment parameters of a network (100) configured to transport a fluid, from at least one source station (106) of the network to at least one destination station (108) of the network and via at least one pipeline (102), the network comprising at least one valve (104) and / or at least one expansion and / or compression station (110), the method comprising: a) determining an initial configuration of the binary states of the at least one valve and / or of the at least one expansion and / or compression station and / or of the at least one source station; b) initializing pressure loss coefficients associated with the at least one pipeline; c) an initial estimate of the parameters, based on the initial configuration, d) the subsequent updating of the pressure loss coefficients, by the calculation device, based on the first estimate of the parameters;e) searching for a new configuration of the binary states, based on the updated pressure drop coefficients; f) a second estimation of the parameters, based on the new configuration; and g) providing the second estimation of the parameters to a network control station.;

2. The method of claim 1, wherein the first estimation is performed by solving a non-linear programming (NLP) problem, by the computing device (200), based on the initial configuration and a pipeline model describing the network.

3. Method according to claim 1 or 2, wherein the initial configuration corresponds to a configuration in which the at least one tap (104) and / or the at least one expansion and / or compression station (110) and / or the at least one source station (108) are in a fixed state, for example in an operating state, or in a non-operating state.

4. Method according to any one of claims 1 to 3, further comprising, following step d), a new performance of step c) resulting in a new first estimation of the parameters, on the basis of the initial configuration.

5. Method according to claim 4, further comprising, following the performance of step d): - the calculation, by the calculation device (200), of a first error value associated with the first estimation of the parameters; - the comparison of the first error value with a first threshold value; and - if the first error value is greater than the first threshold value, a new performance of step c).

6. A method according to any one of claims 1 to 5, wherein the search for the new configuration is carried out by solving a mixed integer nonlinear programming (MINLP) problem, by the computing device (200), on the basis of the updated pressure drop coefficients.

7. Method according to any one of claims 1 to 6, further comprising, following the performance of step e): - the calculation, by the calculation device (200), of an objective value associated with the new configuration; - the comparison of the objective value with an objective value associated with the initial configuration; and - if the objective value associated with the new configuration is better than the objective value associated with the initial configuration (504), the resumption of the method in a new performance of step c), with the new configuration as the initial configuration.

8. A method according to claim 7 as dependent on claim 6, wherein the objective values ​​are calculated, by the computing device (200), on the basis of an objective function associated with the mixed integer nonlinear programming problem.

9. Method according to any one of claims 1 to 8, further comprising, following step e), a new update of the pressure drop coefficients, on the basis of the new configuration.

10. A method according to any one of claims 1 to 9, wherein the network (100) is a gas network comprising, for example, at least 500 pipelines (102).

11. Method according to any one of claims 1 to 9, in which the network (100) is a heat network comprising, for example, at least 500 pipes (102).

12. A method according to any one of claims 1 to 11, wherein the parameters correspond to pressure and / or flow rate values ​​at level of at least one source station (106) and pressure values ​​at the level of at least one expansion and / or compression station (110).

13. A computing device comprising: - a non-volatile memory (202) configured to store instructions (204) implementing the method for estimating control and / or adjustment parameters of a network (100) according to any one of claims 1 to 12; - a processor (206) configured to execute the instructions; - an interface (212) configured to provide the estimated parameters to a network control device.

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