Flow channel hydraulic parameter optimization method and system for long-water-diversion pumped storage power station

Abstract

The invention relates to the technical field of water conservancy projects, and discloses a runner hydraulic parameter optimization method and system for a long-water-diversion pumped storage power station. A mathematical model of the long-water-diversion pumped storage power station considering runner loss nonlinearity and surge chamber impedance loss nonlinearity is constructed; the mathematical model is optimized and solved through the mayfly naiad algorithm, the hydraulic parameter optimization calculation speed is higher, the convergence rate is higher, and therefore the optimal solution set of the flow channel hydraulic parameters of the flow channel system of the long-water-diversion pumped storage power station is obtained; more accurate system decoupling analysis and unit operation control are realized by adopting hydraulic optimization parameters in the optimal solution set of the hydraulic parameters of the flow channel, and the operation stability of the energy storage power station is improved; the technical problem of how to improve the operation stability of the energy storage power station is solved.

Description

Technical Field

[0001] The invention relates to the technical field of water conservancy engineering, and in particular to a method and system for optimizing hydraulic parameters of a flow channel of a long-water diversion pumped-storage power station. Background Art

[0002] The flow channel system is an important part of the pumped storage power station. The optimization of its hydraulic parameters is very critical in the design process of the pumped storage power station. On the one hand, the flow channel system is composed of multiple complex pipeline components, each of which contains key hydraulic parameters. The reasonable configuration and optimization of these parameters are crucial to improving the overall performance of the pumped storage power station; on the other hand, the optimization process of the hydraulic parameters of the flow channel system involves multiple complex control requirements. These control requirements are interrelated and influence each other, which together constitute the difficulty of the optimization design of the flow channel system. Therefore, the optimization of the hydraulic parameters of the flow channel system is essentially a complex coordinated control problem involving multiple variables and multiple objectives. It is necessary to comprehensively consider various factors to achieve the goal of safe, stable and efficient operation of the pumped storage power station.

[0003] The current research on flow channel hydraulic parameter optimization mainly focuses on the control strategy of pumped storage power station units, governor parameter optimization, and guide vane closing law optimization. However, in the control strategy of pumped storage power stations proposed by existing scholars, the study of hydraulic parameters of long-water diversion pumped storage power stations often simplifies the flow channel loss and surge chamber impedance loss, ignoring that the flow channel loss and surge chamber impedance loss are actually nonlinear. These losses will cause pressure fluctuations in the flow channel system, thereby affecting the stable operation of the energy storage power station. Summary of the invention

[0004] The present invention provides a method and system for optimizing the hydraulic parameters of a flow channel of a long-water diversion pumped-storage power station, solving the technical problem of how to improve the operational stability of the storage power station.

[0005] A first aspect of the present invention provides a method for optimizing hydraulic parameters of a flow channel of a long-water diversion pumped-storage power station, comprising:

[0006] In response to the request for parameter optimization of the energy storage power station, a mathematical model of the energy storage power station is established that takes into account the nonlinearity of the flow channel loss and the nonlinearity of the surge chamber impedance loss;

[0007] Setting model constraints of the mathematical model of the energy storage power station;

[0008] Based on the mayfly algorithm, the mathematical model of the energy storage power station is iteratively solved using the model constraints to obtain an optimal solution set of the flow channel hydraulic parameters of the energy storage power station.

[0009] Optionally, the decision variables of the mathematical model of the energy storage power station considering the nonlinearity of flow channel loss and surge chamber impedance loss include the length of the long diversion tunnel, the head loss of the long diversion tunnel, the length of the pressure pipeline, the head loss of the pressure pipeline, the cross-sectional area of ​​the surge chamber, the impedance loss of the surge chamber and the working head of the water pump turbine;

[0010] The model constraints include:

[0011] The length of the long diversion tunnel is greater than or equal to a preset minimum long diversion tunnel length constraint value, and the length of the long diversion tunnel is less than or equal to a preset maximum long diversion tunnel length constraint value;

[0012] The water head loss of the long diversion tunnel is greater than or equal to a preset minimum water head loss constraint value of the long diversion tunnel, and the water head loss of the long diversion tunnel is less than or equal to a preset maximum water head loss constraint value of the long diversion tunnel;

[0013] The pressure pipeline length is greater than or equal to a preset minimum pressure pipeline length constraint value, and the pressure pipeline length is less than or equal to a preset maximum pressure pipeline length constraint value;

[0014] The pressure pipeline head loss is greater than or equal to a preset minimum pressure pipeline head loss constraint value, and the pressure pipeline head loss is less than or equal to a preset maximum pressure pipeline head loss constraint value;

[0015] The cross-sectional area of ​​the surge chamber is greater than or equal to a preset Thomas critical stable cross-sectional area, and the cross-sectional area of ​​the surge chamber is less than or equal to a preset minimum surge chamber cross-sectional area constraint value;

[0016] The voltage regulating chamber impedance loss is greater than or equal to a preset minimum voltage regulating chamber impedance loss constraint value, and the voltage regulating chamber impedance loss is less than or equal to a preset maximum voltage regulating chamber impedance loss constraint value;

[0017] The pump-turbine operating water head is greater than or equal to a preset minimum pump-turbine operating water head constraint value, and the pump-turbine operating water head is less than or equal to a preset maximum pump-turbine operating water head constraint value.

[0018] Optionally, the method of iteratively solving the mathematical model of the energy storage power station by using the model constraints based on the mayfly algorithm to obtain the optimal solution set of hydraulic parameters of the flow channel of the energy storage power station includes:

[0019] Initializing the mayflies, using the energy storage power station mathematical model to solve the initial optimization target value associated with each initial generation of mayflies under the model constraint condition, and performing non-dominated sorting and calculating congestion according to the initial optimization target value to obtain a preset non-dominated optimal solution set stored in the initial generation of mayflies;

[0020] Randomly selecting the first-generation mayflies associated with any of the decision variable parameters from the preset non-dominated optimal solution set as the global optimal position of the mayfly swarm, and updating the positions and velocities of the first-generation mayflies corresponding to the decision variable parameters;

[0021] Performing mating and mutation operations on the updated primary mayfly group in the preset non-dominated optimal solution set, and solving the energy storage power station mathematical model under the model constraint condition to obtain the offspring optimization target value associated with each offspring mayfly;

[0022] Sorting the offspring mayflies according to the offspring optimization target value, and performing non-dominated sorting on the mixed-generation mayfly group obtained by the sorting result to obtain a new preset non-dominated optimal solution set;

[0023] Jump to the step of randomly selecting the first-generation mayflies associated with any of the decision variable parameters from the preset non-dominated optimal solution set as the global optimal position of the mayfly swarm, and updating the positions and speeds of the first-generation mayflies corresponding to each decision variable parameter, until a preset threshold number of iterations is reached, then outputting the optimal solution set of the flow channel hydraulic parameters of the energy storage power station.

[0024] Optionally, the mayfly initialization is performed, and the primary optimization target value associated with each primary mayfly is obtained by using the mathematical model of the energy storage power station under the model constraint condition, and non-dominated sorting and congestion calculation are performed according to the primary optimization target value to obtain a preset non-dominated optimal solution set stored in the primary mayfly, including:

[0025] Initialize the mayfly and obtain the decision variable parameters corresponding to the position of the randomly generated first generation mayfly;

[0026] Under the model constraint conditions, the decision variable parameters associated with each first-generation mayfly are substituted into the objective function in the mathematical model of the energy storage power station for solving, to obtain a plurality of first-generation optimization target values;

[0027] performing non-dominated sorting on the decision variable parameters associated with each of the first-generation mayflies according to the first-generation optimization target value;

[0028] The congestion degree of each of the first-generation mayflies in the first-level dominance layer after non-dominated sorting is calculated, and the decision variable parameters associated with each of the first-generation mayflies in the first-level dominance layer are stored in a preset non-dominated optimal solution set.

[0029] Optionally, performing mating and mutation operations on the updated primary mayfly group in the preset non-dominated optimal solution set, and solving the energy storage power station mathematical model under the model constraint condition to obtain the offspring optimization target value associated with each offspring mayfly, includes:

[0030] Performing a mating operation on the first-generation mayfly group in the preset non-dominated optimal solution set after updating the positions and velocities of the first-generation mayflies to obtain offspring mayflies;

[0031] Performing a mutation operation on the offspring mayfly;

[0032] Under the model constraints, the decision variable parameters associated with the offspring mayflies after each mutation operation are substituted into the objective function in the mathematical model of the energy storage power station for solving, and multiple offspring optimization target values ​​are obtained.

[0033] Optionally, the step of sorting the offspring mayflies according to the offspring optimization target value, and performing non-dominated sorting on the mixed-generation mayfly group obtained as a result of the sorting to obtain a new preset non-dominated optimal solution set includes:

[0034] The offspring mayflies are non-dominatedly sorted according to the offspring optimization target value, and are sorted from high to low according to the dominance level of each of the offspring mayflies;

[0035] Calculating the crowding degree of each of the offspring mayflies, and sorting the offspring mayflies in the same dominance level from large to small according to the crowding degree;

[0036] Eliminating the offspring mayfly individuals exceeding the preset population upper limit according to the sorting result, and adding the retained offspring mayflies to the first generation mayflies in the preset non-dominated optimal solution set to obtain a mixed generation mayfly group;

[0037] Non-dominated sorting is performed on each of the mixed-generation mayflies in the mixed-generation mayfly group, and the decision variable parameters associated with each of the mixed-generation mayflies in the first-level dominated layer after non-dominated sorting are added to the preset non-dominated optimal solution set to obtain a new preset non-dominated optimal solution set.

[0038] A second aspect of the present invention provides a flow channel hydraulic parameter optimization system for a long water diversion pumped storage power station, comprising:

[0039] A response module is used to respond to parameter optimization requests for the energy storage power station and establish a mathematical model of the energy storage power station that takes into account the nonlinearity of the flow channel loss and the nonlinearity of the surge chamber impedance loss;

[0040] A constraint setting module, used to set model constraint conditions of the mathematical model of the energy storage power station;

[0041] The parameter optimization module is used to iteratively solve the mathematical model of the energy storage power station based on the mayfly algorithm and the model constraints to obtain the optimal solution set of the flow channel hydraulic parameters of the energy storage power station.

[0042] A third aspect of the present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for optimizing the hydraulic parameters of the flow channel of a long-water diversion pumped-storage power station as described in any one of the above items.

[0043] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the method for optimizing the hydraulic parameters of a flow channel of a long-water diversion pumped-storage power station as described in any one of the above items.

[0044] A fifth aspect of the present invention provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer executes the method for optimizing the hydraulic parameters of the flow channel of a long-diversion pumped-storage power station as described in any one of the above items.

[0045] It can be seen from the above technical solutions that the present invention has the following advantages:

[0046] In the present invention, in response to a parameter optimization request for an energy-storage power station, a mathematical model of an energy-storage power station considering nonlinearity of flow channel loss and nonlinearity of surge chamber impedance loss is established, model constraints of the mathematical model of the energy-storage power station are set, and based on the mayfly algorithm, the mathematical model of the energy-storage power station is iteratively solved using the model constraints to obtain an optimal solution set of flow channel hydraulic parameters of the energy-storage power station; the present invention constructs a mathematical model of a long-water diversion pumped-storage power station considering nonlinearity of flow channel loss and nonlinearity of surge chamber impedance loss, and uses the mayfly algorithm to optimize and solve the mathematical model, so that the hydraulic parameter optimization calculation speed is faster and the convergence rate is higher, thereby obtaining an optimal solution set of flow channel hydraulic parameters of the flow channel system of the long-water diversion pumped-storage power station, and then adopts the hydraulic optimization parameters in the optimal solution set of flow channel hydraulic parameters to achieve more accurate system decoupling analysis and unit operation control, thereby improving the operating stability of the energy-storage power station; and solves the technical problem of how to improve the operating stability of the energy-storage power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0048] Figure 1 A flowchart of the steps of a method for optimizing hydraulic parameters of a flow channel of a long-water diversion pumped-storage power station provided in Embodiment 1 of the present invention;

[0049] Figure 2 A flow chart of the steps of a method for optimizing hydraulic parameters of a flow channel of a long-water diversion pumped-storage power station provided in the second embodiment of the present invention;

[0050] Figure 3 An analysis flow chart of a method for optimizing flow channel hydraulic parameters of a long-water diversion pumped-storage power station provided in Embodiment 2 of the present invention;

[0051] Figure 4 This is the system schematic diagram of the Changyinshui pumped storage power station.

[0052] Figure 5 This is the flowchart of the mayfly algorithm program;

[0053] Figure 6 It is the Pareto frontier distribution diagram when the load disturbance is -0.1;

[0054] Figure 7 The speed x Pareto frontier distribution diagram when the load disturbance is -0.1;

[0055] Figure 8 The zPareto frontier distribution diagram of the surge chamber water level when the load disturbance is -0.1;

[0056] Fig. 9 This is the time domain diagram of the speed transient response of the super-long diversion tunnel hydropower station under the load disturbance of -0.1;

[0057] Fig.10 This is the time domain diagram of the transient response of the water level in the surge chamber of the super-long diversion tunnel hydropower station under the load disturbance of -0.1;

[0058] Fig.11 A structural block diagram of a flow channel hydraulic parameter optimization system for a long water diversion pumped storage power station provided in Embodiment 3 of the present invention;

[0059] Fig.12 This is a structural block diagram of a computer device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0060] The embodiment of the present invention provides a method and system for optimizing the hydraulic parameters of a flow channel of a long-water diversion pumped-storage power station, which are used to solve the technical problem of how to improve the operational stability of the storage power station.

[0061] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] The present invention proposes a modeling process for a long-water diversion pumped-storage power station, which fully considers the nonlinearity of flow channel loss and surge chamber impedance loss, thereby achieving more accurate power station modeling simulation, system decoupling analysis, and unit operation control.

[0063] The present invention also proposes a method for optimizing the hydraulic parameters of a flow channel system of a long-water diversion pumped-storage power station. The flow channel system hydraulic parameter optimization problem is essentially an n-dimensional variable value optimization problem. The value of the optimization variable is used as the position of the mayfly in the optimization variable space, and the value of the objective function calculated according to the optimization variable value is used to evaluate the position of the mayfly, i.e., the optimization variable. Since the Pareto optimal frontier using the mayfly algorithm is closer to the ideal Pareto frontier, the best objective function under the mayfly algorithm is better than the best objective function under the genetic algorithm and the firefly algorithm, and the optimization process of the mayfly algorithm has a shorter calculation speed and a higher convergence rate, and is more suitable for specific optimization tasks and can avoid falling into a local optimal state.

[0064] See also Figure 1 , Figure 1 A flowchart of the steps of a method for optimizing hydraulic parameters of a flow channel of a long-water diversion pumped-storage power station provided in Embodiment 1 of the present invention.

[0065] The present invention provides a method for optimizing hydraulic parameters of a flow channel of a long-water diversion pumped-storage power station, comprising:

[0066] Step 101, in response to a parameter optimization request for an energy storage power station, a mathematical model of an energy storage power station is established that takes into account nonlinearity of flow channel loss and nonlinearity of surge chamber impedance loss.

[0067] A pumped storage power station refers to a long-distance pumped storage power station with two upper and lower reservoirs. When the power system is under low load, the excess electricity in the system is used to pump water from the lower reservoir to the upper reservoir for storage. When the power system is under peak load, the water in the upper reservoir is released to generate electricity.

[0068] It is worth mentioning that the flow channel hydraulic parameter optimization method of the present invention is applied to long-water diversion pumped-storage power stations.

[0069] Nonlinearity of flow channel loss refers to the energy loss caused by various factors when the fluid flows through the flow channel. Nonlinearity means that there is no simple linear relationship between the flow channel loss and certain variables that affect it (such as flow velocity, flow rate, flow channel geometry, etc.), that is, a simple linear function cannot be used to accurately describe the relationship between this loss and related factors.

[0070] The nonlinearity of the impedance loss of the surge chamber means that there is no simple linear relationship between the energy loss caused by the impedance of the surge chamber and the various factors affecting the loss during operation. This loss is related to many factors such as the structural form of the surge chamber and water flow conditions, that is, a simple linear function cannot be used to accurately describe the relationship between this loss and related factors.

[0071] The mathematical model of a pumped storage power station refers to a mathematical expression or set of equations used to describe the operating characteristics and physical processes of a long-water pumped storage power station. It quantitatively describes the various components of the power station and their interrelationships based on multidisciplinary principles such as fluid mechanics, thermodynamics, and electricity.

[0072] In an embodiment of the present invention, in response to a received request instruction for optimizing flow channel hydraulic parameters for a long diversion pumped storage power station, a mathematical model of the energy storage power station is established that takes into account flow channel loss nonlinearity and surge chamber impedance loss nonlinearity.

[0073] Step 102: setting model constraints of the mathematical model of the energy storage power station.

[0074] In the embodiment of the present invention, constraint conditions of decision variables of the mathematical model of the energy storage power station are set.

[0075] Step 103: Based on the Mayfly algorithm, the mathematical model of the pumped storage power station is iteratively solved using the model constraints to obtain an optimal solution set of the hydraulic parameters of the flow channel of the pumped storage power station.

[0076] The mayfly algorithm refers to an optimization algorithm that solves problems by simulating the survival, reproduction, and group behavior of mayflies in nature.

[0077] In the embodiment of the present invention, based on the mayfly algorithm, the mathematical model of the energy storage power station is iteratively solved under the model constraint condition, so as to obtain the optimal solution set of the hydraulic parameters of the flow channel of the energy storage power station.

[0078] In the present invention, in response to a parameter optimization request for an energy-storage power station, a mathematical model of an energy-storage power station considering nonlinearity of flow channel loss and nonlinearity of surge chamber impedance loss is established, model constraints of the mathematical model of the energy-storage power station are set, and based on the mayfly algorithm, the mathematical model of the energy-storage power station is iteratively solved using the model constraints to obtain an optimal solution set of flow channel hydraulic parameters of the energy-storage power station; the present invention constructs a mathematical model of a long-water diversion pumped-storage power station considering nonlinearity of flow channel loss and nonlinearity of surge chamber impedance loss, and uses the mayfly algorithm to optimize and solve the mathematical model, so that the hydraulic parameter optimization calculation speed is faster and the convergence rate is higher, thereby obtaining an optimal solution set of flow channel hydraulic parameters of the flow channel system of the long-water diversion pumped-storage power station, and then adopts the hydraulic optimization parameters in the optimal solution set of flow channel hydraulic parameters to achieve more accurate system decoupling analysis and unit operation control, thereby improving the operating stability of the energy-storage power station; and solves the technical problem of how to improve the operating stability of the energy-storage power station.

[0079] See also Figure 2-Figure 3 , Figure 2 A flow chart of the steps of a method for optimizing the hydraulic parameters of a flow channel of a long-water diversion pumped-storage power station provided in Embodiment 2 of the present invention.

[0080] Figure 3 An analysis flow chart of a method for optimizing flow channel hydraulic parameters of a long-water diversion pumped-storage power station provided in Embodiment 2 of the present invention.

[0081] The present invention provides a method for optimizing hydraulic parameters of a flow channel of a long-water diversion pumped-storage power station, comprising:

[0082] Step 201, in response to a parameter optimization request for an energy storage power station, a mathematical model of an energy storage power station is established that takes into account nonlinearity of flow channel loss and nonlinearity of surge chamber impedance loss.

[0083] In an embodiment of the present invention, in response to a request for parameter optimization of an energy storage power station, a mathematical model of a long diversion pumped storage power station is established that takes into account the nonlinearity of flow channel losses and the nonlinearity of surge chamber impedance losses, including a mathematical model of the flow channel system of a long diversion tunnel, a surge chamber, and a pressure pipeline, and a mathematical model of the unit including a water pump turbine, a generator motor, and a speed regulator.

[0084] See also Figure 4 The Changyin Pumped Storage Power Station includes an upper reservoir, a long diversion tunnel, an impedance surge chamber, a pressure pipeline, a pump-turbine unit and a lower reservoir.

[0085] Mathematical model of long diversion tunnel:

[0086] Considering the nonlinearity of the flow channel head loss and the nonlinearity of the surge chamber impedance, and ignoring the elasticity of the water flow and the pipe wall, the water diversion tunnel equation is:

[0087]

[0088] The surge tank only considers the continuity equation:

[0089]

[0090] Mathematical model of pressure pipe:

[0091] Considering the nonlinearity of the flow channel head loss and the nonlinearity of the surge chamber impedance, and ignoring the elasticity of the water flow and the pipe wall, the pressure pipe equation is:

[0092]

[0093] Pump turbine torque equation and flow equation:

[0094]

[0095] The generator motor equation of the pumped storage power station in an isolated state:

[0096]

[0097] PI speed regulator has been widely used in pump-turbine speed regulators of pumped storage power stations. Its equation is:

[0098]

[0099] By combining the basic equations, the fifth-order state equation of the long diversion pumped storage power station system considering the nonlinearity of the flow channel head loss and the nonlinearity of the surge chamber impedance loss is obtained:

[0100]

[0101] In the formula, represents the inertia time constant of water flow in the diversion tunnel; z represents the relative value of the water level change in the surge chamber, represents the head loss of the diversion tunnel at the initial moment, Indicates the initial moment value of the working water head of the pump-turbine unit. represents the relative flow rate of the diversion tunnel, , represents the flow rate of the diversion tunnel, represents the initial moment value of the diversion tunnel flow, It represents the head loss coefficient of the surge chamber, represents the initial flow rate of the pump turbine, represents the initial water head of the pump turbine, Indicates the relative flow rate of the pressure pipe; the time constant of the pressure regulating chamber , Indicates the cross-sectional area of ​​the surge chamber; time constant of the pressure pipe , It represents the transfer coefficient of pump-turbine flow to unit head, , It represents the transfer coefficient of pump-turbine flow to guide vane opening, , It represents the transfer coefficient of pump-turbine flow to unit speed, , Indicates the relative speed of the unit. Indicates the relative opening of the guide vane, Indicates the relative head of the pressure pipe; represents the inertia time constant of the pump-turbine, It represents the transfer coefficient of pump-turbine torque to unit head, , It represents the transfer coefficient of pump-turbine torque to unit speed, , It represents the torque transmission coefficient of the pump turbine to the guide vane opening, , represents the relative dynamic torque, Indicates the relative resistance moment of the load; Indicates the self-regulation coefficient of the generator motor; represents the pump-turbine proportional gain; Represents the pump-turbine integral gain.

[0102] Step 202: setting model constraints of the mathematical model of the energy storage power station.

[0103] Determine the decision variables to be optimized in the flow channel system of the long diversion pumped storage power station and determine the constraints of the decision variables.

[0104] Furthermore, the decision variables of the mathematical model of the energy storage power station considering the nonlinearity of the flow channel loss and the impedance loss of the surge chamber include the length of the long diversion tunnel, the head loss of the long diversion tunnel, the length of the pressure pipe, the head loss of the pressure pipe, the cross-sectional area of ​​the surge chamber, the impedance loss of the surge chamber and the working head of the water pump turbine;

[0105] The decision variables represent the parameters to be optimized for the flow system of the long diversion pumped storage power station. The flow system of the long diversion pumped storage power station mainly consists of three parts: the long diversion tunnel, the surge chamber, and the pressure pipeline.

[0106] For long diversion tunnels, their characteristics are mainly represented by three values: , cross-sectional area of ​​the long diversion tunnel , Head loss in long diversion tunnels caused by friction . Long diversion tunnel length Head loss It mainly refers to the layout of the diversion tunnel in the design of pumped storage power station, and the cross-sectional area of ​​the long diversion tunnel It is mainly determined by the design flow and economic flow rate. Therefore, for the long diversion tunnel, the optimization variable is the length of the long diversion tunnel. Head loss in long diversion tunnels .

[0107] The characteristics of the pressure pipeline are similar to those of the long diversion tunnel, so the length of the pressure pipeline is also selected Head loss in pressure pipes As an optimization variable for pressure pipes.

[0108] The characteristics of the impedance surge chamber are determined by the cross-sectional area of ​​the surge chamber and the shape and size of the impedance hole, while the shape and size of the impedance hole can be determined by the impedance loss. Therefore, the optimization variable of the surge chamber is selected as the cross-sectional area of ​​the surge chamber. Impedance loss of surge chamber In addition, the flow channel system of the Changshui pumped storage power station also includes the working water head of the pump turbine , which is determined by the design of the pumped storage power station, so this parameter can be used as an optimization variable.

[0109] In summary, the parameters to be optimized for the flow system of the long diversion pumped storage power station include the length of the long diversion tunnel , Head loss in long diversion tunnel , Length of pressure pipe , Pressure pipe head loss , Surge chamber cross-sectional area , surge chamber impedance loss , Pump turbine working water head .

[0110] The model constraints include:

[0111] The length of the long diversion tunnel is greater than or equal to the preset minimum long diversion tunnel length constraint value, and the length of the long diversion tunnel is less than or equal to the preset maximum long diversion tunnel length constraint value;

[0112] It should be noted that the constraint condition of the length of the long diversion tunnel is encapsulated in the form of a formula:

[0113]

[0114] Long diversion tunnel length Usually several thousand meters, so the minimum diversion tunnel length constraint is preset The maximum length of the diversion tunnel is preferably 1 km, and the maximum length of the diversion tunnel is preset. Preferably 10 km.

[0115] 1km≤ ≤10km

[0116] The head loss of the long diversion tunnel is greater than or equal to the preset minimum head loss constraint value of the long diversion tunnel, and the head loss of the long diversion tunnel is less than or equal to the preset maximum head loss constraint value of the long diversion tunnel;

[0117] It should be noted that the constraint conditions of the head loss of the long diversion tunnel are encapsulated in the form of a formula:

[0118]

[0119] Head loss in long diversion tunnel Due to different geographical conditions, the head loss constraint value of the minimum length of the diversion tunnel can usually range from a few meters to tens of meters. The maximum head loss constraint value of the long diversion tunnel is preset. Preferably 100m.

[0120] 1m≤ ≤100m

[0121] The pressure pipeline length is greater than or equal to the preset minimum pressure pipeline length constraint value, and the pressure pipeline length is less than or equal to the preset maximum pressure pipeline length constraint value;

[0122] It should be noted that the constraint condition of the pressure pipe length is encapsulated in the form of a formula:

[0123]

[0124] Pressure pipe length Usually it is tens of meters to hundreds of meters, so the minimum pressure pipeline length constraint value is preset The preferred value is 10m, and the maximum pressure pipeline length constraint value is preset Preferably 1000m.

[0125] 10m≤ ≤1000m

[0126] The pressure pipeline head loss is greater than or equal to the preset minimum pressure pipeline head loss constraint value, and the pressure pipeline head loss is less than or equal to the preset maximum pressure pipeline head loss constraint value;

[0127] It should be noted that the constraint condition of the head loss of the pressure pipe is encapsulated in the form of a formula:

[0128]

[0129] Pressure pipe head loss It can be from a few meters to more than ten meters, so the minimum pressure pipeline head loss constraint value is preset The preferred value is 1m, and the maximum pressure pipeline head loss constraint value is preset Preferably 20m.

[0130] 1m≤ ≤20m

[0131] The cross-sectional area of ​​the surge chamber is greater than or equal to the preset Thomas critical stable cross-sectional area, and the cross-sectional area of ​​the surge chamber is less than or equal to the preset minimum cross-sectional area constraint value of the surge chamber;

[0132] It should be noted that the constraint condition of the cross-sectional area of ​​the surge chamber is encapsulated in the form of a formula:

[0133] and

[0134] Surge chamber cross-sectional area It should usually be larger than the critical stable cross section of Thomas, but as small as possible to save economic costs. Therefore, the cross section of the surge chamber is greater than or equal to the preset critical stable cross section of Thomas. , and the cross-sectional area of ​​the surge chamber is less than or equal to the preset minimum cross-sectional area constraint value of the surge chamber .

[0135] The impedance loss of the surge chamber is greater than or equal to a preset minimum surge chamber impedance loss constraint value, and the impedance loss of the surge chamber is less than or equal to a preset maximum surge chamber impedance loss constraint value;

[0136] It should be noted that the constraint condition of the voltage regulating chamber impedance loss is encapsulated in the form of a formula:

[0137]

[0138] Impedance loss of surge chamber Determined by the impedance orifice area of ​​the surge chamber, therefore, the minimum surge chamber impedance loss constraint value is preset and preset maximum surge chamber impedance loss constraint value It is based on the impedance orifice area of ​​the surge chamber Decision, specifically:

[0139]

[0140] In the formula, The lower limit coefficient of the impedance orifice area of ​​the surge chamber.

[0141]

[0142] In the formula, Upper limit coefficient of surge chamber impedance orifice area.

[0143] It is more appropriate that the orifice area is 25% to 45% of the cross section of the pressure diversion channel or the pressure tail channel. Therefore:

[0144]

[0145] The pump-turbine operating water head is greater than or equal to a preset minimum pump-turbine operating water head constraint value, and the pump-turbine operating water head is less than or equal to a preset maximum pump-turbine operating water head constraint value.

[0146] It should be noted that the constraints of the working water head of the pump turbine are encapsulated in the form of a formula:

[0147]

[0148] Pump turbine working head The minimum pump-turbine operating head constraint value should be set and the preset maximum pump-turbine operating head constraint value within the range.

[0149] It should be noted that, before step 203, it is also necessary to establish the objective function of the mathematical model of the energy storage power station, specifically including the objective function of the pump-turbine unit and the objective function of the surge chamber.

[0150] When a long-distance pumped storage power station is subjected to load disturbance during operation, it mainly focuses on two parts:

[0151] First, the frequency of the current output by the generator motor, or how the speed of the pump turbine changes,

[0152] The second is how the water level in the surge chamber fluctuates. Therefore, the objective function mainly consists of two functions to evaluate the performance of the pump-turbine unit and the performance of the surge chamber.

[0153] The unit objective function, for the state of the pump-turbine unit is mainly expressed by its speed, so the unit objective function is generated by the pump-turbine speed, which is an indicator used to evaluate the speed transient response characteristics, specifically:

[0154]

[0155]

[0156] Where: , Respectively represent the overshoot of the unit σ p1 and adjustment time t s (Time when the speed deviation is ≤ ±0.2%) as a function of the flow path parameters of a long diversion pumped storage power station.

[0157] Surge chamber objective function. The performance of the surge chamber is mainly expressed by the water level fluctuation of the surge chamber. Therefore, the objective function of the surge chamber is generated by the water level fluctuation of the surge chamber. Here, the overshoot and attenuation rate of the water level fluctuation of the surge chamber are selected:

[0158]

[0159]

[0160] Where: , They represent the overshoot of the water level in the surge chamber σ p2 and decay rate Function of flow path parameters in long diversion pumped storage power station.

[0161] See also Figure 5 , Figure 5 This is a flow chart of the mayfly algorithm program. Steps 203 to 207 are the process of using the mayfly algorithm to solve the problem, wherein the ode function is used to describe the movement behavior and fitness evaluation of the mayfly. By defining a suitable ode function, the behavior of the mayfly can be controlled, thereby helping to form a more effective search strategy. The ode function can also be used to describe the changes in the fitness function, helping the mayfly algorithm to evaluate and select suitable individuals for interaction and innovation.

[0162] Step 203, perform mayfly initialization, use the energy storage power station mathematical model to solve the initial generation of mayflies under model constraints to obtain the initial optimization target value associated with each initial generation of mayflies, and perform non-dominated sorting and calculate congestion according to the initial optimization target value to obtain a preset non-dominated optimal solution set stored in the initial generation of mayflies.

[0163] Further, step 203 may include the following sub-steps:

[0164] S11, performing mayfly initialization to obtain decision variable parameters corresponding to the positions of the randomly generated first-generation mayflies.

[0165] In the embodiment of the present invention, the mayfly is initialized to obtain the mayfly position corresponding to the randomly generated first-generation mayfly, that is, the long diversion pumped storage power station parameter, that is, the decision variable parameter.

[0166] S12. Under the model constraints, the decision variable parameters associated with each first-generation mayfly are substituted into the objective function in the mathematical model of the energy storage power station for solving, and multiple first-generation optimization target values ​​are obtained.

[0167] In the embodiment of the present invention, under the model constraint conditions, the decision variable parameters associated with each primary generation of mayflies are substituted into the objective function in the mathematical model of the energy storage power station for solving, and the primary optimization target value is obtained. The optimization target value includes the overshoot σ of the unit. p1, Adjustment time t s And the overshoot of the surge chamber σ p2 and decay rate .

[0168] S13. Perform non-dominated sorting on the decision variable parameters associated with each first-generation mayfly according to the first-generation optimization target value.

[0169] In an embodiment of the present invention, the decision variable parameters associated with each initial generation of mayflies are Pareto non-dominated sorting (Pareto non-dominated sorting) is performed according to each group of initial optimization target values. The sorting method is to first define non-dominated solutions, and each solution is compared with all other solutions to find out those solutions that are not better than them in all objectives. The set of these solutions is the non-dominated solution of the first-level dominance layer.

[0170] For ease of understanding, the following is a specific application example:

[0171] Assume that there are three target values, that is, three solutions, namely solution A, solution B and solution C. Each solution includes four targets: unit overshoot, adjustment time, surge chamber overshoot and attenuation rate.

[0172] Taking the specific values ​​of the unit overshoot, adjustment time, surge chamber overshoot and attenuation rate as an example, then:

[0173] Solution A = (0.1, 5, 0.2, 0.8)

[0174] Solution B = (0.2, 4, 0.3, 0.7)

[0175] Solution C = (0.15, 6, 0.25, 0.6)

[0176] Compare solution A with solution B

[0177] The overshoot of the unit is 0.1<0.2; the adjustment time is 5>4; the overshoot of the surge chamber is 0.2<0.3; the decay rate is 0.8>0.7. Solution A is not dominated by solution B.

[0178] Compare solution A with solution C

[0179] The overshoot of the unit is 0.1<0.15; the adjustment time is 5<6; the overshoot of the surge chamber is 0.2<0.25; the decay rate is 0.8>0.6. Solution A is not dominated by Solution C.

[0180] Compare Solution B with Solution C

[0181] The overshoot of the unit is 0.2>0.15; the adjustment time is 4<6; the overshoot of the surge chamber is 0.3>0.25; the decay rate is 0.7>0.6. Solution B is not dominated by solution C.

[0182] In summary, the set of first-level solutions is {A,B}, because neither solution A nor solution B is better than them in all objectives by other solutions.

[0183] S14, calculating the crowding degree of each first-generation mayfly in the first-level dominating layer after the non-dominated sorting, and storing the decision variable parameters associated with each first-generation mayfly in the first-level dominating layer into a preset non-dominated optimal solution set.

[0184] In an embodiment of the present invention, the crowding degree of each first-generation mayfly in the first-level dominant layer after non-dominated sorting is calculated. In the multi-objective optimization algorithm, the first-generation mayflies represent different potential solutions. Calculating the crowding degree of each first-generation mayfly in the first-level dominant layer helps to maintain the diversity of solutions. By calculating the crowding degree, those first-generation mayflies in relatively crowded areas can be identified. When performing operations such as selection and reproduction, it is appropriate to avoid selecting individuals in these overly crowded areas, thereby prompting the algorithm to explore more different solution space areas and find a diversified solution set that better balances multiple objectives; and the decision variable parameters associated with each first-generation mayfly in the first-level dominant layer are stored in a preset non-dominated optimal solution set.

[0185] The specific formula for congestion is:

[0186]

[0187] In the formula, Indicates the degree of congestion, Represents an individual mayfly A collection of mayflies within a certain distance. Represents an individual mayfly With mayfly individuals The distance between.

[0188] Step 204: randomly select a first-generation mayfly associated with any decision variable parameter from the preset non-dominated optimal solution set as the global optimal position of the mayfly swarm, and update the position and speed of the first-generation mayfly corresponding to each decision variable parameter.

[0189] In the embodiment of the present invention, decision variable parameters in the preset non-dominated optimal solution set are randomly selected as the global optimal position of the mayfly group, and the mayfly position and speed are updated according to the mayfly movement law. It can be understood that each mayfly position corresponds to a set of parameters.

[0190] Step 205: perform mating and mutation operations on the first generation mayfly group in the updated preset non-dominated optimal solution set, and use the energy storage power station mathematical model to solve the offspring optimization target value associated with each offspring mayfly under the model constraint condition.

[0191] Further, step 205 may include the following sub-steps:

[0192] S21, performing a mating operation on the first-generation mayfly group in the preset non-dominated optimal solution set after the positions and velocities of the first-generation mayflies are updated to obtain offspring mayflies.

[0193] In the embodiment of the present invention, a mating operation is performed on the first-generation mayfly group in the preset non-dominated optimal solution set after the first-generation mayfly position and speed are updated to obtain offspring mayflies, including offspring female mayflies and offspring male mayflies.

[0194] S22. Perform mutation operation on the offspring mayflies.

[0195] In the embodiment of the present invention, a mutation operation is performed on all the descendant mayflies.

[0196] S23. Under the model constraints, the decision variable parameters associated with the offspring mayflies after each mutation operation are substituted into the objective function in the mathematical model of the energy storage power station for solving, and multiple offspring optimization target values ​​are obtained.

[0197] In the embodiment of the present invention, under the model constraint condition, the decision variable parameters associated with the offspring mayflies after each mutation operation are substituted into the objective function in the mathematical model of the energy storage power station for solving, and multiple offspring optimization target values ​​are obtained.

[0198] Step 206: sort the offspring mayflies according to the offspring optimization target value, and perform non-dominated sorting on the mixed-generation mayfly group obtained as a result of the sorting to obtain a new preset non-dominated optimal solution set.

[0199] Further, step 206 may include the following sub-steps:

[0200] S31, performing non-dominated sorting on the offspring mayflies according to the offspring optimization target value, and sorting the offspring mayflies from high to low according to the dominance level of each offspring mayfly.

[0201] In the embodiment of the present invention, the offspring mayflies are sorted in a Pareto non-dominated manner according to the offspring optimization target value. First, the offspring male mayfly group and the offspring female mayfly group are sorted from high to low according to the dominance level they are in.

[0202] S32, calculating the crowding degree of each offspring mayfly, and sorting the offspring mayflies in the same dominance level from large to small according to the crowding degree.

[0203] In the embodiment of the present invention, the crowding degree of each descendant mayfly is calculated, and then the descendant mayflies in the same dominance level are sorted from large to small according to the crowding degree.

[0204] S33. Eliminate offspring mayfly individuals that exceed a preset population upper limit according to the sorting result, and add the retained offspring mayflies to the first-generation mayflies in the preset non-dominated optimal solution set to obtain a mixed-generation mayfly population.

[0205] In an embodiment of the present invention, offspring mayflies that exceed a preset population upper limit are eliminated according to the sorting result. Here, the population upper limit refers to a fixed population size, and the population size is determined at the time of initialization. The mayflies are sorted according to their quality based on the non-dominated level and the crowding degree, and then the retained offspring mayflies are added to the first-generation mayflies in the preset non-dominated optimal solution set in step S14 to obtain a mixed-generation mayfly group.

[0206] S34, performing non-dominated sorting on each mixed-generation mayfly in the mixed-generation mayfly group, and adding the decision variable parameters associated with each mixed-generation mayfly in the first level dominance layer after the non-dominated sorting to the preset non-dominated optimal solution set to obtain a new preset non-dominated optimal solution set.

[0207] In an embodiment of the present invention, each hybrid generation ephemera in a hybrid generation ephemera group is subjected to Pareto non-dominated sorting, and the decision variable parameters associated with each hybrid generation ephemera in the first level dominating layer after non-dominated sorting are added to a preset non-dominated optimal solution set to obtain a new preset non-dominated optimal solution set.

[0208] Step 207, jump to the step of randomly selecting the first-generation mayflies associated with any decision variable parameter from the preset non-dominated optimal solution set as the global optimal position of the mayfly group, and updating the position and speed of the first-generation mayflies corresponding to each decision variable parameter, until the preset iteration number threshold is reached, then outputting the optimal solution set of the flow channel hydraulic parameters of the energy storage power station.

[0209] In the embodiment of the present invention, the process jumps back to the step of randomly selecting any first-generation mayfly associated with any decision variable parameter from the preset non-dominated optimal solution set as the global optimal position of the mayfly group, and updating the position and speed of the first-generation mayfly corresponding to each decision variable parameter, and iterates in turn until the upper limit of the number of iterations is reached, and the final non-dominated optimal solution set is given. The decision variable parameters in the non-dominated optimal solution set, that is, the overshoot σ of the unit p1 , Adjustment time t s And the overshoot of the surge chamber σ p2 and decay rate The decision variable parameters that can reach the Pareto frontier.

[0210] After step 207, the method further includes simulating the energy storage power station by using the optimal solution set of the flow channel hydraulic parameters of the energy storage power station.

[0211] Furthermore, the optimal solution set of the flow channel hydraulic parameters of the energy storage power station is used to simulate the energy storage power station under load reduction disturbance conditions.

[0212] The hydraulic parameters optimization of the flow channel system of a Changyin Water Pumped Storage Power Station under load reduction disturbance was studied, and the load increase and decrease conditions of the Changyin Water Pumped Storage Power Station were simulated and analyzed to illustrate the optimization process and results of the hydraulic parameters of the flow channel of the Changyin Water Pumped Storage Power Station.

[0213] The method of the present invention is described in detail by taking the optimization study of the hydraulic parameters of the flow channel of a long water diversion pumped storage power station under load reduction disturbance. The basic data of the pumped storage power station are as follows: Rated head , rated flow The diversion tunnel is , equivalent cross-sectional area , pressure pipe length , equivalent cross-sectional area The area of ​​the surge chamber is , , , , , , the pump turbine transfer coefficient takes the ideal value , , , , , , the speed regulator parameter value is , , , .

[0214] Based on multi-objective ephemera optimization algorithm, long diversion pumped storage power station is optimized under load disturbance The simulation process of hydraulic parameter optimization of the downstream channel is as follows:

[0215] Initialize the male mayfly population Popm=20, the female mayfly population Popf=20, the upper limit of the Pareto optimal solution set nPareto=40, the number of offspring Popc=20, the mutation probability mu=0.02, the maximum number of evolutionary iterations Maxit=500, and the decision variable constraints are shown in Table 1.

[0216]

[0217] Generate the initial population, calculate the objective function and sort it. Generate a new population by using the operations of mayfly movement, mayfly mating and mayfly mutation. After sorting the population, store the non-dominated individuals in the external Pareto optimal solution set, remove the crossed individuals, and proceed to the next step of mayfly optimization operation.

[0218] Determine whether to terminate: If the termination condition is met, the iterative optimization ends and the Pareto optimal solution set is output; if the termination condition is not met, the optimization of the mayfly algorithm continues.

[0219] The Pareto optimal solution set obtained by simulation calculation is as follows Figure 6-Figure 8 As shown:

[0220] Figure 6 It is the Pareto front distribution diagram when the load disturbance is -0.1.

[0221] Figure 7 It is the speed x Pareto front distribution diagram when the load disturbance is -0.1.

[0222] Figure 8 It is the Pareto front distribution diagram of the surge chamber water level when the load disturbance is -0.1.

[0223] Eight representative long-water pumped storage power stations were selected from these Pareto fronts under load disturbance. The hydraulic parameters and transient response data of the lower flow channel are shown in Table 2:

[0224] Table 2 shows the hydraulic parameters of the flow channels of some long water diversion pumped storage power stations and the load disturbance Transient response data.

[0225]

[0226] analyze Figure 6-Figure 10 From Table 2, we can see that:

[0227] Fig. 9 This is the time domain diagram of the speed transient response of the super-long diversion tunnel hydropower station under the load disturbance of -0.1.

[0228] Fig.10 This is the time domain diagram of the transient response of the surge chamber water level in the super-long diversion tunnel hydropower station under the load disturbance of -0.1.

[0229] The optimization of the hydraulic parameters of the long diversion pumped storage power station under the load disturbance of -0.1 based on the multi-objective ephemera algorithm can make the hydraulic parameters of the flow channel reach the Pareto optimal solution set and make the objective function close to the Pareto frontier. In the objective function, the unit speed overshoot σ p1 and adjustment time t s Contradictory, in order to obtain a smaller speed overshoot σ p1 When the adjustment time t s , and vice versa; the overshoot of the water level in the surge chamber σ p2 and decay rate Contradictory, if you want the surge chamber water level to decay faster, you will get a larger overshoot σ p1 .

[0230] In load disturbance Pump turbine operating head in steady state The larger the water diversion pumped storage power station is, the better the transient response performance can be. From the perspective of mathematical model, the larger the pump turbine working head is, the better the transient response performance can be. It can reduce the hydraulic loss in the pipeline relatively, thus slowing down the change after disturbance. Optimized pressure pipeline head loss They are all close to the lower limit of the variable, and we can know the head loss of the pressure pipe It is not conducive to the load disturbance in the long water diversion pumped storage power station For the surge chamber, we can see that the area of ​​the surge chamber is and impedance loss factor They are close to the upper limit of the variable, so it can be concluded that the larger the surge chamber area and the impedance loss coefficient, the more beneficial it is for the long diversion pumped storage power station to withstand load disturbances. Get a smaller speed overshoot σ p1 With adjustment time t s and surge chamber water level overshoot σ p2 , and make the water level in the surge tank decay faster to obtain a greater decay rate. Head loss in long diversion tunnel Length of pressure pipe It is evenly distributed within the range of variable variation. It can be seen that the optimization of the objective function by these two flow channel hydraulic parameters will contradict each other, and it is impossible to obtain a value that makes both objective functions better. Under the load reduction condition, the flow rate of the pressure pipe will increase first, and the impedance of the surge chamber can reduce the increased flow rate in the pressure pipe, which makes the greater the impedance loss of the surge chamber, the more conducive it is to the stability of the long water diversion tunnel under the load reduction condition.

[0231] In the present invention, in response to a parameter optimization request for an energy-storage power station, a mathematical model of an energy-storage power station considering nonlinearity of flow channel loss and nonlinearity of surge chamber impedance loss is established, model constraints of the mathematical model of the energy-storage power station are set, and based on the mayfly algorithm, the mathematical model of the energy-storage power station is iteratively solved using the model constraints to obtain an optimal solution set of flow channel hydraulic parameters of the energy-storage power station; the present invention constructs a mathematical model of a long-water diversion pumped-storage power station considering nonlinearity of flow channel loss and nonlinearity of surge chamber impedance loss, and uses the mayfly algorithm to optimize and solve the mathematical model, so that the hydraulic parameter optimization calculation speed is faster and the convergence rate is higher, thereby obtaining an optimal solution set of flow channel hydraulic parameters of the flow channel system of the long-water diversion pumped-storage power station, and then adopts the hydraulic optimization parameters in the optimal solution set of flow channel hydraulic parameters to achieve more accurate system decoupling analysis and unit operation control, thereby improving the operating stability of the energy-storage power station; and solves the technical problem of how to improve the operating stability of the energy-storage power station.

[0232] See also Fig.11 , Fig.11 This is a structural block diagram of a flow channel hydraulic parameter optimization system for a long water diversion pumped storage power station provided in Example 3 of the present invention.

[0233] The present invention provides a flow channel hydraulic parameter optimization system for a long water diversion pumped storage power station, comprising:

[0234] A response module 301 is used to respond to a parameter optimization request for an energy storage power station and establish a mathematical model of an energy storage power station that takes into account nonlinearity of flow channel loss and nonlinearity of surge chamber impedance loss;

[0235] The constraint setting module 302 is used to set the model constraint conditions of the mathematical model of the energy storage power station;

[0236] The parameter optimization module 303 is used to iteratively solve the mathematical model of the energy storage power station based on the mayfly algorithm and the model constraints to obtain the optimal solution set of the flow channel hydraulic parameters of the energy storage power station.

[0237] Furthermore, the decision variables of the mathematical model of the energy storage power station considering the nonlinearity of the flow channel loss and the impedance loss of the surge chamber include the length of the long diversion tunnel, the head loss of the long diversion tunnel, the length of the pressure pipe, the head loss of the pressure pipe, the cross-sectional area of ​​the surge chamber, the impedance loss of the surge chamber and the working head of the water pump turbine;

[0238] The model constraints include:

[0239] The length of the long diversion tunnel is greater than or equal to the preset minimum long diversion tunnel length constraint value, and the length of the long diversion tunnel is less than or equal to the preset maximum long diversion tunnel length constraint value;

[0240] The head loss of the long diversion tunnel is greater than or equal to the preset minimum head loss constraint value of the long diversion tunnel, and the head loss of the long diversion tunnel is less than or equal to the preset maximum head loss constraint value of the long diversion tunnel;

[0241] The pressure pipeline length is greater than or equal to the preset minimum pressure pipeline length constraint value, and the pressure pipeline length is less than or equal to the preset maximum pressure pipeline length constraint value;

[0242] The pressure pipeline head loss is greater than or equal to the preset minimum pressure pipeline head loss constraint value, and the pressure pipeline head loss is less than or equal to the preset maximum pressure pipeline head loss constraint value;

[0243] The cross-sectional area of ​​the surge chamber is greater than or equal to the preset Thomas critical stable cross-sectional area, and the cross-sectional area of ​​the surge chamber is less than or equal to the preset minimum cross-sectional area constraint value of the surge chamber;

[0244] The impedance loss of the surge chamber is greater than or equal to a preset minimum surge chamber impedance loss constraint value, and the impedance loss of the surge chamber is less than or equal to a preset maximum surge chamber impedance loss constraint value;

[0245] The pump-turbine operating water head is greater than or equal to a preset minimum pump-turbine operating water head constraint value, and the pump-turbine operating water head is less than or equal to a preset maximum pump-turbine operating water head constraint value.

[0246] Furthermore, the parameter optimization module 303 includes:

[0247] The initialization submodule is used to initialize the mayflies, and obtain the initial optimization target value associated with each initial generation of mayflies by using the mathematical model of the energy storage power station under the model constraint condition, and perform non-dominated sorting and calculate the congestion degree according to the initial optimization target value to obtain the preset non-dominated optimal solution set stored in the initial generation of mayflies;

[0248] An updating submodule is used to randomly select a first-generation mayfly associated with any decision variable parameter from a preset non-dominated optimal solution set as the global optimal position of the mayfly group, and to update the position and speed of the first-generation mayfly corresponding to each decision variable parameter;

[0249] The solution submodule is used to perform mating and mutation operations on the first-generation mayfly group in the updated preset non-dominated optimal solution set, and to obtain the offspring optimization target value associated with each offspring mayfly by using the mathematical model of the energy storage power station under the model constraint condition;

[0250] A sorting submodule is used to sort the offspring mayflies according to the offspring optimization target value, and to perform non-dominated sorting on the mixed-generation mayfly group obtained by the sorting result to obtain a new preset non-dominated optimal solution set;

[0251] The jump submodule is used to jump to the step of randomly selecting the first-generation mayflies associated with any decision variable parameter from the preset non-dominated optimal solution set as the global optimal position of the mayfly group, and updating the position and speed of the first-generation mayflies corresponding to each decision variable parameter until the preset iteration number threshold is reached, and then the optimal solution set of the flow channel hydraulic parameters of the energy storage power station is output.

[0252] Furthermore, the initialization submodule includes:

[0253] A decision variable parameter unit is used to initialize the mayfly and obtain the decision variable parameters corresponding to the positions of the randomly generated first generation mayflies;

[0254] The primary optimization target value unit is used to substitute the decision variable parameters associated with each primary mayfly into the target function in the mathematical model of the energy storage power station under the model constraint conditions to solve the problem and obtain multiple primary optimization target values;

[0255] A non-dominated sorting unit is used to perform non-dominated sorting on the decision variable parameters associated with each first-generation mayfly according to the first-generation optimization target value;

[0256] The solution set unit is used to calculate the crowding degree of each first-generation mayfly in the first-level dominance layer after the non-dominated sorting, and store the decision variable parameters associated with each first-generation mayfly in the first-level dominance layer into a preset non-dominated optimal solution set.

[0257] Furthermore, the solution submodule includes:

[0258] The offspring mayfly unit is used to perform a mating operation on the first-generation mayfly group in the preset non-dominated optimal solution set after the positions and velocities of the first-generation mayflies are updated to obtain offspring mayflies;

[0259] A mutation operation unit, used to perform mutation operations on offspring mayflies;

[0260] The offspring optimization target value unit is used to substitute the decision variable parameters associated with the offspring mayflies after each mutation operation into the target function in the mathematical model of the energy storage power station for solving under the model constraint condition, so as to obtain multiple offspring optimization target values.

[0261] Furthermore, the sorting submodule includes:

[0262] The first processing unit is used to perform non-dominated sorting on the offspring mayflies according to the offspring optimization target value, and sort the offspring mayflies from high to low according to the dominance level of each offspring mayfly;

[0263] The second processing unit is used to calculate the crowding degree of each offspring mayfly, and sort the offspring mayflies in the same dominance level from large to small according to the crowding degree;

[0264] A mixed-generation mayfly group unit is used to remove offspring mayfly individuals exceeding a preset population upper limit according to the sorting result, and add the retained offspring mayflies to the first-generation mayflies in the preset non-dominated optimal solution set to obtain a mixed-generation mayfly group;

[0265] The third processing unit is used to perform non-dominated sorting on each hybrid generation ephemera in the hybrid generation ephemera group, and add the decision variable parameters associated with each hybrid generation ephemera in the first level dominance layer after the non-dominated sorting to the preset non-dominated optimal solution set to obtain a new preset non-dominated optimal solution set.

[0266] In the present invention, in response to a parameter optimization request for an energy-storage power station, a mathematical model of an energy-storage power station considering nonlinearity of flow channel loss and nonlinearity of surge chamber impedance loss is established, model constraints of the mathematical model of the energy-storage power station are set, and based on the mayfly algorithm, the mathematical model of the energy-storage power station is iteratively solved using the model constraints to obtain an optimal solution set of flow channel hydraulic parameters of the energy-storage power station; the present invention constructs a mathematical model of a long-water diversion pumped-storage power station considering nonlinearity of flow channel loss and nonlinearity of surge chamber impedance loss, and uses the mayfly algorithm to optimize and solve the mathematical model, so that the hydraulic parameter optimization calculation speed is faster and the convergence rate is higher, thereby obtaining an optimal solution set of flow channel hydraulic parameters of the flow channel system of the long-water diversion pumped-storage power station, and then adopts the hydraulic optimization parameters in the optimal solution set of flow channel hydraulic parameters to achieve more accurate system decoupling analysis and unit operation control, thereby improving the operating stability of the energy-storage power station; and solves the technical problem of how to improve the operating stability of the energy-storage power station.

[0267] See also Fig.12 , Fig.12 This is a structural block diagram of a computer device provided in Embodiment 4 of the present invention.

[0268] An electronic device according to an embodiment of the present invention comprises: a memory 401 and a processor 402, wherein the memory 402 stores a computer program; when the computer program is executed by the processor 402, the processor 402 executes a flow channel hydraulic parameter optimization method for a long-water diversion pumped-storage power station according to any of the above embodiments.

[0269] The memory 401 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read-only memory), an EPROM, a hard disk or a ROM. The memory 401 has a storage space 403 for a program code 413 for executing any method step in the above method. For example, the storage space 403 for the program code may include individual program codes 413 for implementing the various steps in the above method, respectively. These program codes may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards or floppy disks. The program code may be compressed, for example, in an appropriate form. When these codes are run by a computing and processing device, the computing and processing device performs the various steps in the method described above. These program codes may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards or floppy disks. The program code may be compressed, for example, in an appropriate form. When these codes are executed by a computing and processing device, they cause the computing and processing device to execute the various steps of the above-described method for optimizing hydraulic parameters of a flow channel of a long-water diversion pumped-storage power station.

[0270] Embodiment 5 of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method for optimizing the hydraulic parameters of the flow channel of a long-water diversion pumped-storage power station as described in any of the above embodiments is implemented.

[0271] Embodiment 6 of the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer executes the method for optimizing the flow channel hydraulic parameters of a long-diversion pumped-storage power station as described in any of the above embodiments.

[0272] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0273] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0274] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0275] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0276] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0277] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing hydraulic parameters of a flow channel of a long water diversion pumped storage power station, characterized in that: include: In response to the request for parameter optimization of the energy storage power station, a mathematical model of the energy storage power station is established that takes into account the nonlinearity of the flow channel loss and the nonlinearity of the surge chamber impedance loss; Setting model constraints of the mathematical model of the energy storage power station; Based on the mayfly algorithm, the mathematical model of the energy storage power station is iteratively solved using the model constraints to obtain an optimal solution set of the flow channel hydraulic parameters of the energy storage power station.

2. The method for optimizing flow channel hydraulic parameters of a long water diversion pumped storage power station according to claim 1, characterized in that: The decision variables of the mathematical model of the energy storage power station considering the nonlinearity of the flow channel loss and the impedance loss of the surge chamber include the length of the long diversion tunnel, the head loss of the long diversion tunnel, the length of the pressure pipeline, the head loss of the pressure pipeline, the cross-sectional area of ​​the surge chamber, the impedance loss of the surge chamber and the working head of the water pump turbine; The model constraints include: The length of the long diversion tunnel is greater than or equal to a preset minimum long diversion tunnel length constraint value, and the length of the long diversion tunnel is less than or equal to a preset maximum long diversion tunnel length constraint value; The water head loss of the long diversion tunnel is greater than or equal to a preset minimum water head loss constraint value of the long diversion tunnel, and the water head loss of the long diversion tunnel is less than or equal to a preset maximum water head loss constraint value of the long diversion tunnel; The pressure pipeline length is greater than or equal to a preset minimum pressure pipeline length constraint value, and the pressure pipeline length is less than or equal to a preset maximum pressure pipeline length constraint value; The pressure pipeline head loss is greater than or equal to a preset minimum pressure pipeline head loss constraint value, and the pressure pipeline head loss is less than or equal to a preset maximum pressure pipeline head loss constraint value; The cross-sectional area of ​​the surge chamber is greater than or equal to a preset Thomas critical stable cross-sectional area, and the cross-sectional area of ​​the surge chamber is less than or equal to a preset minimum surge chamber cross-sectional area constraint value; The voltage regulating chamber impedance loss is greater than or equal to a preset minimum voltage regulating chamber impedance loss constraint value, and the voltage regulating chamber impedance loss is less than or equal to a preset maximum voltage regulating chamber impedance loss constraint value; The pump-turbine operating water head is greater than or equal to a preset minimum pump-turbine operating water head constraint value, and the pump-turbine operating water head is less than or equal to a preset maximum pump-turbine operating water head constraint value.

3. The method for optimizing flow channel hydraulic parameters of a long water diversion pumped storage power station according to claim 1, characterized in that: The method based on the mayfly algorithm and using the model constraints to iteratively solve the mathematical model of the energy storage power station to obtain the optimal solution set of the flow channel hydraulic parameters of the energy storage power station includes: Initializing the mayflies, using the energy storage power station mathematical model to solve the initial optimization target value associated with each initial generation of mayflies under the model constraint condition, and performing non-dominated sorting and calculating congestion according to the initial optimization target value to obtain a preset non-dominated optimal solution set stored in the initial generation of mayflies; Randomly selecting the first-generation mayflies associated with any of the decision variable parameters from the preset non-dominated optimal solution set as the global optimal position of the mayfly swarm, and updating the positions and velocities of the first-generation mayflies corresponding to the decision variable parameters; Performing mating and mutation operations on the updated primary mayfly group in the preset non-dominated optimal solution set, and solving the energy storage power station mathematical model under the model constraint condition to obtain the offspring optimization target value associated with each offspring mayfly; Sorting the offspring mayflies according to the offspring optimization target value, and performing non-dominated sorting on the mixed-generation mayfly group obtained by the sorting result to obtain a new preset non-dominated optimal solution set; Jump to the step of randomly selecting the first-generation mayflies associated with any of the decision variable parameters from the preset non-dominated optimal solution set as the global optimal position of the mayfly swarm, and updating the positions and speeds of the first-generation mayflies corresponding to each decision variable parameter, until a preset threshold number of iterations is reached, then outputting the optimal solution set of the flow channel hydraulic parameters of the energy storage power station.

4. The method for optimizing flow channel hydraulic parameters of a long water diversion pumped storage power station according to claim 3 is characterized in that: The mayfly initialization is performed, and the primary optimization target value associated with each primary mayfly is obtained by using the mathematical model of the energy storage power station under the model constraint condition, and non-dominated sorting and congestion calculation are performed according to the primary optimization target value to obtain a preset non-dominated optimal solution set stored in the primary mayfly, including: Initialize the mayfly and obtain the decision variable parameters corresponding to the position of the randomly generated first generation mayfly; Under the model constraint conditions, the decision variable parameters associated with each first-generation mayfly are substituted into the objective function in the mathematical model of the energy storage power station for solving, to obtain a plurality of first-generation optimization target values; performing non-dominated sorting on the decision variable parameters associated with each of the first-generation mayflies according to the first-generation optimization target value; The congestion degree of each of the first-generation mayflies in the first-level dominance layer after non-dominated sorting is calculated, and the decision variable parameters associated with each of the first-generation mayflies in the first-level dominance layer are stored in a preset non-dominated optimal solution set.

5. The method for optimizing flow channel hydraulic parameters of a long water diversion pumped storage power station according to claim 3, characterized in that: The mating operation and mutation operation are performed on the updated primary mayfly group in the preset non-dominated optimal solution set, and the offspring optimization target value associated with each offspring mayfly is obtained by using the energy storage power station mathematical model under the model constraint condition, including: Performing a mating operation on the first-generation mayfly group in the preset non-dominated optimal solution set after updating the positions and velocities of the first-generation mayflies to obtain offspring mayflies; Performing a mutation operation on the offspring mayfly; Under the model constraints, the decision variable parameters associated with the offspring mayflies after each mutation operation are substituted into the objective function in the mathematical model of the energy storage power station for solving, and multiple offspring optimization target values ​​are obtained.

6. The method for optimizing flow channel hydraulic parameters of a long water diversion pumped storage power station according to claim 3, characterized in that: The step of sorting the offspring mayflies according to the offspring optimization target value and performing non-dominated sorting on the mixed-generation mayfly group obtained by the sorting result to obtain a new preset non-dominated optimal solution set includes: The offspring mayflies are non-dominatedly sorted according to the offspring optimization target value, and are sorted from high to low according to the dominance level of each of the offspring mayflies; Calculating the crowding degree of each of the offspring mayflies, and sorting the offspring mayflies in the same dominance level from large to small according to the crowding degree; Eliminating the offspring mayfly individuals exceeding the preset population upper limit according to the sorting result, and adding the retained offspring mayflies to the first generation mayflies in the preset non-dominated optimal solution set to obtain a mixed generation mayfly group; Non-dominated sorting is performed on each of the mixed-generation mayflies in the mixed-generation mayfly group, and the decision variable parameters associated with each of the mixed-generation mayflies in the first-level dominated layer after non-dominated sorting are added to the preset non-dominated optimal solution set to obtain a new preset non-dominated optimal solution set.

7. A flow channel hydraulic parameter optimization system for a long water diversion pumped storage power station, characterized in that: include: A response module is used to respond to parameter optimization requests for the energy storage power station and establish a mathematical model of the energy storage power station that takes into account the nonlinearity of the flow channel loss and the nonlinearity of the surge chamber impedance loss; A constraint setting module, used to set model constraint conditions of the mathematical model of the energy storage power station; The parameter optimization module is used to iteratively solve the mathematical model of the energy storage power station based on the mayfly algorithm and the model constraints to obtain the optimal solution set of the flow channel hydraulic parameters of the energy storage power station.

8. An electronic device, characterized in that: It comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the flow channel hydraulic parameter optimization method of the long diversion pumped storage power station as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the method for optimizing the flow channel hydraulic parameters of a long-water diversion pumped-storage power station as described in any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer executes the flow channel hydraulic parameter optimization method of a long-diversion pumped-storage power station as described in any one of claims 1-6.

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