Thermal power generating unit cold end circulating water system real-time decision-making method based on two-stage optimization

Abstract

The invention provides a thermal power generating unit cold end circulating water system real-time decision-making method based on double-stage optimization. The method comprises an offline stage and an online stage. In the off-line stage, global optimization calculation is conducted through a basin jump method, and an optimal pump set operation parameter set under different working conditions is obtained and stored in a preset working condition optimal solution set; and in the online stage, according to the real-time environment temperature and the electric load requirement, an adjacent working condition solution is searched from the preset working condition optimal solution set, real-time optimization calculation and decision making are carried out based on the pump set combination of the adjacent working conditions, and optimal pump set operation parameters are output. According to the method, global optimization calculation is transferred to an offline stage, on the basis of meeting physical constraints, the real-time performance of online decision making is guaranteed, better pump set operation parameters are obtained, and therefore efficient operation of a pump set system under the working conditions of different environment temperatures and electrical load requirements is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of optimization of a circulating water system of a cold end of a thermal power unit, and particularly relates to a real-time decision-making method for a circulating water system of a cold end of a thermal power unit based on two-stage optimization. BACKGROUND

[0002] The circulating water system of a cold end of a thermal power unit is an important component of a thermal power plant, and its main function is to provide cooling water for a steam turbine to maintain normal operation of the steam turbine. As an important subsystem of the steam turbine, the operating parameters (such as circulating water flow, operating characteristics of the pump, etc.) of the circulating water system directly affect the vacuum degree of the condenser, the power generation of the unit, and the power consumption of the circulating water pump, thereby having a significant impact on the economic efficiency of the unit operation.

[0003] With the increasing requirements of the electricity market for the flexibility and economy of the operation of a thermal power unit, especially the increasing demand for load regulation speed and regulation depth, more and more power plants have carried out double-speed transformation and frequency transformation of the circulating water pump. The working range of the circulating water pump is wider after the transformation, and the combination mode is more, although the energy-saving potential is increased, but the optimization difficulty is also significantly improved. The traditional optimization method mainly relies on thermal performance test and condenser variable condition calculation model, when facing the complex system after transformation, it exposes the problems of high calculation complexity, long time consumption, poor real-time performance, etc., and the test data deviates greatly from the actual situation, which is difficult to meet the needs of dynamic operation environment.

[0004] In recent years, with the rapid development of computing technology and optimization algorithm, a large number of intelligent optimization methods have been applied to the field of optimization of the circulating water pump of the steam turbine, such as genetic algorithm, particle swarm optimization and BP neural network. However, these methods still have some limitations: the standard particle swarm optimization algorithm is easy to fall into a local optimal trap, and the convergence precision is insufficient; the genetic algorithm and the BP neural network have the problems of difficult parameter adjustment and low training efficiency.

[0005] Therefore, when facing a complex circulating water system of a cold end of a thermal power unit, how to complete the optimization calculation and give the optimal operation decision in a short time is a technical problem faced by those skilled in the art. SUMMARY

[0006] The present application provides a real-time decision-making method for a circulating water system of a cold end of a thermal power unit based on two-stage optimization, which solves the technical problem that the global optimization calculation of a complex pump system under different environmental temperature and electrical load demand conditions is time-consuming and difficult to meet the real-time decision-making demand.

[0007] The present application provides a real-time decision-making method for a circulating water system of a cold end of a thermal power unit based on two-stage optimization, which includes:

[0008] Step 1, system parameter acquisition, including determining the basic operating parameters of the system, obtaining the steam turbine power-circulating water flow characteristics, pipeline head characteristics, obtaining the head-flow characteristics and efficiency-flow characteristics of various circulating water pumps in different operating states, determining the system operating constraints, etc.

[0009] Step 2, first stage (offline stage), according to the obtained steam turbine power-circulating water flow characteristics, pipeline head characteristics, head-flow characteristics and efficiency-flow characteristics of various circulating water pumps in different operating states, offline global optimization calculation is carried out by basin jumping method, and the optimization results are stored in the preset working condition optimal solution set.

[0010] Step 3, second stage (online stage), according to the real-time environmental temperature and unit power load demand, the adjacent working condition solution is found from the preset working condition optimal solution set, and the real-time optimization calculation and decision are carried out based on the pump set combination of the adjacent working condition, and the optimal pump set operating parameters are output.

[0011] According to the steam turbine power-circulating water flow characteristics, the output power of the steam turbine under different environmental temperatures and circulating water flow is calculated by the following formula,

[0012] P turbine =f(Q total ,T);

[0013] Where P turbine is the output power of the steam turbine, MW; Q total is the total circulating water flow, m 3 / s; T is the environmental temperature, ℃.

[0014] According to the relationship between the total circulating water flow and the pipeline head, the pipeline head is calculated by the following formula,

[0015] H need =f(Q total );

[0016] Where H need is the pipeline head, m.

[0017] According to the characteristics of various pumps, the following head-flow curves and efficiency-flow curves under different operating states are obtained,

[0018] Head-flow curve and efficiency-flow curve of constant speed pump in open state;

[0019] Head-flow curve and efficiency-flow curve of dual-speed pump in low-speed and high-speed operating modes;

[0020] Head-flow curve and efficiency-flow curve of variable frequency pump at different frequencies.

[0021] According to the flow, head and efficiency of each pump, the power consumption of each pump is calculated by the following formula,

[0022]

[0023] wherein P pump,i is the power consumption of the i-th pump, MW; p is the density of circulating water, kg / m 3 ; g is the acceleration of gravity, m / s 2 ; Q i is the flow of the i-th pump, m 3 / s; H i is the head of the i-th pump, m; and η i is the efficiency of the i-th pump.

[0024] Further, according to the obtained steam turbine power-circulating water flow characteristics, pipeline head characteristics, and characteristics of various pumps, offline global optimization calculation is performed by the basin jumping method, and the main steps are as follows:

[0025] Step 2.1, setting decision variables, including continuous variables and discrete variables;

[0026] Step 2.2, setting constraint conditions, the following is the constraint condition:

[0027] Head matching error is calculated by the following formula:

[0028] |H pump,i -H need |≤δ;

[0029] Wherein, δ is the minimum head error allowed.

[0030] Flow range requirement is calculated by the following formula:

[0031] Q min,i ≤Q i ≤Q max,i ;

[0032] Wherein, Q min,i is the minimum operating flow of the i-th pump, and Q max,i is the maximum operating flow of the i-th pump.

[0033] Circulating water total flow requirement is calculated by the following formula:

[0034]

[0035] Wherein, n is the total number of pumps.

[0036] Step 2.3, setting optimization configuration, including optimization objective function, local optimizer, perturbation parameter, etc.

[0037] Step 2.4, generating initial solution, including initial pump combination, flow distribution and variable frequency pump frequency;

[0038] Step 2.5, iterative optimization, generating perturbation and local optimization;

[0039] Step 2.6, for the solution that meets the constraints, calculate its objective function (system electric power difference) value, if better than the current optimal solution, update the optimal solution. When the termination condition is reached, terminate the optimization process, and output the pump set operation parameters.

[0040] Further, a real-time decision-making method for the cold-end circulating water system of a thermal power generating unit based on two-stage optimization, through the offline stage, the preset multiple environmental temperature-power load combined working conditions are globally optimized and calculated, and the optimal pump set operation parameter set under each working condition point is obtained.

[0041] The pump set operation parameter set includes,

[0042] The on-off state (closed / open) and flow of each constant speed pump;

[0043] The running state (closed / low speed mode / high speed mode) and flow of each double speed pump;

[0044] The on-off state (closed / open), frequency and flow of each variable frequency pump.

[0045] Further, a real-time decision-making method for the cold-end circulating water system of a thermal power generating unit based on two-stage optimization, the optimal pump set operation parameter set is stored in the preset working condition optimal solution set, which is used for fast query and parameter acquisition in the subsequent real-time decision-making stage.

[0046] Further, according to the preset working condition optimal solution set obtained by the offline stage and the real-time working condition environmental temperature and power load demand, optimization calculation and decision-making are carried out through the online stage, and the main steps are:

[0047] Step 3.1, find several adjacent working conditions as adjacent candidate solutions;

[0048] Step 3.2, fix the pump combination of the adjacent candidate solutions respectively, and carry out real-time optimization on the continuous variables;

[0049] Step 3.3, respectively, the real-time optimization results of the adjacent candidate solutions are subjected to feasibility test, and the following is the feasibility test content:

[0050] Whether the difference between the head of each running pump and the pipeline head is within the allowed minimum head error;

[0051] Whether the flow of each running pump is within the allowed range;

[0052] whether the total flow of circulating water meets the minimum requirement (in order to ensure normal operation of the condenser, the minimum flow of the condenser is set according to the unit regulation to prevent the condensate from being supercooled, the flow rate of the fluid in the pipe from being too low and the pipe from being corroded due to the too small cooling water flow; the minimum flow of the condenser cooling water, i.e. the minimum requirement of the total flow of circulating water, is set through the unit operation regulation and the manufacturer's manual);

[0053] whether the frequency of the variable frequency pump is within the range of the minimum variable frequency ratio and the maximum variable frequency ratio.

[0054] Step 3.4, if there is no feasible solution, the number of adjacent candidate solutions is increased or the constraint is relaxed, and the real-time optimization is re-performed; if there is a feasible solution, the electric power difference of each unit in the feasible solution is calculated and sorted;

[0055] Further, a real-time decision-making method for a cold-end circulating water system of a thermal power unit based on two-stage optimization is used to calculate the system electric power difference through the following formula,

[0056]

[0057] wherein P net is the system electric power difference, MW; P turbine,j is the electric power of the jth unit, MW; m is the total number of units, P pump,i represents the ith pump; and n represents the total number of pumps.

[0058] Further, a real-time decision-making method for a cold-end circulating water system of a thermal power unit based on two-stage optimization is used to select the feasible solution with the highest electric power difference as the pump group operation parameter scheme, and output the start-stop state, flow distribution and frequency of each pump.

[0059] Further, a real-time decision-making method for a cold-end circulating water system of a thermal power unit based on two-stage optimization is used, and the circulating water pump operation mode is two-machine six-pump or two-machine five-pump or two-machine four-pump or two-machine three-pump or two-machine two-pump.

[0060] The technical concept of the present application is as follows:

[0061] A real-time decision-making method for a cold-end circulating water system of a thermal power unit based on two-stage optimization is used, in the offline stage, the optimal pump group operation parameters under different environmental temperatures and electric load demand conditions are calculated in advance, and an optimal solution set under preset conditions is established; in the online stage, the adjacent candidate solutions are quickly found according to the current condition, and the pump group combination based on the adjacent candidate solutions is used for real-time optimization, so as to obtain an optimal operation scheme meeting the physical requirements such as head matching and flow constraint.

[0062] The beneficial effects of the present application are as follows:

[0063] By transferring the global optimization calculation to the offline stage, the method meets the physical constraints, ensures the real-time of online decision-making, and obtains the optimal pump set operation parameters, thereby ensuring the efficient operation of the pump set system under different environmental temperatures and electrical load demand conditions. BRIEF DESCRIPTION OF DRAWINGS

[0064] Figure 1 is a total flow process schematic diagram of a real-time decision-making method for a thermal power unit cold-end circulating water system based on two-stage optimization provided by the application;

[0065] Figure 2 is a steam turbine power-circulating water flow characteristic curve diagram of a real-time decision-making method for a thermal power unit cold-end circulating water system based on two-stage optimization provided by the application;

[0066] Figure 3 is a pipeline head characteristic curve diagram of a real-time decision-making method for a thermal power unit cold-end circulating water system based on two-stage optimization provided by the application;

[0067] Figure 4 is a head-flow characteristic curve diagram of various pumps of a real-time decision-making method for a thermal power unit cold-end circulating water system based on two-stage optimization provided by the application;

[0068] Figure 5 is an efficiency-flow characteristic curve diagram of various pumps of a real-time decision-making method for a thermal power unit cold-end circulating water system based on two-stage optimization provided by the application;

[0069] Figure 6 is an offline stage flow process schematic diagram of a real-time decision-making method for a thermal power unit cold-end circulating water system based on two-stage optimization provided by the application;

[0070] Figure 7 is an online stage flow process schematic diagram of a real-time decision-making method for a thermal power unit cold-end circulating water system based on two-stage optimization provided by the application;

[0071] Figure 8 is a circulating water expansion unit system operation schematic diagram of a 2x1000MW ultra-supercritical power generation unit of a real-time decision-making method for a thermal power unit cold-end circulating water system based on two-stage optimization provided by the application. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme in the present application will be described clearly and completely below in combination with the drawings in the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0073] The following is combined with Figures 1-7 This invention describes a real-time decision-making method for a thermal power unit's cold-end circulating water system based on two-stage optimization, comprising:

[0074] Please refer to Figure 1 , Figure 1 This is a schematic diagram of the overall process of a real-time decision-making method for a thermal power unit cold-end circulating water system based on two-stage optimization, provided by the present invention.

[0075] This invention provides a real-time decision-making method for a thermal power unit's cold-end circulating water system based on two-stage optimization, comprising:

[0076] Step 1: System parameter acquisition, including determining the basic operating parameters of the system, obtaining the turbine power-circulating water flow characteristics and pipeline head characteristics, obtaining the head-flow characteristics and efficiency-flow characteristics of various pumps under different operating conditions, and determining the system operating constraints, etc.

[0077] Step 2, First Stage (Offline Stage): Based on the obtained turbine power-circulating water flow characteristics, pipeline head characteristics, and characteristics of various pumps, offline global optimization calculations are performed using the basin jump method, and the optimization results are stored in the preset optimal solution set.

[0078] Step 3, the second stage (online stage): Based on the real-time ambient temperature and external electrical load demand, the system searches for adjacent operating condition solutions from the preset optimal solution set, and performs real-time optimization calculations and decisions based on the pump group combination of adjacent operating conditions, outputting the optimal pump group operating parameters.

[0079] Please refer to Figure 2 , Figure 2 This invention provides a turbine power-circulating water flow characteristic diagram for a real-time decision-making method for the cold-end circulating water system of a thermal power unit based on two-stage optimization.

[0080] Based on the characteristics of the steam turbine, the output power of the steam turbine under different ambient temperatures and circulating water flow rates is calculated using the following formula.

[0081] P turbine =f(Q) total ,T);

[0082] Among them, P turbine Q represents the output power of the steam turbine, in MW; total The total flow rate of the circulating water is m. 3 / s; T is the ambient temperature, °C.

[0083] Please refer to Figure 3 , Figure 3This is a pipeline head characteristic curve of a real-time decision-making method for a thermal power unit cold-end circulating water system based on two-stage optimization, provided by the present invention.

[0084] Based on the relationship between the total circulating water flow rate and the pipeline head, the pipeline head can be calculated using the following formula.

[0085] H need =f(Q) total );

[0086] Among them, H need The pipe head is measured in meters (m).

[0087] Please refer to Figure 4 , Figure 4 This invention provides a real-time decision-making method for the cold-end circulating water system of a thermal power unit based on two-stage optimization, which displays the head-flow characteristic curves of various pumps.

[0088] Based on the characteristics of various pumps, the following head-flow curves under different operating conditions were obtained, including the head-flow curve of a constant speed pump in the on state.

[0089] Head-flow curves of a dual-speed pump in low-speed and high-speed operating modes;

[0090] Head-flow curves of variable frequency pumps at different frequencies.

[0091] Please refer to Figure 5 , Figure 5 This invention provides an efficiency-flow characteristic curve of various pumps in a real-time decision-making method for a thermal power unit's cold-end circulating water system based on a two-stage optimization.

[0092] Based on the characteristics of various pumps, the following efficiency-flow curves under different operating conditions were obtained, including the efficiency-flow curve of a constant speed pump in the on state.

[0093] Efficiency-flow curves of a dual-speed pump in low-speed and high-speed operating modes;

[0094] Efficiency-flow curves of variable frequency pumps at different frequencies.

[0095] Based on the flow rate, head, and efficiency of each pump, the power consumption of each pump is calculated using the following formula.

[0096]

[0097] Among them, P pump,i ρ is the power consumption of the i-th pump, in MW; ρ is the density of the circulating water, in kg / m³. 3 g is the acceleration due to gravity, in m / s². 2 Q i Let m be the flow rate of the i-th pump. 3 / s;Hi Let m be the head of the i-th pump; η be the head of the i-th pump. i Let be the efficiency of the i-th pump.

[0098] Please refer to Figure 6 , Figure 6 This is an offline stage flowchart of a real-time decision-making method for a thermal power unit cold-end circulating water system based on dual-stage optimization, provided by the present invention.

[0099] Based on the obtained turbine power-circulating water flow characteristics, pipeline head characteristics, and characteristics of various pumps, offline global optimization calculations are performed using the basin jump method. The main steps are as follows:

[0100] Step 1: Define the decision variables, including continuous and discrete variables;

[0101] Specifically, continuous variables include: flow rate of each pump and frequency of the variable frequency pump; discrete variables include on / off status of each pump and operating mode of the dual-speed pump.

[0102] Step 2: Set constraints. The constraints are as follows:

[0103] The head matching error is calculated using the following formula:

[0104] |H pump,i -H need |≤δ;

[0105] Where δ is the minimum allowable head error.

[0106] The required flow range is calculated using the following formula:

[0107] Q min,i ≤Q i ≤Q max,i ;

[0108] Among them, Q min,i Q is the minimum operating flow rate of the i-th pump. max,i Let be the maximum operating flow rate of the i-th pump.

[0109] The required total circulating water flow rate is calculated using the following formula:

[0110]

[0111] Where n is the total number of pumps.

[0112] Step 3: Set the optimization configuration, including the objective function, local optimizer, perturbation parameters, etc.

[0113] Specifically, the optimization objective function is to maximize the system electric power difference; the local optimizer uses a sequential least squares quadratic programming (SLSQP) to handle continuous variables, and the perturbation step is set to 25% of the variable range, and for discrete variables, their states are randomly changed with a probability of 0.3.

[0114] Step four, generating an initial solution, including an initial pump combination, flow distribution and variable frequency pump frequency;

[0115] Step five, iterative optimization, generating perturbations and local optimization;

[0116] Specifically, the current optimal solution is perturbed, and for discrete variables, their states are randomly changed with a probability of 0.3; for continuous variables, random perturbations are made within ±25% of their current values. Then, the discrete variables after perturbation are fixed, and the SLSQP optimizer is used to locally optimize the continuous variables.

[0117] Step six, for solutions that meet the constraints, calculate the objective function value, and if it is better than the current optimal solution, update the optimal solution. When the termination condition is reached, terminate the optimization process and output the pump set operating parameters.

[0118] Specifically, the termination condition is that when the optimal value changes less than 0.1% for 50 consecutive iterations, or the maximum number of iterations is reached.

[0119] According to the real-time decision-making method for the cold-end circulating water system of the thermal power generating unit provided by the application, the optimal pump set operating parameter set is stored in the preset working condition optimal solution set, which is used for subsequent online decision-making stage fast query and parameter acquisition.

[0120] Please refer to Figure 7 , Figure 7 is a flow diagram of an online stage of a real-time decision-making method for a cold-end circulating water system of a thermal power generating unit based on two-stage optimization provided by the application.

[0121] According to the preset working condition optimal solution set obtained in the offline stage and the real-time working condition environmental temperature and electrical load demand, optimization calculation and decision-making are performed through the online stage, and the main steps are:

[0122] Step one, find several adjacent working conditions as adjacent candidate solutions;

[0123] Step two, fix the pump combination of the adjacent candidate solutions respectively, and perform real-time optimization on the continuous variables;

[0124] Step three, perform feasibility test on the real-time optimization results of the adjacent candidate solutions, and the following is the feasibility test content:

[0125] Whether the difference between the head of each operating pump and the head of the pipeline is within the allowed minimum head error;

[0126] whether the flow of each operating pump is within an allowable range;

[0127] whether the total flow of circulating water meets a minimum requirement;

[0128] whether the frequency of the variable frequency pump is within an upper and lower limit range of operation (after the variable frequency pump changes the frequency, the change in the rotational speed of the pump causes a change in the head-flow curve of the pump, and the operating point of the pump changes. This change is achieved by the frequency converter, which can cause the variable frequency pump to change within 25 Hz to 50 Hz. At the same time, the variable frequency pump is limited by the static head H0=19.02, and the minimum variable frequency ratio of a single pump is calculated to be 0.693, so the minimum variable frequency ratio is set to 0.7 and the maximum variable frequency ratio is set to 1.

[0129] The minimum variable frequency ratio refers to the ratio of the minimum operating frequency at which the water pump can maintain normal operation of the system to the rated frequency. At this minimum frequency, the water pump just overcomes the static head, and at this time the flow in the pipeline is theoretically zero. Therefore, at the minimum frequency, the head of the water pump at zero flow (zero flow head) must be equal to the static head of the system.

[0130] According to the performance curve provided by the circulating pump manufacturer, the zero flow head of the water pump is about 39.6 meters

[0131] According to the water pump proportionality law:

[0132]

[0133] f rated is the rated frequency, which is 50 Hz in the example; f min is the minimum frequency, Hz; H min is the zero flow head of the pump at the rated frequency, m; H rated is the zero flow head of the pump at the rated frequency.

[0134] The minimum variable frequency ratio is

[0135]

[0136] To reserve a certain safety margin to ensure that the system can be started and operated stably under various operating condition fluctuations, an integer 0.7 slightly larger than the theoretical value is taken as the actual minimum variable frequency ratio setting.

[0137] Step four, if there is no feasible solution, increase the number of adjacent candidate solutions or relax the constraints, and perform real-time optimization again; if there is a feasible solution, calculate the unit electric power difference in the feasible solution and sort them;

[0138] According to the real-time decision method for the cold-end circulating water system of the thermal power unit based on two-stage optimization provided by the application, the system electric power difference is calculated by the following formula,

[0139]

[0140] P is the system electric power difference, MW. net

[0141] According to the real-time decision method for the cold-end circulating water system of the thermal power unit based on two-stage optimization provided by the application, the feasible solution with the highest electric power difference is selected as the pump set operation parameter scheme, and the start-stop state, flow distribution and frequency of the variable frequency pump of each pump are output.

[0142] According to the real-time decision method for the cold-end circulating water system of the thermal power unit based on two-stage optimization provided by the application, the circulating water pump operation mode is two machines and six pumps or two machines and five pumps or two machines and four pumps or two machines and three pumps or two machines and two pumps.

[0143] According to the real-time decision method for the cold-end circulating water system of the thermal power unit based on two-stage optimization provided by the application, the minimum change range of the circulating pump frequency is 0.1 Hz.

[0144] The real-time decision method for the cold-end circulating water system of the thermal power unit based on two-stage optimization provided by the application pre-calculates the optimal pump set operation parameters under different environmental temperatures and power demand conditions through offline global optimization, and establishes a high-quality solution space database; in the real-time operation stage, the near neighbor candidate solution is quickly found according to the current working condition, and the real-time optimization is carried out based on the pump set combination of the near neighbor candidate solution, so as to obtain the optimal operation scheme meeting the physical requirements such as head matching and flow constraint. The time-consuming global optimization calculation is transferred to the offline stage, the unification of online rapid decision and physical constraint satisfaction is realized, the pump set system can obtain the maximum electric power difference under different environmental temperatures and power demands, and the operation safety and reliability are ensured.

[0145] Example 1:

[0146] Reference Figure 8 , Figure 8 is a circulating water expansion unit operation schematic diagram of a 2x1000MW ultra-supercritical power generating unit provided by the real-time decision method for the cold-end circulating water system of the thermal power unit based on two-stage optimization provided by the application;

[0147] The 2x1000MW ultra-supercritical power generating unit has two fixed-speed circulating water pumps, two double-speed circulating water pumps and two variable-frequency circulating water pumps, and the design power of the steam turbine is 1000MW.

[0148] ​In the pump group (#1A, #1B, #1C), a constant speed pump, a double speed pump and a variable frequency pump are included.

[0149] In the pump group (#2A, #2B, #2C), a constant speed pump, a double speed pump and a variable frequency pump are also included.

[0150] Specifically, 2x1000MW ultra-supercritical generating units are provided with a circulating water pump outlet mother pipe communication door in front of the condenser of the No. 1 unit and the condenser of the No. 2 unit, and the circulating water flow between the two units can be flexibly distributed through the communication door. This design feature increases the flexibility of the system operation, and also increases the complexity of the pump group optimization problem.

[0151] The optimization method includes the following steps:

[0152] Step one, system parameter acquisition, including determining the basic operating parameters of the system, obtaining the turbine power-circulating water flow characteristics and pipeline head characteristics, obtaining the head-flow characteristics and efficiency-flow characteristics of various pumps in different operating states, determining the system operating constraints, etc.

[0153] Specifically, the basic operating parameters of the system are divided into 7 power levels and 7 ambient temperature levels, and the seven ambient temperature levels are: 5℃ / 10℃ / 15℃ / 20℃ / 25℃ / 30℃ / 35℃, and the seven power levels are: 1000MW / 900MW / 800MW / 700MW / 600MW / 500MW / 400MW, a total of 49 discrete working conditions.

[0154] Specifically, at least two pumps are required to operate to ensure water supply reliability.

[0155] Step two, first stage (offline stage), according to the obtained turbine power-circulating water flow characteristics, pipeline head characteristics, and characteristics of various pumps, offline global optimization calculation is carried out by basin jumping method, and the optimization results are stored in the preset working condition optimal solution set.

[0156] Specifically, taking 1000MW, 5℃ working condition as an example, the main steps are:

[0157] Step one, set the decision variables, including continuous variables and discrete variables;

[0158] Specifically, the continuous variables include: pump flow, variable frequency pump frequency; the discrete variables include: pump on-off state, double speed pump operating mode.

[0159] Step two, set the constraint conditions;

[0160] Specifically, the flow range of all pumps is 3.18-12.72 m3 / s (on state) or fixed as 0 (off state); the frequency range of variable frequency pumps is 0.7-1.0 (on state) or fixed as 1 (off state); the outlet head of each pump must be within 0.001 m of the system required head;

[0161] Step three, setting the optimization configuration, including the optimization objective function, local optimizer, perturbation parameters, etc.

[0162] Specifically, the optimization objective function is to maximize the system electric power difference; the local optimizer uses the sequential least squares quadratic programming (SLSQP) to handle continuous variables, and the perturbation step is set to 25% of the variable range; for discrete variables, their states are randomly changed with a probability of 0.3; the maximum number of iterations is set to 500.

[0163] Step four, generating an initial solution, including the initial pump combination, flow distribution, and variable frequency pump frequency.

[0164] Specifically, for the on-state constant speed pump, the initial flow is set to 5 m3 / s; for the on-state double-speed pump, the initial flow is set to 5 m3 / s; for the on-state variable frequency pump, the initial flow is set to 5 m3 / s and the initial frequency is set to 0.9; for the off-state pump, the flow is set to 0 and the frequency is set to 1.

[0165] Step five, iterative optimization, generate perturbation and local optimization.

[0166] Step six, for solutions that meet the constraints, calculate their objective function values, and if they are better than the current optimal solution, update the optimal solution. When the termination condition is reached, terminate the optimization process and output the pump group operating parameters.

[0167] Specifically, the termination condition is when the optimal value changes by less than 0.1% for 50 consecutive iterations, or the maximum number of iterations is reached.

[0168] Specifically, for the calculation results of 1000 MW and 5℃ working conditions: No. 1 variable frequency pump is on, the flow is 10.830 m 3 / s, the frequency is 47.5 Hz; No. 2 variable frequency pump is off; No. 1 constant speed pump is off; No. 2 constant speed pump is off; No. 1 double-speed pump is in high speed state, the flow is 11.941 m 3 / s; No. 2 double-speed pump is in low speed state, the flow is 9.227 m 3 / s; the electric power difference of a single unit under this scheme is 1001.629 MW.

[0169] The real-time decision-making method for the cold-end circulating water system of a thermal power unit based on two-stage optimization provided by the application stores the optimal pump set operation parameter set into a preset working condition optimal solution set, which is used for fast query and parameter acquisition in the subsequent online decision-making stage.

[0170] The preset working condition optimal solution set obtained in the offline stage and the real-time working condition environment temperature and electrical load demand are optimized and decided through the online stage.

[0171] Taking the working condition with an electrical load demand of 850 MW and an environment temperature of 13℃ as an example, the main steps are as follows:

[0172] Step one, find several adjacent working conditions as adjacent candidate solutions;

[0173] Specifically, the adjacent working conditions are 800 MW / 10℃, 800 MW / 15℃, 900 MW / 10℃ and 900 MW / 15℃.

[0174] Step two, fix the pump combination of the adjacent candidate solutions respectively, and perform real-time optimization on the continuous variables;

[0175] Specifically, the pump combinations of the four adjacent working conditions are as follows: 800 MW / 10℃: two variable frequency pumps are started, and one double-speed pump is started in high-speed mode; 800 MW / 15℃: one variable frequency pump is started, and two double-speed pumps are started in high-speed mode; 900 MW / 10℃: one variable frequency pump is started, and two double-speed pumps are started in high-speed mode; 900 MW / 15℃: two variable frequency pumps are started, one double-speed pump is started in high-speed mode, and one double-speed pump is started in low-speed mode.

[0176] Step three, perform feasibility test on the real-time optimization results of the adjacent candidate solutions respectively;

[0177] Specifically, the pump combination optimization results of 800 MW / 10℃, 800 MW / 15℃, 800 MW / 15℃ and 900 MW / 15℃ pass the test.

[0178] Step four, if there is no feasible solution, increase the number of adjacent candidate solutions or relax the constraints, and perform real-time optimization again; if there is a feasible solution, calculate the unit electrical power difference in the feasible solution respectively, and sort them;

[0179] According to the real-time decision-making method for the cold-end circulating water system of a thermal power unit based on two-stage optimization provided by the application, the feasible solution with the highest electrical power difference is selected as the pump set operation parameter scheme, and the start-stop state, flow distribution and variable frequency pump frequency of each pump are output.

[0180] Specifically, the real-time optimization result of the working condition with an electrical load demand of 850 MW and an environment temperature of 13℃ is as follows: No. 1 variable frequency pump is started, and the flow is 11.05 m3 / s, the frequency is 48.2Hz; the No.2 variable frequency pump is opened, the flow is 11.05m 3 / s, the frequency is 48.2Hz; the No.1 double-speed pump is in high speed state, the flow is 11.87m 3 / s; the No.2 double-speed pump is closed. Under this operation scheme, the system electric power difference is 845.89MW.

[0181] The effect comparison and parameter comparison of the present application and prior art are shown in the following table 1 and table 2.

[0182] Table 1 effect comparison

[0183]

[0184]

[0185]

[0186] Table 2 parameter comparison

[0187]

[0188]

[0189] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A real-time decision method for a thermal power unit cold-end circulating water system based on two-stage optimization, characterized in that, Comprise: Step 1, system parameter acquisition: including determining the operating parameters of the system, obtaining the steam turbine power-circulating water flow characteristic curve, the pipeline head characteristic curve, the head-flow characteristic curve of various circulating water pumps under different operating conditions, the efficiency-flow characteristic curve of various circulating water pumps under different operating conditions, and determining the system operating constraints; Step 2, offline stage: according to the characteristic curve obtained in step 1, a plurality of environmental temperature-power load combined working conditions are preset, and the offline global optimization calculation is carried out by the basin jumping method to obtain the optimal pump set operating parameter set under each working condition point, and the optimal solution set is stored in the preset working condition; Step 3, online stage: according to the real-time environmental temperature and unit power load demand, the adjacent working condition solution is searched from the preset working condition optimal solution set, and the real-time optimization calculation and decision are carried out based on the pump set combination of the adjacent working condition, and the optimal pump set operating parameter is output.

2. The real-time decision method for the cold-end circulating water system of a thermal power unit based on two-stage optimization according to claim 1, characterized in that, The step 1 is specifically as follows: According to the steam turbine power-circulating water flow characteristic curve, the output power of the steam turbine under different environmental temperatures and circulating water flow is calculated: P turbine = f(Q total ,T). where P turbine is the output power of the turbine; Q total is the total flow of circulating water; and T is the ambient temperature. According to the circulating water total flow and the pipeline head characteristic curve, the pipeline head is calculated: H need = f(Q total ); where H need is the pipe head; According to the characteristics of various pumps, the following head-flow curves and efficiency-flow curves under different operating conditions are obtained, including: The head-flow curve and efficiency-flow curve of the constant speed pump in the open state; The head-flow curve and efficiency-flow curve of the double-speed pump in low-speed and high-speed operating modes; The head-flow curve and efficiency-flow curve of the variable frequency pump under different frequencies; According to the flow, head and efficiency of each pump, the power consumption of each pump is calculated by the following formula, where P pump,i is the power consumption of the i-th pump; p is the circulating water density; g is the gravitational acceleration; Q i is the flow rate of the i-th pump; H i is the head of the i-th pump; and η i is the efficiency of the i-th pump.

3. The method according to claim 1, wherein, The step 2 is specifically as follows: Step 2.1, set the decision variables, including continuous variables and discrete variables; Step 2.2, set the constraint conditions: Head matching error requirement: |H pump,i -H need |≤δ; Wherein, δ is the minimum head error allowed; Flow range requirement: Q min,i ≤Q i ≤Q max,i ; where Q min,i is the minimum operating flow rate of the i-th pump, Q max,i is the maximum operating flow rate of the i-th pump; Circulating water total flow requirement: Wherein, n is the total number of pumps; Step 2.3, set the optimization configuration, including the optimization objective function, the local optimizer, and the disturbance parameter; Step 2.4, generate the initial solution, including the initial pump combination, the flow distribution, and the variable frequency pump frequency; Step 2.5, iterative optimization, generate disturbance and carry out local optimization; Step 2.6, for the solution that meets the constraints, calculate its target function value, if it is better than the current optimal solution, update the optimal solution; when the termination condition is reached, terminate the optimization process, and output the pump set operating parameter.

4. The method according to claim 1, wherein, The pump set operating parameter set includes the open and stop state and flow of each constant speed pump, the operating state and flow of each double-speed pump, and the open and stop state, frequency and flow of each variable frequency pump.

5. The method of claim 1, wherein the method is characterized by, Step 3 is specifically as follows: According to the preset working condition optimal solution set obtained in the offline stage and the real-time working condition environmental temperature and power load demand, the optimization calculation and decision are carried out in the online stage: Step 3.1, find several adjacent working conditions as adjacent candidate solutions; Step 3.2, fix the pump combination of the adjacent candidate solutions respectively, and carry out real-time optimization on the continuous variables; Step 3.3, carry out feasibility test on the real-time optimization results of the adjacent candidate solutions, and the feasibility test content includes: whether the head difference of each operating pump and the pipeline head is within the set allowable minimum head error; whether the flow of each operating pump is within the set allowable range; whether the total flow of circulating water meets the minimum demand; whether the frequency of the variable frequency pump is within the upper and lower limits of operation; Step 3.4, if there is no feasible solution, increase the number of adjacent candidate solutions or relax the constraints, and re-optimize in real time; if there is a feasible solution, calculate the unit electric power difference in the feasible solution respectively, and sort them.

6. The method of claim 1, wherein the method is characterized by, select the feasible solution with the highest electric power difference as the pump group operation parameter scheme, output the start-stop state, flow distribution and variable frequency pump frequency of each pump; The system electric power difference is calculated by the following formula, where P net is the system electric power difference; P turbine,j is the electric power of the jth unit; m is the total number of units, P pump,i represents the ith pump; n represents the total number of pumps.

7. The method according to claim 1, wherein, The circulating water pump operation mode is two machines and six pumps or two machines and five pumps or two machines and four pumps or two machines and three pumps or two machines and two pumps.

Images