Thermal power generating unit heat and power load optimal distribution method considering combined heat and power generation mode

Abstract

The invention relates to the technical field of power systems, and discloses a thermal power generating unit thermoelectric load optimal distribution method considering a combined heat and power generation mode, and the method specifically comprises the following steps: 1, building a thermal power generating unit thermoelectric load distribution model considering the combined heat and power generation mode; step 2, establishing a thermal power generating unit operation constraint condition considering a combined heat and power generation mode; 3, improving the heuristic optimization algorithm according to the operation constraint condition of the thermal power generating unit; 4, optimizing the thermal power load distribution of the thermal power generating unit by adopting an improved heuristic optimization algorithm; under the condition that the system electrical load balance constraint, the thermal load balance constraint and the unit thermal power load feasible region constraint are met, the electrical load and the thermal load of each thermal power generating unit in the combined heat and power generation system are optimally distributed, and the minimum total fuel cost of the multiple combined heat and power generation thermal power generating units is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, in particular to a thermal and electric load optimization distribution method for thermal power generating units considering cogeneration modes. BACKGROUND

[0002] At present, thermal power generating units are still the main technical means for power supply in China and the main technical means for industrial steam and central heating. In the cogeneration operation mode, the use of intermediate steam extraction of the steam turbine for heating greatly reduces the exhaust steam loss, and the comprehensive thermal efficiency of the unit is greatly improved.

[0003] For a cogeneration system composed of multiple thermal power generating units, there are inevitable performance differences between the units. Thermal and electric load optimization distribution refers to reasonably planning the electric load and thermal load of each unit to achieve the optimal overall operation economy of the system under the conditions of meeting the system electric and thermal load balance and the operating characteristics constraints of each unit. This is of great significance to ensuring the safety and stability of the system energy supply and realizing the transformation of China's energy system. At present, many experts have studied the thermal and electric load distribution problem, but the existing research still has the following two problems: First, when building the model of the thermal and electric load distribution problem, the operating characteristics of the thermal power generating units in different cogeneration modes are not considered. The thermal power generating units currently have two cogeneration modes: extraction condensing and back pressure. The energy consumption characteristics and thermal and electric load regulation characteristics (feasible region) of the units in the two cogeneration modes are significantly different. When the unit operates in the extraction condensing cogeneration mode, the electric load can be adjusted within a certain range under the condition of a certain thermal load, and the thermal load can also be adjusted within a certain range under the condition of a certain electric load, and the energy consumption of the unit is determined by the electric load and the thermal load. When the unit operates in the back pressure cogeneration mode, the thermal load and the electric load of the unit are in a one-to-one correspondence, and the energy consumption of the unit is determined by the electric load only. Second, in recent years, a large number of heuristic optimization algorithms have been widely used to solve the thermal and electric load distribution problem, such as particle swarm optimization algorithm, genetic algorithm, and ant colony optimization algorithm. However, the existing research often uses penalty factors to passively handle the constraints, resulting in low solving efficiency and inability to guarantee that the solution meets the constraint conditions.

[0004] Therefore, how to consider the energy consumption characteristics and load regulation characteristics of the thermal power generating units in the extraction condensing and back pressure cogeneration operation modes in the thermal and electric load distribution problem of the units, to improve the solving accuracy of the thermal and electric load distribution problem, and efficiently handle the constraints in the solving process of the heuristic optimization algorithm, to improve the solving efficiency of the thermal and electric load distribution problem, is an urgent problem to be solved at present. SUMMARY

[0005] The present application aims at the deficiencies of the prior art, and provides a thermal and electric load optimization distribution method for thermal power generating units in a cogeneration mode.

[0006] The technical scheme for solving the above problems is as follows: a thermal and electric load optimization distribution method for thermal power generating units in a cogeneration mode, specifically comprising the following steps:

[0007] Step 1, establishing a thermal and electric load distribution model for thermal power generating units in a cogeneration mode;

[0008] Step 2, establishing a thermal and electric load distribution model for thermal power generating units in a cogeneration mode;

[0009] Step 3, improving a heuristic optimization algorithm according to the operation constraint condition of the thermal power generating unit;

[0010] Step 4, optimizing the thermal and electric load distribution of the thermal power generating unit by using the improved heuristic optimization algorithm.

[0011] Further, step 1 specifically comprises the following steps:

[0012] The total fuel cost of multiple thermal and electric cogeneration thermal power generating units is selected as the objective function, the difference in energy consumption characteristics of the thermal power generating unit in the extraction condensing and back pressure two cogeneration modes is considered, and the objective function is as shown in formula (1),

[0013]

[0014] In the formula, F represents the total fuel cost of multiple thermal and electric cogeneration thermal power generating units, i is the number of extraction condensing units, and I is the number of extraction condensing units; represents the fuel cost of the i th extraction condensing unit, j is the number of extraction condensing units, and J is the number of extraction condensing units; represents the fuel cost of the j th back pressure unit;

[0015] which can be represented as formula (2),

[0016]

[0017] In the formula, f i C is the fuel consumption characteristic function of the i th extraction condensing unit, P i C is the electric load of the i th extraction condensing unit; is the thermal load of the i th extraction condensing unit;

[0018] which can be represented as formula (3),

[0019]

[0020] wherein, is the fuel consumption characteristic function of the jth backpressure unit; is the electrical load of the jth backpressure unit.

[0021] Further, the constraint conditions established in step 2 include:

[0022] Constraint condition 1, as shown in equation (4):

[0023] wherein, P D is the electrical load demand of the cogeneration system;

[0024] Constraint condition 2, as shown in equation (5):

[0025] wherein, Q D is the thermal load demand of the cogeneration system; is the thermal load of the jth backpressure unit. When the thermal power unit operates in the backpressure cogeneration mode, the thermal load and the electrical load of the unit are in a one-to-one correspondence, therefore, which can be expressed as equation (6):

[0026]

[0027] wherein, is the thermal load calculation function of the jth backpressure unit;

[0028] Constraint condition 3, as shown in equations (7)-(9):

[0029]

[0030] wherein, and are the minimum electrical load calculation function and the maximum electrical load calculation function of the ith extraction condensing unit, respectively; and are the minimum thermal load calculation function and the maximum thermal load calculation function of the ith extraction condensing unit, respectively; and are the minimum electrical load and the maximum electrical load of the jth backpressure unit on the feasible region, respectively.

[0031] Further, step 3 specifically includes:

[0032] The candidate solution x of the heuristic optimization algorithm is composed of the electrical load and the thermal load of the extraction condensing unit and the electrical load of the backpressure unit, which can be expressed as equation (10):

[0033]

[0034] The correction operation of the candidate solution x is as follows:

[0035] Step 3.1, according to the thermal power unit operation constraint condition 1, the electrical load of all units is corrected, the method is:

[0036]

[0037] Step 3.2, check whether the corrected unit electrical load meets the constraint condition 3, and correct the unit electrical load that does not meet the constraint condition 3;

[0038] Step 3.3, calculate the minimum heat load of the i-th extraction condensing unit And the maximum heat load Calculate the heat load of the j-th back pressure unit

[0039] Step 3.4, according to the thermal power unit operation constraint condition 2, the extraction condensing unit heat load is corrected, the method is:

[0040]

[0041] Step 3.5, check whether the corrected extraction condensing unit heat load meets the constraint condition 3, and correct the extraction condensing unit heat load that does not meet the constraint condition 3.

[0042] Further, the specific method of step 3.2 is:

[0043] Step 3.2.1, traverse all unit electrical loads, check whether the extraction condensing unit electrical load meets formula (14) and the back pressure unit electrical load meets formula (9), set the parameter Used to record the P i C Number that does not meet formula (14), set the parameter Used to record the Number that does not meet formula (9), set the parameter

[0044]

[0045] In the formula, And The minimum electrical load and the maximum electrical load of the i-th extraction condensing unit on the feasible region respectively;

[0046] Step 3.2.2, traverse all extraction condensing unit electrical loads, for the i-th extraction condensing unit If And ΔP D ≥ 0, then let If And ΔP D ≤ 0, then let Otherwise, do not change Pi C ; set parameter for recording the changed P i C number;

[0047] Step 3.2.3, traverse all back pressure unit electric loads, for the jth back pressure unit If and ΔP D ≥ 0, then let If and ΔP D ≤ 0, then let Otherwise, do not change set parameter for recording the changed P number;

[0048] Step 3.2.4, set parameter

[0049] Step 3.2.5, correct the unit electric loads which are not changed in step 3.2.2 and step 3.2.3, the method is:

[0050]

[0051] Step 3.2.6, traverse all unit electric loads, check whether the extraction condensing unit electric load satisfies formula (14) and whether the back pressure unit electric load satisfies formula (9), set parameter for recording the P i C number, set parameter for recording the P number, set parameter If L1 is equal to 0, execute step 3.3, otherwise return to step 3.2.2.

[0052] Further, the specific method of step 3.5 is:

[0053] Step 3.5.1, traverse all extraction condensing unit heat loads, check whether it satisfies formula (8), set parameter for recording the P number;

[0054] Step 3.5.2, traverse all extraction condensing unit heat loads, for the ith extraction condensing unit If and ΔQ D ≥ 0, then let If and ΔQ D≤ 0, then let Otherwise, do not change Set parameter for recording the number of times of changing the thermal load of the extraction condensing unit in this step Number;

[0055] Step 3.5.3, correct the thermal load of the extraction condensing unit which is not changed in step 3.5.2, by the following method:

[0056]

[0057] Step 3.5.4, traverse all the thermal loads of the extraction condensing units, check whether formula (8) is satisfied, set parameter for recording the number of times of not satisfying formula (8); if is equal to 0, complete the correction operation of the candidate solution, otherwise return to step 3.5.2.

[0058] Further, step 4 specifically comprises the following steps:

[0059] Step 4.1, according to the thermal-electric load distribution model of the thermal power unit considering the cogeneration mode established in step 1, set the parameters of the heuristic optimization algorithm, and take the total fuel cost F of the multiple thermal power units with cogeneration as the fitness of the heuristic optimization algorithm;

[0060] Step 4.2, perform the initialization operation of the candidate solution:

[0061] Step 4.3, perform the iterative optimization operation of the candidate solution:

[0062] Step 4.4, output the optimal candidate solution and its fitness.

[0063] Further, the specific method of step 4.2 is as follows:

[0064] Step 4.2.1, randomly generate N candidate solutions based on the feasible region of the thermal-electric load of the multiple thermal power units with cogeneration;

[0065] Step 4.2.2, perform step 3 correction operation on each candidate solution;

[0066] Step 4.2.3, calculate the fitness of each corrected candidate solution;

[0067] Step 4.2.4, sort the candidate solutions according to the fitness, and determine the optimal candidate solution.

[0068] Further, the specific method of step 4.3 is as follows:

[0069] Step 4.3.1, disturb and update each candidate solution;

[0070] ​Step 4.3.2, performing step 3 correction operation on each candidate solution;

[0071] Step 4.3.3, calculating the fitness of each corrected candidate solution;

[0072] Step 4.3.4, ranking the candidate solutions according to the fitness, determining the optimal candidate solution;

[0073] Step 4.3.5, judging whether the iteration optimization termination condition is reached, if not, returning to execute step 4.3.1, and if yes, terminating the iteration optimization operation.

[0074] Further, in steps 3 and 4, the heuristic optimization algorithm includes but is not limited to particle swarm optimization algorithm, genetic algorithm, ant colony optimization algorithm, water wave optimization algorithm.

[0075] The present application has beneficial effects:

[0076] The present application provides a thermal power unit thermal and electric load optimization distribution method considering cogeneration mode, under the conditions of meeting system electric load balance constraint and thermal load balance constraint, unit thermal and electric load feasible region constraint, optimizing distribution of electric load and thermal load of each thermal power unit in cogeneration system, realizing lowest total fuel cost of multiple cogeneration thermal power units.

[0077] (1) Compared with the prior art, the method has the advantages that, on the one hand, the operation characteristics of the thermal power unit under different cogeneration modes are specially considered, the calculation accuracy of the optimal solution is improved; on the other hand, the heuristic optimization algorithm is improved according to the operation constraint conditions of the thermal power unit, the candidate solution correction operation is added in the calculation process, and the calculation speed of the thermal and electric load optimization distribution model of the thermal power unit is improved.

[0078] (2) The present application models the thermal power unit load optimization distribution problem in reality under different cogeneration modes, uses the improved heuristic optimization algorithm to solve the nonlinear non-convex optimization problem, and provides an effective solution for the thermal and electric optimization load distribution of the thermal power unit.

[0079] (3) The present application considers the operation characteristics of the thermal power unit under different cogeneration modes in the thermal and electric load optimization distribution model of the thermal power unit, models the energy consumption characteristics and thermal and electric load regulation characteristics of the thermal power unit under the extraction condensing and back pressure two cogeneration modes, improves the accuracy of the optimal solution of the thermal and electric load optimization distribution model of the thermal power unit, is conducive to reducing the total fuel cost of the cogeneration system, and improves the resource utilization rate.

[0080] (4) In the calculation process of solving the thermal power load optimization allocation problem of thermal power units by heuristic optimization algorithm, the present invention introduces candidate solution correction operation. Compared with the traditional method of passively handling constraints by using penalty factors, the improved heuristic optimization algorithm can ensure that the corrected candidate solution fully satisfies the operating constraints of thermal power units, which greatly improves the convergence speed and calculation efficiency of solving the thermal power load optimization allocation problem of thermal power units. Attached Figure Description

[0081] Figure 1 This is a schematic diagram of the process for optimizing the allocation of thermal power load in thermal power units that takes into account the combined heat and power (CHP) mode, according to the present invention.

[0082] Figure 2 This is a comparison diagram of the feasible regions of thermal power load for the six thermal power units in the embodiment.

[0083] Figure 3 This is a comparison chart of the convergence curves of the improved water wave optimization algorithm and the traditional water wave optimization algorithm in the embodiments. Detailed Implementation

[0084] The present invention will be further described in detail below with reference to the accompanying drawings.

[0085] This embodiment uses the optimized allocation of heat and power load in a cogeneration system with 6 thermal power units as an example. Four units operate in extraction-condensing cogeneration mode, and two units operate in back-pressure cogeneration mode. The electrical load demand P of the cogeneration system is... D =2530MW, heat load demand Q D = 4250 GJ / h. This embodiment uses the water wave optimization algorithm as a specific example of a heuristic optimization algorithm for illustration.

[0086] like Figure 1 As shown, a method for optimizing the allocation of thermal power load in thermal power units considering combined heat and power (CHP) mode specifically includes the following steps:

[0087] Step 1: Establish an optimal allocation model for thermal power load of thermal power units that takes into account the cogeneration mode, based on the composition of the cogeneration system.

[0088] The total fuel cost of multiple cogeneration thermal power units is selected as the objective function. Considering the difference in energy consumption characteristics of thermal power units under the two cogeneration modes of extraction condensation and back pressure, the objective function is shown in Equation (1).

[0089]

[0090] In the formula, F represents the total fuel cost of multiple cogeneration thermal power units; i is the number of the extraction condensing unit; and I is the number of extraction condensing units. represents the fuel cost of the i-th extraction condensing unit; j is the unit number; J is the number of extraction condensing units. represents the fuel cost of the jth back pressure unit.

[0091] When the thermal power unit operates in the extraction condensing cogeneration mode, the electric load can be adjusted within a certain range under the condition of constant thermal load, and the thermal load can also be adjusted within a certain range under the condition of constant electric load. The fuel consumption of the unit is jointly determined by the electric load and the thermal load. Therefore, which can be expressed as formula (2).

[0092]

[0093] In the formula, f i C is the fuel consumption characteristic function of the ith extraction condensing unit; P i C is the electric load of the ith extraction condensing unit. is the thermal load of the ith extraction condensing unit.

[0094] When the thermal power unit operates in the back pressure cogeneration mode, the thermal load and the electric load of the unit are in a one-to-one correspondence, and the fuel consumption of the unit can be determined only by the electric load. Therefore, which can be expressed as formula (3).

[0095]

[0096] In the formula, is the fuel consumption characteristic function of the jth back pressure unit; is the electric load of the jth back pressure unit.

[0097] Step 2: Establish the operation constraint condition of the thermal power unit considering the cogeneration mode according to the operation requirements of the cogeneration system and the operation characteristics of the thermal power unit.

[0098] (2.1) The sum of the electric loads of all thermal power units should be balanced with the electric load demand of the cogeneration system within the distribution range, therefore, the operation constraint condition 1 of the thermal power unit is established, as shown in formula (4).

[0099]

[0100] (2.2) The sum of the thermal loads of all thermal power units should be balanced with the thermal load demand of the cogeneration system within the distribution range, therefore, the operation constraint condition 2 of the thermal power unit is established, as shown in formula (5).

[0101]

[0102] In the formula, Q D is the thermal load demand of the cogeneration system; The thermal load of the jth back pressure unit is the thermal load of the thermal power unit in the back pressure combined heat and power mode, the thermal load and the electric load of the unit are in one-to-one correspondence, therefore, which can be expressed as formula (6).

[0103]

[0104] In the formula, is the thermal load calculation function of the jth back pressure unit.

[0105] (2.3) Each thermal power unit must have both the electric load and the thermal load in the thermal-electric load feasible region when it is in normal operation. If the unit is not operated in the thermal-electric load feasible region, the excessive load will accelerate the aging of the high-temperature and high-pressure equipment of the unit, shorten the service life of the unit, or the too small load will lead to unstable combustion in the boiler, overheating of the low-pressure cylinder impeller and blade of the steam turbine and other safety problems.

[0106] Therefore, the operation constraint condition 3 of the thermal power unit is established, as shown in formula (7)-(9).

[0107]

[0108] In the formula, and are the minimum electric load calculation function and the maximum electric load calculation function of the ith extraction condensing unit, respectively; and are the minimum thermal load calculation function and the maximum thermal load calculation function of the ith extraction condensing unit, respectively; and are the minimum electric load and the maximum electric load of the jth back pressure unit on the feasible region, respectively.

[0109] The parameters of each extraction condensing unit and back pressure unit are determined by Figure 2 The parameters of the extraction condensing unit are shown in Table 1, and the parameters of the back pressure unit are shown in Table 2.

[0110] Table 1

[0111]

[0112]

[0113] Table 2

[0114]

[0115] Step 3, improve the water wave optimization algorithm according to the operation constraint condition of the thermal power unit.

[0116] In the step 1 of the water wave optimization algorithm, the candidate solution x is composed of the electric load and the thermal load of the extraction condensing unit, the electric load of the back pressure unit, and can be expressed as formula (10).

[0117]

[0118] In the process of water wave initialization and iterative optimization (propagation, refraction, and breaking wave operation), random numbers are used to change the value of variables in the water wave x, which is easy to cause the water wave x not to meet the operation constraints of the thermal power unit in step 2. Therefore, the water wave optimization algorithm is improved, and the correction operation is performed in the process of candidate solution initialization and iterative optimization (propagation, refraction, and breaking wave operation), so as to ensure that the corrected water wave x can fully meet the operation constraints of the thermal power unit. The correction operation of the water wave x is as follows:

[0119] (3.1) According to the operation constraint condition 1 of the thermal power unit, the electric load of all units is corrected, and the method is as follows:

[0120]

[0121] (3.2) Check whether the corrected electric load of the unit meets the constraint condition 3, and correct the electric load of the unit that does not meet the constraint condition 3, and the method is as follows:

[0122] (3.2.1) Traverse all the electric loads of the units, check whether the electric load of the extraction condensing unit meets formula (14) and whether the electric load of the back pressure unit meets formula (9), and set the parameters for recording the number of P i C that does not meet formula (14), set the parameters for recording the number of that does not meet formula (9), set the parameters

[0123]

[0124] In the formula, and are the minimum electric load and the maximum electric load of the i-th extraction condensing unit on the feasible region respectively.

[0125] (3.2.2) Traverse all the electric loads of the extraction condensing units, and for the i-th extraction condensing unit If and ΔP D ≥ 0, then let If and ΔP D ≤ 0, then let Otherwise, do not change P iC Set parameters Set parameters i Set parameters C Set parameters Set parameters

[0126] (3.2.3) Traverse all back pressure unit electric loads, for the jth back pressure unit If and ΔP D ≥ 0, then let If and ΔP D ≤ 0, then let Otherwise, do not change Set parameters Set parameters Set parameters Set parameters

[0127] (3.2.4) Set parameters

[0128] (3.2.5) Correct the unit electric loads that have not been changed in steps (3.2.2) and (3.2.3), by the method:

[0129]

[0130] (3.2.6) Traverse all unit electric loads, check whether the extraction condensing unit electric load satisfies equation (14) and the back pressure unit electric load satisfies equation (9), set parameters Set parameters i C Set parameters Set parameters Set parameters If L1 is equal to 0, execute step (3.3), otherwise return to step (3.2.2).

[0131] (3.3) Calculate the minimum heat load and the maximum heat load Calculate the heat load of the jth back pressure unit

[0132] (3.4) Correct the extraction condensing unit heat load according to the thermal power plant operation constraint condition 2, by the method:

[0133]

[0134] (3.5) Check whether the corrected extraction condensing unit heat load satisfies the constraint condition 3, correct the extraction condensing unit heat load that does not satisfy the constraint condition 3, by the method:

[0135] (3.5.1) Traverse all extraction condensing unit heat loads, check if formula (8) is met, set parameter for recording the number of extraction condensing unit heat loads not meeting formula (8).

[0136] (3.5.2) Traverse all extraction condensing unit heat loads, for the i-th extraction condensing unit if and ΔQ D ≥ 0, let if and ΔQ D ≤ 0, let otherwise, do not change set parameter for recording the number of extraction condensing unit heat loads changed in this step.

[0137] (3.5.3) Correct the extraction condensing unit heat loads not changed in step (3.5.2), the method is:

[0138]

[0139]

[0140] (3.5.4) Traverse all extraction condensing unit heat loads, check if formula (8) is met, set parameter for recording the number of extraction condensing unit heat loads not meeting formula (8). If is equal to 0, complete the correction operation of candidate solution, otherwise return to step (3.5.2).

[0141] Step 4, use the improved water wave optimization algorithm to optimize the thermal and electrical load distribution of thermal power units.

[0142] (4.1) According to the thermal and electrical load distribution model of thermal power units considering combined heat and power mode established in step 1, set the parameters of the improved water wave optimization algorithm: maximum iteration number t max = 250, water wave number N = 50, water wave height initial value h max = 12, variable number in each water wave D = 10, wave length of each water wave broken wave coefficient β = 0.15, wave length attenuation coefficient α = 1.00026, zero division coefficient ε = 0.00001.

[0143] (4.2) Perform the initialization operation of candidate solution, i.e. water wave x:

[0144] (4.2.1) Randomly generate water wave x based on the feasible region of thermal and electrical load of multiple combined heat and power thermal power units n ​​​ The method is:

[0145]

[0146] where rand(0,1) is a function that generates a uniformly distributed random number in the range [0,1].

[0147] (4.2.2) Traverse all water waves, and for water wave Perform step 3 correction operation.

[0148] (4.2.3) Traverse all corrected water waves, and for water wave Bring into equations (1), (2), (3) to calculate the total fuel cost F as the fitness f(x n ) of water wave x n .

[0149] (4.2.4) Sort all water waves according to fitness, and assign the water wave with the smallest fitness to the optimal solution x * . Set the current iteration number t = 1.

[0150] (4.3) Traverse all water waves, and for water wave Perform iteration optimization operation:

[0151] (4.3.1) Perform propagation operation, update each dimension variable of water wave x n to generate new water wave x n ', the method is:

[0152]

[0153] where d is the number of variables in water wave x n ; D is the number of variables in water wave x n ; x n (d) is the value of the d-th dimension variable of water wave x n ; x n '(d) is the value of the d-th dimension variable of new water wave x n '; rand(-1,1) is a function that generates a uniformly distributed random number in the range [-1,1]; λ(x n ) is the wavelength of water wave x n (preferably initially set to 0.5); L(d) is the length of the search space of the d-th dimension variable.

[0154] (4.3.2) Perform step 3 correction operation on water wave x n .

[0155] (4.3.3) Bring water wave x n into equations (1), (2), (3) to calculate the total fuel cost F as the fitness f(x n ) of new water wave x n .n The fitness of f(x) n '). If f(x) n ')<f(x n If the condition is met, proceed to step (4.3.4); otherwise, proceed to step (4.3.5).

[0156] (4.3.4) The water wave x n and its fitness f(x) n Replace each with a new water wave x n 'and its fitness f(x) n '), and will water waves x n The wave height h(x n Reset to the initial value h max If f(x) n )<f(x * If the optimal solution x is obtained, then the optimal solution x will be obtained. * and its fitness f(x) * Replace each with x n and its fitness f(x) n ), and for the optimal solution x * Perform the wave-breaking operation:

[0157] (4.3.4.1) In the optimal solution x * Randomly select k-dimensional (1≤k≤D) variables, and update each selected variable according to the formula to generate k new water waves x. * The method is as follows:

[0158] x * '(d)=x * (d)+β·N(0,1)·L(d)

[0159] In the formula, β is the wave break coefficient; N(μ,σ) is a Gaussian random number with mean μ and variance σ.

[0160] (4.3.4.2) For each of the k new water waves x * Perform step 3 to correct the operation.

[0161] (4.3.4.3) Each of the k new water waves x * Substitute equations (1), (2), and (3) to calculate the total fuel cost F, and use this as the new water wave x. * The fitness of f(x) * '). If f(x) * ')<f(x * If the optimal solution x is obtained, then the optimal solution x will be obtained. * and its fitness f(x) * Replace each with x * 'and its fitness f(x) * ').

[0162] (4.3.5) Update the water wave x n by subtracting 1 from the wave height h(x n ). If h(x n ) becomes 0, perform a refraction operation on the water wave x n :

[0163] (4.3.5.1) Update each dimension variable of the water wave x n by:

[0164]

[0165] (4.3.5.2) Perform step 3 correction operation on the water wave x n .

[0166] (4.3.5.3) Put the water wave x n into equations (1), (2), (3) to calculate the total fuel cost F as the fitness f(x n ) of the water wave x n . Reset the wave height h(x n ) of the water wave x n to the initial value h max .

[0167] (4.4) Traverse all water waves to determine the minimum value f min and the maximum value f max of all water wave fitnesses, and update the wavelength of each water wave by:

[0168]

[0169] where a is the wavelength attenuation coefficient; and e is the zero division coefficient.

[0170] (4.5) Determine whether the iteration optimization termination condition is reached, i.e., whether t > t max is true. If not, increase the iteration number t by 1 and return to perform step (4.3) to enter the next iteration. If t > t max is true, perform step (4.6).

[0171] (4.6) Output the optimal solution x * and its fitness f(x * ).

[0172] The improved water wave optimization algorithm and the traditional water wave optimization algorithm (using a penalty factor to passively handle the constraint condition) are respectively used to solve the thermoelectric load optimization allocation model, wherein the water wave optimization algorithm parameters are set as: the maximum iteration number t max = 250, the water wave number N = 50, the water wave wave height initial value h max = 1, the wavelength attenuation coefficient a = 0.1, and the zero division coefficient e = 0.0001.max =12, the variable number in each water wave D=10, the wavelength of each water wave λ=0.5, the broken wave coefficient β=0.15, the wavelength attenuation coefficient α=1.00026, the zero division coefficient ε=0.00001. The comparison of the solving results of the improved water wave optimization algorithm and the traditional water wave optimization algorithm is shown in Table 3. It can be seen that the total fuel cost corresponding to the load optimization distribution result of the improved water wave optimization algorithm is less than that of the traditional water wave optimization algorithm, indicating that the optimal solution solved by the improved water wave optimization algorithm is more accurate. When the improved water wave optimization algorithm is applied to a larger scale of thermal power load optimization distribution problem, its effect will be more obvious, which will be conducive to reducing the total fuel cost of the combined heat and power system and improving the energy utilization efficiency. It is shown that the method for optimizing the thermal power load distribution of the thermal power unit considering the combined heat and power mode can effectively solve the thermal power load optimization distribution problem of the thermal power unit containing complex equality constraints and inequality constraints.

[0173] Table 3

[0174]

[0175]

[0176] The convergence curves of the solving processes of the improved water wave optimization algorithm and the traditional water wave optimization algorithm are compared as shown in Figure 3 It can be seen that the convergence speed of the improved water wave optimization algorithm is faster and the total fuel cost is lower, further indicating that the method for optimizing the thermal power load distribution of the thermal power unit considering the combined heat and power mode has more advantages in solving the thermal power load optimization distribution problem of the thermal power unit containing complex equality constraints and inequality constraints.

[0177] The above is only the preferred embodiment of the present application, and is not intended to limit the present application in other forms. Any person skilled in the art can modify or change the above disclosed technical content to equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made on the basis of the technical essence of the present application to the above embodiments still belongs to the protection scope of the present application technical solution.

Claims

1. A method for optimal distribution of thermal and electric loads of a thermal power unit considering a cogeneration mode, characterized in that: Specifically comprising the following steps: Step 1, establishing a thermal power unit thermal power load distribution model considering cogeneration mode; Step 2, establishing a thermal power unit operation constraint condition considering cogeneration mode; Step 3, improving the heuristic optimization algorithm according to the thermal power unit operation constraint condition; Step 4, optimizing the thermal power load distribution of the thermal power unit by using the improved heuristic optimization algorithm.

2. The method of claim 1, wherein the method is characterized in that: Step 1 specifically comprises the following steps: The total fuel cost of multiple cogeneration thermal power units is selected as the objective function, and the difference in energy consumption characteristics of the thermal power unit in the extraction condensing and back pressure two cogeneration modes is considered, and the objective function is as shown in formula (1), In the formula, F represents the total fuel cost of multiple combined heat and power thermal power generating units; i is the number of extraction condensing units; I is the number of extraction condensing units; represents the fuel cost of the i th extraction condensing unit; j is the number of extraction condensing units; J is the number of extraction condensing units; represents the fuel cost of the j th back pressure unit. may be represented as formula (2), In the formula, f i C is the fuel consumption characteristic function of the i-th extraction condensing unit; P i C is the electric load of the i-th extraction condensing unit; is the thermal load of the i-th extraction condensing unit; may be represented as formula (3), In the formula, is the fuel consumption characteristic function of the jth back pressure unit; is the electric load of the jth back pressure unit.

3. The method of claim 1 or 2, wherein the method further comprises: The constraint conditions established in step 2 include: Constraint 1, as shown in equation (4): In the formula, P D is the electrical load demand of the cogeneration system; Constraint 2, as shown in equation (5): In the formula, Q D is the heat load demand of the cogeneration system; is the heat load of the jth back pressure unit, when the thermal power unit is operated in the back pressure cogeneration mode, the heat load and the electric load of the unit are in a one-to-one correspondence, therefore, can be expressed as formula (6): In the formula, is the heat load calculation function of the jth back pressure unit. Constraint condition 3, as shown in formulas (7)-(9): wherein, and are the minimum and maximum electrical load calculation functions of the i-th extraction condensing unit, respectively; and are the minimum and maximum thermal load calculation functions of the i-th extraction condensing unit, respectively; and are the minimum and maximum electrical load of the j-th back pressure unit on the feasible region, respectively.

4. The method of claim 3, wherein the method further comprises: Step 3 specifically includes: The candidate solution x of the heuristic optimization algorithm is composed of the electric load and thermal load of the extraction condensing unit and the electric load of the back pressure unit, which can be represented by formula (10); The correction operation of the candidate solution x is as follows: Step 3.1, according to the thermal power unit operation constraint condition 1, the electric load of all units is corrected, and the method is: Step 3.2, check whether the corrected unit electric load meets the constraint condition 3, and correct the unit electric load that does not meet the constraint condition 3; Step 3.

3. Calculate the minimum heat duty of the ith extraction condenser and the maximum heat duty Calculate the heat duty of the jth back pressure turbine Step 3.4, according to the thermal power unit operation constraint condition 2, the thermal load of the extraction condensing unit is corrected, and the method is: Step 3.5, check whether the corrected thermal load of the extraction condensing unit meets the constraint condition 3, and correct the thermal load of the extraction condensing unit that does not meet the constraint condition 3.

5. The method of claim 4, wherein the method further comprises: The specific method of step 3.2 is: Step 3.2.1, traverse all the unit electric loads, check whether the extraction condensing unit electric load satisfies formula (14), whether the back pressure unit electric load satisfies formula (9), set parameters For recording P i C Number, set parameters For recording Number, set parameters wherein, and and are the minimum and maximum electrical load of the i-th extraction unit on the feasible region, respectively. Step 3.2.2, iterate over all extraction condensing unit electrical loads, for the i-th extraction condensing unit If and ΔP D ≥ 0, then let If and ΔP D ≤ 0, then let Otherwise, do not change P i C ; Setting parameters for recording the P i C Number; Step 3.2.3, traverse all backpressure units electrical load, for the jth backpressure unit If and ΔP D ≥ 0, then let If and ΔP D ≤ 0, then let Otherwise, do not change Set parameter for recording the number of changed in this step; Step 3.2.4, Set Parameters Step 3.2.5, the unit electric load that has not been changed in step 3.2.2 and step 3.2.3 is corrected, and the method is: Step 3.2.6, traverse all the unit electric loads, check whether the extraction condensing unit electric load satisfies formula (14), whether the back pressure unit electric load satisfies formula (9), set parameters For recording P i C Number, set parameters For recording P Number, set parameters If L1 is equal to 0, then execute step 3.3, otherwise return to step 3.2.

2.

6. The method of claim 4, wherein the method further comprises: The specific method of step 3.5 is: Step 3.5.1, iterate through all extraction condensing unit heat loads, check if equation (8) is satisfied, set parameter for recording the number of times equation (8) is not satisfied number of times; Step 3.5.2, iterate over all extraction condensing units heat loads, for the i-th extraction condensing unit If and AQ D ≥ 0, then let If and AQ D ≤ 0, then let Otherwise, do not change Set parameter to record the number of times changed in this step ; Step 3.5.3, the thermal load of the extraction condensing unit that has not been changed in step 3.5.2 is corrected, and the method is: Step 3.5.4, traverse all extraction condensing units heat load, check if it satisfies equation (8), set parameter for recording the candidate solution which does not satisfy equation (8) number; if equal to 0, complete the modification operation of candidate solution, otherwise return to step 3.5.

2.

7. The method of claim 1, wherein the method further comprises: Step 4 specifically includes the following steps: Step 4.1, according to the thermal power unit thermal power load distribution model considering cogeneration mode established in step 1, set the parameters of the heuristic optimization algorithm, and take the total fuel cost F of multiple cogeneration thermal power units as the fitness of the heuristic optimization algorithm; Step 4.2, perform the initialization operation of the candidate solution: Step 4.3, perform the iterative optimization operation of the candidate solution: Step 4.4, output the optimal candidate solution and its fitness.

8. The method of claim 7, wherein the method further comprises: The specific method of step 4.2 is: Step 4.2.1, randomly generate N candidate solutions based on the thermal power load feasible region of multiple cogeneration thermal power units; Step 4.2.2, perform the correction operation of step 3 for each candidate solution; Step 4.2.3, calculate the fitness of each corrected candidate solution; Step 4.2.4, sort the candidate solutions according to the fitness to determine the optimal candidate solution.

9. The method of claim 7, wherein the method further comprises: The specific method of step 4.3 is: Step 4.3.1, disturb and update each candidate solution; Step 4.3.2, perform the correction operation of step 3 for each candidate solution; Step 4.3.3, calculate the fitness of each corrected candidate solution; Step 4.3.4, sort the candidate solutions according to the fitness to determine the optimal candidate solution; Step 4.3.5, judging whether the iteration optimization termination condition is reached, if not, returning to execute step 4.3.1, and if yes, terminating the iteration optimization operation.

10. The method of claim 1, wherein the method further comprises: In steps 3 and 4, the heuristic optimization algorithm includes but is not limited to a particle swarm optimization algorithm, a genetic algorithm, an ant colony optimization algorithm, and a water wave optimization algorithm.

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