Power load dispatching optimization method

Abstract

The invention discloses a power load scheduling optimization method, which overcomes the problems of high economic cost and premature convergence of a traditional power load scheduling scheme model in the prior art, and comprises the following steps: obtaining power load parameters and original parameters of power generation equipment; determining Jaya algorithm parameters, and establishing a BHM model based on the Jaya algorithm to obtain a B-Jaya optimization algorithm; establishing a power load dispatching model by taking the minimum power load dispatching cost as an objective function; establishing constraint conditions for stable operation of the power generation equipment; and solving the power load dispatching model by adopting a B-Jaya optimization algorithm to obtain a power load dispatching scheme. The original Jaya algorithm is combined with the black hole model (BHM), the diversity of power load dispatching solutions is expanded, the total cost of power load dispatching is reduced, the convergence speed is increased, and good robustness is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric load dispatching, and in particular to an electric load dispatching optimization method. Background Art

[0002] The dispatching problem of power load refers to a dispatching method that reasonably utilizes energy and equipment to ensure reliable power supply to users at the lowest power generation cost or fuel cost under the premise of meeting safety and power quality. The intelligent algorithms used to solve the power load dispatching problem in the prior art are generally relatively old, such as genetic algorithms or particle swarm algorithms. The Jaya algorithm is a powerful heuristic algorithm proposed in 2016. The Jaya algorithm only requires two basic parameters, and its working mechanism is easy to understand and apply. Therefore, it can relatively easily solve various engineering optimization problems, such as the orderly charging and discharging of electric vehicles, the multiple dispatching problem of job shops, and the optimal compensator problem of photovoltaics.

[0003] Therefore, the Jaya algorithm began to be used to solve the problem of power load scheduling. For example, on January 25, 2021, the China Patent Office published an invention patent named an integrated energy system planning method based on an improved Jaya algorithm, with a publication number of CN113971510A. It provides an integrated energy system planning method based on the Jaya algorithm, solves the integrated energy system model more accurately, and provides a more instructive integrated energy system planning scheme. However, if only the Jaya algorithm is used, the model has a high economic cost and there is a problem of premature convergence. Summary of the invention

[0004] The purpose of the present invention is to overcome the problems of high economic cost and premature convergence of traditional power load scheduling scheme models in the prior art, and to provide a power load scheduling optimization method, which combines the original Jaya algorithm with the black hole model (BHM), expands the diversity of power load scheduling solutions, reduces the total cost of power load scheduling, improves the convergence speed, and has good robustness.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: A method for optimizing power load scheduling, characterized in that it comprises the following steps: S1: Obtain power load parameters and original parameters of power generation equipment; S2: Determine the parameters of the Jaya algorithm, establish the BHM model based on the Jaya algorithm, and obtain the B-Jaya optimization algorithm; S3: Taking the minimum power load dispatching cost as the objective function, establish a power load dispatching model; S4: Establish constraints for stable operation of power generation equipment; S5: Use the B-Jaya optimization algorithm to solve the power load scheduling model and obtain the power load scheduling plan.

[0006] The present invention provides a power load dispatch optimization method, which combines the original Jaya algorithm with the black hole model (BHM), expands the diversity of power load dispatch solutions, and avoids the premature convergence problem in the process of solving the power load economic dispatch problem. On this basis, a power load dispatch model is constructed with the lowest dispatch cost as the goal, and then the B-Jaya optimization algorithm is used to solve the mathematical model, which reduces the total cost of power load dispatch, improves the convergence speed, and has good robustness.

[0007] Preferably, the B-Jaya optimization algorithm is used to solve the power load dispatching model, including: S5.1: Initialize the power generation of each generator and determine the initial solution set X; S5.2: Calculate the objective function value corresponding to the initial solution set X and set the initial value of the number of iterations; S5.3: Sort the initial solution set X and determine the global optimal solution and the global worst solution; S5.4: According to the iteration rule of the optimization algorithm B-Jaya, perform X iterations on the initial solution set and update the global optimal solution and the global worst solution; S5.5: Update the number of iterations and determine whether the termination condition is met. If so, output the objective function value and the global optimal solution. If not, return to S5.3.

[0008] Preferably, the iteration rules of the B-Jaya optimization algorithm include: If the random coefficient corresponding to each dimension of the current optimal solution is less than or equal to the probability threshold, the updated value of the sth variable of the rth candidate solution in the t+1th iteration is the sum of the product of the radius of the area where the randomly generated particle is located and the coefficient α and the current optimal solution; If the random coefficient corresponding to each dimension of the current optimal solution is greater than the probability threshold, the updated value of the sth variable of the rth candidate solution after the t+1th iteration is the value of the sth variable of the rth candidate solution after the tth iteration plus the product of the first random coefficient and the difference between the absolute values ​​of the sth variable of the current optimal solution and the rth candidate solution in the tth iteration minus the product of the second random coefficient and the difference between the absolute values ​​of the sth variable of the current worst solution and the rth candidate solution in the tth iteration.

[0009] Preferably, the constraints include that the difference between the current output power of the generator and the previous output power of the generator is less than or equal to the rising limit of the current output power of the generator, and the difference between the previous output power of the generator and the current output power of the generator is less than or equal to the falling limit of the current output power of the generator.

[0010] Preferably, the constraint conditions include that the generator output power is greater than or equal to the minimum value of the generator output power and is less than or equal to the lower limit of the first prohibited operation area of ​​the generator; the generator output power is greater than or equal to the upper limit of the g-1th prohibited operation area of ​​the generator and is less than or equal to the lower limit of the gth prohibited operation area of ​​the generator; the generator output power is greater than or equal to the upper limit of the Zith prohibited operation area of ​​the generator and is less than or equal to the maximum value of the generator output power, Zi is the total number of prohibited operation areas of the generator.

[0011] Preferably, the power load dispatching cost includes the sum of the power generation costs of all generators, and the power generation cost of each generator is the product of the square of the generator output power and the first cost coefficient plus the product of the generator output power and the second cost coefficient plus the third cost coefficient plus the generator rectifier sinusoidal component.

[0012] Preferably, the generator rectification sinusoidal component includes: calculating the difference between the minimum generator power generation and the generator output power, and calculating the sine value of the product of the difference and the second cost coefficient of the generator valve point effect; calculating the product of the sine value and the first cost coefficient of the generator valve point effect to obtain the generator rectification sinusoidal component.

[0013] Preferably, an initial solution set X is constructed based on the constraints of the decision variables and the solution set size pop of the B-Jaya algorithm.

[0014] Preferably, the initial solution set X is sorted according to the size of the objective function value, the global optimal solution is the solution with the smallest objective function value in the initial solution set X, and the global worst solution is the solution with the largest objective function value in the initial solution set X.

[0015] Preferably, the iteration of the initial solution set X includes: if the objective function value of the new generation solution set is better than the objective function value of the previous generation solution set, the initial solution set X is replaced, otherwise, the initial solution set X is kept unchanged; according to the size of the objective function value of the new generation solution set, the new generation solution set is sorted, and the global optimal solution and the global worst solution are updated according to the sorting result.

[0016] Therefore, the present invention has the following beneficial effects: 1. Combining the original Jaya algorithm with the black hole model (BHM), a diversity-enlarged B-Jaya algorithm is proposed, which improves the convergence speed of the algorithm and makes the algorithm more practical; 2. The B-Jaya optimization algorithm is applied to the power load scheduling model to avoid the premature convergence problem that occurs in the process of solving the model. On this basis, the power load angle model is constructed with the minimum power load scheduling cost as the objective function, and the B-Jaya optimization algorithm is used to solve the mathematical model. The model has lower economic cost, faster convergence speed and better robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of the steps of the power load scheduling optimization method in the present invention.

[0018] Figure 2 The present invention is a flowchart of the steps of using the B-Jaya optimization algorithm to solve the power load scheduling model. DETAILED DESCRIPTION

[0019] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments: Embodiment 1: like Figure 1 In the illustrated embodiment, a method for optimizing power load scheduling can be seen, and its operation process is as follows: step one, obtaining power load parameters and original parameters of power generation equipment; step two, determining Jaya algorithm parameters, establishing a BHM model based on the Jaya algorithm, and obtaining a B-Jaya optimization algorithm; step three, establishing a power load scheduling model with the minimum power load scheduling cost as the objective function; step four, establishing constraint conditions for the stable operation of power generation equipment; step five, using the B-Jaya optimization algorithm to solve the power load scheduling model and obtain a power load scheduling plan.

[0020] This embodiment provides a method for optimizing power load scheduling, which applies the B-Jaya optimization algorithm to the scheduling model of power loads. The BHM model is committed to expanding the diversity of solutions and avoiding premature convergence problems in the process of solving power load scheduling problems. At the same time, since the B-Jaya algorithm does not require the complexity and continuous differentiability of the objective function, it is very convenient to solve the scheduling problem of power loads. On this basis, the power load angle model is constructed with the minimum power load scheduling cost as the objective function, and then the B-Jaya algorithm is used to solve the mathematical model. It can be concluded that under the same parameter setting conditions, the B-Jaya algorithm is more competitive than the original Jaya algorithm in terms of model economic cost, convergence speed and robustness, and can provide more references and guidance for solving the economic scheduling problem of power loads.

[0021] The technical scheme and technical effects of the present invention will be further described below through specific examples. The following examples are explanations of the present invention and the present invention is not limited to the following examples.

[0022] Step 1: Input the original parameters such as the load parameters of a certain urban area, the relevant cost coefficient of the generator, etc.

[0023] Step 2: Input the Jaya algorithm parameters, including the solution set size pop and the maximum number of iterations Max.

[0024] The principle of Jaya algorithm is as follows: Assume that the objective function f(x) is required to be minimized or maximized, and assume that the number of variables required is N var , where the parameter s∈{1,2,...,N var}, assuming the maximum number of iterations is N iter , where the parameter t∈{1,2,...,N iter}, assuming the size of the solution is N sol , where the parameter r∈{1,2,...,N sol}.

[0025] Therefore, suppose is the value of the sth variable of the rth candidate solution in the tth iteration of the Jaya algorithm, then the updated solution is (The value of the sth variable of the rth candidate solution after the t+1th iteration) is: The value of the sth variable of the rth candidate solution after the tth iteration Add the first random coefficient μ1 and the current optimal solution and the absolute value of the value of the sth variable of the rth candidate solution at the tth iteration The product of the difference between the second random coefficient μ2 and the absolute value of the value of the sth variable of the current worst solution and the rth candidate solution in the tth iteration is subtracted. The product of the differences.

[0026] The mathematical formula is as follows: in, yes After the update, μ1 is the first random coefficient uniformly generated in [0, 1], and μ2 is the second random coefficient uniformly generated in [0, 1]. is the optimal solution with the best f(x) value, is the worst solution with the worst f(x) value.

[0027] It can be clearly seen from the above formula that the Jaya algorithm does not require algorithm parameters, which saves the huge workload of setting parameters for specific problems. This is one of the most helpful features of using the Jaya algorithm in complex engineering optimization problems.

[0028] Step 3: Based on the Jaya algorithm, establish the BHM model to form an improved B-Jaya optimization algorithm.

[0029] The principle of the BHM model is: In each iteration, a particle is randomly generated as a black hole, and the threshold p is determined from [0, 1] to determine its The black hole is located at the current optimal solution. A region near , ξ is the radius of the region. For each dimension of , we randomly generate a coefficient γ, whose size is between [0, 1]. If γ≤p, then This dimension is absorbed by the black hole, and the coordinates of this dimension are directly replaced by the coordinates of the black hole.

[0030] Specifically, Represents the s-dimensional coordinate of the r-th solution when iterating to the t-th generation; represents the worst solution of the sth dimension when iterating to the tth generation, represents the best solution of the sth dimension when iterating to the tth generation; then, A black hole is generated nearby, and its distance is Secondly, generate the threshold p associated with the black hole and The coefficient γ corresponding to each dimension of . Finally, when γ≤p, is absorbed by the black hole, the update rule of BHM is: the updated value of the s-dimensional coordinate of the r-th candidate solution in the t+1th iteration is the product of the radius ξ of the region where the randomly generated particle is located and the coefficient α and the current optimal solution The sum.

[0031] The mathematical formula is as follows: Among them, α follows a uniform distribution in [0, 1], are the new coordinates updated by the above mathematical formula.

[0032] The above mathematical formula means that BHM can provide solutions for some dimensions that are closer than Jaya. another opportunity (with a probability threshold of p), which actually expands the diversity of understanding.

[0033] This embodiment constructs a B-Jaya optimization algorithm that combines the BHM principle with the Jaya algorithm and expands diversity. The update mechanism of the Jaya algorithm and the BHM model is integrated to obtain the B-Jaya optimization algorithm.

[0034] Specifically, the update mechanism of the B-Jaya optimization algorithm is: (1) If the random coefficient γ corresponding to each dimension of the current optimal solution is less than or equal to the probability threshold p, then the updated value of the sth variable of the rth candidate solution in the t+1th iteration is is the product of the radius ξ of the region where the randomly generated particle is located and the coefficient α and the current optimal solution The sum.

[0035] (2) If the random coefficient γ corresponding to each dimension of the current optimal solution is greater than the probability threshold p, then the updated value of the sth variable of the rth candidate solution in the t+1th iteration is is the value of the sth variable of the rth candidate solution after the tth iteration Add the product of the first random coefficient and the difference between the absolute value of the sth variable of the current best solution and the rth candidate solution in the tth iteration, and subtract the product of the second random coefficient and the difference between the absolute value of the sth variable of the current worst solution and the rth candidate solution in the tth iteration.

[0036] The mathematical formula is as follows: The BHM principle is introduced into Jaya to further expand the diversity of its solutions. according to and Update its position; however, in the B-Jaya optimization algorithm, the BHM model can absorb The surrounding solutions enable some solutions to have new orbits in the search space, thereby expanding the search space and the diversity of solutions.

[0037] The B-Jaya optimization algorithm is more competitive than the original Jaya algorithm in terms of model economic cost, convergence speed and robustness.

[0038] Specific: According to the B-Jaya algorithm, for each solution in the initial solution set In addition to following and It also has the probability of being absorbed by a black hole, thus changing its original orbit and generating new solutions. Therefore, the B-Jaya algorithm expands the diversity of solutions. At the same time, since black holes are generated nearby, assuming Close to the true global optimal point, the black hole will conduct a thorough search around the global optimal point, which will accelerate the convergence speed; on the other hand, assuming In the local optimal state, the black hole will be Providing thrust to start a new trajectory helps avoid the problem of premature convergence. The results show that compared with the original Jaya algorithm, the B-Jaya algorithm successfully reduces the total dispatch cost, improves the convergence speed, and maintains good robustness, which can provide more references and guidance for solving the economic dispatch problem of power load.

[0039] Step 4: Considering the economic efficiency of the power system, construct the objective function of the power load dispatch model and take the minimum dispatch cost as the goal.

[0040] The objective function of the power load dispatch model is to minimize the power load dispatch cost.

[0041] Among them, the power load dispatch cost is the sum of the power generation costs of all generators, and its mathematical formula is as follows: Where O is the total system cost, N is the total number of generators, P i is the power output of the i-th generator, f i (p i ) is the cost function of the i-th generator.

[0042] In traditional power load dispatching, f i (p i ) is composed of a quadratic polynomial, and its calculation method is the generator output power P i The square of the first cost coefficient a i The product of plus the generator output power P i and the second cost coefficient b i The product of plus the third cost coefficient c i .

[0043] The mathematical formula is as follows: f i (p i )=a i p i 2 +b i p i +c i In the formula, a i is the first cost coefficient of the i-th generator; b i is the second cost coefficient of the i-th generator; c i is the third cost coefficient of the i-th generator.

[0044] In practical applications, this embodiment takes into account the valve point effect of the generator and adds a rectified sine component to the cost function. The rectified sine component is: the minimum value of the generator power generation and generator output power P iThe difference between the second cost coefficient f of the generator valve point effect i The sine value of the product of the sine value and the first cost coefficient e of the generator valve point effect i The absolute value of the product of .

[0045] Therefore, in this embodiment, the cost function of the i-th generator is: generator output power P i The square of the first cost coefficient a i The product of plus the generator output power P i and the second cost coefficient b i The product of plus the third cost coefficient c i Plus the minimum generator power and generator output power P i The difference between the value of and the second cost coefficient f of the generator valve point effect i The sine value of the product of and the first cost coefficient of the generator valve point effect e i The absolute value of the product of .

[0046] The mathematical formula is as follows: In the formula, e i is the first cost coefficient of generator i reflecting the valve point effect, f i is the second cost coefficient of generator i reflecting the valve point effect.

[0047] Step 5: Establish constraints for stable operation of generators, including power balance constraints, generation capacity constraints, ramp rate constraints, and prohibited operation area constraints.

[0048] Specifically, the constraints include: (1) Power balance constraint.

[0049] The power balance constraint is that the total power generated by all generators must be equal to the load power P d and transmission loss power P los The sum of .

[0050] The mathematical formula is as follows: Among them, P loss Calculated by Kron formula, the mathematical formula is as follows: In the formula, B ij , B i0 , B 00 The transmission loss power P loss The B matrix coefficients can generally be assumed to be constants under normal operating conditions.

[0051] (2) Power generation capacity constraints.

[0052] The power generation capacity constraint is the generator output power P i Less than or equal to the maximum output power of the generator And the generator output power P i Greater than or equal to the minimum generator output power

[0053] The mathematical formula is as follows: In the formula, represents the maximum output power of the i-th generator, Represents the minimum output power of the i-th generator.

[0054] (3) Ramp rate constraint.

[0055] In actual situations, the generator output power P i It cannot be adjusted instantly, and its uphill and downhill constraints should be considered. That is, the current output power P of the generator i The output power before the generator The difference is less than or equal to the rising limit of the current output power of the generator, and the previous output power of the generator The current output power P of the generator i The difference is less than or equal to the decrease limit of the current output power of the generator.

[0056] The mathematical formula is as follows: Among them, P i is the current output power of the generator, is the output power before the generator, UR i Indicates the current power generation limit of the generator, DR i Indicates the current power reduction limit of the generator.

[0057] (4) Prohibited operation area restrictions.

[0058] Due to the physical limitations of the generator, the entire operating area is not always available. Prohibited operating zones (POZs) result in the generator output power P i The discontinuous areas include: (1) Generator output power P i Greater than or equal to the minimum generator output power And less than or equal to the lower limit of the first prohibited operation area of ​​the generator

[0059] (2) Generator output power Pi Greater than or equal to the upper bound of the g-1th prohibited operation region of the generator And is less than or equal to the lower bound of the g-th prohibited operation region of the generator

[0060] (3) Generator output power P i Greater than or equal to the generator's zi-th prohibited operation area And less than or equal to the maximum output power of the generator Zi is the total number of prohibited operation areas of the generator.

[0061] The mathematical formula is as follows: Where Zi is the total number of prohibited operation zones POZs of generator i, g is the sequence number of POZs, where g∈{1, 2, …, Zi}, represents the lower bound of the g-th POZs of the i-th generator, represents the upper bound of the g-th POZs of the ith generator.

[0062] Step 6: Use the B-Jaya optimization algorithm to solve the mathematical model of the economic dispatch problem of power load.

[0063] The specific process is as follows Figure 2 As shown, including: (1) Based on the constraints of the decision variables and the solution set size pop of the B-Jaya algorithm, construct the initial solution set X, that is, initialize the power generation of each generator.

[0064] (2) Based on the objective function obtained in the fourth step, calculate the objective function value corresponding to the initial solution set X.

[0065] (3) Set the initial value of the number of iterations to iter = 1.

[0066] (4) Sort the initial solution set X according to the value of the objective function, and select the solution with the smallest objective function value in the initial solution set X as the global optimal solution X. best , select the solution with the largest objective function value in the initial solution set X as the global worst solution X worst .

[0067] (5) According to the iterative principle of the B-Jaya algorithm, that is, the update mechanism of the B-Jaya optimization algorithm in the third step, the solution set is iterated. The iterative process is: if the objective function value of the new generation solution set is better than the objective function value of the previous generation solution set, the initial solution set X is replaced; otherwise, the initial solution set X remains unchanged.

[0068] (6) According to the size of the objective function value of the new generation solution set, the new generation solution set is sorted and the global optimal individual X is updated. best and the global worst individual X worst .

[0069] (7) Update the iteration count mark value iter=iter+1.

[0070] (8) Determine whether the termination condition is met (i.e. iter ≥ Max). If the termination condition is met, output the global optimal solution X best and the objective function value, i.e., the optimal power generation of each generator; if not satisfied, return to execute steps (4) to (8) until the termination condition is met.

[0071] In terms of algorithm selection, this embodiment combines the original Jaya algorithm with the black hole model (BHM) and proposes a B-Jaya optimization algorithm with expanded diversity, which improves the convergence speed of the algorithm and makes the algorithm more practical in application value.

[0072] In practical applications, applying the B-Jaya algorithm to the dispatching model of power loads avoids the problem of premature convergence in the process of solving the model, so it has great advantages in solving the dispatching problem of power loads.

[0073] Embodiment 2: This embodiment provides a power load scheduling optimization system, including: The acquisition module is used to obtain the original parameters such as the load parameters of a certain urban area and the relevant cost coefficients of the generator.

[0074] The model building module is used to build a power load scheduling model based on the data collected by the acquisition module, taking the minimum power load scheduling cost as the objective function, and integrating power balance constraints, power generation capacity constraints, ramp rate constraints and prohibited operation area constraints.

[0075] The model analysis module is used to solve the constructed power load dispatching model based on the B-Jaya optimization algorithm and obtain the power load dispatching plan.

[0076] The execution module is used to dispatch the power load according to the solution results of the model analysis module.

[0077] It successfully reduced the total dispatch cost, improved the convergence speed, and maintained good robustness, which can provide more reference and guidance for solving the economic dispatch problem of power load.

[0078] Embodiment three: This embodiment also provides a power load scheduling optimization device, including a data acquisition module, a memory, at least one processor, a computer program stored in the memory and executable on the at least one processor, and at least one communication bus.

[0079] The memory can be used to store computer programs, and the processor implements the steps of the power load scheduling optimization method in Example 1 by running or executing the computer program stored in the memory and calling the data stored in the memory.

[0080] The memory in the power load dispatch optimization device stores multiple instructions to implement a power load dispatch optimization method, and the processor can execute the multiple instructions to implement: Real-time collection of original parameters such as load parameters of a certain urban area and relevant cost coefficients of generators; Based on Jaya algorithm, the BHM model is established to form the improved optimization algorithm B-Jaya; According to the collected load parameters and the original parameters of the generator, the power load dispatching model is established with the minimum power load dispatching cost as the objective function; The mathematical model of the power load dispatching problem is solved using the B-Jaya optimization algorithm.

[0081] If the power load dispatch optimization device provided in this embodiment is implemented in the form of a software functional unit and used as an independent product, it can be stored in a computer-readable medium.

[0082] Therefore, when implementing all or part of the processes in the power load dispatch optimization method in the first embodiment, the relevant hardware can also be instructed to complete by a computer program. The computer program can be stored in a computer-readable storage medium. The computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory and read-only memory.

[0083] The above-described embodiment is only a preferred solution of the present invention and does not limit the present invention in any form. There are other variations and modifications without exceeding the technical solution described in the claims.

Claims

1. A method for optimizing power load scheduling, characterized in that: The following steps are involved: S1: Obtain power load parameters and original parameters of power generation equipment; S2: Determine the parameters of the Jaya algorithm, establish the BHM model based on the Jaya algorithm, and obtain the B-Jaya optimization algorithm; S3: Taking the minimum power load dispatching cost as the objective function, establish a power load dispatching model; S4: Establish constraints for stable operation of power generation equipment; S5: Use the B-Jaya optimization algorithm to solve the power load scheduling model and obtain the power load scheduling plan.

2. The method for optimizing power load dispatching according to claim 1, characterized in that: The B-Jaya optimization algorithm is used to solve the power load dispatching model, including: S5.1: Initialize the power generation of each generator and determine the initial solution set X ; S5.2: Compute the initial solution set X The corresponding objective function value sets the initial value of the number of iterations; S5.3: Initial solution set X Sort and determine the global optimal solution and the global worst solution; S5.4: According to the iteration rule of the optimization algorithm B-Jaya, the initial solution set is obtained X Iterate and update the global optimal solution and the global worst solution; S5.5: Update the number of iterations and determine whether the termination condition is met. If so, output the objective function value and the global optimal solution. If not, return to S5.

3.

3. A method for optimizing power load dispatching according to claim 1 or 2, characterized in that: The iteration rules of the B-Jaya optimization algorithm include: If the random coefficient corresponding to each dimension of the current optimal solution is less than or equal to the probability threshold, the updated value of the sth variable of the rth candidate solution in the t+1th iteration is the sum of the product of the radius of the area where the randomly generated particle is located and the coefficient α and the current optimal solution; If the random coefficient corresponding to each dimension of the current optimal solution is greater than the probability threshold, the updated value of the sth variable of the rth candidate solution after the t+1th iteration is the value of the sth variable of the rth candidate solution after the tth iteration plus the product of the first random coefficient and the difference between the absolute values ​​of the sth variable of the current optimal solution and the rth candidate solution in the tth iteration minus the product of the second random coefficient and the difference between the absolute values ​​of the sth variable of the current worst solution and the rth candidate solution in the tth iteration.

4. The method for optimizing power load dispatching according to claim 1, characterized in that: The constraints include that the difference between the current output power of the generator and the previous output power of the generator is less than or equal to the rising limit of the current output power of the generator, and the difference between the previous output power of the generator and the current output power of the generator is less than or equal to the falling limit of the current output power of the generator.

5. A method for optimizing power load dispatching according to claim 1, 2 or 4, characterized in that: The constraint conditions include that the generator output power is greater than or equal to the minimum value of the generator output power and is less than or equal to the lower limit of the first prohibited operation area of ​​the generator; the generator output power is greater than or equal to the upper limit of the g-1th prohibited operation area of ​​the generator and is less than or equal to the lower limit of the gth prohibited operation area of ​​the generator; the generator output power is greater than or equal to the upper limit of the Zith prohibited operation area of ​​the generator and is less than or equal to the maximum value of the generator output power, where Zi is the total number of prohibited operation areas of the generator.

6. A method for optimizing power load dispatching according to claim 1, 2 or 4, characterized in that: The power load dispatching cost includes the sum of the power generation costs of all generators, and the power generation cost of each generator is the product of the square of the generator output power and the first cost coefficient plus the product of the generator output power and the second cost coefficient plus the third cost coefficient plus the generator rectifier sinusoidal component.

7. A method for optimizing power load dispatching according to claim 6, characterized in that: The generator rectification sinusoidal component includes: calculating the difference between the minimum generator power generation and the generator output power, calculating the sine value of the product of the difference and the second cost coefficient of the generator valve point effect; calculating the absolute value of the product of the sine value and the first cost coefficient of the generator valve point effect to obtain the generator rectification sinusoidal component.

8. The method for optimizing power load dispatching according to claim 2, characterized in that: Constraints based on decision variables and the solution size of the B-Jaya algorithm pop , construct the initial solution set X .

9. A method for optimizing power load dispatching according to claim 2 or 8, characterized in that: According to the size of the objective function value, the initial solution set X Sorting, the global optimal solution is the initial solution set X The solution with the minimum objective function value in the global worst solution is the initial solution set X The solution with the largest objective function value.

10. A method for optimizing power load dispatching according to claim 2 or 8, characterized in that: The initial solution set X The iteration includes: if the objective function value of the new generation solution set is better than the objective function value of the previous generation solution set, the initial solution set X Replace, otherwise, keep the initial solution set X unchanged; sort the new generation of solution sets according to the size of the objective function value of the new generation of solution sets, and update the global optimal solution and the global worst solution according to the sorting results.

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