A multi-region interconnected power system optimal dispatching method and device

Abstract

This invention belongs to the field of power system dispatching technology and discloses a method and apparatus for optimal dispatching of multi-regional interconnected power systems. The method includes the following steps: establishing constraints for the regional interconnected power system based on equality and inequality constraints; establishing a regional optimal dispatching model with the minimization of fuel costs of generator units in the multi-regional interconnected power system as the objective function; and solving the regional optimal dispatching model using a knowledge-learning linear population size reduction differential evolution algorithm to obtain the optimal dispatching scheme for each generator unit. This invention, by introducing a knowledge-learning strategy, avoids the situation where the fitness of particles cannot be improved during iterative evolution, saves iteration resources, and accelerates the convergence speed. This algorithm exhibits good performance in the optimal dispatching of multi-regional interconnected power systems.

Description

Technical Field

[0001] This invention belongs to the field of power system dispatching technology, specifically relating to a method and apparatus for optimizing the dispatching of a multi-regional interconnected power system. Background Technology

[0002] Multi-regional optimal dispatch is one of the core directions of power system optimal dispatch and is an important optimization problem. The essence of this problem is a complex practical engineering optimization problem with highly nonlinear, nonconvex, multimodal, and multi-constraint characteristics. Its solution involves the coordinated dispatch of multiple power generation regions. The goal is to achieve the optimal fuel cost of the entire system by optimizing the power generation allocation within each region and the power transmission between regions, while satisfying various equality and inequality constraints for the safe and stable operation of the system.

[0003] In the 1990s, solving multi-region optimization scheduling problems mainly relied on traditional mathematical programming theory and decomposition optimization methods. Among these, the Lagrange relaxation method became the mainstream approach due to its effectiveness in handling large-scale constrained optimization problems. By relaxing complex network constraints and coupling constraints into the objective function, it achieved the decomposability of the problem. Meanwhile, decomposition coordination algorithms such as Benders decomposition and Dantzig-Wolfe decomposition were widely used to decompose multi-region problems into two levels: intra-region optimization and inter-region coordination, effectively reducing the problem's complexity. Furthermore, linear programming and mixed-integer linear programming methods provided feasible solutions for optimization scheduling by piecewise linearizing nonlinear cost functions and constraints, while dynamic programming showed unique advantages in handling temporally coupled constraints. However, limited by the computational capabilities and the level of algorithmic theory development at the time, these methods generally suffered from problems such as high model simplification, difficulty in guaranteeing computational convergence, and inability to meet real-time scheduling requirements. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention aims to provide a multi-region interconnected power system optimal scheduling method based on knowledge-learning-based linear population size reduction differential evolution. This method satisfies various equality and inequality constraints of system operation while ensuring the safe and stable operation of the power system, determining the power output of generator units in each region and the power output between regions, and minimizing the fuel cost of the entire system. To achieve the above objectives, this invention provides the following solution: A method for optimal dispatching of a multi-regional interconnected power system includes the following steps: Based on the output constraints of generator units, inter-regional power transmission constraints, and power balance constraints of multi-regional interconnected power systems, constraint conditions for regional interconnected power systems are established. A regional optimal scheduling model is established with the objective function of minimizing the fuel cost of generator units in a multi-regional interconnected power system. Based on the constraints of the regional interconnected power system, a knowledge-learning linear population size reduction differential evolution algorithm is used to solve the regional optimal scheduling model and obtain the optimal scheduling scheme for each generator unit. Based on the optimal scheduling scheme obtained for each generator set, the actual power output of each generator set in the multi-regional interconnected power system is regulated.

[0005] Preferably, the output constraints and inter-regional power transmission constraints of the generator sets in the multi-regional interconnected power system are inequality constraints, and the power balance constraints are equality constraints. The output constraint of the generator set is: the actual power output of the j-th generator set in the i-th region must be greater than or equal to its minimum power output and less than or equal to its maximum power output; The inter-regional transmission power constraint is as follows: the transmission power between the i-th region and the k-th region must be greater than or equal to its minimum transmission power and less than or equal to its maximum transmission power. The power balance constraint is: the total power generation of region i is equal to the sum of the load demand of region i and the power transmitted between regions.

[0006] Preferably, the regional optimization scheduling model is as follows: , , Where M represents the total number of regions. This represents the number of generator sets in region i. Let J be the fuel cost of the j-th generator unit in region i. This represents the actual power output of the j-th generator unit in the i-th region. , , Let be the fuel cost coefficient of the j-th generator unit in region i. and Let be the valve point effect coefficient of the j-th generator unit in region i, and This is the minimum power output limit for the j-th generator unit in region i.

[0007] Preferably, the method for obtaining the optimal scheduling scheme for each generator set includes: Initialize population parameters; Repair the actual power output and inter-regional power transmission of generator sets that do not meet the constraints; Calculate the objective function for the initial population; The scaling factor F and crossover rate CR are dynamically generated using a successful history parameter adaptive mechanism, and new parameters are generated through normal or Cauchy distribution. Whether to use a knowledge-learning-guided mutation strategy to generate new offspring individuals depends on whether the knowledge learning base is empty and the learning rate of the particles. Using binomial crossover, new offspring individuals are fused with their parents to generate offspring individuals; Calculate the objective function value of the newly generated offspring individuals and update the knowledge learning base based on the results; The objective function value of the newly generated offspring is compared with the objective function value of the parent to determine the new population or to retain the parent. When the termination condition is met, output the optimal solution in the population and the output of each corresponding unit.

[0008] Preferably, the constraint violation degree of the power balance constraint in the i-th region is calculated. When the constraint is violated When the value is greater than zero, it indicates that the total power output of the current region i is greater than its actual power demand, and the actual power output of the unit needs to be reduced; when the constraint violation degree When the value is less than zero, it indicates that the total power output of region i is less than its actual power demand, and the actual power output of the unit needs to be increased. The formula for calculating the constraint violation degree of region i is as follows: , in, This represents the total power output of all generator sets in region i. This represents the load demand in region i. This represents the network loss in region i. This indicates the transmission power between different regions.

[0009] Preferably, when the power balance constraint of region i is violated... When the actual power output is greater than 0, a generator set j is randomly selected in region i, and its actual power output is reduced to: ; When the power balance constraint of region i is violated When < 0, randomly select a generator set j in region i and increase its actual power output so that its actual power output is: .

[0010] The present invention also provides an optimized dispatching device for a multi-regional interconnected power system, which applies the aforementioned method and includes: The condition constraint unit is used to establish the constraint conditions of the regional interconnected power system based on the output constraints of the generator units, the inter-regional transmission power constraints, and the power balance constraints of the multi-regional interconnected power system. The scheduling model unit is used to establish a regional optimal scheduling model with the objective function of minimizing the fuel cost of generator units in a multi-regional interconnected power system. The optimization solution unit is used to solve the regional optimal scheduling model based on the constraints of the regional interconnected power system, and adopts the linear population size reduction differential evolution algorithm with knowledge learning to obtain the optimal scheduling scheme for each generator unit. The control unit is used to regulate the actual power output of each generator unit in a multi-regional interconnected power system based on the obtained optimal scheduling scheme for each generator unit.

[0011] Preferably, in the condition constraint unit, the output constraint and the inter-regional transmission power constraint of the generator set of the multi-regional interconnected power system are inequality constraints, and the power balance constraint is an equality constraint. The output constraint of the generator set is: the actual power output of the j-th generator set in the i-th region must be greater than or equal to its minimum power output and less than or equal to its maximum power output; The inter-regional transmission power constraint is as follows: the transmission power between the i-th region and the k-th region must be greater than or equal to its minimum transmission power and less than or equal to its maximum transmission power. The power balance constraint is: the total power generation of region i is equal to the sum of the load demand of region i and the power transmitted between regions.

[0012] Preferably, the regional optimization scheduling model in the scheduling model unit is: , , in, Let J be the fuel cost of the j-th generator unit in region i. , , Let be the fuel cost coefficient of the j-th generator unit in region i. and Let be the valve point effect coefficient of the j-th generator unit in region i, and This is the minimum power output limit for the j-th generator unit in region i.

[0013] Preferably, the method for obtaining the optimal scheduling scheme for each generator set in the optimization solution unit includes: Initialize population parameters; Repair the actual power output and inter-regional power transmission of generator sets that do not meet the constraints; Calculate the objective function for the initial population; The scaling factor F and crossover rate CR are dynamically generated using a successful history parameter adaptive mechanism, and new parameters are generated through normal or Cauchy distribution. Whether to use a knowledge-learning-guided mutation strategy to generate new offspring individuals depends on whether the knowledge learning base is empty and the learning rate of the particles. The offspring are generated by fusing mutant individuals with their parents using a binary crossover method. Calculate the objective function value of the newly generated offspring individuals and update the knowledge learning base based on the results; The objective function values ​​of the newly generated offspring are compared with those of the parent generation to determine the new population or to retain the parent generation. When the termination condition is met, output the optimal solution in the population and the output of each corresponding unit.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The principle of this invention is simple, it is easy to implement, it runs fast and is robust, and it is suitable for the optimized scheduling of various multi-regional interconnected power systems. 2. In this method, by introducing a knowledge learning strategy, the situation where the fitness of particles cannot be improved during iterative evolution is avoided to a certain extent, saving iteration resources and accelerating the convergence speed. The algorithm shows good performance in the optimal scheduling of multi-region interconnected power systems. Attached Figure Description

[0015] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly described below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 This is a schematic diagram of the overall process of the multi-regional interconnected power system optimization scheduling method according to Embodiment 1 of the present invention; Figure 2 This is a diagram showing the transmission power limitation between the four regions in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram illustrating the principle of the linear population size reduction differential evolution algorithm based on knowledge learning in Embodiment 1 of the present invention. Figure 4 This is a comparison of the average fuel cost convergence curves of the method in Embodiment 1 of the present invention with other advanced algorithms on a 4-region, 40-unit model. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Swarm intelligence (SI) is a key and powerful optimization technique in the field of computational intelligence. It draws inspiration from the collective behavior of animals and social insects. In SI, each individual entity possesses unique intelligence and behavior. However, it is precisely the collaboration and integration of these individuals that endows SI with a powerful ability to solve complex and challenging problems, demonstrating good results in solving non-convex, nonlinear, high-dimensional, and multi-modal complex problems. Therefore, researchers have proposed applying intelligent optimization algorithms to solve such complex problems. These mainly include: differential evolution algorithm, particle swarm optimization algorithm, genetic algorithm, simulated annealing algorithm, and ant colony optimization algorithm. These algorithms can solve multi-region optimal scheduling problems more quickly and effectively; therefore, current research on multi-region optimal scheduling problems in power systems mainly utilizes heuristic intelligent optimization algorithms.

[0018] Example 1 This invention provides an optimal scheduling method for multi-region interconnected power systems. Specifically, it uses the minimum output of each generator unit in multiple regions as the objective function, and considers various constraints. It employs a knowledge-based linear population size reduction differential evolution method to determine the optimal output of each generator unit.

[0019] This embodiment uses a multi-regional optimized scheduling mathematical model with 4 regions and 40 generating units as an example. The total power load demand of this region is 10500MW. Region 1 consists of the first 10 generating units, with a power load demand of 1575MW, accounting for 15% of the total regional load; Region 2 consists of the last 10 generating units of Region 1, with a power load demand of 4200MW, accounting for 40% of the total regional load; Region 3 consists of the last 10 generating units of Region 2, with a power load demand of 3150MW, accounting for 30% of the total regional load; and Region 4 consists of the last 10 generating units, with a power load demand of 1575MW, accounting for 15% of the total regional load. The operating parameters of the thermal power generating units in each region are shown in Table 1, and the power transmission limits between the four regions are as follows: Figure 2 As shown.

[0020] Table 1 The present invention proposes an optimized scheduling method for multi-regional interconnected power systems, the overall process of which is illustrated as follows: Figure 1As shown, it mainly consists of four steps, the specific contents of which include: S1. Based on the obtained optimal scheduling scheme for each generator set, regulate the actual power output of each generator set in the multi-regional interconnected power system.

[0021] Specifically, based on the output constraints of generator units in multi-regional interconnected power systems, the power transmission constraints between regions, and the power balance constraints, the constraint conditions for regional interconnected power systems are established.

[0022] The output constraint of the generator set is: the actual power output of the j-th generator set in the i-th region must be greater than or equal to its minimum power output and less than or equal to its maximum power output, expressed as: , in, This represents the actual power output of the j-th generator unit in the i-th region. and These represent the maximum and minimum power output limits of the j-th generator set in the i-th region, respectively.

[0023] The inter-regional transmission power constraint is: the transmission power between the i-th region and the k-th region must be greater than or equal to their minimum transmission power and less than or equal to their maximum transmission power, expressed as: , in, The transmission power between the i-th region and the k-th region. and These represent the maximum and minimum transmission power between the corresponding regions, respectively. In actual operation, the power generation of the generator set needs to meet the total load demand and take into account network losses during transmission. Therefore, the power balance constraint of region i is: the total power generation of region i is equal to the sum of the load demand of region i and the power transmitted between regions, expressed as: , in, This represents the total power generation of region i, while This represents the load demand in region i.

[0024] S2. A regional optimal scheduling model is established with the objective function of minimizing the fuel cost of generator units in a multi-regional interconnected power system. Specifically, it is expressed as follows: , , Where M represents the total number of regions. This represents the number of generator sets in region i. Let J be the fuel cost of the j-th generator unit in region i. , , Let be the fuel cost coefficient of the j-th generator unit in region i. and Let be the valve point effect coefficient of the j-th generator unit in region i, and This is the minimum output limit (in MW) for the j-th generator unit in region i.

[0025] S3. Based on the constraints of the regional interconnected power system, a linear population size reduction differential evolution algorithm with knowledge learning is used to solve the regional optimal scheduling model and obtain the optimal scheduling scheme for each generator unit. For example... Figure 3 As shown, specifically, it is divided into the following steps: S3.1: Initialization parameters: Population size popsize, maximum number of evaluations MaxFEs and initial number of calculations Fes=0, initial number of iterations t=1.

[0026] S3.2: Initialize the population in the solution space and also initialize the archive A.

[0027] For a multi-region optimization scheduling system consisting of M regions, the location information of the effective solution is... for: , in, This represents the position of the i-th particle in the fuel cost function when the number of iterations is t. Indicates the first The first region The output of each generator set Indicates the region With the region The power transmitted between them.

[0028] S3.3: When particles in the population do not meet the system constraints, it is necessary to perform constraint repair on the particles that violate the constraints. For the constraint on power generation capacity, the repair method is as follows: , in, For the first The first region The actual power output of each generator set and These are the maximum and minimum power outputs of the unit, respectively.

[0029] For particles that do not meet the transmission capacity constraint between the two regions, the repair method is as follows: , in, For the first The area to the first Transmission power between regions and These are the maximum and minimum transmission power between the corresponding regions, respectively.

[0030] More specifically, in S3.3, the power repair steps for the i-th region are as follows: S3.3.1: Calculate the constraint violation degree of the power balance constraint in the i-th region. When the constraint is violated A value greater than zero indicates that the total power output of region i is greater than its actual power demand, and the actual power output of the unit needs to be reduced. When the constraint violation degree... When the value is less than zero, it indicates that the total power output of region i is less than its actual power demand, and the actual power output of the unit needs to be increased. The formula for calculating the constraint violation degree of region i is as follows: , in, This represents the total power output of all generator sets in region i. This represents the load demand in region i. This represents the network loss in region i. This indicates the transmission power between different regions.

[0031] S3.3.2: When the power balance constraint of region i is violated... When the actual power output is greater than 0, a generator set j is randomly selected in region i, and its actual power output is reduced to: .

[0032] S3.3.3: When the power balance constraint of region i is violated... When < 0, randomly select a generator set j in region i and increase its actual power output so that its actual power output is: .

[0033] S3.3.4: After executing S3.3.2 or S3.3.3, recalculate the constraint violation degree of the power balance constraint in region i. ,when Repair stops when the absolute value is less than a pre-set threshold or equal to zero. If the power balance constraint in region i is violated... If the requirements are still not met, select other units in the area that have not undergone repair operations and repeat S3.3.2 or S3.3.3.

[0034] S3.4: Calculate the objective function of the initial population, and let FES = FES + popsize, where FES is the number of times the current objective function has been calculated, and popsize is the size of the initial population. Also, let the archive A = 0, and put the initialized population P and the archive A into the set B.

[0035] S3.5: The scaling factor F and crossover rate CR are dynamically generated using a successful history parameter adaptive mechanism. The algorithm maintains a history memory array with an initial value of 0.5. and These are used to store parameter values ​​that performed well in past iterations. In each generation, each individual randomly selects a reference value from the historical memory and generates new parameters using either a normal distribution (CR) or a Cauchy distribution (F), while truncating the parameters to ensure effectiveness. The parameters of individuals that successfully generate better solutions are recorded and updated in the historical memory based on a weighted average of their fitness improvement, with individuals having a greater improvement having a greater impact on parameter updates. If the historical memory is entirely zero or marked as a termination value, CR is forced to 0 to enhance local search. The specific formula is as follows: , , in, Indicates the scaling factor. Indicates the first One particle, Indicates the success history scaling factor memory. This represents the memory bank indicating the success rate of historical crossovers.

[0036] S3.6: Based on whether the knowledge learning base is empty and the particle's learning rate The specific formula for determining whether to use a knowledge-learning-guided mutation strategy to generate new offspring individuals is as follows: , Here, ranki represents the fitness ranking of a particle within the population.

[0037] S3.7: Use binomial crossover to merge new offspring individuals with their parents to generate offspring individuals. The specific formula is as follows: , in, A random number in the range (0,1). Let be a random integer in the range [1, D], where D is the dimension of the objective function. This represents the test vector of the i-th particle. Represents the mutation vector. Represents an individual particle.

[0038] S3.8: Calculate the objective function value of the newly generated offspring individuals, update the knowledge learning base KL based on the result, and let FEs = FEs + popsize, t = t + 1.

[0039] S3.9: Perform a selection operation, comparing the objective function value of the newly generated offspring with that of the parent. If the offspring's objective function value is better than the parent's, the offspring replaces the parent to form a new population, and the discarded parent randomly replaces an individual in external file A. If the parent is better than the offspring, the parent remains in the subpopulation while the offspring is discarded.

[0040] S3.10: Determine whether the current number of evaluations Fes has reached the maximum number of evaluations MaxFEs. If it has, the termination condition is met; otherwise, repeat S3.5.

[0041] S3.11: Output the optimal solution in the population and the output of each corresponding unit.

[0042] S4. Based on the output of S3.11, obtain the optimal scheduling scheme for each generator set, determine and regulate the actual power output and corresponding cost of each generator set in the multi-regional interconnected power system.

[0043] Table 2 shows the power output of each unit in the region and the transmission power between regions under the optimal scheduling scheme. Table 3 compares the optimal scheduling scheme provided by the method of this invention with those provided by several other advanced optimization algorithms to demonstrate the performance of the method provided by this invention. It can be seen that, based on a comprehensive evaluation of average cost, minimum cost, and maximum cost, the optimal scheduling scheme determined by the method provided by this invention has superior performance and advantages. Figure 4 The figure shows a comparison of the convergence of the method in this invention with several other advanced optimization algorithms. It can be seen that the method in this invention has advantages in both convergence speed and accuracy. In summary, this invention demonstrates that it can effectively solve multi-region optimization scheduling problems.

[0044] Table 2 Table 3 The present invention provides a knowledge-based linear population size reduction differential evolution method for optimal scheduling of multi-region interconnected power systems. This method is used to solve multi-region optimal scheduling problems with various equality and inequality constraints. It also provides a method to repair the constraints of generator output and regional power balance, thereby improving the feasibility of the solution. It allocates the optimal output and inter-regional transmission power to each generator unit in each region, thereby minimizing the fuel cost of the entire system.

[0045] Example 2 An optimized dispatching device for a multi-regional interconnected power system mainly includes a condition constraint unit, a dispatching model unit, an optimization solution unit, and a control unit. Specifically: The constraint unit is used to establish the constraint conditions of a multi-regional interconnected power system based on the output constraints of generator units, the inter-regional power transmission constraints, and the power balance constraints. Specifically, the output constraints of generator units and the inter-regional power transmission constraints are inequality constraints, while the power balance constraints are equality constraints. The generator unit output constraint is: the actual power output of the j-th generator unit in the i-th region must be greater than or equal to its minimum power output and less than or equal to its maximum power output. The inter-regional power transmission constraint is: the power transmitted between the i-th region and the k-th region must be greater than or equal to its minimum transmission power and less than or equal to its maximum transmission power. The power balance constraint is: the total power generation of region i is equal to the sum of the load demand of region i and the inter-regional power transmission.

[0046] The scheduling model unit is used to establish a regional optimal scheduling model with the objective function of minimizing the fuel cost of generator units in a multi-regional interconnected power system, and is expressed as follows: , , in, Let J be the fuel cost of the j-th generator unit in region i. This represents the actual power output of the j-th generator unit in the i-th region. , , Let be the fuel cost coefficient of the j-th generator unit in region i. and Let be the valve point effect coefficient of the j-th generator unit in region i, and This represents the minimum output limit of the j-th generator unit in region i.

[0047] The optimization solution unit is used to solve the regional optimal scheduling model based on the constraints of the regional interconnected power system, employing a knowledge-learning linear population size reduction differential evolution algorithm to obtain the optimal scheduling scheme for each generator unit. Specifically, it performs the following steps: initializing population parameters; correcting the actual power output and inter-regional transmission power of generator units that do not meet the constraints; calculating the objective function of the initial population; dynamically generating the scaling factor F and crossover rate CR using a successful history parameter adaptive mechanism, and generating new parameters through normal or Cauchy distribution; determining whether to generate new offspring individuals using a knowledge-learning-guided mutation strategy based on whether the knowledge learning base is empty and the particle's learning rate; fusing mutated individuals with their parents using binomial crossover to generate offspring individuals; calculating the objective function value of the newly generated offspring individuals and updating the knowledge learning base based on the results; comparing the objective function value of the newly generated offspring with the objective function value of the parent to determine a new population or retain the parent; and outputting the optimal solution in the population and the corresponding output of each generator unit when the termination condition is met.

[0048] The control unit is used to regulate the actual power output of each generator unit in a multi-regional interconnected power system based on the obtained optimal scheduling scheme for each generator unit.

[0049] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for optimized dispatching of a multi-regional interconnected power system, characterized in that, Includes the following steps: Based on the output constraints of generator units, inter-regional power transmission constraints, and power balance constraints of multi-regional interconnected power systems, constraint conditions for regional interconnected power systems are established. A regional optimal scheduling model is established with the objective function of minimizing the fuel cost of generator units in a multi-regional interconnected power system. Based on the constraints of the regional interconnected power system, a knowledge-learning linear population size reduction differential evolution algorithm is used to solve the regional optimal scheduling model and obtain the optimal scheduling scheme for each generator unit. Based on the optimal scheduling scheme obtained for each generator set, the actual power output of each generator set in the multi-regional interconnected power system is regulated.

2. The method according to claim 1, characterized in that, The output constraints and inter-regional power transmission constraints of generator units in a multi-regional interconnected power system are inequality constraints, while the power balance constraints are equality constraints. The output constraint of the generator set is: the actual power output of the j-th generator set in the i-th region must be greater than or equal to its minimum power output and less than or equal to its maximum power output; The inter-regional transmission power constraint is as follows: the transmission power between the i-th region and the k-th region must be greater than or equal to its minimum transmission power and less than or equal to its maximum transmission power. The power balance constraint is: the total power generation of region i is equal to the sum of the load demand of region i and the power transmitted between regions.

3. The method according to claim 1, characterized in that, The regional optimization scheduling model is as follows: ， ， Where M represents the total number of regions. This represents the number of generator sets in region i. Let J be the fuel cost of the j-th generator unit in region i. This represents the actual power output of the j-th generator unit in the i-th region. , , Let be the fuel cost coefficient of the j-th generator unit in region i. and Let be the valve point effect coefficient of the j-th generator unit in region i, and This is the minimum power output limit for the j-th generator unit in region i.

4. The method according to claim 1, characterized in that, Methods for obtaining the optimal scheduling scheme for each generator set include: Initialize population parameters; Repair the actual power output and inter-regional power transmission of generator sets that do not meet the constraints; Calculate the objective function for the initial population; The scaling factor F and crossover rate CR are dynamically generated using a successful history parameter adaptive mechanism, and new parameters are generated through normal or Cauchy distribution. Whether to use a knowledge-learning-guided mutation strategy to generate new offspring individuals depends on whether the knowledge learning base is empty and the learning rate of the particles. Using binomial crossover, new offspring individuals are fused with their parents to generate offspring individuals; Calculate the objective function value of the newly generated offspring individuals and update the knowledge learning base based on the results; The objective function value of the newly generated offspring is compared with the objective function value of the parent to determine the new population or to retain the parent. When the termination condition is met, output the optimal solution in the population and the output of each corresponding unit.

5. The method according to claim 4, characterized in that, Calculate the constraint violation degree of the power balance constraint in the i-th region. When the constraint is violated When the value is greater than zero, it indicates that the total power output of the current region i is greater than its actual power demand, and the actual power output of the unit needs to be reduced; when the constraint violation degree When the value is less than zero, it indicates that the total power output of region i is less than its actual power demand, and the actual power output of the unit needs to be increased. The formula for calculating the constraint violation degree of region i is as follows: ， in, This represents the total power output of all generator sets in region i. This represents the load demand in region i. This represents the network loss in region i. This indicates the transmission power between different regions.

6. The method according to claim 5, characterized in that, When the power balance constraint of region i is violated When the actual power output is greater than 0, a generator set j is randomly selected in region i, and its actual power output is reduced to: ； When the power balance constraint of region i is violated When < 0, randomly select a generator set j in region i and increase its actual power output so that its actual power output is: 。 7. A multi-regional interconnected power system optimized dispatching device, employing the method described in any one of claims 1-6, characterized in that, include: The condition constraint unit is used to establish the constraint conditions of the regional interconnected power system based on the output constraints of the generator units, the inter-regional transmission power constraints, and the power balance constraints of the multi-regional interconnected power system. The scheduling model unit is used to establish a regional optimal scheduling model with the objective function of minimizing the fuel cost of generator units in a multi-regional interconnected power system. The optimization solution unit is used to solve the regional optimal scheduling model based on the constraints of the regional interconnected power system, and adopts the linear population size reduction differential evolution algorithm with knowledge learning to obtain the optimal scheduling scheme for each generator unit. The control unit is used to regulate the actual power output of each generator unit in a multi-regional interconnected power system based on the obtained optimal scheduling scheme for each generator unit.

8. The apparatus according to claim 7, characterized in that, In the condition constraint unit, the output constraint and the inter-regional transmission power constraint of the generator set of the multi-regional interconnected power system are inequality constraints, and the power balance constraint is an equality constraint. The output constraint of the generator set is: the actual power output of the j-th generator set in the i-th region must be greater than or equal to its minimum power output and less than or equal to its maximum power output; The inter-regional transmission power constraint is as follows: the transmission power between the i-th region and the k-th region must be greater than or equal to its minimum transmission power and less than or equal to its maximum transmission power. The power balance constraint is: the total power generation of region i is equal to the sum of the load demand of region i and the power transmitted between regions.

9. The apparatus according to claim 7, characterized in that, The regional optimization scheduling model in the scheduling model unit is: ， ， in, Let J be the fuel cost of the j-th generator unit in region i. This represents the actual power output of the j-th generator unit in the i-th region. , , Let be the fuel cost coefficient of the j-th generator unit in region i. and Let be the valve point effect coefficient of the j-th generator unit in region i, and This is the minimum power output limit for the j-th generator unit in region i.

10. The apparatus according to claim 7, characterized in that, The optimization solution unit includes the following methods for obtaining the optimal scheduling scheme for each generator set: Initialize population parameters; Repair the actual power output and inter-regional power transmission of generator sets that do not meet the constraints; Calculate the objective function for the initial population; The scaling factor F and crossover rate CR are dynamically generated using a successful history parameter adaptive mechanism, and new parameters are generated through normal or Cauchy distribution. Whether to use a knowledge-learning-guided mutation strategy to generate new offspring individuals depends on whether the knowledge learning base is empty and the learning rate of the particles. The offspring are generated by fusing mutant individuals with their parents using a binary crossover method. Calculate the objective function value of the newly generated offspring individuals and update the knowledge learning base based on the results; The objective function values ​​of the newly generated offspring are compared with those of the parent generation to determine the new population or to retain the parent generation. When the termination condition is met, output the optimal solution in the population and the output of each corresponding unit.

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