Reactive power optimization method for power distribution network containing distributed energy based on improved teaching and learning optimization algorithm

Abstract

The invention provides a distributed energy-containing power distribution network reactive power optimization method based on an improved teaching and learning optimization algorithm, the background is established on the multi-point access power distribution network of a distributed energy system, and the problems of voltage out-of-limit, reactive power compensation demand increase and the like caused by the access of the distributed energy to the power distribution network are solved. And a reactive power optimization model aiming at reducing the active power loss and node voltage deviation of the power distribution network is constructed. According to the improved teaching and learning optimization algorithm, the global search capability and the local search precision of the algorithm are enhanced by introducing a self-adaptive learning factor and an elitist retention strategy, and the problem that a traditional algorithm is prone to falling into local optimum is effectively avoided. The result shows that the method can significantly reduce the active loss and node voltage deviation of the power distribution network, improves the voltage stability and the operation economy of the power distribution network, and verifies the effectiveness and superiority of the proposed method.

Description

Technical Field

[0001] The present invention relates to the technical field of voltage stabilization and reactive power optimization of distribution networks, and in particular to a reactive power optimization method for distribution networks containing distributed energy sources based on an improved teaching and learning optimization algorithm. Background Art

[0002] With the widespread adoption of distributed photovoltaic (DPV) technology, the problem of reactive power optimization in distribution networks has become increasingly complex. The integration of DPV not only changes the power flow distribution of distribution networks but also significantly impacts the reactive power balance. Traditional reactive power optimization methods face numerous challenges when handling DPV integration, such as increased computational complexity and reduced optimization accuracy. Therefore, the study of a reactive power optimization method for distribution networks with DPVs based on an improved teaching-learning-based optimization (ITLBO) algorithm is of great theoretical and practical significance. In recent years, research on reactive power optimization in distribution networks has made significant progress. Traditional methods are primarily based on optimization techniques such as linear programming and nonlinear programming. However, these methods have limitations when handling DPV integration. Research based on intelligent algorithms, such as genetic algorithms and particle swarm optimization, has gradually emerged. These algorithms have demonstrated excellent performance in solving complex optimization problems, but their application to reactive power optimization in distribution networks with DPVs remains an urgent challenge. Therefore, this paper proposes a reactive power optimization method for distribution networks with DPVs based on an improved teaching-learning-based optimization algorithm to address these challenges. Summary of the Invention

[0003] In view of this, the object of the present invention is to provide a reactive power optimization method for a distribution network containing distributed energy sources based on an improved teaching and learning optimization algorithm, so as to at least solve the above problems.

[0004] The technical solution adopted in the present invention is as follows: A reactive power optimization method for a distribution network containing distributed energy resources based on an improved teaching and learning optimization algorithm, the method comprising the following steps: S1. Construct a reactive power optimization model with the dual objectives of reducing active power loss and node voltage deviation in the distribution network; S2, using an improved teaching and learning optimization algorithm ITLBO to solve the model, the algorithm dynamically adjusts the learning step size through an adaptive learning factor, and retains the best individuals through an elite retention strategy; S3. Output the optimal reactive power compensation solution, including the reactive power output of distributed energy and the configuration of reactive power compensation equipment.

[0005] Furthermore, in step S1, the objective function A of the model is:

[0006] in, is the sum of the number of distributed photovoltaic systems and wind turbine systems, is the number of distributed energy storage systems, is the actual node voltage value of the distributed photovoltaic system and the distributed wind power system, is the node voltage reference value of the distributed photovoltaic system and distributed wind power system, The actual power generated by the distributed energy storage system.

[0007] Furthermore, the adjustment formula of the adaptive learning factor is:

[0008] in, The adaptive factors corresponding to distributed wind power, photovoltaic and energy storage units, is a random teaching factor, taking 1 or 2, is the optimal individual in the current population, is the population average position.

[0009] Furthermore, the best performing population is retained in each iteration individuals, and use them to replace the worst performing individuals in the population after the iteration individuals, among which Reserve quantity for pre-set elites.

[0010] Furthermore, the improved teaching and learning optimization algorithm ITLBO introduces a multi-learning mechanism in the learning stage. Specifically, two student individuals are randomly selected for comparison, and the individual positions are updated according to the quality of the objective function value. The formula is: in, 、 For randomly selected individuals, is the objective function value.

[0011] Furthermore, in step S1, the constraints of the model include: Node voltage range: ; Distributed energy power output limits: ; Reactive power compensation equipment capacity limitation: ; Transformer tap location: .

[0012] Furthermore, when the population is initialized in step S2, a reactive power compensation scheme for distributed energy access nodes is randomly generated, and each individual includes the following parameters: Reactive output of distributed wind turbines; Reactive power output of distributed photovoltaic systems; The charging and discharging power of the energy storage system; Capacity configuration of reactive power compensation equipment; Transformer tap locations.

[0013] Furthermore, in step S2, the convergence determination condition is: Reaching the preset maximum number of iterations; continuous The relative change rate of the objective function value in the iterations is less than the threshold ,in, , .

[0014] Furthermore, the method is applicable to a distribution network including distributed wind power, photovoltaic and energy storage systems, and the distributed energy is connected to the distribution network through an inverter, and its reactive output characteristics are modeled by instantaneous power theory.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention proposes a reactive power optimization method for a distribution network containing distributed energy based on an improved teaching and learning optimization algorithm. By constructing a reactive power optimization model with the dual objectives of reducing the active power loss and node voltage deviation of the distribution network, the reactive output characteristics of the distributed energy are fully considered in the model. The access of a single distributed energy source has a significant impact on the power flow distribution and voltage level of the distribution network. Its reactive output characteristics are incorporated into the model, making the optimization results more consistent with the actual operation of the distribution network.

[0016] The present invention proposes a reactive power optimization method for a distribution network containing distributed energy sources based on an improved teaching and learning optimization algorithm, by introducing an adaptive learning factor: the improved teaching and learning optimization algorithm introduces an adaptive learning factor, which can dynamically adjust the learning step size according to the algorithm's iterative process and the distribution of the current population. This enables the algorithm to explore the solution space more flexibly during the search process, which not only enhances the global search capability but also improves the search efficiency. Adopting an elite retention strategy: by introducing an elite retention strategy, the algorithm can retain a portion of excellent individuals during the iteration process, avoiding the loss of excellent solutions. This helps to improve the convergence speed and stability of the algorithm, while enhancing the local search accuracy, enabling the algorithm to more accurately find the global optimal solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only preferred embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 This is a schematic diagram of the overall process of a reactive power optimization method for a distribution network containing distributed energy based on an improved teaching and learning optimization algorithm proposed in an embodiment of the present invention.

[0019] Figure 2 This is a distributed energy distribution network architecture diagram of a reactive power optimization method for a distribution network containing distributed energy based on an improved teaching and learning optimization algorithm proposed in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the present invention more apparent, exemplary embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described herein. Based on the embodiments of the present invention described in the present invention, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of the present invention.

[0021] In the following description, numerous specific details are provided to provide a more thorough understanding of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced without one or more of these details. In other instances, certain technical features well known in the art are not described to avoid confusion with the present invention.

[0022] It should be understood that the present invention can be implemented in different forms and should not be interpreted as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to make disclosure thorough and complete and to fully convey the scope of the present invention to those skilled in the art.

[0023] The purpose of the terms used herein is only to describe specific embodiments and is not intended to limit the present invention. When used herein, the singular forms "a", "an", and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "comprising" and / or "comprising", when used in this specification, determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0024] The principles and features of the present invention are described below with reference to the accompanying drawings. The enumerated embodiments are only used to explain the present invention and are not used to limit the scope of the present invention.

[0025] Reference Figure 1 and Figure 2 The present invention provides a method for optimizing reactive power of a distribution network containing distributed energy based on an improved teaching and learning optimization algorithm, the method comprising the following steps: S1. Construct a reactive power optimization model with the dual objectives of reducing active power loss and node voltage deviation in the distribution network; S2, using an improved teaching and learning optimization algorithm ITLBO to solve the model, the algorithm dynamically adjusts the learning step size through an adaptive learning factor, and retains the best individuals through an elite retention strategy; S3. Output the optimal reactive power compensation solution, including the reactive power output of distributed energy and the configuration of reactive power compensation equipment.

[0026] In step S1, the objective function A of the model is:

[0027] in, is the sum of the number of distributed photovoltaic systems and wind turbine systems, is the number of distributed energy storage systems, is the actual node voltage value of the distributed photovoltaic system and the distributed wind power system, is the node voltage reference value of the distributed photovoltaic system and distributed wind power system, The actual power generated by the distributed energy storage system.

[0028] In step S1, the constraints of the model include: Node voltage range: ; Distributed energy power output limits: ; Reactive power compensation equipment capacity limitation: ; Transformer tap location: .

[0029] For example, a distributed wind power reactive power optimization model suitable for teaching and learning algorithm is established. Now the d-axis of the synchronous coordinate system is fixed to the direction of the grid voltage vector, that is, , the active power and reactive power between the grid-side converter and the grid can be expressed as:

[0030] 、 are the d-axis and q-axis components of the grid-side converter voltage respectively; 、 are the d-axis and q-axis components of the grid-side converter current respectively.

[0031] Establishment of a distributed photovoltaic reactive power optimization model suitable for teaching and learning algorithms The inverter output active power and reactive power can be calculated by instantaneous power theory, that is:

[0032] Establishment of a distributed energy storage reactive power optimization model suitable for teaching and learning algorithms The inverter output active power and reactive power can be calculated by instantaneous power theory, that is:

[0033] The goal of reactive power optimization in distribution networks is to achieve voltage stability, reduce network losses, and improve equipment utilization by adjusting the distribution of reactive power while meeting system operation constraints. The reactive power optimization model can be expressed as: The objective function is set to A

[0034] The adjustment formula of the adaptive learning factor is:

[0035] in, The adaptive factors corresponding to distributed wind power, photovoltaic and energy storage units, is a random teaching factor, taking 1 or 2, is the optimal individual in the current population, is the population average position.

[0036] In each iteration, the best performing individuals, and use them to replace the worst performing individuals in the population after the iteration individuals, among which Reserve quantity for pre-set elites.

[0037] For example, before each iteration, the NES individuals with the best performance in the population are selected and retained as elite solutions. After each iteration, the NES individuals with the worst performance in the population are replaced with the elite solutions.

[0038] Convergence check: Determine whether the convergence conditions are met (such as reaching the maximum number of iterations or the objective function value no longer changes significantly).

[0039] The improved teaching and learning optimization algorithm ITLBO introduces a multi-learning mechanism in the learning stage. Specifically, two student individuals are randomly selected for comparison, and the individual positions are updated according to the quality of the objective function value. The formula is: in, 、 For randomly selected individuals, is the objective function value.

[0040] For example, during the learning phase, students are allowed to learn from each other rather than just from the teacher. By increasing communication and learning between students, the diversity of the population is improved, further enhancing the global search capability of the algorithm. When the population is initialized in step S2, the reactive power compensation scheme of the distributed energy access node is randomly generated, and each individual contains the following parameters: Reactive output of distributed wind turbines; Reactive power output of distributed photovoltaic systems; The charging and discharging power of the energy storage system; Capacity configuration of reactive power compensation equipment; Transformer tap locations.

[0041] The convergence determination condition in step S2 is: Reaching the preset maximum number of iterations; continuous The relative change rate of the objective function value in the iterations is less than the threshold ,in, , .

[0042] The method is applicable to a distribution network including distributed wind power, photovoltaic and energy storage systems, wherein the distributed energy is connected to the distribution network via an inverter, and its reactive output characteristics are modeled using instantaneous power theory.

[0043] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A reactive power optimization method for a distribution network containing distributed energy based on an improved teaching and learning optimization algorithm, characterized in that: The method comprises the following steps: S1. Construct a reactive power optimization model with the dual objectives of reducing active power loss in distribution network and node voltage deviation; S2, using an improved teaching and learning optimization algorithm ITLBO to solve the model, the algorithm dynamically adjusts the learning step size through an adaptive learning factor, and retains the best individuals through an elite retention strategy; S3. Output the optimal reactive power compensation solution, including the reactive power output of distributed energy and the configuration of reactive power compensation equipment.

2. The reactive power optimization method of a distribution network containing distributed energy based on an improved teaching and learning optimization algorithm according to claim 1 is characterized in that: In step S1, the objective function A of the model is: in, is the sum of the number of distributed photovoltaic systems and wind turbine systems, is the number of distributed energy storage systems, is the actual node voltage value of the distributed photovoltaic system and the distributed wind power system, is the node voltage reference value of the distributed photovoltaic system and the distributed wind power system, The actual power generated by the distributed energy storage system.

3. The reactive power optimization method of a distribution network containing distributed energy based on an improved teaching and learning optimization algorithm according to claim 1 is characterized in that: The adjustment formula of the adaptive learning factor is: in, The adaptive factors corresponding to distributed wind power, photovoltaic and energy storage units respectively, is a random teaching factor, taking 1 or 2, is the optimal individual in the current population, is the average position of the population.

4. The reactive power optimization method for a distribution network containing distributed energy based on an improved teaching and learning optimization algorithm according to claim 1, characterized in that: In each iteration, the best performing individuals, and use them to replace the worst performing individuals in the population after the iteration. individuals, among which Amount reserved for the preset elite.

5. The reactive power optimization method of a distribution network containing distributed energy based on an improved teaching and learning optimization algorithm according to claim 1, characterized in that: The improved teaching and learning optimization algorithm ITLBO introduces a multi-learning mechanism in the learning stage, specifically: randomly select two student individuals for comparison, and update the individual position according to the quality of the objective function value. The formula is: in, , is a randomly selected individual, is the objective function value.

6. The reactive power optimization method of a distribution network containing distributed energy based on an improved teaching and learning optimization algorithm according to claim 1, characterized in that: In step S1, the constraints of the model include: Node voltage range: ; Distributed energy power output limitations: ; Reactive power compensation equipment capacity limitation: ; Transformer tap locations: .

7. The reactive power optimization method of a distribution network containing distributed energy based on an improved teaching and learning optimization algorithm according to claim 1, characterized in that: When the population is initialized in step S2, the reactive power compensation scheme of the distributed energy access node is randomly generated, and each individual contains the following parameters: Reactive power output of distributed wind turbines; Reactive power output of distributed photovoltaic systems; The charging and discharging power of the energy storage system; Capacity configuration of reactive power compensation equipment; Transformer tap locations.

8. The reactive power optimization method for a distribution network containing distributed energy based on an improved teaching and learning optimization algorithm according to claim 1 is characterized in that: The convergence determination condition in step S2 is: The preset maximum number of iterations is reached; continuous The relative change rate of the objective function value in the iterations is less than the threshold ,in, , .

9. The reactive power optimization method of a distribution network containing distributed energy based on an improved teaching and learning optimization algorithm according to claim 1, characterized in that: The method is applicable to a distribution network including distributed wind power, photovoltaic and energy storage systems, and the distributed energy is connected to the distribution network through an inverter, and its reactive output characteristics are modeled through instantaneous power theory.

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