Power electronic transformer micro-grid multi-objective optimization method, device, medium and product
By improving the particle swarm optimization algorithm, establishing a comprehensive objective function, and dynamically adjusting the inertia weight and influence coefficient, the problem of imbalance between exploration and development capabilities in multi-objective optimization of microgrids was solved, realizing multi-objective optimization control of microgrids and improving operating costs, power loss, and voltage stability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ZHEJIANG JIANGSHAN TRANSFORMER CO LTD
- Filing Date
- 2026-01-20
- Publication Date
- 2026-05-01
AI Technical Summary
Traditional particle swarm optimization algorithms are prone to causing an imbalance between exploration and development capabilities during the search process in multi-objective optimization problems of microgrids, making it impossible to find the optimal solution in the global scope, which affects the operating cost, power loss and voltage stability of microgrids.
An improved particle swarm optimization algorithm is adopted. By establishing a comprehensive objective function and combining it with the weight coefficient method to transform it into a single objective fitness function, the inertia weight and the influence coefficient based on the population distribution state are dynamically adjusted to update the velocity and position of the particles, ensuring the updating of the global historical best position and the cooperative search efficiency of the particle swarm.
It significantly improves the global search capability and optimization efficiency of microgrids, achieving reduced operating costs, reduced power loss, and improved voltage stability, forming a complete closed loop from intelligent optimization to precise control.
Smart Images
Figure CN121965573A_ABST
Abstract
Description
Multi-objective optimization methods, equipment, media, and products for power electronic transformer microgrids Technical Field
[0001] This application relates to the field of microgrid operation and control technology, and in particular to a multi-objective optimization method, equipment, medium and product for power electronic transformer microgrids. Background Technology
[0002] In the field of operation and control of power electronic transformer microgrids, multi-objective optimization control has always been a core issue of great concern in the industry. As the scale of microgrids continues to expand and more and more distributed energy sources are integrated into them, the operating characteristics of microgrids are becoming increasingly complex and variable. This makes the need for multi-objective collaborative optimization of microgrids even more urgent. At the same time, when traditional particle swarm optimization algorithms are applied to multi-objective optimization problems in microgrids, particle updates rely solely on individual historical optimal and global historical optimal information. This can easily lead to an imbalance between exploration and development capabilities during the search process. The algorithm may stop searching in a local region of the solution space, failing to find the optimal solution globally, thus failing to provide global optimization control parameters for the microgrid. Summary of the Invention
[0003] The purpose of this application is to provide a multi-objective optimization method, device, medium, and product for power electronic transformer microgrids, which can realize the collaborative optimization of multiple objectives of microgrid operation, improve the global search capability and search efficiency of the optimization process, and comprehensively improve the economy, energy efficiency, and stability of microgrid operation.
[0004] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a multi-objective optimization method for power electronic transformer microgrids, comprising: establishing a comprehensive objective function for microgrid operation, wherein the objective function includes at least a comprehensive objective function of operating cost, microgrid power loss, and microgrid voltage stability; determining the global historical optimal position of the particle swarm optimization algorithm; and applying the control parameters corresponding to the finally obtained global historical optimal position to the actual control of the power electronic transformer microgrid to achieve multi-objective optimization of microgrid operating cost, power loss, and voltage stability; wherein, determining the global historical optimal position of the particle swarm optimization algorithm specifically includes: initializing the parameters of the improved particle swarm optimization algorithm, and in the feasible solution space... A particle swarm with initial position and initial velocity is generated within a given time interval. The position of each particle in the swarm represents a set of control parameters for a power electronic transformer. The following steps are iteratively executed until the maximum number of iterations is reached: the comprehensive objective function is transformed into a single-objective fitness function using a weighted coefficient method, and the fitness value of each particle at the current iteration number is calculated; the individual historical best position of each particle and the global historical best position of the entire particle swarm are updated based on the fitness value; the velocity and position of each particle are updated based on the inertial weights that are dynamically adjusted according to the iteration process and the influence coefficients calculated based on the population distribution state; after completing the iteration, the fitness value of each particle updated after the last iteration is calculated, and the individual historical best position of each particle and the global historical best position of the entire particle swarm are updated.
[0005] Optionally, the objective function for the operating cost of the microgrid is specifically expressed as: In the formula, Let n be the objective function for the operating cost of the microgrid, where n is the number of conventional power sources in the microgrid, and m is the number of energy storage devices. is the thermoelectric conversion micro-defect coefficient of the i-th conventional power source material; Let be the topology complexity coefficient of the j-th power electronic transformer.
[0006] Optionally, the objective function for power loss in the microgrid is specifically expressed as: In the formula, Let z be the objective function for power loss in the microgrid, z be the number of temperature-related terms, and d be the number of electromagnetic compatibility interference-related terms. y is the nonlinear quantization value of the temperature coefficient of thermal conductivity of the y-th material; It is the electromagnetic wave polarization direction weight of the cth electromagnetic compatibility interference factor; Let be the dielectric constant distortion coefficient of the c-th electromagnetic interference and power loss.
[0007] Optionally, the objective function for the voltage stability of the microgrid is specifically expressed as: In the formula, Let h be the objective function for the voltage stability of the microgrid, h be the number of terms related to load fluctuation factors, and l be the number of terms related to harmonic effects. The charge density wave weight of the g-th load fluctuation factor is... The higher harmonic spectral weights of the k-th harmonic factor.
[0008] Optionally, in updating the velocity and position of each particle based on the inertial weights that are dynamically adjusted according to the iteration process and the influence coefficients calculated based on the population distribution state, the velocity of each particle is updated using the following velocity update formula: In the formula, For the first Speed at the next iteration , As the impact factor, The influence coefficient, For inertial weights, Let be the velocity of particle i in the t-th iteration. , As a learning factor, Let be the individual historical best position of particle i. Let i be the position of particle i in the t-th iteration. This represents the global historical best position for the entire population.
[0009] Optionally, in updating the velocity and position of each particle based on the inertial weights that are dynamically adjusted according to the iteration process and the influence coefficients calculated based on the population distribution state, the position of each particle is updated using the following position update formula: In the formula, for Position at the next iteration This represents the position at the t-th iteration. For the first Speed at the next iteration Indicates the first The time taken for each iteration.
[0010] Optionally, updating the individual historical best position of each particle and the global historical best position of the entire particle swarm based on the fitness value specifically includes: if the particle's current fitness is better than its own individual historical best fitness, then update the current fitness to the individual historical best fitness and update the position corresponding to the current fitness to the individual historical best position; compare the individual historical best fitness of all particles, and update the position corresponding to the best individual historical best fitness among all individual historical best fitnesss to the global historical best position; if the particle's current fitness is not better than its own individual historical best fitness, then the particle's individual historical best position remains unchanged; compare the individual historical best fitness of all particles, and update the position corresponding to the best individual historical best fitness among all individual historical best fitnesss to the global historical best position.
[0011] In a second aspect, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the multi-objective optimization method for power electronic transformer microgrids described in the first aspect.
[0012] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the multi-objective optimization method for power electronic transformer microgrids described in the first aspect.
[0013] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the multi-objective optimization method for power electronic transformer microgrids described in the first aspect.
[0014] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a multi-objective optimization method, device, medium, and product for power electronic transformer microgrids. By establishing a comprehensive objective function that simultaneously covers operating costs, power losses, and voltage stability, and using a weighting coefficient method to transform it into a unified fitness evaluation standard, it overcomes the limitation of traditional methods that often only focus on a single objective. It can coordinate multiple operating objectives in a single optimization process, significantly improving the overall operating performance of the microgrid. The improved particle swarm optimization algorithm used in this application not only includes a standard iterative update loop, but also, after completing all iterations, recalculates the fitness of the particle positions after the last update and updates the optimal position. This ensures that all potential optimal solutions discovered by the algorithm during the entire search process can be effectively captured and evaluated, thereby improving the reliability of the final output global historical optimal position (i.e., the optimal control parameter set). By adopting an inertia weight that is dynamically adjusted according to the iteration process, this invention enables the algorithm to maintain a large inertia weight in the early stage of the search to enhance global exploration capabilities and broadly search the solution space. In the later stage of the search, the inertia weight is reduced to enhance local development capabilities and finely converge to the optimal region. This dynamic balancing strategy effectively avoids premature convergence of the algorithm to local optima. This invention introduces an influence coefficient calculated based on the real-time distribution state of the population. This allows particles to sense the aggregation or dispersion of the entire population when updating their speed, and adjust their search behavior accordingly, thereby significantly improving the cooperative search efficiency of the particle swarm and accelerating the overall convergence speed. This application outputs the power electronic transformer control parameters corresponding to the globally historical optimal position. This allows theoretical optimization results to be directly applied to the actual control system of the microgrid, forming a complete closed loop from "intelligent optimization" to "precise control," effectively transforming the optimization objective into practical operational benefits such as reduced operating costs, reduced power loss, and improved voltage stability. Attached Figure Description
[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0016] Figure 1 is a flowchart illustrating a multi-objective optimization method for a power electronic transformer microgrid according to an embodiment of this application; Figure 2 is a functional module diagram illustrating a multi-objective optimization device for a power electronic transformer microgrid according to an embodiment of this application; Figure 3 is a structural diagram illustrating a computer device according to an embodiment of this application. Detailed Implementation
[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0018] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0019] In an exemplary embodiment, as shown in FIG1, a multi-objective optimization method for a power electronic transformer microgrid is provided. The method is executed by a computer device, specifically by a computer device such as a terminal or a server alone, or by a terminal and a server together. In this embodiment, the method includes the following steps 101 to 104, wherein: Step 101, establishing a comprehensive objective function for microgrid operation, the objective function including at least a comprehensive objective function of operating cost objective function, microgrid power loss objective function and microgrid voltage stability objective function.
[0020] As an optional implementation, the objective function for the microgrid operating cost is specifically expressed as follows: In the formula, Let n be the objective function for the operating cost of the microgrid, where n is the number of conventional power sources in the microgrid, and m is the number of energy storage devices. is the thermoelectric conversion microdefect coefficient of the i-th conventional power material, which reflects the impact on cost caused by the reduction in energy conversion efficiency due to microdefects in the thermoelectric conversion process of conventional power materials. Let be the topology complexity coefficient of the j-th power electronic transformer. The more complex the topology, the higher the cost of design, manufacturing and other processes.
[0021] This implementation method, by adding a thermoelectric conversion micro-defect coefficient to the operating cost objective function, can quantify the conversion efficiency loss of conventional power sources from the perspective of material micro-defects, directly linking material science characteristics with operational economics, and making cost prediction closer to the actual physical process. At the same time, adding a topology complexity coefficient to the operating cost objective function transforms the topology complexity of the power electronic transformer into a cost influencing factor, reflecting the differences in efficiency, loss, and control difficulty of different topologies, and can distinguish the cost characteristics of different equipment designs.
[0022] As an optional implementation, the objective function for power loss in the microgrid is specifically expressed as follows: In the formula, Let z be the objective function for power loss in the microgrid, z be the number of temperature-related terms, and d be the number of electromagnetic compatibility interference-related terms. Let y be the nonlinear quantization value of the temperature coefficient of the thermal conductivity of the material. The nonlinear change of the thermal conductivity of the material with temperature will affect the heat dissipation of the equipment, and thus affect the power loss. The effect of temperature on power loss is calculated in this way. It is the electromagnetic wave polarization direction weight of the c-th electromagnetic compatibility interference factor. Different electromagnetic wave polarization directions result in different degrees of interference to the equipment, which in turn affects power loss. Let be the dielectric constant distortion coefficient of the c-th electromagnetic interference and power loss. Dielectric constant distortion can affect the insulation and other properties of the equipment, resulting in power loss.
[0023] This implementation method quantifies the losses caused by changes in conduction and heat dissipation efficiency due to temperature increases by considering the nonlinear effect of temperature on the thermal conductivity of equipment materials. It also incorporates electromagnetic compatibility (EMC) interference factors and dielectric constant distortion coefficients to reflect the dynamic additional losses caused by EMC. Combining these two approaches allows for a more comprehensive characterization of the multi-source, time-varying, and coupled nature of power losses in actual systems. This provides a detailed and accurate reflection of the composition and changes in power losses during microgrid operation, offering precise quantitative data for subsequent multi-objective optimization control of microgrids based on improved particle swarm optimization algorithms. This makes the optimization process more closely aligned with the complex characteristics of power losses in actual microgrid operation.
[0024] As an optional implementation, the microgrid voltage stability objective function is specifically expressed as follows: In the formula, Let h be the objective function for the voltage stability of the microgrid, h be the number of terms related to load fluctuation factors, and l be the number of terms related to harmonic effects. Let be the charge density wave weight of the g-th load fluctuation factor. Changes in charge density wave will cause load fluctuations, which in turn will affect voltage stability. The higher harmonic spectral weights are the spectral weights of the k-th harmonic factor. The spectral characteristics of higher harmonics affect the voltage quality of the power grid.
[0025] This implementation method integrates the voltage stability influencing factors such as load fluctuations and harmonics into the microgrid voltage stability objective function in a mathematical form with micro-quantitative parameters. This can accurately reflect the status and changes of microgrid voltage stability, providing precise voltage stability quantification basis for subsequent multi-objective optimization control of microgrids based on improved particle swarm optimization algorithms. This makes the optimization process more consistent with the complex characteristics of voltage stability in actual microgrid operation.
[0026] Step 102: Determine the global historical optimal position of the particle swarm using an improved particle swarm optimization algorithm.
[0027] As an optional implementation, step 102 determines the global historical optimal position of the particle swarm using an improved particle swarm optimization algorithm. Specifically, this includes: initializing the parameters of the improved particle swarm optimization algorithm and generating a particle swarm with initial position and initial velocity in the feasible solution space, wherein the position of each particle in the particle swarm represents a set of control parameters of a power electronic transformer.
[0028] The following steps are performed iteratively until the maximum number of iterations is reached: the comprehensive objective function is transformed into a single-objective fitness function using the weighting coefficient method, and the fitness value of each particle at the current iteration number is calculated; the individual historical best position of each particle and the global historical best position of the entire particle swarm are updated according to the fitness value.
[0029] Based on the inertial weights that are dynamically adjusted according to the iteration process and the influence coefficients calculated based on the population distribution state, the velocity and position of each particle are updated. After the iteration is completed, the fitness value of each particle updated after the last iteration needs to be calculated, and the individual historical best position of each particle and the global historical best position of the entire particle swarm are updated.
[0030] In this embodiment, the parameters of the particle swarm optimization algorithm include particle swarm size, maximum number of iterations, and learning factor.
[0031] In this embodiment, the single-target fitness function The calculation formula is: In the formula, , , The weighting coefficients corresponding to the microgrid operating cost objective function, the microgrid power loss objective function, and the microgrid voltage stability objective function are used to weigh the importance of the microgrid operating cost objective function, the power loss objective function, and the voltage stability objective function in the fitness calculation.
[0032] This implementation method, by constructing a single-objective fitness function, can transform the comprehensive consideration of multiple objectives into the fitness calculation of a single objective. This provides a foundation for the iteration of particles and the search for the optimal solution in the subsequent multi-objective optimization control of microgrids based on the improved particle swarm optimization algorithm, enabling the algorithm to effectively find the optimal control parameters under the premise of comprehensively balancing various objectives.
[0033] As an optional implementation, updating the individual historical best position of each particle and the global historical best position of the entire population based on the particle's fitness value specifically includes: if the particle's current fitness is better than its own individual historical best fitness, then update the current fitness to the individual historical best fitness and update the position corresponding to the current fitness to the individual historical best position; compare the individual historical best fitness of all particles, and update the position corresponding to the best individual historical best fitness among all individual historical best fitnesss to the global historical best position; if the particle's current fitness is not better than its own individual historical best fitness, then the particle's individual historical best position remains unchanged; compare the individual historical best fitness of all particles, and update the position corresponding to the best individual historical best fitness among all individual historical best fitnesss to the global historical best position.
[0034] In this embodiment of the application, if the current fitness of particle i is The individual's historical best fitness is superior to its own. If the current fitness is not found, then update the current fitness to the individual's historical best fitness, and set the position corresponding to the current fitness. Updated to the individual's historical best position Specifically, it means: if < Then update = , = Specifically, for each particle, its current fitness value is... The individual best fitness value of this particle Compare. If the current fitness value... Less than the individual's historical best fitness value This means that the current particle has found a better solution, and at this point, it is necessary to update the particle's individual historical best fitness value, that is, let = At the same time, the current position of the particle Record as the individual's historical best position .
[0035] After updating the individual best-in-history positions of all particles, the individual best-in-history fitness values of all particles are compared, and the minimum fitness value is found. Then, the position corresponding to this minimum fitness value is set as the global best-in-history position for the entire population. By continuously iterating and updating the individual and global optimal positions, the particle swarm optimization algorithm can progressively search the solution space and ultimately find the control parameters that comprehensively optimize multiple objectives such as microgrid operating cost, power loss, and voltage stability, thereby achieving multi-objective optimization control of power electronic transformer microgrids.
[0036] As an optional implementation, in updating the velocity and position of each particle based on the inertial weights that are dynamically adjusted according to the iteration process and the influence coefficients calculated based on the population distribution state, the velocity of each particle is updated using the following velocity update formula: In the formula, For the first Speed at the next iteration , As an influencing factor, it introduces randomness into the learning process and increases the diversity of the search; As an influence coefficient, additional adjustments can be made to the speed update; Inertial weights are used to dynamically balance global exploration and local development capabilities during algorithm iteration by adjusting the degree to which particles inherit their velocity from the previous moment. Let be the velocity of particle i at the t-th iteration, which reflects the particle's motion state; , These are learning factors, used to adjust the degree to which a particle learns from its individual historical best position and its global historical best position, respectively. Let be the individual historical best position of particle i, representing the position of the best solution found by the particle in its own history; Let i be the position of particle i in the t-th iteration. This represents the global historical best position for the entire population.
[0037] In this implementation method, the particle's velocity is influenced by a combination of factors, including its historical motion state, learning from its individual optimal position, learning from its global optimal position, and additional influence coefficients, through the aforementioned velocity update formula. This influence guides the particle to move in a more optimal direction.
[0038] In this embodiment of the application, the inertia weight is calculated using the following formula. : In the formula, For maximum inertia weight, For minimum inertia weight, This represents the number of iterations; as the number of iterations increases, the inertia weight... The nonlinearity increases, enabling the transition from global search to local fine-grained search.
[0039] In this implementation method, nonlinear inertial weights are set. It can balance the global exploration and local development capabilities of the particle swarm optimization algorithm, and dynamically adjust it with the iteration process, allowing particles to have a larger exploration range in the early stage of the search and more accurate convergence in the later stage.
[0040] In this embodiment, the influence coefficient is calculated using the following formula. : In the formula, It is a coordination coefficient used to adjust the effect of positional differences between particles. The degree of influence, where N is the total number of particles; and These are the positions of particle j and particle i at the t-th iteration, respectively.
[0041] In this embodiment, by summing the position differences of all particle pairs, the distribution state (degree of dispersion or aggregation) of the particle swarm in the solution space can be reflected, thereby affecting the velocity update. This allows the particle swarm optimization algorithm to expand the search range when the particle distribution is dispersed and to search more accurately when the distribution is clustered. This embodiment calculates the... As an influence coefficient, it participates in the particle velocity update formula, enabling the particle swarm optimization algorithm to update particle velocity more accurately when considering inter-particle cooperative factors. This improves the ability of the particle swarm optimization algorithm to solve multi-objective optimization control problems of power electronic transformer microgrids, helps to find better control parameters, and achieves multi-objective optimization of microgrid operating costs, power loss and voltage stability.
[0042] As an optional implementation, in updating the velocity and position of each particle based on the inertial weights that are dynamically adjusted according to the iteration process and the influence coefficients calculated based on the population distribution state, the position of each particle is updated using the following position update formula: In the formula, for Position at the next iteration This represents the position at the t-th iteration. For the first Speed at the next iteration Indicates the first The time taken for each iteration.
[0043] In this embodiment of the application, speed It reflects the motion trend and amplitude of the particle after adjustments based on factors such as the individual optimal position, the global optimal position, and the nonlinear inertial weight in the current iteration. After calculating the particle's new velocity, multiplying it by the time taken for the next iteration and adding it to the particle's current position will give the particle's new position in the next iteration.
[0044] Step 103: After the iteration terminates, the control parameters corresponding to the final global historical optimal position are applied to the actual control of the power electronic transformer microgrid to achieve multi-objective optimization of microgrid operating cost, power loss and voltage stability.
[0045] By implementing steps 101 to 103 above, a comprehensive objective function encompassing operating cost, power loss, and voltage stability is established. This function is then transformed into a unified fitness evaluation standard using a weighted coefficient method. This overcomes the limitations of traditional methods that often focus only on a single objective, enabling the coordinated management of multiple operational objectives during a single optimization process and significantly improving the overall operational performance of the microgrid. The improved particle swarm optimization algorithm used in this application not only includes a standard iterative update loop but also, after completing all iterations, recalculates the fitness of the particles after the last update and updates the optimal position. This ensures that all potential optimal solutions discovered by the algorithm throughout the search process are effectively captured and evaluated, thereby improving the reliability of the final output global historical optimal position (i.e., the optimal control parameter set). This invention employs dynamically adjusted inertia weights based on the iteration process. This allows the algorithm to maintain a large inertia weight in the early stages of the search to enhance global exploration capabilities and broadly search the solution space, while reducing the inertia weight in the later stages to enhance local exploration capabilities and finely converge to the optimal region. This dynamic balancing strategy effectively avoids premature convergence to local optima. This invention introduces an influence coefficient calculated based on the real-time distribution state of the population. This technology enables particles to sense the aggregation or dispersion of the entire population when updating their speed, and adjust their search behavior accordingly, thereby significantly improving the cooperative search efficiency of the particle swarm and accelerating the overall convergence speed. This application outputs the power electronic transformer control parameters corresponding to the globally optimal historical position. This allows theoretical optimization results to be directly applied to the actual control system of the microgrid, forming a complete closed loop from "intelligent optimization" to "precise control," effectively transforming optimization objectives into practical operational benefits such as reduced operating costs, reduced power loss, and improved voltage stability.
[0046] The objective function is constructed by considering three core objectives: microgrid operating cost, power loss, and voltage stability. Compared to single-objective optimization, this approach more comprehensively reflects the actual operational needs of the microgrid, achieving a multi-dimensional optimization balance and improving the microgrid's economy, energy efficiency, and stability. In the algorithm initialization phase, the particle swarm size, maximum number of iterations, and learning factor are reasonably set, and initial particle positions and velocities are randomly generated in the feasible solution space to ensure a broad search range and avoid limitations imposed by initial conditions. The comprehensive objective function is transformed into a single-objective fitness function using a weighted coefficient method, scientifically integrating the influence of multiple objectives. This allows the algorithm to evaluate particle performance based on a unified fitness value during iteration, simplifying the complexity of multi-objective optimization and improving optimization efficiency. In the optimal particle position update stage, the individual and global optimal positions are dynamically updated based on the fitness value, allowing particles to continuously converge towards better solutions, ensuring the algorithm converges towards the global optimum and enhancing the accuracy of the optimization results. Finally, applying the parameters corresponding to the globally optimal location to the actual control of the microgrid can effectively achieve multi-objective optimization such as reduced operating costs, reduced power loss, and improved voltage stability, providing strong technical support for the efficient and reliable operation of the microgrid. It is applicable to various scenarios such as distributed energy access, independent or grid-connected operation of the microgrid, and has broad practical application value.
[0047] Based on the same inventive concept, this application also provides a power electronic transformer microgrid multi-objective optimization device for implementing the aforementioned multi-objective optimization method for power electronic transformer microgrids. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the power electronic transformer microgrid multi-objective optimization device provided below can be found in the limitations of the power electronic transformer microgrid multi-objective optimization method described above, and will not be repeated here.
[0048] In an exemplary embodiment, as shown in FIG2, a multi-objective optimization device for a power electronic transformer microgrid is provided, comprising: a function establishment unit 201, used to establish a comprehensive objective function for microgrid operation, wherein the objective function includes at least a comprehensive objective function of an operating cost objective function, a microgrid power loss objective function, and a microgrid voltage stability objective function; a calculation unit 202, used to determine the global historical optimal position of the particle swarm optimization algorithm; and an output unit 203, used to apply the control parameters corresponding to the finally obtained global historical optimal position to the actual control of the power electronic transformer microgrid, so as to achieve multi-objective optimization of microgrid operating cost, power loss, and voltage stability; as an optional implementation, determining the global historical optimal position of the particle swarm optimization algorithm specifically includes: initializing the improved particle swarm optimization algorithm. The parameters of the swarm optimization algorithm are determined, and a swarm of particles with initial positions and velocities is generated in the feasible solution space. The position of each particle in the swarm represents a set of control parameters of a power electronic transformer. The following steps are iteratively executed until the maximum number of iterations is reached: the comprehensive objective function is transformed into a single-objective fitness function using the weighting coefficient method, and the fitness value of each particle at the current iteration number is calculated; the individual historical best position of each particle and the global historical best position of the entire swarm are updated according to the fitness value; the velocity and position of each particle are updated based on the inertial weights that are dynamically adjusted according to the iteration process and the influence coefficients calculated based on the population distribution state; after the iteration is completed, the fitness value of each particle updated after the last iteration is calculated, and the individual historical best position of each particle and the global historical best position of the entire swarm are updated.
[0049] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram is shown in Figure 3. The computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is connected to the system bus via the I / O interfaces. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores multi-objective optimization data for power electronic transformer microgrids. The I / O interfaces of the computer device are used for information exchange between the processor and external devices. The communication interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a multi-objective optimization method for power electronic transformer microgrids.
[0050] Those skilled in the art will understand that the structure shown in Figure 3 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or may combine certain components, or may have different component arrangements.
[0051] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.
[0052] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0053] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0054] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.
[0055] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
[0056] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0057] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0058] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A multi-objective optimization method for power electronic transformer microgrids, characterized in that, The multi-objective optimization method for power electronic transformer microgrids includes: establishing a comprehensive objective function for microgrid operation, wherein the objective function includes at least a comprehensive objective function of operating cost, microgrid power loss, and microgrid voltage stability; determining the global historical optimal position of the particle swarm using an improved particle swarm optimization algorithm; and applying the control parameters corresponding to the finally obtained global historical optimal position to the actual control of the power electronic transformer microgrid to achieve multi-objective optimization of microgrid operating cost, power loss, and voltage stability. Specifically, determining the global historical optimal position of the particle swarm using the improved particle swarm optimization algorithm includes: initializing the parameters of the improved particle swarm optimization algorithm and generating an initial position and initial velocity within the feasible solution space. The particle swarm, where the position of each particle represents a set of control parameters for a power electronic transformer, is used to iteratively execute the following steps until the maximum number of iterations is reached: The comprehensive objective function is transformed into a single-objective fitness function using a weighted coefficient method, and the fitness value of each particle at the current iteration number is calculated; the individual historical best position of each particle and the global historical best position of the entire particle swarm are updated based on the fitness value; the velocity and position of each particle are updated based on the inertial weights that are dynamically adjusted according to the iteration process and the influence coefficients calculated based on the population distribution state; after completing the iteration, the fitness value of each particle updated after the last iteration is calculated, and the individual historical best position of each particle and the global historical best position of the entire particle swarm are updated.
2. The multi-objective optimization method for power electronic transformer microgrids according to claim 1, characterized in that, The objective function for the operating cost of the microgrid is specifically expressed as follows: In the formula, Let n be the objective function for the operating cost of the microgrid, where n is the number of conventional power sources in the microgrid, and m is the number of energy storage devices. is the thermoelectric conversion micro-defect coefficient of the i-th conventional power source material; Let be the topology complexity coefficient of the j-th power electronic transformer.
3. The multi-objective optimization method for power electronic transformer microgrids according to claim 1, characterized in that, The objective function for power loss in the microgrid is specifically expressed as follows: In the formula, Let z be the objective function for power loss in the microgrid, z be the number of temperature-related terms, and d be the number of electromagnetic compatibility interference-related terms. y is the nonlinear quantization value of the temperature coefficient of thermal conductivity of the y-th material; It is the electromagnetic wave polarization direction weight of the cth electromagnetic compatibility interference factor; Let be the dielectric constant distortion coefficient of the c-th electromagnetic interference and power loss.
4. The multi-objective optimization method for power electronic transformer microgrids according to claim 1, characterized in that, The objective function for the voltage stability of the microgrid is specifically expressed as follows: In the formula, Let h be the objective function for the voltage stability of the microgrid, h be the number of terms related to load fluctuation factors, and l be the number of terms related to harmonic effects. The charge density wave weight of the g-th load fluctuation factor is... The higher harmonic spectral weights of the k-th harmonic factor.
5. The multi-objective optimization method for power electronic transformer microgrids according to claim 1, characterized in that, In the process of updating the velocity and position of each particle based on the inertial weights that are dynamically adjusted according to the iteration process and the influence coefficients calculated based on the population distribution state, the velocity of each particle is updated using the following velocity update formula: In the formula, For the first Speed at the next iteration 、 As the impact factor, The influence coefficient, For inertial weights, Let be the velocity of particle i in the t-th iteration. 、 As a learning factor, Let be the individual historical best position of particle i. Let i be the position of particle i in the t-th iteration. This represents the global historical best position for the entire population.
6. The multi-objective optimization method for power electronic transformer microgrids according to claim 1 or 5, characterized in that, In the process of updating the velocity and position of each particle based on the inertial weights that are dynamically adjusted according to the iteration process and the influence coefficients calculated based on the population distribution state, the position of each particle is updated using the following position update formula: In the formula, for Position at the next iteration This represents the position at the t-th iteration. For the first Speed at the next iteration Indicates the first The time taken for each iteration.
7. The multi-objective optimization method for power electronic transformer microgrids according to claim 1, characterized in that, The step of updating the individual historical best position of each particle and the global historical best position of the entire particle swarm based on the fitness value specifically includes: if the current fitness of a particle is better than its own individual historical best fitness, then the current fitness is updated to the individual historical best fitness, and the position corresponding to the current fitness is updated to the individual historical best position; comparing the individual historical best fitness of all particles, and updating the position corresponding to the best individual historical best fitness among all individual historical best fitnesss to the global historical best position; if the current fitness of a particle is not better than its own individual historical best fitness, then the individual historical best position of the particle remains unchanged; comparing the individual historical best fitness of all particles, and updating the position corresponding to the best individual historical best fitness among all individual historical best fitnesss to the global historical best position.
8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the multi-objective optimization method for power electronic transformer microgrids according to any one of claims 1-7.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-objective optimization method for power electronic transformer microgrids as described in any one of claims 1-7.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-objective optimization method for power electronic transformer microgrids as described in any one of claims 1-7.