Hybrid energy power station optimization method and device, equipment and storage medium

Abstract

The invention provides a hybrid energy power station optimization method and device, equipment and a storage medium, and belongs to the technical field of energy power station optimization, and the method comprises the steps: building a node feature vector matrix based on the electrical parameters and real-time operation data of each node in a power grid topological structure; establishing an energy storage characteristic vector matrix based on the physical parameters of each energy storage module; constructing an incidence matrix representing the matching degree of the node feature vectors and the energy storage feature vectors by calculating the similarity of the node feature vectors and the energy storage feature vectors; based on the node eigenvector matrix, the energy storage eigenvector matrix and the optimization target parameters, obtaining an optimal solution set of each energy storage module by using a multi-target particle swarm algorithm; and acquiring node power and node voltage in the power grid in real time by using the energy management module, and optimizing output parameters of the plurality of energy storage modules based on the incidence matrix in response to the situation that the node power or the node voltage does not belong to the range of the optimal solution set. The operation efficiency of the power grid can be improved.

Description

Technical Field

[0001] The present application belongs to the technical field of energy power station optimization, and more specifically, relates to a hybrid energy power station optimization method and device, equipment, and storage medium. Background Art

[0002] With the continued growth of global energy demand and the accelerated transformation of the energy structure, the proportion of renewable energy in electricity supply is increasing. However, the intermittent and fluctuating nature of renewable energy poses a significant challenge to the stable operation of the power system. To effectively address this challenge, energy storage technology has emerged and has developed rapidly. Energy storage systems can store energy when there is excess renewable energy generation and release energy when there is a shortage, thus playing a "peak shaving and valley filling" role, thereby improving the stability and reliability of the power system. As an integrated energy system that integrates multiple energy forms and energy storage devices, hybrid energy power stations can fully leverage the advantages of different energy sources and energy storage devices to achieve efficient energy utilization and optimal allocation, becoming a research hotspot and development direction in the current energy field.

[0003] In the research and application of hybrid power plants, most current energy storage deployments place storage equipment on the power generation side and rely on historical operating data and empirical formulas to regulate the storage equipment. This regulation approach can easily lead to energy storage failing to effectively suppress voltage overshoots and alleviate line congestion when grid node voltages change, resulting in reduced grid efficiency. Summary of the Invention

[0004] Based on the above problems, the present application provides a hybrid energy power station optimization method and device, equipment, and storage medium, which respectively set energy storage modules on the power generation side, transmission side, and distribution side, and can optimize the adjustment parameters of each energy storage module based on the power grid topology, thereby improving the operating efficiency of the power grid.

[0005] In a first aspect of an embodiment of the present application, a hybrid energy power station optimization method is provided, which is applied to a hybrid energy power station. The hybrid energy power station includes an energy management module and multiple energy storage modules, where the multiple energy storage modules are respectively arranged on the power generation side, the power transmission side, and the power distribution side. The hybrid energy power station optimization method includes: A node eigenvector matrix is ​​established based on the electrical parameters and real-time operation data of each node in the power grid topology. Each row of the node eigenvector matrix corresponds to a node eigenvector, and each column corresponds to a performance parameter of the node. An energy storage characteristic vector matrix is ​​established based on the physical parameters of each energy storage module, where each row of the energy storage characteristic vector matrix corresponds to an energy storage characteristic vector of an energy storage module, and each column corresponds to a performance parameter of the energy storage module; By calculating the similarity between the node feature vector and the energy storage characteristic vector, a correlation matrix representing the matching degree between the two is constructed; Based on the node eigenvector matrix, energy storage characteristic vector matrix and optimization target parameters, the multi-objective particle swarm algorithm is used to obtain the optimal solution set of each energy storage module. The energy management module is used to obtain the node power and node voltage in the power grid in real time. In response to the node power or node voltage not belonging to the optimal solution set, the output parameters of multiple energy storage modules are optimized based on the correlation matrix.

[0006] A second aspect of an embodiment of the present application provides a hybrid energy power station optimization device, which is applied to a hybrid energy power station. The hybrid energy power station includes an energy management module and multiple energy storage modules, where the multiple energy storage modules are respectively arranged on the power generation side, the power transmission side, and the power distribution side. The hybrid energy power station optimization device includes: A first data processing unit is configured to establish a node feature vector matrix based on electrical parameters and real-time operation data of each node in the power grid topology, wherein each row of the node feature vector matrix corresponds to a node feature vector of a node, and each column corresponds to a performance parameter of the node; A second data processing unit is configured to establish an energy storage characteristic vector matrix based on the physical parameters of each energy storage module, wherein each row of the energy storage characteristic vector matrix corresponds to an energy storage characteristic vector of an energy storage module, and each column corresponds to a performance parameter of the energy storage module; A first calculation unit is used to calculate the similarity between the node feature vector and the energy storage characteristic vector to construct a correlation matrix representing the matching degree between the two; The second calculation unit is used to obtain the optimal solution set of each energy storage module based on the node characteristic vector matrix, the energy storage characteristic vector matrix and the optimization target parameters using a multi-objective particle swarm algorithm; The parameter optimization unit is used to use the energy management module to obtain the node power and node voltage in the power grid in real time, and in response to the node power or node voltage not belonging to the range of the optimal solution set, optimize the output parameters of multiple energy storage modules based on the correlation matrix.

[0007] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned hybrid energy power station optimization method when executing the computer program.

[0008] In a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned hybrid energy power station optimization method are implemented.

[0009] The hybrid energy power station optimization method, device, equipment, and storage medium provided in the embodiments of the present application have the following beneficial effects: The embodiment of the present application establishes a node feature vector matrix based on the electrical parameters and real-time operation data of each node in the power grid topology, and establishes an energy storage characteristic vector matrix based on the physical parameters of each energy storage module, which can accurately characterize the operating status and characteristics of each node in the power grid, as well as the physical properties and performance characteristics of each energy storage module. Furthermore, by calculating the similarity between the node feature vector and the energy storage characteristic vector, and constructing an association matrix that represents the matching degree between the two, it is possible to clearly define the matching relationship between each node in the power grid and different energy storage modules, so that in the subsequent optimization process, the most suitable energy storage module can be quickly and accurately found for adjustment according to the actual needs of the node. Finally, a multi-objective particle swarm algorithm is used to obtain the optimal solution set for each energy storage module. If the node power or node voltage does not fall within the range of the optimal solution set, the output parameters of multiple energy storage modules are optimized separately based on the association matrix. This real-time monitoring and dynamic optimization method in the embodiment of the present application can respond to changes in the power grid operation status in a timely manner. When problems such as voltage exceeding the limit and line congestion occur, the output parameters of the energy storage module are quickly adjusted, effectively suppressing the further deterioration of the problem, ensuring the stable operation of the power grid, and improving the operation efficiency and reliability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0011] Figure 1 A flow chart of a hybrid energy power station optimization method provided in one embodiment of the present application; Figure 2 A structural block diagram of a hybrid energy power station optimization device provided in one embodiment of the present application; Figure 3 A schematic block diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0012] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0013] In order to make the purpose, technical solutions and advantages of this application clearer, specific embodiments will be described below with reference to the accompanying drawings.

[0014] Please refer to Figure 1 , Figure 1 This is a flow chart of a hybrid energy power station optimization method provided in one embodiment of the present application, which can be executed by an electronic device. The hybrid energy power station includes an energy management module and multiple energy storage modules, with the multiple energy storage modules being respectively arranged on the power generation side, the power transmission side, and the power distribution side. The method may include S101 to S105.

[0015] In this embodiment, the hybrid energy power station also includes multiple energy generation modules, and energy storage modules are provided on the power generation, transmission, and distribution sides, with at least one energy storage module provided on each of the power generation, transmission, and distribution sides. The energy management module is configured to obtain operational data of the hybrid energy power station, including power generation data of each energy generation module, operational data of the energy storage module, and operational data of the power grid connected to the hybrid energy power station, and transmit the acquired data to the electronic device for processing.

[0016] S101: Establishing a node feature vector matrix based on electrical parameters and real-time operation data of each node in the power grid topology.

[0017] Each row of the node feature vector matrix corresponds to the node feature vector of a node, and each column corresponds to the performance parameter of the node.

[0018] In this embodiment, the grid topology represents the physical connections and layout of electrical components (such as power generation equipment, transmission lines, distribution devices, energy storage module access points, and loads) within the power network where the hybrid energy power station resides. This includes the connection methods, hierarchical relationships, and geographical / logical distribution between components, used to clearly define the electrical connection paths and mutual influence relationships between nodes. Electrical equipment includes power generation equipment, transmission lines, distribution devices, and energy storage module access points, and component connections can be in series or parallel.

[0019] Each node in the grid topology represents a connection point or functional unit for electrical components within the grid. Specifically, these nodes may include: generation-side nodes, such as the grid connection points for renewable energy generation modules like photovoltaic and wind turbines; transmission-side nodes, such as the starting and ending points of transmission lines and transformer connection points; and distribution-side nodes, such as user load access points and distribution network branch connection points. The electrical parameters corresponding to each node, such as the node's rated voltage, node voltage level, line connection impedance, and node's maximum power carrying capacity, can be used to construct a node eigenvector matrix. Real-time operating data represents data reflecting the node's dynamic operating status, acquired by the energy management module through the grid monitoring port. This data includes, but is not limited to, real-time voltage values, real-time active and reactive power values, power fluctuation data (the change in power per unit time), and voltage deviation data (the difference between the real-time voltage and the rated voltage).

[0020] In this embodiment, a node feature vector matrix can be established based on the electrical parameters and real-time operating data of each node in the power grid topology. Each row of the matrix corresponds to a node feature vector, and each column corresponds to a performance parameter of a node. For example, the power grid topology contains three nodes: Node 1, Node 2, and Node 3. The electrical parameters and real-time operating data corresponding to each node are shown in Table 1.

[0021] Table 1 Electrical parameters and real-time operating data of each node

[0022] The performance parameters for each node include the node load fluctuation coefficient, transmission capacity margin, and voltage sensitivity coefficient. The node load fluctuation coefficient is the product of the load fluctuation frequency and the difference between peak and valley power, divided by the maximum loadable power. The transmission capacity margin is the difference between the maximum loadable power and the current average power, divided by the maximum loadable power. The voltage sensitivity coefficient is the product of the absolute value of the voltage deviation and the voltage level weight (assuming 1 for 110kV, 0.5 for 220kV, and 2 for 10kV).

[0023] Based on the above method, the node feature vector matrix can be obtained as X, and the expression of X is:

[0024] The values ​​of the column vectors of the node eigenvector matrix have been normalized to eliminate dimensional differences, laying the foundation for the subsequent calculation of the matching degree between the node and the energy storage module.

[0025] S102: Establishing an energy storage characteristic vector matrix based on the physical parameters of each energy storage module.

[0026] Each row of the energy storage characteristic vector matrix corresponds to an energy storage characteristic vector of an energy storage module, and each column corresponds to a performance parameter of the energy storage module.

[0027] In this embodiment, energy storage modules are provided on the power generation, transmission, and distribution sides, respectively. The energy storage modules on the power generation side are primarily used to smooth power fluctuations in renewable energy generation (such as output power fluctuations caused by sudden changes in sunlight or unstable wind speeds) and to assist in the stable output of each energy generation module. The energy storage modules on the power generation side can be lithium battery modules, flow battery modules, or flywheel modules. The energy storage modules on the power transmission side are primarily used to maintain voltage stability in power transmission lines and alleviate power congestion on these lines, requiring large capacity and long-term regulation capabilities. The energy storage modules on the power transmission side can be flow battery modules. The energy storage modules on the distribution side are primarily used to respond to load fluctuations and maintain distribution voltage stability, requiring rapid response and flexible regulation. The energy storage modules on the distribution side can be supercapacitors or lead-acid batteries, among others.

[0028] The physical parameters of an energy storage module represent the static parameters of its inherent technical characteristics, primarily including capacity, power, response, voltage, efficiency, and stability. The energy storage characteristic vector matrix is ​​normalized by normalizing the values ​​corresponding to the physical parameters of each energy storage module. Similar to the node eigenvector matrix, the energy storage characteristic vector matrix is ​​also normalized to eliminate dimensional differences, laying the foundation for subsequent calculations of the matching between nodes and energy storage modules.

[0029] There is no strict execution order between S101 and S102. S101 can be executed before S102, after S102, or at the same time. Figure 1 The execution method is only an example and is not intended to be limiting.

[0030] S103: By calculating the similarity between the node feature vector and the energy storage characteristic vector, a correlation matrix representing the matching degree between the two is constructed.

[0031] In this embodiment, similarity is a quantitative indicator that measures the degree of similarity between two vectors in terms of multi-dimensional features, and its value range is generally [0, 1]. Common similarity calculation methods include cosine similarity, Euclidean distance, and Pearson correlation coefficient. The association matrix represents a matrix formed by arranging the "similarity" between all nodes and all energy storage modules in order by rows and columns. Its dimension is "number of nodes × number of energy storage modules". The element value in the jth row and i-th column of the matrix represents the similarity (i.e., matching degree) between the jth node and the i-th energy storage module.

[0032] S104: Based on the node characteristic vector matrix, the energy storage characteristic vector matrix and the optimization target parameters, the optimal solution set of each energy storage module is obtained using a multi-objective particle swarm algorithm.

[0033] In this embodiment, the optimization target parameters represent the core technical indicators of the hybrid power station's operational performance and serve as the optimization direction for the multi-objective particle swarm algorithm. These parameters include, but are not limited to, an energy loss indicator, a grid stability indicator, and a response timeliness indicator. The energy loss indicator represents the total energy loss during the energy storage module's charging and discharging process; the grid stability indicator represents the node voltage deviation rate and power fluctuation amplitude; and the response timeliness indicator represents the deviation between the energy storage module's actual response time and the theoretical requirement.

[0034] The multi-objective particle swarm algorithm is a heuristic optimization algorithm based on swarm intelligence, which is used to solve optimization problems with multiple conflicting objectives, such as minimizing energy loss and optimizing grid stability. Based on the multi-objective particle swarm algorithm, the position and velocity of particles can be iteratively updated to gradually approach the optimal solution. When the iteration meets the termination condition, such as reaching the preset number of iterations, or the difference in the fitness function of the optimal solution set in multiple consecutive iterations is less than the preset threshold, the iteration is stopped. From the global optimal solution set, all non-dominated solutions corresponding to each energy storage module are extracted according to the energy storage module classification to form the optimal solution set of each energy storage module. Each optimal solution set contains the optimal output parameters of the energy storage module under different optimization target parameters.

[0035] This embodiment uses a multi-objective particle swarm algorithm to obtain the optimal solution set for each energy storage module, enhancing the grid voltage stability and power balancing capabilities. This allows the energy storage modules to quickly respond to real-time changes in node power and voltage, ensuring that they can effectively smooth fluctuations and provide stability in different operating scenarios, significantly improving the overall operational reliability of the hybrid energy power station.

[0036] S105: Utilize the energy management module to obtain node power and node voltage in the power grid in real time, and in response to the node power or node voltage not belonging to the optimal solution set, optimize the output parameters of the multiple energy storage modules based on the correlation matrix.

[0037] The energy management module performs data collection, status determination, and command issuance. In this embodiment, it acquires real-time node power and voltage data from the power grid and, based on this data, determines whether the data falls within the optimal solution set. If the data falls within the optimal solution set, no parameter optimization is required. If the data falls outside the optimal solution set, the output parameters of the multiple energy storage modules must be optimized individually based on the correlation matrix.

[0038] Node power represents the active or reactive power values ​​at key nodes in the power grid. It reflects the power supply and demand status of the nodes (such as load power consumption) and is a core indicator for measuring power balance in the power grid. Node voltage represents the voltage amplitude at each node in the power grid and is a fundamental indicator for stable grid operation. It must be maintained within the allowable deviation range of the rated voltage (e.g., ±5%). Abnormal voltages can cause equipment damage or grid failure. The output parameters of the energy storage module represent key adjustable parameters during operation, such as charge and discharge power and response time.

[0039] From the above, we can conclude that traditional hybrid power plants mostly centrally locate energy storage equipment on the power generation side. This layout is relatively simple and cannot fully address the complex needs of the power system at different stages. However, the hybrid power plant used in this optimization method places multiple energy storage modules on the power generation side, transmission side, and distribution side. This multi-side layout can fully utilize the unique advantages of energy storage modules in different locations, allowing the energy storage system to play a role in all stages of the power system, better adapting to the various problems caused by the intermittent and fluctuating nature of renewable energy, and effectively improving the adaptability of the entire hybrid power plant to different operating conditions and energy fluctuations.

[0040] Specifically, the embodiment of the present application establishes a node feature vector matrix based on the electrical parameters and real-time operation data of each node in the power grid topology, and establishes an energy storage characteristic vector matrix based on the physical parameters of each energy storage module, which can accurately characterize the operating status and characteristics of each node in the power grid, as well as the physical properties and performance characteristics of each energy storage module. Furthermore, by calculating the similarity between the node feature vector and the energy storage characteristic vector, and constructing an association matrix that represents the matching degree between the two, it is possible to clarify the matching relationship between each node in the power grid and different energy storage modules, so that in the subsequent optimization process, the most suitable energy storage module can be quickly and accurately found for adjustment according to the actual needs of the node. Finally, a multi-objective particle swarm algorithm is used to obtain the optimal solution set for each energy storage module. If the node power or node voltage does not fall within the range of the optimal solution set, the output parameters of multiple energy storage modules are optimized separately based on the association matrix. This real-time monitoring and dynamic optimization method in the embodiment of the present application can respond to changes in the power grid operation status in a timely manner. When problems such as voltage exceeding the limit and line congestion occur, the output parameters of the energy storage module are quickly adjusted, effectively suppressing the further deterioration of the problem, ensuring the stable operation of the power grid, and improving the operation efficiency and reliability of the power grid.

[0041] In one embodiment of the present application, based on the node eigenvector matrix, the energy storage characteristic vector matrix, and the optimization target parameters, a multi-objective particle swarm algorithm is used to obtain the optimal solution set for each energy storage module, including: Determine the number of particles of the multi-objective particle swarm algorithm based on the number of energy storage modules; Determine the initial position and initial velocity of particles in the multi-objective particle swarm optimization algorithm based on the node eigenvector matrix and the energy storage characteristic vector matrix; Iterative calculations are performed based on the number of particles, initial positions of particles, and initial velocities of particles until the following conditions are met to obtain the optimal solution set for each energy storage module; Among them, the conditions include: The number of iterations is equal to the preset number of iterations, or the difference in fitness functions of consecutive target numbers is less than the preset threshold.

[0042] In this embodiment, the number of particles is directly related to the number of energy storage modules. For example, the number of particles is K times the number of energy storage modules, where K is an empirical value. If there are five energy storage modules, the number of particles can be set to 5 × 10 = 50 (each module corresponds to 10 potential solutions), ensuring that each energy storage module has sufficient solution space for search. The initial position of the particle is determined based on the node eigenvector matrix and the energy storage characteristic vector matrix. For example, the position coordinates of each particle correspond to a set of energy storage module output parameters (such as charge and discharge power). The initial value can be set based on the match between the node eigenvector and the energy storage characteristic vector. That is, parameter combinations with a high match are prioritized as the initial position, reducing inefficient searches. The initial velocity of the particle is also set based on the difference in the characteristics of the two matrices. For example, the greater the difference between the node and energy storage capacity, the greater the initial velocity; the smaller the difference, the lower the initial velocity, thus balancing global exploration and local development.

[0043] In addition to the above parameters, you can also set an inertia weight reference value to balance the particle's global exploration (large-scale search for new solutions) and local development; set a cognitive factor to guide particles toward their own historical optimal solution; and set a social factor to guide particles toward the group's optimal solution. These parameters can be set based on experience.

[0044] In one embodiment, the hybrid energy power station optimization method further includes: In response to the number of iterations being less than or equal to the first number, increasing the particle swarm algorithm inertia weight reference value by a first inertia step size; In response to the number of iterations being greater than the first number, the inertia weight reference value of the particle swarm algorithm is reduced by a second inertia step size.

[0045] In this embodiment, considering that in the early stages of the particle swarm algorithm iteration, increasing the inertia weight reference value can give particles greater inertia, allowing them to conduct a wide range of searches in the search space with a larger step size. This helps particles explore the entire search space more fully in the initial stage, avoid falling into a local optimal solution too early, and increase the possibility of finding the global optimal solution. When the number of iterations exceeds a certain number, the particle swarm algorithm has already gained a certain understanding of the search space. At this time, lowering the inertia weight reference value can reduce the particle search step size and focus more on performing a detailed search within the current local area. Because as the iteration progresses, the particles get closer to the optimal solution, it is necessary to search for the optimal solution more accurately within the local range. A smaller inertia weight allows particles to conduct more detailed exploration near the current optimal position, improving the algorithm's convergence accuracy.

[0046] Therefore, a threshold can be set. When the number of iterations exceeds the threshold, the inertia weight reference value is appropriately reduced. When the number of iterations is less than or equal to the threshold, the inertia weight reference value is appropriately increased.

[0047] This threshold is the first number. It should be noted that the first number should be set to be less than the target number of iterations. The first number can be determined as a certain ratio of the target number of iterations, for example, one-third to one-half of the target number of iterations. The first inertia step length and the second inertia step length are the step lengths for adjusting the inertia weight reference value, and can be set based on experimental data.

[0048] In this embodiment, an iterative calculation is performed based on parameters such as the number of particles, the initial position and velocity of the particles, and the inertia weight reference value until the number of iterations is equal to a preset number of iterations, or the difference in the fitness function of the consecutive target number is less than a preset threshold, thereby obtaining the optimal solution set for each energy storage module.

[0049] The fitness function includes a first fitness function and a second fitness function; the preset threshold includes a first preset threshold and a second preset threshold; Determining a first fitness function based on an energy loss parameter in the node characteristic vector matrix and a charge and discharge efficiency parameter in the energy storage characteristic vector matrix; The second fitness function is determined based on the voltage stability parameter and the power fluctuation parameter in the node characteristic vector matrix and the response speed parameter in the energy storage characteristic vector matrix.

[0050] The difference in fitness function of consecutive target numbers is less than the preset threshold, including: The difference of the first fitness function of the consecutive target numbers is less than a first preset threshold; The differences of the second fitness functions of the consecutive target quantities are all less than the second preset threshold.

[0051] In this embodiment, based on the energy loss parameter in the node characteristic vector matrix and the charge and discharge efficiency parameter in the energy storage characteristic vector matrix, a first fitness function is determined using a first formula. The first formula is:

[0052] Where k is the number of energy storage modules, T is the total number of time intervals in the optimization cycle, is the charging power of the i-th energy storage module in the t-th time interval, is the charging efficiency of the i-th energy storage module, is the discharge power of the i-th energy storage module in the t-th time interval, is the discharge efficiency of the i-th energy storage module, is the energy loss rate during charging, is the energy loss rate during the discharge process.

[0053] In the first fitness function, the node energy loss parameter and the energy storage charging and discharging efficiency parameter are coupled through the power-efficiency term in the first formula ( and ), and during optimization, it can simultaneously take into account the reduction of grid transmission loss and the reduction of energy storage itself, making the reduction of energy loss more in line with the actual physical process.

[0054] In this embodiment, based on the voltage stability parameter and the power fluctuation parameter in the node characteristic vector matrix and the response speed parameter in the energy storage characteristic vector matrix, a second fitness function is determined using a second formula. The second formula is:

[0055] Where n is the total number of grid nodes, is the real-time voltage of the jth node, The rated voltage of the jth node, is the real-time power of the j-th node, is the rated power of the jth node, is the difference between the actual response time and the theoretical response time of the i-th energy storage module, is the maximum allowable response delay of the i-th energy storage module, , and is the weight coefficient.

[0056] In this embodiment, the node voltage parameters, node power parameters, and energy storage response speed parameters are weightedly coupled through the deviation rate, so that during optimization, not only the current stability deviation of the power grid is considered, but also the response time of the energy storage module is introduced, making the regulation of the equipment more dynamically adaptable and avoiding the problem of focusing only on static deviations and ignoring the timeliness of regulation.

[0057] As can be seen from the above, the embodiment of the present application sets the number of particles based on the number of energy storage modules and determines the initial position and velocity of the particles based on the association matrix between the nodes and the energy storage modules, thereby reducing the number of iterations and shortening the iteration time of the algorithm. Secondly, by setting multiple fitness functions and optimizing multiple target parameters, the optimal solution set for each energy storage module is ultimately obtained, providing a more reliable reference for the optimization of the energy storage modules, ensuring the stable operation of the power grid, and improving the operating efficiency of the power grid.

[0058] In one embodiment of the present application, based on the correlation matrix, the output parameters of multiple energy storage modules are optimized separately, including: Based on the matching degree between each node in the association matrix and the energy storage module, the power grid is divided into different control areas, each of which corresponds to an energy storage module. A dual-factor weighted algorithm of matching degree and capacity elasticity coefficient is used to calculate the compensation power of each energy storage module on the power generation side. The capacity elasticity coefficient is the coefficient of the capacity adjustment capability of the energy storage module as the power of the corresponding node changes dynamically. Based on the line transient impedance correction coefficient and the matching degree in the correlation matrix, a power regulation dynamic coefficient matrix is ​​constructed. Based on the power regulation dynamic coefficient matrix, the charging and discharging power of each energy storage module on the transmission side is corrected. The line transient impedance correction coefficient is the ratio of the current real-time impedance of the line to the rated impedance. The matching degrees in the correlation matrix are sorted, and a voltage response virtual queue is established. Based on the voltage response virtual queue, the reactive power response parameters of each energy storage module on the distribution side participating in voltage regulation are adjusted.

[0059] In this embodiment, the power grid is divided into different control regions based on the matching degree between each node and the energy storage module in the association matrix. Each control region corresponds to an energy storage module, and each energy storage module corresponds to one or more nodes. By optimizing the output parameters of the energy storage modules on the generation, transmission, and supply sides in different ways, the control capabilities of the energy storage modules on each side are highly adapted to the real-time needs of the corresponding nodes. This allows for precise smoothing of power grid power fluctuations, stabilizing voltage within the rated range, and significantly reducing energy losses during transmission. Ultimately, this achieves a comprehensive improvement in the coordinated efficiency and overall stability of all aspects of the hybrid power station.

[0060] In one embodiment, a dual-factor weighted algorithm of matching degree and capacity elasticity coefficient is used to calculate the compensation power of each energy storage module on the power generation side, including: The compensation power of each energy storage module on the power generation side is calculated based on the following method: Obtaining benchmark load values ​​of multiple nodes corresponding to each energy storage module; The ratio of each node's real-time load value to the total load value within the service range of the corresponding energy storage module is used as the node weight coefficient; Calculate the compensation power of each node based on the benchmark load value of each node and the double-factor weighted algorithm; The compensation power of the corresponding energy storage module is obtained by performing a weighted sum based on the partial compensation power of each node and the node weight coefficient corresponding to each node.

[0061] In this embodiment, the capacity elasticity coefficient is the coefficient by which the energy storage module's capacity adjustment capability changes dynamically with the corresponding node power. The greater the node power fluctuation, the higher the coefficient. Compensation power is the regulated power output by the generation-side energy storage module to smooth out power fluctuations from renewable energy sources (such as photovoltaic and wind power). It must match the node power fluctuation to maintain power balance.

[0062] In this embodiment, the compensation power of each energy storage module on the power generation side is calculated in the following manner. First, the benchmark load value (such as planned load or historical average load) of each node within the service range of the energy storage module is obtained as a benchmark for judging real-time load fluctuations, and the power deviation base to be compensated is clarified. Secondly, the proportion of the real-time load value of each node to the total real-time load within the service range of the energy storage module is calculated as the node weight coefficient, that is, the higher the proportion of the real-time load value of the node, the greater the impact on the total power balance of the region, and the higher the weight of the proportion in the total compensation power. Thirdly, for each node, combined with the deviation between its benchmark load value and the real-time load, the node's partial compensation power is obtained by weighting the double factors of "matching degree + capacity elasticity coefficient". Finally, the partial compensation power of each node is weighted and summed according to its node weight coefficient to obtain the total compensation power of the energy storage module.

[0063] From the above, it can be concluded that the compensation power not only reflects the differences in load proportions of each node, but also takes into account the adaptability and dynamic adjustment capabilities of energy storage and nodes. It can ensure that power fluctuations on the power generation side are accurately smoothed, thereby improving the efficiency and stability of regional power balance.

[0064] In one embodiment, adjusting reactive power response parameters of each energy storage module on the distribution side when participating in voltage regulation based on a voltage response virtual queue includes: Periodically obtain the voltage fluctuation amplitude and energy storage module charge of each node; Update the virtual queue order based on the voltage fluctuation amplitude and energy storage module charge of each node; Based on the updated virtual queue ranking, reactive power response parameters of each energy storage module on the distribution side participating in voltage regulation are adjusted.

[0065] In this embodiment, the voltage fluctuation amplitude and energy storage module charge at each node are periodically acquired—for example, at intervals of 1 minute, 2 minutes, or 5 minutes—to ensure that the data reflects the real-time state of the power grid. The sorting rules for the voltage response virtual queue are updated based on the collected data. Generally, nodes with greater voltage fluctuation amplitudes are ranked higher (emergency requests are prioritized). Energy storage modules with charges within a reasonable range are ranked higher (modules with strong regulation capabilities are prioritized), thus preventing regulation failures caused by abnormal energy storage charge states.

[0066] Based on the updated virtual queue ranking, the reactive power response parameters of the energy storage modules are adjusted differentially: energy storage modules with higher rankings can be allocated faster response speeds and larger adjustment limits, giving priority to regulating nodes with the most severe voltage fluctuations; modules with lower rankings are on standby or assist in regulation according to suboptimal parameters, ensuring that limited energy storage resources are used first to meet the most critical voltage stability needs, thereby improving the efficiency and reliability of voltage regulation on the distribution side.

[0067] Corresponding to the hybrid energy power station optimization method of the above embodiment, Figure 2 This is a block diagram of the structure of a hybrid energy power station optimization device provided by an embodiment of the present application. For ease of explanation, only the parts related to the embodiment of the present application are shown. The hybrid energy power station optimization device is applied to a hybrid energy power station, which includes an energy management module and multiple energy storage modules, which are respectively arranged on the power generation side, the transmission side, and the distribution side. Figure 2 The hybrid energy power station optimization device 20 includes: a first data processing unit 21, a second data processing unit 22, a first calculation unit 23, a second calculation unit 24 and a parameter optimization unit 25.

[0068] The first data processing unit 21 is configured to establish a node feature vector matrix based on electrical parameters and real-time operation data of each node in the power grid topology, wherein each row of the node feature vector matrix corresponds to a node feature vector of a node, and each column corresponds to a performance parameter of the node; A second data processing unit 22 is configured to establish an energy storage characteristic vector matrix based on the physical parameters of each energy storage module, wherein each row of the energy storage characteristic vector matrix corresponds to an energy storage characteristic vector of an energy storage module, and each column corresponds to a performance parameter of the energy storage module; A first calculation unit 23 is configured to calculate the similarity between the node feature vector and the energy storage characteristic vector to construct a correlation matrix representing the matching degree between the two; The second calculation unit 24 is used to obtain the optimal solution set of each energy storage module based on the node characteristic vector matrix, the energy storage characteristic vector matrix and the optimization target parameters using a multi-objective particle swarm algorithm; The parameter optimization unit 25 is used to use the energy management module to obtain the node power and node voltage in the power grid in real time, and in response to the node power or node voltage not belonging to the range of the optimal solution set, optimize the output parameters of multiple energy storage modules based on the correlation matrix.

[0069] In one embodiment of the present application, the second calculation unit 24 is specifically configured to: Determine the number of particles of the multi-objective particle swarm algorithm based on the number of energy storage modules; Determine the initial position and initial velocity of particles in the multi-objective particle swarm optimization algorithm based on the node eigenvector matrix and the energy storage characteristic vector matrix; Iterative calculations are performed based on the number of particles, initial positions of particles, and initial velocities of particles until the following conditions are met to obtain the optimal solution set for each energy storage module; The conditions include: The number of iterations is equal to the preset number of iterations, or the difference in fitness functions of consecutive target numbers is less than the preset threshold.

[0070] In one embodiment of the present application, the fitness function includes a first fitness function and a second fitness function; the preset threshold includes a first preset threshold and a second preset threshold. The second calculation unit 24 is specifically configured to: The difference of the first fitness function of the consecutive target numbers is less than a first preset threshold; The differences of the second fitness functions of the consecutive target quantities are all less than the second preset threshold.

[0071] In one embodiment of the present application, the second calculation unit 24 is further configured to: Determining a first fitness function based on an energy loss parameter in the node characteristic vector matrix and a charge and discharge efficiency parameter in the energy storage characteristic vector matrix; The second fitness function is determined based on the voltage stability parameter and the power fluctuation parameter in the node characteristic vector matrix and the response speed parameter in the energy storage characteristic vector matrix.

[0072] In one embodiment of the present application, the parameter optimization unit 25 is specifically configured to: Based on the matching degree between each node in the association matrix and the energy storage module, the power grid is divided into different control areas, each of which corresponds to an energy storage module. A dual-factor weighted algorithm of matching degree and capacity elasticity coefficient is used to calculate the compensation power of each energy storage module on the power generation side. The capacity elasticity coefficient is the coefficient of the capacity adjustment capability of the energy storage module as the power of the corresponding node changes dynamically. Based on the line transient impedance correction coefficient and the matching degree in the correlation matrix, a power regulation dynamic coefficient matrix is ​​constructed. Based on the power regulation dynamic coefficient matrix, the charging and discharging power of each energy storage module on the transmission side is corrected. The line transient impedance correction coefficient is the ratio of the current real-time impedance of the line to the rated impedance. The matching degrees in the correlation matrix are sorted, and a voltage response virtual queue is established. Based on the voltage response virtual queue, the reactive power response parameters of each energy storage module on the distribution side participating in voltage regulation are adjusted.

[0073] In one embodiment of the present application, the parameter optimization unit 25 is specifically configured to: The compensation power of each energy storage module on the power generation side is calculated based on the following method: Obtaining benchmark load values ​​of multiple nodes corresponding to each energy storage module; The ratio of each node's real-time load value to the total load value within the service range of the corresponding energy storage module is used as the node weight coefficient; Calculate the compensation power of each node based on the benchmark load value of each node and the double-factor weighted algorithm; The compensation power of the corresponding energy storage module is obtained by performing a weighted sum based on the partial compensation power of each node and the node weight coefficient corresponding to each node.

[0074] In one embodiment of the present application, the parameter optimization unit 25 is specifically configured to: Periodically obtain the voltage fluctuation amplitude and energy storage module charge of each node; Update the virtual queue order based on the voltage fluctuation amplitude and energy storage module charge of each node; Based on the updated virtual queue ranking, reactive power response parameters of each energy storage module on the distribution side participating in voltage regulation are adjusted.

[0075] See also Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided in one embodiment of the present application. Figure 3 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of each unit in the above-mentioned device embodiments, such as Figure 2The functions of the first data processing unit 21 , the second data processing unit 22 , the first calculation unit 23 , the second calculation unit 24 and the parameter optimization unit 25 are shown.

[0076] It should be understood that in the embodiment of the present application, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0077] The input device 302 may include a touchpad, a fingerprint collection sensor (for collecting user fingerprint information and fingerprint direction information), a microphone, etc. The output device 303 may include a display (LCD, etc.), a speaker, etc.

[0078] The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include a nonvolatile random access memory.

[0079] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiment of the present application can execute the implementation method described in the hybrid energy power station optimization method provided in the embodiment of the present application, and can also execute the implementation method of the electronic device described in the embodiment of the present application, which will not be repeated here.

[0080] In another embodiment of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, all or part of the process of the method in the above embodiment is implemented. The computer program can also be used to instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above method embodiments are implemented. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0081] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the aforementioned embodiments, such as the electronic device's hard drive or memory. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the computer-readable storage medium can include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or is about to be output.

[0082] Those skilled in the art will appreciate that the modules / units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0083] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0084] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of modules / units is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules, units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or modules / units, or it can be an electrical, mechanical or other form of connection.

[0085] Modules / units described as separate components may or may not be physically separate, and components displayed as modules / units may or may not be physical modules / units, that is, they may be located in one place or distributed across multiple network modules / units. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0086] In addition, the functional modules / units in the various embodiments of the present application may be integrated into a single processing module / unit, or each module / unit may exist physically separately, or two or more modules / units may be integrated into a single module / unit. The aforementioned integrated modules / units may be implemented in the form of hardware or software functional modules / units.

[0087] The above are only specific embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A hybrid energy power station optimization method, characterized in that: Applied to the hybrid energy power station, the hybrid energy power station includes an energy management module and multiple energy storage modules, and the multiple energy storage modules are respectively arranged on the power generation side, the power transmission side and the power distribution side; the optimization method includes: Establishing a node feature vector matrix based on electrical parameters and real-time operating data of each node in the power grid topology, wherein each row of the node feature vector matrix corresponds to a node feature vector of a node, and each column corresponds to a performance parameter of the node; Establishing an energy storage characteristic vector matrix based on the physical parameters of each energy storage module, wherein each row of the energy storage characteristic vector matrix corresponds to an energy storage characteristic vector of an energy storage module, and each column corresponds to a performance parameter of the energy storage module; By calculating the similarity between the node feature vector and the energy storage characteristic vector, a correlation matrix representing the matching degree between the two is constructed; Based on the node eigenvector matrix, the energy storage characteristic vector matrix and the optimization target parameters, a multi-objective particle swarm algorithm is used to obtain the optimal solution set of each energy storage module; The energy management module is used to obtain node power and node voltage in the power grid in real time. In response to the node power or node voltage not belonging to the range of the optimal solution set, the output parameters of multiple energy storage modules are optimized based on the association matrix.

2. The method according to claim 1, wherein The method of obtaining the optimal solution set of each energy storage module based on the node characteristic vector matrix, the energy storage characteristic vector matrix and the optimization target parameters using a multi-objective particle swarm algorithm includes: Determining the number of particles of the multi-objective particle swarm algorithm based on the number of energy storage modules; Determine the particle initial position and particle initial velocity of a multi-objective particle swarm algorithm based on the node eigenvector matrix and the energy storage characteristic vector matrix; Performing iterative calculations based on the number of particles, the initial positions of the particles, and the initial velocities of the particles until the following conditions are met, thereby obtaining an optimal solution set for each energy storage module; The following conditions include: The number of iterations is equal to the preset number of iterations, or the difference in fitness functions of consecutive target numbers is less than the preset threshold.

3. The method according to claim 2, wherein The fitness function includes a first fitness function and a second fitness function; the preset threshold includes a first preset threshold and a second preset threshold; The difference in fitness function of the consecutive target numbers is less than a preset threshold, including: The difference of the first fitness function of the consecutive target numbers is less than a first preset threshold; The differences of the second fitness functions of the consecutive target quantities are all less than the second preset threshold.

4. The method according to claim 3, wherein Also includes: Determining a first fitness function based on an energy loss parameter in the node characteristic vector matrix and a charge and discharge efficiency parameter in the energy storage characteristic vector matrix; A second fitness function is determined based on the voltage stability parameter and the power fluctuation parameter in the node characteristic vector matrix and the response speed parameter in the energy storage characteristic vector matrix.

5. The method according to claim 1, wherein The optimizing the output parameters of the plurality of energy storage modules based on the correlation matrix includes: Dividing the power grid into different control areas based on the matching degree between each node in the association matrix and the energy storage module, each control area corresponding to an energy storage module; The compensation power of each energy storage module on the power generation side is calculated using a dual-factor weighted algorithm of matching degree and capacity elasticity coefficient. The capacity elasticity coefficient is the coefficient of the capacity adjustment capability of the energy storage module that changes dynamically with the power of the corresponding node. Based on the line transient impedance correction coefficient and the matching degree in the correlation matrix, a power regulation dynamic coefficient matrix is ​​constructed, and the charge and discharge power of each energy storage module on the transmission side is corrected based on the power regulation dynamic coefficient matrix. The line transient impedance correction coefficient is the ratio of the current real-time impedance of the line to the rated impedance; The matching degrees in the association matrix are sorted, a voltage response virtual queue is established, and reactive power response parameters of each energy storage module on the distribution side participating in voltage regulation are adjusted based on the voltage response virtual queue.

6. The method according to claim 5, wherein The dual-factor weighted algorithm of matching degree and capacity elasticity coefficient is used to calculate the compensation power of each energy storage module on the power generation side, including: The compensation power of each energy storage module on the power generation side is calculated based on the following method: Obtaining benchmark load values ​​of multiple nodes corresponding to each energy storage module; The ratio of each node's real-time load value to the total load value within the service range of the corresponding energy storage module is used as the node weight coefficient; Calculating the compensation power of each node based on the benchmark load value of each node and the double-factor weighted algorithm; The compensation power of the corresponding energy storage module is obtained by performing a weighted sum based on the partial compensation power of each node and the node weight coefficient corresponding to each node.

7. The method according to claim 5, wherein The step of adjusting reactive power response parameters of each energy storage module on the distribution side when participating in voltage regulation based on the voltage response virtual queue includes: Periodically obtain the voltage fluctuation amplitude and energy storage module charge of each node; Updating the order of the virtual queue based on the voltage fluctuation amplitude of each node and the charge of the energy storage module; Based on the updated order of the virtual queue, reactive power response parameters of each energy storage module on the distribution side when participating in voltage regulation are adjusted.

8. A hybrid energy power station optimization device, characterized in that: Applied to the hybrid energy power station, the hybrid energy power station includes an energy management module and multiple energy storage modules, the multiple energy storage modules are respectively arranged on the power generation side, the power transmission side and the power distribution side; the optimization device includes: A first data processing unit is configured to establish a node feature vector matrix based on electrical parameters and real-time operation data of each node in the power grid topology, wherein each row of the node feature vector matrix corresponds to a node feature vector of a node, and each column corresponds to a performance parameter of the node; A second data processing unit is configured to establish an energy storage characteristic vector matrix based on the physical parameters of each energy storage module, wherein each row of the energy storage characteristic vector matrix corresponds to an energy storage characteristic vector of an energy storage module, and each column corresponds to a performance parameter of the energy storage module; A first calculation unit is configured to calculate the similarity between the node feature vector and the energy storage characteristic vector to construct a correlation matrix representing the matching degree between the two; A second calculation unit is used to obtain an optimal solution set for each energy storage module based on the node characteristic vector matrix, the energy storage characteristic vector matrix and the optimization target parameter using a multi-objective particle swarm algorithm; A parameter optimization unit is used to use the energy management module to obtain node power and node voltage in the power grid in real time, and in response to the node power or node voltage not falling within the range of the optimal solution set, optimize the output parameters of multiple energy storage modules based on the association matrix.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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