Virtual power plant adjustable capacity configuration method and system based on optimization analysis

Abstract

The invention relates to the technical field of intelligent power supply systems, in particular to a virtual power plant adjustable capacity configuration method and system based on optimization analysis. In the method, a first power demand of a particle is obtained. And adjusting the first power demand according to the distance to obtain a second power demand. And further analyzing the power configuration proportion of the renewable energy sources to obtain the green configuration degree of each particle. The particle swarm optimization based on the green configuration degree can consider the energy supply and demand relationship and the green and environmental protection concept at the same time, so that the optimization result better conforms to the expectation. In the particle updating process, the generating capacity stability and the configuration power of the configured energy distribution points are considered, the updating speed of each particle is weighted through the updating speed weight, and finally the optimal configuration scheme of the virtual power plant is obtained. According to the invention, power distribution is carried out on the energy distribution points and the configuration points through the optimal configuration scheme, so that energy supply and demand can be ensured and an environment-friendly concept is met.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent power supply systems, and in particular to a method and system for configuring the adjustable capacity of a virtual power plant based on optimization analysis. Background Art

[0002] The conceptual model or entity that realizes power supply through intelligent scheduling and integration of multiple distributed energy sources is called a virtual power plant. The energy that can be deployed by a virtual power plant includes renewable energy such as solar energy and wind energy, as well as traditional energy such as natural gas and biomass energy. Compared with traditional energy, the use of renewable energy can not only significantly reduce the emission of harmful pollutants, help improve air quality, but also can be continuously used. Therefore, when configuring energy in a virtual power plant, it is crucial to improve the utilization rate of renewable energy.

[0003] Because there are many energy distribution points and configuration points designed for virtual power plants, there are multiple configuration schemes. In the existing technology, each configuration scheme can be regarded as a particle, and then the particle swarm optimization algorithm is used to find the optimal solution in the entire particle swarm. In the optimization process of the existing technology, the fitness of the particles is often set only by a single goal such as resource utilization and layout rationality. It is impossible to perform targeted optimization for the green and environmental protection of the power resources configured in the virtual power plant. In addition, the adaptability of the particles is weak during the optimization process, and the particles cannot be updated safely, quickly and effectively. Summary of the invention

[0004] In order to solve the technical problem that the optimization results of the prior art using the particle swarm optimization algorithm to optimize the configuration scheme of the virtual power plant cannot meet the green environmental protection requirements, the purpose of the present invention is to provide a method and system for configuring the adjustable capacity of a virtual power plant based on optimization analysis. The technical scheme adopted is as follows:

[0005] The present invention proposes a method for configuring the adjustable capacity of a virtual power plant based on optimization analysis, the method comprising:

[0006] Determine a configuration scheme between energy distribution points and configuration points; the energy distribution points include renewable energy and traditional energy;

[0007] Each configuration scheme is used as a particle in the particle swarm optimization algorithm, and optimization is performed to determine the optimal configuration scheme; the particle swarm optimization algorithm specifically includes the following in the optimization process:

[0008] For each particle, a first power demand of each particle is obtained according to a difference between a configured power of an energy distribution point corresponding to the particle and a required power of the configured point; the first power demand is adjusted according to a distance between the configured point and the energy distribution point to obtain a second power demand of each particle;

[0009] Adjusting the second electricity demand according to the proportion of renewable energy power configuration in each particle to obtain a green configuration degree of each particle;

[0010] According to the power generation stability and configured power of the energy distribution point configured by the configuration point in the particle, the update speed weight of each particle is obtained, and the update speed of each particle is weighted according to the update speed weight; based on the green configuration degree and the weighted update speed of each particle, all particles are iteratively updated until the update condition is met, and the global optimal particle is obtained, and the global optimal particle is used as the optimal configuration scheme.

[0011] Furthermore, the method for obtaining the first electricity demand includes:

[0012] For each particle, the sum of configured power obtained by configuring each configuration point by all energy distribution points is obtained; the difference between the sum of configured power and the required power of each configuration point is processed by a sign function to obtain a sign label value of each configuration point, where the sign label value is 1 or -1; the sign label values ​​of all configuration points of each particle are accumulated and normalized to obtain the first power demand.

[0013] Furthermore, the method for obtaining the second electricity demand includes:

[0014] For each particle, configuration points and energy distribution points are combined in pairs to obtain configuration combinations; for each configuration combination, the distance between the configuration point and the energy distribution point is negatively correlated and normalized to obtain a distance weight, the power received by the configuration point from the energy distribution point is multiplied by the distance weight to obtain an initial adjustment weight of the configuration combination, the initial adjustment weights of all configuration combinations are accumulated and normalized to obtain an adjustment weight for each particle; the product of the adjustment weight and the first electricity demand is used as the second electricity demand.

[0015] Furthermore, the method for obtaining the green configuration degree includes:

[0016] For each configuration point in each particle, the ratio of the power required by the configuration point to the total configured power is taken as the energy utilization rate of the configuration point, and the product of the energy utilization rate and the proportion of the power configuration of renewable energy corresponding to the configuration point is taken as the initial green weight of the configuration point; the initial green weights of all configuration points in each particle are accumulated and normalized to obtain the green weight of each particle, and the green weight is multiplied by the second electricity demand to obtain the green configuration degree of each particle.

[0017] Furthermore, the method for obtaining the stability of power generation includes:

[0018] A preset time period is divided according to a preset time frequency, the average power generation of each energy distribution point in the preset time period is obtained, the power generation difference between the power generation of the energy distribution point per unit time in the preset time period and the average power generation is obtained, the accumulated value of the power generation difference of the energy distribution point in the preset time period is used as the power generation volatility, the power generation volatility is negatively correlated mapped and normalized, and the power generation stability is obtained.

[0019] Furthermore, the method for acquiring the update speed weight includes:

[0020] For each configuration point in each particle, the configuration power of the configuration point configured to the energy distribution point is multiplied by the power generation stability to obtain the initial security of each energy distribution point, and the initial security of all energy distribution points configured by the configuration point is accumulated to obtain the security of the configuration point;

[0021] For each particle, the security of all the configuration points is accumulated and normalized to obtain the update speed weight.

[0022] Further, weighting the update speed of each particle according to the update speed weight includes:

[0023] The update speed weight is multiplied by the initial update speed to obtain a weighted update speed.

[0024] Furthermore, the update condition is that the number of iterations reaches a preset maximum number of iterations.

[0025] Furthermore, the method for obtaining the required power of each configuration point includes:

[0026] The average power consumption per unit time of each configuration point in the preset historical time period is counted to obtain the required power of each configuration point.

[0027] The present invention also proposes a virtual power plant adjustable capacity configuration system based on optimization analysis, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any one of the steps of a method for configuring the adjustable capacity of a virtual power plant based on optimization analysis when executing the computer program.

[0028] The present invention has the following beneficial effects:

[0029] In the optimization process of the present invention, the first power demand of the particle is first obtained. The first power demand can characterize the rationality of the power supply and demand relationship in the configuration scheme. Further considering that the distance between the configuration point and the energy distribution point will cause energy loss, the first power demand is adjusted in combination with the distance to obtain the second power demand. On the basis of the second power demand, the proportion of power configuration of renewable energy is further analyzed to obtain the green configuration degree of each particle. Particle swarm optimization based on green configuration degree can simultaneously take into account the energy supply and demand relationship and the concept of green environmental protection, so that the optimization result is more in line with expectations. Further, in the process of particle update, the stability of the power generation and the configured power of the configured energy distribution point are taken into account, and the update speed weight is used to weight the update speed of each particle, so that the particle update process has strong adaptability, and particles with relatively no energy risk and green configuration scheme can be finally obtained. Finally, the optimal configuration scheme of the virtual power plant is obtained. The optimal configuration scheme is used to allocate power to the energy distribution point and the configuration point, which can ensure energy supply and demand while meeting the concept of green environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 A flow chart of a method for configuring adjustable capacity of a virtual power plant based on optimization analysis provided by one embodiment of the present invention;

[0032] Figure 2 A relationship distribution diagram between a configuration point and an energy distribution point provided by an embodiment of the present invention;

[0033] Figure 3 A flow chart of an optimization method of a particle swarm optimization algorithm provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a virtual power plant adjustable capacity configuration method and system based on optimization analysis proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0035] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0036] The specific scheme of a method and system for configuring the adjustable capacity of a virtual power plant based on optimization analysis provided by the present invention is described in detail below in conjunction with the accompanying drawings.

[0037] See also Figure 1 , which shows a flow chart of a method for configuring adjustable capacity of a virtual power plant based on optimization analysis provided by an embodiment of the present invention, the method comprising:

[0038] Step S101: Determine a configuration scheme between energy distribution points and configuration points; the energy distribution points include renewable energy and traditional energy.

[0039] Virtual power plant is a management system that integrates multiple energy resources and realizes scientific distribution of electricity through intelligent scheduling and optimization. Figure 2 , which shows a relationship distribution diagram between a configuration point and an energy distribution point provided by an embodiment of the present invention. In a virtual power plant, the deployable energy includes multiple renewable energy sources and multiple traditional energy sources, which are collectively referred to as energy distribution points, and also includes multiple configuration points, that is, the configuration points are user points where electricity needs to be configured. In the virtual power plant of the embodiment of the present invention, the energy distribution points of renewable energy are regarded as n1, the energy distribution points of traditional energy are regarded as n2, the configuration points are regarded as m, and the total number of energy distribution points is regarded as n.

[0040] There are multiple configuration schemes between energy distribution points and configuration points. In one configuration scheme, each configuration point can obtain the power allocated by each energy distribution point.

[0041] Step S102: taking each configuration scheme as a particle in a particle swarm optimization algorithm, and performing optimization to determine the optimal configuration scheme.

[0042] The embodiment of the present invention aims to use a particle swarm optimization algorithm to find the optimal configuration solution among multiple configuration solutions. Therefore, each configuration solution can be regarded as a massless particle in space, and then a particle swarm is obtained. The particle swarm optimization algorithm is used for optimization to obtain the optimal configuration solution.

[0043] It should be noted that the basic principle of the particle swarm optimization algorithm is a technical means well known to those skilled in the art, and will not be described in detail here. In the embodiment of the present invention, the number of particles is set to 100, the maximum number of iterations is 1000, the learning factor is set to 2, and the inertia weight is set to 0.5, and the optimization can be started by initializing the particle position and speed.

[0044] In order to ensure that the final optimization result meets the energy supply and demand relationship and complies with the green environmental protection strategy, please refer to Figure 3 The particle swarm optimization algorithm in the embodiment of the present invention specifically includes the following steps in the optimization process:

[0045] Step S301: for each particle, according to the difference between the required power of the energy distribution point corresponding to the particle and the configured power of the configuration point, obtain a first power demand of each particle; according to the distance between the configuration point and the energy distribution point, adjust the first power demand to obtain a second power demand of each particle;

[0046] For a particle, if its corresponding configuration scheme is a better configuration scheme, the configuration scheme should at least meet the supply and demand relationship between energy sources, that is, the configuration store of the energy distribution point should meet the power required by the configuration point, which is the basic power demand. Therefore, for each particle, the greater the difference between the configuration power of the energy distribution point corresponding to the particle and the power required by the configuration point, it means that the configuration point in the configuration scheme has enough electric energy to use and can meet the basic supply and demand relationship. Therefore, the first power demand of each particle can be obtained according to the difference between the configuration power of the energy distribution point corresponding to the particle and the power required by the configuration point.

[0047] Further considering that the power distribution process should also be distributed as close as possible, the farther the distance between the configuration point and the energy distribution point, the higher the loss in the energy transmission process. Therefore, it is necessary to further consider the distance between the configuration point and the energy distribution point. The shorter the distance, the higher the rationality of the configuration scheme, the higher the energy utilization efficiency and the lower the potential energy risk. Therefore, the first power demand is further adjusted according to the distance between the configuration point and the energy distribution point to obtain the second power demand of each particle. The second power demand can be used to evaluate the rationality of the configuration scheme corresponding to the particle in the basic energy supply and demand relationship.

[0048] Preferably, in one embodiment of the present invention, in order to effectively quantify the first power demand and highlight the characteristics of whether the supply and demand relationship is satisfied, for each particle, a sign function is used to process the difference between the total configured power and the required power of each configuration point. The total configured power is the total configured power obtained by configuring all energy distribution points for each configuration point, which represents the total power allocated to the configuration point in the configuration scheme corresponding to the particle. If the difference between the total configured power and the required power of the configuration point is a negative number, it means that the configuration scheme of the particle cannot meet the supply and demand relationship of the configuration point; if it is a positive value, it means that it can be satisfied. Therefore, the sign of the difference can be output by the sign function to obtain a sign label value, that is, the sign label value is 1 or -1, and the sign label value is 1, which means that the power supply and demand relationship of the configuration point is satisfied, and -1 means that it is not satisfied. Therefore, the sign label values ​​of all configuration points of each particle are accumulated and normalized to obtain the first power demand. The larger the first power demand, the more reasonable the configuration scheme corresponding to the particle is for the power supply and demand relationship of the configuration point.

[0049] As an example, the first power demand is expressed by the formula:

[0050] Among them, Y ′ (A) is the first power demand of the Ath particle, m is the number of configuration points, n is the number of energy distribution points, is the configured power received by the i-th configuration point from the j-th energy distribution point, is the required power of the ith configuration point, H() represents the sign function, and f() represents the normalized function.

[0051] It should be noted that the normalization method used in the embodiment of the present invention is a maximum and minimum value normalization method, and the data of all particles updated in each iteration are used as the data set for normalization.

[0052] Furthermore, the method for obtaining the required power of each configuration point includes: calculating the average power consumption of each configuration point per unit time within a preset historical time period to obtain the required power of each configuration point. Because the virtual power plant needs to allocate each node at a certain moment, the historical time period composed of multiple historical moments before this moment can be used as a reference time period, and the average power consumption of the configuration point within the historical time period can be calculated to obtain the required power of each configuration point. In the embodiment of the present invention, the historical time period is one month, and the moment is set to one day, that is, the average power consumption of the configuration point every day within one month is calculated as the required power. It should be noted that because the required power can be regarded as the daily power consumption of the configuration point, the configured power received by the configuration point from the energy distribution point in the analysis of the first power demand is also the configured power for one day.

[0053] Preferably, in one embodiment of the present invention, the distance between nodes in the configuration scheme is considered to be an important factor in evaluating the rationality of the configuration scheme, and on the other hand, more energy should be allocated to energy distribution points with closer distances to ensure that energy loss is reduced. Therefore, the second method for obtaining the power demand includes:

[0054] For each particle, the configuration points and energy distribution points are combined in pairs to obtain the configuration combination; for each configuration combination, the distance between the configuration point and the energy distribution point is negatively correlated and normalized to obtain the distance weight, that is, the shorter the distance, the greater the distance weight, indicating that the configuration point in the configuration combination should receive more configuration power. Therefore, the power received by the configuration point from the energy distribution point is multiplied by the distance weight to obtain the initial adjustment weight of the configuration combination. That is, for each configuration combination, the smaller the distance between the configuration point and the energy distribution point, the greater the power configured by the configuration point, which means that the configuration of the configuration combination is more reasonable, and the initial adjustment weight is greater.

[0055] Because there are multiple configuration points in each particle, there is a configuration combination between each configuration point and each energy distribution point, that is, there are a large number of configuration combinations in each particle. The initial adjustment weights of all configuration combinations are accumulated and normalized to obtain the adjustment weight of each particle. The larger the adjustment weight, the more reasonable the configuration scheme of the particle is in the dimension of power transportation loss. Therefore, the product of the adjustment weight and the first power demand is taken as the second power demand.

[0056] As an example, the second power demand is expressed by the formula:

[0057] where Y ″ (A) is the second power demand of particle A, R i , j is the distance between the i-th configuration point and the j-th energy distribution point, and ε is a preset hyperparameter. ε is set to 1, and the purpose of ε is to prevent the denominator from being 0, that is, is the distance weight, which is the result of negative correlation mapping and normalization of the distance.

[0058] Step S302: adjusting the second electricity demand according to the proportion of renewable energy power configuration in each particle to obtain the green configuration degree of each particle;

[0059] The second electricity demand obtained in the above steps represents the evaluation of the basic electricity supply and demand relationship in the configuration scheme. In order to focus on the development concept of green environmental protection, it is also necessary to analyze the rationality of the allocation of renewable energy in the configuration scheme during the power distribution process. The higher the utilization rate of renewable energy, the greener the configuration scheme. Therefore, the embodiment of the present invention further adjusts the second electricity demand according to the proportion of renewable energy in the power configuration of each particle to obtain the green configuration degree of each particle. That is, the larger the proportion of renewable energy in the power configuration, the greener the power distribution in the configuration scheme. At this time, the larger the second electricity demand, the configuration scheme satisfies the green environmental protection concept while satisfying the basic power supply and demand relationship, and the greater the green configuration degree.

[0060] Preferably, in one embodiment of the present invention, it is further necessary to consider the utilization rate of the configured power at the configuration point, that is, while ensuring the basic power supply and demand relationship, the required power should not be much different from the total configured power, avoiding waste of power allocation, which is also one of the factors of green environmental protection. Therefore, the method for obtaining the green configuration degree includes:

[0061] For each configuration point in each particle, the ratio of the power required by the configuration point to the total power of the configuration is taken as the energy utilization rate of the configuration point. The larger the ratio is, that is, the closer it is to 1, the greater the energy utilization rate is.

[0062] The product of the energy utilization rate and the proportion of renewable energy power configuration corresponding to the configuration point is taken as the initial green weight of the configuration point. The larger the initial green weight, the more the configuration of the configuration point in the particle configuration scheme conforms to the concept of green environmental protection.

[0063] Because a particle contains multiple configuration points, the initial green weights of all configuration points in each particle are accumulated and normalized to obtain the green weight of each particle, and the green weight is multiplied by the second power demand to obtain the green configuration degree of each particle.

[0064] As an example, the green configuration degree is expressed as:

[0065] Where S(A) is the green configuration degree of particle A, is the configured power of the i-th configuration point configured by the k-th renewable energy source.

[0066] Step S303: According to the power generation stability of the energy distribution point configured by the configuration point in the particle and the configured power, the update speed weight of each particle is obtained, and the update speed of each particle is weighted according to the update speed weight; based on the green configuration degree and the weighted update speed of each particle, all particles are iteratively updated until the update condition is met, and the global optimal particle is obtained, and the global optimal particle is used as the optimal configuration scheme.

[0067] For each initially generated particle, the green configuration degree of each particle can be obtained through the above steps, and the green environmental protection of each particle based on the reasonable power configuration is evaluated. In the optimization process, it is necessary to update the individual best position first. By comparing the green configuration degree of the current particle with the maximum green configuration degree recorded by the particle in the historical iteration, if the current green configuration degree is higher, it means that this particle is a better configuration scheme, then the position of the particle is updated to the best position, that is, the position is the local optimal solution found in the entire search process, which helps to guide the particle to move in a better direction in subsequent iterations. At the same time, the particle swarm optimization algorithm will also look for the best example of green configuration degree among all particles and update the global optimal position. It means that the example with the highest green configuration degree is found in the entire particle group and the position is recorded. The global optimal solution represents the best solution found by all particles at the current position of the iteration. In the above optimization process, the existing particle swarm algorithm will update the update speed and position of the particles according to the existing formula and preset parameters, but this update method does not combine the actual needs of the virtual power plant scenario, and cannot guarantee that the optimal configuration scheme with stable power configuration, satisfying supply and demand and green environmental protection can be obtained quickly and accurately within a limited number of iterations.

[0068] Therefore, the embodiment of the present invention adopts a weighted strategy to give a relatively large weight to the update speed of particles with higher security, so that they occupy a more important position in the entire optimization algorithm. In the optimization process, the weight corresponding to particles with higher risks is smaller, and the impact on the final result will be weakened, which helps to guide the algorithm to update in a safer and more reasonable direction and reduce potential risks.

[0069] Since energy distribution points have the risk of unstable power supply, intermittency and instability, especially renewable energy, such as wind energy, which depends on natural conditions, the power generation will be affected by weather and seasons. Therefore, when determining the safety of each configuration point, the greater the stability of the power generation of the energy distribution point configured, the higher the safety of the configuration point, and the corresponding update speed weight should also be greater. Therefore, the embodiment of the present invention obtains the update speed weight of each particle based on the power generation stability and configured power of the energy distribution point configured by the configuration point in the particle, that is, the greater the stability of the power generation and the greater the configured power, the safer the corresponding configuration point is, and the greater the update speed weight of the corresponding particle is.

[0070] By weighting the update speed of each particle according to the update speed weight, a new update speed and position can be obtained during the optimization process. Through the weighted mechanism, the optimization process can be more stable, and finally focus on particles that meet basic needs and have high security. The reliability of the optimization process is improved.

[0071] Preferably, in one embodiment of the present invention, the method for obtaining power generation stability includes:

[0072] The preset time period is divided according to the preset time frequency, the average power generation of each energy distribution point in the preset time period is obtained, the power generation difference between the power generation of each unit time of the energy distribution point in the preset time period and the average power generation is obtained, and the accumulated value of the power generation difference of the energy distribution point in the preset time period is used as the power generation volatility. In the embodiment of the present invention, the time frequency is set to every day, and the preset time period is one month, that is, the average power generation of each day in a month is obtained, which is used as the basic reference data. The greater the power generation difference between the daily power generation and the average power generation in the time period, the greater the power generation volatility. The power generation volatility is negatively correlated and normalized to obtain the power generation stability. In the embodiment of the present invention, the power generation volatility is negatively correlated and normalized in the form of an inverse, and the inverse of the result of adding the power generation volatility to the positive integer 1 is used as the power generation stability.

[0073] Preferably, in one embodiment of the present invention, the method for acquiring the update speed weight includes:

[0074] For each configuration point in each particle, the configuration power of the configuration point to the energy distribution point is multiplied by the stability of power generation to obtain the initial security of each energy distribution point. That is, the larger the configuration power, the greater the stability of power generation, which means that the power distribution of the configuration point under the energy distribution point is more stable and reliable, and the initial security is greater. A configuration point can be distributed by multiple energy distribution points, so the initial security of all energy distribution points configured by the configuration point is accumulated to obtain the security of the configuration point. Combined with all configuration points in a particle, for each particle, the security of all configuration points is accumulated and normalized to obtain the update speed weight.

[0075] In the embodiment of the present invention, the update speed weight is multiplied by the initial update speed to obtain the weighted update speed. It should be noted that the initial update speed here is the update speed obtained according to the update speed calculation formula in the traditional particle swarm optimization algorithm, that is, each time the update speed is calculated using the traditional algorithm, the update speed weight is used for further adjustment to obtain the weighted update speed.

[0076] In the embodiment of the present invention, the update condition is set to the number of iterations reaching the preset maximum number of iterations. After the update condition is met, the global optimal particle can be obtained, and the configuration scheme of the global optimal particle is the optimal configuration scheme. Through the particle swarm optimization of the embodiment of the present invention, the configuration scheme of the virtual power plant can be made to take into account the green environmental protection concept on the premise of considering the basic power supply and demand relationship, and minimize the risk of power consumption at the configuration point, thereby realizing the optimal allocation of power resources of the virtual power plant.

[0077] In summary, in the optimization process of the embodiment of the present invention, the first power demand of the particle is first obtained. The first power demand can characterize the rationality of the power supply and demand relationship in the configuration scheme. Further considering that the distance between the configuration point and the energy distribution point will cause energy loss, the first power demand is adjusted in combination with the distance to obtain the second power demand. On the basis of the second power demand, the proportion of power configuration of renewable energy is further analyzed to obtain the green configuration degree of each particle. Particle swarm optimization based on green configuration degree can simultaneously take into account the energy supply and demand relationship and the concept of green environmental protection, so that the optimization result is more in line with expectations. Further, in the process of particle update, the stability of the power generation and the configuration power of the configured energy distribution point are taken into account, and the update speed weight is used to weight the update speed of each particle, so that the particle update process has strong adaptability, and particles with relatively no energy risk and green configuration scheme can be finally obtained. Finally, the optimal configuration scheme of the virtual power plant is obtained. The optimal configuration scheme is used to allocate power to the energy distribution point and the configuration point, which can ensure energy supply and demand while meeting the concept of green environmental protection.

[0078] The present invention also proposes a virtual power plant adjustable capacity configuration system based on optimization analysis, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any one of the steps of a method for configuring the adjustable capacity of a virtual power plant based on optimization analysis.

[0079] It should be noted that the sequence of the above embodiments of the present invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0080] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for configuring adjustable capacity of a virtual power plant based on optimization analysis, characterized in that: The method comprises: Determine a configuration scheme between energy distribution points and configuration points; the energy distribution points include renewable energy and traditional energy; Each configuration scheme is used as a particle in the particle swarm optimization algorithm, and optimization is performed to determine the optimal configuration scheme; the particle swarm optimization algorithm specifically includes the following in the optimization process: For each particle, a first power demand of each particle is obtained according to a difference between a configured power of an energy distribution point corresponding to the particle and a required power of the configured point; the first power demand is adjusted according to a distance between the configured point and the energy distribution point to obtain a second power demand of each particle; Adjusting the second electricity demand according to the proportion of renewable energy power configuration in each particle to obtain a green configuration degree of each particle; According to the power generation stability and configured power of the energy distribution point configured by the configuration point in the particle, the update speed weight of each particle is obtained, and the update speed of each particle is weighted according to the update speed weight; based on the green configuration degree and the weighted update speed of each particle, all particles are iteratively updated until the update condition is met, and the global optimal particle is obtained, and the global optimal particle is used as the optimal configuration scheme.

2. The method for configuring adjustable capacity of a virtual power plant based on optimization analysis according to claim 1, characterized in that: The method for obtaining the first electricity demand includes: For each particle, the sum of configured power obtained by configuring each configuration point by all energy distribution points is obtained; the difference between the sum of configured power and the required power of each configuration point is processed by a sign function to obtain a sign label value of each configuration point, where the sign label value is 1 or -1; the sign label values ​​of all configuration points of each particle are accumulated and normalized to obtain the first power demand.

3. The method for configuring adjustable capacity of a virtual power plant based on optimization analysis according to claim 1, characterized in that: The method for obtaining the second electricity demand includes: For each particle, configuration points and energy distribution points are combined in pairs to obtain configuration combinations; for each configuration combination, the distance between the configuration point and the energy distribution point is negatively correlated and normalized to obtain a distance weight, the power received by the configuration point from the energy distribution point is multiplied by the distance weight to obtain an initial adjustment weight of the configuration combination, the initial adjustment weights of all configuration combinations are accumulated and normalized to obtain an adjustment weight for each particle; the product of the adjustment weight and the first electricity demand is used as the second electricity demand.

4. The method for configuring adjustable capacity of a virtual power plant based on optimization analysis according to claim 2, characterized in that: The method for obtaining the green configuration degree includes: For each configuration point in each particle, the ratio of the power required by the configuration point to the total configured power is taken as the energy utilization rate of the configuration point, and the product of the energy utilization rate and the proportion of the power configuration of renewable energy corresponding to the configuration point is taken as the initial green weight of the configuration point; the initial green weights of all configuration points in each particle are accumulated and normalized to obtain the green weight of each particle, and the green weight is multiplied by the second electricity demand to obtain the green configuration degree of each particle.

5. The method for configuring adjustable capacity of a virtual power plant based on optimization analysis according to claim 1, characterized in that: The method for obtaining the stability of power generation includes: A preset time period is divided according to a preset time frequency, the average power generation of each energy distribution point in the preset time period is obtained, the power generation difference between the power generation of the energy distribution point per unit time in the preset time period and the average power generation is obtained, the accumulated value of the power generation difference of the energy distribution point in the preset time period is used as the power generation volatility, the power generation volatility is negatively correlated mapped and normalized, and the power generation stability is obtained.

6. The method for configuring adjustable capacity of a virtual power plant based on optimization analysis according to claim 1, characterized in that: The method for obtaining the update speed weight includes: For each configuration point in each particle, the configuration power of the configuration point configured to the energy distribution point is multiplied by the power generation stability to obtain the initial security of each energy distribution point, and the initial security of all energy distribution points configured by the configuration point is accumulated to obtain the security of the configuration point; For each particle, the security of all the configuration points is accumulated and normalized to obtain the update speed weight.

7. The method for configuring adjustable capacity of a virtual power plant based on optimization analysis according to claim 1, characterized in that: The step of weighting the update speed of each particle according to the update speed weight comprises: The update speed weight is multiplied by the initial update speed to obtain a weighted update speed.

8. The method for configuring adjustable capacity of a virtual power plant based on optimization analysis according to claim 1, characterized in that: The update condition is that the number of iterations reaches a preset maximum number of iterations.

9. The method for configuring adjustable capacity of a virtual power plant based on optimization analysis according to claim 2, characterized in that: The method for obtaining the required power of each configuration point includes: The average power consumption per unit time of each configuration point in the preset historical time period is counted to obtain the required power of each configuration point.

10. A virtual power plant adjustable capacity configuration system based on optimization analysis, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of a method for configuring the adjustable capacity of a virtual power plant based on optimization analysis as described in any one of claims 1 to 9 are implemented.

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