Virtual power plant distributed power capacity optimization method based on improved grey wolf algorithm

Abstract

This invention relates to the field of power plant planning technology and discloses a method for optimizing the distributed power generation capacity of a virtual power plant based on an improved gray wolf algorithm. This method, based on the operating strategy of the virtual power plant, simulates the configuration scheme represented by each individual gray wolf in the initialized gray wolf population to obtain operating data. Then, based on the operating data and the distributed power generation capacity optimization configuration model, the initial fitness value of each gray wolf is obtained. Based on the distance between gray wolf individuals and the radius of their niche, a corresponding niche is constructed with each gray wolf as the center. Through a sharing mechanism, based on the number and distribution of gray wolves within each niche, the initial fitness value of each gray wolf is adjusted to obtain a target fitness value. Based on the target fitness value, the gray wolf individuals in the population are iteratively updated until the convergence condition is met, resulting in the optimal configuration strategy.

Description

Technical Field

[0001] This invention relates to the field of power grid planning technology, and relates to, but is not limited to, a method and system for optimizing the capacity of distributed power sources in virtual power plants based on an improved gray wolf algorithm. Background Technology

[0002] Virtual Power Plant (VPP) is a new type of energy management technology that integrates multiple distributed energy resources. Through information and communication technology and advanced control systems, it can aggregate and coordinate geographically dispersed distributed power sources such as wind power, photovoltaic power, gas turbines, and energy storage systems to participate in grid dispatch and electricity market transactions, thereby achieving efficient energy utilization and safe and stable system operation.

[0003] In the planning and operation of virtual power plants, the optimal configuration of distributed generation capacity is a crucial factor determining their economic efficiency and reliability. A reasonable capacity configuration can not only effectively reduce investment and operating costs but also improve energy utilization efficiency, reduce wind and solar power curtailment, and enhance the system's adaptability to load fluctuations. Existing technologies for optimizing the capacity of distributed generation in virtual power plants largely revolve around traditional optimization methods or single intelligent algorithms, such as particle swarm optimization and genetic algorithms. However, virtual power plant systems are characterized by multi-source heterogeneity, complex operating modes, and high uncertainty, leading to poor optimization results from these methods. When facing complex and ever-changing operating environments, they often struggle to effectively handle high-dimensional, nonlinear, and multi-constraint optimization problems, failing to meet the needs of practical applications. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method for optimizing the capacity of distributed power sources in virtual power plants based on an improved gray wolf algorithm, which can meet the requirements of economic benefits and operational stability of virtual power plants.

[0005] The specific technical solutions of this invention are as follows: This application provides a method for optimizing the capacity of distributed power sources in a virtual power plant based on an improved gray wolf algorithm, including: Obtain the operation strategy of the virtual power plant and the distributed power source capacity optimization configuration model; wherein, the distributed power source capacity optimization configuration model includes an objective function and constraints. The objective function is used to minimize the monthly comprehensive economic cost of the virtual power plant, and the constraints include at least one of decision variable constraints, power balance constraints, device constraints, power supply reliability constraints, and energy waste constraints. Initialize the gray wolf population; the gray wolf population consists of multiple gray wolf individuals, each representing a configuration scheme; Based on the operational strategy, the configuration scheme of each gray wolf individual is simulated and run to obtain the corresponding operational data. Based on the operational data, objective function and constraints, the initial fitness value of each gray wolf individual is obtained. Determine the distance between individual gray wolves, and based on the distance and the radius of the niche, construct a corresponding niche centered on each individual gray wolf; Through a sharing mechanism, the initial fitness value of each gray wolf individual is adjusted based on the number and distribution of gray wolves within each small territory to obtain the target fitness value. Based on the target fitness value, the gray wolf individuals in the gray wolf population are iteratively updated until the convergence condition is met to obtain the optimal configuration strategy. The optimal configuration strategy is used to indicate the optimal installed capacity of wind power, photovoltaic, gas turbine and energy storage systems in the virtual power plant.

[0006] In some embodiments, the initial fitness value of each individual gray wolf is obtained based on runtime data, an objective function, and constraints, including: Determine whether the running data meets the constraints; If the operating data meets the constraints, the monthly comprehensive economic cost corresponding to the operating data is determined based on the operating data and the objective function, and the monthly comprehensive economic cost is determined as the initial fitness value of the gray wolf individual corresponding to the operating data; wherein, the monthly comprehensive economic cost includes at least one of the following: monthly average investment and installation cost, monthly average operation and maintenance cost, monthly average equipment replacement cost, monthly environmental cost, monthly wind and solar curtailment penalty fee, monthly fuel cost, and monthly load interruption compensation fee.

[0007] In some embodiments, based on distance and niche radius, a corresponding niche is constructed with each individual gray wolf as the center, including: A small habitat is constructed with the first gray wolf individual as the center and the radius of the small habitat as the radius. The second gray wolf individual in the gray wolf population whose distance from the first gray wolf individual is less than or equal to the radius of the small habitat is identified as a gray wolf individual within the small habitat of the first gray wolf individual. The first gray wolf individual can be any gray wolf individual in the gray wolf population.

[0008] In some embodiments, through a sharing mechanism, the initial fitness value of each gray wolf individual is adjusted based on the number and distribution of gray wolf individuals within each small territory to obtain a target fitness value, including: Based on the distance between each individual gray wolf and other gray wolves within its territory, the total sharing degree of each gray wolf is determined by a sharing function; The target fitness value for each gray wolf is determined based on its initial fitness value and total sharing value.

[0009] In some embodiments, a shared function is represented by the following formula: in, Let be the degree of sharing between the i-th gray wolf and the j-th gray wolf. For the radius of the small habitat, Let be the distance between the i-th gray wolf and the j-th gray wolf. The shape parameter for the shared function.

[0010] In some embodiments, the target fitness value is expressed by the following formula: in, Let be the target fitness value of the i-th gray wolf individual. Let be the initial fitness value of the i-th gray wolf individual. Let i be the degree of sharing between the i-th gray wolf individual and the j-th gray wolf individual; Let be the distance between the i-th gray wolf and the j-th gray wolf. This represents the total number of individual gray wolves within the territory of Xiaosheng.

[0011] In some embodiments, the gray wolf population is iteratively updated based on the target fitness value, including: Based on the adjusted fitness value of each individual gray wolf in the gray wolf population, determine the best, second-best, and third-best gray wolf individuals in the current iteration round. Based on the positions of the best, second-best, and third-best gray wolf individuals, the positions of other gray wolf individuals in the gray wolf population are updated. Then, through a chaotic mutation mechanism, some gray wolf individuals in the updated gray wolf population are mutated to obtain a new gray wolf population. The new gray wolf population will be updated as the gray wolf population for the next iteration.

[0012] In some embodiments, power supply reliability constraints are expressed by the following formula: in, This indicates the load shortage rate of the virtual power plant. This indicates the maximum allowable load deficit rate for the virtual power plant. This represents the amount of load interruption during time period t. This represents the load demand during time period t. This represents the total number of scheduling periods; Energy waste constraints are expressed by the following formula: in, This represents the amount of wind and solar power curtailed by the virtual power plant. This indicates the maximum allowable wind and solar power curtailment capacity of the virtual power plant. This represents the amount of wind and solar power curtailed during time period t. This represents the load demand during time period t. This represents the total number of scheduling periods.

[0013] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: In this embodiment of the invention, the operation strategy of the virtual power plant and the distributed power supply capacity optimization configuration model are first obtained. The distributed power supply capacity optimization configuration model includes an objective function and constraints. The objective function minimizes the monthly comprehensive economic cost of the virtual power plant, and the constraints include at least one of decision variable constraints, power balance constraints, device constraints, power supply reliability constraints, and energy waste constraints. A gray wolf population is initialized, comprising multiple gray wolf individuals, each representing a configuration scheme. Based on the operation strategy, the configuration scheme represented by each gray wolf individual is simulated to obtain corresponding operation data. Based on the operation data, the objective function, and the constraints, the initial fitness value of each gray wolf individual is obtained. The relationships between gray wolf individuals are then determined. The distance to the individual gray wolf is determined, and based on the distance and the radius of the niche, a corresponding niche is constructed with each individual gray wolf as the center. Through a sharing mechanism, the initial fitness value of each individual gray wolf is adjusted based on the number and distribution of gray wolves within each niche to obtain the target fitness value. Based on the target fitness value, the gray wolf population is iteratively updated until the convergence condition is met, resulting in the optimal configuration strategy. The optimal configuration strategy is used to indicate the optimal installed capacity of wind power, photovoltaic, gas turbine, and energy storage systems in the virtual power plant. In this way, the optimal combination of wind turbines, photovoltaic, gas turbines, and energy storage devices can be obtained under various constraints, thereby achieving the optimized configuration of distributed power capacity in the virtual power plant and meeting the economic benefits and operational stability requirements of the virtual power plant. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 A flowchart illustrating the distributed power generation capacity optimization method for virtual power plants based on the improved gray wolf algorithm provided in an embodiment of the present invention; Figure 2 Iterative curves for scenario one, scenario two, and scenario three are provided for embodiments of the present invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The following embodiments are used to illustrate the present invention, but are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0017] It should be noted that the terms "first, second, and third" used in the embodiments of the present invention are only used to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, and third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present invention described herein can be implemented in an order other than that illustrated or described herein.

[0018] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which these embodiments of the invention pertain. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0019] Figure 1 This is a flowchart illustrating a virtual power plant distributed power capacity optimization method based on an improved gray wolf algorithm, provided by an embodiment of the present invention. The method can be executed via a control device, which may include at least one of a personal computer, laptop computer, smartphone, tablet computer, and portable wearable device; however, this embodiment does not limit the type of control device.

[0020] like Figure 1 As shown, the virtual power plant distributed power capacity optimization method based on the improved gray wolf algorithm provided in this embodiment of the invention may include steps S101-S105.

[0021] S101. Obtain the operation strategy of the virtual power plant and the distributed power supply capacity optimization configuration model.

[0022] In some embodiments, a virtual power plant consists of wind power generation, photovoltaic power generation, gas turbines, and energy storage systems. The operation strategy of the virtual power plant is determined based on the operating characteristics of the wind power generation, photovoltaic power generation, gas turbines, and energy storage systems within the virtual power plant, and is used to determine the operation plan for each distributed power source.

[0023] For example, the operation strategy of the virtual power plant is pre-configured in the control device and can be dynamically adjusted according to the energy demand and supply situation in different seasons and time periods. For instance, during seasons with abundant wind resources, wind power is prioritized to meet part of the load demand; during periods of sufficient sunlight, the output of photovoltaic power generation is increased. Simultaneously, the gas turbine and energy storage system can be rationally scheduled according to the peak and valley changes in load. During peak load periods, the power generation of the gas turbine is increased, and the energy storage system's energy is released; during off-peak load periods, excess electricity is used to charge the energy storage system. This application does not limit the scope of the embodiments described herein.

[0024] In some embodiments, the distributed power generation capacity optimization configuration model includes an objective function and constraints. The objective function and constraints are constructed based on wind turbine power generation models, photovoltaic power generation models, energy storage system models, and gas turbine models.

[0025] In some embodiments, wind speed can be modeled using a Weibull distribution, resulting in the wind speed probability density function shown below. : in, Let be the shape parameter of the Weibull distribution. This refers to the actual wind speed. This is the scale parameter.

[0026] The output power of wind power generation, i.e., the output power of wind turbines, can be expressed by the following formula: in, The output power of wind power generation, air density, The radius of the wind turbine's rotor blades. This refers to the actual wind speed. This is the wind energy utilization coefficient.

[0027] In practical applications, to simplify calculations, an approximate mathematical model describing the wind turbine output can be established based on the above formula, as shown in the following formula, i.e., the wind turbine power generation model: in, This refers to the actual wind speed. To cut off the wind speed, To cut into wind speed, Rated wind speed, This refers to the rated power of the fan.

[0028] In some embodiments, photovoltaic output is not only affected by its own performance but also closely related to its geographical location, prevailing sunlight conditions, and other climatic factors. Therefore, it can be represented by a beta probability density function, i.e., the photovoltaic power generation model can be expressed by the following formula: in, This represents the solar radiation value. and Let be the shape parameter of the beta distribution. and This can be expressed by the following formula: in, This represents the average light intensity. denoted as the standard deviation of light intensity.

[0029] In some embodiments, solar radiation values ​​are obtained through a photovoltaic power generation model. Then, the output power of the photovoltaic panel can be obtained using the following formula: in, The output power of the photovoltaic panel. The area of ​​the photovoltaic panel. For the conversion efficiency of photovoltaic panels, This represents the solar radiation value.

[0030] In some embodiments, the energy storage system model includes the state of charge (SOC) of the energy storage system in the charging state and the state of charge of the energy storage system in the discharging state.

[0031] The state of charge of an energy storage system during charging can be represented as: in, This refers to the state of charge of the energy storage system during time period t while it is in a charging state. It is a time series. This refers to the state of charge of the energy storage system during time period t-1 while it is in a charging state. To indicate the rated capacity of an energy storage system, For energy storage charging efficiency, for The charging power of the time-of-use energy storage system This is the time interval of the scheduling cycle.

[0032] The state of charge of an energy storage system during discharge can be expressed as: in, This represents the state of charge of the energy storage system during time period t while it is in a discharge state. It is a time series. This represents the state of charge of the energy storage system during time period t-1 when it is in a discharge state. To indicate the rated capacity of an energy storage system, The time interval of the scheduling cycle. For the discharge efficiency of the energy storage system for Discharge power of the time-limited energy storage system.

[0033] In some embodiments, the gas turbine model can be represented as: in, The cost of generating electricity from gas turbines, The total number of scheduling periods. It is a time series. , , This represents the power generation cost coefficient for gas turbines. The output of the gas turbine during time period t.

[0034] In some embodiments, the objective function is used to minimize the monthly comprehensive economic cost of the virtual power plant. The objective function in the distributed power generation capacity optimization configuration model can be expressed by the following formula: in, To minimize the overall monthly economic cost, This represents the average monthly investment and installation cost. For the average monthly operating and maintenance cost; This represents the average monthly equipment replacement cost. For monthly environmental costs; The penalty for abandoning the moon's light and wind; Monthly fuel costs; This refers to the monthly load interruption compensation cost. The monthly comprehensive economic cost includes the average monthly investment and installation cost, average monthly operation and maintenance cost, average monthly equipment replacement cost, monthly environmental cost, monthly wind and solar curtailment penalty cost, monthly fuel cost, and monthly load interruption compensation cost.

[0035] In some embodiments, the average monthly investment installation cost Involving wind power generation, photovoltaic power generation, gas turbines, and energy storage systems, the calculation formula is shown below: in, , , , These are the annual values ​​of investment costs for wind power generation, photovoltaic power generation, gas turbines, and energy storage systems, respectively. , , , These are the quantities of wind turbines, photovoltaic systems, gas turbines, and energy storage devices, respectively. , , , These are the investment costs for a single wind turbine, photovoltaic system, gas turbine, and energy storage device, respectively. , , , These refer to the service life of wind turbines, photovoltaic systems, gas turbines, and energy storage devices, respectively. This represents the depreciation rate.

[0036] In some embodiments, average monthly operating and maintenance costs Involving wind power generation, photovoltaic power generation, gas turbines, and energy storage systems, the calculation formula is shown below: in, , , , These are the annual operating and maintenance costs for wind power generation, photovoltaic power generation, gas turbines, and energy storage systems, respectively. , , , These are the operating and maintenance costs for a single wind turbine, photovoltaic system, gas turbine, and energy storage device, respectively. , , , These are the rated power of a single wind turbine, photovoltaic system, gas turbine, and energy storage device, respectively.

[0037] In some embodiments, average monthly equipment replacement cost The devices involved in the calculation include wind power generation, photovoltaic power generation, gas turbines, and energy storage systems. The calculation formula is as follows: in, , , , These are the annual equipment replacement costs for wind power generation, photovoltaic power generation, gas turbines, and energy storage systems, respectively. , , , The replacement costs are for a single wind turbine, photovoltaic system, gas turbine, and energy storage device, respectively.

[0038] In some embodiments, monthly environmental costs For gas turbines, the monthly environmental cost includes the emission costs of carbon dioxide, sulfur dioxide, and nitrogen oxides, calculated as follows: in, These are the emission coefficients for carbon dioxide, sulfur dioxide, and nitrogen oxides per unit of electricity generated by a gas turbine; These are the trading prices for emissions of carbon dioxide, sulfur dioxide, and nitrogen oxides, respectively.

[0039] In some embodiments, within a virtual power plant, wind and solar curtailment may occur when the output of wind turbines and solar power exceeds the maximum power that the virtual power plant can absorb. Monthly wind and solar curtailment penalty fees. The expression is as follows: in, The penalty for abandoning the wind and light for the sake of the moon; for Wasted power from wind and solar power during certain periods ; for The output power of photovoltaics during a given period; for The output power of wind power during a given period Let t be the output of the gas turbine during time period t. for Discharge power of the time-limited energy storage system Let t represent the load demand during time period t.

[0040] In some embodiments, the fuel costs in a virtual power plant are primarily generated by the gas turbine, with monthly fuel costs... It can be calculated using the following formula: in, Price per unit of fuel.

[0041] In some embodiments, for ease of calculation, an equivalent load can be used to represent the degree of matching between the total output of each distributed power source and the load demand. Therefore, the virtual power plant needs to provide a certain economic compensation for the disconnected load, namely, a monthly load interruption compensation fee. Monthly load interruption compensation cost The calculation formula is expressed as follows: in, for Interruption amount of total load of virtual power plant during the time period; Costs for compensating for load interruptions; for Power of the equivalent load during the time period; for Load demand during a given time period.

[0042] In some embodiments, the constraints include at least one of decision variable constraints, power balance constraints, device constraints, power supply reliability constraints, and energy waste constraints.

[0043] In some embodiments, decision variable constraints can be expressed by the following formula: in, , , , These are the quantities of wind turbines, photovoltaic systems, gas turbines, and energy storage devices, respectively. , , , These represent the maximum number of wind turbines, photovoltaics, gas turbines, and energy storage devices that a virtual power plant is allowed to install.

[0044] In some embodiments, the power balance constraint can be expressed by the following formula: in, for The output power of photovoltaics during a given period; for The output power of wind power during a given period; for Wasted power from wind and solar power during certain periods and They cannot both exist simultaneously and both be non-negative, that is, when hour, Similarly, when hour, .

[0045] In some embodiments, device constraints (also known as energy storage charge / discharge and capacity constraints) are expressed by the following formula: in, and These are the maximum and minimum values ​​of the energy storage charging power, respectively. and These represent the maximum and minimum values ​​of the energy storage discharge power, respectively. and Energy storage The charging and discharging state variables for each time period take values ​​of 1 or 0; This represents the capacity of the energy storage at the initial moment. The energy storage capacity at the end of the time period; For energy storage Remaining capacity for the time period.

[0046] In some embodiments, power supply reliability constraints are expressed by the following formula: in, This indicates the load shortage rate of the virtual power plant. This indicates the maximum allowable load deficit rate for the virtual power plant. for Interruption amount of total load of virtual power plant during time period. This represents the load demand during time period t. This represents the total number of scheduling periods.

[0047] In some embodiments, energy waste constraints can be expressed by the following formula: in, This represents the amount of wind and solar power curtailed by the virtual power plant. This indicates the maximum allowable wind and solar power curtailment capacity of the virtual power plant. This represents the amount of wind and solar power curtailed during time period t. This represents the load demand during time period t. This represents the total number of scheduling periods.

[0048] It is understood that the embodiments of this application comprehensively consider the operational constraints of wind power, photovoltaics, gas turbines and energy storage, aiming at the lowest monthly comprehensive economic cost, to construct a distributed power supply capacity optimization configuration model, and formulate corresponding operational strategies to ensure the stable and reliable operation of the virtual power plant. By solving the distributed power supply capacity optimization configuration model based on the operational strategies, the optimal combination of wind turbines, photovoltaics, gas turbines and energy storage devices under various constraints can be obtained, thereby realizing the optimized configuration of distributed power supply capacity of the virtual power plant and meeting the economic benefits and operational stability of the virtual power plant.

[0049] S102. Initialize the gray wolf population; the gray wolf population includes multiple gray wolf individuals, and each gray wolf individual represents a configuration scheme.

[0050] In some embodiments, the gray wolf population is the initial solution set in the improved gray wolf optimization algorithm, used to characterize candidate capacity configuration schemes for distributed power sources in a virtual power plant. Each gray wolf represents a configuration scheme, and each configuration scheme consists of a set of decision variables indicating the installed capacity of each distributed power source in the virtual power plant, such as the number of wind turbines, photovoltaic units, gas turbines, and energy storage devices. That is, the decision variables include the number of wind turbines, photovoltaic units, gas turbines, and energy storage devices.

[0051] In some embodiments, within the constraints of decision variables, multiple gray wolf individuals can be generated using a uniform random distribution to obtain a gray wolf population.

[0052] S103. Based on the operating strategy, the configuration scheme of each gray wolf individual is simulated and run to obtain the corresponding operating data. Based on the operating data, objective function and constraints, the initial fitness value of each gray wolf individual is obtained.

[0053] In some embodiments, obtaining the initial fitness value of each individual gray wolf based on operational data, an objective function, and constraints includes: determining whether the operational data satisfies the constraints; if the operational data satisfies the constraints, determining the monthly comprehensive economic cost corresponding to the operational data based on the operational data and the objective function, and setting the monthly comprehensive economic cost as the initial fitness value of the individual gray wolf corresponding to the operational data. The monthly comprehensive economic cost includes at least one of the following: monthly average investment and installation cost, monthly average operation and maintenance cost, monthly average equipment replacement cost, monthly environmental cost, monthly wind and solar curtailment penalty fee, monthly fuel cost, and monthly load interruption compensation fee.

[0054] In some embodiments, for each individual gray wolf in the initialized gray wolf population, a simulation can be performed based on the operating strategy to evaluate the economy and feasibility of the configuration scheme corresponding to each gray wolf individual under actual operating conditions.

[0055] For example, based on the decision variables represented by each gray wolf individual—namely, the number of wind turbines, photovoltaic units, gas turbines, and energy storage devices—and combined with the rated power of each device, simulation operation is performed to obtain the operational data of the configuration scheme represented by each gray wolf individual. This operational data may include information such as the actual output power of each distributed power source at different time periods, the charging and discharging status and remaining capacity of the energy storage devices, and the amount of load interruption. After obtaining the operational data, it can be determined whether the operational data meets the constraints. If it does, the monthly comprehensive economic cost corresponding to the operational data is determined through the objective function, and this monthly comprehensive economic cost is set as the initial fitness value of the gray wolf individual corresponding to the operational data. If the operational data does not meet the constraints, such as the decision variables exceeding the maximum allowed number, power imbalance, energy storage charging and discharging violating restrictions, load shortage rate, or wind and solar curtailment exceeding the maximum allowed value, the configuration scheme corresponding to the gray wolf individual for that operational data is eliminated.

[0056] S104. Determine the distance between individual gray wolves, and based on the distance and the radius of the niche, construct a corresponding niche with each individual gray wolf as the center.

[0057] In some embodiments, the Euclidean distance between individual gray wolves can be determined, and a corresponding niche can be constructed with each individual gray wolf as the center based on the Euclidean distance between individual gray wolves and the niche radius.

[0058] In some embodiments, the Euclidean distance between individual gray wolves can be determined using the following formula: in, Let be the Euclidean distance between the i-th gray wolf and the j-th gray wolf. This represents the total number of individual gray wolves in the niche habitat. For the i-th gray wolf individual, For the j-th gray wolf individual, Let k be the decision variable for the i-th gray wolf individual. Let be the k-th decision variable for the j-th gray wolf individual.

[0059] In some embodiments, constructing a corresponding niche based on distance and niche radius for each individual gray wolf includes: constructing a niche with the first individual gray wolf as the center and the niche radius as the radius, and identifying a second individual gray wolf in the gray wolf population whose distance from the first individual gray wolf is less than or equal to the niche radius as a gray wolf within the niche of the first individual gray wolf; wherein, the first individual gray wolf is any individual gray wolf in the gray wolf population.

[0060] In some embodiments, each individual gray wolf in the gray wolf population can be traversed, and a microhabitat can be constructed around each individual wolf in turn, thereby completing the construction of all microhabitats.

[0061] S105. Through a sharing mechanism, based on the number and distribution of gray wolves within each small territory, the initial fitness value of each gray wolf individual is adjusted to obtain the target fitness value; based on the target fitness value, the gray wolf individuals in the gray wolf population are iteratively updated until the convergence condition is met, and the optimal configuration strategy is obtained.

[0062] In some embodiments, the optimal configuration strategy is used to indicate the optimal installed capacity of wind power, photovoltaic, gas turbine and energy storage systems in a virtual power plant.

[0063] In some embodiments, the initial fitness value of each gray wolf individual is adjusted based on the number and distribution of gray wolf individuals within each small territory through a sharing mechanism to obtain the target fitness value. This includes: determining the total sharing degree of each gray wolf individual based on the distance between each gray wolf individual and other gray wolf individuals within its small territory using a sharing function; and determining the target fitness value of each gray wolf individual based on its initial fitness value and the total sharing degree.

[0064] In some embodiments, a shared function is represented by the following formula: in, Let be the degree of sharing between the i-th gray wolf and the j-th gray wolf. For the radius of the small habitat, Let be the distance between the i-th gray wolf and the j-th gray wolf. The shape parameter for the shared function.

[0065] In some embodiments, the target fitness value is expressed by the following formula: in, Let be the target fitness value of the i-th gray wolf individual. Let be the initial fitness value of the i-th gray wolf individual. Let i be the degree of sharing between the i-th gray wolf individual and the j-th gray wolf individual; Let be the distance between the i-th gray wolf and the j-th gray wolf. This represents the total number of individual gray wolves within the territory of Xiaosheng.

[0066] In some embodiments, if the convergence speed is too fast during the iterative update process, it may lead to the localization of the Pareto front. Therefore, this application introduces a chaotic mutation mechanism to perform mutation operations on some gray wolf individuals through chaotic mapping in order to increase the diversity of the population and avoid the algorithm from getting trapped in local optima.

[0067] In some embodiments, iteratively updating gray wolf individuals in a gray wolf population based on a target fitness value includes: determining the best, second-best, and third-best gray wolf individuals in the current iteration based on the adjusted fitness value of each gray wolf individual in the gray wolf population; updating the positions of other gray wolf individuals in the gray wolf population based on the positions of the best, second-best, and third-best gray wolf individuals, and performing mutation operations on some gray wolf individuals in the updated gray wolf population through a chaotic mutation mechanism to obtain a new gray wolf population; and updating the new gray wolf population as the gray wolf population for the next iteration.

[0068] In some embodiments, the chaotic mutation mechanism can be represented by the following formula: in, Let be the chaotic variable corresponding to the j-th gray wolf in the (k+1)-th iteration; Let be the chaotic variable corresponding to the j-th gray wolf in the k-th iteration; It is a random number.

[0069] Understandably, a niche can simulate the competition and communication between species in nature, allowing individual gray wolves to improve themselves and find optimal solutions through competition and communication, just like biological individuals. Compared to the traditional gray wolf optimization algorithm that uses random numbers to determine the initial population, the introduction of a niche in this application can avoid getting stuck in the optimal solution.

[0070] In some embodiments, to verify the feasibility and effectiveness of the methods provided in this application, the distributed power generation capacity optimization method based on the improved gray wolf algorithm, the traditional gray wolf optimization algorithm, and the traditional particle swarm optimization algorithm provided in this application can be used to optimize the configuration of distributed power generation capacity. The configuration results and economic costs are shown in Tables 1 and 2, respectively. Scenario 1 involves optimizing the distributed power generation capacity using the virtual power plant distributed power generation capacity optimization method based on the improved gray wolf algorithm provided in this application; Scenario 2 involves optimizing the distributed power generation capacity using the traditional gray wolf optimization algorithm; and Scenario 3 involves optimizing the distributed power generation capacity using the traditional particle swarm optimization algorithm.

[0071] Table 1. Capacity configuration results under three scenarios Table 2 Economic Costs in Three Scenarios As shown in Tables 1 and 2, the configuration capacity of Scenario 1 is smaller than that of Scenario 2 and Scenario 3, and the overall cost (i.e., the aforementioned monthly comprehensive economic cost) is also lower. This is because the amount of new energy storage and photovoltaic capacity added in Scenario 1 is less than that in Scenario 2 and Scenario 3.

[0072] Figure 2 Figure 2 shows the iteration curves for scenarios one, two, and three. In scenario two, the traditional gray wolf optimization algorithm gets stuck in a local optimum after 25 iterations; in scenario three, the traditional particle swarm optimization algorithm gets stuck in a local optimum around 15 iterations. However, in scenario one, the virtual power plant distributed power capacity optimization method based on the improved gray wolf algorithm provided in this application successfully finds the optimal value around 200 iterations. Thus, the virtual power plant distributed power capacity optimization method based on the improved gray wolf algorithm provided in this application not only has high convergence accuracy but also better global search capability.

[0073] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of the invention. Therefore, the phrase "in one embodiment" or "in an embodiment" appearing in every place throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in each embodiment of the invention, the sequence number of each process described above does not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the invention. The sequence numbers of the embodiments of the invention described above are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0074] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0075] In the several embodiments provided by this invention, it should be understood that the disclosed methods can be implemented in other ways. The methods disclosed in the several method embodiments provided by this invention can be arbitrarily combined without conflict to obtain new method embodiments. The features disclosed in the several method embodiments provided by this invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0076] The above description is merely an embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimizing the capacity of distributed power sources in a virtual power plant based on an improved gray wolf algorithm, characterized in that, The method includes: Obtain the operation strategy of the virtual power plant and the distributed power source capacity optimization configuration model; wherein, the distributed power source capacity optimization configuration model includes an objective function and constraints. The objective function is used to minimize the monthly comprehensive economic cost of the virtual power plant, and the constraints include at least one of decision variable constraints, power balance constraints, device constraints, power supply reliability constraints, and energy waste constraints. Initialize the gray wolf population; the gray wolf population consists of multiple gray wolf individuals, each representing a configuration scheme; Based on the operational strategy, the configuration scheme of each gray wolf individual is simulated and run to obtain the corresponding operational data. Based on the operational data, objective function and constraints, the initial fitness value of each gray wolf individual is obtained. Determine the distance between individual gray wolves, and based on the distance and the radius of the niche, construct a corresponding niche centered on each individual gray wolf; Through a sharing mechanism, the initial fitness value of each gray wolf individual is adjusted based on the number and distribution of gray wolves within each small territory to obtain the target fitness value. Based on the target fitness value, the gray wolf individuals in the gray wolf population are iteratively updated until the convergence condition is met to obtain the optimal configuration strategy. The optimal configuration strategy is used to indicate the optimal installed capacity of wind power, photovoltaic, gas turbine and energy storage systems in the virtual power plant.

2. The method according to claim 1, characterized in that, Based on the runtime data, objective function, and constraints, the initial fitness value for each individual gray wolf is obtained, including: Determine whether the running data meets the constraints; If the operating data meets the constraints, the monthly comprehensive economic cost corresponding to the operating data is determined based on the operating data and the objective function, and the monthly comprehensive economic cost is determined as the initial fitness value of the gray wolf individual corresponding to the operating data; wherein, the monthly comprehensive economic cost includes at least one of the following: monthly average investment and installation cost, monthly average operation and maintenance cost, monthly average equipment replacement cost, monthly environmental cost, monthly wind and solar curtailment penalty fee, monthly fuel cost, and monthly load interruption compensation fee.

3. The method according to claim 1, characterized in that, Based on distance and niche radius, a corresponding niche is constructed centered on each individual gray wolf, including: A small habitat is constructed with the first gray wolf individual as the center and the radius of the small habitat as the radius. The second gray wolf individual in the gray wolf population whose distance from the first gray wolf individual is less than or equal to the radius of the small habitat is identified as a gray wolf individual within the small habitat of the first gray wolf individual. The first gray wolf individual can be any gray wolf individual in the gray wolf population.

4. The method according to claim 1, characterized in that, Through a sharing mechanism, based on the number and distribution of gray wolves within each small territory, the initial fitness value of each gray wolf is adjusted to obtain the target fitness value, including: Based on the distance between each individual gray wolf and other gray wolves within its territory, the total sharing degree of each gray wolf is determined by a sharing function; The target fitness value for each gray wolf is determined based on its initial fitness value and total sharing value.

5. The method according to claim 4, characterized in that, Shared functions are represented by the following formula: in, Let be the degree of sharing between the i-th gray wolf and the j-th gray wolf. For the radius of the small habitat, Let be the distance between the i-th gray wolf and the j-th gray wolf. The shape parameter for the shared function.

6. The method according to claim 4, characterized in that, The target fitness value is expressed by the following formula: in, Let be the target fitness value of the i-th gray wolf individual. Let be the initial fitness value of the i-th gray wolf individual. Let i be the degree of sharing between the i-th gray wolf individual and the j-th gray wolf individual; Let be the distance between the i-th gray wolf and the j-th gray wolf. This represents the total number of individual gray wolves within the territory of Xiaosheng.

7. The method according to claim 1, characterized in that, Based on the target fitness value, the gray wolf individuals in the gray wolf population are iteratively updated, including: Based on the adjusted fitness value of each individual gray wolf in the gray wolf population, determine the best, second-best, and third-best gray wolf individuals in the current iteration round. Based on the positions of the best, second-best, and third-best gray wolf individuals, the positions of other gray wolf individuals in the gray wolf population are updated. Then, through a chaotic mutation mechanism, some gray wolf individuals in the updated gray wolf population are mutated to obtain a new gray wolf population. The new gray wolf population will be updated as the gray wolf population for the next iteration.

8. The method according to claim 1, characterized in that, Power supply reliability constraints are expressed by the following formula: in, This indicates the load shortage rate of the virtual power plant. This indicates the maximum allowable load deficit rate for the virtual power plant. This represents the amount of load interruption during time period t. This represents the load demand during time period t. This represents the total number of scheduling periods; Energy waste constraints are expressed by the following formula: in, This represents the amount of wind and solar power curtailed by the virtual power plant. This indicates the maximum allowable wind and solar power curtailment capacity of the virtual power plant. This represents the amount of wind and solar power curtailed during time period t. This represents the load demand during time period t. This represents the total number of scheduling periods.

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