A multi-resource collaborative optimization method for virtual power plants based on edge computing

Abstract

The present invention discloses a virtual power plant multi-resource collaborative optimization method based on edge computing, which relates to the technical field of virtual power plants, including: forming a virtual power plant edge resource network information library by collecting various resource data; calculating the overall power generation of the virtual power plant through the virtual power plant edge resource network information library; calculating the energy storage cost of the virtual power plant through the virtual power plant edge resource network information library; constructing a multi-resource collaborative optimization model based on a multi-objective Harris Hawk optimization algorithm; minimizing the energy storage cost of the virtual power plant and maximizing the overall power generation of the virtual power plant through the multi-resource collaborative optimization model, and obtaining the optimal solution for virtual power plant resource allocation. The present invention can improve resource allocation efficiency and reduce delays, thereby quickly adapting to various changes and demands, and ensuring the efficiency and reliability of power supply.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plants, and in particular to a multi-resource collaborative optimization method for virtual power plants based on edge computing. Background Art

[0002] With the rapid development of renewable energy and distributed energy, more and more small energy generation facilities are connected to the power system, forming a distributed energy network. These distributed energy resources have the characteristics of wide geographical distribution, different sizes, and large fluctuations in production capacity, which brings challenges to the dispatch and management of the power system. In order to better coordinate and manage distributed energy resources and improve the operating efficiency and stability of the power system, virtual power plants (VPPs) have gradually attracted attention as a platform for integrating and optimizing distributed energy resources.

[0003] Against the backdrop of green development and reforms on the energy supply and consumption sides, virtual power plants, which are based on the aggregation and flexible scheduling of distributed power resources, energy storage, flexible loads and other resources to achieve optimal allocation of multiple resources and economical operation, have rapidly generated application demand in China.

[0004] The publication number is CN116644924A, and the name is a virtual power plant resource dispatching method. The virtual power plant calculates the total dispatching demand based on the dispatching demand and the first dispatching margin, wherein the first dispatching margin is calculated by a first model using the dispatching demand as input data; the virtual power plant reports the total dispatching demand to the dispatching platform, and obtains the total dispatching capacity issued by the dispatching platform; the virtual power plant constructs a day-ahead dispatching model based on the dispatching demand, the first dispatching margin and the total dispatching capacity, and uses the day-ahead dispatching model to calculate the first dispatching capacity; the virtual power plant dispatches the first power resource for the user based on the first dispatching capacity. However, the real-time variability of the environment during the dispatching process is not taken into account, so that the virtual power plant dispatching strategy cannot be updated in real time, and abnormalities cannot be discovered and handled in a timely manner.

[0005] The publication number is CN115049323B, and the name is a virtual power plant supervision system based on distributed resource collaboration, a distributed resource management module and a park virtual power plant module interconnected with the distributed resource management module, the other end of the park virtual power plant module is connected to a scheduling evaluation module, and the park virtual power plant module includes a resource manager for receiving information from the distributed resource management module, the output end of the resource manager is connected to a grid-connected differential module, and the output end of the grid-connected differential module is connected to a robust evaluation module. Through the settings of the distributed resource management module, the park virtual power plant module, the grid-connected differential module and the robust evaluation module, the frequencies of different power systems can be time-coupled and adjusted, solving the problem of poor power supply reliability due to different power supply parameters of each power supply subsystem, but there is no real-time feedback on the scheduling results, and the scheduling strategy cannot be adjusted in real time, which reduces the scheduling efficiency and stability of the virtual power plant. Summary of the invention

[0006] In order to solve the above technical problems, a technical solution adopted by the present invention is to provide a multi-resource collaborative optimization method of a virtual power plant based on edge computing, the method comprising:

[0007] S10. Constructing a virtual power plant edge resource network information database by collecting various resource data;

[0008] S20. The overall power generation of the virtual power plant is calculated by the virtual power plant edge resource network information base;

[0009] S30. The energy storage cost of the virtual power plant is calculated by the virtual power plant edge resource network information base;

[0010] S40. Construct a multi-resource collaborative optimization model based on the multi-objective Harris Hawk optimization algorithm;

[0011] S50. By minimizing the energy storage cost of the virtual power plant and maximizing the overall power generation of the virtual power plant through the multi-resource collaborative optimization model, the optimal solution for resource allocation of the virtual power plant is obtained;

[0012] The S30 includes:

[0013] S31. Calculate the hydroelectric energy cost by using the energy storage resource data and the controllable load resource data;

[0014] The energy cost of hydroelectric power The calculation formula is:

[0015] ;

[0016] in, is the i-th hydroelectric power generation equipment, is the number of hydroelectric power generation equipment, is the hydroelectric power generation coefficient of the i-th hydroelectric power generation equipment, is the power generation of the i-th hydroelectric power generation equipment;

[0017] S32. Calculating the energy cost of wind power generation by using the energy storage resource data and the controllable load resource data;

[0018] The energy cost of wind power The calculation formula is:

[0019] ;

[0020] in, is the i-th wind turbine, is the number of wind power generation equipment, is the rated power of wind power, is the wind power generation coefficient, is the average construction cost per unit installed capacity, is the installed rated capacity of the i-th wind power generation equipment, is the wind load rate;

[0021] S33. Calculate the energy cost of solar power generation by using the energy storage resource data and the controllable load resource data;

[0022] The solar power energy cost The calculation formula is:

[0023] C SP = ∑ sp_i=1 spnum π sp_i *[ 1+θ sp _i T run - T amb ] ;

[0024] in, is the i-th solar power generation device, is the number of solar power generation equipment, is the solar power generation coefficient of the i-th solar power generation device, is the temperature coefficient, is the actual operating temperature of the photovoltaic panel, is the ambient temperature;

[0025] S34. Calculating energy storage loss costs through energy storage resource data and controllable load resource data;

[0026] The energy storage loss cost The calculation formula is:

[0027] ;

[0028] in, is the loss cost coefficient, is the hydropower generation loss cost coefficient, is the wind power generation loss cost coefficient, is the solar power generation loss cost coefficient, is the maximum energy storage value, is the wind power generation, is the hydroelectric power generation, The amount of electricity generated by solar energy;

[0029] S35. The energy storage cost of the virtual power plant is calculated by the hydroelectric energy cost, wind power energy cost, solar power energy cost, and energy storage loss cost;

[0030] The Virtual Power Plant Energy Storage Costs The calculation formula is:

[0031] ;

[0032] The S40 comprises:

[0033] S41. Initialize resource allocation population size;

[0034] S42. Define resource allocation member positions;

[0035] The resource allocation member location It is expressed as:

[0036] ;

[0037] in, is the overall power generation coefficient, is the energy storage loss coefficient, is the energy storage cost of the virtual power plant, For the overall power generation of the virtual power plant, the resource allocation member positions are continuously updated iteratively to minimize the energy storage cost of the virtual power plant and maximize the overall power generation of the virtual power plant;

[0038] S43. Define the target resource allocation member escape energy;

[0039] The target resource allocation member escapes energy It is expressed as:

[0040] ;

[0041] in, The initial escape energy of the member allocated to the target resource is a random number between [-1,1], t is the current evolutionary generation, and T is the maximum evolutionary generation of the population;

[0042] S44. Calculate the average position of individual members of resource allocation;

[0043] The average position of the individual resource allocation members It is expressed as;

[0044] ;

[0045] Among them, M is the number of resource allocation members, Assign individual member locations to resources;

[0046] S45. Determine whether the escape energy of the target resource allocation member is greater than or equal to 1. If so, enter the search phase and update the resource allocation member position; if not, enter the development phase and continuously perform iterative updates to obtain the optimal solution for virtual power plant resource allocation.

[0047] Furthermore, the various resource data include renewable distributed resource data, energy storage resource data, and controllable load resource data, including:

[0048] The renewable distributed resource data includes wind power generation resource data, solar power generation resource data, and hydropower generation resource data;

[0049] The energy storage resource data includes pumped storage resource data, battery storage resource data, and capacitor storage resource data;

[0050] The controllable load resource data includes industrial production load resource data and charging equipment load resource data.

[0051] Further, the S20 includes:

[0052] S21. Calculate hydroelectric power generation using hydroelectric power resource data;

[0053] The amount of hydroelectric power The calculation formula is:

[0054] ;

[0055] in, is the amount of electricity generated by hydropower in one hour, is the volume of water flowing in one second, is the density of water, is the rated power of hydroelectric power, is the water flow drop;

[0056] S22. Calculating wind power generation using wind power resource data;

[0057] Wind power generation The calculation formula is:

[0058] ;

[0059] Where WP is wind power generation, is the rated power of wind power, For the cut-in wind speed, is the wind speed at the current moment, To cut out the wind speed, The minimum wind speed for the motor to work. The maximum wind speed of the motor;

[0060] S23. Calculating solar power generation using solar power generation resource data;

[0061] The solar power generation The calculation formula is:

[0062] SP= P su n * S ev * A ev * μ sp *[1- ( T b -20) G sp 2 800 ] ;

[0063] in, is the rated power of solar power generation, is the number of solar panels, is the solar panel area, The standard temperature for solar panel energy storage. is the solar radiation, is the temperature coefficient;

[0064] S24. The overall power generation of the virtual power plant is calculated by the hydroelectric power generation, wind power generation, and solar power generation;

[0065] The overall power generation of the virtual power plant The calculation formula is:

[0066] .

[0067] Furthermore, the search phase refers to a global search of target resource allocation members with equal probability through two strategies; when the probability When the probability When , the resource allocation members will randomly move within the resource allocation population. The expression of the search phase is as follows:

[0068] ;

[0069] in, For iteration After that, the resource allocation member position, Assign member individual positions to target resources, is a random number between [0,1], Assign individual member locations to random target resources.

[0070] Furthermore, the development stage includes:

[0071] The development phase refers to the resource allocation members adopting four hunting strategies to imitate the hunting behavior of Harris's hawks after finding the target resource allocation members;

[0072] The four hunting strategies are soft encirclement, hard encirclement, soft encirclement with gradual rapid dive, and hard encirclement with gradual rapid dive;

[0073] S441. Allocate member escape probability by target resource Combine target resources to allocate members' escape energy The absolute value of is used for judgment, and one of the four hunting strategies is executed according to the judgment result;

[0074] The target resource allocation member escape probability is a random number between (0,1), There is a chance to escape. It means there is no chance of escape;

[0075] The target resource allocation member escapes energy ,when , indicating that there is energy escaping, when , indicating that there is no escaping energy;

[0076] S442. When Soft encirclement is performed when

[0077] The soft encirclement means that the target resource allocation member still has the energy to escape but no chance of escape, and the update formula is:

[0078] ;

[0079] in, For iteration After that, the resource allocation member position, Assign member individual positions to target resources, is a random number between [0,2];

[0080] S443. When Hard encirclement;

[0081] The hard encirclement means that the target resource allocation member has no energy to escape and no chance to escape. The update formula is:

[0082] ;

[0083] S444. When A soft encirclement with a gradual and rapid dive;

[0084] The soft encirclement of the progressive rapid dive means that the target resource allocation member has a chance to escape from the encirclement and has enough energy to escape the capture of the resource allocation members. The resource allocation members will adopt a soft encirclement of the progressive rapid dive around the target resource allocation member, which is implemented through two strategies. When the first strategy If invalid, execute the second strategy , the update formula is:

[0085] ;

[0086] ;

[0087] ;

[0088] in, is the fitness function, is a two-dimensional random vector, whose elements are random numbers between [0,1]. is the mathematical expression of Levy flight, Assign member individual positions to target resources, is a random number between [0,2];

[0089] S445. When A hard encirclement with a gradual and rapid dive;

[0090] The progressive fast dive hard encirclement means that the target resource allocation member has no energy to escape, but still has a chance to escape. The resource allocation members will adopt a progressive fast dive hard encirclement around the target resource allocation member to shorten the distance from the average position of the individual resource allocation members. This is implemented through two strategies. When the first strategy If invalid, execute the second strategy , the update formula is:

[0091] ;

[0092] ;

[0093] ;

[0094] in, The average position of individual members for resource allocation, is the fitness function, is a two-dimensional random vector, whose elements are random numbers between [0,1]. is the mathematical expression of Levy flight, Assign member individual positions to target resources, is a random number between [0,2];

[0095] S446. Repeat iterative steps S442-S446 until the iteration threshold 1000 or the resource allocation member position is reached Achieve target resource allocation member individual position The iteration is stopped when , and the optimal solution for virtual power plant resource allocation is obtained.

[0096] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0097] 1. The invention provides a virtual power plant multi-resource collaborative optimization method based on edge computing, which collects and processes various resource data by establishing a virtual power plant resource network information library and saves it in the resource information library, so that the virtual power plant can manage and dispatch resources quickly and conveniently; through centralized management and real-time monitoring, the virtual power plant can allocate and utilize resources more efficiently, thereby improving the overall resource management efficiency.

[0098] 2. The multi-resource collaborative optimization method of a virtual power plant based on edge computing provided by the present invention optimizes the resource allocation of the virtual power plant by utilizing a multi-objective optimization algorithm. Through precise resource allocation strategies, the virtual power plant can reasonably allocate various resources to ensure that each edge device can be fully utilized, thereby improving the overall operating efficiency, which not only helps to reduce energy consumption, but also reduces operating costs.

[0099] 3. The multi-resource collaborative optimization method of a virtual power plant based on edge computing provided by the present invention helps to improve the sustainability of the virtual power plant by optimizing resource allocation; effective resource management and optimization reduce resource waste and energy consumption, thereby having a smaller impact on the environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0100] Figure 1 It is a flow chart of a multi-resource collaborative optimization method of a virtual power plant based on edge computing provided by the present invention.

[0101] Figure 2 It is a flow chart for solving the overall power generation of a virtual power plant based on a virtual power plant multi-resource collaborative optimization method based on edge computing provided by the present invention.

[0102] Figure 3 It is a flow chart of solving the energy storage cost of a virtual power plant based on a virtual power plant multi-resource collaborative optimization method based on edge computing provided by the present invention.

[0103] Figure 4 It is a flow chart of constructing a multi-resource collaborative optimization model of a virtual power plant multi-resource collaborative optimization method based on edge computing provided by the present invention.

[0104] Figure 5 It is a flow chart of a hunting strategy for a multi-resource collaborative optimization method of a virtual power plant based on edge computing provided by the present invention. DETAILED DESCRIPTION

[0105] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making a clearer and more definite definition of the protection scope of the present invention.

[0106] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present invention, rather than all of the embodiments.

[0107] Figure 1 : is a flowchart of a multi-resource collaborative optimization method for a virtual power plant based on edge computing provided by an embodiment of the present invention, the method comprising:

[0108] S10. Constructing a virtual power plant edge resource network information database by collecting various resource data;

[0109] The various resource data include renewable distributed resource data, energy storage resource data, and controllable load resource data; the renewable distributed resource data include wind power resource data, solar power resource data, and hydropower resource data;

[0110] The energy storage resource data includes pumped storage resource data, battery storage resource data, and capacitor storage resource data;

[0111] The controllable load resource data includes industrial production load resource data and charging equipment load resource data.

[0112] S20. The overall power generation of the virtual power plant is calculated by the virtual power plant edge resource network information base;

[0113] Further, refer to Figure 2 , the S20 comprises:

[0114] S21. Calculate hydroelectric power generation using hydroelectric power resource data;

[0115] The amount of hydroelectric power The calculation formula is:

[0116] ;

[0117] in, is the amount of electricity generated by hydropower in one hour, is the volume of water flowing in one second, is the density of water, is the rated power of hydroelectric power, is the water flow drop;

[0118] S22. Calculating wind power generation using wind power resource data;

[0119] Wind power generation The calculation formula is:

[0120] ;

[0121] Where WP is wind power generation, is the rated power of wind power, For the cut-in wind speed, is the wind speed at the current moment, To cut out the wind speed, The minimum wind speed for the motor to work. The maximum wind speed of the motor;

[0122] S23. Calculating solar power generation using solar power generation resource data;

[0123] The solar power generation The calculation formula is:

[0124] SP= P su n * S ev * A ev * μ sp *[1- ( T b -20) G sp 2 800 ] ;

[0125] in, is the rated power of solar power generation, is the number of solar panels, is the solar panel area, The standard temperature for solar panel energy storage. is the solar radiation, is the temperature coefficient;

[0126] S24. The overall power generation of the virtual power plant is calculated by the hydroelectric power generation, wind power generation, and solar power generation;

[0127] The overall power generation of the virtual power plant The calculation formula is:

[0128] .

[0129] S30. The energy storage cost of the virtual power plant is calculated by the virtual power plant edge resource network information base;

[0130] Further, refer to Figure 3 , the S30 comprises:

[0131] S31. Calculate the hydroelectric energy cost by using the energy storage resource data and the controllable load resource data;

[0132] The energy cost of hydroelectric power The calculation formula is:

[0133] ;

[0134] in, is the i-th hydroelectric power generation equipment, is the number of hydroelectric power generation equipment, is the hydroelectric power generation coefficient of the i-th hydroelectric power generation equipment, is the power generation of the i-th hydroelectric power generation equipment;

[0135] S32. Calculating the energy cost of wind power generation by using the energy storage resource data and the controllable load resource data;

[0136] The energy cost of wind power The calculation formula is:

[0137] ;

[0138] in, is the i-th wind turbine, is the number of wind power generation equipment, is the rated power of wind power, is the wind power generation coefficient, is the average construction cost per unit installed capacity, is the installed rated capacity of the i-th wind power generation equipment, is the wind load rate;

[0139] S33. Calculate the energy cost of solar power generation by using the energy storage resource data and the controllable load resource data;

[0140] The solar power energy cost The calculation formula is:

[0141] C SP = ∑ sp_i=1 spnum π sp_i *[ 1+θ sp _i T run - T amb ] ;

[0142] in, is the i-th solar power generation device, is the number of solar power generation equipment, is the solar power generation coefficient of the i-th solar power generation device, is the temperature coefficient, is the actual operating temperature of the photovoltaic panel, is the ambient temperature;

[0143] S34. Calculating energy storage loss costs through energy storage resource data and controllable load resource data;

[0144] The energy storage loss cost The calculation formula is:

[0145] ;

[0146] in, is the loss cost coefficient, is the hydropower generation loss cost coefficient, is the wind power generation loss cost coefficient, is the solar power generation loss cost coefficient, is the maximum energy storage value, is the wind power generation, is the hydroelectric power generation, The amount of electricity generated by solar energy;

[0147] S35. The energy storage cost of the virtual power plant is calculated by the hydroelectric energy cost, wind power energy cost, solar power energy cost, and energy storage loss cost;

[0148] The Virtual Power Plant Energy Storage Costs The calculation formula is:

[0149] .

[0150] S40. Construct a multi-resource collaborative optimization model based on the multi-objective Harris Hawk optimization algorithm;

[0151] Further, refer to Figure 4 , the S40 comprises:

[0152] S41. Initialize resource allocation population size;

[0153] S42. Define resource allocation member positions;

[0154] The resource allocation member location It is expressed as:

[0155] ;

[0156] in, is the overall power generation coefficient, is the energy storage loss coefficient, is the energy storage cost of the virtual power plant, For the overall power generation of the virtual power plant, the resource allocation member positions are continuously updated iteratively to minimize the energy storage cost of the virtual power plant and maximize the overall power generation of the virtual power plant;

[0157] S43. Define the target resource allocation member escape energy;

[0158] The target resource allocation member escapes energy It is expressed as:

[0159] ;

[0160] in, The initial escape energy of the member allocated to the target resource is a random number between [-1,1], t is the current evolutionary generation, and T is the maximum evolutionary generation of the population;

[0161] S44. Calculate the average position of individual members of resource allocation;

[0162] The average position of the individual resource allocation members It is expressed as;

[0163] ;

[0164] Among them, M is the number of resource allocation members, Assign individual member locations to resources;

[0165] S45. Determine whether the escape energy of the target resource allocation member is greater than or equal to 1. If so, enter the search phase and update the resource allocation member position; if not, enter the development phase and continuously perform iterative updates to obtain the optimal solution for virtual power plant resource allocation.

[0166] Furthermore, the search phase refers to a global search of target resource allocation members with equal probability through two strategies; when the probability When the probability When , the resource allocation members will randomly move within the resource allocation population. The expression of the search phase is as follows:

[0167] ;

[0168] in, For iteration After that, the resource allocation member position, Assign member individual positions to target resources, is a random number between [0,1], Assign individual member locations to random target resources.

[0169] Further, refer to Figure 5 The development phase includes:

[0170] The development phase refers to the resource allocation members adopting four hunting strategies to imitate the hunting behavior of Harris's hawks after finding the target resource allocation members;

[0171] The four hunting strategies are soft encirclement, hard encirclement, soft encirclement with gradual rapid dive, and hard encirclement with gradual rapid dive;

[0172] S441. Allocate member escape probability by target resource Combine target resources to allocate members' escape energy The absolute value of is used for judgment, and one of the four hunting strategies is executed according to the judgment result;

[0173] The target resource allocation member escape probability is a random number between (0,1), There is a chance to escape. It means there is no chance of escape;

[0174] The target resource allocation member escapes energy ,when , indicating that there is energy escaping, when , indicating that there is no escaping energy;

[0175] S442. When Soft encirclement is performed when

[0176] The soft encirclement means that the target resource allocation member still has the energy to escape but no chance of escape, and the update formula is:

[0177] ;

[0178] in, For iteration After that, the resource allocation member position, Assign member individual positions to target resources, is a random number between [0,2];

[0179] S443. When Hard encirclement;

[0180] The hard encirclement means that the target resource allocation member has no energy to escape and no chance to escape. The update formula is:

[0181] ;

[0182] S444. When A soft encirclement with a gradual and rapid dive;

[0183] The soft encirclement of the progressive rapid dive means that the target resource allocation member has a chance to escape from the encirclement and has enough energy to escape the capture of the resource allocation members. The resource allocation members will adopt a soft encirclement of the progressive rapid dive around the target resource allocation member, which is implemented through two strategies. When the first strategy If invalid, execute the second strategy , the update formula is:

[0184] ;

[0185] ;

[0186] ;

[0187] in, is the fitness function, is a two-dimensional random vector, whose elements are random numbers between [0,1]. is the mathematical expression of Levy flight, Assign member individual positions to target resources, is a random number between [0,2];

[0188] S445. When A hard encirclement with a gradual and rapid dive;

[0189] The progressive fast dive hard encirclement means that the target resource allocation member has no energy to escape, but still has a chance to escape. The resource allocation members will adopt a progressive fast dive hard encirclement around the target resource allocation member to shorten the distance from the average position of the individual resource allocation members. This is implemented through two strategies. When the first strategy If invalid, execute the second strategy , the update formula is:

[0190] ;

[0191] ;

[0192] ;

[0193] in, The average position of individual members for resource allocation, is the fitness function, is a two-dimensional random vector, whose elements are random numbers between [0,1]. is the mathematical expression of Levy flight, Assign member individual positions to target resources, is a random number between [0,2];

[0194] S446. Repeat iterative steps S442-S446 until the iteration threshold 1000 or the resource allocation member position is reached Achieve target resource allocation member individual position The iteration is stopped when , and the optimal solution for virtual power plant resource allocation is obtained.

[0195] S50. The multi-resource collaborative optimization model is used to minimize the energy storage cost of the virtual power plant and maximize the overall power generation of the virtual power plant, thereby obtaining an optimal solution for the resource allocation of the virtual power plant.

[0196] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0197] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A virtual power plant multi-resource collaborative optimization method based on edge computing, characterized in that: include: S10. Constructing a virtual power plant edge resource network information database by collecting various resource data; S20. The overall power generation of the virtual power plant is calculated by the virtual power plant edge resource network information base; S30. The energy storage cost of the virtual power plant is calculated by the virtual power plant edge resource network information base; S40. Construct a multi-resource collaborative optimization model based on the multi-objective Harris Hawk optimization algorithm; S50. By minimizing the energy storage cost of the virtual power plant and maximizing the overall power generation of the virtual power plant through the multi-resource collaborative optimization model, the optimal solution for resource allocation of the virtual power plant is obtained; The S30 includes: S31. Calculate the hydroelectric energy cost by using the energy storage resource data and the controllable load resource data; The energy cost of hydroelectric power The calculation formula is: ； in, is the i-th hydroelectric power generation equipment, is the number of hydroelectric power generation equipment, is the hydroelectric power generation coefficient of the i-th hydroelectric power generation equipment, is the power generation of the i-th hydroelectric power generation equipment; S32. Calculating the energy cost of wind power generation by using the energy storage resource data and the controllable load resource data; The energy cost of wind power The calculation formula is: ； in, is the i-th wind turbine, is the number of wind power generation equipment, is the rated power of wind power, is the wind power generation coefficient, is the average construction cost per unit installed capacity, is the installed rated capacity of the i-th wind power generation equipment, is the wind load rate; S33. Calculate the energy cost of solar power generation by using the energy storage resource data and the controllable load resource data; The solar power energy cost The calculation formula is: ； in, is the i-th solar power generation device, is the number of solar power generation equipment, is the solar power generation coefficient of the i-th solar power generation device, is the temperature coefficient, is the actual operating temperature of the photovoltaic panel, is the ambient temperature; S34. Calculating energy storage loss costs through energy storage resource data and controllable load resource data; The energy storage loss cost The calculation formula is: ； in, is the loss cost coefficient, is the hydropower generation loss cost coefficient, is the wind power generation loss cost coefficient, is the solar power generation loss cost coefficient, is the maximum energy storage value, is the wind power generation, is the hydroelectric power generation, The amount of electricity generated by solar energy; S35. The energy storage cost of the virtual power plant is calculated by the hydroelectric energy cost, wind power energy cost, solar power energy cost, and energy storage loss cost; The Virtual Power Plant Energy Storage Costs The calculation formula is: ； The S40 comprises: S41. Initialize resource allocation population size; S42. Define resource allocation member positions; The resource allocation member location It is expressed as: ； in, is the overall power generation coefficient, is the energy storage loss coefficient, is the energy storage cost of the virtual power plant, For the overall power generation of the virtual power plant, the resource allocation member positions are continuously updated iteratively to minimize the energy storage cost of the virtual power plant and maximize the overall power generation of the virtual power plant; S43. Define the target resource allocation member escape energy; The target resource allocation member escapes energy It is expressed as: ； in, The initial escape energy of the member allocated to the target resource is a random number between [-1,1], t is the current evolutionary generation, and T is the maximum evolutionary generation of the population; S44. Calculate the average position of individual members of resource allocation; The average position of the individual resource allocation members It is expressed as; ； Where M is the number of resource allocation members, Assign individual member locations to resources; S45. Determine whether the escape energy of the target resource allocation member is greater than or equal to 1. If so, enter the search phase and update the resource allocation member position; if not, enter the development phase and continuously perform iterative updates to obtain the optimal solution for virtual power plant resource allocation.

2. A virtual power plant multi-resource collaborative optimization method based on edge computing as claimed in claim 1, characterized in that: The various types of resource data include renewable distributed resource data, energy storage resource data, and controllable load resource data; The renewable distributed resource data includes wind power generation resource data, solar power generation resource data, and hydropower generation resource data; The energy storage resource data includes pumped storage resource data, battery storage resource data, and capacitor storage resource data; The controllable load resource data includes industrial production load resource data and charging equipment load resource data.

3. The multi-resource collaborative optimization method of a virtual power plant based on edge computing according to claim 1, characterized in that: The S20 includes: S21. Calculate hydroelectric power generation using hydroelectric power resource data; The amount of hydroelectric power The calculation formula is: ； in, is the amount of electricity generated by hydropower in one hour, is the volume of water flowing in one second, is the density of water, is the rated power of hydroelectric power, is the water flow drop; S22. Calculating wind power generation using wind power resource data; Wind power generation The calculation formula is: ； Where WP is wind power generation, is the rated power of wind power, For the cut-in wind speed, is the wind speed at the current moment, To cut out the wind speed, The minimum wind speed for the motor to work. The maximum wind speed of the motor; S23. Calculating solar power generation using solar power generation resource data; The solar power generation The calculation formula is: ； in, is the rated power of solar power generation, is the number of solar panels, is the solar panel area, The standard temperature for solar panel energy storage. is the solar radiation, is the temperature coefficient; S24. The overall power generation of the virtual power plant is calculated by the hydroelectric power generation, wind power generation, and solar power generation; The overall power generation of the virtual power plant The calculation formula is: 。 4. The method for multi-resource collaborative optimization of a virtual power plant based on edge computing according to claim 1, characterized in that: The search phase refers to a global search of target resource allocation members with equal probability through two strategies; when the probability When the probability When , the resource allocation members will randomly move within the resource allocation population. The expression of the search phase is as follows: ； in, For iteration After that, the resource allocation member position, Assign member individual positions to target resources, is a random number between [0,1], Assign individual member locations to random target resources.

5. The method for multi-resource collaborative optimization of a virtual power plant based on edge computing according to claim 1, characterized in that: The development stage refers to the resource allocation members adopting four hunting strategies to imitate the hunting behavior of Harris's hawks after finding the target resource allocation members; The four hunting strategies are soft encirclement, hard encirclement, soft encirclement with gradual rapid dive, and hard encirclement with gradual rapid dive; S441. Allocate member escape probability by target resource Combine target resources to allocate members' escape energy The absolute value of is used for judgment, and one of the four hunting strategies is executed according to the judgment result; The target resource allocation member escape probability is a random number between (0,1), There is a chance to escape. It means there is no chance of escape; The target resource allocation member escapes energy ,when , indicating that there is energy escaping, when , indicating that there is no escaping energy; S442. When Soft encirclement is performed when The soft encirclement means that the target resource allocation member still has the energy to escape but no chance of escape, and the update formula is: ； in, For iteration After that, the resource allocation member position, Assign member individual positions to target resources, is a random number between [0,2]; S443. When Hard encirclement; The hard encirclement means that the target resource allocation member has no energy to escape and no chance to escape. The update formula is: ； S444. When A soft encirclement with a gradual and rapid dive; The soft encirclement of the progressive rapid dive means that the target resource allocation member has a chance to escape from the encirclement and has enough energy to escape the capture of the resource allocation members. The resource allocation members will adopt a soft encirclement of the progressive rapid dive around the target resource allocation member, which is implemented through two strategies. When the first strategy If invalid, execute the second strategy , the update formula is: ； ； ； in, is the fitness function, is a two-dimensional random vector, whose elements are random numbers between [0,1]. is the mathematical expression of Levy flight, Assign member individual positions to target resources, is a random number between [0,2]; S445. When A hard encirclement with a gradual and rapid dive; The progressive fast dive hard encirclement means that the target resource allocation member has no energy to escape, but still has a chance to escape. The resource allocation members will adopt a progressive fast dive hard encirclement around the target resource allocation member to shorten the distance from the average position of the individual resource allocation members. This is implemented through two strategies. When the first strategy If invalid, execute the second strategy , the update formula is: ； ； ； in, The average position of individual members for resource allocation, is the fitness function, is a two-dimensional random vector, whose elements are random numbers between [0,1]. is the mathematical expression of Levy flight, Assign member individual positions to target resources, is a random number between [0,2]; S446. Repeat iterative steps S442-S446 until the iteration threshold 1000 or the resource allocation member position is reached Achieve target resource allocation member individual position The iteration is stopped when , and the optimal solution for virtual power plant resource allocation is obtained.

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