Virtual power plant distributed resource aggregation optimization method and device under homomorphic encryption

Abstract

The invention provides a method and equipment for aggregation optimization of distributed resources of a virtual power plant under homomorphic encryption. The method comprises the steps of obtaining distributed resource data of a virtual power plant; performing classification; homomorphic encryption processing is carried out to obtain encrypted data; performing homomorphic encryption optimization to obtain a ciphertext of the distributed resource data of the virtual power plant; based on the encrypted virtual power plant distributed resource data, determining a target and constraint conditions of virtual power plant distributed resource aggregation optimization, and constructing a virtual power plant distributed resource aggregation optimization model; and solving the virtual power plant distributed resource aggregation optimization model through a heuristic algorithm to obtain a virtual power plant distributed resource aggregation optimization scheme. Under the condition that original data are not exposed, efficient aggregation and optimization of the distributed resources of the virtual power plant are achieved. The security and privacy of the distributed resource data of the virtual power plant are ensured, the energy utilization efficiency and the system stability are improved, and technical support is provided for the construction of an intelligent power grid and the virtual power plant.

Description

Technical Field

[0001] The present application relates to the technical field of virtual power plants, and in particular to a method and device for optimizing distributed resource aggregation of virtual power plants under homomorphic encryption. Background Art

[0002] Virtual power plants are an efficient way to allocate power resources and are the key to improving energy utilization, alleviating power supply and demand tensions, and alleviating carbon emissions. However, with the continuous expansion and increasing complexity of distributed resources, how to effectively aggregate and optimize virtual power plant resources while ensuring data privacy and security has become an urgent problem to be solved. Summary of the invention

[0003] In view of this, the purpose of this application is to propose a method and device for distributed resource aggregation optimization of a virtual power plant under state encryption.

[0004] Based on the above objectives, the present application provides a method for distributed resource aggregation optimization of a virtual power plant under homomorphic encryption, comprising: Obtain distributed resource data of virtual power plants; Classifying the distributed resource data of the virtual power plant; Performing homomorphic encryption processing on the classified distributed resource data of the virtual power plant to obtain encrypted data; Performing homomorphic encryption optimization on the sensitive data in the encrypted data to obtain ciphertext of the distributed resource data of the virtual power plant; Based on the encrypted virtual power plant distributed resource data, determine the objectives and constraints of virtual power plant distributed resource aggregation optimization, and build a virtual power plant distributed resource aggregation optimization model; the objectives include the highest operating efficiency, the highest renewable energy utilization rate, and the lowest carbon emissions; The virtual power plant distributed resource aggregation optimization model is solved by a heuristic algorithm to obtain a virtual power plant distributed resource aggregation optimization solution.

[0005] In some embodiments, the performing homomorphic encryption optimization on the sensitive data in the encrypted data includes: determining a decryption vector for the encrypted data; Determining an inner product compensation coefficient of the encrypted data based on the decryption vector and random bits of the encrypted data; Based on the inner product compensation coefficient, performing homomorphic encryption optimization on the sensitive data in the encrypted data; The data obtained by the homomorphic encryption optimization is stored in a database to obtain the ciphertext of the distributed resource data of the virtual power plant.

[0006] In some embodiments, the random bits of the encrypted data are obtained by randomly intercepting the bits of the encrypted data; the ciphertext of the virtual power plant distributed resource data includes: ; Wherein, Q is the ciphertext of the distributed resource data of the virtual power plant; is the database dimension; is a random error; is the plausibility function; Optimize the resulting data for homomorphic encryption; ; is the number of ciphertexts generated; is the attenuation constant; is a positive reciprocal matrix; is the decision function; is the inner product compensation coefficient; ; is the inner product parameter corresponding to the decrypted ciphertext; is the mixing coefficient; is a linear function; is the number of randomly intercepted bits in the vector element of the encrypted data; is the decryption vector; ; is the coefficient of dispersion; is the scaling factor; is the reconstruction error; is the encryption key; ; is the public key function; is the time conversion constant; is the distance function; The distributed resource data of virtual power plants obtained for classification; ; is the sampling time interval; is the linear frequency; is the random variation; is the unit length of the distributed resource data of the virtual power plant.

[0007] In some embodiments, performing homomorphic encryption on the classified distributed resource data of the virtual power plant to obtain encrypted data includes: Based on a public key function, generating an encryption key for the classified distributed resource data of the virtual power plant; The encryption key is superimposed on the classified virtual power plant distributed resource data to obtain encrypted data.

[0008] In some of the embodiments, the virtual power plant distributed resource aggregation optimization model includes: ;in, For the constraints Distributed resource aggregation optimization model under ; is the objective function of operating efficiency; is the objective function of renewable energy utilization; is the objective function of carbon emissions; Constraints include ;in, For the Distributed resource electricity consumption in each quarter; is the load power supply; is the coefficient matrix; For the Minimum electricity consumption per quarter; is the number of dispatchable wind turbines; For the The predicted output of each generating unit; The power demand during the operation of the virtual power plant; Charging power for distributed resources; is the discharge power; The upper limit of resource contribution; Storage of electricity for virtual power plants; ; is the distributed resource ciphertext; is the maximum load value of the daily load curve; For the Demand response loads; is the daily load fluctuation rate; is a function of a variable; ; is the amount of renewable energy resources; For the The weight of renewable energy sources; is the cost function associated with the decision variables; Actual contribution to distributed resources; ; For the The amount of electricity generated from renewable sources; is the total capacity of distributed resources; is the average daily response capacity of the virtual power plant; It is an adjustable parameter.

[0009] In some embodiments, the heuristic algorithm is a bat optimization algorithm; and solving the virtual power plant distributed resource aggregation optimization model by using the heuristic algorithm includes: Initialization parameters; Update individual speed and individual position; Calculate the set of individual objective functions; In response to determining that the individual objective function set does not have a dominating relationship with the initial solution set, moving the individual objective function set into the current solution set; In response to determining that the current number of iterations is the maximum number of iterations, the position of the individual with the highest moving speed is output as the final solution to obtain a distributed resource aggregation optimization solution for the virtual power plant.

[0010] In some embodiments, the speed of updating the individual and the position of the individual are updated by the formula Conduct; among them, is the updated position of the individual, is the updated individual speed; is the pulse frequency; is the pulse loudness; is the resolution coefficient; is the number of individuals; The individual objective function set is calculated by the formula Conduct; among them, is the set of individual objective functions; for A random vector within .

[0011] An embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the methods described above when executing the program.

[0012] An embodiment of the present application also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute any of the methods described above.

[0013] An embodiment of the present application further provides a computer program product, comprising computer program instructions, which, when executed on a computer, enable the computer to execute any of the methods described above.

[0014] From the above, it can be seen that the method and device for aggregation and optimization of distributed resources of virtual power plants under homomorphic encryption provided by this application, with the support of homomorphic encryption technology, realizes efficient aggregation and optimization of distributed resources of virtual power plants without exposing original data. It not only ensures the security and privacy of distributed resource data of virtual power plants, but also improves energy utilization efficiency and system stability, providing technical support for the construction of smart grids and virtual power plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A flowchart of a method for optimizing distributed resource aggregation in a virtual power plant under homomorphic encryption according to an embodiment of the present application; Figure 2 A schematic diagram of a process for optimizing homomorphic encryption of sensitive data in the encrypted data according to an embodiment of the present application; Figure 3 A flowchart of solving the distributed resource aggregation optimization model of the virtual power plant by a heuristic algorithm in an embodiment of the present application; Figure 4 A schematic diagram of a distributed resource aggregation system structure of a virtual power plant according to an embodiment of the present application; Figure 5 This is a schematic diagram of the results of the new energy utilization rate in the verification example of this application; Figure 6 A schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0018] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be understood by people with ordinary skills in the field to which the present application belongs. The words "including" or "comprising" and the like used in the embodiments of the present application mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. The words "connect" or "connected" and the like are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect.

[0019] In related technologies, real-time operation data of distributed energy is collected through network sensing technology, and distributed energy resources are aggregated and optimized in terms of resource scheduling, balanced load distribution, and full utilization of energy using optimization algorithms. This method can obtain real-time information on the operating status and demand of distributed energy, thereby achieving rapid optimization and scheduling. However, the real-time collection and processing of large amounts of data may involve data security and privacy protection issues, resulting in a low utilization rate of new energy after optimization.

[0020] In the related technology (method 1), a distributed generation model considering uncertainty is established based on the Gaussian mixture model (GMM), and the vertex enumeration method is used to achieve the coordination and optimization operation of the entire system. This method can effectively reduce the peak load in the power grid and enhance the stability of the power grid, but this method requires a large amount of real-time data support. When faced with incomplete basic data, the utilization rate of the optimized system is not high.

[0021] In the related technology (method 2), performance indicators are established from three aspects: response time, response capability, and daily load fluctuation rate, and the subsequent optimization scheduling is completed in combination with the grey target theory. This method can objectively and accurately analyze the characteristics of flexible resources and realize the reasonable classification and aggregation of resources. However, this method requires a large amount of real-time data support, has high requirements for data accuracy and real-time performance, and cannot guarantee the utilization rate of distributed resources.

[0022] In the related technology, based on the typical scenario of photovoltaic output and the maximum flexible operating range of the thermal network power, a system random optimization operation model is established, and then a joint scheduling strategy for the regional electric and thermal integrated energy system is obtained. This method can aggregate the flexible resources of the thermal network, realize the decoupling of thermal and electric scheduling, and improve the overall flexibility of the energy system. However, the parameter setting of the optimization operation model established by this method has a great influence on the scheduling results. If the parameter setting is unreasonable, it will cause a waste of new energy resources.

[0023] Therefore, the current virtual power plant distributed resource aggregation optimization all have the problem of low utilization rate of new energy to a certain extent. Based on this, the embodiment of the present application provides a virtual power plant distributed resource aggregation optimization algorithm under homomorphic encryption, which classifies the distributed resources of the virtual power plant, obtains the encryption key and decryption vector, randomly intercepts the bits in the plaintext vector elements, and obtains the inner product compensation coefficient, thereby completing the privacy protection of the distributed resource data of the virtual power plant. With the highest operating efficiency, the highest utilization rate of renewable energy and the lowest carbon emissions as the goals, the corresponding constraints are comprehensively considered, an optimization model is established, and the bat optimization algorithm is introduced to solve the model to obtain the best aggregation optimization solution, which can improve the new energy utilization rate of the system to a certain extent and solve the problem of low utilization rate of new energy of distributed resources in virtual power plants in related technologies.

[0024] like Figure 1 As shown, an embodiment of the present application provides a method for distributed resource aggregation optimization of a virtual power plant under homomorphic encryption, and the method may include: S100, obtaining distributed resource data of a virtual power plant; S200, classifying the distributed resource data of the virtual power plant; S300, performing homomorphic encryption processing on the classified distributed resource data of the virtual power plant to obtain encrypted data; S400, performing homomorphic encryption optimization on the sensitive data in the encrypted data to obtain ciphertext of the distributed resource data of the virtual power plant; S500, based on the encrypted virtual power plant distributed resource data, determine the objectives and constraints of virtual power plant distributed resource aggregation optimization, and build a virtual power plant distributed resource aggregation optimization model; the objectives include the highest operating efficiency, the highest renewable energy utilization rate, and the lowest carbon emissions; S600, solving the virtual power plant distributed resource aggregation optimization model through a heuristic algorithm to obtain a virtual power plant distributed resource aggregation optimization solution.

[0025] The method for aggregation optimization of distributed resources of a virtual power plant under homomorphic encryption provided in the embodiment of the present application can achieve aggregation optimization while ensuring the security of distributed resource data, thereby improving the utilization rate of new energy in the virtual power plant.

[0026] In some of the embodiments, in step S100, the distributed resource data of the virtual power plant usually has the characteristics of multi-source heterogeneity, and may include various distributed resources such as distributed power sources, energy storage equipment, and controllable loads.

[0027] In some embodiments, in step S200, classifying the virtual power plant distributed resource data before encryption can improve the encryption effect of the virtual power plant distributed resource data. Classifying the virtual power plant distributed resource data may include: randomly extracting a length of The distributed resource data of the virtual power plant is reconstructed and its components are obtained. Resource data types in the Class window .in, (1). It can be the sampling time interval; is the linear frequency; is the random variation; is the unit length of the distributed resource data of the virtual power plant.

[0028] In some embodiments, in step S300, performing homomorphic encryption on the classified distributed resource data of the virtual power plant to obtain encrypted data may include: Based on the public key function, an encryption key for the classified distributed resource data of the virtual power plant is generated. Specifically, the encryption key may be: (2). Among them, is the encryption key; is the public key function; is the time conversion constant; is the distance function. As shown in the above formula (1), a specific algorithm can be used to generate the public key and private key required for encryption. The public key is used to encrypt the plaintext data (ie, the distributed resource data of the virtual power plant).

[0029] The encryption key is superimposed on the classified virtual power plant distributed resource data to obtain encrypted data. Specifically, the encryption key can be superimposed on the plaintext data of the classified virtual power plant distributed resource data. The encrypted data obtained after superposition can be a polynomial.

[0030] In some embodiments, such as Figure 2 As shown, in step S400, the homomorphic encryption optimization of the sensitive data in the encrypted data to obtain the ciphertext of the virtual power plant distributed resource data may include: Step S410, determining a decryption vector of the encrypted data; Step S420, determining an inner product compensation coefficient of the encrypted data based on the decryption vector and the random bits of the encrypted data; Step S430, performing homomorphic encryption optimization on the sensitive data in the encrypted data based on the inner product compensation coefficient; Step S440, store the data obtained by the homomorphic encryption optimization in a database to obtain the ciphertext of the distributed resource data of the virtual power plant.

[0031] In this way, by determining the inner product compensation coefficient, it is possible to avoid the change of the inner product of the virtual power plant distributed resource data (i.e., plaintext) and the key before homomorphic encryption during the decryption of the encrypted data obtained by homomorphic encryption, which leads to variables in the ciphertext of the encrypted data obtained by homomorphic encryption, thereby reducing the accuracy of the encryption result.

[0032] In some embodiments, in step S410, the decryption vector of the encrypted data may be: (3). Among them, is the decryption vector; is the coefficient of dispersion; is the scaling factor; is the reconstruction error; Is the encryption key. The details are as shown in the above formula (2).

[0033] In some embodiments, in step S420, the inner product compensation coefficient of the encrypted data can be obtained by randomly intercepting the bits of the encrypted data. For example, the inner product compensation coefficient of the encrypted data can be obtained by randomly intercepting the bits of the encrypted data in the plaintext vector element after the superposition operation with the key. bits to obtain the inner product compensation coefficient.

[0034] Specifically, the inner product compensation coefficient can be expressed as (4). Among them, is the inner product compensation coefficient. is the inner product parameter corresponding to the decrypted ciphertext; is the mixing coefficient; is a linear function; is the number of randomly selected bits in the vector element of the encrypted data. exp is a natural exponential function. To decrypt the vector, it can be specifically shown as the above formula (3).

[0035] In some embodiments, in step S430, performing homomorphic encryption optimization on the sensitive data in the encrypted data based on the inner product compensation coefficient may include: (5) Performing homomorphic encryption optimization on the sensitive data in the encrypted data. Optimize the resulting data for homomorphic encryption; is the number of ciphertexts generated; is the attenuation constant; is a positive reciprocal matrix; is the decision function; is the inner product compensation coefficient, which can be specifically shown in the above formula (4). After homomorphic encryption optimization, several ciphertexts can be obtained. The sensitive data may include a length of Sensitive data such as energy production, consumption or price in the distributed resource data of virtual power plants.

[0036] It should be understood that the optimization of homomorphic encryption of sensitive data in the encrypted data based on the inner product compensation coefficient emphasizes taking additional protection measures for these key data (i.e., sensitive data) to ensure their security, rather than two independent encryption operations in the traditional sense. The encryption effect is mainly optimized by adjusting the specific algorithm (i.e., the introduction of the inner product compensation coefficient) on the basis of maintaining the homomorphic encryption mechanism.

[0037] In this way, after homomorphic encryption optimization, it is possible to better prevent these data from being illegally obtained or tampered with during transmission and calculation.

[0038] In some of the embodiments, in step S440, storing the data obtained by the homomorphic encryption optimization in a database, and obtaining the ciphertext of the virtual power plant distributed resource data may specifically include: packing the encrypted ciphertext of all virtual power plant distributed resources (that is, the data of the virtual power plant distributed resources obtained after homomorphic encryption optimization), and placing it in a database with set dimensions, establishing a resource encryption mechanism, and finally outputting the ciphertext of the distributed resource data.

[0039] In some embodiments, the ciphertext of the virtual power plant distributed resource data may include the formula (6). Among them, The ciphertext of the distributed resource data of the virtual power plant; is the database dimension; is a random error; is the plausibility function; The data obtained by homomorphic encryption optimization can be specifically shown as the above formula (5).

[0040] In this way, homomorphic encryption technology can be used to encrypt the classified data of the virtual power plant's distributed resources. By obtaining the encryption key and decryption vector and combining the inner product compensation coefficient, the privacy protection of the virtual power plant's distributed resource data can be completed, preparing for the subsequent construction of a virtual power plant distributed resource aggregation optimization model.

[0041] In some of the embodiments, in step S500, the core of the distributed resource aggregation optimization of the virtual power plant is to integrate and optimize the dispersed resources and data in an effective way. Therefore, by building a distributed resource aggregation optimization model for a virtual power plant on the basis of sensitive data privacy protection, with the highest operating efficiency, the highest renewable energy utilization rate and the lowest carbon emissions as the goal, it is possible to better achieve a balance between economy and efficiency, reduce the amount of wind and solar power abandoned in the virtual power plant, and facilitate intelligent scheduling and optimization, thereby improving the efficiency and operational reliability of the entire system.

[0042] In some of the embodiments, the objective function of the operation efficiency of the virtual power plant distributed resource aggregation optimization model can be expressed as: (7). Among them, is the distributed resource ciphertext; is the maximum load value of the daily load curve; For the Demand response loads; is the daily load fluctuation rate; is a variable function. When Class resources participate in aggregation; when , it indicates that this type of resource does not participate in aggregation.

[0043] In this way, the operating efficiency of the system can be considered when establishing a distributed resource aggregation optimization model, which can effectively improve resource utilization and reduce system losses, thereby better achieving a balance between economy and efficiency.

[0044] In some embodiments, the renewable energy utilization rate of the virtual power plant distributed resource aggregation optimization model can be expressed as: (8). Among them, is the objective function of renewable energy utilization. is the amount of renewable energy resources; For the The weight of renewable energy sources; is the cost function associated with the decision variables; The actual output of distributed resources. The use of this renewable energy utilization rate can reduce the amount of wind and solar power abandoned in the virtual power plant.

[0045] In some embodiments, the carbon emissions of the virtual power plant distributed resource aggregation optimization model can be expressed as: (9). Among them, is the objective function of carbon emissions. For the The amount of electricity generated from renewable sources; is the total capacity of distributed resources; is the average daily response capacity of the virtual power plant; is an adjustable parameter. In this way, the supply and demand relationship of the virtual power plant can be balanced, facilitating intelligent scheduling and optimization.

[0046] In some embodiments, the constraint condition is determined based on the aforementioned objective function of operating efficiency, objective function of renewable energy utilization rate and objective function of carbon emissions. Specifically, the constraint condition may include the formula (10). Among them, For the Distributed resource electricity consumption in each quarter; is the load power supply; is the coefficient matrix; For the Minimum electricity consumption per quarter; is the number of dispatchable wind turbines; For the The predicted output of each generating unit; The power demand during the operation of the virtual power plant; Charging power for distributed resources; is the discharge power; The upper limit of resource contribution; The stored electricity of the virtual power plant.

[0047] By setting this constraint condition based on the aforementioned objective function of operating efficiency, objective function of renewable energy utilization and objective function of carbon emissions, it is possible to comprehensively consider the scale constraints of distributed resource aggregates, power balance constraints and equipment output limitation constraints, and thus output the most satisfactory solution within the feasible solution range.

[0048] In some of the embodiments, the objective function and the corresponding constraints can be combined to construct a distributed resource aggregation optimization model for a virtual power plant. Specifically, the distributed resource aggregation optimization model for a virtual power plant includes the formula (11). Among them, For the constraints Distributed resource aggregation optimization model under .

[0049] In some embodiments, in step S600, the heuristic algorithm may be a bat optimization algorithm. Solving the virtual power plant distributed resource aggregation optimization model by the bat algorithm in the heuristic algorithm can better adapt to the influence of the multi-objective virtual power plant distributed resource aggregation optimization model on the search speed and search space range of feasible solutions.

[0050] In some embodiments, such as Figure 3 As shown, solving the virtual power plant distributed resource aggregation optimization model by a heuristic algorithm may include: Initialization parameters; Update individual speed and individual position; Calculate the set of individual objective functions; Determine whether there is a dominating relationship between the objective function set and the initial solution set; In response to determining that the individual objective function set does not have a dominating relationship with the initial solution set, moving the individual objective function set into the current solution set; In response to determining that the current number of iterations is the maximum number of iterations, the position of the individual with the highest moving speed is output as the final solution to obtain a distributed resource aggregation optimization solution for the virtual power plant.

[0051] In some embodiments, the speed and position of the individual can be updated by the formula (12) is carried out. Among them, is the updated position of the individual, is the updated individual speed; is the pulse frequency; is the pulse loudness; is the resolution coefficient; The number of individuals.

[0052] In some embodiments, the calculation of the individual objective function set can be performed by the formula (13) is carried out. Among them, is the set of individual objective functions; for A random vector within .

[0053] In practical applications, the distributed resource aggregation optimization model of the virtual power plant can be solved by Python and MATLAB. The specific content can be as follows Python import numpy as np from scipy.optimize import minimize import matplotlib.pyplot as plt # Define the objective function def objective_function(x): # Define the objective function, such as minimizing energy consumption or maximizing renewable energy utilization return np.sum(x**2)# Example: Minimize the sum of squares # Define constraints def constraint(x): # Define constraints, such as total resource limits, etc. return np.sum(x) - 1# Example: The total amount of resources is 1 # Initial guess value x0 = np.array([0.5, 0.5])# Example initial value # Use SciPy's minimize function for optimization result = minimize(objective_function, x0, method='SLSQP', constraints={'type': 'eq', 'fun': constraint}) print("Optimal solution:", result.x).

[0054] Matlab % Define the objective function function f = objective(x) f = sum(x.^2);% Example: Minimize the sum of squares end % Define constraints function [c, ceq] = constraint(x) c = [];% inequality constraint ceq = sum(x) - 1;% Equality constraint: the total amount of resources is 1 end % Initial guess value x0 = [0.5, 0.5]; % Use MATLAB's fmincon function for optimization options = optimoptions('fmincon', 'Display', 'iter'); [x, fval] = fmincon(@(x) objective(x), x0, [], [], [], [], [0, 0],[1, 1], @(x) constraint(x), options); disp('Optimal solution:'); disp(x).

[0055] Verification Example In order to verify the effect of the virtual power plant distributed resource aggregation optimization algorithm under homomorphic encryption designed by the present invention in practical applications, the method of the present invention is applied in a virtual power plant, and distributed resource aggregation optimization is performed on it. The aggregation optimization effect of the method is analyzed based on the experimental results.

[0056] 1.1 Experimental Preparation A virtual power plant includes distributed generation resources, energy storage systems, controllable loads, and new energy vehicles. Among them, DG includes small gas turbines, small photovoltaics, small wind power, hydropower, biomass, fuel cells, etc. The distributed resource aggregation system structure of the virtual power plant can be as follows Figure 4 shown.

[0057] The basic performance parameters of the distributed resources of the virtual power plant can be shown in Table 1.

[0058] Table 1 Basic performance parameters of distributed resources in virtual power plants

[0059] The hardware equipment of the experimental environment is a high-performance computer cluster consisting of 10 computing nodes, equipped with high-performance CPUs and sufficient memory, which is used to simulate distributed resource aggregation and optimize the calculation in the virtual power plant; the software environment installs the Linux operating system, configures Paillier's homomorphic encryption library and Hadoop's distributed computing framework. MATLAB simulation software is used to build a virtual power plant simulation platform to simulate dynamic processes such as the output and load changes of distributed resources. The encryption communication module is integrated into the simulation platform to achieve secure communication between distributed resources and ensure the privacy and integrity of data transmission. The homomorphic encryption algorithm and the virtual power plant resource aggregation optimization algorithm are written in Python programming language.

[0060] The simulation parameters during the experiment are set as follows. The power generation efficiency of photovoltaic resources is 15%, the output characteristics vary with the light intensity, the response time is 1min, and the ramp rate is 0.2 MW / min; the cut-in wind speed of wind power resources is 3 m / s, the cut-out wind speed is 25 m / s, the response time is 2mion, and the ramp rate is 0.23MW / min; the charging and discharging efficiency of the energy storage system is 90%, the response time is 30s, and the ramp rate is 0.5 MW / min. The public key length of the homomorphic encryption algorithm is set to 2048 bits, and the private key length is 1024 bytes. In the bat optimization algorithm, the initial number of iterations is set to 1000, the population size is 50, and the frequency range is , the scale range is , the minimum loudness is 0.1, the loudness control parameter is 0.95, the pulse emission rate control parameter is 0.75, and the position vector range is .

[0061] The experimental process is as follows. Start the computer cluster and simulation platform, load the distributed resource parameters and virtual power plant parameters; generate public and private keys for the homomorphic encryption algorithm, and establish an encrypted communication channel between distributed resources to build a homomorphic encryption system; simulate the dynamic processes such as the output and load changes of distributed resources in the simulation platform, and record the relevant data; use homomorphic encryption technology to encrypt distributed resources, run the resource aggregation optimization method to calculate the optimal resource scheduling plan; apply the optimized resource scheduling plan to the simulation platform, verify its impact on the performance of the virtual power plant, and analyze the effectiveness and performance of the distributed resource aggregation optimization algorithm of the virtual power plant under homomorphic encryption.

[0062] 1.2 Results and analysis of distributed resource aggregation optimization in virtual power plants In order to verify the effectiveness of the method of the present invention, a comparative experiment was conducted. In the experiment, the method 1 as described above and the method 2 as described above were respectively used as the comparative group, and the method of the embodiment of the present application was the experimental group, so as to more clearly demonstrate the advantages of the method proposed by the present invention. Three methods were respectively used to aggregate and optimize the distributed resources of the virtual power plant. It should be understood that for the three methods, the relevant parameters are the same or remain consistent in terms of key parameters. The key parameters may be, for example, relevant parameters of photovoltaic resources, relevant parameters of energy storage systems, and other relevant parameters. Among them, the relevant parameters of photovoltaic resources may include installed capacity (MW): the total installed capacity of photovoltaic power generation equipment; maximum output (MW): the maximum power generation capacity of photovoltaic equipment under optimal conditions and minimum output (MW): the power generation capacity of photovoltaic equipment under minimum conditions. The relevant parameters of the energy storage system may include energy storage capacity (MWh): the total energy storage capacity of the energy storage system; charging and discharging efficiency: the energy conversion efficiency of the energy storage system during charging and discharging, and response time: the reaction time required for the energy storage system to go from standby state to full load operation. Other relevant parameters may include wind energy resource parameters (if applicable): such as installed capacity of wind power equipment, maximum / minimum output, etc.; load demand parameters: such as the average daily load demand of the virtual power plant, peak-valley load differences, etc.; and operating constraints: such as equipment output limitations, grid stability and reliability requirements, etc.

[0063] Experimental results: The experimental results are shown in Table 2.

[0064] Table 2 Optimization results of distributed resource aggregation in virtual power plants

[0065] Result analysis: By analyzing the data in Table 2, it can be seen that the distributed resources of the virtual power plant are aggregated and optimized using the method designed in this application. In the same operating period, the wind and solar power abandonment of the virtual power plant under this method are significantly lower than those of the control group method, indicating that the method of this application can maximize the use of new energy to meet the operating needs of the virtual power plant. Therefore, the method of the embodiment of this application can effectively improve the utilization rate of distributed resources.

[0066] 1.3 Comparative Experiment and Analysis To further verify the feasibility of the method of this application, the energy conversion efficiency of virtual power plants based on different methods is statistically analyzed. This indicator represents the ratio of effectively utilized energy to actual consumed energy. A higher energy conversion efficiency means a higher amount of effectively utilized energy in distributed resources, that is, a higher utilization rate of new energy. The comparison results are as follows: Figure 5 shown.

[0067] like Figure 5As shown, compared with the other two methods, after the distributed resources of the new year are aggregated and optimized using the method of the embodiment of the present application, the conversion efficiency of new energy is higher, indicating that the virtual power plant can effectively utilize new energy and has a higher utilization rate for distributed resources.

[0068] It is understandable that before using the technical solutions of each embodiment of the present disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved will be informed to the user in an appropriate manner, and the user's authorization will be obtained.

[0069] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly remind the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can independently choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0070] As an optional but non-limiting implementation, in response to receiving the user's active request, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0071] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0072] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the described method.

[0073] It should be noted that the above describes some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0074] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for optimizing distributed resource aggregation of a virtual power plant under homomorphic encryption as described in any of the above embodiments is implemented.

[0075] Figure 6 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0076] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0077] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0078] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0079] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0080] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010 , the memory 1020 , the input / output interface 1030 , and the communication interface 1040 ).

[0081] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0082] The electronic device of the above-mentioned embodiment is used to implement the method of distributed resource aggregation optimization of a virtual power plant under homomorphic encryption corresponding to any of the above-mentioned embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0083] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method for distributed resource aggregation optimization of a virtual power plant under homomorphic encryption as described in any of the above embodiments.

[0084] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0085] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the method for distributed resource aggregation optimization of a virtual power plant under homomorphic encryption as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0086] Based on the same inventive concept, corresponding to the method for optimizing the aggregation of distributed resources of a virtual power plant under homomorphic encryption described in any of the above embodiments, the present disclosure also provides a computer program product, which includes computer program instructions. In some embodiments, the computer program instructions can be executed by one or more processors of a computer so that the computer and / or the processor execute the method for optimizing the aggregation of distributed resources of a virtual power plant under homomorphic encryption. Corresponding to the execution subject corresponding to each step in each embodiment of the method for optimizing the aggregation of distributed resources of a virtual power plant under homomorphic encryption, the processor that executes the corresponding step may belong to the corresponding execution subject.

[0087] The computer program product of the above-mentioned embodiment is used to enable the computer and / or the processor to execute the method for distributed resource aggregation optimization of a virtual power plant under homomorphic encryption as described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0088] A person skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0089] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power / ground connections to the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device may be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present application are to be implemented (that is, these details should be fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or with changes in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0090] Although the present application has been described in conjunction with specific embodiments of the present application, many alternatives, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the discussed embodiments.

[0091] The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present application.

Claims

1. A method for distributed resource aggregation optimization of virtual power plants under homomorphic encryption, characterized in that: include: Obtain distributed resource data of virtual power plants; Classifying the distributed resource data of the virtual power plant; Performing homomorphic encryption processing on the classified distributed resource data of the virtual power plant to obtain encrypted data; Performing homomorphic encryption optimization on the sensitive data in the encrypted data to obtain ciphertext of the distributed resource data of the virtual power plant; Based on the encrypted virtual power plant distributed resource data, determine the objectives and constraints of virtual power plant distributed resource aggregation optimization, and build a virtual power plant distributed resource aggregation optimization model; the objectives include the highest operating efficiency, the highest renewable energy utilization rate, and the lowest carbon emissions; The virtual power plant distributed resource aggregation optimization model is solved by a heuristic algorithm to obtain a virtual power plant distributed resource aggregation optimization solution.

2. The method for distributed resource aggregation optimization of virtual power plants under homomorphic encryption according to claim 1 is characterized in that: The performing homomorphic encryption optimization on the sensitive data in the encrypted data includes: determining a decryption vector for the encrypted data; Determining an inner product compensation coefficient of the encrypted data based on the decryption vector and random bits of the encrypted data; Based on the inner product compensation coefficient, performing homomorphic encryption optimization on the sensitive data in the encrypted data; The data obtained by the homomorphic encryption optimization is stored in a database to obtain the ciphertext of the distributed resource data of the virtual power plant.

3. The method for distributed resource aggregation optimization of virtual power plants under homomorphic encryption according to claim 2 is characterized in that: The random bits of the encrypted data are obtained by randomly intercepting the bits of the encrypted data; the ciphertext of the virtual power plant distributed resource data includes: ; Wherein, Q is the ciphertext of the distributed resource data of the virtual power plant; is the database dimension; is a random error; is the plausibility function; Optimize the resulting data for homomorphic encryption; ; is the number of ciphertexts generated; is the attenuation constant; is a positive reciprocal matrix; is the decision function; is the inner product compensation coefficient; ; is the inner product parameter corresponding to the decrypted ciphertext; is the mixing coefficient; is a linear function; is the number of randomly intercepted bits in the vector element of the encrypted data; is the decryption vector; ; is the coefficient of dispersion; is the scaling factor; is the reconstruction error; is the encryption key; ; is the public key function; is the time conversion constant; is the distance function; The distributed resource data of virtual power plants obtained for classification; ; is the sampling time interval; is the linear frequency; is the random variation; is the unit length of the distributed resource data of the virtual power plant.

4. The method for distributed resource aggregation optimization of virtual power plants under homomorphic encryption according to claim 1 is characterized in that: The encrypted data obtained by homomorphically encrypting the classified distributed resource data of the virtual power plant includes: Based on a public key function, generating an encryption key for the classified distributed resource data of the virtual power plant; The encryption key is superimposed on the classified virtual power plant distributed resource data to obtain encrypted data.

5. The method for distributed resource aggregation optimization of virtual power plants under homomorphic encryption according to claim 1 is characterized in that: The virtual power plant distributed resource aggregation optimization model includes: ;in, For the constraints Distributed resource aggregation optimization model under ; is the objective function of operating efficiency; is the objective function of renewable energy utilization; is the objective function of carbon emissions; Constraints include ;in, For the Distributed resource electricity consumption in each quarter; is the load power supply; is the coefficient matrix; For the Minimum electricity consumption per quarter; is the number of dispatchable wind turbines; For the The predicted output of each generating unit; The power demand during the operation of the virtual power plant; Charging power for distributed resources; is the discharge power; The upper limit of resource contribution; Storage of electricity for virtual power plants; ; is the distributed resource ciphertext; is the maximum load value of the daily load curve; For the Demand response loads; is the daily load fluctuation rate; is a function of a variable; ; is the amount of renewable energy resources; For the The weight of renewable energy sources; is the cost function associated with the decision variables; Actual contribution to distributed resources; ; For the The amount of electricity generated from renewable sources; is the total capacity of distributed resources; is the average daily response capacity of the virtual power plant; It is an adjustable parameter.

6. The method for distributed resource aggregation optimization of virtual power plants under homomorphic encryption according to claim 1 is characterized in that: The heuristic algorithm is a bat optimization algorithm; the method of solving the virtual power plant distributed resource aggregation optimization model by using the heuristic algorithm includes: Initialization parameters; Update individual speed and individual position; Calculate the set of individual objective functions; In response to determining that the individual objective function set does not have a dominating relationship with the initial solution set, moving the individual objective function set into the current solution set; In response to determining that the current number of iterations is the maximum number of iterations, the position of the individual with the highest moving speed is output as the final solution to obtain a distributed resource aggregation optimization solution for the virtual power plant.

7. The method for distributed resource aggregation optimization of virtual power plants under homomorphic encryption according to claim 6 is characterized in that: The speed of updating the individual and the position of the individual are expressed by Conduct; among them, is the updated position of the individual, is the updated individual speed; is the pulse frequency; is the pulse loudness; is the resolution coefficient; is the number of individuals; The individual objective function set is calculated by the formula Conduct; among them, is the set of individual objective functions; for A random vector within .

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the program.

9. A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the method according to any one of claims 1 to 7.

10. A computer program product, comprising computer program instructions, which, when executed on a computer, cause the computer to execute the method according to any one of claims 1 to 7.

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