Virtual power plant distributed scheduling method and device considering subject privacy protection

Abstract

The invention relates to a virtual power plant distributed scheduling method and device considering subject privacy protection. The method comprises the following steps: S1, obtaining a scheduling strategy of each distributed virtual power plant; s2, calculating disturbance attenuation of each virtual power plant according to the current iteration round; s3, based on the disturbance attenuation, differential privacy disturbance is added in the scheduling strategies, and the scheduling strategies of all the virtual power plants after disturbance is added are calculated respectively; s4, on the basis of the scheduling strategy after disturbance is added to each virtual power plant, an energy selling strategy is obtained through optimization scheduling; and S5, judging whether a preset convergence condition is met or not, if not, executing the step S1, and if yes, recalculating the scheduling strategy based on the current energy selling strategy to realize distributed scheduling of the virtual power plant. Compared with the prior art, the method has the advantages of realizing privacy protection in the virtual power plant interaction process, considering the cost of privacy protection and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of power dispatching, and in particular to a virtual power plant distributed dispatching method and device taking subject privacy protection into consideration. Background Art

[0002] Virtual Power Plant (VPP) is used as an effective management form to aggregate distributed renewable energy and realize precise control of internal resources. With the further advancement of the goals of "dual carbon" and "new power system", the coexistence of multiple VPPs in a regional distribution network will be an important development direction. The power resources between VPPs have the characteristics of temporal and spatial complementarity. Therefore, the coordinated optimization of multiple VPPs in the same regional distribution network taking into account the power mutual assistance between VPPs to achieve the balance and optimization of interests between VPPs will obtain better economic benefits than each VPP participating in the power market independently.

[0003] Carbon trading is a mechanism designed to reduce greenhouse gas emissions. It allows the buying and selling of emission rights to incentivize emission reductions and promote the development of low-carbon technologies.

[0004] The peak-shaving capacity market obtains market benefits from the peak-shaving capacity of the units, explores and reserves sufficient peak-shaving capacity for the power grid, and encourages the units to carry out deep peak-shaving transformation.

[0005] However, the current virtual power plant scheduling scheme does not fully consider the privacy leakage problem when multiple virtual power plants trade electricity and carbon. Summary of the invention

[0006] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a virtual power plant distributed scheduling method and device that takes into account the privacy protection of the subject, so as to solve or partially solve the privacy leakage problem during electricity and carbon trading in virtual power plants.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] One aspect of the present invention provides a distributed scheduling method for a virtual power plant considering subject privacy protection, comprising the following steps:

[0009] Step S1, obtaining the dispatching strategy of each distributed virtual power plant;

[0010] Step S2, calculating the disturbance attenuation of each virtual power plant according to the current iteration round;

[0011] Step S3, based on the disturbance attenuation, adding differential privacy disturbance to the scheduling strategy, and calculating the scheduling strategy of each virtual power plant after adding the disturbance;

[0012] Step S4, based on the dispatching strategies of each virtual power plant after adding disturbances, an energy sales strategy is obtained by optimizing the dispatching;

[0013] Step S5, determine whether the preset convergence condition is met, if not, execute step S1, if yes, recalculate the scheduling strategy based on the current energy sales strategy to realize the distributed scheduling of the virtual power plant.

[0014] As a preferred technical solution, the step S1 includes the following sub-steps:

[0015] Step S101, constructing an objective function of a distributed virtual power plant;

[0016] Step S102, establishing constraints for the scheduling process;

[0017] Step S103, obtaining the dispatching strategy of each distributed virtual power plant by applying the constraint condition to the augmented Lagrangian function.

[0018] As a preferred technical solution, the objective function is to minimize the total operating cost, wherein the total operating cost takes into account the peak-shaving benefits, carbon market benefits and equipment operating costs.

[0019] As a preferred technical solution, the constraints include virtual power plant transaction balance conditions and virtual power plant internal equipment constraints, wherein the virtual power plant internal equipment includes coal-fired units, new energy units, power-to-gas equipment, gas turbines and energy storage equipment.

[0020] As a preferred technical solution, in step S2, the disturbance attenuation is calculated using the following formula:

[0021]

[0022] Among them, ε j,k is the disturbance attenuation of virtual power plant j in the kth iteration, K is the maximum number of iterations, k≤K.

[0023] As a preferred technical solution, in step S3, adding differential privacy perturbation to the scheduling strategy is implemented using the following formula:

[0024]

[0025] in, is the purchased power vector of virtual power plant j after differential privacy processing, that is, the dispatch strategy after adding disturbance, is the Laplace perturbation added by virtual power plant j to the purchased power vector, 1 is the unit vector, α j is the differential privacy sensitivity of virtual power plant j, is the maximum load power of virtual power plant j, ε jis the disturbance attenuation of virtual power plant j, Lap(·) is the Laplace mechanism, is the real purchased energy power vector of virtual power plant j.

[0026] As a preferred technical solution, in step S5, the convergence condition is that the residual is less than a preset value and / or the privacy budget consumption is greater than a preset value, wherein the residual is calculated using the following formula:

[0027]

[0028] in, is the power vector of the local energy market in the k+1th round, i.e., the energy sales strategy, It is the purchased power vector of virtual power plant j after differential privacy processing in the k+1th round, that is, the scheduling strategy after adding disturbance.

[0029] The privacy budget consumption is calculated using the following formula:

[0030]

[0031] Where: E j is the privacy budget consumption of virtual power plant j, ε j,m is the privacy budget of virtual power plant j at the mth iteration.

[0032] Another aspect of the present invention provides a virtual power plant distributed scheduling device considering subject privacy protection, including:

[0033] Multiple distributed virtual power plants, used to obtain the dispatching strategy of each distributed virtual power plant, calculate the disturbance attenuation of each virtual power plant according to the current iteration round, add differential privacy perturbation to the dispatching strategy based on the disturbance attenuation, and calculate the dispatching strategy of each virtual power plant after adding the disturbance;

[0034] The local market module is used to add disturbance-based dispatching strategies to each virtual power plant, obtain energy sales strategies through optimized dispatching, and determine whether the preset convergence conditions are met. If not, continue to iterate. If so, recalculate the dispatching strategy based on the current energy sales strategy to realize distributed dispatching of virtual power plants.

[0035] Another aspect of the present invention provides an electronic device, comprising: one or more processors and a memory, wherein the memory stores one or more programs, and the one or more programs include instructions for executing the aforementioned virtual power plant distributed scheduling method considering subject privacy protection.

[0036] Another aspect of the present invention provides a computer-readable storage medium comprising one or more programs for execution by one or more processors of an electronic device, wherein the one or more programs include instructions for executing the aforementioned virtual power plant distributed scheduling method considering subject privacy protection.

[0037] Compared with the prior art, the present invention has at least one of the following beneficial effects:

[0038] (1) Realizing privacy protection in the virtual power plant interaction process: To address the privacy leakage problem during electricity and carbon trading in virtual power plants, the present invention calculates the disturbance attenuation of each virtual power plant according to the current iteration round and adds differential privacy disturbance to the scheduling strategy of the virtual power plant.

[0039] (2) Considering the cost of privacy protection: Considering that each additional disturbance for privacy protection will incur consumption, the present invention incorporates the privacy budget consumption greater than the preset value into the convergence condition. Each virtual power plant can set different differential privacy sensitivities and total privacy budgets according to its own needs, and add disturbances of different sizes as needed, thereby fully considering the balance between privacy protection and cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A schematic diagram of a flow chart of a distributed scheduling method for a virtual power plant taking into account subject privacy protection in an embodiment;

[0041] Figure 2 This is a schematic diagram of a distributed dispatching device of a virtual power plant that takes subject privacy protection into consideration in an embodiment, wherein 11 is an upper-level market module, 12-14 are virtual power plant modules, and 15 is a local market module. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0043] Example 1

[0044] In view of the problems existing in the aforementioned prior art, this embodiment provides a virtual power plant distributed scheduling method that takes into account the privacy protection of the subject, the purpose of which is to realize the distributed scheduling of virtual power plants in the peak-shaving-electricity-carbon coupling market that takes into account the privacy protection demands of the subject, while realizing the optimal allocation of resources of multiple virtual power plants and the mutual assistance and complementarity of energy, and taking into account the economy and privacy of the scheduling and operation of virtual power plants. Specifically, this method adds disturbances to the marginal information to solve the problem of privacy leakage during electricity and carbon trading of multiple virtual power plants; considering the impact of adding disturbances on the convergence of the algorithm, a disturbance attenuation mechanism is proposed to optimize the computational performance of the algorithm.

[0045] See also Figure 1 , the method comprises the following steps:

[0046] Step S0, initializing each virtual power plant, local market optimization scheduling parameters, and distributed algorithm parameters, with the initial number of iterations k=1.

[0047] Step S1, optimal dispatch of each virtual power plant: each virtual power plant optimizes dispatch parameters according to the local market, and optimizes dispatch in combination with its own model to obtain the optimal dispatch results of the electric and thermal power of each virtual power plant. The pre-built virtual power plant collaborative dispatch model is used to obtain the optimal dispatch strategy of each virtual power plant.

[0048] Specifically, the process of establishing the virtual power plant optimization scheduling model includes the following:

[0049] (1) Objective function.

[0050] The model takes the lowest total operating cost of each virtual power plant as its objective function. The total operating cost f total As shown in formula (1).

[0051]

[0052] Where: S T is the time period set; S P is the virtual power plant collection; f j,t is the operating cost of virtual power plant j in the tth period.

[0053] The operating cost function of virtual power plant j in the tth period is shown in formula (2).

[0054]

[0055] Where: P j is the set of virtual power plant j equipment, f i,t is the operating cost of equipment i in the tth period, R F is the peak load-shaving income of the virtual power plant, R CM Generate benefits for virtual power plants participating in the carbon market.

[0056] Peak shaving cost function:

[0057]

[0058] Where: is the flexible peak-shaving price at time t, is the peak load capacity at time t, P t baseline is the baseline capacity at time t, is the peak shift penalty coefficient, is the electrical power of device i in the tth time period.

[0059] The profit function of participating in the carbon market is:

[0060]

[0061] Where: c CM is the carbon quota price, x a1 Issuing carbon quota factor for gas turbines, x a2 Apply for CCER for wind turbines to obtain carbon offset quota coefficient, x e is the carbon emission coefficient of the gas turbine.

[0062] (2)Constraints.

[0063] 1) Balance conditions for virtual power plant transactions.

[0064] The virtual power plant transaction balance condition is shown in formula (4).

[0065]

[0066] Where: is the traded electric power of virtual power plant j in the tth period, is the trading carbon quota of virtual power plant j in the tth period.

[0067] 2) Internal constraints of virtual power plants.

[0068] 1. Coal-fired units.

[0069] The coal-fired unit model is shown in formula (5).

[0070]

[0071] Where: P i min , P i max are the minimum and maximum output power of device i, respectively, c coal is the unit coal cost.

[0072] 2. New energy units.

[0073] The new energy unit model is shown in formula (6).

[0074]

[0075] Where: is the predicted power of the new energy unit in the tth period.

[0076] 3. Power-to-gas equipment.

[0077] The power-to-gas equipment model is shown in formula (7).

[0078]

[0079] Where: is the gas power of equipment i in the tth period, η g,P2G The efficiency of the power-to-gas equipment.

[0080] 4. Gas turbine.

[0081] The gas turbine model is shown in equation (8).

[0082]

[0083] Where: P MT,max and P MT,min Respectively represent the upper and lower limits of its output; and They represent the ramp up and ramp down rates of the gas turbine respectively; is a Boolean variable, indicating the power generation status of the gas turbine, 1 for operation and 0 for shutdown; and Respectively represent the continuous power generation and shutdown time of the gas turbine; M on and M off Respectively represent the minimum continuous power generation and shutdown time of the unit; C f is the gas turbine fuel cost; C qd is the gas turbine start-up and shutdown cost; P t MT is the power generation capacity of the gas turbine; a, b, c are the power generation cost coefficients of the gas turbine; SU and SD are the start-up and shutdown costs of the unit; It is a Boolean variable, indicating the power generation status of the gas turbine, 1 for operation and 0 for shutdown.

[0084] 5. Energy trading.

[0085] The energy trading model is shown in formula (9).

[0086]

[0087] Where: To purchase electricity, is the amount of surplus electricity connected to the grid (its value is negative), c e+ is the unit electricity purchase cost, c e- The unit electricity sales cost.

[0088] 6. Energy storage.

[0089] Unified modeling of electric energy storage equipment in each virtual power plant.

[0090]

[0091] Where: are the charging and discharging power of energy storage device i in the tth time period respectively; are binary variables, which are the charging and discharging state parameters of energy storage device i in the tth period, Indicates that it is in charging state. Indicates that it is in the energy release state; η cha,ES , η dis,ES are the charging and discharging efficiency of energy storage equipment; S i,t is the capacity percentage of energy storage device i in the tth period; is the rated capacity of energy storage device i; are the upper and lower limits of the capacity percentage of energy storage device i respectively.

[0092] 7. Internal power balance of virtual power plant.

[0093] The internal power balance of the virtual power plant is the supply and demand balance of electricity, heat and gas. The specific mathematical expression is shown in formula (11).

[0094]

[0095] Where: is a collection of equipment for a virtual power plant; are the electrical power, thermal power and gas power of equipment i in the tth period, are the electric load, thermal load and gas load of virtual power plant j in the tth time period.

[0096] The conventional distributed scheduling solution method is as follows:

[0097] First, formula (4) is the transaction balance constraint between virtual power plants. It decouples the power transaction balance constraint and adds power replication variables. The original variables with superscript I are optimized by the local energy market, and the replication variables with superscript P are introduced to be optimized by each virtual power plant. The constraints on the replication variables are added to make the energy sales power equal to the energy purchase power:

[0098]

[0099] According to the idea of ​​distributed algorithm, the constraint is relaxed to the original objective function by applying the augmented Lagrangian function:

[0100]

[0101] Where: P I is the local energy market power vector; P P is the virtual power plant power vector; λ is the energy price vector; ρ is the penalty factor.

[0102] It should be noted that the final virtual power plant equipment model in this embodiment is a coal-fired unit, a new energy unit, a power-to-gas equipment, a gas turbine, and an energy storage device. Under the premise of no conflict, it can be replaced with other virtual power plant equipment models.

[0103] Through the above steps, the dispatching strategy of each distributed virtual power plant can be obtained, including the output of each coal-fired unit, new energy unit, power-to-gas equipment, gas turbine, and energy storage equipment.

[0104] Step S2: Each virtual power plant performs differential privacy disturbance attenuation.

[0105] Disturbance attenuation: Each virtual power plant calculates disturbance attenuation according to the following formula;

[0106]

[0107] Where: K is the maximum number of iterations, k≤K. The privacy budget consumed by the virtual power plant j in the i-th iteration is ε j,i , after k iterations, the privacy budget consumption of virtual power plant j is Virtual power plant j can set the total privacy budget E according to its own needs j As the basis for determining whether the privacy budget consumption reaches the threshold, that is, When other parameters are constant, E j The smaller it is, the higher the privacy protection requirements of the virtual power plant.

[0108] In step S3, each virtual power plant adds differential privacy perturbations and returns the results to the local market.

[0109] Add differential privacy perturbation: Each virtual power plant performs differential privacy processing on the optimized scheduling strategy according to the following formula, and returns the processed results to the local energy market;

[0110]

[0111] Where: is the purchased energy power vector of virtual power plant j after differential privacy processing, is the Laplace perturbation added by virtual power plant j to the purchased power vector, α j is the differential privacy sensitivity of virtual power plant j, is the maximum electrical load power of virtual power plant j.

[0112] The differential privacy of this embodiment is designed for a given data set and a given data analysis requirement. The data processing algorithm is used to obtain a perturbed data set, so that the data analysis result is correct in the expected sense, but the original data cannot be inferred. This method processes the original data to obtain a random number with the original value as the average value and subject to a certain distribution.

[0113] It should be noted that each virtual power plant can set different differential privacy sensitivities and total privacy budgets according to its own needs, and add disturbances of different sizes as needed.

[0114] Step S4: local market optimization scheduling.

[0115] Optimizing the local energy market: The local energy market optimizes the dispatching results based on the feedback from each virtual power plant (including the purchased power of each virtual power plant). Optimize the scheduling with the power balance of each virtual power plant as the goal, and calculate the local energy market energy sales strategy, including the energy price signal λ k And the energy sales to each virtual power plant

[0116] Step S5: Calculate the algorithm residual and determine whether the algorithm has converged based on the privacy budget consumption.

[0117] Calculate residuals and determine convergence conditions: Residuals The residual threshold is And calculate the privacy budget consumption according to the following formula;

[0118]

[0119] Where: E j is the privacy budget consumption of virtual power plant j, ε j,m is the privacy budget of virtual power plant j at the mth iteration.

[0120] Determine whether the residual is less than the convergence criterion, or determine whether the consumed privacy budget reaches the set value according to the virtual power plant quotation strategy based on differential privacy. If the residual meets the convergence criterion, or the consumed privacy budget reaches the set value, the iteration is stopped, and each virtual power plant recalculates the optimization scheduling result according to the energy sales strategy issued by the local market; otherwise, return to step S1 to continue iteration.

[0121] In summary, this method establishes a virtual power plant model under the peak-shaving-electricity-carbon coupling market that takes into account the subject's privacy protection demands. For the scheduling and operation of virtual power plants under the electricity-carbon coupling market, this method designs a peak-shaving mechanism and a privacy protection mechanism for the interaction process, taking into account both the economy and privacy of virtual power plant scheduling and operation.

[0122] This embodiment adopts the method of perturbation attenuation at each iteration, so that the calculation error caused by differential privacy is gradually reduced, and the calculation accuracy of the algorithm is improved; on the other hand, perturbation attenuation reduces the number of iterations of the algorithm, thereby improving the running speed of the algorithm. Therefore, the perturbation correction attenuation algorithm proposed in this embodiment eliminates the adverse effects of differential privacy on calculation accuracy and algorithm running speed.

[0123] Example 2

[0124] Based on Example 1, see Figure 2 This embodiment provides a virtual power plant distributed scheduling device considering subject privacy protection, including:

[0125] The upper-level market module 11 includes the electricity spot market, the electricity peak-shaving market, and the carbon trading market.

[0126] Multiple distributed virtual power plants (corresponding to Figure 2 The virtual power plant modules 12, 13, and 14 in the distributed virtual power plant are used to obtain the scheduling strategy of each distributed virtual power plant, calculate the disturbance attenuation of each virtual power plant according to the current iteration round, add differential privacy disturbances to the scheduling strategy based on the disturbance attenuation, and calculate the scheduling strategy of each virtual power plant after adding disturbances. Each virtual power plant module may include coal-fired units, new energy units, power-to-gas equipment, gas turbines, energy storage equipment, and loads.

[0127] Local Market Module (corresponding to Figure 2 The local market module 15) is used to add a scheduling strategy after disturbance to each virtual power plant, obtain the energy sales strategy through optimized scheduling, and determine whether the preset convergence conditions are met. If not, continue to iterate. If so, recalculate the scheduling strategy based on the current energy sales strategy to realize distributed scheduling of virtual power plants.

[0128] In this embodiment, modules 12, 13, and 14 conduct electricity trading and carbon trading with module 11 respectively according to their own energy needs. Modules 12, 13, and 14 realize mutual trading of electricity and carbon quotas through module 15. Modules 12, 13, and 14 realize privacy protection in the interaction process with module 15 through differential privacy technology.

[0129] Example 3

[0130] This embodiment provides an electronic device, including: one or more processors and a memory, wherein the memory stores one or more programs, and the one or more programs include instructions for executing the virtual power plant distributed scheduling method considering subject privacy protection as described in Example 1.

[0131] Example 4

[0132] This embodiment provides a computer-readable storage medium, including one or more programs for execution by one or more processors of an electronic device, wherein the one or more programs include instructions for executing a virtual power plant distributed scheduling method that takes into account subject privacy protection as described in Example 1.

[0133] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A distributed scheduling method for virtual power plants considering subject privacy protection, characterized in that: The steps include: Step S1, obtaining the dispatching strategy of each distributed virtual power plant; Step S2, calculating the disturbance attenuation of each virtual power plant according to the current iteration round; Step S3, based on the disturbance attenuation, adding differential privacy disturbance to the scheduling strategy, and calculating the scheduling strategy of each virtual power plant after adding the disturbance; Step S4, based on the dispatching strategies of each virtual power plant after adding disturbances, an energy sales strategy is obtained by optimizing the dispatching; Step S5, determine whether the preset convergence condition is met, if not, execute step S1, if yes, recalculate the scheduling strategy based on the current energy sales strategy to realize the distributed scheduling of the virtual power plant.

2. A distributed scheduling method for virtual power plants considering subject privacy protection according to claim 1, characterized in that: The step S1 includes the following sub-steps: Step S101, constructing an objective function of a distributed virtual power plant; Step S102, establishing constraints for the scheduling process; Step S103, obtaining the dispatching strategy of each distributed virtual power plant by applying the constraint condition to the augmented Lagrangian function.

3. A distributed scheduling method for virtual power plants considering subject privacy protection according to claim 2, characterized in that: The objective function is to minimize the total operating cost, wherein the total operating cost takes into account the peak-shaving benefits, carbon market benefits and equipment operating costs.

4. A distributed scheduling method for virtual power plants considering subject privacy protection according to claim 2, characterized in that: The constraints include virtual power plant transaction balance conditions and virtual power plant internal equipment constraints, wherein the virtual power plant internal equipment includes coal-fired units, new energy units, power-to-gas equipment, gas turbines and energy storage equipment.

5. A distributed scheduling method for virtual power plants considering subject privacy protection according to claim 1, characterized in that: In the step S2, the disturbance attenuation is calculated using the following formula: Among them, ε j,k is the disturbance attenuation of virtual power plant j in the kth iteration, K is the maximum number of iterations, k≤K.

6. A distributed scheduling method for virtual power plants considering subject privacy protection according to claim 1, characterized in that: In the step S3, adding differential privacy perturbation to the scheduling strategy is implemented by the following formula: in, is the purchased power vector of virtual power plant j after differential privacy processing, that is, the dispatch strategy after adding disturbance, is the Laplace perturbation added by virtual power plant j to the purchased power vector, 1 is the unit vector, α j is the differential privacy sensitivity of virtual power plant j, is the maximum load power of virtual power plant j, ε j is the disturbance attenuation of virtual power plant j, Lap(·) is the Laplace mechanism, is the real purchased energy power vector of virtual power plant j.

7. A distributed scheduling method for virtual power plants considering subject privacy protection according to claim 1, characterized in that: In step S5, the convergence condition is that the residual is less than a preset value and / or the privacy budget consumption is greater than a preset value, wherein the residual and the privacy budget consumption are calculated using the following formula: in, is the power vector of the local energy market in the k+1th round, i.e., the energy sales strategy, is the purchased power vector of virtual power plant j after differential privacy processing in the k+1th round, that is, the scheduling strategy after adding disturbance, The privacy budget consumption is calculated using the following formula: Where: E j is the privacy budget consumption of virtual power plant j, ε j,m is the privacy budget of virtual power plant j at the mth iteration.

8. A distributed dispatching device for a virtual power plant considering subject privacy protection, characterized in that: include: Multiple distributed virtual power plants, used to obtain the dispatching strategy of each distributed virtual power plant, calculate the disturbance attenuation of each virtual power plant according to the current iteration round, add differential privacy perturbation to the dispatching strategy based on the disturbance attenuation, and calculate the dispatching strategy of each virtual power plant after adding the disturbance; The local market module is used to add disturbance-based dispatching strategies to each virtual power plant, obtain energy sales strategies through optimized dispatching, and determine whether the preset convergence conditions are met. If not, continue to iterate. If so, recalculate the dispatching strategy based on the current energy sales strategy to realize distributed dispatching of virtual power plants.

9. An electronic device, characterized in that: include: One or more processors and a memory, wherein the memory stores one or more programs, and the one or more programs include instructions for executing a virtual power plant distributed scheduling method that takes into account subject privacy protection as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: It includes one or more programs for execution by one or more processors of an electronic device, and the one or more programs include instructions for executing a virtual power plant distributed scheduling method that takes into account subject privacy protection as described in any one of claims 1-7.

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