A virtual power plant day-ahead accurate peak shaving method, system, device and storage medium

Abstract

The present application relates to the technical field of power dispatching, and discloses a virtual power plant day-ahead accurate peak shaving method, system, device and storage medium. When receiving a day-ahead peak shaving demand, the present application acquires real-time power supply paths of each user according to user account data, and queries each real-time power supply path according to the identification of the heavy overload equipment in the day-ahead peak shaving demand. If the heavy overload equipment is queried in the power supply equipment set corresponding to the real-time power supply path, the user corresponding to the real-time power supply path is taken as a target user, a target user set is constructed, and then an invitation is initiated to each target user in the target user set. According to the feedback of each target user to the invitation, a winning user is determined, and a day-ahead dispatching plan curve is issued to each winning user, so that each winning user performs peak shaving feedback according to the day-ahead dispatching plan curve. The present application can realize accurate peak shaving demand specific to a certain main transformer, distribution transformer or feeder, and improve the accuracy of peak shaving.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power dispatching, and in particular to a virtual power plant day-ahead accurate peak shaving method, system, device and storage medium. BACKGROUND

[0002] Due to the large fluctuation of the power supply side, the power supply may be short in some local time periods and local areas, and it is necessary to shave the peak on the load side to maintain the real-time balance between power generation and power consumption.

[0003] Currently, when shaving the peak on the load side, all users in the region are usually invited, and it is impossible to accurately determine the day-ahead peak shaving invitation range and realize effective interaction with the corresponding users, so that accurate peak shaving cannot be realized, and the problem of local area overload cannot be effectively solved. SUMMARY

[0004] The present application provides a virtual power plant day-ahead accurate peak shaving method, system, device and storage medium, which solves the technical problem of low accuracy caused by the inability to accurately determine the day-ahead peak shaving invitation range and realize effective interaction with the corresponding users when the existing virtual power plant performs day-ahead peak shaving.

[0005] The first aspect of the present application provides a virtual power plant day-ahead accurate peak shaving method, comprising:

[0006] receiving a day-ahead peak shaving demand, the day-ahead peak shaving demand comprising an identification of an overload device, a peak shaving period and a power demand curve of each preset time interval within the peak shaving period;

[0007] obtaining real-time power supply paths of each user according to pre-established user account data, wherein each real-time power supply path comprises a corresponding power distribution transformer and a set of power supply devices participating in power supply to the corresponding power distribution transformer;

[0008] querying each real-time power supply path according to the identification of the overload device, and if one or more overload devices are queried in the set of power supply devices corresponding to the real-time power supply path, regarding the user corresponding to the real-time power supply path as a target user, and constructing a target user set;

[0009] initiating an invitation corresponding to the day-ahead peak shaving demand to each target user in the target user set;

[0010] determining a winning user according to the feedback of each target user to the invitation, and issuing a day-ahead dispatching plan curve to each winning user, so that each winning user performs peak shaving feedback according to the day-ahead dispatching plan curve.

[0011] According to an implementable manner of the first aspect of the present application, the power supply device comprises a 220 kV main transformer, a 110 kV main transformer, a 110 kV line and a 10 kV feeder, the real-time power supply path of each user is obtained according to the pre-established user account data, comprising:

[0012] The day-ahead power distribution network topology information is obtained, and the day-ahead power distribution network topology information is modified according to the planned power supply device outage maintenance or power supply switching operation from the day-ahead to the peak shaving day;

[0013] According to the modified power distribution network topology information, the 10 kV feeder corresponding to each power distribution transformer in the user account data is queried to form a feeder set corresponding to the power distribution transformer;

[0014] For each 10 kV feeder in the feeder set, the corresponding 110 kV main transformer is queried according to the power distribution network topology information to form a first main transformer set corresponding to the 10 kV feeder;

[0015] For each 110 kV main transformer in the first main transformer set, the corresponding 110 kV line is queried according to the power distribution network topology information to form a line set corresponding to the 110 kV main transformer;

[0016] For each 110 kV line in the line set, the corresponding 220 kV main transformer is queried according to the power distribution network topology information to form a second main transformer set corresponding to the 110 kV line;

[0017] According to the feeder set, the first main transformer set, the line set and the second main transformer set, the real-time power supply path of each user from the power distribution transformer, the 10 kV feeder, the 110 kV main transformer, the 110 kV line to the 220 kV main transformer is determined.

[0018] According to an implementable manner of the first aspect of the present application, the day-ahead power distribution network topology information is obtained, comprising:

[0019] A request for obtaining the day-ahead power distribution network topology information is sent to a power grid system storing the power distribution network topology information;

[0020] The day-ahead power distribution network topology information sent by the power grid system according to the request is received.

[0021] According to an implementable manner of the first aspect of the present application, the determination of the winning user according to the feedback of each target user to the invitation comprises:

[0022] According to the feedback of each target user to the invitation, the bid of each target user is determined;

[0023] The adjustment performance coefficient of each target user is determined according to the adjustable response amount, the adjustment rate and the response time length;

[0024] The winning user and the clearing price are determined by using an optimized clearing model according to the bid and the adjustment performance coefficient. According to an implementable manner of the first aspect of the application, the adjustment performance coefficient of each target user is determined according to the adjustable response amount, the adjustment rate and the response time length, and specifically:

[0025] The adjustment performance coefficient is determined according to the following formula:

[0026] R = p x v x h

[0027] In the formula, R represents the adjustment performance coefficient, p represents the adjustable response amount, v represents the adjustment rate, and h represents the response time length.

[0028] According to an implementable manner of the first aspect of the application, the winning user and the clearing price are determined by using an optimized clearing model, and specifically:

[0029] The objective function is to minimize the total cost of unit adjustment performance, the winning amount of all users in the tth time period is solved, and the maximum clearing price corresponding to the tth time period is taken as the marginal clearing price.

[0030] The optimized clearing model is:

[0031] The objective function is:

[0032]

[0033] The constraint condition is:

[0034]

[0035] In the formula, C f represents the total cost of unit adjustment performance of the virtual power plant, T represents the total time length of system peak shaving, N represents the total number of target users, P x,t represents the bid of the xth target user in the tth time period, G x,t represents the response amount of the xth target user in the tth time period, R x represents the adjustment performance coefficient of the xth target user, G x,min represents the lower limit of the response amount of the xth target user, G x,max represents the upper limit of the response amount of the xth target user, P min represents the lower limit of the compensation price declaration set by the system, P max represents the upper limit of the compensation price declaration set by the system, Q t represents the total power demand of system peak shaving in the tth time period, h x,t represents the response time length of the xth target user in the tth time period, h ta total system peak shaving duration demand of a t-th time period, v x,t a t-th time period, v t a total system peak shaving rate demand of a t-th time period.

[0036] According to an implementable manner of the first aspect of the present application, the issuing of the day-ahead scheduling plan curve to each of the winning users comprises:

[0037] If the winning user is a load aggregator, the day-ahead scheduling plan curve to be issued is decomposed;

[0038] The obtained decomposition result is sent to the corresponding load aggregator, so that the corresponding load aggregator sends a corresponding scheduling plan curve to each user aggregated by the load aggregator according to the decomposition result, to ensure that the sum of the scheduling plan curves of the users aggregated by the load aggregator is consistent with the day-ahead scheduling plan curve to be issued.

[0039] According to an implementable manner of the first aspect of the present application, the decomposition of the day-ahead scheduling plan curve to be issued specifically comprises:

[0040] The day-ahead scheduling plan curve to be issued is decomposed according to a proportional decomposition strategy; the proportional decomposition strategy is to decompose the day-ahead scheduling plan curve to be issued according to the following formula:

[0041]

[0042] In the formula, g j,t is a winning amount of a j-th user of a load aggregator in a t-th time period, G t is a total winning amount of the load aggregator in the t-th time period, m is a total number of users aggregated by the load aggregator, g j,t is a bidding response amount of the j-th user of the load aggregator in the t-th time period.

[0043] The second aspect of the present application provides a virtual power plant day-ahead accurate peak shaving system, comprising:

[0044] A demand receiving module is configured to receive a day-ahead peak shaving demand, the day-ahead peak shaving demand comprising an identifier of an overloaded device, a peak shaving time period, and a power demand curve of each preset time interval in the peak shaving time period;

[0045] A real-time power supply path obtaining module is configured to obtain real-time power supply paths of users according to pre-established user account data, wherein each real-time power supply path comprises a corresponding distribution transformer and a set of power supply devices participating in power supply to the corresponding distribution transformer;

[0046] a target user set construction module, configured to query each of the real-time power supply paths according to the identification of the heavy overload equipment, and if one or more heavy overload equipment is queried in the power supply equipment set corresponding to the real-time power supply path, take the user corresponding to the real-time power supply path as a target user, and construct a target user set;

[0047] an invitation module, configured to initiate an invitation corresponding to the day-ahead peak shaving demand to each target user in the target user set;

[0048] a decision module, configured to determine a winning user according to the feedback of each target user to the invitation, and issue a day-ahead scheduling plan curve to each winning user, so that each winning user performs peak shaving feedback according to the day-ahead scheduling plan curve.

[0049] According to an implementable manner of the second aspect of the present application, the power supply equipment includes a 220kV main transformer, a 110kV main transformer, a 110kV line and a 10kV feeder, and the real-time power supply path acquisition module includes:

[0050] an acquisition unit, configured to acquire day-ahead power distribution network topology information, and modify the day-ahead power distribution network topology information according to power supply equipment power-off maintenance or power transfer operations scheduled from the day-ahead to the peak shaving day;

[0051] a first query unit, configured to query, according to the modified power distribution network topology information, 10kV feeders corresponding to each power distribution transformer in user account data, and form a feeder set corresponding to the power distribution transformer;

[0052] a second query unit, configured to, for each 10kV feeder in the feeder set, query a corresponding 110kV main transformer according to the power distribution network topology information, and form a first main transformer set corresponding to the 10kV feeder;

[0053] a third query unit, configured to, for each 110kV main transformer in the first main transformer set, query a corresponding 110kV line according to the power distribution network topology information, and form a line set corresponding to the 110kV main transformer;

[0054] a fourth query unit, configured to, for each 110kV line in the line set, query a corresponding 220kV main transformer according to the power distribution network topology information, and form a second main transformer set corresponding to the 110kV line;

[0055] a path determination unit, configured to determine, according to the feeder set, the first main transformer set, the line set and the second main transformer set, a real-time power supply path of each user from a power distribution transformer, a 10kV feeder, a 110kV main transformer, a 110kV line to a 220kV main transformer.

[0056] According to an implementable manner of the second aspect of the present application, the obtaining unit comprises:

[0057] The request sending subunit is configured to send a request for obtaining the day-ahead power distribution network topology information to a power grid system in which the power distribution network topology information is stored.

[0058] The receiving subunit is configured to receive the day-ahead power distribution network topology information sent by the power grid system according to the request.

[0059] According to an implementable manner of the second aspect of the present application, the decision module comprises a first sub-module for determining the winning user according to the feedback of each target user to the invitation, and the first sub-module comprises:

[0060] The compensation price determining unit is configured to determine the bid of each target user according to the feedback of each target user to the invitation.

[0061] The adjustment performance coefficient determining unit is configured to determine the adjustment performance coefficient of each target user according to the adjustable response amount, the adjustment rate and the response duration.

[0062] The winning user determining unit is configured to determine the winning user and the clearing price by using an optimization clearing model according to the bid and the adjustment performance coefficient.

[0063] According to an implementable manner of the second aspect of the present application, the adjustment performance coefficient determining unit is specifically configured to:

[0064] The adjustment performance coefficient is determined according to the following formula:

[0065] R = p × v × h

[0066] In the formula, R represents the adjustment performance coefficient, p represents the adjustable response amount, v represents the adjustment rate, and h represents the response duration.

[0067] According to an implementable manner of the second aspect of the present application, the winning user determining unit is specifically configured to:

[0068] The target function is to minimize the total cost of unit adjustment performance, the winning amount of all users in the tth time period is solved, and the maximum clearing price corresponding to the tth time period is taken as the marginal clearing price.

[0069] The optimization clearing model is:

[0070] The target function is:

[0071]

[0072] The constraint condition is:

[0073]

[0074] C = ∑ (Gx * T * N) / P f C is the total cost of virtual power plant unit regulation performance, T is the total duration of system peak shaving, N is the total number of target users, P x,t Gx is the bid of the xth target user in the tth period, G x,t Gx is the response amount of the xth target user in the tth period, G x Gx is the regulation performance coefficient of the xth target user, G x,min Gx is the lower limit of the response amount of the xth target user, G x,max Gx is the upper limit of the response amount of the xth target user, P min P is the lower limit of the compensation price declaration set by the system, P max Q is the upper limit of the compensation price declaration set by the system, Q t h is the total power demand of system peak shaving in the tth period, h x,t h is the response duration of the xth target user in the tth period, h t v is the total duration demand of system peak shaving in the tth period, v x,t v is the regulation rate of the xth target user in the tth period, v t v is the total regulation rate demand of system peak shaving in the tth period.

[0075] According to an implementable manner of the second aspect of the present application, the decision module further comprises a second submodule for issuing a day-ahead scheduling plan curve to each of the winning users, and the second submodule comprises:

[0076] a decomposition unit configured to, when the winning user is a load aggregator, decompose the day-ahead scheduling plan curve to be issued;

[0077] a sending unit configured to send the obtained decomposition result to the corresponding load aggregator, so that the corresponding load aggregator sends a corresponding scheduling plan curve to each user aggregated by the load aggregator according to the decomposition result, to ensure that the sum of the scheduling plan curves of each user aggregated by the load aggregator is consistent with the day-ahead scheduling plan curve to be issued.

[0078] According to an implementable manner of the second aspect of the present application, the decomposition unit is specifically configured to, when the winning user is a load aggregator:

[0079] decompose the day-ahead scheduling plan curve to be issued according to a proportional decomposition strategy; the proportional decomposition strategy is to decompose the day-ahead scheduling plan curve to be issued according to the following formula:

[0080]

[0081] wherein, g j,t Gj is the winning amount of the jth user of the load aggregator in the tth period, G tis the total amount of winning bids of the load aggregator in the t period, m is the total number of users aggregated by the load aggregator, g j,t is the response amount of the jth user of the load aggregator in the t period.

[0082] The third aspect of the present application provides a virtual power plant day-ahead accurate peak shaving system, comprising:

[0083] The virtual power plant is used for receiving a day-ahead peak shaving demand, the day-ahead peak shaving demand comprising an identification of a heavy overload device, a peak shaving period and a power demand curve of each preset time interval in the peak shaving period; obtaining real-time power supply paths of each user according to pre-established user account data, wherein each real-time power supply path comprises a corresponding distribution transformer and a set of power supply devices participating in power supply to the corresponding distribution transformer; querying each real-time power supply path according to the identification of the heavy overload device, if one or more heavy overload devices are queried in the set of power supply devices corresponding to the real-time power supply path, taking the user corresponding to the real-time power supply path as a target user, and constructing a target user set; initiating an invitation corresponding to the day-ahead peak shaving demand to each target user in the target user set; determining winning users according to feedback of each target user to the invitation, and issuing a day-ahead scheduling plan curve to each winning user, so that each winning user responds to the grid company according to the day-ahead scheduling plan curve in the peak shaving period;

[0084] The grid company is used for calculating a user response baseline, and calculating a subsidy fee of a response amount of each winning user according to the user response baseline.

[0085] According to an implementable manner provided by the third aspect of the present application, when the grid company calculates the user response baseline, it is specifically used for:

[0086] determining a corresponding response day type when the winning user responds, the response day type comprising a weekday, a double holiday, a holiday and a special national holiday;

[0087] obtaining historical load data of the same type corresponding to the corresponding response day type;

[0088] calculating a corresponding average load curve according to the historical load data according to the following formula, and taking the obtained average load curve as the user response baseline:

[0089]

[0090] In the formula, G represents the average load of the bth winning user in the t period, G d,b,t D represents the historical load of the bth winning user in the t period, and D is the corresponding number of days of historical load data.

[0091] According to the third aspect of the present application, the power grid company can implement the following mode: when the power grid company calculates the subsidy fee of the response amount of each winning user according to the user response baseline, the power grid company is specifically used for:

[0092] Let the actual load monitoring value of the bth winning user in the tth time period on the response day be L b,t The corresponding winning amount is G b,t The load of the corresponding user response baseline is When the following condition is met: The corresponding subsidy fee M b is calculated according to the following formula:

[0093]

[0094] In the formula, T is the total duration of system peak shaving, P b,t is the bid of the bth winning user in the tth time period;

[0095] When the following condition is met: The corresponding subsidy fee M b is calculated according to the following formula:

[0096]

[0097] When the following condition is met: The corresponding subsidy fee M b is calculated according to the following formula:

[0098]

[0099] The fourth aspect of the present application provides a virtual power plant day-ahead accurate peak shaving device, comprising:

[0100] A memory for storing instructions; wherein the instructions are instructions for implementing the virtual power plant day-ahead accurate peak shaving method of any one of the above implementable modes;

[0101] A processor for executing the instructions in the memory.

[0102] The fifth aspect of the present application is a computer readable storage medium, and the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the virtual power plant day-ahead accurate peak shaving method of any one of the above implementable modes.

[0103] From the above technical solutions, the present application has the following advantages:

[0104] The application obtains real-time power supply paths of each user according to pre-established user account data when receiving day-ahead peak shaving demand, and queries each real-time power supply path according to the identification of the heavy overload equipment in the day-ahead peak shaving demand, if one or more heavy overload equipment is queried in the power supply equipment set corresponding to the real-time power supply path, the user corresponding to the real-time power supply path is taken as a target user, a target user set is constructed, then an invitation is initiated to each target user in the target user set, a winning user is determined according to the feedback of each target user to the invitation, and a day-ahead scheduling plan curve is issued to each winning user, so that each winning user performs peak shaving feedback according to the day-ahead scheduling plan curve; the application uses the load aggregation and informationized regulation and control means of the virtual power plant, obtains the real-time power supply path of the user based on the topological association relationship of the distribution network, realizes accurate peak shaving demand on a certain main transformer, distribution transformer or feeder, not only can avoid the decline of power supply quality and social influence caused by orderly power utilization, but also can improve the accuracy of the peak shaving invitation range, avoid large-scale user invitation, thereby effectively solve the technical problem that the existing virtual power plant cannot accurately determine the day-ahead peak shaving invitation range and realize effective interaction with the corresponding user when performing day-ahead peak shaving, which is beneficial to reduce the invalid response to the power grid and reduce the subsidy cost of the virtual power plant to call the load side resources. BRIEF DESCRIPTION OF DRAWINGS

[0105] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor.

[0106] Figure 1 The flow chart of a virtual power plant day-ahead accurate peak shaving method provided for an optional embodiment of the present application;

[0107] Figure 2 The structure connection block diagram of a virtual power plant day-ahead accurate peak shaving system provided for an optional embodiment of the present application.

[0108] Reference signs:

[0109] 1-demand receiving module; 2-real-time power supply path acquisition module; 3-target user set construction module; 4-invitation module; 5-decision module. DETAILED DESCRIPTION

[0110] The embodiment of the present application provides a virtual power plant day-ahead accurate peak shaving method, system, device and storage medium, and is used for solving the technical problem that the existing virtual power plant has low accuracy when performing day-ahead peak shaving due to the inability to accurately determine a day-ahead peak shaving invitation range and to realize effective interaction with corresponding users.

[0111] In order to make the application purpose, features and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the following described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0112] The present application provides a virtual power plant day-ahead accurate peak shaving method, which can be executed by a platform of the virtual power plant.

[0113] Please refer to Figure 1 , Figure 1 A flowchart of a virtual power plant day-ahead accurate peak shaving method provided by the embodiment of the present application is shown.

[0114] The virtual power plant day-ahead accurate peak shaving method provided by the embodiment of the present application comprises steps S1-S5.

[0115] Step S1, receiving a day-ahead peak shaving demand, wherein the day-ahead peak shaving demand comprises an identification of a heavy overload device, a peak shaving period and a power demand curve of each preset time interval in the peak shaving period.

[0116] Preferably, the preset time interval can be a 15-minute interval.

[0117] It should be noted that the day-ahead dispatching plan curve is a curve composed of plan power values of preset time intervals.

[0118] When specifically implemented, the power regulation platform initiates the day-ahead peak shaving demand to all virtual power plants accessed to the power regulation platform. After the power grid company arranges the next day operation mode, obtains the power and energy balance analysis result according to the load prediction and the unit start-up condition, judges that there is a heavy load or overload in a local area, and then issues the day-ahead peak shaving demand to the virtual power plants accessed to the power regulation platform.

[0119] Step S2, obtaining real-time power supply paths of each user according to pre-established user account data, wherein each real-time power supply path comprises a corresponding distribution transformer and a set of power supply devices participating in power supply to the corresponding distribution transformer.

[0120] Wherein, the pre-established user station account data is the account data established by a user individual or a load aggregator when registering on the platform of the virtual power plant, and the account data includes but is not limited to: user unified social credit code, power consumption household number, distribution transformer plaque number, user electric meter number and adjustable resource capacity.

[0121] Considering that the user power supply path will change due to the influence of distribution network power supply, the real-time power supply path needs to query the latest distribution network topology information after the power grid issues the peak shaving demand in advance, and the latest distribution network topology information is usually in the power grid GIS system, distribution network dispatching system and the like, and the query mode can be real-time interface query or offline manual query.

[0122] In a specific implementation, as an implementable manner, the power supply equipment includes a 220kV main transformer, a 110kV main transformer, a 110kV line and a 10kV feeder, and the real-time power supply path of each user is obtained according to the pre-established user station account data, including:

[0123] Obtaining the topology information of the day-ahead distribution network, and modifying the topology information of the day-ahead distribution network according to the power supply equipment outage maintenance or power supply switching operation arranged from the day-ahead to the peak shaving day;

[0124] According to the modified topology information of the distribution network, querying the 10kV feeder connected to each distribution transformer in the user account data to form a feeder set corresponding to the distribution transformer;

[0125] For each 10kV feeder in the feeder set, querying the corresponding 110kV main transformer according to the topology information of the distribution network to form a first main transformer set corresponding to the 10kV feeder;

[0126] For each 110kV main transformer in the first main transformer set, querying the corresponding 110kV line according to the topology information of the distribution network to form a line set corresponding to the 110kV main transformer;

[0127] For each 110kV line in the line set, querying the corresponding 220kV main transformer according to the topology information of the distribution network to form a second main transformer set corresponding to the 110kV line;

[0128] According to the feeder set, the first main transformer set, the line set and the second main transformer set, the real-time power supply path of each user from the distribution transformer, the 10kV feeder, the 110kV main transformer, the 110kV line to the 220kV main transformer is determined.

[0129] According to the embodiment of the present application, the real-time power supply path is queried according to the latest topology information of the distribution network, the actual power supply path of each user can be accurately found, and the accuracy of subsequent determination of the peak shaving invitation range is improved.

[0130] In the embodiment, the day-ahead power distribution network topology information is acquired, and the day-ahead power distribution network topology information is modified according to scheduled power supply equipment outage maintenance or power supply switching operation from the day-ahead to the peak shaving day.

[0131] sending a request for acquiring day-ahead power distribution network topology information to a power grid system in which the power distribution network topology information is stored;

[0132] receiving day-ahead power distribution network topology information sent by the power grid system according to the request.

[0133] To make the method of determining the real-time power supply path clearer, a specific embodiment is described below.

[0134] The power distribution transformer set of a user σ is acquired from the user account data of the virtual power plant, and the power distribution transformer set is Φ1={10kv power distribution transformer α}. In the power distribution network topology, it is found that the upper feeder of the 10kv power distribution transformer α is two, which are 10kV feeder β1 and 10kV feeder β2. Then the feeder set corresponding to the power distribution transformer α is formed as Φ2={β1,β2}.

[0135] For the 10kV feeder β1, in the power distribution network topology, it is found that the upper 110kV main transformer of the 10kV feeder β1 is γ1, and the upper 110kV main transformer of the 10kV feeder β2 is γ2. Then the first main transformer set corresponding to the 10kV feeder is formed as Φ3={γ1,γ2}.

[0136] Similarly, the line set corresponding to the 110kV main transformer is formed as Φ4={δ1,δ2}, where δ1 is the 110kV line connected to the 110kV main transformer γ1, and δ2 is the 110kV line connected to the 110kV main transformer γ2.

[0137] The second main transformer set corresponding to the 110kV line is formed as Φ5={ε1}, where ε1 is the 220kV main transformer connected to the 110kV line δ1 and δ2.

[0138] Then, the real-time power supply path of the user σ from the power distribution transformer, the 10kV feeder, the 110kV main transformer, the 110kV line to the 220kV main transformer is determined according to the sets Φ1, Φ2, Φ3, Φ4 and Φ5.

[0139]

[0140] Similarly, the real-time power supply path of other users can be formed. The virtual power plant updates the account information of all users about the real-time power supply path according to the real-time power supply path result.

[0141] Step S3, querying each real-time power supply path according to the identifier of the heavy overload equipment, if one or more heavy overload equipments are queried in the power supply equipment set corresponding to the real-time power supply path, taking the user corresponding to the real-time power supply path as a target user, and constructing a target user set.

[0142] In combination with the specific embodiment in step S2, step S2 is described in another specific embodiment:

[0143] When the identifier of the heavy overload equipment is Ω = {110kV main transformer γ1, 10kv distribution transformer α}, the 110kV main transformer γ1 and the 10kv distribution transformer α are queried in the real-time power supply path of each user, and it can be seen that the heavy overload equipment can be found in the real-time power supply path L σ , and the user corresponding to the real-time power supply path L σ is taken as a target user.

[0144] According to the identifier of the heavy overload equipment, the real-time power supply path is queried in the embodiment of the application to determine the target user, and the query method is simple and convenient.

[0145] Step S4, initiating an invitation corresponding to the day-ahead peak shaving demand to each target user in the target user set.

[0146] Step S5, determining a winning user according to the feedback of each target user to the invitation, and issuing a day-ahead scheduling plan curve to each winning user, so that each winning user performs peak shaving feedback according to the day-ahead scheduling plan curve.

[0147] After each target user receives the invitation, the target user reports response amount, price, up and down adjustment range, duration and other parameters. According to the feedback of the target user, the embodiment of the application will further determine the winning user. As an implementable way, when the winning user is determined according to the feedback of each target user to the invitation, the following is specifically executed:

[0148] According to the feedback of each target user to the invitation, the bid of each target user is determined.

[0149] According to the adjustable response amount, the adjustment rate and the response duration, the adjustment performance coefficient of each target user is determined.

[0150] According to the bid and the adjustment performance coefficient, the winning user and the clearing price are determined by using an optimization clearing model.

[0151] In an implementable way, the adjustment performance coefficient of each target user is determined according to the adjustable response amount, the adjustment rate and the response duration, and the adjustment performance coefficient is specifically:

[0152] The adjustment performance coefficient is determined according to the following formula:

[0153] R = p x v x h

[0154] In the formula, R represents a regulation performance coefficient, p is an adjustable response amount, v is a regulation rate, and h is a response time length.

[0155] The embodiment of the application provides a calculation formula of the regulation performance coefficient, so that the determination of the regulation performance coefficient is more simple and convenient.

[0156] Further, when the winning users are determined, the target function of minimizing the total cost of unit regulation performance is used to obtain the winning amount of all users in the t period, and the maximum clearing price corresponding to the t period is used as the marginal clearing price.

[0157] The optimization clearing model is as follows:

[0158] The target function is as follows:

[0159]

[0160] The constraint condition is as follows:

[0161]

[0162] In the formula, C f is the total cost of unit regulation performance of the virtual power plant, T is the total time length of system peak regulation, N is the total number of target users, P x,t is the bid of the xth target user in the t period, G x,t is the response amount of the xth target user in the t period, R x is the regulation performance coefficient of the xth target user, G x,min is the lower limit of the response amount of the xth target user, G x,max is the upper limit of the response amount of the xth target user, P min is the lower limit of the compensation price declaration set by the system, P max is the upper limit of the compensation price declaration set by the system, Q t is the total power demand of system peak regulation in the t period, h x,t is the response time length of the xth target user in the t period, h t is the total time length demand of system peak regulation in the t period, v x,t is the regulation rate of the xth target user in the t period, v t is the total regulation rate demand of system peak regulation in the t period.

[0163] The unified marginal clearing price is determined through the above marginal clearing algorithm model, the efficiency of the determination of the marginal clearing price is improved, and the market is standardized, and malicious bidding is prevented.

[0164] When the day-ahead scheduling plan curve is issued to each of the winning users, if the winning user is an individual user, the corresponding day-ahead scheduling plan curve can be directly issued to the winning user.

[0165] If the winning user is a load aggregator, the following operations can be performed:

[0166] If the winning user is a load aggregator, the day-ahead scheduling plan curve to be issued is decomposed.

[0167] The decomposition result is sent to the corresponding load aggregator, so that the corresponding load aggregator sends the corresponding scheduling plan curve to each user aggregated by the load aggregator according to the decomposition result, to ensure that the sum of the scheduling plan curves of the users aggregated by the load aggregator is consistent with the day-ahead scheduling plan curve to be issued.

[0168] As an implementable manner, if the winning user is a load aggregator, the day-ahead scheduling plan curve to be issued is decomposed according to an equal proportion decomposition strategy; the equal proportion decomposition strategy is to decompose the day-ahead scheduling plan curve to be issued according to the following formula:

[0169]

[0170] In the formula, g j,t is the winning amount of the jth user of the load aggregator in the tth period, G t is the total winning amount of the load aggregator in the tth period, m is the total number of users aggregated by the load aggregator, g j,t ' is the declared response amount of the jth user of the load aggregator in the tth period.

[0171] It should be noted that the day-ahead scheduling plan curve to be issued can also be decomposed according to other self-defined decomposition strategies.

[0172] It should be noted that when the winning user is a load aggregator, the virtual power plant does not necessarily decompose the day-ahead scheduling plan curve to be issued. The execution subject of decomposing the day-ahead scheduling plan curve can be the load aggregator itself, and the specific decomposition strategy can refer to the above description of the curve decomposition strategy performed by the virtual power plant.

[0173] The embodiment of the application can ensure that the sum of the scheduling plan curves of the aggregated users is consistent with the scheduling plan curve issued by the power grid.

[0174] The power grid company can calculate the subsidy cost in a manner of post-verification of the response amount, and in the specific implementation, the user response baseline can be calculated according to certain rules, each individual user or load aggregator responds according to the scheduling plan curve, and the power grid company calculates the subsidy cost in the manner of post-verification of the response amount.

[0175] The user response baseline is calculated according to certain rules, specifically, the user response baseline needs to select the average value of historical load data of different days according to the date type of the response day, the date type of the response day is specifically a working day, a double holiday, a holiday and a special national holiday (for example, March 3 in Guangxi, water-splashing festival in Yunnan, etc.), and the different days can be any number of days in 1-10 days, which is selected according to the actual situation.

[0176] The application further provides a virtual power plant day-ahead accurate peak shaving system.

[0177] Please refer to Figure 2 , Figure 2 The structure connection block diagram of the virtual power plant day-ahead accurate peak shaving system provided by the embodiment of the application is shown.

[0178] The embodiment of the application provides a virtual power plant day-ahead accurate peak shaving system, which comprises:

[0179] A demand receiving module 1 is used for receiving a day-ahead peak shaving demand, wherein the day-ahead peak shaving demand comprises the identification of a heavy overload device, a peak shaving period and a power demand curve of each preset time interval in the peak shaving period;

[0180] A real-time power supply path acquisition module 2 is used for acquiring the real-time power supply path of each user according to the user account data established in advance, wherein each real-time power supply path comprises a power supply device set participating in power supply to a corresponding distribution transformer;

[0181] A target user set construction module 3 is used for querying each real-time power supply path according to the identification of the heavy overload device, and if one or more heavy overload devices are queried in the power supply device set corresponding to the real-time power supply path, the user corresponding to the real-time power supply path is regarded as a target user, and a target user set is constructed;

[0182] An invitation module 4 is used for initiating an invitation corresponding to the day-ahead peak shaving demand to each target user in the target user set;

[0183] A decision module 5 is used for determining a winning user according to the feedback of each target user to the invitation, and issuing a day-ahead scheduling plan curve to each winning user, so that each winning user performs peak shaving feedback according to the day-ahead scheduling plan curve.

[0184] In an implementable manner, the power supply device comprises a 220kV main transformer, a 110kV main transformer, a 110kV line and a 10kV feeder, and the real-time power supply path acquisition module 2 comprises:

[0185] The acquisition unit is configured to acquire day-ahead power distribution network topology information, and modify the day-ahead power distribution network topology information according to planned power supply equipment outage maintenance or power transfer operation arranged from the day-ahead to a peak shaving day;

[0186] The first query unit is configured to query, according to the modified power distribution network topology information, 10kV feeder lines corresponding to each power distribution transformer in user account data, to form a feeder line set corresponding to the power distribution transformer;

[0187] The second query unit is configured to, for each 10kV feeder line in the feeder line set, query a corresponding 110kV main transformer according to the power distribution network topology information, to form a first main transformer set corresponding to the 10kV feeder line;

[0188] The third query unit is configured to, for each 110kV main transformer in the first main transformer set, query a corresponding 110kV line according to the power distribution network topology information, to form a line set corresponding to the 110kV main transformer;

[0189] The fourth query unit is configured to, for each 110kV line in the line set, query a corresponding 220kV main transformer according to the power distribution network topology information, to form a second main transformer set corresponding to the 110kV line;

[0190] The path determination unit is configured to determine real-time power supply paths of each user from a power distribution transformer, a 10kV feeder line, a 110kV main transformer, a 110kV line to a 220kV main transformer according to the feeder line set, the first main transformer set, the line set and the second main transformer set.

[0191] In an implementable manner, the acquisition unit comprises:

[0192] The request sending subunit is configured to send a request for acquiring day-ahead power distribution network topology information to a power grid system in which the power distribution network topology information is stored;

[0193] The receiving subunit is configured to receive day-ahead power distribution network topology information sent by the power grid system according to the request.

[0194] In an implementable manner, the decision module 5 comprises a first sub-module for determining a winning user according to feedback of each target user to the invitation, and the first sub-module comprises:

[0195] The compensation price determination unit is configured to determine a bid of each target user according to feedback of each target user to the invitation;

[0196] The adjustment performance coefficient determination unit is configured to determine an adjustment performance coefficient of each target user according to an adjustable response amount, an adjustment rate and a response time length;

[0197] The winning user determination unit is configured to determine a winning user and a clearing price based on the bid and the adjustment performance coefficient using an optimization clearing model.

[0198] In an implementable manner, the adjustment performance coefficient determination unit is specifically configured to:

[0199] The adjustment performance coefficient is determined according to the following formula:

[0200] R = p x v x h

[0201] In the formula, R represents the adjustment performance coefficient, p represents the adjustable response quantity, v represents the adjustment rate, and h represents the response duration.

[0202] In an implementable manner, the winning user determination unit is specifically configured to:

[0203] The target function is to minimize the total cost of unit adjustment performance, and the winning quantity of all users in the tth time period is determined, and the maximum clearing price corresponding to the tth time period is taken as the marginal clearing price.

[0204] The optimization clearing model is:

[0205] The target function is:

[0206]

[0207] The constraint condition is:

[0208]

[0209] In the formula, C f represents the total cost of unit adjustment performance of the virtual power plant, T represents the total duration of system peak shaving, N represents the total number of target users, P x,t represents the bid of the xth target user in the tth time period, G x,t represents the response quantity of the xth target user in the tth time period, R x represents the adjustment performance coefficient of the xth target user, G x,min represents the lower limit of the response quantity of the xth target user, G x,max represents the upper limit of the response quantity of the xth target user, P min represents the lower limit of the compensation price declaration set by the system, P max represents the upper limit of the compensation price declaration set by the system, Q t represents the total power demand of system peak shaving in the tth time period, h x,t represents the response duration of the xth target user in the tth time period, h t represents the total duration demand of system peak shaving in the tth time period, v x,t represents the adjustment rate of the xth target user in the tth time period, v ta total system peak shaving regulation rate requirement for the t period.

[0210] According to an implementable manner of the second aspect of the present application, the decision module further comprises a second submodule for issuing a day-ahead scheduling plan curve to each of the winning users, the second submodule comprising:

[0211] a decomposition unit configured to, when the winning user is a load aggregator, decompose the day-ahead scheduling plan curve to be issued;

[0212] a sending unit configured to send the obtained decomposition result to the corresponding load aggregator, so that the corresponding load aggregator sends a corresponding scheduling plan curve to each user aggregated by the load aggregator according to the decomposition result, to ensure that the sum of the scheduling plan curves of the users aggregated by the load aggregator is consistent with the day-ahead scheduling plan curve to be issued.

[0213] According to an implementable manner of the second aspect of the present application, the decomposition unit is specifically configured to, when the winning user is a load aggregator:

[0214] decompose the day-ahead scheduling plan curve to be issued according to an equal proportion decomposition strategy; the equal proportion decomposition strategy is to decompose the day-ahead scheduling plan curve to be issued according to the following formula:

[0215]

[0216] wherein, g j,t is the winning amount of the jth user of the load aggregator in the t period, G t is the total winning amount of the load aggregator in the t period, m is the total number of users aggregated by the load aggregator, g j,t ′ is the declared response amount of the jth user of the load aggregator in the t period.

[0217] The present application further provides a virtual power plant day-ahead accurate peak shaving system, comprising:

[0218] The virtual power plant is used for receiving a day-ahead peak shaving demand, the day-ahead peak shaving demand comprising an identification of a heavy overload device, a peak shaving period and a power demand curve of each preset time interval in the peak shaving period; obtaining real-time power supply paths of each user according to pre-established user account data, wherein each real-time power supply path comprises a corresponding power distribution transformer and a set of power supply devices participating in power supply to the corresponding power distribution transformer; querying each real-time power supply path according to the identification of the heavy overload device, if one or more heavy overload devices are queried in the set of power supply devices corresponding to the real-time power supply path, taking a user corresponding to the real-time power supply path as a target user, and constructing a target user set; initiating an invitation corresponding to the day-ahead peak shaving demand to each target user in the target user set; determining a winning user according to feedback of each target user to the invitation, and issuing a day-ahead scheduling plan curve to each winning user, so that each winning user responds to a power grid company according to the day-ahead scheduling plan curve in the peak shaving period;

[0219] The power grid company is used for calculating a user response baseline, and calculating a subsidy fee of a response amount of each winning user according to the user response baseline.

[0220] It should be noted that the virtual power plant of the embodiment of the present application can also be used to execute other embodiments of the above method.

[0221] As an implementable manner, when the power grid company calculates the user response baseline, it is specifically used for:

[0222] determining a corresponding response day type when the winning user responds, the response day type comprising a weekday, a double holiday, a holiday and a special national holiday;

[0223] obtaining historical load data of the same type according to the corresponding response day type;

[0224] calculating a corresponding average load curve according to the historical load data according to the following formula, and taking the obtained average load curve as the user response baseline:

[0225]

[0226] In the formula, G b,t represents the average load of the bth winning user in the tth period, G b,t represents the historical load of the bth winning user in the tth period of the dth day, and D represents the corresponding number of days of the historical load data. d,b,t In the formula,

[0227] Wherein, the corresponding number of days of different response day types can be determined according to actual conditions. For example, the number of days corresponding to the weekday is 5 days, and the number of days corresponding to the double holiday is 2 days. The embodiment of the present application is not limited thereto.

[0228] As a mode that can be implemented, when the power grid company calculates the subsidy fee of the response amount of each winning user according to the user response baseline, the method is specifically used for:

[0229] Let the actual load monitoring value of the bth winning user in the tth time period on the response day be L b,t The corresponding winning amount is G b,t The load of the corresponding user response baseline is When the following condition is met: The corresponding subsidy fee M b Is calculated according to the following formula:

[0230]

[0231] In the formula, T is the total duration of system peak shaving, P b，t is the bid of the bth winning user in the tth time period;

[0232] When the following condition is met: The corresponding subsidy fee M b Is calculated according to the following formula:

[0233]

[0234] When the following condition is met: The corresponding subsidy fee M b Is calculated according to the following formula:

[0235]

[0236] The application also provides a virtual power plant day-ahead accurate peak shaving device, comprising:

[0237] A memory for storing instructions; wherein the instructions are instructions for implementing the virtual power plant day-ahead accurate peak shaving method of any one of the above embodiments;

[0238] A processor for executing the instructions in the memory.

[0239] The application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the virtual power plant day-ahead accurate peak shaving method of any one of the above embodiments.

[0240] The above embodiment of the present application utilizes the load aggregation and informationized regulation and control means of the virtual power plant, acquires the real-time power supply path of the user based on the topology correlation of the distribution network, realizes the precise peak shaving demand on a certain main transformer, distribution transformer or feeder, can not only avoid the decline in power supply quality and social influence caused by orderly power utilization, but also improve the precision of the peak shaving invitation range, avoid large-scale user invitation, thereby effectively solving the technical problem that the existing virtual power plant cannot accurately determine the day-ahead peak shaving invitation range and realize effective interaction with the corresponding user during day-ahead peak shaving, and reducing the response to the invalid power grid, and reducing the subsidy expense of the virtual power plant in calling the load side resources.

[0241] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and module can refer to the corresponding processes in the foregoing method embodiments, and the specific beneficial effects of the above-described system, device and module can refer to the corresponding beneficial effects in the foregoing method embodiments, which will not be repeated here.

[0242] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and actual implementation can have another division manner, for example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed modules can be indirect coupling or communication connection through some interface, device or module, and can be electrical, mechanical or other forms.

[0243] The modules described as separate components can or can not be physically separated, and the components shown as modules can or can not be physical modules, that is, they can be located in one place, or can be distributed on a plurality of network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment.

[0244] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can exist physically, or two or more modules can be integrated in one module. The above integrated module can be realized in the form of hardware or in the form of a software functional module.

[0245] The integrated module, if implemented in the form of a software function module and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0246] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A virtual power plant day-ahead accurate peak shaving method, characterized in that, The method comprises the following steps: receiving a day-ahead peak shaving demand, the day-ahead peak shaving demand comprising an identification of a heavily overloaded device, a peak shaving period, and a power demand curve for each preset time interval within the peak shaving period; obtaining real-time power supply paths of each user according to pre-established user account data, wherein each real-time power supply path comprises a corresponding power distribution transformer and a set of power supply devices participating in power supply to the corresponding power distribution transformer; querying each real-time power supply path according to the identification of the heavily overloaded device, and if one or more heavily overloaded devices are found in the set of power supply devices corresponding to the real-time power supply path, regarding the user corresponding to the real-time power supply path as a target user and constructing a target user set; initiating an invitation corresponding to the day-ahead peak shaving demand to each target user in the target user set; determining a winning user according to feedback of each target user to the invitation, and issuing a day-ahead dispatching plan curve to each winning user to enable each winning user to respond to the power grid company according to the day-ahead dispatching plan curve during the peak shaving period; the step of determining a winning user according to feedback of each target user to the invitation comprises: determining a bid of each target user according to feedback of each target user to the invitation; determining an adjustment performance coefficient of each target user according to an adjustable response amount, an adjustment rate, and a response duration; determining a winning user and a clearing price by using an optimization clearing model according to the bid and the adjustment performance coefficient; determining the adjustment performance coefficient according to the following formula: ； wherein denotes the regulation performance coefficient, is the adjustable response quantity, is the regulation rate, is the response duration; the step of determining a winning user and a clearing price by using an optimization clearing model comprises: taking the total cost of unit adjustment performance as an objective function, determining the winning amount of all users in the tth period, and taking the maximum clearing price corresponding to the tth period as a marginal clearing price; the optimization clearing model is as follows: the objective function is as follows: ； the constraint condition is as follows: ； In the formula, is the total cost of virtual power plant unit regulation performance, is the total duration of system peak shaving, is the total number of target users, is the bid of the th target user in the th time period, is the response amount of the th target user in the th time period, is the regulation performance coefficient of the th target user, is the lower limit of the response amount of the th target user, is the upper limit of the response amount of the th target user, is the lower limit of the compensation price declaration set by the system, is the upper limit of the compensation price declaration set by the system, is the total power demand of system peak shaving in the th time period, is the response duration of the th target user in the th time period, is the total duration demand of system peak shaving in the th time period, is the regulation rate of the th target user in the th time period, is the total regulation rate demand of system peak shaving in the th time period.

2. The virtual power plant day-ahead accurate peak clipping method according to claim 1, characterized in that, the power supply device comprises a 220kV main transformer, a 110kV main transformer, a 110kV line, and a 10kV feeder, and the step of obtaining real-time power supply paths of each user according to pre-established user account data comprises: obtaining day-ahead power distribution network topology information, and modifying the day-ahead power distribution network topology information according to power supply device power-off maintenance or power supply switching operations scheduled from the day-ahead to the peak shaving day; querying 10kV feeders corresponding to each power distribution transformer in the user account data according to the modified power distribution network topology information, to form a feeder set corresponding to the power distribution transformer; for each 10kV feeder in the feeder set, querying a 110kV main transformer corresponding to the 10kV feeder according to the power distribution network topology information, to form a first main transformer set corresponding to the 10kV feeder; for each 110kV main transformer in the first main transformer set, querying a 110kV line corresponding to the 110kV main transformer according to the power distribution network topology information, to form a line set corresponding to the 110kV main transformer; for each 110kV line in the line set, querying a 220kV main transformer corresponding to the 110kV line according to the power distribution network topology information, to form a second main transformer set corresponding to the 110kV line; Determine real-time power supply paths of each user from distribution transformer, 10kV feeder, 110kV main transformer, 110kV line to 220kV main transformer according to the feeder set, the first main transformer set, the line set and the second main transformer set.

3. The virtual power plant day-ahead precise peak-cutting method according to claim 2, characterized in that, The obtaining of the day-ahead power distribution network topology information comprises: sending a request for obtaining day-ahead power distribution network topology information to a power grid system storing power distribution network topology information; receiving day-ahead power distribution network topology information sent by the power grid system according to the request. 4.The virtual power plant day-ahead accurate peak clipping method according to claim 1, characterized in that, The sending of the day-ahead dispatching plan curve to each of the winning users comprises: if the winning user is a load aggregator, decomposing the day-ahead dispatching plan curve to be sent; sending the decomposition result to the corresponding load aggregator, so that the corresponding load aggregator sends a corresponding dispatching plan curve to each user aggregated by the load aggregator according to the decomposition result, to ensure that the sum of the dispatching plan curves of each user aggregated by the load aggregator is consistent with the day-ahead dispatching plan curve to be sent.

5. The virtual power plant day-ahead precise peak-cutting method according to claim 4, characterized in that, The decomposing of the day-ahead dispatching plan curve to be sent specifically comprises: decomposing the day-ahead dispatching plan curve to be sent according to an equal proportion decomposition strategy; the equal proportion decomposition strategy is decomposing the day-ahead dispatching plan curve to be sent according to the following formula: ； In the formula, The first load aggregator The user The number of bids won during the time period For the load aggregator in the first Total number of bids won during the period The total number of users aggregated by the load aggregator during the specified time period. The first load aggregator The user The number of applications submitted during a given time period.

6. A virtual power plant day-ahead accurate peak shaving system, characterized in that, comprises: a demand receiving module configured to receive a day-ahead peak shaving demand, the day-ahead peak shaving demand comprising an identifier of an overload device, a peak shaving time period, and a power demand curve for each preset time interval within the peak shaving time period; a real-time power supply path obtaining module configured to obtain real-time power supply paths of each user according to pre-established user account data, wherein each real-time power supply path comprises a corresponding distribution transformer and a set of power supply devices participating in power supply to the corresponding distribution transformer; a target user set constructing module configured to query each real-time power supply path according to the identifier of the overload device, and if one or more overload devices are queried in the set of power supply devices corresponding to the real-time power supply path, take the user corresponding to the real-time power supply path as a target user, and construct a target user set; an invitation module configured to initiate an invitation corresponding to the day-ahead peak shaving demand to each target user in the target user set; a decision module configured to determine winning users according to feedback of each target user to the invitation, and send a day-ahead dispatching plan curve to each winning user, so that each winning user performs peak shaving feedback according to the day-ahead dispatching plan curve; The decision module comprises a first submodule configured to determine winning users according to feedback of each target user to the invitation, and the first submodule comprises: a compensation price determining unit configured to determine a bid of each target user according to feedback of each target user to the invitation; an adjustment performance coefficient determining unit configured to determine an adjustment performance coefficient of each target user according to an adjustable response amount, an adjustment rate, and a response duration; a winning user determining unit configured to determine winning users and a clearing price by using an optimization clearing model according to the bid and the adjustment performance coefficient; The adjustment performance coefficient determining unit is specifically configured to: determine the adjustment performance coefficient according to the following formula: ； wherein denotes the regulation performance coefficient, is the adjustable response quantity, is the regulation rate, is the response duration; The winning user determination unit is specifically configured to: taking the total cost of unit regulation performance as the objective function, solving the winning amount of all users in the t th period, and taking the maximum clearing price of the corresponding t th period as the marginal clearing price; The optimization clearing model is: The objective function is: ； The constraint condition is: ； In the formula, is the total cost of virtual power plant unit regulation performance, is the total system peak shaving time, is the total number of target users, is the bid of the th target user in the th time period, is the response amount of the th target user in the th time period, is the regulation performance coefficient of the th target user, is the lower limit of the response amount of the th target user, is the upper limit of the response amount of the th target user, is the lower limit of the compensation price declaration set by the system, is the upper limit of the compensation price declaration set by the system, is the total system peak shaving power demand in the th time period, is the response time of the th target user in the th time period, is the total system peak shaving time demand in the th time period, is the regulation rate of the th target user in the th time period, is the total system peak shaving regulation rate demand in the th time period.

7. A virtual power plant day-ahead accurate peak shaving system, characterized in that, including: The virtual power plant is configured to receive a day-ahead peak shaving demand, the day-ahead peak shaving demand including an identification of a heavy overload device, a peak shaving period, and a power demand curve of each preset time interval in the peak shaving period; obtain real-time power supply paths of each user according to pre-established user account data, wherein each real-time power supply path includes a corresponding distribution transformer and a set of power supply devices participating in power supply to the corresponding distribution transformer; query each real-time power supply path according to the identification of the heavy overload device, and if one or more heavy overload devices are queried in the set of power supply devices corresponding to the real-time power supply path, take the user corresponding to the real-time power supply path as a target user, and construct a target user set; initiate an invitation corresponding to the day-ahead peak shaving demand to each target user in the target user set; determine winning users according to feedback of each target user to the invitation, and issue a day-ahead dispatching plan curve to each winning user, so that each winning user responds to the power grid company in the peak shaving period according to the day-ahead dispatching plan curve; The power grid company is configured to calculate a user response baseline, and calculate a subsidy fee of a response amount of each winning user according to the user response baseline; The determination of the winning users according to the feedback of each target user to the invitation includes: determine a bid of each target user according to the feedback of each target user to the invitation; determine a regulation performance coefficient of each target user according to an adjustable response amount, a regulation rate, and a response duration; determine the winning users and a clearing price by using an optimization clearing model according to the bid and the regulation performance coefficient; The regulation performance coefficient is determined according to the following formula: ； wherein denotes a regulation performance coefficient, is an adjustable response quantity, is a regulation rate, is a response duration; The determination of the winning users and the clearing price by using the optimization clearing model specifically includes: taking the total cost of unit regulation performance as the objective function, solving the winning amount of all users in the t th period, and taking the maximum clearing price of the corresponding t th period as the marginal clearing price; The optimization clearing model is: The objective function is: ； The constraint condition is: ； In the formula, is the total cost of virtual power plant unit regulation performance, is the total duration of system peak regulation, is the total number of target users, is the bid of the target user in the time period, is the response amount of the target user in the time period, is the regulation performance coefficient of the target user, is the lower limit of the response amount of the target user, is the upper limit of the response amount of the target user, is the lower limit of the compensation price declaration set by the system, is the upper limit of the compensation price declaration set by the system, is the total power demand of system peak regulation in the time period, is the response duration of the target user in the time period, is the total duration demand of system peak regulation in the time period, is the regulation rate of the target user in the time period, is the total regulation rate demand of system peak regulation in the time period.

8. The virtual power plant day-ahead precise peak-cutting system according to claim 7, characterized in that, When the power grid company calculates the user response baseline, it is specifically configured to: determine a corresponding response day type when the winning users respond, the response day type including a weekday, a double holiday, a holiday, and a special national holiday; obtain historical load data of the same type for a corresponding number of days according to the corresponding response day type; calculate a corresponding average load curve according to the historical load data, and take the obtained average load curve as the user response baseline: ； In the formula, Indicates the first The winning bidder was in the first Average load over the period Indicates the first The first winning bidder Heaven is in Historical load over a period of time This represents the corresponding number of days for historical load data.

9. The virtual power plant day-ahead precise peak cutting system according to claim 7, characterized in that, When the power grid company calculates the subsidy fee of the response amount of each winning user according to the user response baseline, it is specifically configured to: Let the actual load monitoring value of the winning user in the first time period of the response day be , the corresponding winning amount be , and the load of the user response reference line be . When is satisfied, the corresponding subsidy cost is calculated according to the following formula: ​​ ； In the formula, is the total length of system peak-shaving, is the bid of the th successful bidder in the th time period; When the conditions are met the corresponding subsidy cost is calculated as follows: ； When the condition is met, the corresponding subsidy cost is calculated as follows: 。 10. A virtual power plant day-ahead precise peak shaving device, characterized in that, including: a memory configured to store instructions; wherein the instructions are instructions that can implement the virtual power plant day-ahead accurate peak shaving method of any one of claims 1-5; a processor configured to execute the instructions in the memory.

11. A computer readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the virtual power plant day-ahead accurate peak shaving method in any one of claims 1-5.

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