Method, device and storage medium for optimizing virtual power plant scheduling

Abstract

The present application discloses a method, device, and storage medium for optimizing the scheduling of a virtual power plant. The method includes: constructing a first scheduling model based on the uncertainty factors corresponding to prosumers; constructing a second scheduling model based on the uncertainty factors corresponding to the virtual power plant; and solving the first scheduling model and the second scheduling model based on the alternating direction multiplier method to obtain the optimal solution for the scheduling of the virtual power plant.

Description

Technical Field

[0001] The present application relates to the technical field of virtual power plant scheduling, and specifically relates to a method, device and storage medium for optimizing virtual power plant scheduling. Background Art

[0002] The source and load dual attributes of prosumers and the uncertainty of the output of Renewable Distributed Generation (RDG) will affect the economy and security of the operation of a Virtual Power Plant (VPP). In order to make full use of the dispatchable resources in virtual power plants and prosumers and mitigate the negative impact of uncertainty on the operation of virtual power plants, the optimal scheduling of virtual power plants with prosumers has become one of the problems that need to be solved urgently. Summary of the Invention

[0003] The present application provides a method, device and storage medium for optimizing virtual power plant scheduling. By combining the uncertainty factors corresponding to prosumers and virtual power plants, a corresponding first scheduling model and second scheduling model are respectively constructed, and then the first scheduling model and the second scheduling model are solved by combining the alternating direction multiplier method, and the optimal solution of the virtual power plant optimization scheduling problem with prosumers can be obtained.

[0004] In a first aspect, the present application provides a method for optimizing virtual power plant scheduling, the method comprising:

[0005] Constructing a first scheduling model based on the uncertainty factors corresponding to prosumers;

[0006] Constructing a second scheduling model based on the uncertainty factors corresponding to the virtual power plant;

[0007] Solving the first scheduling model and the second scheduling model based on the alternating direction multiplier method to obtain the optimal solution for virtual power plant scheduling.

[0008] In a second aspect, the present application provides a device for optimizing virtual power plant scheduling, the device comprising: an acquisition unit and a processing unit;

[0009] The acquisition unit is configured to acquire the uncertainty factors corresponding to prosumers and the uncertainty factors corresponding to the virtual power plant;

[0010] The processing unit is configured to construct a first scheduling model based on the uncertainty factors corresponding to prosumers; and construct a second scheduling model based on the uncertainty factors corresponding to the virtual power plant;

[0011] The processing unit is further configured to solve the first scheduling model and the second scheduling model based on the alternating direction multiplier method to obtain the optimal solution for virtual power plant scheduling.

[0012] In a third aspect, the present application provides an electronic device, including: a processor and a memory. The processor is connected to the memory. The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory so that the electronic device executes the method according to the first aspect.

[0013] In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it executes the method according to the first aspect.

[0014] In a fifth aspect, the present application provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it executes the method according to the first aspect.

[0015] Implementing the present application has the following beneficial effects:

[0016] It can be seen that in the embodiments of the present application, based on the uncertainty factors corresponding to prosumers, a first scheduling model is constructed; and based on the uncertainty factors corresponding to the virtual power plant, a second scheduling model is constructed; then, based on the alternating direction multiplier method, the first scheduling model and the second scheduling model are solved to obtain the optimal solution for the virtual power plant scheduling, considering the differences in the known degrees of uncertainty factors in the optimization scheduling problems of different entities, and "adapting to local conditions" to use different uncertainty optimization methods to construct scheduling models and solve the scheduling problems of virtual power plants and prosumers. While optimizing the scheduling of virtual power plants with prosumers, the conservativeness of the results can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a schematic flowchart of a method for optimizing the scheduling of a virtual power plant provided by an embodiment of the present application;

[0019] Figure 2 It is a block diagram of the functional units of a device for optimizing the scheduling of a virtual power plant provided by an embodiment of the present application;

[0020] Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0022] The terms "first", "second", "third", "fourth", etc. in the specification, claims and drawings of the present application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0023] Referring to "embodiments" herein means that the specific features, results or characteristics described in connection with the embodiments can be included in at least one embodiment of the present application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0024] First, relevant terms and related technologies in the present application will be explained:

[0025] Prosumer: In the power system, it refers to a user who can both produce electricity and consume electricity. For example, these users can be households, enterprises or other entities that own distributed energy sources (such as solar energy, wind energy, battery energy storage, etc.).

[0026] Virtual Power Plant (VPP): It is a digital technology and intelligent control system that integrates multiple distributed energy resources (such as wind energy, solar energy, energy storage devices, electric vehicles, etc.) located in different places to simulate a single, centrally managed power plant.

[0027] It should be explained that for the uncertainty of RDG output, existing research mainly uses dispatchable devices within prosumers such as controllable distributed generation (CDG) and energy storage to provide reserve power. However, the interactive power between prosumers and virtual power plants is also adjustable. If only relying on the dispatchable devices corresponding to prosumers to provide reserve power, it may lead to insufficient reserve power of prosumers, thus affecting the safe accommodation of RDG output. Existing research only regards the reserve power provided by virtual power plants as the sole source of prosumer reserve power, which will cause overuse of reserve capacity, resulting in relatively large fluctuations in the interactive power between allowed prosumers and virtual power plants, and affecting the economy and security of virtual power plant operation.

[0028] Therefore, in the embodiments of this application, considering uncertain environmental factors, the day-ahead interactive power between the virtual power plant and prosumers and the tie-line reserve power provided to prosumers are jointly used as coupling variables for coordinated optimization to further improve the economy and security of the operation of the entire virtual power plant.

[0029] In addition, since virtual power plants and prosumers are generally operated by different entities, considering the issue of their entity privacy, some operating parameters are difficult to be completely transparent to each other between different entities. Therefore, there will be differences in the known degree of uncertainty factors in the optimal dispatching problems of virtual power plants and prosumers.

[0030] From the perspective of prosumers, the uncertainty factors in prosumers include the prediction errors of distributed generation devices. Therefore, in the embodiments of this application, the prediction errors of distributed generation can be fitted according to the historical prediction error data of distributed generation devices, and the conditional value-at-risk (CVaR) is used to handle the uncertainty of RDG output to reduce the conservatism of robust optimization (RO).

[0031] From the perspective of the operator of the virtual power plant, the uncertainty factor in the virtual power plant is the interactive power with prosumers. Since the operator of the virtual power plant cannot obtain the detailed parameters of the specific equipment operation in prosumers, the operator of the virtual power plant can only obtain the fluctuation range of tie-line power based on the tie-line reserve capacity provided to prosumers. Currently, the operator of the virtual power plant generally uses robust optimization (RO) to handle the uncertainty of interactive power.

[0032] However, they share a commonality that ignoring the differences in the known degrees of uncertainty factors in the optimization scheduling problems of different entities will lead to an excessively high conservatism of the results. Therefore, in the embodiments of the present application, an optimization scheduling strategy for a virtual power plant considering uncertainty is proposed. Meanwhile, the interactive power between the virtual power plant and prosumers and the reserve power provided to prosumers are coordinated and optimized. According to the differences in the known degrees of uncertainty factors in the virtual power plant and prosumers, different uncertainty optimization methods are "adopted according to local conditions" to solve the scheduling problems of the virtual power plant and prosumers, so as to reduce the conservatism of the results.

[0033] The following explains the method for optimizing the virtual power plant scheduling of the present application with specific embodiments:

[0034] First of all, it should be noted that the number of prosumers in the embodiments of the present application can be one or more, and the present application mainly takes one as an example for explanation. Refer to Figure 1 , Figure 1 is a schematic flowchart of a method for optimizing virtual power plant scheduling provided by an embodiment of the present application. This method is applied to a device for optimizing virtual power plant scheduling, and this method includes but is not limited to steps S101 - S103:

[0035] S101. Construct a first scheduling model based on the uncertainty factors corresponding to the prosumers.

[0036] In the embodiments of the present application, first, a first objective function, a first constraint condition, a second constraint condition, and a third constraint condition corresponding to the prosumers are constructed; then, based on the first objective function, the first constraint condition, the second constraint condition, and the third constraint condition, a first scheduling model is constructed. Among them, when constructing the first objective function, specifically:

[0037] For each first distributed generation device corresponding to the prosumers, based on the first output power of each first distributed generation device at time t, the first upward reserve power and the first downward reserve power provided by each first distributed generation device at time t, and the first unit upward reserve cost and the first unit downward reserve cost corresponding to each first distributed generation device, a first cost function corresponding to the prosumers is determined. For example, the first cost function is expressed by formula (1):

[0038] (1)

[0039] Among them, is the scheduling period, t represents the t-th moment in the scheduling period, N is the N first distributed generation devices corresponding to the prosumers, and the specific value of N in the present application is not limited; g is the g-th first distributed generation device among the N first distributed generation devices; is the first output power of the g-th first distributed power generation device at time t; is the first upward reserve power provided by the g-th first distributed power generation device at time t, is the first downward reserve power provided by the g-th first distributed power generation device at time t; is the first unit upward reserve cost corresponding to the g-th first distributed power generation device, is the first unit downward reserve cost corresponding to the g-th first distributed power generation device; and are preset parameters, and the present application does not limit their specific values.

[0040] Then, based on the charging power and discharging power of the energy storage device corresponding to the prosumer at time t, the second upward reserve power and the second downward reserve power provided by the energy storage device at time t in the charging mode, the third upward reserve power and the third downward reserve power provided by the energy storage device at time t in the discharging mode, as well as the second unit upward reserve cost and the second unit downward reserve cost corresponding to the energy storage device, determine the second cost function corresponding to the prosumer. For example, the second cost function is expressed by formula (2):

[0041] (2)

[0042] Wherein, is the charging power of the energy storage device at time t, is the discharging power of the energy storage device at time t; is the second upward reserve power provided by the energy storage device at time t in the charging mode, is the second downward reserve power provided by the energy storage device at time t in the charging mode; is the third upward reserve power provided by the energy storage device at time t in the discharging mode, is the third downward reserve power provided by the energy storage device at time t in the discharging mode; is the second unit upward reserve cost, is the second unit downward reserve cost.

[0043] Then, based on the first expected value of wind and light abandonment corresponding to the prosumer at time t, the second expected value of load curtailment, as well as the first penalty cost corresponding to wind and light abandonment and the second penalty cost corresponding to load curtailment, determine the third cost function corresponding to the prosumer. For example, the third cost function is expressed by formula (3):

[0044] (3)

[0045] Wherein, is the first expected value of wind and light abandonment at time t, To reduce the second expected value corresponding to the load at time t, is the first penalty cost, is the second penalty cost.

[0046] In an alternative embodiment, before determining the third cost function, it is also possible to determine the first expected value corresponding to the curtailment of wind and light by the prosumer at time t and the second expected value corresponding to the load reduction. Specifically:

[0047] First, based on the historical prediction error, the prediction error of the first output power of each first distributed generation device at time t is fitted to obtain a probability distribution function. At this time, the corresponding prediction error of each first distributed generation device at time t satisfies the probability distribution of this probability distribution function.

[0048] Then, based on the first upward reserve power of each first distributed generation device at time t, the second upward reserve power provided by the energy storage device in the charging mode at time t, and the third upward reserve power provided by the energy storage device in the discharging mode at time t, such as a summation operation, the total upward reserve power corresponding to the prosumer at time t is obtained.

[0049] And based on the first downward reserve power of each first distributed generation device at time t, the second downward reserve power provided by the energy storage device in the charging mode at time t, and the third downward reserve power provided by the energy storage device in the discharging mode at time t, such as a summation operation, the total downward reserve power corresponding to the prosumer at time t is determined. Then, based on the power distribution function, the total upward reserve power corresponding to the prosumer at time t, and the total downward reserve power corresponding to the prosumer at time t, the first expected value corresponding to the curtailment of wind and light by the prosumer at time t and the second expected value corresponding to the load reduction can be determined. For example, the first expected value and the second expected value can be expressed by formula (4):

[0050] (4)

[0051] Where, is the first expected value corresponding to the curtailment of wind and light at time t, is the second expected value corresponding to the load reduction at time t, is the probability distribution function, is the total upward reserve power corresponding to the prosumer at time t, is the total up and down reserve power corresponding to the prosumer at time t, is the maximum prediction error corresponding to the distributed scheduling device at time t, is the prediction error, i.e., the variable.

[0052] Then, based on the first cost function, the second cost function, and the third cost function, a first objective function can be constructed. For example, the first objective function is obtained through Equation (5):

[0053] (5)

[0054] It should be noted that the meanings of the various parameters in Equation (5) will not be elaborated here, and the corresponding explanations in the above Equations (1)-(3) can be referred to.

[0055] Furthermore, based on the operating state, minimum output, and maximum output corresponding to each first distributed generation device, a first constraint condition corresponding to each first distributed generation device can be determined. For example, the first constraint condition includes Equation (6):

[0056] (6)

[0057] Among them, is the minimum output corresponding to the g-th first distributed generation device, is the maximum output corresponding to the g-th first distributed generation device; is the binary variable corresponding to the operating state of the g-th first distributed generation device,

[0058] For example, if the operating state is the operating state, then it is 1, and if the operating state is the shutdown state, then it is 0.

[0059] And based on the energy storage state and the maximum charge and discharge power corresponding to the energy storage device, a second constraint condition corresponding to the energy storage device is determined. For example, the second constraint condition includes Equation (7):

[0060] (7)

[0061] Among them, is the maximum charge and discharge power corresponding to the energy storage device; and are binary variables corresponding to the energy storage state of the energy storage device. For example, in the charging mode, if the energy storage device is in the charging state, then is 1, and if it is in the discharging state, then is 0; and in the discharging mode, if the energy storage device is in the discharging state, then is 1, and if it is in the charging state, then is 0.

[0062] It should be noted that the meanings of the other parameters in Equation (7) can be referred to the corresponding explanations in the above equations, which will not be elaborated here. In an optional embodiment, when the energy storage device provides standby power, the second constraint condition further includes Equations (8) and (9):

[0063] (8)

[0064] (9)

[0065] Among them, represents the th moment in the scheduling period; is the charge-discharge efficiency corresponding to the energy storage device; is the initial energy value of the energy storage device, is the maximum energy value of the energy storage device, is the minimum energy value of the energy storage device; is the time slot between the th moment and the tth moment; is the charging power corresponding to the energy storage device at the th moment, is the discharging power of the energy storage device at the moment; is the second uplink reserve power provided by the energy storage device at the moment in the charging mode, is the second downlink reserve power provided by the energy storage device at the moment in the charging mode; is the third uplink reserve power provided by the energy storage device at the moment in the discharging mode, is the third downlink reserve power provided by the energy storage device at the moment in the discharging mode.

[0066] It should be noted that for the meanings of other parameters in formulas (8) and (9), reference can be made to the explanations in the above formulas, which will not be elaborated here.

[0067] And the third constraint condition for determining the interaction power between the prosumer and the virtual power plant. For example, the third constraint condition may also include formula (10):

[0068] (10)

[0069] Among them, is the interaction power between the prosumer and the virtual power plant at the t moment; is the maximum interaction power between the prosumer and the virtual power plant; , are respectively the fourth uplink reserve power and the fourth downlink reserve power provided by the virtual power plant at the t moment when the virtual power plant provides reserve power to the prosumer.

[0070] After determining the first objective function, the first constraint condition, the second constraint condition, and the third constraint condition, a first scheduling model can be constructed based on the first objective function, the first constraint condition, the second constraint condition, and the third constraint condition.

[0071] S102. Construct a second scheduling model based on the uncertainty factors corresponding to the virtual power plant.

[0072] In the embodiments of the present application, first, a second objective function, a fourth constraint condition, a fifth constraint condition, and a sixth constraint condition corresponding to the virtual power plant are constructed; then, a second scheduling model is constructed based on the second objective function, the fourth constraint condition, the fifth constraint condition, and the sixth constraint condition. Among them, when constructing the second objective function, specifically:

[0073] First, based on the electricity price of the power grid at time t and the day-ahead interaction power between the virtual power plant and the power grid at time t in the day-ahead operation stage, a fourth cost function corresponding to the virtual power plant is determined. For example, the fourth cost function is obtained through formula (11):

[0074] (11)

[0075] Among them, is the electricity price of the power grid at time t, is the day-ahead interaction power between the virtual power plant and the power grid at time t in the day-ahead operation stage, and min represents minimization.

[0076] Then, for each second distributed generation device corresponding to the virtual power plant, based on the second output power of each second distributed generation device at time t in the day-ahead operation stage, the fifth upward reserve power and the fifth downward reserve power provided by each second distributed generation device at time t in the day-ahead operation stage, a fifth cost function corresponding to the virtual power plant is determined. For example, the fifth cost function is obtained through formula (12):

[0077] (12)

[0078] Among them, are N second distributed generation devices corresponding to the virtual power plant, is the second output power of the gth second distributed generation device at time t in the day-ahead operation stage; is the fifth upward reserve power provided by the gth second distributed generation device at time t, is the fifth downward reserve power provided by the gth second distributed generation device at time t in the day-ahead operation stage.

[0079] It should be noted that other parameters of formula (12) can be correspondingly referred to the corresponding explanations in the above formula, and will not be elaborated here.

[0080] Then, based on the first unbalanced power and the second unbalanced power of the virtual power plant at time t, as well as the first unit cost corresponding to the first unbalanced power and the second unit cost corresponding to the second unbalanced power, a sixth cost function corresponding to the virtual power plant is determined.

[0081] Among them, the first unbalanced power represents the unbalanced power caused by the insufficient upward reserve power of the virtual power plant operator, and the second unbalanced power represents the unbalanced power caused by the insufficient downward reserve power of the virtual power plant operator. For example, the sixth cost function can be obtained by Equation (13):

[0082] (13)

[0083] Among them, is the first unbalanced power of the virtual power plant at time t during the intraday operation stage, is the second unbalanced power of the virtual power plant at time t during the intraday operation stage, is the first unit cost, is the second unit cost; is the unit voltage violation penalty cost, is the node set of the virtual power plant during the intraday operation stage, is the voltage violation amount of the i-th node in the node set at time t during the intraday operation stage; is the uncertainty set in the virtual power plant scheduling problem, which can also be understood as the uncertainty set of the power purchase of prosumers. max represents maximization.

[0084] Then, based on the fourth cost function, the fifth cost function, and the sixth cost function, a second objective function can be constructed. For example, the second objective function is obtained by Equation (14):

[0085] (14)

[0086] Among them, represents minimizing the second objective function. It should be noted that the meanings of the parameters in Equation (14) will not be elaborated here. The corresponding explanations can be referred to the above Equations (11)-(13).

[0087] Furthermore, based on the working status, minimum output, and maximum output of each second distributed generation device, a fourth constraint condition corresponding to each second distributed generation device is determined. For example, the fourth constraint condition includes Equation (15):

[0088] (15)

[0089] Among them, $P_{g,2}(t)$ is the second output power of the $g$-th second distributed generation device at time $t$ in the day-ahead operation stage. $R_{g,5}^{up}(t)$ is the fifth upward reserve power provided by the $g$-th second distributed generation device at time $t$ in the day-ahead operation stage. $R_{g,5}^{down}(t)$ is the fifth downward reserve power provided by the $g$-th second distributed generation device at time $t$ in the day-ahead operation stage. $P_{g,\min}$ is the minimum output of the $g$-th second distributed generation device. $P_{g,\max}$ is the maximum output of the $g$-th second distributed generation device. $u_{g}$ is a binary variable corresponding to the operating state of the $g$-th second distributed generation device. For example, if the operating state is the operating state, it is 1; if the operating state is the shutdown state, it is 0.

[0090] And determine the fifth constraint condition corresponding to the interaction power between the virtual power plant and prosumers in the day-ahead operation stage. For example, the fifth constraint condition includes equations (16) and (17):

[0091] (16)

[0092] (17)

[0093] Where $P_{vp - p}(t)$ is the interaction power between the virtual power plant and prosumers in the day-ahead operation stage. $R_{vp,6}^{up}(t)$ is the sixth upward reserve power provided by the virtual power plant for prosumers at time $t$ in the day-ahead operation stage, that is, the upward tie-line reserve power provided by the virtual power plant. $R_{vp,6}^{down}(t)$ is the sixth downward reserve power provided by the virtual power plant for prosumers at time $t$ in the day-ahead operation stage, based on the downward tie-line reserve power provided by the virtual power plant. $P_{max}$ is the maximum interaction power between prosumers and the virtual power plant.

[0094] $\Xi_{vp - p}$ is the uncertainty set of the interaction power between the virtual power plant and prosumers. $\Delta P_{vp - p}^{up}$ is the upward fluctuation amount of the interaction power between the virtual power plant and prosumers. $\Delta P_{vp - p}^{down}$ is the downward fluctuation amount of the interaction power between the virtual power plant and prosumers. $\sigma_{vp - p}^{up}$ is the upward fluctuation degree of the interaction power between the virtual power plant and prosumers. $\sigma_{vp - p}^{down}$ is the downward fluctuation degree of the interaction power between the virtual power plant and prosumers.

[0095] And determine the sixth constraint condition corresponding to the virtual power plant based on the upward power adjustment amount and downward power adjustment amount corresponding to each second distributed generation device in the intra-day operation stage. For example, the sixth constraint condition includes equations (18) and (19):

[0096] (18)

[0097] (19)

[0098] Among them, and are respectively the upward power adjustment amount and the downward power adjustment amount corresponding to the g-th second distributed power generation device during the intraday operation stage. It should be noted that for the meanings of other parameters in formulas (18) and (19), reference can be made to the corresponding explanations in the above formulas, which will not be elaborated here.

[0099] After determining the second objective function, the third constraint condition, the fourth constraint condition, and the fifth constraint condition, a second scheduling model can be constructed based on the second objective function, the third constraint condition, the fourth constraint condition, and the fifth constraint condition.

[0100] S103. Solve the first scheduling model and the second scheduling model based on the alternating direction method of multipliers to obtain the optimal solution for the virtual power plant scheduling.

[0101] Exemplarily, the optimization problem of the virtual power plant scheduling with prosumers can be represented by formula (20):

[0102] (20)

[0103] Among them, assume the number of prosumers is ; is the second objective function corresponding to the virtual power plant, is the first objective function corresponding to the m-th prosumer; is the variable to be optimized corresponding to the virtual power plant, is the variable to be optimized corresponding to the m-th prosumer; is the coupling variable related to the interaction power between the virtual power plant and the m-th prosumer, is the coupling variable related to the interaction power corresponding to the m-th prosumer.

[0104] and are the inequality and equality constraints in the virtual power plant optimal scheduling problem; and are the inequality and equality constraints in the scheduling problem of the m-th prosumer; is the subject to in the alternating direction method of multipliers.

[0105] Since the optimization problem shown in Equation (17) is decomposable, the Alternating Direction Method of Multipliers (ADMM) algorithm can be directly used to relax the coupling constraints into Lagrangian penalty functions and add them to the corresponding first objective function and second objective function. After relaxation, the original problem can be decomposed into the optimization scheduling problem of the virtual power plant operator and the optimization scheduling problem of each prosumer, and the optimal solution of the virtual power plant optimization scheduling with prosumers can be obtained independently, as well as the optimal solution of the optimization scheduling problem of each prosumer.

[0106] It can be seen that in the embodiments of the present application, based on the uncertainty factors corresponding to the prosumers, a first scheduling model is constructed; and based on the uncertainty factors corresponding to the virtual power plant, a second scheduling model is constructed; then, based on the Alternating Direction Method of Multipliers, the first scheduling model and the second scheduling model are solved to obtain the optimal solution of the virtual power plant scheduling, taking into account the differences in the known degrees of uncertainty factors in the optimization scheduling problems of different entities, coordinating and optimizing the interactive power between the virtual power plant and the prosumers and the reserve power provided to the prosumers, and "adapting measures to local conditions" to use different uncertainty optimization methods to solve the scheduling problems of the virtual power plant and the prosumers according to the differences in the known degrees of uncertainty factors in the virtual power plant and the prosumers. While optimizing the virtual power plant scheduling with prosumers, the conservativeness of the results can be reduced.

[0107] Refer to Figure 2 , Figure 2 It is a functional unit composition block diagram of a device for optimizing virtual power plant scheduling provided by an embodiment of the present application. The device 200 for optimizing virtual power plant scheduling includes: an acquisition unit 201 and a processing unit 202;

[0108] The acquisition unit 201 is used to acquire the uncertainty factors corresponding to the prosumers and the uncertainty factors corresponding to the virtual power plant;

[0109] The processing unit 202 is used to construct a first scheduling model based on the uncertainty factors corresponding to the prosumers; and construct a second scheduling model based on the uncertainty factors corresponding to the virtual power plant;

[0110] The processing unit 202 is further used to solve the first scheduling model and the second scheduling model based on the Alternating Direction Method of Multipliers to obtain the optimal solution of the virtual power plant scheduling.

[0111] In specific implementation, the acquisition unit 201 and the processing unit 202 described in the embodiments of the present invention can also execute other implementation manners described in the embodiments of the method for optimizing virtual power plant scheduling provided by the embodiments of the present invention, which will not be elaborated here.

[0112] Refer to Figure 3 , Figure 3 which is a schematic structural diagram of an electronic device provided by an embodiment of the present application. As Figure 3 shown, the electronic device 300 includes a transceiver 301, a processor 302, and a memory 303. They are connected through a bus 304. The memory 303 is used to store computer programs and data, and can transmit the data stored in the memory 303 to the processor 302.

[0113] The processor 302 is used to read the computer program in the memory 303 and perform the following operations:

[0114] Control the transceiver 301 to obtain the uncertainty factors corresponding to the prosumer users and the uncertainty factors corresponding to the virtual power plant;

[0115] Based on the uncertainty factors corresponding to the prosumer users, construct a first scheduling model; and based on the uncertainty factors corresponding to the virtual power plant, construct a second scheduling model;

[0116] Based on the alternating direction multiplier method, solve the first scheduling model and the second scheduling model to obtain the optimal solution for the virtual power plant scheduling.

[0117] In specific implementation, the transceiver 301 and the processor 302 described in the embodiments of the present invention may also execute other implementation manners described in the embodiments of the method for optimizing the virtual power plant scheduling provided by the embodiments of the present invention, which will not be elaborated here.

[0118] Specifically, the above transceiver 301 may be Figure 2 the acquisition unit 201 of the device 200 for optimizing the virtual power plant scheduling in the embodiments of Figure 2 the above, and the above processor 302 may be

[0119] the processing unit 202 of the device 200 for optimizing the virtual power plant scheduling in the embodiments of

[0120] It should be understood that the embodiments of the present application also provide 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 some or all of the steps of any one of the methods for optimizing the virtual power plant scheduling described in the above method embodiments.

[0121] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0122] In the above embodiments, the descriptions of the respective embodiments have their own focuses. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0123] In several embodiments provided in this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0124] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0125] In addition, in each embodiment of this application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software program modules.

[0126] When the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs.

[0127] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program. This program can be stored in a computer-readable memory, and the memory can include: flash drives, read-only memories (English: Read-Only Memory, abbreviated: ROM), random access memories (English: Random Access Memory, abbreviated: RAM), magnetic disks, or optical discs, etc.

[0128] The above has introduced the embodiments of this application in detail. Specific examples are used in this article to elaborate on the principle and implementation manner of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A method for optimizing virtual power plant scheduling, characterized in that: The method comprises: Based on the uncertainty factors corresponding to the prosumer, a first scheduling model is constructed, which specifically includes: for each first distributed power generation device corresponding to the prosumer, based on the first output power of each first distributed power generation device at time t, the first upstream backup power and the first downstream backup power provided by each first distributed power generation device at time t, and the first unit upstream backup cost and the first unit downstream backup cost corresponding to each first distributed power generation device, a first cost function corresponding to the prosumer is determined; based on the charging power and discharging power of the energy storage device corresponding to the prosumer at time t, the second upstream backup power provided by the energy storage device at time t in the charging mode, power and the second downlink backup power, the third uplink backup power and the third downlink backup power provided by the energy storage device at time t in the discharge mode, and the second unit uplink backup cost and the second unit downlink backup cost corresponding to the energy storage device, determine the second cost function corresponding to the prosumer; based on the first expected value of wind and solar power abandonment corresponding to the prosumer at time t, the second expected value of load reduction, and the first penalty cost corresponding to wind and solar power abandonment and the second penalty cost corresponding to load reduction, determine the third cost function corresponding to the prosumer; based on the first cost function, the second cost function and the third cost function, construct the first scheduling model; Based on the uncertainty factors corresponding to the virtual power plant, a second scheduling model is constructed, specifically including: based on the electricity price of the power grid at time t and the day-ahead interaction power between the virtual power plant and the power grid at time t in the day-ahead operation stage, a fourth cost function corresponding to the virtual power plant is determined; for each second distributed power generation device corresponding to the virtual power plant, based on the second output power of each second distributed power generation device at time t in the day-ahead operation stage, a fifth upstream backup power and a fifth downstream backup power provided by each second distributed power generation device at time t in the day-ahead operation stage, a fifth cost function corresponding to the virtual power plant is determined; based on the first unbalanced power and the second unbalanced power of the virtual power plant at time t in the intraday operation stage, as well as the first unit cost corresponding to the first unbalanced power and the second unit cost corresponding to the second unbalanced power, a sixth cost function corresponding to the virtual power plant is determined; based on the fourth cost function, the fifth cost function and the sixth cost function, the second scheduling model is constructed; Based on the alternating direction multiplier method, the first scheduling model and the second scheduling model are solved to obtain the optimal solution for the virtual power plant scheduling.

2. The method according to claim 1, characterized in that The method further comprises: Based on the historical prediction error, the prediction error of the first output power of each first distributed power generation device at time t is fitted to obtain a probability distribution function; Determine the total upstream reserve power corresponding to the prosumer at time t based on the first upstream reserve power of each first distributed power generation device at time t, the second upstream reserve power provided by the energy storage device at time t in the charging mode, and the third upstream reserve power provided by the energy storage device at time t in the discharging mode; Determine the total downstream reserve power corresponding to the prosumer at time t based on the first downstream reserve power of each first distributed power generation device at time t, the second downstream reserve power provided by the energy storage device at time t in the charging mode, and the third downstream reserve power provided by the energy storage device at time t in the discharging mode; Based on the probability distribution function, the total uplink reserve power corresponding to the production and consumption user at time t, and the total downlink reserve power corresponding to the production and consumption user at time t, determine the first expected value corresponding to the wind and solar power abandonment of the production and consumption user at time t and the second expected value corresponding to the load reduction.

3. The method according to claim 2, characterized in that The constructing the first scheduling model based on the first cost function, the second cost function and the third cost function includes: Constructing a first objective function based on the first cost function, the second cost function and the third cost function; Determine a first constraint condition corresponding to each first distributed power generation device based on the working state, minimum output, and maximum output corresponding to each first distributed power generation device; Determining a second constraint condition corresponding to the energy storage device based on the energy storage state and the maximum charge and discharge power corresponding to the energy storage device; Determining a third constraint condition corresponding to the interaction power between the prosumer and the virtual power plant; The first scheduling model is constructed based on the first objective function, the first constraint condition, the second constraint condition and the third constraint condition.

4. The method according to claim 3, characterized in that: The formula corresponding to the first cost function is: The formula corresponding to the second cost function is: The formula corresponding to the third cost function is: in, is the scheduling period, t is the time t in the scheduling period, N is the N first distributed generation devices corresponding to the prosumer, and g is the g-th first distributed generation device among the N first distributed generation devices; is the first output power of the g-th first distributed generation device at time t; is the first upstream reserve power provided by the g-th first distributed generation device at time t, The first downlink reserve power provided by the g-th first distributed generation device at time t; is the first unit upstream backup cost corresponding to the g-th first distributed generation device, is the first unit downstream backup cost corresponding to the g-th first distributed generation device, and is the preset parameter; is the charging power of the energy storage device at time t, is the discharge power of the energy storage device at time t; is the second uplink backup power provided by the energy storage device at time t in the charging mode, The second downlink backup power provided by the energy storage device at time t in the charging mode; is the third uplink backup power provided by the energy storage device at time t in the discharge mode, is the third downlink standby power provided by the energy storage device at time t in the discharge mode; is the second unit uplink standby cost, is the second unit downlink standby cost; is the first expected value corresponding to wind and solar power abandonment at time t, is the second expected value corresponding to the load reduction at time t, is the first penalty cost, is the second penalty cost.

5. The method according to claim 4, characterized in that The formula corresponding to the first objective function is: Among them, min means minimization, and minF means minimizing the first objective function.

6. The method according to claim 5, characterized in that The formula corresponding to the first constraint condition is: in, is the minimum output corresponding to the g-th first distributed generation device, is the maximum output corresponding to the g-th first distributed generation device, is a binary variable corresponding to the working state of the g-th first distributed power generation device.

7. The method according to claim 6, characterized in that The formula corresponding to the second constraint is: in, is the maximum charge and discharge power corresponding to the energy storage device, and is a binary variable corresponding to the energy storage state of the energy storage device.

8. The method according to claim 7, characterized in that When the energy storage device provides backup power, the formula corresponding to the second constraint condition also includes: in, The first a moment, is the charge and discharge efficiency corresponding to the energy storage device; is the initial energy value of the energy storage device, is the maximum energy value of the energy storage device, is the minimum energy value of the energy storage device, For the The time slot between the moment to the tth moment; The energy storage device is The charging power at each moment, The energy storage device is Discharge power at the moment; The energy storage device is in charging mode The second uplink backup power provided at all times, The energy storage device is in charging mode The second downlink backup power provided at all times; The energy storage device is in the discharge mode The third uplink backup power provided at all times, The energy storage device is in the discharge mode The third downlink reserve power provided at all times.

9. The method according to claim 8, characterized in that The formula corresponding to the third constraint condition is: in, is the interactive power between the prosumer and the virtual power plant at time t, is the maximum interactive power between the prosumer and the virtual power plant; , They are respectively the fourth uplink backup power and the fourth downlink backup power provided by the virtual power plant at time t when the virtual power plant provides backup power to the production and consumption user.

10. The method according to claim 9, characterized in that The constructing the second scheduling model based on the fourth cost function, the fifth cost function and the sixth cost function includes: constructing a second objective function based on the fourth cost function, the fifth cost function and the sixth cost function; Determine a fourth constraint condition corresponding to each second distributed power generation device based on the working state, minimum output, and maximum output corresponding to each second distributed power generation device; Determine a fifth constraint condition corresponding to the interactive power between the virtual power plant and the prosumer during the day-ahead operation phase; Determine a sixth constraint condition corresponding to the virtual power plant based on an upward power adjustment amount and a downward power adjustment amount corresponding to each second distributed power generation device during the intraday operation phase; The second scheduling model is constructed based on the second objective function, the fourth constraint, the fifth constraint and the sixth constraint.

11. The method according to claim 10, characterized in that The formula corresponding to the fourth cost function is: The formula corresponding to the fifth cost function is: The formula corresponding to the sixth cost function is: in, is the electricity price of the power grid at time t, is the day-ahead interaction power between the virtual power plant and the power grid at time t in the day-ahead operation phase; N second distributed power generation devices corresponding to the virtual power plant, is the second output power of the g-th second distributed generation equipment at time t during the day-ahead operation phase, The fifth upstream reserve power provided by the g-th second distributed generation equipment at time t in the day-ahead operation phase, The fifth downlink reserve power provided for the g-th second distributed generation device at time t during the day-ahead operation phase; is the first unbalanced power of the virtual power plant at time t during the intraday operation phase, is the second unbalanced power of the virtual power plant at time t during the intraday operation phase, is the first unit cost, is the second unit cost; is the penalty cost for unit voltage exceeding the limit, is the node set of the virtual power plant in the daily operation phase, is the voltage exceeding the limit of the ith node in the node set at time t during the intraday operation phase, The uncertainty set in the virtual power plant scheduling problem.

12. The method according to claim 11, characterized in that The formula corresponding to the fifth constraint condition includes: in, is the interactive power between the virtual power plant and the prosumer during the day-ahead operation phase, is the sixth upstream reserve power provided by the virtual power plant to the prosumer at time t during the day-ahead operation phase, is the sixth downlink reserve power provided by the virtual power plant to the prosumer at time t during the day-ahead operation phase, is the maximum interactive power between the prosumer and the virtual power plant; is an uncertain set of interaction powers between the virtual power plant and the prosumer, is the upward fluctuation of the interactive power between the virtual power plant and the prosumer, is the downward fluctuation of the interactive power between the virtual power plant and the prosumer, is the upward fluctuation degree of the interactive power between the virtual power plant and the prosumer, It is the downward fluctuation degree of the interaction power between the virtual power plant and the production and consumption user.

13. The method according to claim 12, characterized in that The formula corresponding to the sixth constraint condition includes: in, and They are respectively the upward power adjustment amount and the downward power adjustment amount corresponding to the g-th second distributed generation equipment in the daily operation stage.

14. A device for optimizing virtual power plant scheduling, characterized in that: The device comprises: an acquisition unit and a processing unit; The acquisition unit is used to acquire uncertainty factors corresponding to the prosumer and uncertainty factors corresponding to the virtual power plant; The processing unit is used to construct a first scheduling model based on the uncertainty factors corresponding to the prosumer, specifically including: for each first distributed generation device corresponding to the prosumer, based on the first output power of each first distributed generation device at time t, the first upstream backup power and the first downstream backup power provided by each first distributed generation device at time t, and the first unit upstream backup cost and the first unit downstream backup cost corresponding to each first distributed generation device, determine the first cost function corresponding to the prosumer; based on the charging power and the discharging power of the energy storage device corresponding to the prosumer at time t, the second upstream backup power and the second downstream backup power provided by the energy storage device at time t in the charging mode, the third upstream backup power and the third downstream backup power provided by the energy storage device at time t in the discharging mode, and the second unit upstream backup cost and the second unit downstream backup cost corresponding to the energy storage device, determine the second cost function corresponding to the prosumer; based on the first expected value of wind and solar power abandonment corresponding to the prosumer at time t, the second expected value of load reduction, and the first penalty cost corresponding to wind and solar power abandonment and the second penalty cost corresponding to load reduction. , determine the third cost function corresponding to the production and consumption user; construct the first dispatch model based on the first cost function, the second cost function and the third cost function; and construct the second dispatch model based on the uncertainty factors corresponding to the virtual power plant, specifically including: based on the electricity price of the power grid at time t and the day-ahead interaction power between the virtual power plant and the power grid at time t in the day-ahead operation stage, determine the fourth cost function corresponding to the virtual power plant; for each second distributed power generation device corresponding to the virtual power plant, based on the second output power of each second distributed power generation device at time t in the day-ahead operation stage, the fifth upstream backup power and the fifth downstream backup power provided by each second distributed power generation device at time t in the day-ahead operation stage, determine the fifth cost function corresponding to the virtual power plant; based on the first unbalanced power and the second unbalanced power of the virtual power plant at time t in the intraday operation stage, as well as the first unit cost corresponding to the first unbalanced power and the second unit cost corresponding to the second unbalanced power, determine the sixth cost function corresponding to the virtual power plant; construct the second dispatch model based on the fourth cost function, the fifth cost function and the sixth cost function; The processing unit is also used to solve the first scheduling model and the second scheduling model based on the alternating direction multiplier method to obtain the optimal solution for the virtual power plant scheduling.

15. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method according to any one of claims 1 to 13.

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