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

Abstract

The invention discloses a method and device for optimizing virtual power plant scheduling and a storage medium. The method comprises the following steps: constructing a first scheduling model based on uncertainty factors corresponding to production and elimination users; constructing a second scheduling model based on the uncertainty factors corresponding to the virtual power plant; and based on an alternating direction multiplier method, solving the first scheduling model and the second scheduling model to obtain an optimal solution of virtual power plant scheduling.

Description

Technical Field

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

[0002] The dual attributes of source and load of prosumers and the uncertainty of renewable distributed generation (RDG) output will affect the economy and safety of virtual power plant (VPP) operation. In order to make full use of the dispatchable resources in virtual power plants and prosumers and alleviate the negative impact of uncertainty on the operation of virtual power plants, the optimal dispatch of virtual power plants containing 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 the production and consumption users and the virtual power plant, corresponding first scheduling models and second scheduling models are constructed respectively, and then the first scheduling model and the second scheduling model are solved in combination with the alternating direction multiplier method, so as to obtain the optimal solution to the optimization scheduling problem of the virtual power plant containing production and consumption users.

[0004] In a first aspect, the present application provides a method for optimizing virtual power plant scheduling, the method comprising: Based on the uncertainty factors corresponding to the production and consumption users, a first scheduling model is constructed; Based on the uncertainty factors corresponding to the virtual power plant, a 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 virtual power plant scheduling.

[0005] 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; An acquisition unit, used to acquire uncertainty factors corresponding to the prosumer and uncertainty factors corresponding to the virtual power plant; A processing unit, configured to construct a first dispatch model based on uncertainty factors corresponding to the prosumer; and to construct a second dispatch model based on uncertainty factors corresponding to the virtual power plant; 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.

[0006] In a third aspect, the present application provides an electronic device, comprising: a processor and a memory, the processor being connected to the memory, the memory being used to store a computer program, and the processor being used to execute the computer program stored in the memory, so that the electronic device performs the method of the first aspect.

[0007] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method of the first aspect is performed.

[0008] 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, the method of the first aspect is performed.

[0009] The implementation of this application has the following beneficial effects: It can be seen that in the embodiments of the present application, a first scheduling model is constructed based on the uncertainty factors corresponding to the prosumers; and a second scheduling model is constructed based on the uncertainty factors corresponding to the virtual power plant; 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, taking into account the differences in the known degree of uncertainty factors in the optimization scheduling problems of different entities, and adopting different uncertainty optimization methods "according to local conditions" to construct the scheduling model and solve the scheduling problems of the virtual power plant and the prosumers, which can reduce the conservatism of the results while optimizing the scheduling of the virtual power plant containing prosumers. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0011] Figure 1 A flowchart of a method for optimizing virtual power plant scheduling provided in an embodiment of the present application; Figure 2 A block diagram of the functional units of a device for optimizing virtual power plant scheduling provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0013] The terms "first", "second", "third" and "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 "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. 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 includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0014] Reference to "embodiments" herein means that a particular feature, result, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0015] First, the relevant terms and related technologies in this application are explained: Prosumers: refers to users who can both produce and consume electricity in the power system. For example, these users can be households, businesses or other entities that own distributed energy resources (such as solar energy, wind energy, battery storage, etc.).

[0016] Virtual Power Plant (VPP): A virtual power plant that uses digital technology and intelligent control systems to integrate multiple distributed energy resources (such as wind power, solar power, energy storage equipment, electric vehicles, etc.) distributed in different locations to simulate a single, centrally managed power plant.

[0017] It should be explained that in response to the uncertainty of RDG output, existing research mainly provides backup power through dispatchable equipment within prosumers, such as controllable distributed generation (CDG) and energy storage. However, the interactive power between prosumers and virtual power plants is also adjustable. If the backup power is only provided by the dispatchable equipment corresponding to prosumers, it may lead to insufficient backup power for prosumers, thus affecting the safe absorption of RDG output. Existing research only regards the backup power provided by virtual power plants as the only source of backup power for prosumers, which will cause excessive use of backup capacity, resulting in large fluctuations in the allowed interactive power between prosumers and virtual power plants, affecting the economy and safety of virtual power plant operations.

[0018] Therefore, in an embodiment of the present application, taking into account uncertain environmental factors, the day-ahead interactive power between the virtual power plant and the production and consumption users and the interconnection line backup power provided to the production and consumption users are coordinated and optimized as coupling variables to further improve the economy and safety of the entire virtual power plant operation.

[0019] In addition, since virtual power plants and prosumers are generally operated by different entities, considering their privacy issues, some operating parameters are difficult to be completely transparent between different entities. Therefore, there will be differences in the degree of knowledge of uncertainty factors in the optimization scheduling problems of virtual power plants and prosumers.

[0020] From the perspective of prosumers, the uncertainty factors in prosumers include the prediction error of distributed generation equipment. Therefore, in the embodiment of the present application, the prediction error of distributed generation can be fitted according to the historical prediction error data of distributed generation equipment, and the conditional value-at-risk (CVaR) is used to handle the uncertainty of RDG output to reduce the conservatism of robust optimization (RO).

[0021] From the perspective of the virtual power plant operator, the uncertainty factor in the virtual power plant is the interactive power with the prosumer. Since the virtual power plant operator cannot obtain the detailed parameters of the specific equipment operation in the prosumer, the virtual power plant operator can only obtain the fluctuation range of the interconnection line power based on the interconnection line backup capacity provided to the prosumer. At present, the virtual power plant operator generally adopts robust optimization (RO) to deal with the uncertainty of interactive power.

[0022] However, the two have one thing in common, which is that they ignore the differences in the degree of knowledge of uncertainty factors in the optimization scheduling problems of different entities, which will lead to overly conservative results. Therefore, in the embodiment of the present application, a virtual power plant optimization scheduling strategy that takes uncertainty into account is proposed, and the interactive power between the virtual power plant and the prosumer and the backup power provided to the prosumer are coordinated and optimized. According to the differences in the degree of knowledge of uncertainty factors in the virtual power plant and the prosumer, different uncertainty optimization methods are used to solve the scheduling problems of the virtual power plant and the prosumer in accordance with local conditions to reduce the conservatism of the results.

[0023] The following is an explanation of the method for optimizing virtual power plant scheduling in this application in conjunction with specific embodiments: First of all, it should be noted that the number of the production and consumption users in the embodiment of the present application can be one or more, and the present application mainly uses one as an example for explanation. Figure 1 , Figure 1 A flowchart of a method for optimizing virtual power plant scheduling provided in an embodiment of the present application. The method is applied to a device for optimizing virtual power plant scheduling, and the method includes but is not limited to steps S101-S103: S101. Construct a first scheduling model based on uncertainty factors corresponding to production and consumption users.

[0024] In the embodiment of the present application, firstly, a first objective function, a first constraint condition, a second constraint condition, and a third constraint condition corresponding to the production and consumption user 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. Specifically, when constructing the first objective function: For each first distributed generation device corresponding to the production and consumption user, 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, the first cost function corresponding to the production and consumption user is determined. For example, the first cost function is expressed by formula (1): (1) in, is the scheduling period, t represents the time t in the scheduling period, N represents the N first distributed power generation devices corresponding to the production and consumption users, and the specific value of N is not limited in this application; g represents the g-th first distributed power generation device among the N first distributed power 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 It is a preset parameter, and this application does not limit its specific value.

[0025] Then, based on the charging power and discharging power of the energy storage device corresponding to the prosumer at time t, the second uplink backup power and the second downlink backup power provided by the energy storage device at time t in the charging mode, the third uplink backup power and the third downlink backup power provided by the energy storage device at time t in the discharging mode, and the second unit uplink backup cost and the second unit downlink backup cost corresponding to the energy storage device, the second cost function corresponding to the prosumer is determined. For example, the second cost function is expressed by formula (2): (2) in, 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 charging mode, is 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 backup power provided by the energy storage device at time t in the discharge mode; is the second unit uplink backup cost, is the second unit downstream standby cost.

[0026] Then, 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, the first penalty cost corresponding to wind and solar power abandonment, and the second penalty cost corresponding to load reduction, the third cost function corresponding to the prosumer is determined. For example, the third cost function is expressed by formula (3): (3) in, 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.

[0027] In an optional embodiment, before determining the third cost function, a first expected value corresponding to wind and solar power abandonment and a second expected value corresponding to load reduction by the prosumer at time t may also be determined. Specifically: First, 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. At this time, the corresponding prediction error of each first distributed power generation device at time t satisfies the probability distribution of the probability distribution function.

[0028] Then, based on the first upstream backup power of each first distributed power generation device at time t, the second upstream backup power provided by the energy storage device at time t in the charging mode, and the third upstream backup power provided by the energy storage device at time t in the discharging mode, for example, a summation operation is performed to obtain the total upstream backup power corresponding to the production and consumption user at time t.

[0029] And 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, such as a summation operation, the total downstream reserve power corresponding to the production and consumption user at time t is determined. Then, based on the power distribution function, the total upstream reserve power corresponding to the production and consumption user at time t, and the total downstream reserve power corresponding to the production and consumption user at time t, 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 can be determined. For example, the first expected value and the second expected value can be expressed by formula (4): (4) in, 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 probability distribution function, is the total uplink reserve power corresponding to the production and consumption user at time t, is the total uplink and downlink reserve power corresponding to the production and consumption user 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.

[0030] Then, the first objective function can be constructed based on the first cost function, the second cost function and the third cost function. For example, the first objective function is obtained by equation (5): (5) It should be noted that the meaning of each parameter in formula (5) will not be further explained here, and reference may be made to the corresponding explanations of the above formulas (1) to (3).

[0031] Furthermore, the first constraint condition corresponding to each first distributed power generation device may be determined based on the working state, minimum output, and maximum output corresponding to each first distributed power generation device. For example, the first constraint condition includes formula (6): (6) 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 the binary variable corresponding to the working state of the g-th first distributed generation device, For example, if the working state is running, it is 1, and if the working state is stopping, it is 0.

[0032] And based on the energy storage state and the maximum charge and discharge power corresponding to the energy storage device, determine the second constraint condition corresponding to the energy storage device. For example, the second constraint condition includes formula (7): (7)

[0033] in, is the maximum charging and discharging power corresponding to the energy storage device; and is a binary variable corresponding to the energy storage state of the energy storage device. For example, in charging mode, if the energy storage device is in charging state, then is 1, if it is in the discharge state, then is 0; and in the discharge mode, if the energy storage device is in the discharge state, then If it is in charging state, is 0.

[0034] It should be noted that the meanings of other parameters in formula (7) can be referred to the explanations in the above formula, and will not be repeated here. In an optional embodiment, when the energy storage device provides backup power, the second constraint condition also includes formulas (8) and (9): (8) (9) in, Indicates the first a moment; is the charging and discharging 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 gap between the moment and the tth moment; For energy storage equipment The charging power corresponding to each moment is: For energy storage devices Discharge power at the moment; In charging mode, the energy storage device The second uplink backup power provided at all times, In charging mode, the energy storage device The second downlink backup power provided at all times; In discharge mode, the energy storage device The third uplink backup power provided at all times, In discharge mode, the energy storage device The third downlink reserve power provided at all times.

[0035] It should be noted that the meanings of other parameters in equations (8) and (9) can be referred to the explanations in the above equations and will not be repeated here.

[0036] And determine the third constraint condition corresponding to the interactive power between the prosumer and the virtual power plant. For example, the third constraint condition may also include formula (10): (10) 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 reserve power and the fourth downlink reserve power provided by the virtual power plant at time t when the virtual power plant provides reserve power to the production and consumption users.

[0037] After the first objective function, the first constraint condition, the second constraint condition and the third constraint condition are determined, a first scheduling model may be constructed based on the first objective function, the first constraint condition, the second constraint condition and the third constraint condition.

[0038] S102. Construct a second scheduling model based on uncertainty factors corresponding to the virtual power plant.

[0039] In the embodiment of the present application, firstly, the second objective function, the fourth constraint condition, the fifth constraint condition, and the sixth constraint condition corresponding to the virtual power plant are constructed; then, based on the second objective function, the fourth constraint condition, the fifth constraint condition, and the sixth constraint condition, a second scheduling model is constructed. Specifically, when constructing the second objective function: First, based on the electricity price of the grid at time t and the day-ahead interaction power between the virtual power plant and the grid at time t in the day-ahead operation phase, the fourth cost function corresponding to the virtual power plant is determined. For example, the fourth cost function is obtained through formula (11): (11) 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 grid at time t in the day-ahead operation phase, and min represents minimization.

[0040] 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 phase, the fifth upstream backup power and the fifth downstream backup power provided by each second distributed generation device at time t in the day-ahead operation phase, the fifth cost function corresponding to the virtual power plant is determined. For example, the fifth cost function is obtained by formula (12): (12) in, 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 in the day-ahead operation stage; The fifth uplink reserve power provided by the g-th second distributed generation device at time t, The fifth downstream reserve power provided for the g-th second distributed generation device at time t in the day-ahead operation phase.

[0041] It should be noted that the other parameters of formula (12) can refer to the corresponding explanations in the above formula and will not be repeated here.

[0042] Then, based on the first unbalanced power and the second unbalanced power of the virtual power plant at time t, the first unit cost corresponding to the first unbalanced power, and the second unit cost corresponding to the second unbalanced power, the sixth cost function corresponding to the virtual power plant is determined.

[0043] Among them, the first unbalanced power represents the unbalanced power caused by the virtual power plant operator due to insufficient uplink backup power, and the second unbalanced power represents the unbalanced power caused by insufficient downlink backup power of the virtual power plant operator. For example, the sixth cost function can be obtained by formula (13): (13) in, 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 per unit voltage limit, is the node set of the virtual power plant during the intraday operation phase, is the voltage exceeding the limit of the ith node in the node set at time t during the intraday operation phase; is the uncertainty set in the virtual power plant scheduling problem, which can also be understood as the uncertainty set of electricity purchase by producers and consumers, and max represents maximization.

[0044] Then, the second objective function can be constructed based on the fourth cost function, the fifth cost function and the sixth cost function. For example, the second objective function is obtained by equation (14): (14) in, It represents minimizing the second objective function. It should be noted that the meaning of each parameter in equation (14) is not explained here in detail, and the corresponding explanations of equations (11) to (13) above can be referred to.

[0045] Furthermore, based on the working state, minimum output and maximum output corresponding to each second distributed power generation device, a fourth constraint condition corresponding to each second distributed power generation device is determined. For example, the fourth constraint condition includes formula (15): (15) in, 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 minimum output corresponding to the g-th second distributed generation device, is the maximum output corresponding to the g-th second distributed generation device; is a binary variable corresponding to the working state of the g-th second distributed power generation device, for example, if the working state is the running state, it is 1, and if the working state is the shutdown state, it is 0.

[0046] And determine the fifth constraint corresponding to the interactive power between the virtual power plant and the production and consumption users in the day-ahead operation phase. For example, the fifth constraint includes equations (16) and (17): (16) (17) in, is the interactive power between the virtual power plant and the prosumer in the day-ahead operation phase, The sixth uplink reserve power provided by the virtual power plant to the production and consumption users at time t during the day-ahead operation phase, that is, the uplink tie line reserve power provided by the virtual power plant; is the sixth downstream reserve power provided by the virtual power plant to the prosumer at time t during the day-ahead operation phase, based on the downstream tie line reserve power provided by the virtual power plant; It is the maximum interaction power between the prosumer and the virtual power plant.

[0047] is the uncertain set of interactive power 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 interactive power between the virtual power plant and the production and consumption users.

[0048] And based on the upward power adjustment amount and the downward power adjustment amount corresponding to each second distributed power generation device in the daily operation stage, determine the sixth constraint condition corresponding to the virtual power plant. For example, the sixth constraint condition includes equations (18) and (19): (18) (19) in, and They are the upward power adjustment and downward power adjustment corresponding to the g-th second distributed generation equipment in the daily operation stage. It should be noted that the meanings of other parameters in equations (18) and (19) can refer to the corresponding explanations in the above equations and will not be repeated here.

[0049] After the second objective function, the third constraint condition, the fourth constraint condition and the fifth constraint condition are determined, a second scheduling model may be constructed based on the second objective function, the third constraint condition, the fourth constraint condition and the fifth constraint condition.

[0050] S103. 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.

[0051] For example, the optimization problem of virtual power plant scheduling with prosumers can be expressed by equation (20): (20) It is assumed that the number of production and consumption users is indivual; is the second objective function corresponding to the virtual power plant, is the first objective function corresponding to the mth prosumer; is the variable to be optimized corresponding to the virtual power plant, is the variable to be optimized corresponding to the mth producer and consumer; is the coupling variable related to the interactive power between the virtual power plant and the mth prosumer, is the coupling variable related to the interaction power corresponding to the mth producer and consumer.

[0052] and Inequality and equality constraints in optimizing scheduling problems for virtual power plants; and is the inequality and equality constraints in the scheduling problem of the mth producer and consumer; is the subject to in the alternating direction multiplier method.

[0053] 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 and second objective functions. After relaxation, the original problem can be decomposed into the optimal scheduling problem of the virtual power plant operator and the optimal scheduling problem of each prosumer, and the optimal solution of the optimal scheduling of the virtual power plant containing prosumers and the optimal solution of the optimal scheduling problem of each prosumer can be obtained by independently solving them.

[0054] It can be seen that in an embodiment of the present application, a first scheduling model is constructed based on the uncertainty factors corresponding to the prosumers; and a second scheduling model is constructed based on the uncertainty factors corresponding to the virtual power plant; 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, taking into account the differences in the known degree of uncertainty factors in the optimization scheduling problems of different subjects, and coordinating and optimizing the interaction power between the virtual power plant and the prosumers in the scheduling problem and the backup power provided to the prosumers. According to the differences in the known degree of uncertainty factors in the virtual power plant and the prosumers, different uncertainty optimization methods are adopted "according to local conditions" to solve the scheduling problems of the virtual power plant and the prosumers, which can reduce the conservatism of the results while optimizing the scheduling of the virtual power plant containing prosumers.

[0055] See also Figure 2 , Figure 2 A functional unit composition block diagram of a device for optimizing virtual power plant scheduling provided in 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; An acquisition unit 201 is used to acquire uncertainty factors corresponding to prosumers and uncertainty factors corresponding to virtual power plants; The processing unit 202 is configured to construct a first scheduling model based on uncertainty factors corresponding to the prosumer; and to construct a second scheduling model based on uncertainty factors corresponding to the virtual power plant; The processing unit 202 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.

[0056] In a specific implementation, the acquisition unit 201 and the processing unit 202 described in the embodiment of the present invention may also execute other implementation methods described in the embodiment of the method for optimizing virtual power plant scheduling provided in the embodiment of the present invention, which will not be repeated here.

[0057] See also Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 300 includes a transceiver 301, a processor 302 and a memory 303. They are connected via 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.

[0058] The processor 302 is used to read the computer program in the memory 303 and perform the following operations: Controlling the transceiver 301 to obtain uncertainty factors corresponding to the prosumer and uncertainty factors corresponding to the virtual power plant; Based on the uncertainty factors corresponding to the prosumer, a first dispatch model is constructed; and based on the uncertainty factors corresponding to the virtual power plant, a second dispatch 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 virtual power plant scheduling.

[0059] In a specific implementation, the transceiver 301 and the processor 302 described in the embodiment of the present invention may also execute other implementation methods described in the embodiment of the method for optimizing virtual power plant scheduling provided in the embodiment of the present invention, which will not be repeated here.

[0060] Specifically, the transceiver 301 may be Figure 2 The acquisition unit 201 of the device 200 for optimizing virtual power plant scheduling of the embodiment, the processor 302 may be Figure 2 The processing unit 202 of the apparatus 200 for optimizing virtual power plant scheduling of an embodiment.

[0061] It should be understood that an embodiment of the present application also provides a computer-readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement part or all of the steps of any method for optimizing virtual power plant scheduling as recorded in the above method embodiments.

[0062] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all of the steps of any method for optimizing virtual power plant scheduling as recorded in the above method embodiments.

[0063] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0064] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0065] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0066] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0067] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software program module.

[0068] If 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 the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0069] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable memory, which may include a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0070] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present 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 production and consumption users, a first scheduling model is constructed; Based on the uncertainty factors corresponding to the virtual power plant, a 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 constructing of a first scheduling model based on uncertainty factors corresponding to production and consumption users includes: For each first distributed generation device corresponding to the production and consumption user, 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 production and consumption user; Determine a second cost function corresponding to the prosumer based on the charging power and discharging power of the energy storage device corresponding to the prosumer at time t, the second uplink backup power and the second downlink backup power provided by the energy storage device at time t in the charging mode, the third uplink backup power and the third downlink backup power provided by the energy storage device at time t in the discharging mode, and the second unit uplink backup cost and the second unit downlink backup cost corresponding to the energy storage device; Determine a third cost function corresponding to the prosumer based on a first expected value of wind and solar power abandonment corresponding to the prosumer at time t, a second expected value of load reduction, a first penalty cost corresponding to wind and solar power abandonment, and a second penalty cost corresponding to load reduction; The first scheduling model is constructed based on the first cost function, the second cost function and the third cost function.

3. The method according to claim 2, 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.

4. The method according to claim 3, 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.

5. The method according to claim 4, 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.

6. The method according to claim 5, 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.

7. The method according to claim 6, 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.

8. The method according to claim 7, 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.

9. The method according to claim 8, 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.

10. The method according to claim 9, 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.

11. The method according to claim 10, characterized in that The second scheduling model is constructed based on the uncertainty factors corresponding to the virtual power plant, including: Determine a fourth cost function corresponding to the virtual power plant based on the electricity price of the power grid at time t and the day-ahead interactive power between the virtual power plant and the power grid at time t in the day-ahead operation phase; 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 phase, and the fifth upstream reserve power and the fifth downstream reserve power provided by each second distributed power generation device at time t in the day-ahead operation phase, determine a fifth cost function corresponding to the virtual power plant; Determine a sixth cost function corresponding to the virtual power plant based on a first unbalanced power and a second unbalanced power at time t during the intraday operation phase of the virtual power plant, a first unit cost corresponding to the first unbalanced power, and a second unit cost corresponding to the second unbalanced power; Based on the fourth cost function, the fifth cost function and the sixth cost function, the second scheduling model is constructed.

12. The method according to claim 11, 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.

13. The method according to claim 12, 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.

14. The method according to claim 13, 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.

15. The method according to claim 14, 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.

16. 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 configured to construct a first scheduling model based on uncertainty factors corresponding to the prosumer; and to construct a second scheduling model based on uncertainty factors corresponding to the virtual power plant; 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.

17. 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 15.

Images