A reliability optimization method, equipment, medium and product for multi-stage peak-shaving tasks of a virtual power plant

Abstract

The present application discloses a reliability optimization method, equipment, medium and product for multi-stage peak-shaving tasks of a virtual power plant, which relates to the field of virtual power plant peak-shaving. The method includes constructing a virtual power plant information channel availability model and a virtual power plant information-physical equipment availability model; establishing a binary decision diagram model for all-stage tasks of the virtual power plant; determining the reliability index of all-stage tasks of the virtual power plant based on the above model; establishing and solving a reliability optimization model that considers the uncertainty of real-time peak-shaving service demand and a reliability optimization model that considers the uncertainty of the main scheme equipment state, optimizing the reliability index of all-stage tasks of the virtual power plant, and completing the optimization of the reliability of the multi-stage peak-shaving tasks of the virtual power plant. The present application can take into account the impact of information-physical interaction on the expressiveness of the virtual power plant in the process of participating in the electricity market, and realize the optimization of the reliability of the multi-stage peak-shaving tasks of the virtual power plant.

Description

Technical Field

[0001] The present application relates to the field of virtual power plant peak regulation, and in particular to a method, equipment, medium and product for optimizing the reliability of multi-stage peak regulation tasks of a virtual power plant. Background Art

[0002] Traditional reliability assessments primarily focus on physical equipment such as transmission and distribution systems, distribution transformers, and energy storage devices. Currently, there is a lack of reliability assessment methods specifically for virtual power plants. Virtual power plants, as new energy management entities encompassing distributed renewable energy equipment, energy storage, and flexible loads, generate revenue by participating in power ancillary services market transactions. The internal operation and control of virtual power plants involves a complex cyber-physical interaction process, and it is crucial to consider the impact of cyber-physical interaction on the performance of virtual power plants in the electricity market. Summary of the Invention

[0003] The purpose of this application is to provide a method, equipment, medium and product for optimizing the reliability of multi-stage peak-shaving tasks of a virtual power plant, which can take into account the impact of information-physical interaction on the performance of the virtual power plant in participating in the electricity market, and realize the optimization of the reliability of the multi-stage peak-shaving tasks of the virtual power plant.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a method for optimizing the reliability of multi-stage peak-shaving tasks in a virtual power plant, comprising:

[0006] Construct a normal-fault state transition model for cyber-physical devices;

[0007] Establishing a virtual power plant information channel availability model; the virtual power plant information channel availability model is determined based on packet loss, transmission delay and bit error availability of the transmission channel;

[0008] Establishing a virtual power plant cyber-physical device availability model based on the normal state probability of the cyber-physical device in the normal-fault state transition model and the actual capacity of the cyber-physical device;

[0009] Establishing a binary decision diagram model for all-stage tasks of a virtual power plant; the binary decision diagram model for all-stage tasks of a virtual power plant is established based on a binary decision diagram model for a single-stage task of a virtual power plant; the binary decision diagram model for a single-stage task of a virtual power plant is established based on a fault tree for a single-stage task of a virtual power plant using Shannon's decomposition law;

[0010] Determining a reliability index of the virtual power plant's full-stage mission according to the virtual power plant information channel availability model, the virtual power plant's cyber-physical equipment availability model, and the virtual power plant's full-stage mission binary decision diagram model;

[0011] Establish a reliability optimization model that considers the uncertainty of real-time peak-shaving service demand and a reliability optimization model that considers the uncertainty of the main scheme equipment status; the reliability optimization model that considers the uncertainty of real-time peak-shaving service demand includes a first objective function and a first constraint condition set; the first objective function aims to minimize the virtual power plant operator's operating coefficient in the day-ahead peak-shaving auxiliary service market and minimize the real-time peak-shaving auxiliary service market operating coefficient; the first constraint condition set includes the active power balance constraint of the virtual power plant, the output constraint of distributed new energy equipment, the power constraint of the energy storage equipment, the upward and downward adjustment constraint of the flexible load, and the constraint of the virtual power plant equipment in the real-time peak-shaving auxiliary service market on the output of the day-ahead peak-shaving auxiliary service market; the reliability optimization model that considers the uncertainty of the main scheme equipment status includes a second objective function and a second constraint condition set; the second objective function aims to minimize the virtual power plant operator's operating coefficient in the peak-shaving auxiliary service market;

[0012] The reliability optimization model considering the uncertainty of real-time peak-shaving service demand and the reliability optimization model considering the uncertainty of main scheme equipment state are solved respectively to obtain optimization parameters; the optimization parameters include the output of distributed new energy equipment, the charging power of energy storage equipment, the discharge power of energy storage equipment, the output of distributed new energy equipment in the real-time peak-shaving auxiliary service market, the charging amount of energy storage equipment in the real-time peak-shaving auxiliary service market, the discharge amount of energy storage equipment in the real-time peak-shaving auxiliary service market, the downward adjustment amount of flexible load in the real-time peak-shaving auxiliary service market, and the upward adjustment amount of flexible load in the real-time peak-shaving auxiliary service market;

[0013] Based on the optimization parameters, the reliability index of the virtual power plant's full-stage mission is optimized to complete the optimization of the reliability of the virtual power plant's multi-stage peak-shaving mission.

[0014] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-mentioned methods for optimizing the reliability of multi-stage peak-shaving tasks of a virtual power plant.

[0015] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the reliability optimization method for multi-stage peak-shaving tasks of a virtual power plant as described in any one of the above.

[0016] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the reliability optimization method for multi-stage peak-shaving tasks of a virtual power plant as described above.

[0017] According to the specific embodiments provided in this application, this application has the following technical effects:

[0018] The present application provides a method, device, medium and product for optimizing the reliability of multi-stage peak-shaving tasks of a virtual power plant, constructing a normal-fault state transition model of information-physical equipment; establishing a virtual power plant information channel availability model; establishing a virtual power plant information-physical equipment availability model based on the normal state probability of the information-physical equipment in the normal-fault state transition model and the actual capacity of the information-physical equipment; establishing a virtual power plant full-stage task binary decision diagram model; determining the reliability index of the virtual power plant full-stage task according to the virtual power plant information channel availability model, the virtual power plant information-physical equipment availability model and the virtual power plant full-stage task binary decision diagram model; establishing a reliability optimization model that considers the uncertainty of real-time peak-shaving service demand and a reliability optimization model that considers the uncertainty of the main scheme equipment state and solving them to obtain optimization parameters; optimizing the reliability index of the virtual power plant full-stage task based on the optimization parameters, and completing the optimization of the reliability of the virtual power plant multi-stage peak-shaving tasks. The present application can take into account the impact of information-physical interaction on the expressiveness of the virtual power plant in the process of participating in the electricity market, and realize the optimization of the reliability of the virtual power plant multi-stage peak-shaving tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A flowchart of a method for optimizing the reliability of multi-stage peak-shaving tasks in a virtual power plant provided in one embodiment of the present application;

[0021] Figure 2 A schematic diagram of a two-state transition model for a device provided in one embodiment of the present application;

[0022] Figure 3 A schematic diagram of a single-stage task fault tree for a virtual power plant provided in one embodiment of the present application;

[0023] Figure 4 A schematic diagram of a basic graphical representation of an operation rule provided in an embodiment of the present application;

[0024] Figure 5 A schematic diagram of a binary decision diagram model for a single-stage task of a virtual power plant provided in one embodiment of the present application;

[0025] Figure 6A schematic diagram of a full-stage task fault tree model for a virtual power plant provided in one embodiment of the present application;

[0026] Figure 7 A simplified single-stage binary decision diagram provided in one embodiment of the present application;

[0027] Figure 8 A binary decision diagram for all-stage peak-shaving tasks of a virtual power plant provided in one embodiment of the present application;

[0028] Figure 9 A flowchart for reliability optimization of multi-stage peak-shaving tasks of a virtual power plant provided in one embodiment of the present application;

[0029] Figure 10 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0031] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0032] In an exemplary embodiment, Figure 1 As shown, a reliability optimization method for multi-stage peak-shaving tasks of a virtual power plant is provided, comprising the following steps:

[0033] S1: Construct a normal-fault state transition model for cyber-physical devices.

[0034] In practical applications, the equipment basic "normal-fault" two-state transformation model based on Markov process is as follows: Figure 2 shown.

[0035] Among them, N i,t Indicates that device i is in normal state; F i,t Indicates that device i is in a faulty state; i represents the failure rate of device i; μ i is the repair rate of equipment i, both of which are historical statistical data; represents the probability that device i, t is in a normal state; represents the probability that the device i is in a faulty state at time t. The dynamic transition process of the device between the "normal-fault" state can be expressed by formulas (1) and (2):

[0036]

[0037] The analytical expressions of the probability of the equipment being in normal and faulty states are shown in formulas (3) and (4):

[0038]

[0039] From formula (3) and formula (4), we can see that the probability of the equipment being in a normal state decreases as the operating time increases, while the probability of being in a faulty state increases as the operating time increases; both will eventually converge to a fixed value.

[0040] S2: Establishing a virtual power plant information channel availability model; the virtual power plant information channel availability model is determined based on packet loss, transmission delay and bit error availability of the transmission channel.

[0041] In practical applications, communication transmission methods include wired and wireless transmission. For wireless transmission, the equipment is limited to node devices; for wired transmission, the equipment includes both node devices and line devices, i.e., communication transmission links. Node device transmission characteristics include latency, bit errors, and packet loss, while line devices only have transmission latency. The availability of a data transmission channel is determined by the transmission characteristics of both node and line devices.

[0042] 1) Communication system routing feature set.

[0043] The routing characteristics of a communication system represent the transmission characteristics between node devices and only include propagation delay, as shown in formula (5). For wired communication, propagation delay is determined only by the line length and can be calculated using formula (6):

[0044]

[0045]

[0046] in, Routing characteristics of the communication system; DL l (t) is the propagation delay; L l is the length of the communication transmission line l, and ε is the electromagnetic wave propagation velocity.

[0047] Unlike wired communications, packet retransmission is inevitable due to interference in wireless communications, which may increase propagation delay. The propagation delay is determined by the line length and the number of retransmissions, and is expressed as:

[0048]

[0049] Where r is the number of times the data packet is retransmitted, Pr(r) is the probability of the data packet being retransmitted r times, is the upper limit of the number of times the data packet is retransmitted; p RT is the probability of data packet retransmission, is the number of combinations.

[0050] 2) Communication node feature set.

[0051] Communication node characteristics represent the transmission characteristics of different node devices, which are defined by packet loss, node switching delay, and bit error characteristics, as shown in formula (9):

[0052]

[0053] in, is a set of node attributes; LS j (t) indicates node packet loss; DL j (t) represents the node switching delay; ER j (t) represents the bit error characteristic.

[0054] The Gilbert-Elliot model based on Markov chains is used to simulate node packet loss characteristics. First, based on historical data, packet loss is divided into multiple states with representative packet loss rates. Next, the probability of each state is obtained by solving the Kolmogorov equation. Finally, the probability mass function corresponding to the node data pass rate is obtained:

[0055]

[0056] in, Indicates the data passing rate of the kth group of nodes; Indicates the probability of occurrence of the kth group of node data, and both values ​​range from 0 to 100%. For example It means that the probability that 75% of the data can pass through node j smoothly is 6%. is the total number of packets of the node data pass rate. For the node error characteristics and node switching delay characteristics, Poisson distribution and normal distribution are used to describe their respective uncertainties, as shown in the following formula:

[0057]

[0058] Among them, s j is the number of bit errors per second, γ j is its corresponding expected value. j represents the switching delay of node device j; T de represents the switching delay threshold of node device j. represents the mean value of exchange delay; It represents the variance of the switching delay, which can be determined through historical statistics.

[0059] 3) Transmission channel availability set.

[0060] The transmission channel of a communication system includes the node devices and communication routes that data passes through from the sending point to the receiving point, which can be defined by formula (13):

[0061]

[0062] in, Represents the set of node devices included in the communication transmission channel h; represents the set of communication routes contained in the communication transmission channel h. The characteristics of the information transmission channel are determined by the corresponding communication node characteristics and routing characteristics, as shown in formulas (14) to (17):

[0063]

[0064] in, is the feature set of information transmission channel; LS h (t), DL h (t) and ER h (t) represent the packet loss, delay and bit error characteristics of the transmission channel h, which are defined by formula (15), formula (16) and formula (17) respectively. j (t) is the packet loss of the jth node device in the transmission channel h; DL j (t) is the switching delay of the jth node device in the transmission channel h; DL l (t) is the switching delay of the lth communication route in the transmission channel h; ER j (t) is the bit error characteristic of the jth node device in the transmission channel h; it can be seen from formulas (15) and (17) that the packet loss and bit error of the transmission channel h depend on the worst case of the corresponding node characteristics. Formula (16) shows that the transmission delay of the transmission channel h is the sum of the delays of all node devices and the communication routing delay. Only when the packet loss, transmission delay and bit error of the transmission channel h are within the limited range can the data be successfully transmitted. Therefore, the availability of the transmission channel is defined by the product of the packet loss, transmission delay and bit error availability rate. The virtual power plant information channel availability model is:

[0065]

[0066] in, is the availability rate of the virtual power plant information channel; is the packet loss of the h-th transmission channel at time t; is the transmission delay of the h-th transmission channel at time t; is the bit error availability rate of the hth transmission channel at time t. The calculation method is shown in the following formula:

[0067]

[0068] in, is the probability that the packet loss of transmission channel h is less than or equal to the total number of packets of node data passing rate; Node device j * The probability of being in a normal state, Indicates device j * The packet loss rate is greater than that of other node communication devices; Pr(DL h (t)≤T de ) is the probability that the switching delay of transmission channel h is less than or equal to the switching delay threshold of node device j; is the probability that the exchange delay of the jth node device in the transmission channel h is less than or equal to the upper limit of the node exchange delay; is the upper limit of the switching delay of the j-th node device; is the probability that device j is in normal state; Pr(DL l (t)≤T l DP ) is the probability that the exchange delay of the lth communication route in the transmission channel h is less than or equal to the upper limit of the communication route exchange delay; T l DP The upper limit of the exchange delay for the lth communication route; is the probability that device j is in a normal state; is the probability that the bit error probability of the lth communication transmission channel is less than the upper limit of the bit error rate; ES h (t) is the bit error rate of the lth communication transmission channel at time t; is the upper limit of the bit error rate of the communication transmission channel; ES j (t) is the bit error rate of the communication device of the jth node at time t; Indicates the jth * The bit error rate of the node communication device is greater than that of other node devices.

[0069] S3: Based on the normal state probability of the cyber-physical device in the normal-fault state transition model and the actual capacity of the cyber-physical device, establish a virtual power plant cyber-physical device availability model.

[0070] In practical applications, in most traditional studies, the availability of physical equipment is simplified to a two-state model that includes normal and fault states. However, it is not enough to limit the availability of physical equipment to the two-state dimension of "normal-fault". The actual capacity of the physical equipment will also have a profound impact on its operational availability. For example, if the actual capacity of the equipment is too small to support the task, it will be considered as having low availability even though it is in an operational state. Therefore, the availability of the physical equipment is defined not only by the probability of being in a normal state, but also by the actual capacity. The availability model of the cyber-physical equipment of the virtual power plant is:

[0071]

[0072] in, is the state availability rate of the i-th cyber-physical device at time t; Pr i N (t) is the probability that the i-th cyber-physical device is in a normal state at time t; is the actual capacity of the i-th cyber-physical device at time t; DER is the distributed new energy device; ES is the energy storage device; Ω t is the set of physical devices corresponding to time t; T is the time series set of multi-stage peak-shaving tasks of the virtual power plant.

[0073] 1) Actual capacity of distributed new energy equipment.

[0074] The prediction process for distributed renewable energy device power generation ignores the impact of natural disasters, which may affect network operators' judgment. Therefore, it is urgent to take into account the occurrence of meteorological disasters and re-evaluate the actual power generation capacity of distributed renewable energy devices. Assuming that different disasters occur independently, the actual power generation capacity of the i-th distributed renewable energy device at time t can be expressed as follows:

[0075]

[0076] in, is the predicted capacity of distributed new energy equipment i at time t; Pr(ξ f,t ) is the probability of occurrence of the fth natural disaster at time t, which is a historical statistical value; η f,i,t To reflect the factor of the disaster f on the distributed new energy equipment i at time t, the value is between 0 and 1; dis It is a collection of natural disasters, including flood disasters, freezing disasters and typhoon disasters.

[0077] 2) The actual capacity of the energy storage equipment.

[0078] The capacity decay of energy storage batteries includes calendar decay and cycle decay. Calendar decay refers to the phenomenon that the capacity of energy storage batteries gradually decreases over time when not in use; the corresponding capacity loss can be calculated according to Eyring's law, as shown in the following formula:

[0079]

[0080] in, is the capacity loss of energy storage device i due to calendar decay at time t; k B is the Boltzmann constant; T i,b,τ is the ambient temperature of the energy storage battery b in the energy storage device i; is the total number of batteries in energy storage device i; A τ is the attenuation coefficient of the battery, is the activation energy of the energy storage battery b in the energy storage device i, and Δτ is the working time of the battery.

[0081] Cycle attenuation refers to the capacity attenuation of energy storage batteries during cyclic charge and discharge. The capacity loss is affected by the number of cycles and can be calculated by the following formula:

[0082]

[0083] in, is the capacity loss of energy storage device i during cycle decay at time t; V0 and is the standard voltage and initial capacity of energy storage battery b; is the equivalent charge and discharge cycle number of the battery up to the time τ-1; B and c are the cycle aging coefficients of the battery; kT is the charge / discharge capacity of battery cell b in energy storage device i at time τ; i,b,τ It is a factor that reflects the relationship between temperature and capacity loss and is calculated by the following formula:

[0084]

[0085] in, is the activation energy of battery cycle aging; T i,b,τ is the temperature of battery b in energy storage device i at the time, and T ref is the reference value of ambient temperature. Taking into account calendar attenuation and cycle attenuation, the actual power generation capacity of energy storage device i is expressed by the following formula:

[0086]

[0087] in, is the actual capacity of energy storage device i at time t, is the initial capacity of the energy storage device without considering power attenuation.

[0088] S4: Establish a binary decision diagram model for all-stage tasks of a virtual power plant; the binary decision diagram model for all-stage tasks of a virtual power plant is established based on a binary decision diagram model for a single-stage task of a virtual power plant; the binary decision diagram model for a single-stage task of a virtual power plant is established based on a fault tree for a single-stage task of a virtual power plant using Shannon's decomposition law.

[0089] In practical applications, a single-phase task of a virtual power plant can be decomposed into three subtasks, including instruction reception, distribution, and task execution. There are two schemes for executing the task, namely the main scheme and the auxiliary scheme; if the main scheme or the auxiliary scheme is effectively executed, the task can be successfully completed. When all subtasks are successfully executed, the system's single-phase task is judged to be successful. The fault tree model of a single-phase task is as follows: Figure 3 shown.

[0090] According to the above single-stage fault tree model, the single-stage binary decision diagram of the virtual power plant can be obtained by Shannon's decomposition law, as shown in the following formula:

[0091]

[0092] Among them, G is a Boolean function based on the binary logic variable set Z = {z1, z2, z3, z4}, where z1, z2, z3, z4 represent the execution status of task A, task B, task C1 and task C2 respectively, and the variable order is: z3 <z4<z2<z1。 Indicates that the mth task is successfully executed, z m is the execution status of the mth task; Represents the variable z m Take the logical opposite, if z m =1 vice versa; Indicates that the mth task failed to execute smoothly. ite is an abstract operator representing the conditional statement "if-then-else". According to formula (28) and the corresponding variable sorting, the single-stage task binary decision diagram model is constructed from the bottom up according to the above rules, and the expression is as follows:

[0093]

[0094] Among them, G and H are two Boolean functions that traverse the fault tree, G1, G0 and H1, H2 are the sub-function expressions corresponding to Boolean functions G and H, respectively, as follows Figure 4 The above rules are applied recursively until one of the sub-expressions becomes a constant expression '0' or '1'.

[0095] Based on the above single-stage task fault tree model and binary decision diagram construction rules, a single-stage task binary decision diagram model of the virtual power plant is generated, such as Figure 5 shown.

[0096] like Figure 5 As shown in the figure, the proposed binary decision graph model for a single-stage task in a virtual power plant is a rooted, directed acyclic graph with two convergence points and multiple non-convergence points. The non-convergence points represent different subtasks, and the convergence points marked as "0" and "1" represent the failure and success of the single-stage task respectively. Two edges are output from each non-convergence point, and the edges marked as "0" and "1" represent the failure and success of the subtask corresponding to the non-convergence point respectively. Figure 5 It can be seen that the success of subtasks A and B requires that the availability of the communication system transmission channel be maintained at a high level, while the success of subtask C requires that the availability of the physical system equipment be maintained at a high level.

[0097] The success of the full-stage peak-shaving task of the virtual power plant is highly dependent on the successful execution of the single-stage tasks. If each single-stage task of the virtual power plant is successfully executed, the corresponding full-stage peak-shaving task of the virtual power plant can be considered completely reliable. To this end, a fault tree model of the full-stage task of the virtual power plant is proposed, such as Figure 6 shown.

[0098] In addition, in order to obtain the full-stage task binary decision diagram based on the above full-stage task fault tree model, it is necessary to simplify the single-stage task binary decision diagram, such as Figure 7 shown.

[0099] Apply the combination rule between single-stage subtasks, i.e., formula (29), to the simplified binary decision diagrams of each stage, and obtain the full-stage task binary decision diagram composed of the single-stage task binary decision diagrams, as shown in Figure 8 shown.

[0100] S5: Determine the reliability index of the virtual power plant's full-stage tasks based on the virtual power plant information channel availability model, the virtual power plant's cyber-physical equipment availability model, and the virtual power plant's full-stage tasks binary decision diagram model.

[0101] like Figure 8 As shown in the figure, the red dotted line with an arrow constitutes the main path for the full-stage peak-shaving task of the virtual power plant to be completely reliable. According to this path, the probability reliability index of the full-stage peak-shaving task determined by the reliability of all single-stage peak-shaving tasks is defined as follows:

[0102]

[0103] Among them, X and Respectively represent the success and failure of subtask X (X = A p , B p , C p,1 orC p,2 ); Is the reflection period tp The coefficient of task importance is also used to adjust the value of RE1, N pha is the total number of stages of the multi-stage peak-shaving task; T phy＝p is the moment corresponding to stage p; t p The probability that Task A, Task B, and Task C1 are successfully executed in the time period; t p The probability that Task A, Task B, and Task C2 are successfully executed during a period, while Task C1 is not successfully executed. To assume that the tasks in each stage and the subtasks within a single stage are independent of each other, the above formula can be rewritten as follows:

[0104]

[0105] Where Pr(X) is the probability of successful execution of task X. The successful completion of the task depends on the status of the participating devices. Unless all devices are available, the successful execution of the task cannot be guaranteed; therefore, the probability of task success is defined as follows:

[0106]

[0107] in, and To participate in Task A p and Task B p The communication transmission channel set; and and Then it is the participation task C p,1 and C p,2 A collection of physical devices; is the availability rate of the hth information channel; pha＝p is the time series set of the peak-shaving tasks of the virtual power plant in stage p.

[0108] In addition to the above-mentioned probabilistic reliability of the virtual power plant's full-stage peak-shaving mission, reliability can be further defined by the lack of demand for peak-shaving services, which can be expressed as follows:

[0109]

[0110]

[0111] in, The peak-shaving service supply-demand deviation for subtask C1; The supply and demand deviation of the peak shaving service for subtask C2; Service requirements for subtask C1; and are the physical device sets corresponding to the primary solution and the secondary solution respectively; i (t p) is the output value of the i-th physical device during the execution of the main scheme; p j (t p ) is the output value of the jth device during the execution of the auxiliary scheme; is the correlation coefficient between subtask A and subtask C1 in stage p; is the correlation coefficient between subtask A and subtask C2 in stage p, and is the equipment availability matrix and The elements in are shown in formulas (39) and (40). Similarly, is the correlation coefficient between subtask B and subtask C1 in stage p, is the correlation coefficient between subtask B and subtask C2 in stage p, and is the equipment availability matrix and The elements in are shown in formula (41) and formula (42):

[0112]

[0113] Among them, M C1-A is the coupling matrix between the physical devices participating in subtask C1 and the communication channels participating in task A; M C2-A is the coupling matrix between the physical devices participating in subtask C2 and the communication channels participating in task A; M C1-B is the coupling matrix between the physical devices participating in subtask C1 and the communication channels participating in task B; M C2-B is the coupling matrix between the physical devices participating in subtask C2 and the communication channels participating in task B; is the availability matrix of the communication transmission channels participating in subtask A; is the availability matrix of the communication transmission channel participating in subtask B, as shown below:

[0114]

[0115] in, and is the total number of physical devices participating in subtasks C1 and C2; is the availability rate of the hth information channel; and are the total number of communication transmission channels involved in task A and task B respectively.

[0116] S6: Establish a reliability optimization model that considers the uncertainty of real-time peak-shaving service demand and a reliability optimization model that considers the uncertainty of the main scheme equipment status; the reliability optimization model that considers the uncertainty of real-time peak-shaving service demand includes a first objective function and a first set of constraints; the first objective function aims to minimize the operating coefficient of the virtual power plant operator in the day-ahead peak-shaving auxiliary service market and minimize the operating coefficient of the real-time peak-shaving auxiliary service market; the first set of constraints includes the active power balance constraint of the virtual power plant, the output constraint of distributed new energy equipment, the power constraint of the energy storage equipment, the up and down adjustment constraint of the flexible load, and the constraint of the output of the day-ahead peak-shaving auxiliary service market on each device of the virtual power plant in the real-time peak-shaving auxiliary service market; the reliability optimization model that considers the uncertainty of the main scheme equipment status includes a second objective function and a second set of constraints; the second objective function aims to minimize the operating coefficient of the virtual power plant operator in the peak-shaving auxiliary service market.

[0117] In practical applications, the peak-shaving service supply-demand deviation of subtasks C1 and C2 will have a significant impact on the full-stage peak-shaving service reliability index RE2 of the virtual power plant. The service supply-demand deviation of subtasks C1 and C2 is defined as follows:

[0118]

[0119] in, and They represent the service requirements of subtask C1 and the set of physical devices involved in subtask C1 respectively; and Represent the service demand for subtask C2 and the set of physical devices participating in subtask C2, respectively. In practice, once the price and quantity of peak-shaving ancillary services in the day-ahead market are determined, the virtual power plant operator allocates power demand to each entity based on the contracted quantity. However, to ensure operational stability and reliability, the system operator adjusts the total peak-shaving service demand of the virtual power plant operator in the real-time market; therefore, the demand for subtask C1 is uncertain.

[0120] To improve the reliability of virtual power plants in the face of uncertainty in real-time peak-shaving services, this application proposes a two-stage robust optimization model for the day-ahead market and the real-time market. The goal of the virtual power plant operator is to minimize its total coefficient in the two markets. The first objective function is:

[0121]

[0122] in, and are the operator's operating coefficients in the day-ahead market and the real-time market respectively. The coefficients of the day-ahead market are shown as follows:

[0123]

[0124] in, is the operating coefficient of the virtual power plant operator in the day-ahead peak load ancillary service market; the first item represents the power generation coefficient of distributed new energy equipment, is the number of distributed new energy devices at t p Output at a moment; α RG is the cost coefficient of distributed new energy equipment; the second item is the charge and discharge coefficient of energy storage equipment, is the energy storage device i at t p Charging power at the moment; is the energy storage device i at t p The discharge power at the moment; α ES is the cost coefficient of energy storage equipment; pha＝p is the time series set of the virtual power plant’s peak-shaving tasks in phase p. The constraints corresponding to the day-ahead market cost optimization problem are as follows.

[0125] The active power balance constraint of the virtual power plant is:

[0126]

[0127] in, A set of distributed new energy equipment for executing the main plan; A set of energy storage devices for executing the main plan; The flexibility load set for executing the main plan; is the i-th flexibility load t p Increase power at all times; is the i-th flexibility load t p Reduce power at all times; t p The demand for day-ahead peak-shaving services of the virtual power plant at each moment.

[0128] The output constraints of the distributed new energy equipment are:

[0129]

[0130] The power constraint of the energy storage device is:

[0131]

[0132] Formulas (52) and (53) are the charge and discharge constraints for energy storage device i, respectively. Formula (54) indicates that energy storage device i cannot be in both the charge and discharge states at the same time. Formulas (55) and (56) represent the capacity constraints for energy storage device i.

[0133] in, tp A Boolean variable indicating the charge of the i-th energy storage device at the moment; for t p A Boolean variable indicating the discharge of the i-th energy storage device at the moment; t p The lower limit of the charging power of the i-th energy storage device at the moment; t p The upper limit of the charging power of the i-th energy storage device at the moment; t p The lower limit of the discharge power of the i-th energy storage device at the moment; t p The upper limit of the discharge power of the i-th energy storage device at the moment; t p The storage capacity of the i-th energy storage device at the moment; t p-1 The storage capacity of the i-th energy storage device at the moment; is the discharge coefficient of the energy storage device; is the charging coefficient of the energy storage device; t p The actual capacity of the i-th energy storage device at the moment.

[0134] The upward and downward adjustment constraints of the flexibility load are:

[0135]

[0136] in, t p A Boolean variable indicating the upward adjustment of the i-th flexibility load at time instant; t p Boolean variable indicating the reduction of the flexibility load at time i; t p The upper limit of the power increase of the i-th flexible load at time; t p The lower limit of the power increase of the i-th flexible load at time; t p The upper limit of the power reduction of the i-th flexible load at time; t p The lower limit of the power reduction of the i-th flexible load at time. According to formula (59), the flexible load point can only be in the upward or downward state at the same time.

[0137] The operating costs of the real-time market are determined by the output of distributed renewable energy equipment, the charging and discharging of energy storage, and the amount of flexible load adjustment.

[0138]

[0139] in, Operating coefficients for virtual power plant operators in the real-time peak-shaving ancillary services market; is the i-th distributed new energy device t p The contribution of real-time peak load ancillary service market at all times; RG The cost coefficient of distributed new energy equipment in the real-time peak-shaving auxiliary service market; is the i-th energy storage device t p Charging power in the real-time peak-shaving auxiliary service market at all times; is the i-th energy storage device t p The discharge power in the real-time peak-shaving auxiliary service market at all times; β ES The cost coefficient of energy storage equipment in the real-time peak-shaving auxiliary service market; is the i-th flexibility load t p Always adjust power in the real-time peak-shaving ancillary service market; is the i-th flexibility load t p The power reduction in the real-time peak load ancillary service market; β FL is the cost coefficient of the flexible load in the real-time peak-shaving ancillary service market. U is the uncertainty set corresponding to the demand in the real-time peak-shaving ancillary service market.

[0140] Further introduction of standardized auxiliary variables and To describe the uncertainty of real-time market demand, the uncertainty set can be further defined as shown in the following formula:

[0141]

[0142] Δd C1 =μ d -3σ d (61-4)

[0143]

[0144] in, and Δd C1 The upper and lower limits of the real-time market service demand deviation can be calculated by formulas (61-3) and (61-4). Formula (61-5) is for auxiliary variables and The uncertainty intensity of the real-time market can be controlled by adjusting its value.

[0145] The constraints imposed on the output of the day-ahead peak-shaving ancillary service market by each device of the virtual power plant in the real-time peak-shaving ancillary service market are:

[0146]

[0147]

[0148] Among them, κ RG is the adjustment coefficient of the output of distributed new energy equipment; κ ES+ is the adjustment coefficient of the charging power of the energy storage device; κ ES- is the adjustment coefficient of the discharge power of the energy storage device; κ FL+ The adjustment coefficient for increasing the power of flexible loads; κ FL- Adjustment factor for reducing power for flexible loads.

[0149] In practical applications, a reliability enhancement backup model was established, taking into account the uncertainty of the "normal-fault" state of the physical equipment participating in the primary solution. Similar to the primary solution, the objective function of the backup solution is also to minimize the operating cost of the virtual power plant. The secondary objective function is:

[0150]

[0151] in, A collection of distributed new energy devices to implement auxiliary solutions; A set of energy storage devices for executing auxiliary schemes; A set of flexible loads for implementing auxiliary schemes; The jth distributed new energy device at t p The effort of every moment; The jth energy storage device at t p Charging power at the moment; The jth energy storage device at t p Discharge power at the moment; is the jth flexibility load t p Increase power at all times; is the jth flexibility load t p Reduce power at all times.

[0152] The output constraints for each device in the auxiliary solution are similar to those in the primary solution. However, the key difference is that the set of devices involved in the auxiliary solution is different from the set of devices in the primary solution. The specific constraints are expressed as follows:

[0153] The second set of constraints is:

[0154]

[0155] in, The jth distributed new energy device at t p The actual power generation capacity at the moment.

[0156]

[0157] in, t p A Boolean variable indicating the charge of the jth energy storage device at the moment; t p The lower limit of the charging power of the jth energy storage device at time; t p The upper limit of the charging power of the j-th energy storage device at time.

[0158]

[0159] in, for t p A Boolean variable indicating the discharge of the jth energy storage device at the moment; t p The lower limit of the discharge power of the jth energy storage device at the moment; t p The upper limit of the discharge power of the jth energy storage device at time.

[0160]

[0161] in, t p+1 The storage capacity of the jth energy storage device at the moment; t p-1 The storage capacity of the jth energy storage device at the moment.

[0162]

[0163] in, t p The storage capacity of the jth energy storage device at the moment; t p The actual capacity of the i-th energy storage device at the moment.

[0164]

[0165] in, t p Boolean variable indicating the increase in flexibility load at time j; t p The upper limit of the power increase of the j-th flexible load at time; t p The lower limit of the power increase of the j-th flexible load at time.

[0166]

[0167] in, t p Boolean variable indicating the jth flexibility load reduction at time instant; t p The upper limit of the power reduction of the jth flexible load at time; t p The lower limit of the power reduction of the j-th flexible load at time.

[0168]

[0169] In addition, the total demand of the auxiliary scheme is jointly affected by the day-ahead market peak-shaving demand corresponding to the main scheme and the output of the distributed new energy equipment and energy storage system participating in the main scheme, as shown in formula (77):

[0170]

[0171] in, t p The auxiliary solution of the virtual power plant at any moment is the demand for peak-shaving services on the day before; t p The day-ahead peak load regulation service demand of the virtual power plant's main solution; is the i-th distributed new energy device t p The actual output at any moment is a discrete random variable; is the i-th energy storage device t p The actual output at a given moment is a discrete random variable. It is defined by the probability of being in the "normal-fault" state and the actual output in the primary solution, as shown in Equations (78) and (79). In addition, the supply and demand balance of the auxiliary solution is shown in Equation (80).

[0172]

[0173] in, is the i-th distributed new energy device t p The probability distribution of the output at each moment; is the i-th distributed new energy device t p The discrete random variable of actual output at time t is equal to the value of the i-th distributed new energy device at t p The probability of output at a given moment; is the i-th distributed new energy device t p The probability that the discrete random variable of actual output at a given moment is equal to 0; For the i-th device t p The probability of being in a normal state at all times; For the i-th device t p The probability of being in a fault state at any time.

[0174]

[0175] in, is the i-th energy storage device t p The probability distribution of the output at each moment; is the i-th energy storage device t p The discrete random variable of actual output at time t is equal to the value of the i-th energy storage device at t p The discharge power at time t is equal to the discharge power of the i-th energy storage device at time t p The probability of the difference in charging power at the moment; is the i-th energy storage device t p The probability that the discrete random variable of actual output at a given moment is equal to 0.

[0176]

[0177] S7: Solve the reliability optimization model considering the uncertainty of real-time peak-shaving service demand and the reliability optimization model considering the uncertainty of the main scheme equipment status respectively to obtain optimization parameters; the optimization parameters include the output of distributed new energy equipment, the charging power of energy storage equipment, the discharge power of energy storage equipment, the output of distributed new energy equipment in the real-time peak-shaving auxiliary service market, the charging amount of energy storage equipment in the real-time peak-shaving auxiliary service market, the discharge amount of energy storage equipment in the real-time peak-shaving auxiliary service market, the downward adjustment amount of flexible load in the real-time peak-shaving auxiliary service market, and the upward adjustment amount of flexible load in the real-time peak-shaving auxiliary service market.

[0178] S8: Optimize the reliability index of the virtual power plant's full-stage mission based on the optimization parameters, and complete the optimization of the reliability of the virtual power plant's multi-stage peak-shaving mission.

[0179] The reliability optimization method of multi-stage peak-shaving task of virtual power plant in this application is as follows Figure 9 As shown, first, based on the "normal-fault" state transition model of the information-physical equipment proposed in step 1, the information transmission error, delay and packet loss characteristics are further considered, and the virtual power plant information channel availability model in step 2 is proposed; combined with the actual capacity of the physical equipment, the virtual power plant physical equipment availability model in step 3 is proposed.

[0180] Next, considering the actual response of the virtual power plant's actual single-stage peak-shaving task, the failure process of the single-stage peak-shaving service is analyzed, and the virtual power plant single-stage task fault tree and binary decision diagram model in step 4 are proposed; based on the single-stage binary decision diagram model, considering the inter-stage coupling relationship of the virtual power plant's multi-stage peak-shaving task, the virtual power plant full-stage binary decision diagram model in step 5 is proposed.

[0181] Subsequently, based on the virtual power plant information channel availability model, the physical equipment availability model and the binary decision diagram model of the virtual power plant full-stage peak-shaving task, the full-stage peak-shaving task probability reliability index RE1 and the reliability index RE2 reflecting the service quality deviation were proposed.

[0182] Finally, in order to reduce the deviation of the peak-shaving service of the virtual power plant in the whole stage and further improve the mission reliability of the virtual power plant, a reliability improvement main plan considering the uncertainty of the real-time peak-shaving service in step 7 and a reliability improvement backup plan considering the uncertainty of the equipment status of the main plan in step 8 were proposed.

[0183] (1) This application proposes a "normal-fault" state transition model for cyber-physical devices. Based on a Markov process and combined with historical statistical data on the device's reliability indicators, failure rate and repair rate, the probability of the device being in the normal and faulty states is obtained. This serves as a foundation for constructing information channel availability models and physical device availability models.

[0184] (2) This application proposes a virtual power plant information channel availability model. This model comprehensively considers the effects of propagation delay in communication system routing and the packet loss, switching delay, and bit error characteristics of communication system node devices, defining the virtual power plant information channel availability model. Furthermore, by combining the state probabilities of communication system line devices and node devices, this model comprehensively considers these factors, enabling a relatively objective evaluation of the virtual power plant information channel availability.

[0185] (3) This application proposes a model for the availability of physical equipment in a virtual power plant. The model comprehensively considers the impact of the state probability of physical system equipment and the actual capacity of the equipment on the availability. For the distributed new energy equipment in the virtual power plant, a model for actual power generation capacity that takes into account the impact of natural disasters is proposed; for the distributed energy storage system in the virtual power plant, a model for actual storage capacity that takes into account calendar decay and cyclic decay is proposed. The evaluation angles are diverse, fully reflecting the relationship between the availability of physical equipment and the reliability of the peak-shaving service of the virtual power plant throughout the entire period.

[0186] (4) This application proposes a single-stage task fault tree and binary decision diagram model for a virtual power plant. Based on the actual response of the virtual power plant's peak-shaving service, the failure process of the single-stage peak-shaving service is analyzed, and a single-stage task fault tree and binary decision diagram model are proposed. The binary decision diagram can more vividly describe the relationship between each virtual power plant's single-stage peak-shaving subtask and the overall task.

[0187] (5) This application proposes a full-stage binary decision diagram model for a virtual power plant. Based on the single-stage task fault tree and binary decision diagram model of the virtual power plant, the inter-stage coupling relationship of the multi-stage peak-shaving task of the virtual power plant is fully considered. The constructed full-stage binary decision diagram model of the virtual power plant can clearly reflect the impact of each subtask of the virtual power plant's cyber-physical system on the reliability of the full-stage peak-shaving task, providing a new idea for the reliability assessment of the virtual power plant.

[0188] (6) This application proposes a reliability index for the full-stage mission of a virtual power plant. Based on the binary decision diagram model of the full-stage peak-shaving mission of a virtual power plant, a probabilistic reliability index RE1 for the full-stage peak-shaving mission is proposed. Then, based on the probabilistic reliability, considering the insufficient demand for peak-shaving services, a reliability index RE2 reflecting the deviation of service quality is proposed. These two indicators can fully describe the comprehensive performance of the virtual power plant in providing peak-shaving services.

[0189] (7) This application proposes a reliability improvement scheme that considers the uncertainty of real-time peak-shaving services. Taking into account the uncertainty deviation between the day-ahead service demand and the real-time service demand during the peak-shaving service process, a reliability improvement scheme model is proposed with the minimum operating cost of the virtual power plant as the objective function. By applying this model, the service demand deviation of the virtual power plant can be effectively reduced and the reliability of the virtual power plant can be improved.

[0190] (8) This application proposes a backup solution to improve reliability by considering the uncertainty of the equipment status of the primary solution. Taking into account the uncertainty of the equipment status of the primary solution, the backup solution is used to adjust the service demand deviation of the primary solution, further improving the reliability of the virtual power plant in all stages from the perspective of the backup solution.

[0191] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the above-mentioned virtual power plant multi-stage peak-shaving task reliability optimization method is implemented.

[0192] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which, when executed by a processor, implements the above-mentioned method for optimizing the reliability of multi-stage peak-shaving tasks of a virtual power plant.

[0193] In an exemplary embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the above-mentioned method for optimizing the reliability of multi-stage peak-shaving tasks of a virtual power plant.

[0194] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 10As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a reliability optimization method for multi-stage peak-shaving tasks of a virtual power plant is implemented.

[0195] Those skilled in the art will understand that Figure 10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0196] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0197] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0198] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0199] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0200] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A reliability optimization method for multi-stage peak-shaving tasks of a virtual power plant, characterized in that: include: Construct a normal-fault state transition model for cyber-physical devices; Establish a virtual power plant information channel availability model; The virtual power plant information channel availability model is determined based on packet loss, transmission delay and bit error availability of the transmission channel; Establishing a virtual power plant cyber-physical device availability model based on the normal state probability of the cyber-physical device in the normal-fault state transition model and the actual capacity of the cyber-physical device; Establishing a binary decision diagram model for all-stage tasks of a virtual power plant; the binary decision diagram model for all-stage tasks of a virtual power plant is established based on a binary decision diagram model for a single-stage task of a virtual power plant; the binary decision diagram model for a single-stage task of a virtual power plant is established based on a fault tree for a single-stage task of a virtual power plant using Shannon's decomposition law; Determining a reliability index of the virtual power plant's full-stage mission according to the virtual power plant information channel availability model, the virtual power plant's cyber-physical equipment availability model, and the virtual power plant's full-stage mission binary decision diagram model; Establish a reliability optimization model that considers the uncertainty of real-time peak-shaving service demand and a reliability optimization model that considers the uncertainty of the main scheme equipment status; the reliability optimization model that considers the uncertainty of real-time peak-shaving service demand includes a first objective function and a first constraint condition set; the first objective function aims to minimize the virtual power plant operator's operating coefficient in the day-ahead peak-shaving auxiliary service market and minimize the real-time peak-shaving auxiliary service market operating coefficient; the first constraint condition set includes the active power balance constraint of the virtual power plant, the output constraint of distributed new energy equipment, the power constraint of the energy storage equipment, the upward and downward adjustment constraint of the flexible load, and the constraint of the virtual power plant equipment in the real-time peak-shaving auxiliary service market on the output of the day-ahead peak-shaving auxiliary service market; the reliability optimization model that considers the uncertainty of the main scheme equipment status includes a second objective function and a second constraint condition set; the second objective function aims to minimize the virtual power plant operator's operating coefficient in the peak-shaving auxiliary service market; The first objective function is: in, The operating coefficient of the virtual power plant operator in the day-ahead peak load ancillary service market; Operating coefficients for virtual power plant operators in the real-time peak-shaving ancillary services market; is the number of distributed new energy devices at t p Output at a moment; α RG is the cost coefficient of distributed new energy equipment; is the energy storage device i at t p Charging power at the moment; is the energy storage device i at t p The discharge power at the moment; α ES is the cost coefficient of energy storage equipment; pha＝p is the time series set of the peak-shaving tasks of the virtual power plant in stage p; A set of distributed new energy equipment for executing the main plan; is the set of energy storage devices that implement the main plan; U is the uncertainty set corresponding to the market demand for real-time peak-shaving auxiliary services; is the i-th distributed new energy device t p The contribution of real-time peak load ancillary service market at all times; RG The cost coefficient of distributed new energy equipment in the real-time peak-shaving auxiliary service market; is the i-th energy storage device t p Charging power in the real-time peak-shaving auxiliary service market at all times; is the i-th energy storage device t p The discharge power in the real-time peak-shaving auxiliary service market at all times; β ES The cost coefficient of energy storage equipment in the real-time peak-shaving auxiliary service market; is the i-th flexibility load t p Always adjust power in the real-time peak-shaving ancillary service market; is the i-th flexibility load t p The power reduction in the real-time peak load ancillary service market; β FL The cost coefficient of flexibility load in the real-time peak load ancillary service market; The flexibility load set for executing the main plan; The second objective function is: Among them, T pha＝p is the time series set of the peak-shaving tasks of the virtual power plant in stage p; A collection of distributed new energy devices to implement auxiliary solutions; A set of energy storage devices for executing auxiliary schemes; A set of flexible loads for implementing auxiliary schemes; The jth distributed new energy device at t p The effort of every moment; The jth energy storage device at t p Charging power at the moment; The jth energy storage device at t p Discharge power at the moment; is the jth flexibility load t p Increase power at all times; is the jth flexibility load t p Reduce power at all times; The second set of constraints is: in, A collection of distributed new energy devices to implement auxiliary solutions; The jth distributed new energy device at t p Actual power generation capacity at the moment; The jth distributed new energy device at t p The effort of every moment; in, A set of energy storage devices for executing auxiliary schemes; t p A Boolean variable indicating the charge of the jth energy storage device at the moment; The jth energy storage device at t p Charging power at the moment; t p The lower limit of the charging power of the jth energy storage device at time; t p The upper limit of the charging power of the jth energy storage device at time; in, t p A Boolean variable indicating the discharge of the jth energy storage device at the moment; The jth energy storage device at t p Discharge power at the moment; t p The lower limit of the discharge power of the jth energy storage device at the moment; t p The upper limit of the discharge power of the jth energy storage device at the moment; in, t p+1 The storage capacity of the jth energy storage device at the moment; t p-1 The storage capacity of the jth energy storage device at the moment; is the discharge coefficient of the energy storage device; is the charging coefficient of the energy storage device; in, t p The storage capacity of the jth energy storage device at the moment; t p The actual capacity of the i-th energy storage device at the moment; in, A set of flexible loads for implementing auxiliary schemes; t p Boolean variable indicating the increase in flexibility load at time j; is the jth flexibility load t p Increase power at all times; t p The upper limit of the power increase of the j-th flexible load at time; t p The lower limit of the power increase of the j-th flexible load at time; in, t p Boolean variable indicating the jth flexibility load reduction at time instant; is the jth flexibility load t p Reduce power at all times; t p The upper limit of the power reduction of the jth flexible load at time; t p The lower limit of the power reduction of the j-th flexible load at time; in, t p The auxiliary solution of the virtual power plant at any moment is the demand for peak-shaving services on the day before; t p The day-ahead peak load regulation service demand of the virtual power plant's main solution; A set of distributed new energy equipment for executing the main plan; A set of energy storage devices for executing the main plan; The flexibility load set for executing the main plan; is the i-th distributed new energy device t p The actual output at any moment is a discrete random variable; is the i-th energy storage device t p The actual output at any moment is a discrete random variable; is the i-th flexibility load t p Increase power at all times; is the i-th flexibility load t p Reduce power at all times; in, is the i-th distributed new energy device t p The probability distribution of the output at each moment; is the i-th distributed new energy device t p The discrete random variable of actual output at time t is equal to the value of the i-th distributed new energy device at t p The probability of output at a given moment; is the number of distributed new energy devices at t p The effort of every moment; is the i-th distributed new energy device t p The probability that the discrete random variable of actual output at a given moment is equal to 0; For the i-th device t p The probability of being in a normal state at all times; For the i-th device t p The probability of being in a fault state at all times; in, is the i-th energy storage device t p The probability distribution of the output at each moment; is the i-th energy storage device t p The discrete random variable of actual output at time t is equal to the value of the i-th energy storage device at t p The discharge power at time t is equal to the discharge power of the i-th energy storage device at time t p The probability of the difference in charging power at the moment; is the energy storage device i at t p Discharge power at the moment; is the energy storage device i at t p Charging power at the moment; is the i-th energy storage device t p The probability that the discrete random variable of actual output at a given moment is equal to 0; The reliability optimization model considering the uncertainty of real-time peak-shaving service demand and the reliability optimization model considering the uncertainty of main scheme equipment state are solved respectively to obtain optimization parameters; the optimization parameters include the output of distributed new energy equipment, the charging power of energy storage equipment, the discharge power of energy storage equipment, the output of distributed new energy equipment in the real-time peak-shaving auxiliary service market, the charging amount of energy storage equipment in the real-time peak-shaving auxiliary service market, the discharge amount of energy storage equipment in the real-time peak-shaving auxiliary service market, the downward adjustment amount of flexible load in the real-time peak-shaving auxiliary service market, and the upward adjustment amount of flexible load in the real-time peak-shaving auxiliary service market; Based on the optimization parameters, the reliability index of the virtual power plant's full-stage mission is optimized to complete the optimization of the reliability of the virtual power plant's multi-stage peak-shaving mission.

2. The reliability optimization method for multi-stage peak-shaving tasks of a virtual power plant according to claim 1 is characterized in that: The virtual power plant information channel availability model is: in, is the availability rate of the virtual power plant information channel; is the packet loss of the h-th transmission channel at time t; is the transmission delay of the h-th transmission channel at time t; is the bit error availability rate of the h-th transmission channel at time t.

3. The reliability optimization method for multi-stage peak-shaving tasks of a virtual power plant according to claim 1 is characterized in that: The availability model of the cyber-physical equipment of the virtual power plant is: in, is the state availability of the i-th cyber-physical device at time t; is the probability that the i-th cyber-physical device is in a normal state at time t; is the actual capacity of the i-th cyber-physical device at time t; DER is the distributed new energy device; ES is the energy storage device; Ω t is the set of physical devices corresponding to time t; T is the time series set of multi-stage peak-shaving tasks of the virtual power plant.

4. The reliability optimization method for multi-stage peak-shaving tasks of a virtual power plant according to claim 1 is characterized in that: The active power balance constraint of the virtual power plant is: in, is the number of distributed new energy devices at t p The effort of every moment; is the energy storage device i at t p Charging power at the moment; is the energy storage device i at t p Discharge power at the moment; A set of distributed new energy equipment for executing the main plan; A set of energy storage devices for executing the main plan; The flexibility load set for executing the main plan; is the i-th flexibility load t p Increase power at all times; is the i-th flexibility load t p Reduce power at all times; t p The demand for day-ahead peak-shaving services of the virtual power plant; The output constraints of the distributed new energy equipment are: in, is the number of distributed new energy devices at t p Actual power generation capacity at the moment; The power constraint of the energy storage device is: in, t p A Boolean variable indicating the charge of the i-th energy storage device at the moment; for t p A Boolean variable indicating the discharge of the i-th energy storage device at the moment; t p The lower limit of the charging power of the i-th energy storage device at the moment; t p The upper limit of the charging power of the i-th energy storage device at the moment; t p The lower limit of the discharge power of the i-th energy storage device at the moment; t p The upper limit of the discharge power of the i-th energy storage device at the moment; t p The storage capacity of the i-th energy storage device at the moment; t p-1 The storage capacity of the i-th energy storage device at the moment; is the discharge coefficient of the energy storage device; is the charging coefficient of the energy storage device; t p The actual capacity of the i-th energy storage device at the moment; The upward and downward adjustment constraints of the flexibility load are: in, t p A Boolean variable indicating the upward adjustment of the i-th flexibility load at time instant; t p Boolean variable indicating the reduction of the flexibility load at time i; t p The upper limit of the power increase of the i-th flexible load at time; t p The lower limit of the power increase of the i-th flexible load at time; t p The upper limit of the power reduction of the i-th flexible load at time; t p The lower limit of the power reduction of the i-th flexible load at time; The constraints imposed on the output of the day-ahead peak-shaving ancillary service market by each device of the virtual power plant in the real-time peak-shaving ancillary service market are: Among them, κ RG is the adjustment coefficient of the output of distributed new energy equipment; κ ES+ is the adjustment coefficient of the charging power of the energy storage device; κ ES- is the adjustment coefficient of the discharge power of the energy storage device; κ FL+ The adjustment coefficient for increasing the power of flexible loads; κ FL- Adjustment factor for reducing power for flexible loads.

5. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the reliability optimization method for multi-stage peak-shaving tasks of a virtual power plant according to any one of claims 1 to 4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the reliability optimization method for multi-stage peak-shaving tasks of a virtual power plant according to any one of claims 1 to 4 is implemented.

7. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the reliability optimization method for multi-stage peak-shaving tasks of a virtual power plant according to any one of claims 1 to 4 is implemented.

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