Converter station constant volume method, device and equipment, storage medium and program product

Abstract

The invention relates to a constant volume method, device and equipment of a converter station, a storage medium and a program product. The method comprises the following steps: on the basis of an expected value of a system investment cost parameter, an expected value of a renewable energy non-utilization rate and an expected value of a commutation loss amount, constructing a corresponding relationship among the system investment cost parameter, the renewable energy non-utilization rate, the commutation loss amount and the capacity; based on the corresponding relation, a preset constraint condition and a normal distribution optimization algorithm, determining a capacity parameter of each converter station in the power grid system; the preset constraint condition comprises at least one of a power balance constraint, an energy storage charging and discharging constraint, an installed capacity constraint, a commutation power constraint, a capacity-load ratio constraint and a power supply reliability constraint. By adopting the method, the determination accuracy of the capacity parameters of the converter station can be ensured.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method, device, equipment, storage medium, and program product for sizing a converter station. Background Art

[0002] A low-voltage converter station is a type of converter station. It is a facility in the power system used to convert AC power into DC power, or vice versa. It is mainly used in low-voltage distribution systems, and its voltage level is usually below 1 kV.

[0003] In practical applications, it's often necessary to rationally determine the active and reactive power capacities of a converter station based on the actual needs of the power system in which it operates. This is known as low-voltage converter station sizing. This is a crucial step in converter station design and planning, directly impacting its operational efficiency, economy, and reliability.

[0004] Therefore, how to accurately determine the capacity of the converter station in the power system has become an urgent problem to be solved. Summary of the Invention

[0005] Based on this, it is necessary to provide a method, device, equipment, storage medium and program product for determining the capacity of a converter station in a power system, which can accurately determine the capacity of the converter station in the power system in order to address the above technical problems.

[0006] In a first aspect, the present application provides a method for determining the capacity of a converter station, comprising:

[0007] Based on the expected value of the system investment cost parameter, the expected value of the renewable energy non-utilization rate and the expected value of the commutation loss, the corresponding relationship between the system investment cost parameter, the renewable energy non-utilization rate, the commutation loss and the capacity is established;

[0008] Based on the corresponding relationship, preset constraints and normal distribution optimization algorithm, the capacity parameters of each converter station in the power grid system are determined; the preset constraints include at least one of power balance constraints, energy storage charging and discharging constraints, installed capacity constraints, commutation power constraints, capacity-to-load ratio constraints and power supply reliability constraints.

[0009] In one embodiment, the above-mentioned construction of the corresponding relationship between the system investment cost parameter, the renewable energy unutilization rate, the commutation loss amount and the capacity based on the expected value of the system investment cost parameter, the expected value of the renewable energy unutilization rate and the expected value of the commutation loss amount includes:

[0010] Determining a first corresponding relationship corresponding to the system investment cost according to the system investment cost parameter, the preset system investment cost weight, and the expected value of the system investment cost parameter;

[0011] Determining a second corresponding relationship corresponding to the renewable energy unutilization rate according to the renewable energy unutilization rate, the preset weight of the renewable energy unutilization rate, and the expected value of the renewable energy unutilization rate;

[0012] determining a third corresponding relationship corresponding to the commutation loss amount according to the commutation loss amount, a preset commutation loss amount weight, and an expected value of the commutation loss amount;

[0013] According to the first corresponding relationship, the second corresponding relationship and the third corresponding relationship, a converter station capacity configuration model is constructed.

[0014] In one embodiment, the method further includes:

[0015] Obtain investment and operating costs and electricity purchase and sales fees from the large power grid;

[0016] Determine the system investment cost parameters based on the investment and operating costs and the electricity purchase and sales costs of the large power grid.

[0017] In one embodiment, the method further includes:

[0018] Obtain photovoltaic power parameters, total load demand parameters and energy storage power parameters;

[0019] The renewable energy unutilization rate is determined based on photovoltaic power parameters, total load demand parameters and energy storage power parameters.

[0020] In one embodiment, the method further includes:

[0021] Obtain the transfer power at each moment and the commutation coefficient of each converter station;

[0022] The commutation loss is determined based on the transferred power and commutation coefficient.

[0023] In one embodiment, the method of determining the capacity parameters of each converter station in the power grid system based on the corresponding relationship, the preset constraints, and the normal distribution optimization algorithm includes:

[0024] According to the preset constraints and the normal distribution optimization algorithm, the converter station capacity configuration model is solved to obtain the initial total capacity parameters of the converter station and the initial capacity parameters of each converter station;

[0025] Update the parameters of the normal distribution optimization algorithm, return to the step of solving the converter station capacity configuration model according to the preset constraints and the normal distribution optimization algorithm, and obtain new initial total capacity parameters and new initial capacity parameters;

[0026] If the initial total capacity parameter and the new initial total capacity parameter are consistent, and the initial capacity parameter and the new initial capacity parameter are consistent, determining the new initial total capacity parameter as the target total capacity parameter, and determining the new initial capacity parameter as the target initial capacity parameter;

[0027] If the initial total capacity parameter and the new initial total capacity parameter are inconsistent, or the initial capacity parameter and the new initial capacity parameter are inconsistent, then return to the step of updating the parameters of the normal distribution optimization algorithm until the initial total capacity parameter and the new initial total capacity parameter are consistent, and the initial capacity parameter and the new initial capacity parameter are consistent.

[0028] In a second aspect, the present application further provides a constant capacity device for a converter station, comprising:

[0029] A construction module is used to construct a corresponding relationship between the system investment cost parameter, the renewable energy non-utilization rate, the commutation loss amount and the capacity based on the expected value of the system investment cost parameter, the expected value of the renewable energy non-utilization rate and the expected value of the commutation loss amount;

[0030] A determination module is used to determine the capacity parameters of each converter station in the power grid system based on the corresponding relationship, preset constraints and a normal distribution optimization algorithm; the preset constraints include at least one of power balance constraints, energy storage charging and discharging constraints, installed capacity constraints, commutation power constraints, capacity-to-load ratio constraints and power supply reliability constraints.

[0031] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0032] Based on the expected value of the system investment cost parameter, the expected value of the renewable energy non-utilization rate and the expected value of the commutation loss, the corresponding relationship between the system investment cost parameter, the renewable energy non-utilization rate, the commutation loss and the capacity is established;

[0033] Based on the corresponding relationship, preset constraints and normal distribution optimization algorithm, the capacity parameters of each converter station in the power grid system are determined; the preset constraints include at least one of power balance constraints, energy storage charging and discharging constraints, installed capacity constraints, commutation power constraints, capacity-to-load ratio constraints and power supply reliability constraints.

[0034] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0035] Based on the expected value of the system investment cost parameter, the expected value of the renewable energy non-utilization rate and the expected value of the commutation loss, the corresponding relationship between the system investment cost parameter, the renewable energy non-utilization rate, the commutation loss and the capacity is established;

[0036] Based on the corresponding relationship, preset constraints and normal distribution optimization algorithm, the capacity parameters of each converter station in the power grid system are determined; the preset constraints include at least one of power balance constraints, energy storage charging and discharging constraints, installed capacity constraints, commutation power constraints, capacity-to-load ratio constraints and power supply reliability constraints.

[0037] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0038] Based on the expected value of the system investment cost parameter, the expected value of the renewable energy non-utilization rate and the expected value of the commutation loss, the corresponding relationship between the system investment cost parameter, the renewable energy non-utilization rate, the commutation loss and the capacity is established;

[0039] Based on the corresponding relationship, preset constraints and normal distribution optimization algorithm, the capacity parameters of each converter station in the power grid system are determined; the preset constraints include at least one of power balance constraints, energy storage charging and discharging constraints, installed capacity constraints, commutation power constraints, capacity-to-load ratio constraints and power supply reliability constraints.

[0040] The above-mentioned converter station capacity determination method, device, equipment, storage medium and program product ensure the accuracy of determining the capacity parameters of the converter station by setting multiple targets, namely the expected value of the system investment cost parameter, the expected value of the renewable energy unutilization rate and the expected value of the conversion loss, and taking into account multiple constraints, namely at least one of the power balance constraint, energy storage charging and discharging constraint, installed capacity constraint, conversion power constraint, capacity-to-load ratio constraint and power supply reliability constraint. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 FIG. 1 is an application environment diagram of a method for determining the capacity of a converter station in one embodiment;

[0043] Figure 2 1 is a flow chart of a method for determining the capacity of a converter station in one embodiment;

[0044] Figure 3 Schematic diagram of a flow chart of a method for determining the capacity of a converter station in another embodiment;

[0045] Figure 4 Schematic diagram of a flow chart of a method for determining the capacity of a converter station in another embodiment;

[0046] Figure 5 Schematic diagram of a flow chart of a method for determining the capacity of a converter station in another embodiment;

[0047] Figure 6 Schematic diagram of a flow chart of a method for determining the capacity of a converter station in another embodiment;

[0048] Figure 7 Schematic diagram of a flow chart of a method for determining the capacity of a converter station in another embodiment;

[0049] Figure 8 FIG. 1 is a structural block diagram of a constant capacity device of a converter station in one embodiment. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0051] A low-voltage converter station is a type of converter station. It is a facility in the power system used to convert AC power into DC power, or vice versa. It is mainly used in low-voltage distribution systems, and its voltage level is usually below 1 kV.

[0052] In practical applications, it's often necessary to rationally determine the active and reactive power capacities of a converter station based on the actual needs of the power system in which it operates. This is known as low-voltage converter station sizing. This is a crucial step in converter station design and planning, directly impacting its operational efficiency, economy, and reliability.

[0053] Therefore, how to accurately determine the capacity of converter stations in power systems has become an urgent problem to be solved. This application aims to solve this problem.

[0054] After introducing the background technology of the converter station capacity determination method provided by the embodiment of the present application, the implementation environment involved in the converter station capacity determination method provided by the embodiment of the present application will be briefly described below. The converter station capacity determination method provided by the embodiment of the present application can be applied to Figure 2The computer device shown in FIG. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, while the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless means, where wireless means can be implemented via Wi-Fi, mobile cellular networks, NFC (near-field communication), or other technologies. When executed by the processor, the computer program implements a method for determining the capacity of a converter station. The display unit of the computer device is used to produce a visual image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0055] Those skilled in the art will understand that Figure 2 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 terminal may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0056] After introducing the application scenarios of the converter station capacity determination method provided by the embodiments of the present application, the following focuses on the converter station capacity determination method described in the present application.

[0057] In one embodiment, Figure 3 As shown, a method for determining the capacity of a converter station is provided, which is applied to Figure 2 The computer device in the example is used to illustrate the process, including the following steps:

[0058] S201. Based on the expected value of the system investment cost parameter, the expected value of the renewable energy unutilization rate, and the expected value of the commutation loss, a corresponding relationship between the system investment cost parameter, the renewable energy unutilization rate, the commutation loss, and the capacity is established.

[0059] In this embodiment, a converter station economic configuration and sizing method is introduced during the flexible interconnection of multiple distribution substations to securely and economically accommodate a high proportion of distributed energy. This method effectively optimizes the system's converter station capacity, improves the rationality of system design, and thus optimizes initial system investment and enhances the cost-effectiveness of system design and construction. The model and objective function components establish a multi-objective optimization model for converter station capacity configuration, targeting distributed energy utilization and system investment costs while satisfying constraints such as power balance, converter power, and installed capacity.

[0060] In the process of processing multi-objective functions, there are two main basic values, namely the actual value and relative value of each sub-objective. Because different sub-objective functions have different meanings and there are certain differences in dimensionality, if they are not processed, the accuracy of the calculation results will be reduced. If the order of magnitude difference between the sub-objectives is too large, using the direct addition method to solve the multi-objective function will reduce the proportion of the sub-objective with a smaller order of magnitude in the multi-objective function optimization problem, and may even bury this sub-objective function and fail to reflect the expected optimization goal. To address this problem, the concept of per-unit value in mathematics is generally used as a theoretical basis to convert each sub-objective into the same order of magnitude and normalize the multi-objective function. Among them, the specific expression of the multi-objective function is the following formula (1):

[0061]

[0062] Where, represents the comprehensive objective function, Represent each sub-objective function respectively.

[0063] Process it according to the above normalization method and obtain formula (2):

[0064]

[0065] Where, Represents the weight of each sub-goal; Represents the expected value of each sub-objective function.

[0066] Optional, see Figure 3 , further provides a specific implementation method for establishing a correspondence between system investment cost parameters, renewable energy non-utilization rate, commutation loss, and capacity, namely, the above-mentioned S201 "establishing a correspondence between system investment cost parameters, renewable energy non-utilization rate, commutation loss, and capacity based on the expected value of the system investment cost parameter, the expected value of the renewable energy non-utilization rate, and the expected value of the commutation loss" includes:

[0067] S301: Determine a first corresponding relationship corresponding to the system investment cost according to a system investment cost parameter, a preset system investment cost weight, and an expected value of the system investment cost parameter.

[0068] Optionally, referring to formula (3), a specific method for determining the first corresponding relationship corresponding to the system investment cost is provided according to the system investment cost parameter, the preset system investment cost weight, and the expected value of the system investment cost parameter.

[0069]

[0070] in, is the weight of the preset system investment cost, is the system investment cost parameter, is the expected value of the system investment cost parameter, This is the first correspondence between the system investment cost.

[0071] S302: Determine a second corresponding relationship corresponding to the renewable energy unutilization rate according to the renewable energy unutilization rate, a preset weight of the renewable energy unutilization rate, and an expected value of the renewable energy unutilization rate.

[0072] Optionally, referring to formula (4), a specific method for determining the second corresponding relationship corresponding to the renewable energy unutilization rate is provided according to the renewable energy unutilization rate, the preset weight of the renewable energy unutilization rate, and the expected value of the renewable energy unutilization rate.

[0073]

[0074] in, To preset the weight of renewable energy unutilized rate, is the unutilized rate of renewable energy, is the expected value of the unutilized rate of renewable energy, This is the second corresponding relationship corresponding to the system investment cost.

[0075] S303: Determine a third corresponding relationship corresponding to the commutation loss according to the commutation loss, the preset weight of the commutation loss, and the expected value of the commutation loss.

[0076] Optionally, referring to formula (5), a specific method for determining the third corresponding relationship corresponding to the commutation loss amount according to the commutation loss amount, the preset commutation loss amount weight and the expected value of the commutation loss amount is provided.

[0077]

[0078] in, is the weight of the preset commutation loss, is the commutation loss, is the expected value of the commutation loss.

[0079] S304: Construct a converter station capacity configuration model according to the first corresponding relationship, the second corresponding relationship, and the third corresponding relationship.

[0080] In this embodiment, after the first corresponding relationship, the second corresponding relationship, and the third corresponding relationship are determined, a converter station capacity configuration model may be constructed according to the first corresponding relationship, the second corresponding relationship, and the third corresponding relationship.

[0081] Optionally, referring to formula (6), a specific method for constructing a converter station capacity configuration model according to the first corresponding relationship, the second corresponding relationship, and the third corresponding relationship is provided.

[0082]

[0083] S202. Determine the capacity parameters of each converter station in the power grid system based on the corresponding relationship, preset constraints and normal distribution optimization algorithm; the preset constraints include at least one of power balance constraints, energy storage charging and discharging constraints, installed capacity constraints, commutation power constraints, capacity-to-load ratio constraints and power supply reliability constraints.

[0084] The power balance constraint means that in order to ensure the normal operation of the system, the power flow between the converters in the system must meet the power balance constraint, that is, the following formula (7):

[0085]

[0086] Where, Indicates the power exchanged between the system and the grid, represents the energy storage power, represents photovoltaic power, Indicates the power consumed by load 1, represents the power consumed by load 2, represents the power consumed by load 3, Indicates the converter power loss.

[0087] Energy storage charge and discharge constraints refer to the following: the magnitude of the energy storage charge and discharge power and the depth of charge and discharge have a decisive influence on its service life. Therefore, in order to extend the service life of the energy storage battery and reduce system investment and operation and maintenance costs, it is necessary to constrain the charge and discharge depth and the magnitude of the charge and discharge power of the energy storage battery. That is, the following formula (8):

[0088]

[0089] Where, Indicates the remaining battery power at time t; Indicates the maximum value of battery SOC; Indicates the minimum value of battery SOC; represents the charging power at time t; Indicates the maximum charging power at time t; Indicates the minimum charging power at time t; represents the discharge power at time t; Indicates the maximum discharge power at time t; Indicates the minimum discharge power at time t.

[0090] The installed capacity constraint means that due to the limitations of construction cost, production process and site, the capacity of the converter station will be limited during design, that is, the following formula (9):

[0091]

[0092] Where, 、 、 are the lower limits of the installed capacity of the three converter stations, 、 、 These are the upper limits of the installed capacity of the three converter stations.

[0093] The commutation power constraint means that when transferring power between AC and DC buses, there must be certain constraints, which cannot exceed the maximum value of the converter station, that is, the following formula (10):

[0094]

[0095] Where, 、 、 These are the power transferred between the AC and DC buses of the three converter stations. 、 、 are the maximum values of power transferred between the AC and DC buses of the three converter stations.

[0096] The capacity-load ratio constraint means that in order to ensure the rationality and economy of the system design, it is necessary to impose certain constraints on the capacity-load ratio of the converter station, namely the following formula (11):

[0097]

[0098] Where, 、 、 Respectively represent the minimum load ratio limits of the three converter stations, 、 、 They represent the maximum load ratio limits of the three converter stations respectively. 、 、 They represent the capacity ratios of the three converter stations respectively.

[0099] Among them, power supply reliability constraints mean that in order to ensure the power demand of users, the power supply reliability of the medium-voltage flexible direct current interconnection system needs to be constrained. For the medium-voltage flexible direct current interconnection ring network system, in order to ensure that there is sufficient capacity between converter stations to meet the power mutual assistance demand, the redundant capacity of the converter stations is constrained according to the annual average load growth rate. In this study, a capacity-load ratio correction factor is introduced to constrain the redundant capacity of the converter stations, that is, the following formula (12):

[0100]

[0101] Where, Indicates the corresponding load ratio correction factor, Indicates the minimum constraint on the load ratio correction factor.

[0102] Among them, the improved generalized normal distribution optimization algorithm is a new intelligent optimization algorithm. Its most notable feature is that it does not require any special control parameters; only the necessary population size and termination conditions must be set in advance. Furthermore, its structure is very simple, and individual positions are updated using a constructed generalized normal distribution formula. The generalized normal distribution optimization algorithm is inspired by normal distribution theory.

[0103] The optimization of the sizing problem of low-voltage converter stations is a mixed nonlinear integer programming problem with multiple objectives, multiple constraints, and multiple variables. To address the problem that the existing generalized normal distribution optimization algorithm has fast convergence speed but low reliability, an adaptive improved generalized normal distribution optimization algorithm with optional external archiving is proposed. Based on the generalized normal distribution optimization algorithm, the improved generalized normal distribution optimization algorithm proposes a DE / current-to-pbest mutation strategy with external archiving. Through external archiving and parameter adaptive improvement, the convergence of the algorithm is improved. The main process of the normal distribution optimization algorithm includes:

[0104] Step 1: Initialize the variables and assume a random variable x obeys the location parameter and scale parameters The probability distribution of , its probability density function can be expressed as the following formula (13):

[0105]

[0106] Among them, the position parameter and scale parameters are used to represent the mean and standard deviation of random variables respectively.

[0107] Step 2: Based on the relationship between the distribution of individuals in the population and the normal distribution, an improved generalized normal distribution model can be established by the following method:

[0108]

[0109] in, For the The trajectory vector of an individual at time t, For the The generalized mean position of each individual, is the generalized standard deviation, is the penalty factor. 、 as well as They are defined as the following formula (15):

[0110]

[0111] in, 、 、 as well as are random numbers between 0 and 1 respectively; is the current best position; is the current average position of the population.

[0112] Generalized mean position and the current best individual Contains useful information related to the global optimal solution. Individual Pulled towards the current best individual The average position M changes during the iteration process, which is conducive to finding better solutions. Therefore, introducing the average position M into the local development strategy improves the probability of avoiding local optimality to a certain extent.

[0113] Step 3: Global exploration is to search for a potential area in the global scope. The global exploration of the improved generalized normal distribution optimization algorithm is based on three randomly selected individuals and can be expressed as the following formula (16):

[0114]

[0115] in, and are two random numbers that follow a standard normal distribution; adjust the parameters is a random number between 0 and 1; and are two trajectory vectors. and The calculation formula is as follows: Formula (17) and Formula (18):

[0116] (17);

[0117] (18);

[0118] in, 、 and are 3 random integers selected from 1 to N that satisfy ≠ ≠ Adjust the parameters Used to balance global information and local information sharing strategies. and It is a random number that satisfies the standard normal distribution, which enables the improved generalized normal distribution optimization algorithm to have a larger search space in the process of performing global search.

[0119] Step 4: Execute steps 2 to 3 until the end.

[0120] In this embodiment, after the corresponding relationship is determined, the capacity parameters of each converter station in the power grid system are determined based on the corresponding relationship, preset constraints and a normal distribution optimization algorithm.

[0121] Optional, see below Figure 4 The process of obtaining the capacity parameters of each converter station in the power grid system can be further described as follows: the above-mentioned S202 "solving the converter station capacity configuration model according to preset constraints and the normal distribution optimization algorithm to obtain the capacity parameters of each converter station in the power grid system" includes:

[0122] S2021. Solve the converter station capacity configuration model based on preset constraints and the normal distribution optimization algorithm to obtain the capacity parameters of each converter station in the power grid system.

[0123] S2022. Update the parameters of the normal distribution optimization algorithm, return to the step of solving the converter station capacity configuration model according to the preset constraints and the normal distribution optimization algorithm, and obtain new initial total capacity parameters and new initial capacity parameters.

[0124] S2023. If the initial total capacity parameter and the new initial total capacity parameter are consistent, and the initial capacity parameter and the new initial capacity parameter are consistent, determine the new initial total capacity parameter as the target total capacity parameter, and determine the new initial capacity parameter as the target initial capacity parameter.

[0125] S2024. If the initial total capacity parameter and the new initial total capacity parameter are inconsistent, or the initial capacity parameter and the new initial capacity parameter are inconsistent, return to the step of updating the parameters of the normal distribution optimization algorithm until the initial total capacity parameter and the new initial total capacity parameter are consistent, and the initial capacity parameter and the new initial capacity parameter are consistent.

[0126] In the embodiment of the present application, by setting multiple targets, namely, the expected value of the system investment cost parameter, the expected value of the renewable energy unutilization rate and the expected value of the commutation loss, and taking into account multiple constraints, namely, at least one of the power balance constraint, energy storage charging and discharging constraint, installed capacity constraint, commutation power constraint, capacity-load ratio constraint and power supply reliability constraint, the accuracy of determining the capacity parameters of the converter station is guaranteed.

[0127] In one embodiment, a method for determining system investment cost parameters is also provided. Figure 5 , the above method further includes:

[0128] S203. Obtain investment and operation costs and electricity purchase and sales costs of the large power grid.

[0129] S204. Determine system investment cost parameters based on investment and operation costs and electricity purchase and sales costs of the large power grid.

[0130] Optionally, the system investment cost parameter is the sum of the annual investment and operating cost Cyear and the electricity purchase and sales fee Ccharge of the large grid. The system investment cost parameter is determined by referring to the following formula (19):

[0131] (19);

[0132] The equal annual investment operating cost Cyear is composed of the replacement cost Cchange, the annual operation and maintenance cost COM, and the equal annual investment cost Cinitial of the system. Its expression is shown in the following formula (20):

[0133]

[0134] in, 、 and The expressions of are shown in the following formulas (21), (22) and (23):

[0135] (twenty one);

[0136] (twenty two);

[0137]

[0138] Where: represents the discount rate; L represents the project life; 、 、 Represent the unit prices of photovoltaic, energy storage and commutation devices respectively; 、 、 Represents the annual operation and maintenance costs of a single photovoltaic, energy storage, and converter device respectively; 、 、 、 、 Respectively represent the installed capacity of photovoltaic, energy storage, converter station 1, converter station 2, and converter station 3; Represents the total replacement cost over the years.

[0139] The expression of transaction costs with the large power grid is shown in the following formula (24):

[0140] (twenty four);

[0141] Where, represents the electricity sold between the microgrid and the large grid at time t; represents the power purchased between the microgrid and the large grid at time t; represents the electricity price at time t; represents the electricity purchase price at time t.

[0142] In one embodiment, a method for determining the unutilized rate of renewable energy is also provided. Figure 6 , the above method further includes:

[0143] S205: Obtain photovoltaic power parameters, total load demand parameters, and energy storage power parameters.

[0144] S206: Determine the renewable energy unutilization rate based on the photovoltaic power parameter, the total load demand parameter, and the energy storage power parameter.

[0145] Optionally, the renewable energy utilization rate can be used as an indicator to evaluate the level of renewable energy utilization in the system power capacity configuration. The expression of renewable energy non-utilization rate can be found in the following formula (25):

[0146] (25);

[0147] Where, represents the total load demand at time t.

[0148] In one embodiment, a method for determining the amount of commutation loss is also provided. Figure 7 , the above method further includes:

[0149] S207: Obtain the transfer power at each moment and the commutation coefficient of each converter station.

[0150] S208. Determine the commutation loss amount according to the transferred power and the commutation coefficient.

[0151] Optionally, in a multi-distribution substation flexible DC interconnection system, a certain number of power electronic converters are required to achieve the integration of renewable energy and AC / DC loads. Due to the efficiency issues of power electronic converters, a certain amount of power loss will be generated during operation. For specific calculations, see formula (26):

[0152] (26);

[0153] Where, represents the commutation loss, represents the transferred (unbalanced) power at time t, and k represents the commutation coefficient of the converter.

[0154] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0155] Based on the same inventive concept, embodiments of the present application also provide a converter station capacity stabilization device for implementing the aforementioned converter station capacity stabilization method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of the one or more converter station capacity stabilization device embodiments provided below can be found in the limitations of the converter station capacity stabilization method described above and will not be further elaborated here.

[0156] In an exemplary embodiment, Figure 8 As shown, a capacity determination device for a converter station is provided, comprising: a construction module 10 and a determination module 11, wherein:

[0157] The construction module 10 is used to construct a corresponding relationship between the system investment cost parameter, the renewable energy non-utilization rate, the commutation loss amount and the capacity based on the expected value of the system investment cost parameter, the expected value of the renewable energy non-utilization rate and the expected value of the commutation loss amount.

[0158] The determination module 11 is used to determine the capacity parameters of each converter station in the power grid system based on the corresponding relationship, preset constraints and the normal distribution optimization algorithm; the preset constraints include at least one of power balance constraints, energy storage charging and discharging constraints, installed capacity constraints, commutation power constraints, capacity-to-load ratio constraints and power supply reliability constraints.

[0159] In an exemplary embodiment, the building module includes: a first determining unit, a second determining unit, a third determining unit, and a fourth determining unit, wherein:

[0160] A first determining unit is specifically configured to determine a first corresponding relationship corresponding to the system investment cost according to the system investment cost parameter, a preset system investment cost weight, and an expected value of the system investment cost parameter;

[0161] The second determining unit is specifically configured to determine a second corresponding relationship corresponding to the renewable energy unutilization rate according to the renewable energy unutilization rate, a preset weight of the renewable energy unutilization rate, and an expected value of the renewable energy unutilization rate;

[0162] a third determining unit, specifically configured to determine a third corresponding relationship corresponding to the commutation loss amount according to the commutation loss amount, the preset commutation loss amount weight, and the expected value of the commutation loss amount;

[0163] The fourth determining unit is specifically configured to construct a converter station capacity configuration model according to the first corresponding relationship, the second corresponding relationship, and the third corresponding relationship.

[0164] In an exemplary embodiment, the apparatus further comprises:

[0165] The first acquisition module is used to obtain investment and operation costs and electricity purchase and sales costs of the large power grid;

[0166] The first determination module is used to determine the system investment cost parameters based on the investment and operation costs and the electricity purchase and sales costs of the large power grid.

[0167] In an exemplary embodiment, the apparatus further comprises:

[0168] The second acquisition module is used to obtain photovoltaic power parameters, total load demand parameters and energy storage power parameters;

[0169] The second determining unit is specifically configured to determine the renewable energy unutilization rate according to the photovoltaic power parameter, the total load demand parameter and the energy storage power parameter.

[0170] In an exemplary embodiment, the apparatus further comprises:

[0171] The third acquisition module is used to obtain the transfer power at each moment and the commutation coefficient of each converter station;

[0172] The third determining module is used to determine the commutation loss amount according to the transferred power and the commutation coefficient.

[0173] In an exemplary embodiment, the determination module 11 includes:

[0174] A solving unit, specifically used to solve the converter station capacity configuration model according to preset constraints and a normal distribution optimization algorithm, and obtain the initial total capacity parameters of the converter station and the initial capacity parameters of each converter station;

[0175] An updating unit, specifically configured to update the parameters of the normal distribution optimization algorithm, return to executing the step of solving the converter station capacity configuration model according to the preset constraints and the normal distribution optimization algorithm, and obtain new initial total capacity parameters and new initial capacity parameters;

[0176] a first determining unit, specifically configured to, when the initial total capacity parameter and the new initial total capacity parameter are consistent, determine the new initial total capacity parameter as the target total capacity parameter and determine the new initial capacity parameter as the target initial capacity parameter;

[0177] The second determination unit is specifically used to return to the step of updating the parameters of the normal distribution optimization algorithm when the initial total capacity parameter and the new initial total capacity parameter are inconsistent, or when the initial capacity parameter and the new initial capacity parameter are inconsistent, until the initial total capacity parameter and the new initial total capacity parameter are consistent, and the initial capacity parameter and the new initial capacity parameter are consistent.

[0178] Each module in the converter station constant capacity device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0179] 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 following steps are implemented:

[0180] Based on the expected value of the system investment cost parameter, the expected value of the renewable energy non-utilization rate and the expected value of the commutation loss, the corresponding relationship between the system investment cost parameter, the renewable energy non-utilization rate, the commutation loss and the capacity is established;

[0181] Based on the corresponding relationship, preset constraints and normal distribution optimization algorithm, the capacity parameters of each converter station in the power grid system are determined; the preset constraints include at least one of power balance constraints, energy storage charging and discharging constraints, installed capacity constraints, commutation power constraints, capacity-to-load ratio constraints and power supply reliability constraints.

[0182] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0183] Determining a first corresponding relationship corresponding to the system investment cost according to the system investment cost parameter, the preset system investment cost weight, and the expected value of the system investment cost parameter;

[0184] Determining a second corresponding relationship corresponding to the renewable energy unutilization rate according to the renewable energy unutilization rate, the preset weight of the renewable energy unutilization rate, and the expected value of the renewable energy unutilization rate;

[0185] determining a third corresponding relationship corresponding to the commutation loss amount according to the commutation loss amount, a preset commutation loss amount weight, and an expected value of the commutation loss amount;

[0186] According to the first corresponding relationship, the second corresponding relationship and the third corresponding relationship, a converter station capacity configuration model is constructed.

[0187] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0188] Obtain investment and operating costs and electricity purchase and sales fees from the large power grid;

[0189] Determine the system investment cost parameters based on the investment and operating costs and the electricity purchase and sales costs of the large power grid.

[0190] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0191] Obtain photovoltaic power parameters, total load demand parameters and energy storage power parameters;

[0192] The renewable energy unutilization rate is determined based on photovoltaic power parameters, total load demand parameters and energy storage power parameters.

[0193] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0194] Obtain the transfer power at each moment and the commutation coefficient of each converter station;

[0195] The commutation loss is determined based on the transferred power and commutation coefficient.

[0196] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0197] According to the preset constraints and the normal distribution optimization algorithm, the converter station capacity configuration model is solved to obtain the initial total capacity parameters of the converter station and the initial capacity parameters of each converter station;

[0198] Update the parameters of the normal distribution optimization algorithm, return to the step of solving the converter station capacity configuration model according to the preset constraints and the normal distribution optimization algorithm, and obtain new initial total capacity parameters and new initial capacity parameters;

[0199] If the initial total capacity parameter and the new initial total capacity parameter are consistent, and the initial capacity parameter and the new initial capacity parameter are consistent, determining the new initial total capacity parameter as the target total capacity parameter, and determining the new initial capacity parameter as the target initial capacity parameter;

[0200] If the initial total capacity parameter and the new initial total capacity parameter are inconsistent, or the initial capacity parameter and the new initial capacity parameter are inconsistent, then return to the step of updating the parameters of the normal distribution optimization algorithm until the initial total capacity parameter and the new initial total capacity parameter are consistent, and the initial capacity parameter and the new initial capacity parameter are consistent.

[0201] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0202] Based on the expected value of the system investment cost parameter, the expected value of the renewable energy non-utilization rate and the expected value of the commutation loss, the corresponding relationship between the system investment cost parameter, the renewable energy non-utilization rate, the commutation loss and the capacity is established;

[0203] Based on the corresponding relationship, preset constraints and normal distribution optimization algorithm, the capacity parameters of each converter station in the power grid system are determined; the preset constraints include at least one of power balance constraints, energy storage charging and discharging constraints, installed capacity constraints, commutation power constraints, capacity-to-load ratio constraints and power supply reliability constraints.

[0204] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0205] Determining a first corresponding relationship corresponding to the system investment cost according to the system investment cost parameter, a preset system investment cost weight, and an expected value of the system investment cost parameter;

[0206] Determining a second corresponding relationship corresponding to the renewable energy unutilization rate according to the renewable energy unutilization rate, a preset weight of the renewable energy unutilization rate, and an expected value of the renewable energy unutilization rate;

[0207] determining a third corresponding relationship corresponding to the commutation loss amount according to the commutation loss amount, a preset weight of the commutation loss amount, and an expected value of the commutation loss amount;

[0208] The converter station capacity configuration model is constructed according to the first corresponding relationship, the second corresponding relationship, and the third corresponding relationship.

[0209] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0210] Obtain investment and operating costs and electricity purchase and sales fees from the large power grid;

[0211] The system investment cost parameters are determined based on the investment and operation costs and the electricity purchase and sales costs of the large power grid.

[0212] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0213] Obtain photovoltaic power parameters, total load demand parameters and energy storage power parameters;

[0214] The renewable energy unutilization rate is determined according to the photovoltaic power parameter, the total load demand parameter, and the energy storage power parameter.

[0215] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0216] Obtaining the transfer power at each moment and the commutation coefficient of each converter station;

[0217] The commutation loss amount is determined according to the transfer power and the commutation coefficient.

[0218] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0219] Solving the converter station capacity configuration model according to the preset constraints and the normal distribution optimization algorithm to obtain the initial total capacity parameter of the converter station and the initial capacity parameter of each converter station;

[0220] Updating the parameters of the normal distribution optimization algorithm, returning to the step of solving the converter station capacity configuration model according to the preset constraints and the normal distribution optimization algorithm, and obtaining new initial total capacity parameters and new initial capacity parameters;

[0221] If the initial total capacity parameter and the new initial total capacity parameter are consistent, and the initial capacity parameter and the new initial capacity parameter are consistent, determining the new initial total capacity parameter as the target total capacity parameter, and determining the new initial capacity parameter as the target initial capacity parameter;

[0222] If the initial total capacity parameter is inconsistent with the new initial total capacity parameter, or the initial capacity parameter is inconsistent with the new initial capacity parameter, return to the step of updating the parameters of the normal distribution optimization algorithm until the initial total capacity parameter is consistent with the new initial total capacity parameter, and the initial capacity parameter is consistent with the new initial capacity parameter.

[0223] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0224] Based on the expected value of the system investment cost parameter, the expected value of the renewable energy non-utilization rate and the expected value of the commutation loss, the corresponding relationship between the system investment cost parameter, the renewable energy non-utilization rate, the commutation loss and the capacity is established;

[0225] Based on the corresponding relationship, preset constraints and normal distribution optimization algorithm, the capacity parameters of each converter station in the power grid system are determined; the preset constraints include at least one of power balance constraints, energy storage charging and discharging constraints, installed capacity constraints, commutation power constraints, capacity-to-load ratio constraints and power supply reliability constraints.

[0226] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0227] Determining a first corresponding relationship corresponding to the system investment cost according to the system investment cost parameter, a preset system investment cost weight, and an expected value of the system investment cost parameter;

[0228] Determining a second corresponding relationship corresponding to the renewable energy unutilization rate according to the renewable energy unutilization rate, a preset weight of the renewable energy unutilization rate, and an expected value of the renewable energy unutilization rate;

[0229] determining a third corresponding relationship corresponding to the commutation loss amount according to the commutation loss amount, a preset weight of the commutation loss amount, and an expected value of the commutation loss amount;

[0230] The converter station capacity configuration model is constructed according to the first corresponding relationship, the second corresponding relationship, and the third corresponding relationship.

[0231] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0232] Obtain investment and operating costs and electricity purchase and sales fees from the large power grid;

[0233] The system investment cost parameters are determined based on the investment and operation costs and the electricity purchase and sales costs of the large power grid.

[0234] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0235] Obtain photovoltaic power parameters, total load demand parameters and energy storage power parameters;

[0236] The renewable energy unutilization rate is determined according to the photovoltaic power parameter, the total load demand parameter, and the energy storage power parameter.

[0237] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0238] Obtaining the transfer power at each moment and the commutation coefficient of each converter station;

[0239] The commutation loss amount is determined according to the transfer power and the commutation coefficient.

[0240] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0241] Solving the converter station capacity configuration model according to the preset constraints and the normal distribution optimization algorithm to obtain the initial total capacity parameter of the converter station and the initial capacity parameter of each converter station;

[0242] Updating the parameters of the normal distribution optimization algorithm, returning to the step of solving the converter station capacity configuration model according to the preset constraints and the normal distribution optimization algorithm, and obtaining new initial total capacity parameters and new initial capacity parameters;

[0243] If the initial total capacity parameter and the new initial total capacity parameter are consistent, and the initial capacity parameter and the new initial capacity parameter are consistent, determining the new initial total capacity parameter as the target total capacity parameter, and determining the new initial capacity parameter as the target initial capacity parameter;

[0244] If the initial total capacity parameter is inconsistent with the new initial total capacity parameter, or the initial capacity parameter is inconsistent with the new initial capacity parameter, return to the step of updating the parameters of the normal distribution optimization algorithm until the initial total capacity parameter is consistent with the new initial total capacity parameter, and the initial capacity parameter is consistent with the new initial capacity parameter.

[0245] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. 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. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can 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 can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). 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, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0246] The technical features of the above embodiments can be combined arbitrarily. In order 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 application.

[0247] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for determining the capacity of a converter station, characterized in that: The method comprises: Based on the expected value of the system investment cost parameter, the expected value of the renewable energy non-utilization rate and the expected value of the commutation loss, the corresponding relationship between the system investment cost parameter, the renewable energy non-utilization rate, the commutation loss and the capacity is established; Based on the corresponding relationship, preset constraints and normal distribution optimization algorithm, the capacity parameters of each converter station in the power grid system are determined; the preset constraints include at least one of power balance constraints, energy storage charging and discharging constraints, installed capacity constraints, commutation power constraints, capacity-to-load ratio constraints and power supply reliability constraints.

2. The method according to claim 1, characterized in that The constructing of a corresponding relationship between the system investment cost parameter, the renewable energy unutilization rate, the commutation loss amount and the capacity based on the expected value of the system investment cost parameter, the expected value of the renewable energy unutilization rate and the expected value of the commutation loss amount includes: Determining a first corresponding relationship corresponding to the system investment cost according to the system investment cost parameter, a preset system investment cost weight, and an expected value of the system investment cost parameter; Determining a second corresponding relationship corresponding to the renewable energy unutilization rate according to the renewable energy unutilization rate, a preset weight of the renewable energy unutilization rate, and an expected value of the renewable energy unutilization rate; determining a third corresponding relationship corresponding to the commutation loss amount according to the commutation loss amount, a preset weight of the commutation loss amount, and an expected value of the commutation loss amount; The converter station capacity configuration model is constructed according to the first corresponding relationship, the second corresponding relationship, and the third corresponding relationship.

3. The method according to claim 1 or 2, characterized in that The method further comprises: Obtain investment and operating costs and electricity purchase and sales fees from the large power grid; The system investment cost parameters are determined based on the investment and operation costs and the electricity purchase and sales costs of the large power grid.

4. The method according to claim 1 or 2, characterized in that The method further comprises: Obtain photovoltaic power parameters, total load demand parameters and energy storage power parameters; The renewable energy unutilization rate is determined according to the photovoltaic power parameter, the total load demand parameter, and the energy storage power parameter.

5. The method according to claim 1 or 2, characterized in that The method further comprises: Obtaining the transfer power at each moment and the commutation coefficient of each converter station; The commutation loss amount is determined according to the transfer power and the commutation coefficient.

6. The method according to claim 1, characterized in that The determining of the capacity parameters of each converter station in the power grid system based on the corresponding relationship, the preset constraint conditions and the normal distribution optimization algorithm includes: Solving the converter station capacity configuration model according to the preset constraints and the normal distribution optimization algorithm to obtain the initial total capacity parameter of the converter station and the initial capacity parameter of each converter station; Updating the parameters of the normal distribution optimization algorithm, returning to the step of solving the converter station capacity configuration model according to the preset constraints and the normal distribution optimization algorithm, and obtaining new initial total capacity parameters and new initial capacity parameters; If the initial total capacity parameter and the new initial total capacity parameter are consistent, and the initial capacity parameter and the new initial capacity parameter are consistent, determining the new initial total capacity parameter as the target total capacity parameter, and determining the new initial capacity parameter as the target initial capacity parameter; If the initial total capacity parameter is inconsistent with the new initial total capacity parameter, or the initial capacity parameter is inconsistent with the new initial capacity parameter, return to the step of updating the parameters of the normal distribution optimization algorithm until the initial total capacity parameter is consistent with the new initial total capacity parameter, and the initial capacity parameter is consistent with the new initial capacity parameter.

7. A constant capacity device for a converter station, characterized in that: The device comprises: A construction module is used to construct a corresponding relationship between the system investment cost parameter, the renewable energy non-utilization rate, the commutation loss amount and the capacity based on the expected value of the system investment cost parameter, the expected value of the renewable energy non-utilization rate and the expected value of the commutation loss amount; A determination module is used to determine the capacity parameters of each converter station in the power grid system based on the corresponding relationship, preset constraints and a normal distribution optimization algorithm; the preset constraints include at least one of power balance constraints, energy storage charging and discharging constraints, installed capacity constraints, commutation power constraints, capacity-to-load ratio constraints and power supply reliability constraints.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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