Method and device for optimal configuration of converter station capacity based on flexible hvdc interconnection system

Abstract

The application belongs to the technical field of power systems, and discloses a method and device for optimizing configuration of converter station capacity based on a flexible HVDC interconnected system. The method comprises the following steps: in the case that population initialization of a pre-stored outer optimization model is completed, population initialization of a pre-stored inner optimization model is performed; based on a constraint condition, a target function of the inner optimization model is solved to obtain an inner target optimal solution; optimal load scheduling quantity and current converter station capacity are calculated according to the inner target optimal solution; and based on the current converter station capacity, the outer optimization model is solved to obtain an optimization result. The method solves the problem of low converter station capacity configuration in the prior art.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method and apparatus for optimizing the configuration of converter station capacity based on a flexible DC interconnection system. Background Technology

[0002] With the increasing severity of the global energy crisis and environmental problems, the development and utilization of renewable energy are receiving more and more attention from countries around the world. Flexible DC interconnection of multiple distribution substations provides energy flow channels to these substations via DC. On the one hand, DC interconnection facilitates the integration of renewable energy and energy storage, strongly supporting the development of renewable energy. On the other hand, flexible DC interconnection of distribution substations enables flexible energy allocation and flow between substations through unified coordination and dispatch, promoting energy balance between substations, improving equipment utilization, and alleviating the pressure of substation expansion.

[0003] The selection of converter station capacity directly affects the economic issues such as investment cost and cost recovery of multi-distribution station flexible DC interconnection system, and also affects the energy interaction between distribution stations, which directly relates to the overall operation and maintenance cost of the system.

[0004] Therefore, optimizing the capacity configuration of converter stations to increase their capacity, reduce power system investment costs, and improve the economic efficiency of the power system is an urgent problem to be solved. Summary of the Invention

[0005] This invention provides a method and apparatus for optimizing the capacity configuration of converter stations based on a flexible DC interconnection system, addressing the problem of low capacity configuration in existing converter stations. To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments. Its sole purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.

[0006] According to a first aspect of the present invention, a method for optimizing the configuration of converter station capacity based on a flexible DC interconnection system is provided.

[0007] In some embodiments, the method includes:

[0008] After the pre-stored outer optimization model has completed population initialization, the pre-stored inner optimization model is then initialized.

[0009] Based on the constraints, the objective function of the inner optimization model is solved to obtain the optimal solution of the inner objective.

[0010] Calculate the optimal load scheduling amount and the current converter station capacity based on the optimal solution of the inner target;

[0011] Based on the current converter station capacity, the outer optimization model is solved to obtain the optimization results.

[0012] In one embodiment, the converter station capacity optimization configuration method based on a flexible DC interconnection system, with the objective of minimizing investment cost, has the following objective function for the outer optimization model:

[0013] minF=I year

[0014] In the formula, I year The system's equivalent annual investment and operating cost is expressed as follows:

[0015] I year =I initial +I OM +I change

[0016] In the formula, I change Indicates replacement cost; I OM Indicates annual operating and maintenance costs; I initial This represents the annualized investment cost.

[0017] In one embodiment, a converter station capacity optimization configuration method based on a flexible DC interconnection system, wherein the replacement cost I... change The expression is:

[0018]

[0019] The annual operation and maintenance cost I OM The expression is:

[0020]

[0021] The equivalent annual value investment cost I initial The expression is:

[0022]

[0023] In the formula, r0 represents the discount rate; L represents the project duration; These represent the unit prices of photovoltaic, energy storage, and converter devices, respectively. These represent the annual operating and maintenance costs per unit of the photovoltaic, energy storage, and converter devices, respectively; N pv N bat N load These represent the installed capacity of photovoltaic, energy storage, and converter stations, respectively; I change_total This represents the total replacement cost over the specified number of years.

[0024] In one embodiment, when the converter station capacity optimization configuration method based on a flexible DC interconnection system aims to optimize system revenue and renewable energy utilization rate, the objective function of the inner optimization model is:

[0025]

[0026] In the formula, w er C represents the weighting coefficients of the system's revenue sub-objective function. total For the benefit of the system, The system yields the independent optimal solution; w REN η represents the weighting coefficients of the renewable energy utilization rate sub-objective function. REN For renewable energy utilization rate, This is the independent optimal solution for renewable energy utilization.

[0027] In one embodiment, the converter station capacity optimization configuration method based on a flexible DC interconnection system has the following sub-objective function for system benefits:

[0028]

[0029] In the formula, C users (t) represents the revenue from selling electricity to users, C adj (t) represents the cost of flexible load dispatching, C grid (t) represents the cost of purchasing electricity from the grid.

[0030] In one embodiment, the objective function for renewable energy utilization rate in the converter station capacity optimization configuration method based on a flexible DC interconnection system is:

[0031]

[0032] In the formula, P pv (t) represents the photovoltaic power generation at the current moment. Let T represent the power sold to the grid at the current moment, T represent the total optimization time length (24 hours in this paper), and t represent the current time.

[0033] In one embodiment, the converter station capacity optimization configuration method based on a flexible DC interconnection system includes at least one of the following constraints:

[0034] Power balance constraints, energy storage charging and discharging constraints, installed capacity constraints, converter power constraints, and capacity-to-load ratio constraints.

[0035] In one embodiment, the power balance constraint in the converter station capacity optimization configuration method based on a flexible DC interconnection system is expressed as follows:

[0036] P grid +P bat +Ppv =P load +P loss

[0037] In the formula, P grid P represents the power exchanged between the system and the power grid. bat P represents energy storage capacity. pv P represents photovoltaic power. load P represents the power consumed by the load. loss This indicates the power loss of the converter.

[0038] In one embodiment, the expression for the energy storage charging and discharging constraint in the converter station capacity optimization configuration method based on a flexible DC interconnection system is:

[0039] SOC min ≤SOC(t)≤SOC max

[0040]

[0041]

[0042] In the formula, SOC(t) represents the remaining charge of the energy storage battery at time t; SOC max This represents the maximum SOC (State of Charge) of the energy storage battery; SOC min P represents the minimum SOC (State of Charge) of the energy storage battery. bat，c (t) represents the charging power at time t; Indicates the maximum charging power; Indicates the minimum charging power; P bat，d (t) represents the discharge power at time t; Indicates the maximum discharge power; This indicates the minimum discharge power.

[0043] In one embodiment, the converter station capacity optimization configuration method based on a flexible DC interconnection system, the expression for the installed capacity constraint is:

[0044] N min，load ≤N load ≤N max，load

[0045] In the formula, N min，load N represents the lower limit of the installed capacity of the converter station. max，load N represents the upper limit of the installed capacity of the converter station. load Install capacity for the converter station.

[0046] In one embodiment, the converter station capacity optimization configuration method based on a flexible DC interconnection system includes the following expression for the converter power constraint:

[0047] P trans ≤P max，trans

[0048] In the formula, P trans For the power transfer between converter stations in the distribution area, P max，trans This represents the maximum power transfer value between converter stations.

[0049] In one embodiment, the capacity optimization configuration method for converter stations based on a flexible DC interconnection system includes the following expression for the capacity-to-load ratio constraint:

[0050] λ min，load ≤λ load ≤λ max，load

[0051] In the formula, λ load λ represents the capacity ratio of the converter station. min，load λ represents the minimum capacity ratio limit of the converter station. max，load This indicates the maximum capacity limit of the converter station.

[0052] In one embodiment, a converter station capacity optimization configuration method based on a flexible DC interconnection system is described.

[0053] The step of initializing the population of the pre-stored inner-layer optimization model includes:

[0054] Set inner-layer optimization objective parameters to complete the population initialization of the inner-layer optimization model; wherein, the inner-layer optimization objective parameters include at least population size, spatial dimension, number of iterations, constraint conditions, and inertia weight parameters;

[0055] The step of solving the objective function of the inner-layer optimization model based on constraints to obtain the optimal solution of the inner-layer objective includes:

[0056] Based on the constraints, the objective function of the inner optimization model is solved to obtain the current optimal solution;

[0057] Update the position and velocity of each particle in the population based on the current optimal solution;

[0058] When the maximum number of inner-layer iterations in updating the position and velocity of each particle in the population based on the current optimal solution reaches a first threshold, the current optimal solution is taken as the inner-layer target optimal solution and the iteration ends.

[0059] In one embodiment, a converter station capacity optimization configuration method based on a flexible DC interconnection system is described.

[0060] The step of completing population initialization in the pre-stored outer optimization model includes:

[0061] The outer layer parameters are initialized and the outer layer optimization target parameters are set to complete the population initialization of the outer layer optimization model; wherein, the outer layer parameters include at least cost, engineering and capacity, and the outer layer optimization target parameters include at least population size, spatial dimension, number of iterations, constraint conditions and inertia weight parameters;

[0062] The step of solving the outer optimization model based on the current converter station capacity to obtain the optimization result includes:

[0063] Based on the current converter station capacity, the outer optimization model is solved to obtain the current optimization result;

[0064] Update the position and velocity of each particle in the population based on the current optimization results;

[0065] When the maximum number of outer-layer iterations for updating the position and velocity of each particle in the population based on the current optimization result reaches the second threshold, the current optimization result is used as the optimization result and the iteration ends.

[0066] According to a second aspect of the present invention, a converter station capacity optimization configuration device based on a flexible DC interconnection system is provided, the device comprising:

[0067] An initialization unit is used to initialize the population of a pre-stored inner optimization model after the pre-stored outer optimization model has completed population initialization.

[0068] The inner solving unit is used to solve the objective function of the inner optimization model based on the constraints, so as to obtain the optimal solution of the inner objective.

[0069] The capacity calculation unit is used to calculate the optimal load scheduling amount and the current converter station capacity based on the optimal solution of the inner target.

[0070] The result output unit is used to solve the outer optimization model based on the current converter station capacity to obtain the optimization result.

[0071] According to a third aspect of the present invention, a computer device is provided.

[0072] In some embodiments, the computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described above.

[0073] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0074] The converter station capacity optimization configuration method and apparatus based on a flexible DC interconnection system provided by this invention employs a two-layer optimization model. The inner optimization model aims to maximize system revenue and renewable energy utilization, while the outer optimization model aims to minimize system construction investment costs. Considering constraints such as power balance, energy storage charging and discharging limitations, and converter capacity limitations, a converter station capacity optimization configuration model considering optimized scheduling and operation is formed. This optimizes the converter station capacity configuration, increases converter station capacity, reduces power system investment costs, improves the economic efficiency of the power system, and solves the problem of low converter station capacity configuration in existing technologies.

[0075] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0076] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0077] Figure 1 This is one of the flowcharts illustrating a converter station capacity optimization configuration method based on a flexible DC interconnection system according to an exemplary embodiment;

[0078] Figure 2 This is a second flowchart illustrating a converter station capacity optimization configuration method based on a flexible DC interconnection system according to an exemplary embodiment;

[0079] Figure 3 This is a schematic diagram of a converter station capacity optimization configuration device based on a flexible DC interconnection system, according to an exemplary embodiment.

[0080] Figure 4 This is a schematic diagram of the structure of a computer device according to an exemplary embodiment.

[0081] Figure label:

[0082] 301 - Initialization unit, 302 - Inner layer solution unit, 303 - Capacity calculation unit, 304 - Result output unit. Detailed Implementation

[0083] The following description and accompanying drawings fully illustrate specific embodiments described herein to enable those skilled in the art to practice them. Some embodiments may include or substitute parts and features of other embodiments. The scope of the embodiments herein encompasses the entire scope of the claims and all available equivalents thereof. Throughout this document, the terms “first,” “second,” etc., are used only to distinguish one element from another without requiring or implying any actual relationship or order between the elements. Indeed, a first element can also be referred to as a second element, and vice versa. Furthermore, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a structure, apparatus, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a structure, apparatus, or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the structure, apparatus, or device that includes said element. The various embodiments described herein are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments; similar or identical parts between embodiments can be referred to interchangeably.

[0084] The terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer" used in this document to indicate orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings. They are used solely for the convenience of describing the document and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In the description herein, unless otherwise specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to mechanical or electrical connections, or internal connections between two elements; they can be direct connections or indirect connections through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.

[0085] In this document, unless otherwise stated, the term "multiple" means two or more.

[0086] In this article, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0087] In this article, the term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0088] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0089] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a converter station capacity optimization configuration method based on a flexible DC interconnection system, according to an exemplary embodiment.

[0090] In one specific embodiment, the converter station capacity optimization configuration method based on a flexible DC interconnection system provided by the present invention includes the following steps:

[0091] Step S101: With the pre-stored outer optimization model having completed population initialization, the pre-stored inner optimization model is then initialized. Specifically, the inner optimization model's population initialization is completed by setting inner optimization target parameters; wherein the inner optimization target parameters include at least population size, spatial dimension, number of iterations, constraints, and inertia weight parameters. The outer optimization model's population initialization is completed by initializing outer parameters and setting outer optimization target parameters; wherein the outer parameters include at least cost, engineering, and capacity, and the outer optimization target parameters include at least population size, spatial dimension, number of iterations, constraints, and inertia weight parameters.

[0092] Step S102: Based on the constraints, solve the objective function of the inner layer optimization model to obtain the optimal solution of the inner layer objective.

[0093] Step S103: Calculate the optimal load scheduling amount and the current converter station capacity based on the inner target optimal solution;

[0094] Step S104: Based on the current converter station capacity, solve the outer optimization model to obtain the optimization results.

[0095] To improve the accuracy of the optimal solution for the inner target, the optimal solution can be obtained through iterative optimization. Therefore, step S102 specifically includes:

[0096] Based on the constraints, the objective function of the inner optimization model is solved to obtain the current optimal solution;

[0097] Update the position and velocity of each particle in the population based on the current optimal solution;

[0098] When the maximum number of inner-layer iterations in updating the position and velocity of each particle in the population based on the current optimal solution reaches a first threshold, the current optimal solution is taken as the inner-layer target optimal solution and the iteration ends.

[0099] Furthermore, to improve the accuracy of the optimization results, iterative optimization can also be used to obtain the results. Therefore, step S104 specifically includes:

[0100] The process of solving the outer optimization model based on the current converter station capacity to obtain the optimization result specifically includes:

[0101] Based on the current converter station capacity, the outer optimization model is solved to obtain the current optimization result;

[0102] Update the position and velocity of each particle in the population based on the current optimization results;

[0103] When the maximum number of outer-layer iterations for updating the position and velocity of each particle in the population based on the current optimization result reaches the second threshold, the current optimization result is used as the optimization result and the iteration ends.

[0104] Theoretically speaking, for multi-objective optimization problems, to improve optimization accuracy, the concept of "per-unit value" is often referenced to normalize each sub-objective function. That is, when the specific expression of the multi-objective function is:

[0105] minF = (f1, f2, ..., f n (1)

[0106] In the formula, F represents the comprehensive objective function, f1, f2, ..., f n Let each represent a sub-objective function. After normalizing them using the method described above, we have:

[0107]

[0108] In the formula, w n Represents the weights of each sub-objective function; This represents the independent optimal solution for each sub-objective function.

[0109] Accordingly, the objective function of the outer optimization model in the converter station capacity optimization configuration method based on the flexible DC interconnection system, with the goal of minimizing investment cost, is:

[0110] minF=I year (3)

[0111] In the formula, I year The system's equivalent annual investment operating cost mainly includes replacement costs. change Annual operation and maintenance costs I OM Annual investment costs for systems, etc. initial Composition, then I year The expression is:

[0112] I year =I initial +I OM +I change (4)

[0113] In the formula, I change Indicates replacement cost; I OM Indicates annual operating and maintenance costs; I initial This represents the annualized investment cost.

[0114] Wherein, the replacement cost I change The expression is:

[0115]

[0116] The annual operation and maintenance cost I OM The expression is:

[0117]

[0118] The equivalent annual value investment cost I initial The expression is:

[0119]

[0120] In the formula, r0 represents the discount rate; L represents the project duration; These represent the unit prices of photovoltaic, energy storage, and converter devices, respectively. These represent the annual operating and maintenance costs per unit of the photovoltaic, energy storage, and converter devices, respectively; N pv N bat N load These represent the installed capacity of photovoltaic, energy storage, and converter stations, respectively; I change_total This represents the total replacement cost over the specified number of years.

[0121] In one embodiment, when the converter station capacity optimization configuration method based on a flexible DC interconnection system aims to optimize system revenue and renewable energy utilization rate, the objective function of the inner optimization model is:

[0122]

[0123] In the formula, w er C represents the weighting coefficients of the system's revenue sub-objective function. total For the benefit of the system, The system yields the independent optimal solution; w REN η represents the weighting coefficients of the renewable energy utilization rate sub-objective function. REN For renewable energy utilization rate, This is the independent optimal solution for renewable energy utilization.

[0124] The system benefit sub-objective function is as follows:

[0125]

[0126] In the formula, C users (t) represents the revenue from selling electricity to users, C adj (t) represents the cost of flexible load dispatching, C grid (t) represents the cost of purchasing electricity from the grid, P imb The difference between photovoltaic power generation and load power represents the current unbalanced power level of the system.

[0127] Specifically, in the system revenue sub-objective function, the revenue C from selling electricity to users is... users Primarily determined by the current electricity price and the amount of electricity sold, the user's revenue from selling electricity, C, is... users The expression is:

[0128] C users (t)=P users α users (t) (10)

[0129] In the formula, P users α represents the current electricity sold to users. users (t) represents the current electricity price.

[0130] In the system revenue sub-objective function, flexible loads participate in grid dispatch. When there is power redundancy in the system, a certain dispatch fee is paid to flexible load users to guide them to engage loads and absorb the redundant power. Similarly, when there is power deficit in the system, a certain compensation fee is paid to users to guide them to actively disconnect loads and reduce the power deficit in the system. Therefore, the flexible load dispatch cost mainly consists of the cost of guiding load engagement and the cost of guiding load disconnection. The flexible load dispatch cost C... adj The expression is:

[0131]

[0132] in,

[0133]

[0134] In the formula, C on (t) represents the cost of guiding flexible load input, C off (t) represents the cost of guiding flexible load shedding, ΔP imb，adj (t) represents the actual flexible load scheduling capacity of the system at the current moment. This represents the coefficient of determination of the flexible load that can be incentivized to be applied at the current moment in the system. β1, β2, γ1, and γ2 represent the flexible load excitation cutoff determination coefficients at the current moment of the system, and represent the flexible load participation depth, which is determined by the flexible load dispatchable ratio.

[0135] In the system revenue sub-objective function, when neither energy storage nor flexible loads can meet the system's power balance requirements, the expression for the cost / revenue of purchasing / selling electricity to the grid to meet the system's power balance through power exchange with the grid is as follows:

[0136]

[0137] In the formula, This represents the electricity sold between the system and the main power grid at time t; α represents the power purchased between the system and the main power grid at time t; sell (t) represents the electricity price at time t; α buy (t) represents the electricity price at time t.

[0138] Furthermore, the objective function for the utilization rate of renewable energy is:

[0139]

[0140] In the formula, P pv (t) represents the photovoltaic power generation at the current moment. Let T represent the power sold to the grid at the current moment, T represent the total optimization time length (24 hours in this paper), and t represent the current time.

[0141] In one embodiment, the converter station capacity optimization configuration method based on flexible DC interconnection system provided by the present invention includes constraints such as power balance constraints, energy storage charging and discharging constraints, installed capacity constraints, converter power constraints, and capacity-to-load ratio constraints.

[0142] Among them, the power balance constraint refers to the power balance condition that the system must meet during normal operation. The expression of the power balance constraint is:

[0143] P grid +P bat +P pv =P load +P loss (14)

[0144] In the formula, P grid P represents the power exchanged between the system and the power grid. bat P represents energy storage capacity. pv P represents photovoltaic power. load P represents the power consumed by the load. loss This indicates the power loss of the converter.

[0145] The magnitude of the charge / discharge power and the depth of charge / discharge have a decisive impact on the lifespan of energy storage batteries. Therefore, to extend the lifespan of energy storage batteries and reduce the later maintenance and replacement costs of the system, it is necessary to constrain the depth of charge / discharge and the charge / discharge power of the energy storage batteries. The expression for the energy storage charge / discharge constraint is:

[0146]

[0147] In the formula, SOC(t) represents the remaining charge of the energy storage battery at time t; SOC max This represents the maximum SOC (State of Charge) of the energy storage battery; SOC min P represents the minimum SOC (State of Charge) of the energy storage battery. bat，c (t) represents the charging power at time t; Indicates the maximum charging power; Indicates the minimum charging power; P bat，d (t) represents the discharge power at time t; Indicates the maximum discharge power; This indicates the minimum discharge power.

[0148] Due to limitations in construction costs and production processes, the capacity of converter stations is subject to certain restrictions during design. To ensure model accuracy, it is also necessary to set installed capacity constraints, the expression of which is as follows:

[0149] N min，load ≤N load ≤N max，load (16)

[0150] In the formula, N min，load N represents the lower limit of the installed capacity of the converter station. max，load N represents the upper limit of the installed capacity of the converter station. load Install capacity for the converter station.

[0151] In practical applications, power transfer between distribution substations requires certain constraints to ensure that the maximum allowable transfer power of the converter station is not exceeded. Therefore, a converter power constraint is also necessary. The expression for the converter power constraint is:

[0152] P trans ≤P max，trans (17)

[0153] In the formula, P trans For the power transfer between converter stations in the distribution area, P max，trans This represents the maximum power transfer value between converter stations.

[0154] To ensure the rationality and economy of the system design, the capacity ratio of the converter station needs to be constrained. The expression for the capacity ratio constraint is as follows:

[0155] λ min，load ≤λ load ≤λ max，load (18)

[0156] In the formula, λ load λ represents the capacity ratio of the converter station. min，load λ represents the minimum capacity ratio limit of the converter station. max，load This indicates the maximum capacity limit of the converter station.

[0157] The following uses a specific application scenario as an example to briefly describe the process of the converter station capacity optimization configuration method based on the flexible DC interconnection system provided by this invention.

[0158] like Figure 2 As shown, the converter station capacity optimization configuration method includes the following steps:

[0159] Step (1): Starting from the outer layer, input the basic data required for optimization, such as light intensity, temperature, population size, etc.;

[0160] Step (2): Set the population size M for the outer capacity optimization configuration, and randomly generate M particles using a random function to form the initial population X = {X}. i}(i = 1, 2, ..., M), where each particle represents a possible solution, represented as a 3D vector, expressed as:

[0161]

[0162] In the formula, These represent the capacities of converter stations 1, 2, and 3 under the i-th particle scheme, respectively.

[0163] Step (3): Set the population size N of the inner optimization scheduling model, and use a random function to randomly generate N particles to form the initial population Y = {Y}. j}(i = 1, 2, ..., N), where each particle represents a possible solution, represented as a 2D vector, expressed as:

[0164]

[0165] In the formula, Let each represent the load excitation input coefficient under the j-th particle scheme. Excitation cut-out coefficient

[0166] Step (4): Assume an individual mechanism. For the particle i with the best fitness value in the outer model, set it as the individual extreme value pBest. For the population extreme value, search for the particle with the best fitness value among multiple individual extreme values ​​and set it as the outer population extreme value gBest. Similarly, for the inner model, set the particle j with the best fitness value as the individual extreme value ypBest, and search for the optimal ypBest among multiple individual extreme values ​​and set it as the inner population extreme value ygBest.

[0167] Step (5): Under the constraints of battery charging and discharging power, charging and discharging depth, converter station capacity, etc., the position and velocity of particle i in the outer population are randomly initialized using a random function;

[0168] Step (6): Calculate the position X of outer particle i based on the mathematical models of the energy storage battery and photovoltaic cell. i The energy storage rechargeable (discharge) power and photovoltaic output power are calculated. The position X of outer particle i is also calculated. i The scheduling resources under the grid are the power that needs to be allocated to the grid and flexible loads for scheduling.

[0169] Step (7): Set the position X of particle i i The scheduling resources are brought into the inner model. Under the premise of satisfying the constraints, the velocity and position of the inner population particle j are randomly initialized using a random function, and the individual optimal value ypBest and the population optimal value ygBest of the inner model are calculated.

[0170] Step (8): Based on the principle of the example group algorithm, update the velocity and position of particle j in the inner model;

[0171] Step (9): Update the individual optimal value; if the fitness value of particle j is better than the original fitness value ypBest, then the inner model updates the fitness value ypBest to the current fitness value, corresponding to the optimal position Y. j Update to current location;

[0172] Step (10): Update the population optimum. If the fitness value of particle j is better than the original fitness value ygBest, then the inner model updates the fitness value ygBest to the current fitness value, and the corresponding global best position is updated to the current position;

[0173] Step (11): Determine the termination condition of the inner optimization model. Determine whether the number of iterations of the inner model has reached the maximum number of iterations. If yes, proceed to step (12); otherwise, proceed to step (8).

[0174] Step (12): Based on the optimization results, calculate the optimal load dispatch amount and the system load under this dispatch amount, and calculate the converter station capacity at this time;

[0175] Step (13): Based on the computational capacity, perform optimization calculations for the outer optimization model to calculate the individual fitness value pBest and the population fitness value gBest of outer particle i;

[0176] Step (14): Based on the principle of particle swarm optimization, update the velocity and position of particle i in the outer model population, and repeat steps (6) to (13).

[0177] Step (15): Update the individual optimal value. If the fitness value of particle i is better than the original individual fitness pBest, then the outer model updates the fitness value pBest to the current fitness value, and the corresponding outer particle optimal position X. i Updated to the current particle position;

[0178] Step (16): Update the swarm optimum. If the fitness value of particle i is better than the original swarm fitness gBest, then the outer model updates the fitness value gBest to the current fitness value, and the corresponding best position X of the outer particle. i Updated to the current particle position;

[0179] Step (17): Determine the termination condition of the outer optimization model and determine whether the outer model has reached the maximum number of iterations. If yes, proceed to step (18); otherwise, proceed to step (14).

[0180] Step (18): Output the optimization results. Output particle (X) i Y j ) optimal fitness and particle position X i Y j .

[0181] In the specific embodiments described above, the converter station capacity optimization configuration method based on a flexible DC interconnection system provided by this invention employs a two-layer optimization model. The inner optimization model aims to maximize system revenue and renewable energy utilization, while the outer optimization model aims to minimize system construction investment costs. Considering constraints such as power balance, energy storage charging and discharging limitations, and converter capacity limitations, a converter station capacity optimization configuration model considering optimized scheduling and operation is formed. This optimizes the converter station capacity configuration, increases converter station capacity, reduces power system investment costs, improves the economic efficiency of the power system, and solves the problem of low converter station capacity configuration in existing technologies.

[0182] In addition to the methods described above, this invention also provides a converter station capacity optimization configuration device based on a flexible DC interconnection system, such as... Figure 3 As shown, the device includes:

[0183] The initialization unit 301 is used to initialize the population of the pre-stored inner optimization model after the pre-stored outer optimization model has completed population initialization.

[0184] The inner solving unit 302 is used to solve the objective function of the inner optimization model based on the constraints, so as to obtain the optimal solution of the inner objective.

[0185] The capacity calculation unit 303 is used to calculate the optimal load scheduling amount and the current converter station capacity based on the optimal solution of the inner target.

[0186] The result output unit 304 is used to solve the outer optimization model based on the current converter station capacity to obtain the optimization result.

[0187] Optionally, when the optimization objective is to minimize investment cost, the objective function of the outer optimization model is:

[0188] minF=I year

[0189] In the formula, I year The system's equivalent annual investment and operating cost is expressed as follows:

[0190] I year =I initial +I OM +I change

[0191] In the formula, I change Indicates replacement cost; I OM Indicates annual operating and maintenance costs; I initial This represents the annualized investment cost.

[0192] Optionally, the replacement cost I change The expression is:

[0193]

[0194] The annual operation and maintenance cost I OM The expression is:

[0195]

[0196] The equivalent annual value investment cost I initial The expression is:

[0197]

[0198] In the formula, r0 represents the discount rate; L represents the project duration; These represent the unit prices of photovoltaic, energy storage, and converter devices, respectively. These represent the annual operating and maintenance costs per unit of the photovoltaic, energy storage, and converter devices, respectively; N pv N bat N load These represent the installed capacity of photovoltaic, energy storage, and converter stations, respectively; I change_total This represents the total replacement cost over the specified number of years.

[0199] Optionally, when the optimization objectives are system revenue and renewable energy utilization rate, the objective function of the inner optimization model is:

[0200]

[0201] In the formula, w er C represents the weighting coefficients of the system's revenue sub-objective function. total For the benefit of the system, The system yields the independent optimal solution; w REN η represents the weighting coefficients of the renewable energy utilization rate sub-objective function. REN For renewable energy utilization rate, This is the independent optimal solution for renewable energy utilization.

[0202] Optionally, the system benefit sub-objective function is:

[0203]

[0204] In the formula, C users (t) represents the revenue from selling electricity to users, C adj (t) represents the cost of flexible load dispatching, C grid (t) represents the cost of purchasing electricity from the grid, P imb The difference between photovoltaic power generation and load power represents the current unbalanced power level of the system.

[0205] Optionally, the objective function for the utilization rate of renewable energy is:

[0206]

[0207] In the formula, P pv (t) represents the photovoltaic power generation at the current moment. Let T represent the power sold to the grid at the current moment, T represent the total optimization time length (24 hours in this paper), and t represent the current time.

[0208] Optionally, the constraints include at least one of the following:

[0209] Power balance constraints, energy storage charging and discharging constraints, installed capacity constraints, converter power constraints, and capacity-to-load ratio constraints.

[0210] Optionally, the expression for the power balance constraint is:

[0211] P grid +P bat +P pv =P load +P loss

[0212] In the formula, P grid P represents the power exchanged between the system and the power grid. bat P represents energy storage capacity. pv P represents photovoltaic power. load P represents the power consumed by the load. loss This indicates the power loss of the converter.

[0213] Optionally, the expression for the energy storage charge / discharge constraint is:

[0214] SOC min ≤SOC(t)≤SOC max

[0215]

[0216]

[0217] In the formula, SOC(t) represents the remaining charge of the energy storage battery at time t; SOC max This represents the maximum SOC (State of Charge) of the energy storage battery; SOC min P represents the minimum SOC (State of Charge) of the energy storage battery. bat，c (t) represents the charging power at time t; Indicates the maximum charging power; Indicates the minimum charging power; P bat，d (t) represents the discharge power at time t; Indicates the maximum discharge power; This indicates the minimum discharge power.

[0218] Optionally, the expression for the installed capacity constraint is:

[0219] N min，load ≤N load ≤N max，load

[0220] In the formula, N min，load N represents the lower limit of the installed capacity of the converter station. max，load N represents the upper limit of the installed capacity of the converter station. load Install capacity for the converter station.

[0221] Optionally, the expression for the commutation power constraint is:

[0222] P trans ≤P max，trans

[0223] In the formula, P trans For the power transfer between converter stations in the distribution area, P max，trans This represents the maximum power transfer value between converter stations.

[0224] Optionally, the expression for the capacity ratio constraint is:

[0225] λ min，load ≤λ load ≤λ max，load

[0226] In the formula, λ load λ represents the capacity ratio of the converter station. min，load λ represents the minimum capacity ratio limit of the converter station. max，load This indicates the maximum capacity limit of the converter station.

[0227] In the above specific embodiments, the converter station capacity optimization configuration device based on a flexible DC interconnection system provided by the present invention adopts a two-layer optimization model. The inner optimization model aims to maximize system revenue and renewable energy utilization, while the outer optimization model aims to minimize system construction investment costs. Considering constraints such as power balance, energy storage charging and discharging limitations, and converter capacity limitations, a converter station capacity optimization configuration model considering optimized scheduling and operation is formed. This optimizes the converter station capacity configuration, increases converter station capacity, reduces power system investment costs, improves the economic efficiency of the power system, and solves the problem of low converter station capacity configuration in existing technologies.

[0228] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores static and dynamic information data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the above method embodiments.

[0229] Those skilled in the art will understand that Figure 4The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0230] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0231] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the method embodiments described above.

[0232] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0233] This invention is not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this invention is limited only by the appended claims.

Claims

1. A method for optimizing the capacity configuration of converter stations based on a flexible DC interconnection system, characterized in that, The method includes: After the pre-stored outer optimization model has completed population initialization, the pre-stored inner optimization model is then initialized. Based on the constraints, the objective function of the inner optimization model is solved to obtain the optimal solution of the inner objective. The optimal load scheduling amount and current converter station capacity are calculated based on the optimal solution of the inner layer objective, including: when the optimization objectives are system revenue and renewable energy utilization rate, the objective function of the inner layer optimization model is: In the formula, w er C represents the weighting coefficients of the system's revenue sub-objective function. total For the benefit of the system, The system yields the independent optimal solution; w REN η represents the weighting coefficients of the renewable energy utilization rate sub-objective function. REN For renewable energy utilization rate, The solution is the independent optimal solution for renewable energy utilization. Based on the current converter station capacity, the outer optimization model is solved to obtain the optimization results, including: when the optimization objective is to minimize investment cost, the objective function of the outer optimization model is: minF=I year In the formula, I year The system's equivalent annual investment and operating cost is expressed as follows: I year =I initial +I OM +I change In the formula, I change Indicates replacement cost; I OM Indicates annual operating and maintenance costs; I initial This represents the annualized investment cost.

2. The converter station capacity optimization configuration method based on a flexible DC interconnection system according to claim 1, characterized in that, The replacement cost I change The expression is: The annual operation and maintenance cost I OM The expression is: The equivalent annual value investment cost I initial The expression is: In the formula, r0 represents the discount rate; L represents the project duration; These represent the unit prices of photovoltaic, energy storage, and converter devices, respectively. These represent the annual operating and maintenance costs per unit of the photovoltaic, energy storage, and converter devices, respectively; N pv N bat N load These represent the installed capacity of photovoltaic, energy storage, and converter stations, respectively; I change_total This represents the total replacement cost over the specified number of years.

3. The converter station capacity optimization configuration method based on a flexible DC interconnection system according to claim 1, characterized in that, The sub-objective function for system returns is: In the formula, C users (t) represents the revenue from selling electricity to users, C adj (t) represents the cost of flexible load dispatching, C grid (t) represents the cost of purchasing electricity from the grid, P imb The difference between photovoltaic power generation and load power represents the current unbalanced power level of the system.

4. The converter station capacity optimization configuration method based on a flexible DC interconnection system according to claim 1, characterized in that, The objective function for the utilization rate of renewable energy is: In the formula, P pv (t) represents the photovoltaic power generation at the current moment. Let T represent the power sold to the grid at the current moment, T represent the total optimization time, and t represent the current time.

5. The converter station capacity optimization configuration method based on a flexible DC interconnection system according to claim 1, characterized in that, The constraints include at least one of the following: Power balance constraints, energy storage charging and discharging constraints, installed capacity constraints, converter power constraints, and capacity-to-load ratio constraints.

6. The converter station capacity optimization configuration method based on a flexible DC interconnection system according to claim 5, characterized in that, The expression for the power balance constraint is: P grid +P bat +P pv =P load +P loss In the formula, P grid P represents the power exchanged between the system and the power grid. bat P represents energy storage capacity. pv P represents photovoltaic power. load P represents the power consumed by the load. loss This indicates the power loss of the converter.

7. The converter station capacity optimization configuration method based on a flexible DC interconnection system according to claim 5, characterized in that, The expression for the energy storage charge and discharge constraint is: SOC min ≤SOC(t)≤SOC max In the formula, SOC(t) represents the remaining charge of the energy storage battery at time t; SOC max This represents the maximum SOC (State of Charge) of the energy storage battery; SOC min P represents the minimum SOC (State of Charge) of the energy storage battery. bat,c (t) represents the charging power at time t; Indicates the maximum charging power; Indicates the minimum charging power; P bat,d (t) represents the discharge power at time t; Indicates the maximum discharge power; This indicates the minimum discharge power.

8. The converter station capacity optimization configuration method based on a flexible DC interconnection system according to claim 5, characterized in that, The expression for the installed capacity constraint is: N min,load ≤N load ≤N max,load In the formula, N min,load N represents the lower limit of the installed capacity of the converter station. max,load N represents the upper limit of the installed capacity of the converter station. load Install capacity for the converter station.

9. The method for optimizing the capacity configuration of converter stations based on a flexible DC interconnection system according to claim 5, characterized in that, The expression for the commutator power constraint is: P trans ≤P max,trans In the formula, P trans For the power transfer between converter stations in the distribution area, P max,trans This represents the maximum power transfer value between converter stations.

10. The converter station capacity optimization configuration method based on a flexible DC interconnection system according to claim 5, characterized in that, The expression for the capacity ratio constraint is: l min,load ≤λ load ≤λ max,load In the formula, λ load λ represents the capacity ratio of the converter station. min,load λ represents the minimum capacity ratio limit of the converter station. max,load This indicates the maximum capacity limit of the converter station.

11. The method for optimizing the capacity configuration of converter stations based on a flexible DC interconnection system according to any one of claims 1-10, characterized in that, The step of initializing the population of the pre-stored inner-layer optimization model includes: Set inner-layer optimization objective parameters to complete the population initialization of the inner-layer optimization model; wherein, the inner-layer optimization objective parameters include at least population size, spatial dimension, number of iterations, constraint conditions, and inertia weight parameters; The step of solving the objective function of the inner-layer optimization model based on constraints to obtain the optimal solution of the inner-layer objective includes: Based on the constraints, the objective function of the inner optimization model is solved to obtain the current optimal solution; Update the position and velocity of each particle in the population based on the current optimal solution; When the maximum number of inner-layer iterations in updating the position and velocity of each particle in the population based on the current optimal solution reaches a first threshold, the current optimal solution is taken as the inner-layer target optimal solution and the iteration ends.

12. The method for optimizing converter station capacity configuration based on a flexible DC interconnection system according to any one of claims 1-10, characterized in that, The step of completing population initialization in the pre-stored outer optimization model includes: The outer layer parameters are initialized and the outer layer optimization target parameters are set to complete the population initialization of the outer layer optimization model; wherein, the outer layer parameters include at least cost, engineering and capacity, and the outer layer optimization target parameters include at least population size, spatial dimension, number of iterations, constraint conditions and inertia weight parameters; The step of solving the outer optimization model based on the current converter station capacity to obtain the optimization result includes: Based on the current converter station capacity, the outer optimization model is solved to obtain the current optimization result; Update the position and velocity of each particle in the population based on the current optimization results; When the maximum number of outer-layer iterations for updating the position and velocity of each particle in the population based on the current optimization result reaches the second threshold, the current optimization result is used as the optimization result and the iteration ends.

13. A converter station capacity optimization configuration device based on a flexible DC interconnection system, characterized in that, The device includes: An initialization unit is used to initialize the population of a pre-stored inner optimization model after the pre-stored outer optimization model has completed population initialization. The inner-layer solver unit is used to solve the objective function of the inner-layer optimization model based on constraints to obtain the optimal solution of the inner-layer objective. This includes: when the optimization objectives are system revenue and renewable energy utilization rate, the objective function of the inner-layer optimization model is: In the formula, w er C represents the weighting coefficients of the system's revenue sub-objective function. total For the benefit of the system, The system yields the independent optimal solution; w REN η represents the weighting coefficients of the renewable energy utilization rate sub-objective function. REN For renewable energy utilization rate, The solution is the independent optimal solution for renewable energy utilization. The capacity calculation unit is used to calculate the optimal load scheduling amount and the current converter station capacity based on the optimal solution of the inner target. The result output unit is used to solve the outer optimization model based on the current converter station capacity to obtain the optimization result, including: when the optimization objective is to minimize investment cost, the objective function of the outer optimization model is: minF=I year In the formula, I year The system's equivalent annual investment and operating cost is expressed as follows: I year =I initial +I OM +I change In the formula, I change Indicates replacement cost; I OM Indicates annual operating and maintenance costs; I initial This represents the annualized investment cost.

14. The converter station capacity optimization configuration device based on a flexible DC interconnection system according to claim 13, characterized in that, The replacement cost I change The expression is: The annual operation and maintenance cost I OM The expression is: The equivalent annual value investment cost I initial The expression is: In the formula, r0 represents the discount rate; L represents the project duration; These represent the unit prices of photovoltaic, energy storage, and converter devices, respectively. These represent the annual operating and maintenance costs per unit of the photovoltaic, energy storage, and converter devices, respectively; N pv N bat N load These represent the installed capacity of photovoltaic, energy storage, and converter stations, respectively; I change_total This represents the total replacement cost over the specified number of years.

15. The converter station capacity optimization configuration device based on a flexible DC interconnection system according to claim 13, characterized in that, The sub-objective function for system returns is: In the formula, C users (t) represents the revenue from selling electricity to users, C adj (t) represents the cost of flexible load dispatching, C grid (t) represents the cost of purchasing electricity from the grid, P imb The difference between photovoltaic power generation and load power represents the current unbalanced power level of the system.

16. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 12.

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