Alternating current and direct current matching method, device and equipment for key channel of alternating current and direct current hybrid power grid

Abstract

The invention provides an AC-DC matching method, device and equipment for key channels of an AC-DC hybrid power grid, and relates to the technical field of power grid planning and operation of a power system. The method comprises the following steps: converting a spinning reserve capacity constraint used for representing the uncertainty of wind power and load in the AC / DC hybrid power grid from a fuzzy opportunity constraint into a deterministic constraint by using similar Cauchy distribution, and obtaining the converted spinning reserve capacity constraint; constructing a double-layer optimization model according to the converted spinning reserve capacity constraint; the double-layer optimization model comprises a power transmission network expansion planning model and a security constraint unit combination operation model; and solving the double-layer optimization model, and determining an AC-DC ratio scheme of the key channel of the AC-DC hybrid power grid.

Description

Technical Field

[0001] This application relates to the field of power grid planning and operation technology, and more specifically, to a method, apparatus and equipment for AC / DC matching of key channels in an AC / DC hybrid power grid. Background Technology

[0002] The continuous increase in grid-connected capacity of renewable energy has brought new challenges to the stable operation, security, and economic benefits of regional power grids. These challenges are mainly reflected in the imbalance of power source and load distribution within the regional power grid, the surge in transmission pressure on key channels (river crossings), and difficulties in integrating new energy sources. Embedded DC, as a novel DC interconnection technology, can transfer power flow from high-voltage transmission lines while maintaining the original short-circuit level of the AC grid, significantly improving the inter-regional power exchange capacity. However, the related equipment and technologies for DC grid construction are still under development, and the overall technological maturity and economic reliability cannot yet meet the needs of large-scale development. Considering that traditional AC transmission technology also has certain advantages in terms of technological maturity and economics, combining the advantages of both would be of great significance in achieving a balance between the economic efficiency of regional power grid investment and the demand for flexible power transmission. Summary of the Invention

[0003] In view of this, this application provides a method, apparatus and equipment for AC / DC matching in key channels of AC / DC hybrid power grids.

[0004] One aspect of this application provides a method for AC / DC allocation in key channels of an AC / DC hybrid power grid, comprising: using a Cauchy-like distribution to transform the spinning reserve capacity constraint, which characterizes the uncertainty of wind power and load in the AC / DC hybrid power grid, from a fuzzy opportunistic constraint to a deterministic constraint, thereby obtaining the transformed spinning reserve capacity constraint; constructing a two-level optimization model based on the transformed spinning reserve capacity constraint; the two-level optimization model including a transmission network expansion planning model and a safety-constrained unit combination operation model; and solving the two-level optimization model to determine the AC / DC allocation scheme for the key channels of the AC / DC hybrid power grid.

[0005] According to an embodiment of this application, the above-mentioned transmission network expansion planning model is obtained by using constraints on the number of line loops, node connectivity, and line capacity as constraints, and minimizing the sum of the network flexibility and uniformity index, network efficiency applicability index, and opportunity constraint reliability index of the AC / DC hybrid power grid as the first objective function.

[0006] According to the embodiments of this application, the above-mentioned safety-constrained unit combination operation model is obtained by using the converted spinning reserve capacity constraint, embedded DC power constraint, power balance constraint, power flow and node phase angle constraint, generator output constraint and unit ramping constraint as constraints, and minimizing the sum of the AC / DC line cost, unit annual operating cost, embedded DC cost, wind curtailment and load shedding penalty cost and wind power spinning reserve cost of the above-mentioned key channels of the AC / DC hybrid power grid as the second objective function.

[0007] According to an embodiment of this application, solving the above-mentioned two-layer optimization model to determine the AC / DC ratio scheme of the key channel of the AC / DC hybrid power grid includes: determining multiple initial AC / DC ratio schemes in a random manner; calculating the individual fitness value of each initial AC / DC ratio scheme using the weighted sum of the first objective function and the second objective function as the fitness function; determining the global optimal fitness value among the multiple initial AC / DC ratio schemes based on the individual fitness values; adjusting the nodes and transmission lines in the multiple initial AC / DC ratio schemes to obtain modified initial AC / DC ratio schemes, such that the modified initial AC / DC ratio schemes satisfy the node connectivity constraints and the line capacity constraints; determining multiple target AC / DC ratio schemes that satisfy preset constraints under all fault conditions from the modified initial AC / DC ratio schemes according to the single fault safety check rule; and utilizing the initial step size, the multiple target AC / DC ratio schemes, the initial AC / DC ratio schemes corresponding to the global fitness value, and the adaptive... According to the update step size determined by the weight, multiple target AC / DC ratio schemes are updated to obtain multiple updated AC / DC ratio schemes. If the comparison result between the individual fitness values ​​of the multiple updated AC / DC ratio schemes and the individual fitness values ​​of the initial AC / DC ratio schemes corresponding to the updated AC / DC ratio schemes satisfies the first preset condition, the individual fitness values ​​of the multiple updated AC / DC ratio schemes and the global fitness values ​​are updated. The updated individual fitness values ​​are used as individual fitness values, the updated global fitness values ​​are used as global fitness values, and the multiple updated AC / DC ratio schemes are used as multiple initial AC / DC ratio schemes. The operations of adjusting nodes and transmission lines, determining target AC / DC ratio schemes, updating AC / DC ratio schemes, updating individual fitness values, and updating global fitness values ​​are iteratively executed until the second preset condition is reached. Based on the updated AC / DC ratio scheme corresponding to the global fitness value obtained when the second preset condition is reached, the AC / DC ratio scheme of the key channel of the AC / DC hybrid power grid is obtained.

[0008] According to an embodiment of this application, the above-mentioned adjustment of nodes and transmission lines in the plurality of initial AC / DC distribution schemes to obtain modified plurality of initial AC / DC distribution schemes includes: adjusting the nodes in the line planning schemes corresponding to the initial AC / DC distribution schemes with islanded nodes according to the above-mentioned node connectivity constraints, until there are no islanded nodes in the line planning schemes corresponding to the plurality of initial AC / DC distribution schemes, thus obtaining updated plurality of initial AC / DC distribution schemes; and adjusting the transmission lines in the line planning schemes corresponding to the updated initial AC / DC distribution schemes with transmission lines exceeding capacity limits according to the above-mentioned line capacity constraints, until there are no transmission lines exceeding capacity limits in the line planning schemes corresponding to the updated plurality of initial AC / DC distribution schemes, thus obtaining modified plurality of initial AC / DC distribution schemes.

[0009] According to an embodiment of this application, the first objective function is:

[0010] ;

[0011] ;

[0012] ;

[0013] ;

[0014] in, Let this be the first objective function; As an indicator of network flexibility and uniformity; As an indicator of network efficiency applicability; For opportunity-constrained reliability indicators; Indicates the number of lines. The flexibility allowance for line i in time period j; It is the average value of the flexibility allowance for each line; Runtime; This represents the total remaining capacity rate. This represents the ratio of the actual power flow of line i to its maximum transmission capacity during time period t. The number of times the joint opportunity constraint has been violated.

[0015] According to an embodiment of this application, the second objective function is:

[0016] ;

[0017] ;

[0018] ;

[0019] ;

[0020] ;

[0021] ;

[0022] in, The second objective function; This represents the total investment cost of the newly constructed line. Annual operating cost of the unit; For the operating cost of embedded DC; The cost of curtailing wind power and cutting off loads; For wind power rotation reserve costs; This represents the construction cost coefficient per unit length of the DC system. For the length of the newly created embedded line, Construction cost coefficient per unit length of the AC system This is the length of the newly built AC line; For the number of generator sets, For runtime, To reduce the number of piecewise linearizations, Let be the linearized coal consumption cost coefficient for the s-th segment of unit i. For unit i to output power during the s-th segment of time period t, This represents the operating status of unit i during time period t. Let be the secondary cost coefficient of operating unit i; Let i be the primary cost coefficient for operating unit i; Let i be the constant cost coefficient for operating unit i. Let $t$ be the startup cost of unit $i$ during time period $t$. Let $t$ be the shutdown cost of unit $i$ during time period $t$. Minimum output of the i-th generator; A collection of DC lines. Let be the unit loss cost coefficient for line l. Let be the transmission power of line l at time t. Let be the resistance per unit length of line l; The length of line l; For the number of nodes, These represent the wind curtailment cost coefficient and the load shedding cost coefficient, respectively. These represent the predicted wind power output and the actual wind power output absorbed at time t, respectively. This represents the load shedding amount of node i during time period t; It is the positive spinning reserve cost coefficient of unit i. It is the positive spinning reserve capacity of unit i during time period t. It is the negative spinning reserve cost coefficient for unit i. It is the negative spinning reserve capacity of unit i during time period t.

[0023] According to an embodiment of this application, the converted spinning reserve capacity constraint is as follows:

[0024] ;

[0025] ;

[0026] ;

[0027] ;

[0028] in, Let represent the positive spinning reserve capacity and negative spinning reserve capacity provided by the i-th generator at time t, respectively; For the number of generator sets, Let represent the quantiles of the positive and negative load prediction errors for node n at time t, respectively. These are the quantiles for the positive and negative prediction errors of wind power at time t. For the number of wind farms, For the number of nodes, Let be the maximum output of the i-th unit at time t; Let be the minimum output of the i-th unit at time t; Let i be the output of the i-th unit at time t; The uphill speed limit for the i-th generator; This represents the operating status of unit i during time period t. The downhill speed limit is set for the i-th generator.

[0029] Another aspect of this application provides an AC / DC ratio device for key channels of an AC / DC hybrid power grid, comprising: a constraint transformation module, used to transform the spinning reserve capacity constraint, which characterizes the uncertainty of wind power and load in the AC / DC hybrid power grid, from a fuzzy opportunistic constraint to a deterministic constraint using a Cauchy-like distribution, to obtain the transformed spinning reserve capacity constraint; a model building module, used to construct a two-level optimization model based on the transformed spinning reserve capacity constraint; the two-level optimization model includes a transmission network expansion planning model and a safety-constrained unit combination operation model; and a scheme determination module, used to solve the two-level optimization model to determine the AC / DC ratio scheme for the key channels of the AC / DC hybrid power grid.

[0030] Another aspect of this application provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to perform the methods described above.

[0031] According to embodiments of this application, compared to existing methods that independently model the uncertainties of wind power and load as fuzzy variables, this application constructs joint fuzzy chance constraints and utilizes a Cauchy-like distribution for deterministic transformation, thus more accurately characterizing the coupling relationship between wind power and load uncertainties and avoiding the problem of overly conservative or inaccurate planning results. Under the premise of satisfying a given confidence level, the proposed joint chance constraint model can ensure that system reliability is not affected, proving the effectiveness of the model in dealing with scenarios involving uncertainties in renewable energy. Furthermore, this application constructs a two-layer power generation and transmission coordinated planning model: compared to methods that simply pursue economic or reliability optimization, this application effectively controls investment costs while meeting system reliability and flexibility requirements through iterative optimization at both the upper and lower layers. Therefore, this application can improve the overall operating efficiency and economy of the system, enhancing the regional power grid's capacity to absorb renewable energy while ensuring economic benefits and system safety. Attached Figure Description

[0032] The above and other objects, features and advantages of this application will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0033] Figure 1 A flowchart illustrating the AC / DC ratio method for key channels in an AC / DC hybrid power grid according to an embodiment of this application is shown schematically.

[0034] Figure 2 A schematic diagram of a two-layer optimization model structure according to an embodiment of this application is shown.

[0035] Figure 3 The flowchart illustrating the solution of a two-level optimization model using an improved particle swarm optimization algorithm according to an embodiment of this application is shown in the illustration.

[0036] Figure 4 This schematically illustrates the Garver-18 system architecture diagram before planning according to an embodiment of this application;

[0037] Figure 5 This schematically illustrates the structure of the Garver-18 system according to an embodiment of this application.

[0038] Figure 6 This schematically illustrates the unit combined output diagram before planning according to an embodiment of this application;

[0039] Figure 7 This schematically illustrates the combined output diagram of the units after planning according to an embodiment of this application;

[0040] Figure 8 A schematic diagram illustrating the convergence curve of a two-layer optimization model according to an embodiment of this application is shown.

[0041] Figure 9 A block diagram illustrating an AC / DC power grid key channel AC / DC ratio device according to an embodiment of this application is shown.

[0042] Figure 10 A block diagram of an electronic device suitable for implementing an AC / DC ratio method for key channels in a hybrid AC / DC power grid, according to an embodiment of this application, is shown schematically. Detailed Implementation

[0043] The embodiments of this application will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of this application. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of this application for ease of explanation. However, it will be apparent that one or more embodiments may be implemented without these specific details. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0044] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0045] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0046] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0047] In the embodiments of this application, the collection, updating, analysis, processing, use, transmission, provision, disclosure, and storage of data (e.g., including but not limited to user personal information) comply with relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures have been taken to prevent unauthorized access to user personal information data and to safeguard user personal information security, network security, and national security.

[0048] In the embodiments of this application, the user's authorization or consent was obtained before obtaining or collecting the user's personal information.

[0049] To address the uncertainties brought about by the integration of new energy sources, fuzzy opportunity-constrained programming (FOCPC) is widely used due to its ability to quantitatively describe risks. Existing fuzzy modeling methods for uncertainties in hybrid power grid systems mainly include: first, directly modeling output based on historical data; and second, transforming uncertainty into fuzzy prediction errors and modeling those errors. These methods effectively improve computational speed and reduce errors, and are widely used in power grid planning. However, existing research generally treats the uncertainties of wind power and load as independent fuzzy variables, neglecting the coupling relationship between wind power and load. This results in joint fuzzy opportunity constraints that are overly conservative or inaccurate, failing to reflect the actual risks of power grid operation.

[0050] To address the aforementioned issues, this application proposes a two-layer planning method for the AC / DC ratio of key channels in a hybrid AC / DC power grid, considering the joint fuzzy opportunity constraints of wind power and load. By constructing joint fuzzy opportunity constraints of wind power and load, the upper layer introduces network flexibility and uniformity indicators, network efficiency adaptability indicators, and joint opportunity constraint reliability indicators to initially evaluate the planning scheme. The lower layer introduces comprehensive economic indicators to analyze the planning model, thereby effectively improving the regional power grid's capacity to absorb renewable energy while ensuring system safety and economy.

[0051] Figure 1 A flowchart illustrating the AC / DC ratio method for key channels in an AC / DC hybrid power grid according to an embodiment of this application is shown.

[0052] like Figure 1 As shown, an embodiment of this application provides a method for AC / DC matching of key channels in an AC / DC hybrid power grid, including operations S101 to S103.

[0053] In operation S101: using a Cauchy-like distribution, the spinning reserve capacity constraint, which characterizes the uncertainty of wind power and load in AC / DC hybrid power grids, is transformed from a fuzzy opportunistic constraint into a deterministic constraint, resulting in the transformed spinning reserve capacity constraint.

[0054] In operation S102: Based on the transformed spinning reserve capacity constraints, a two-layer optimization model is constructed; the two-layer optimization model includes a transmission network expansion planning model and a safety-constrained unit combination operation model.

[0055] In operation S103: Solve the two-layer optimization model to determine the AC / DC ratio scheme of key channels in the AC / DC hybrid power grid.

[0056] According to the embodiments of this application, by coordinating the configuration of AC / DC transmission capacity and the coordinated operation of power generation and grid, the capacity ratio of key channels in the AC / DC hybrid power grid is determined, thereby improving the reliability and economy of the planning of high-proportion new energy power systems. This enables the planning of AC / DC ratio schemes for key channels and the improvement of renewable energy consumption levels while ensuring system safety and economy, thus providing technical support for the planning and operation of AC / DC hybrid power grids.

[0057] In the embodiments of this application, operation S101 addresses the prediction uncertainty of wind power output and system load by modeling the prediction uncertainty of wind power output and system load as fuzzy variables and using a Cauchy-like distribution to describe its probability distribution characteristics.

[0058] The percentages of wind power and load forecasting errors are defined as follows:

[0059] ;

[0060] ;

[0061] in, This refers to wind power forecasting errors; This refers to load forecasting error; To actually contribute to wind power; Contribute to wind power forecasting, This represents the actual load. For load forecasting.

[0062] Errors can be divided into positive errors (actual output is higher than predicted output) and negative errors (actual output is lower than predicted output), and their membership degrees are shown below:

[0063] ;

[0064] in, For membership degree, These represent the statistical averages of the positive and negative errors, respectively. The weights are those of the Cauchy distribution. Indicates prediction error, including and Based on the definition of the confidence measure function, the error confidence measure can be derived as follows:

[0065] ;

[0066] in, Let be the error confidence measure function. It is a fuzzy parameter for credibility measurement.

[0067] To ensure that the confidence level of the above set of constraint events is not lower than the pre-set confidence level, the original uncertain programming model is transformed into a deterministic equivalent constraint for solution.

[0068] According to uncertainty theory, for fuzzy chance constraints Equivalent to , The quantile for fuzzy constraints is defined as follows:

[0069] ;

[0070] In this context, inf is an abbreviation for infimum, and sup is an abbreviation for supremum. is the confidence level (a value between 0 and 1), and K is the value of the statistic that satisfies the quantile condition.

[0071] Based on the error confidence measure, the method for calculating the positive and negative error quantiles based on the Cauchy distribution can be derived as follows:

[0072] ;

[0073] In the formula, For the corresponding error quantiles, Error average parameter.

[0074] A spinning reserve capacity constraint involving fuzzy parameters is constructed. This constraint is then transformed using a Cauchy-like distribution, resulting in a transformed spinning reserve capacity constraint. The transformed constraint includes a first constraint derived from joint chance constraint modeling of the spinning reserve constraint based on positive and negative error quantiles and considering the uncertainties of wind power and load; and a second constraint, where the generator-wind power-load spinning reserve capacity in the grid is also subject to the unit's own capacity and ramping constraints.

[0075] The first constraint is:

[0076] ;

[0077] ;

[0078] The second constraint is:

[0079] ;

[0080] ;

[0081] in, Let represent the positive spinning reserve capacity and negative spinning reserve capacity provided by the i-th generator at time t, respectively; For the number of generator sets, Let represent the quantiles of the positive and negative load prediction errors for node n at time t, respectively. These are the quantiles for the positive and negative prediction errors of wind power at time t. For the number of wind farms, For the number of nodes, Let be the maximum output of the i-th unit at time t; Let be the minimum output of the i-th unit at time t; Let i be the output of the i-th unit at time t; The uphill speed limit for the i-th generator; This represents the operating status of unit i during time period t. The downhill speed limit is set for the i-th generator.

[0082] Figure 2 A schematic diagram of a two-layer optimization model structure according to an embodiment of this application is shown.

[0083] like Figure 2 As shown, the upper-level model of the two-layer optimization model is the power transmission network expansion planning model; the lower-level model is the safety-constrained unit combination operation model.

[0084] The transmission network expansion planning model is obtained by using constraints such as the number of line loops, node connectivity, and line capacity as constraints, and minimizing the sum of the network flexibility and uniformity index, network efficiency applicability index, and opportunity constraint reliability index of the AC / DC hybrid power grid as the first objective function.

[0085] The network flexibility uniformity index reflects the balance of line capacity utilization; essentially, it is the ratio of the standard deviation to the mean of flexibility reserves. It reflects the degree of even distribution of flexibility resources across the network. A smaller value indicates a more even distribution of flexibility resources among lines, which helps improve the system's overall adaptability to uncertainty and avoids situations where some lines have excessive flexibility while others lack it.

[0086] Network efficiency adaptability index is used to evaluate the efficiency and balance of line resource utilization in a power system, reflecting the balance between grid utilization and safety margin during system operation.

[0087] The Joint Opportunity Constraint Reliability Index assesses the ability of a power system to maintain stable operation in the face of uncertain wind power loads, particularly in environments with a high proportion of renewable energy connected to the grid. This index measures the degree to which the system meets opportunity constraints at a pre-set confidence level.

[0088] Therefore, the first objective function is:

[0089] ;

[0090] ;

[0091] ;

[0092] ;

[0093] in, Let this be the first objective function; As an indicator of network flexibility and uniformity; As an indicator of network efficiency applicability; For opportunity-constrained reliability indicators; Indicates the number of lines. The flexibility allowance for line i in time period j; It is the average value of the flexibility allowance for each line; Runtime; This represents the total remaining capacity rate. This represents the ratio of the actual power flow of line i to its maximum transmission capacity during time period t. The number of times the joint opportunity constraint has been violated.

[0094] Average value of flexibility allowance for each line The calculation formula can be:

[0095] .

[0096] Total Remaining Capacity Rate The calculation formula can be:

[0097] .

[0098] The constraints of the transmission network expansion planning model include the number of line loops, node connectivity, and line capacity.

[0099] The circuit loop count constraint is used to limit the number of new lines that can be built in critical corridors. In the planning model, an upper limit is set on the total number of circuit loops between node pairs (i, j). The circuit loop count constraint can be expressed as:

[0100] ;

[0101] ;

[0102] ;

[0103] in, Let be the total number of loops between node pairs (i, j); , These represent the existing number of loops and the newly created number of loops between nodes ij, respectively. , These represent the upper and lower limits of the number of loops between nodes ij, respectively. Represents the set of integers.

[0104] Node connectivity constraints are introduced to address the issue of isolated nodes becoming disconnected from the main network during the planning of new power grid lines, which may occur due to the randomness inherent in particle swarm optimization (PSO) algorithms. To prevent this, the upper-level model incorporates node connectivity constraints to ensure the overall power grid remains connected without any isolated nodes or regions. Node connectivity constraints can be expressed as:

[0105] ;

[0106] ;

[0107] ;

[0108] in, This refers to the virtual traffic flowing from node k to node i. For flow from node i to node Virtual traffic, , Representing nodes respectively The set of parent nodes and the set of child nodes, It is the maximum value. is the number of nodes. c is the line identifier. This is the set of all routes.

[0109] Line capacity constraints are implemented after the upper-level model has initially planned AC / DC lines. It's necessary to check whether the power flow of each line meets the capacity constraints. If not, the number of line loops is increased until the constraints are met. Therefore, line capacity constraints can be expressed as: .

[0110] in, For the line The current power flow on, line Single-loop capacity limit, Line is a circuit The number of circuits, The total number of lines in the system. Check each line to see if it exceeds its maximum capacity. If it exceeds the maximum capacity but not the line's construction capacity, add one loop; if the number of loops has reached the limit, add loops to other lines that have not reached the limit to reduce the line load.

[0111] This application establishes a multi-dimensional quantitative evaluation system comprising network flexibility and uniformity indicators, network efficiency and adaptability indicators, and joint opportunity reliability indicators. This avoids the problems of uneven robustness or wasted investment in AC / DC power distribution schemes caused by relying on only a single indicator, as is common in traditional methods. By comprehensively considering these multiple indicators, a balance between system flexibility and overall economic efficiency is achieved, thus improving the comprehensive indicators of the planning scheme.

[0112] The safety-constrained unit combination operation model is obtained by using the converted spinning reserve capacity constraint, embedded DC power constraint, power balance constraint, power flow and node phase angle constraint, generator output constraint, and unit ramping constraint as constraints, and minimizing the sum of AC / DC line cost, unit annual operating cost, embedded DC cost, wind curtailment and load shedding penalty cost, and wind power spinning reserve cost of the key channel of the AC / DC hybrid power grid as the second objective function.

[0113] The total investment cost of new power lines includes both AC and DC line costs, encompassing the construction investment for planned new embedded DC and AC lines. Annual operating costs for generating units include fuel consumption and start-up / shutdown costs for conventional thermal power units. A piecewise linearization method is used to represent the operating costs of thermal power units. The operating cost of embedded DC lines considers transmission losses during transmission. Curtailment and load shedding penalties are also included. High penalties are imposed on wind power curtailment and load losses to quantify unused renewable energy and unmet load demand. Wind power spinning reserve costs are also considered; to meet reserve requirements under uncertainties in wind power and load forecasting, conventional units must provide both on- and off-spinning reserve capacity.

[0114] The lower-level safety-constrained unit combination operation model aims to minimize the overall operating cost and conducts a comprehensive economic evaluation of the given AC / DC power distribution scheme at the upper level. The overall operating cost is the sum of AC / DC line costs, annual unit operating costs, embedded DC costs, wind curtailment and load shedding penalty costs, and wind power spinning reserve costs. Therefore, the second objective function is:

[0115] ;

[0116] ;

[0117] ;

[0118] ;

[0119] ;

[0120] ;

[0121] in, The second objective function; This represents the total investment cost of the newly constructed line. Annual operating cost of the unit; For the operating cost of embedded DC; The cost of curtailing wind power and cutting off loads; For wind power rotation reserve costs; This represents the construction cost coefficient per unit length of the DC system. For the length of the newly created embedded line, Construction cost coefficient per unit length of the AC system This is the length of the newly built AC line; For the number of generator sets, For runtime, To reduce the number of piecewise linearizations, Let be the linearized coal consumption cost coefficient for the s-th segment of unit i. For unit i to output power during the s-th segment of time period t, This represents the operating status of unit i during time period t. Let be the secondary cost coefficient of operating unit i; Let i be the primary cost coefficient for operating unit i; Let i be the constant cost coefficient for operating unit i. Let $t$ be the startup cost of unit $i$ during time period $t$. Let $t$ be the shutdown cost of unit $i$ during time period $t$. Minimum output of the i-th generator; A collection of DC lines. Let be the unit loss cost coefficient for line l. Let be the transmission power of line l at time t. Let be the resistance per unit length of line l; The length of line l; For the number of nodes, These represent the wind curtailment cost coefficient and the load shedding cost coefficient, respectively. These represent the predicted wind power output and the actual wind power output absorbed at time t, respectively. This represents the load shedding amount of node i during time period t; It is the positive spinning reserve cost coefficient of unit i. It is the positive spinning reserve capacity of unit i during time period t. It is the negative spinning reserve cost coefficient for unit i. It is the negative spinning reserve capacity of unit i during time period t.

[0122] The constraints of the safety-constrained unit combination operation model include converted spinning reserve capacity constraints, embedded DC power constraints, power balance constraints, power flow and node phase angle constraints, generator output constraints, and unit ramping constraints. Among them, the converted spinning reserve capacity constraints involve grid power balance constraints, generator output upper and lower limit constraints, and line transmission capacity constraints.

[0123] The converted spinning reserve capacity constraints are as described above and will not be repeated here.

[0124] Embedded DC power constraints refer to the use of discrete tap-level modeling for the power transmission of each planned and newly constructed embedded DC transmission line. Only one DC power tap can be selected at any given time. The embedded DC power constraint can be expressed as:

[0125] ;

[0126] Where K represents the total number of power levels. Let t be the power transfer value of the DC line. This represents the power value corresponding to the k-th gear. Let be a binary variable, representing whether the kth power level is selected at time t.

[0127] At any given time, only one power level can be selected, that is: .

[0128] Constraining DC power variations, i.e.: ;in, The power transfer value of the DC line at time t-1. The maximum allowable power variation between adjacent time periods.

[0129] Constraining node power balance:

[0130] ;

[0131] ;

[0132] in, DC connection matrix The power injection vector of the DC line at the two connection nodes at time t. The power generation injection vector (MW) of all nodes at time t. The load extraction vector for all nodes at time t.

[0133] Power balance constraint refers to the requirement that the output of various power sources and the load in a hybrid AC / DC power grid must be balanced at any given time. The total active power output of thermal power units, wind power units, and energy storage devices should equal the total system load at that moment minus the load shedding. The power balance constraint can be expressed as:

[0134] ;

[0135] in, The output of the i-th generating unit at time t. Let t be the wind power output at time t. The discharge and charge power of the energy storage system at time t. Let be the total load demand at time t. The amount of load shedding at node n at time t.

[0136] Power flow and node phase angle constraints refer to the use of a DC power flow model approximation in power flow calculations, requiring that the injected power and outflow power at each node satisfy a balance relationship, i.e., satisfying the node power balance equation and the line power-phase angle constraint. Power flow and node phase angle constraints can be expressed as:

[0137] ;

[0138] ;

[0139] ;

[0140] ;

[0141] ;

[0142] ;

[0143] ;

[0144] in, This represents the output vector of a conventional generator unit; This represents the output vector of the adjustable generator unit; This represents the output vector of the new energy unit; Represents the load vector; Represents the load shear vector. This represents the correlation matrix of conventional generating units; Represents the correlation matrix of adjustable generating units; Represents the correlation matrix of new energy units. Here is the nodal admittance matrix. Let be the node voltage phase angle vector; D represents the set of all nodes. Let i represent the i-th conventional unit connected to node d; For conventional units connected to node d; The power of conventional unit i at time t is the power output under normal operating conditions or under N-1 fault operating conditions. For the i-th adjustable unit connected to node d; Adjustable unit i is in normal operating condition or N-1 fault operating condition; For adjustable units connected to node d; For new energy generating units connected to node d; Let i be the state variable connected to the i-th renewable energy unit on node d; The power of new energy unit i at time t is the power output under normal operating conditions or under N-1 fault operating conditions. For the load connected to node d; The set of load shedding connected to node d; The power of the load at time t is the power under normal operating conditions or under N-1 fault operating conditions. The power at time t represents the load shedding power under normal operating conditions or N-1 fault operating conditions. The node admittance at node d at time t is for normal operation or N-1 fault operation. The node voltage phase angle at node d at time t is for normal operation or N-1 fault operation. The minimum phase angle under normal operating conditions or N-1 fault operating conditions; This represents the maximum phase angle under normal operating conditions or under N-1 fault operating conditions. The phase angle of the reference node under normal operating conditions or under N-1 fault operating conditions; The active power flow of the c-th circuit of line ij at time t is under normal operating conditions or under N-1 fault operating conditions. This represents the admittance of the c-th circuit of line ij under normal operating conditions or under N-1 fault operating conditions; The voltage phase angle at node i at time t is for normal operation or N-1 fault operation. The voltage phase angle at node j at time t is for normal operation or N-1 fault operation. This represents the upper limit of the power flow of the c-th loop of line ij under normal operating conditions. This represents the active power flow of the c-th circuit of line ij under normal operating conditions. This represents the upper limit of the power flow of the c-th circuit of line ij under the N-1 fault operating condition; For the active power flow of the c-th circuit of line ij under the N-1 fault operating condition, Let c be the construction state variable of the c-th loop of line ij. This is the set of all routes.

[0145] The generator set output constraint and the unit ramping constraint constitute the unit combined constraint clause. The generator set output constraint can be expressed as:

[0146] ;

[0147] ;

[0148] in, To provide power to the generator set; Let be the minimum output of the i-th generator. This represents the maximum output of the i-th generator. The operating status of unit i during time period t (0-1 variable). It is the continuous start-up time of unit g; It is the minimum continuous start-up time of unit g. It is the continuous shutdown time of unit g; It is the minimum continuous shutdown time of unit g.

[0149] The unit ramp-up constraint can be expressed as:

[0150] ;

[0151] ;

[0152] in, The output of the i-th generator at time t The output of the i-th generator at time t-1 Downhill speed limit for the i-th generator Uphill speed limit for the i-th generator The switching state of the i-th generator at time t. The switching state of the i-th generator at time t-1.

[0153] Taking the upslope constraint as an example, when the unit remained in the on state in the previous period ( The increase in power cannot exceed its uphill rate limit, that is: When the unit starts from the off state ( The constraint is: .

[0154] In the embodiments of this application, the two-layer optimization model can be solved using PSO (Particle Swarm Optimization). In this application, the AC / DC ratio is defined as the ratio of the total transmission capacity of the key channel AC lines to the total transmission capacity of the DC lines. ;in, The total transmission capacity of the AC line. This represents the total transmission capacity of the DC line.

[0155] Figure 3 The flowchart illustrating the solution of a two-layer optimization model according to an embodiment of this application is shown in the illustration.

[0156] like Figure 3 As shown, the process of solving the two-layer optimization model includes operations S301~S308.

[0157] In operation S301: multiple initial AC / DC ratio schemes are determined randomly.

[0158] In the embodiments of this application, parameter initialization is first performed, including clearing temporary data and reading the power grid topology, candidate line set, and load and generation parameters of each node. The particle swarm size is then set. Maximum number of iterations Initial value and convergence factor of inertia weight w, self and global learning factors Set the AMPSO (Adaptive Mutation Particle Swarm Optimization) algorithm parameters. Then perform population initialization, including: setting the iteration counter. Set to zero. Generate randomly. Each particle is assigned a random initial position and velocity, thus obtaining... Different initial critical channel AC / DC ratio planning schemes (particle position codes correspond to the configuration of newly built AC or DC lines for each candidate channel), i.e., randomly generated. In each individual particle, there is an initial AC / DC ratio scheme; the position encoding of the particle can be achieved by representing AC lines with 0 and DC lines with 1, and the AC / DC ratio scheme of the key section can be randomly initialized.

[0159] In operation S302: using the weighted sum of the first objective function and the second objective function as the fitness function, calculate the individual fitness value of each initial AC / DC ratio scheme.

[0160] In operation S303: Based on the fitness values ​​of each individual, determine the global fitness values ​​of multiple initial AC / DC ratio schemes.

[0161] In the embodiments of this application, the initial fitness of each particle is calculated, and the individual optimal fitness value of each particle is recorded respectively. and the global optimum of the entire population .

[0162] The first objective function corresponding to the upper-level model is used as part of the fitness function, and the second objective function corresponding to the lower-level model is used as the other part of the fitness function to calculate the optimal fitness value of an individual. and the global optimum of the entire population .

[0163] In operation S304: Adjust the nodes and transmission lines in multiple initial AC / DC ratio schemes to obtain multiple modified initial AC / DC ratio schemes, so that the modified multiple initial AC / DC ratio schemes satisfy the node connectivity constraints and line capacity constraints.

[0164] In the embodiments of this application, nodes in the line planning scheme corresponding to the initial AC / DC ratio scheme with isolated nodes can be adjusted according to node connectivity constraints until there are no isolated nodes in the line planning schemes corresponding to multiple initial AC / DC ratio schemes, thus obtaining multiple updated initial AC / DC ratio schemes.

[0165] During connectivity constraint verification, network connectivity is checked for the route planning scheme corresponding to each particle. If an isolated node disconnected from the main network is found, node connectivity constraints are introduced for that particle's scheme, and the particle's position is adjusted accordingly until the connectivity requirements are met.

[0166] Based on line capacity constraints, the transmission lines in the line planning schemes corresponding to the updated initial AC / DC ratio schemes that have transmission lines exceeding capacity limits are adjusted until there are no transmission lines exceeding capacity limits in the line planning schemes corresponding to the updated multiple initial AC / DC ratio schemes, thus obtaining multiple corrected initial AC / DC ratio schemes.

[0167] During line capacity verification, for particle schemes that pass the connectivity check, it is evaluated whether the transmission power of each line exceeds the capacity limit. If the power flow of a certain line exceeds its allowable capacity, the number of loops of that line is increased or part of the power flow is reallocated to other lines according to the line capacity constraints of the upper-level model to eliminate the overload phenomenon. The adjusted particle position represents a modified line configuration scheme.

[0168] In operation S305: According to the single fault safety test rule, from the multiple modified initial AC / DC ratio schemes, determine multiple target AC / DC ratio schemes that meet the preset constraints under all fault conditions.

[0169] In the embodiments of this application, during power flow calculation and safety verification, the corresponding network parameters are extracted based on the network topology determined by the particle scheme. Typical load and wind power output scenario data are loaded, and a commercial linear extended solver can be used to solve the lower-level unit combination optimization scheduling model. Wind power absorption and power flow distribution are calculated under normal operation (N) mode to obtain unit output and power flow schemes that meet preset constraints. Based on this, power flow recalculation and safety verification are performed for each N-1 fault scenario to check whether the power grid violates safety constraints such as power flow exceeding limits or having no solution when critical lines or units fail. If a particle scheme cannot meet safety constraints under any N-1 scenario, the scheme is deemed infeasible and subsequent iterations of that particle are terminated.

[0170] The preset constraints include constraints such as the number of line loops, the converted spinning reserve capacity, the embedded DC power constraints, the power balance constraints, the power flow and node phase angle constraints, the generator output constraints, and the unit ramping constraints.

[0171] In operation S306: using the initial step size, multiple target AC / DC ratio schemes, the initial AC / DC ratio scheme corresponding to the global fitness value, and the update step size determined by the adaptive weight, multiple target AC / DC ratio schemes are updated to obtain multiple updated AC / DC ratio schemes.

[0172] In the embodiments of this application, the initial step size refers to the initial velocity of each individual particle. During the process of updating the particle velocity and position, for feasible particle schemes that satisfy all constraints (i.e., the multiple target AC / DC ratio schemes determined by operation S305), based on the current particle velocity (i.e., the initial step size) and the individual's historical best position... (i.e., the individual historical best position of multiple target AC / DC ratio schemes) and the global optimal position An adaptive inertia weight adjustment strategy is used to update the particle velocity, and then the particle position is corrected based on the updated velocity (i.e., a new route configuration scheme is generated). The inertia weight w can be dynamically adjusted during the iteration process to balance the algorithm's global search capability and local convergence speed. The calculation formula is as follows:

[0173] ;

[0174] ;

[0175] in, Uniformly distributed random numbers, The learning factor is usually taken as... , It is the velocity of particle i in generation t. It is the position of particle i in generation t. It is the velocity of particle i in generation t+1. It is the position of particle i in generation t+1; The optimal position of particle i in the t-th generation of its history. It is the global optimal position of the particle swarm in generation t, with parameters It is the coefficient that allows the particle to maintain its original velocity; it is the inertial weight.

[0176] In operation S307: If the comparison result between the individual fitness values ​​of the multiple updated AC / DC ratio schemes and the individual fitness values ​​of the initial AC / DC ratio scheme corresponding to the updated AC / DC ratio schemes satisfies the first preset condition, the individual fitness values ​​and the global fitness values ​​of the multiple updated AC / DC ratio schemes are updated.

[0177] In the embodiments of this application, when updating individual and global extreme values, the fitness of a particle after the update position is compared with its historical best fitness pbest. If the former is better, the pbest of the particle is updated as the current solution. After all particles have been evaluated, a new global optimal solution gbest is selected and saved.

[0178] The first preset condition refers to the comparison result indicating that the individual fitness value of the updated AC / DC ratio scheme is better than the individual fitness value of the initial AC / DC ratio scheme corresponding to the updated AC / DC ratio scheme. In this case, the individual fitness value of the initial AC / DC ratio scheme corresponding to the updated AC / DC ratio scheme that meets the first preset condition is updated, and the better individual fitness value is used as the individual fitness value of the updated AC / DC ratio scheme. If the comparison result does not meet the first preset condition, the individual fitness value of the initial AC / DC ratio scheme corresponding to the updated AC / DC ratio scheme is used as the individual fitness value of the updated AC / DC ratio scheme.

[0179] In operation S308: the updated individual fitness value is used as the individual fitness value, the updated global fitness value is used as the global fitness value, and multiple updated AC / DC ratio schemes are used as multiple initial AC / DC ratio schemes. The operation of adjusting nodes and transmission lines, determining the target AC / DC ratio scheme, updating the AC / DC ratio scheme, updating individual fitness values ​​and global fitness values ​​is iteratively executed until the second preset condition is met.

[0180] In the embodiments of this application, the second preset condition is that the number of iterations reaches the maximum number of iterations or the population fitness converges. Taking the second preset condition of reaching the maximum number of iterations as an example, the iteration counter Gen is incremented by 1 to determine whether the maximum number of iterations has been reached. If the target has not yet been reached, return to operation S304 and enter the next iteration to continue performing cyclical operations such as connectivity and capacity checks, scheduling calculations, and fitness assessments on the particle population.

[0181] In operation S309: Based on the updated AC / DC ratio scheme corresponding to the global fitness value obtained when the second preset condition is met, the AC / DC ratio scheme of the key channel of the AC / DC hybrid power grid is obtained.

[0182] In the embodiments of this application, the algorithm terminates when the number of iterations reaches a preset upper limit or the population fitness converges. The final key channel network expansion construction scheme and the corresponding unit combination operation and scheduling results are output, forming an optimized planning scheme for the AC / DC ratio of key channels in the AC / DC hybrid power grid. This scheme determines the optimal AC / DC configuration ratio of key transmission sections and the layout of newly built lines, which can effectively improve the power flow transmission efficiency of the regional power grid, enhance the capacity for renewable energy absorption, and reduce the overall construction and operation costs.

[0183] To verify the effectiveness of the AC / DC ratio method for key channels of AC / DC hybrid power grids provided in the embodiments of this application, the improved Garver-18 system was used for testing.

[0184] The following is for reference. Figures 4-7 In conjunction with specific embodiments, Figure 1 The method shown will be further explained.

[0185] Figure 4 This schematically illustrates the Garver-18 system architecture diagram before planning according to an embodiment of this application; Figure 5 The schematic diagram illustrates the structure of the Garver-18 system according to an embodiment of this application.

[0186] like Figure 4 As shown, an analysis of the power grid model between two regions is conducted, including two sub-grids, Region A and Region B, to simulate the structure of a load center and its corresponding energy base. Region A has an installed generating capacity of approximately 1120MW and a load demand of approximately 1725MW, resulting in a power shortage of approximately 605MW that needs to be supported by Region B through a critical channel. Region B has a generating capacity of approximately 2467MW (including a 500MW wind farm), a local load of approximately 1656MW, and a power surplus of approximately 811MW, which can be transmitted to Region A via a critical channel (e.g., a river-crossing channel between Region A and Region B). The black dashed lines represent planned AC / DC lines, and the black solid lines represent existing lines. The six generating units are located at nodes 3, 5, 11, 15, 10, and 18, with node 18 being the balancing node.

[0187] like Figure 5As shown, the improved Garver-18 system includes two types of constructable transmission lines and nine expandable transmission corridors. Each transmission corridor has a maximum of three loops, with 0 AC lines and 1 DC line. The AC / DC ratio is determined using a particle swarm optimization algorithm. A 500MW wind turbine is located at node 16, representing a renewable energy penetration rate of 14%. Flexible turbines are located at node 3. Curtailment and load shedding penalties are 200 RMB / MW and 500 RMB / MW, respectively. The discount rate is 0.1%, and the repayment period is 10 years. The prediction error for both load and renewable energy is set to 10%. The line congestion risk threshold is 0.8, the flexibility cost is 32.85, and the congestion penalty cost is 51.1. The daily operating time is 24 hours, and the annual operating time is 8760 hours. The particle dimension is set to 10, the number of iterations is 30, the inertia factor ranges from 0.4 to 0.95, the learning factors c1 and c2 are both 2, and the mutation control parameter is 0.35.

[0188] The AC / DC ratio method for key channels in a hybrid AC / DC power grid provided in this application is applied to the aforementioned Garver-18 system. Four cases—Case 1, Case 2, Case 3, and Case 4—are used for comparative analysis. The specific implementation steps are as follows:

[0189] This application's embodiments consider capacity expansion optimization of AC / DC transmission channels, and the settings for each case are as follows:

[0190] Case 1: The initial plan only considered the existing grid structure and did not optimize the AC / DC ratio.

[0191] Case 2: The planning model only allows the construction of new AC transmission lines, and does not add any DC lines. That is, all critical corridors are planned only by adding new AC lines.

[0192] Case 3: The planning model only allows the construction of new DC transmission lines, and does not add any new AC lines. That is, all critical corridors are planned only through embedded DC lines.

[0193] Case 4: The planning model considers both AC and DC lines, allowing for flexible selection of AC / DC ratios to optimize capacity configuration in critical corridors. This scheme is the AC / DC ratio optimization scheme proposed in this invention.

[0194] The optimal planning results for the AC / DC ratio of the key channels in the four cases are shown in Tables 1 and 2.

[0195] Table 1 Costs of Different Options

[0196]

[0197] Table 2. Indicators for different schemes

[0198]

[0199] According to Table 1, Table 2, Figure 4 and Figure 5 Case 1 has relatively high overall planning and operating costs. Compared to Case 1, Case 4's line construction and operating costs decreased by approximately 5.83% and 4.12%, respectively, with an overall economic indicator decrease of approximately 1.52%. More importantly, compared to Case 2 and Case 3, Case 4's economic advantages are more prominent. Case 4's overall economic indicator is approximately 3.59% lower than Case 3 and approximately 2.94% lower than Case 2, fully demonstrating the economic rationality of the AC / DC ratio. Looking at the specific cost structure, Case 4's line construction cost is approximately 14.88% lower than Case 3, mainly due to avoiding the high investment in DC converter-related equipment; while the operating cost is approximately 14.28% lower than Case 2, reflecting the operational economy brought about by the rational utilization of the low-loss characteristics of DC transmission. This indicates that considering the characteristics of different types of AC / DC lines can effectively reduce resource construction costs. However, in Case 2, despite the trade-off between economic costs and reliability indicators, opportunity-constrained reliability reduction and insufficient network flexibility and uniformity still exist. In comparison, Case 4 offers better investment costs and system flexibility than Case 1, Case 2, and Case 3. This is because Case 4 takes into account the transmission characteristics of both AC and DC lines, improving the utilization efficiency of critical channels.

[0200] Further comparison of the network flexibility and uniformity, and network efficiency adaptability indicators of each scheme reveals that Case 4 shows a significant improvement in network flexibility and uniformity, increasing by approximately 88.87% compared to Case 1, and significantly exceeding that of Case 2 and Case 3. This substantial increase in flexibility primarily stems from the full utilization of the AC network's adjustment flexibility and the DC network's precise control capabilities after the AC / DC matching, making it particularly suitable for the new energy power fluctuation regulation needs of Region B. Although the network efficiency adaptability indicator slightly decreases, it still outperforms Case 2 and is second only to Case 3. This indicates that the improved particle swarm optimization algorithm strikes a reasonable balance between network adaptability and network transmission efficiency during the planning and optimization process, sacrificing some transmission efficiency for greater system flexibility.

[0201] Further comparison of the joint fuzzy opportunity reliability indices of each scheme reveals that the opportunity constraint reliability index reaches 100% before and after the AC / DC power distribution ratio planning, while Case 2 shows a decrease in reliability. This result fully demonstrates that the optimization process ensures that system reliability is not affected, indicating that considering the joint opportunity constraint of wind power-load can guarantee the satisfaction of uncertainty scenarios under the given confidence level. This is particularly important for systems containing renewable energy output, proving the effectiveness of the opportunity constraint model proposed in this paper in dealing with the uncertainty of renewable energy.

[0202] Figure 6 This schematically illustrates the unit combined output diagram before planning according to an embodiment of this application; Figure 7 The diagram illustrates the planned unit combination output according to an embodiment of this application.

[0203] Depend on Figure 6 and Figure 7 A comparison of the unit combination diagrams clearly shows that the load shedding in Case 4 decreased from the significant peak-period fluctuations in Case 1 to an almost imperceptible level, greatly improving the power supply reliability and stability of the grid. Simultaneously, the output curves of each generator unit in Case 4 are smoother, and the peak-to-valley difference is reduced, indicating enhanced system regulation capabilities, which is more conducive to economic dispatch and equipment lifespan protection. Particularly during periods of significant wind power fluctuations, Case 4's adaptability is significantly enhanced, wind curtailment is greatly reduced, and the capacity for renewable energy absorption is improved.

[0204] Figure 8 A schematic diagram of the convergence curve of a two-layer optimization model according to an embodiment of this application is shown.

[0205] like Figure 8 The overall fitness curve shown indicates that as the number of particle swarm iterations increases, the optimal fitness value continuously decreases and converges after approximately 13 iterations. The convergence curve is smooth and monotonic, indicating that the designed improved particle swarm algorithm has good search performance, effectively avoiding local optima traps and finding the global optimum. Notably, the convergence speed is fastest in the first 10 iterations, which is consistent with the dynamic adjustment strategy of inertia weights set in the algorithm, demonstrating the algorithm's efficiency.

[0206] In summary, through comparison of different scheme states and a comprehensive comparison with pure AC and pure DC schemes, the AC / DC ratio method for key channels in the hybrid AC / DC power grid proposed in this application demonstrates significant overall advantages when the AC / DC ratio in the Garver-18 node system is 1.67. While ensuring the reliability of fuzzy opportunity constraints, it achieves improved economic efficiency and enhanced adaptability to new energy sources. In particular, for the simulated regional characteristics of dense load in region A and abundant new energy in region B, the hybrid AC / DC ratio scheme effectively alleviates the source-load imbalance problem and improves the overall system performance by optimizing the transmission capacity of key channels. These results not only verify the effectiveness of the optimization model proposed in this invention but also provide valuable reference for power grid planning for large-scale inter-regional new energy transmission.

[0207] Figure 9 A block diagram of an AC / DC matching device for a key channel of an AC / DC hybrid power grid according to an embodiment of this application is shown schematically.

[0208] like Figure 9As shown, the AC / DC ratio device 900 for key channels of AC / DC hybrid power grids includes a constraint conversion module 910, a model building module 920, and a scheme determination module 930.

[0209] The constraint transformation module 910 is used to transform the spinning reserve capacity constraint, which is used to characterize the uncertainty of wind power and load in AC / DC hybrid power grid, from a fuzzy opportunistic constraint to a deterministic constraint using a Cauchy-like distribution, thus obtaining the transformed spinning reserve capacity constraint.

[0210] Model building module 920 is used to build a two-layer optimization model based on the transformed spinning reserve capacity constraints; the two-layer optimization model includes a transmission network expansion planning model and a safety-constrained unit combination operation model.

[0211] The scheme determination module 930 is used to solve the two-layer optimization model and determine the AC / DC ratio scheme of the key channels of the AC / DC hybrid power grid.

[0212] Any one or more of the modules, submodules, units, and subunits according to the embodiments of this application, or at least part of the functions of any one or more of them, can be implemented in one module. Any one or more of the modules, submodules, units, and subunits according to the embodiments of this application can be implemented by dividing them into multiple modules. Any one or more of the modules, submodules, units, and subunits according to the embodiments of this application can be at least partially implemented as hardware circuits, such as field-programmable gate arrays (FPGAs), programmable logic arrays (PLAs), systems-on-a-chip, systems-on-a-substrate, systems-on-package, application-specific integrated circuits (ASICs), or implemented by hardware or firmware in any other reasonable manner by integrating or packaging circuits, or implemented in any one of software, hardware, and firmware, or in a suitable combination of any of these. Alternatively, one or more of the modules, submodules, units, and subunits according to the embodiments of this application can be at least partially implemented as computer program modules, which, when run, can perform corresponding functions.

[0213] For example, any plurality of the constraint transformation module 910, model building module 920, and scheme determination module 930 can be combined into one module / unit / subunit, or any one of these modules / units / subunits can be split into multiple modules / units / subunits. Alternatively, at least part of the functionality of one or more of these modules / units / subunits can be combined with at least part of the functionality of other modules / units / subunits and implemented in one module / unit / subunit. According to embodiments of this application, at least one of the constraint transformation module 910, model building module 920, and scheme determination module 930 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging the circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, at least one of the constraint transformation module 910, model building module 920, and scheme determination module 930 may be implemented at least partially as a computer program module, which can perform corresponding functions when the computer program module is run.

[0214] It should be noted that the data processing system part in the embodiments of this application corresponds to the data processing method part in the embodiments of this application. The specific description of the data processing system part is referred to in the data processing method part, and will not be repeated here.

[0215] Figure 10 A block diagram of an electronic device suitable for implementing an AC / DC ratio method for key channels in a hybrid AC / DC power grid, according to an embodiment of this application, is shown schematically. Figure 10 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0216] like Figure 10 As shown, an electronic device 1000 according to an embodiment of this application includes a processor 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage portion 1008 into a random access memory (RAM) 1003. The processor 1001 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 1001 may also include onboard memory for caching purposes. The processor 1001 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this application.

[0217] RAM 1003 stores various programs and data required for the operation of electronic device 1000. Processor 1001, ROM 1002, and RAM 1003 are interconnected via bus 1004. Processor 1001 executes various operations of the method flow according to embodiments of this application by executing programs in ROM 1002 and / or RAM 1003. It should be noted that the programs may also be stored in one or more memories other than ROM 1002 and RAM 1003. Processor 1001 may also execute various operations of the method flow according to embodiments of this application by executing programs stored in said one or more memories.

[0218] According to embodiments of this application, the electronic device 1000 may further include an input / output (I / O) interface 1005, which is also connected to a bus 1004. The electronic device 1000 may also include one or more of the following components connected to the input / output (I / O) interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the input / output (I / O) interface 1005 as needed. A removable medium 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 1010 as needed so that computer programs read from it can be installed into the storage section 1008 as needed.

[0219] According to embodiments of this application, the method flow according to embodiments of this application can be implemented as a computer software program. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by processor 1001, it performs the functions defined in the system of embodiments of this application. According to embodiments of this application, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0220] This application also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0221] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium. Examples include, but are not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0222] For example, according to embodiments of this application, a computer-readable storage medium may include the ROM 1002 and / or RAM 1003 described above and / or one or more memories other than ROM 1002 and RAM 1003.

[0223] Embodiments of this application also include a computer program product comprising a computer program containing program code for executing the methods provided in the embodiments of this application. When the computer program product is run on an electronic device, the program code is used to enable the electronic device to implement the AC / DC ratio method for key channels of AC / DC hybrid power grids provided in the embodiments of this application.

[0224] When the computer program is executed by the processor 1001, it performs the functions defined in the system / apparatus of this application embodiment. According to the embodiments of this application, the systems, apparatuses, modules, units, etc. described above can be implemented by computer program modules.

[0225] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 1009, and / or installed from a removable medium 1011. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0226] According to embodiments of this application, program code for executing the computer programs provided in the embodiments of this application can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0227] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions. Those skilled in the art will understand that the features described in the various embodiments of this application can be combined and / or combined in various ways, even if such combinations are not explicitly described in this application. In particular, without departing from the spirit and teachings of this application, the features described in the various embodiments of this application can be combined and / or combined in various ways. All such combinations and / or combinations fall within the scope of this application.

[0228] The embodiments of this application have been described above. However, these embodiments are merely illustrative and not intended to limit the scope of this application. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Without departing from the scope of this application, those skilled in the art can make various substitutions and modifications, all of which should fall within the scope of this application.

Claims

1. A method for AC / DC matching in key channels of an AC / DC hybrid power grid, characterized in that, The method includes: Using a Cauchy-like distribution, the spinning reserve capacity constraint, which characterizes the uncertainty of wind power and load in AC / DC hybrid power grids, is transformed from a fuzzy chance constraint into a deterministic constraint, resulting in the transformed spinning reserve capacity constraint. Based on the transformed spinning reserve capacity constraints, a two-level optimization model is constructed; the two-level optimization model includes a transmission network expansion planning model and a safety-constrained unit combination operation model; Solve the two-layer optimization model to determine the AC / DC ratio scheme of the key channels of the AC / DC hybrid power grid.

2. The AC / DC ratio method for key channels of AC / DC hybrid power grids according to claim 1, characterized in that, The transmission network expansion planning model is obtained by using constraints such as the number of line loops, node connectivity, and line capacity as constraints, and minimizing the sum of the network flexibility and uniformity index, network efficiency applicability index, and opportunity constraint reliability index of the AC / DC hybrid power grid as the first objective function.

3. The AC / DC ratio method for key channels of AC / DC hybrid power grids according to claim 1, characterized in that, The safety-constrained unit combination operation model is obtained by using the converted spinning reserve capacity constraint, embedded DC power constraint, power balance constraint, power flow and node phase angle constraint, generator output constraint, and unit ramping constraint as constraints, and minimizing the sum of the AC / DC line cost, unit annual operating cost, embedded DC cost, wind curtailment and load shedding penalty cost, and wind power spinning reserve cost of the key channel of the AC / DC hybrid power grid as the second objective function.

4. The AC / DC ratio method for key channels of AC / DC hybrid power grids according to claim 2, characterized in that, Solving the two-layer optimization model to determine the AC / DC ratio scheme for key channels of the AC / DC hybrid power grid includes: Multiple initial AC / DC ratio schemes are determined randomly. Using the weighted sum of the first objective function and the second objective function as the fitness function, the individual fitness value of each of the initial AC / DC ratio schemes is calculated respectively; Based on the fitness values ​​of each individual, determine the global optimal fitness value among the multiple initial AC / DC ratio schemes; Adjustments are made to the nodes and transmission lines in the multiple initial AC / DC ratio schemes to obtain multiple modified initial AC / DC ratio schemes, such that the modified multiple initial AC / DC ratio schemes satisfy the node connectivity constraints and the line capacity constraints. According to the single fault safety test rule, from the multiple modified initial AC / DC ratio schemes, multiple target AC / DC ratio schemes that meet the preset constraints under all fault conditions are determined; Using the initial step size, multiple target AC / DC ratio schemes, the initial AC / DC ratio scheme corresponding to the global fitness value, and the update step size determined by the adaptive weight, multiple target AC / DC ratio schemes are updated to obtain multiple updated AC / DC ratio schemes. If the comparison result between the individual fitness values ​​of the multiple updated AC / DC ratio schemes and the individual fitness value of the initial AC / DC ratio scheme corresponding to the updated AC / DC ratio scheme satisfies the first preset condition, the individual fitness values ​​of the multiple updated AC / DC ratio schemes and the global fitness value are updated. The updated individual fitness value is used as the individual fitness value, the updated global fitness value is used as the global fitness value, and multiple updated AC / DC ratio schemes are used as multiple initial AC / DC ratio schemes. The operations of adjusting nodes and transmission lines, determining target AC / DC ratio schemes, updating AC / DC ratio schemes, updating individual fitness values ​​and global fitness values ​​are iteratively executed until the second preset condition is met. Based on the updated AC / DC ratio scheme corresponding to the global fitness value obtained when the second preset condition is met, the AC / DC ratio scheme of the key channel of the AC / DC hybrid power grid is obtained.

5. The AC / DC ratio method for key channels of AC / DC hybrid power grids according to claim 4, characterized in that, The adjustment of nodes and transmission lines in the plurality of initial AC / DC power distribution schemes to obtain revised plurality of initial AC / DC power distribution schemes includes: Based on the node connectivity constraints, the nodes in the line planning scheme corresponding to the initial AC / DC ratio scheme with isolated nodes are adjusted until there are no isolated nodes in the line planning scheme corresponding to the multiple initial AC / DC ratio schemes, thus obtaining the updated multiple initial AC / DC ratio schemes. Based on the line capacity constraints, the transmission lines in the line planning scheme corresponding to the updated initial AC / DC ratio scheme that have transmission lines exceeding the capacity limit are adjusted until there are no transmission lines exceeding the capacity limit in the line planning scheme corresponding to the updated multiple initial AC / DC ratio schemes, thus obtaining the corrected multiple initial AC / DC ratio schemes.

6. The AC / DC ratio method for key channels of AC / DC hybrid power grids according to claim 1, characterized in that, The first objective function is: ； ； ； ； in, Let this be the first objective function; As an indicator of network flexibility and uniformity; As an indicator of network efficiency applicability; For opportunity-constrained reliability indicators; Indicates the number of lines. The flexibility allowance for line i in time period j; It is the average value of the flexibility allowance for each line; Runtime; This represents the total remaining capacity rate. This represents the ratio of the actual power flow of line i to its maximum transmission capacity during time period t. The number of times the joint opportunity constraint has been violated.

7. The AC / DC ratio method for key channels of AC / DC hybrid power grids according to claim 1, characterized in that, The second objective function is: ； ； ； ； ； ； in, The second objective function; This represents the total investment cost of the newly constructed line. Annual operating cost of the unit; For the operating cost of embedded DC; The cost of curtailing wind power and cutting off loads; For wind power rotation reserve costs; This represents the construction cost coefficient per unit length of the DC system. For the length of the newly created embedded line, Construction cost coefficient per unit length of the AC system This is the length of the newly built AC line; For the number of generator sets, For runtime, To reduce the number of piecewise linearizations, Let be the linearized coal consumption cost coefficient for the s-th segment of unit i. For unit i to output power during the s-th segment of time period t, This represents the operating status of unit i during time period t. Let be the secondary cost coefficient of operating unit i; Let i be the primary cost coefficient for operating unit i; Let i be the constant cost coefficient for operating unit i. Let $t$ be the startup cost of unit $i$ during time period $t$. Let $t$ be the shutdown cost of unit $i$ during time period $t$. Minimum output of the i-th generator; A collection of DC lines. Let be the unit loss cost coefficient for line l. Let be the transmission power of line l at time t. Let be the resistance per unit length of line l; The length of line l; For the number of nodes, These represent the wind curtailment cost coefficient and the load shedding cost coefficient, respectively. These represent the predicted wind power output and the actual wind power output absorbed at time t, respectively. This represents the load shedding amount of node i during time period t; It is the positive spinning reserve cost coefficient of unit i. It is the positive spinning reserve capacity of unit i during time period t. It is the negative spinning reserve cost coefficient of unit i. It is the negative spinning reserve capacity of unit i during time period t.

8. The AC / DC ratio method for key channels of AC / DC hybrid power grids according to claim 1, characterized in that, The converted spinning reserve capacity constraint is: ； ； ； ； in, Let represent the positive spinning reserve capacity and negative spinning reserve capacity provided by the i-th generator at time t, respectively; For the number of generator sets, Let represent the quantiles of the positive and negative load prediction errors for node n at time t, respectively. These are the quantiles for the positive and negative prediction errors of wind power at time t. For the number of wind farms, For the number of nodes, Let be the maximum output of the i-th unit at time t; Let be the minimum output of the i-th unit at time t; Let i be the output of the i-th unit at time t; The uphill speed limit for the i-th generator; The operating status of unit i during time period t; The downhill speed limit for the i-th generator.

9. A key AC / DC matching device for a hybrid AC / DC power grid, characterized in that, The device includes: The constraint transformation module is used to transform the spinning reserve capacity constraint, which characterizes the uncertainty of wind power and load in AC / DC hybrid power grid, from a fuzzy opportunistic constraint to a deterministic constraint using a Cauchy-like distribution, thus obtaining the transformed spinning reserve capacity constraint. The model building module is used to construct a two-layer optimization model based on the transformed spinning reserve capacity constraints; the two-layer optimization model includes a transmission network expansion planning model and a safety-constrained unit combination operation model. The scheme determination module is used to solve the two-layer optimization model and determine the AC / DC ratio scheme of the key channels of the AC / DC hybrid power grid.

10. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1 to 8.

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