Water, wind and light coupling system external sending capacity evaluation method and system and storage medium

Abstract

The application discloses a kind of water and wind coupling system external sending capacity evaluation method, system and storage medium, establishes the maximum of system direct current external sending channel total power as objective function of direct current external sending capacity evaluation model, and determines unit output, direct current channel external sending power, node voltage and other core decision variables;Coupling linearization ac power flow constraint to accurately quantify node actual voltage and system power flow distribution, and then build the improved new energy multi-station operation short-circuit ratio constraint covering actual node voltage phasor and new energy injection power, for the strong nonlinear calculation bottleneck of this constraint, using the iterative tangent plane method based on improved SVM active learning, the complex nonlinear operating boundary is reconstructed into linear constraint that can be efficiently calculated, to obtain the optimal direct current external sending scheme that can guarantee voltage support strength.The precision and reliability of the external sending capacity evaluation conclusion are improved.

Description

Technical Field

[0001] This invention relates to the field of power system safety and stable operation technology, and in particular to a method, system and storage medium for evaluating the power transmission capacity of a hydro-wind-solar coupled system. Background Technology

[0002] The high penetration rate of renewable energy in hydro-wind-solar coupled systems, coupled with the continuous grid connection of a high proportion of wind and solar power and the commissioning of large-capacity AC / DC transmission projects, has significantly reduced the overall voltage support strength of the system, exacerbating the problem of grid safety and stability. Current methods for assessing DC transmission capacity often rely on simplified DC power flow models, failing to account for the dynamic shifts in reactive power distribution and node voltage amplitude. The handling of short-circuit ratios at multiple renewable energy sites is mostly limited to static equivalent calculations, and the operational short-circuit ratio constraint itself is highly nonlinear, making it difficult to achieve efficient and accurate calculations within the traditional mixed-integer programming framework. Therefore, there is an urgent need for an assessment method that can accurately account for voltage fluctuations and effectively handle nonlinear short-circuit ratio constraints. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method, system and storage medium for evaluating the transmission capacity of a water-wind-solar coupling system, and to obtain the optimal DC transmission scheme that can guarantee the voltage support strength of the sending-end power grid, in order to address the shortcomings of the existing technology.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a method for evaluating the transmission capacity of a water-wind-solar coupling system, comprising the following steps:

[0005] Construct the following objective function: ; ; ; ; ; ; ; ;

[0006] Calculate the objective function to obtain the optimal DC power transmission scheme;

[0007] Where F represents the total DC power transmitted. N represents the power transmitted by the k-th DC transmission line during time period t. HVDC This represents the total number of DC transmission channel loops, and T represents the total number of time periods considered in the study. This represents the active power flow of AC line l during time period t. This represents the reactive power flow of AC line l during time period t. and Let represent the conductance and susceptance of AC line l, respectively. This represents the voltage amplitude at node i during time period t. This represents the voltage phase angle of node i during time period t. and These represent the active power flow safety upper limit and the reactive power flow safety upper limit of AC line l, respectively. and These represent the safe upper and lower limits of the voltage amplitude at node i, respectively. AC line l refers to the transmission line connecting node i and node j. This represents the short-circuit ratio of node i during time period t. This represents the per-unit voltage value of node i during time period t. This represents the nominal voltage of node i during time period t. Represents the equivalent impedance matrix of an AC power grid The element in the i-th row and i-th column, MRSCR represents the actual apparent power of renewable energy injected into renewable energy grid-connected bus node i during time period t, N represents the number of renewable energy grid-connected nodes, and MRSCR represents the actual apparent power of renewable energy injected into the grid during time period t. S This indicates the safe limit for the short-circuit ratio during operation.

[0008] The specific calculation process for the objective function includes:

[0009] 1) Set the maximum number of iterations and construct an objective function without adding a short-circuit ratio constraint; determine whether the maximum number of iterations has been reached. If it has, end the process; otherwise, proceed to step 2).

[0010] 2) Call the CPLEX solver to calculate the current objective function, extract the actual apparent power of new energy sources, node voltage amplitude, and voltage phase angle of each node, and substitute the extracted operating state variables into the operating short-circuit ratio constraint formula to calculate the actual operating short-circuit ratio of each new energy node in the current system. ;

[0011] 3) Determine whether the operating short-circuit ratio of all new energy nodes is greater than or equal to the preset short-circuit ratio safety threshold value MRSCR. S If yes, it is determined that the voltage support strength is met, the final result is output, and the process ends; if the operating short-circuit ratio of any new energy node is lower than the short-circuit ratio safety threshold, then proceed to step 4); the output result refers to the optimal DC transmission scheme that meets the operating short-circuit ratio constraint, including the unit combination result, unit output, DC transmission power curve, and operating short-circuit ratio.

[0012] 4) Extract the current active power output vector of new energy sources and define it as an unsafe sample point. By introducing a preset safety margin coefficient α, safe sample points are constructed. ;

[0013] 5) The unsafe sample points As negative samples, safe sample points As positive class samples, an active learning training set is constructed; a linear kernel support vector machine with a maximum penalty coefficient is used to classify and train the active learning training set to find the optimal separating hyperplane that strictly divides the two points, and the normal vector and bias term b of the optimal separating hyperplane are extracted; the normal vector is the weight vector w.

[0014] 6) Calculate the safe sample points using the normal vector and bias term b. Projection value along the normal direction of the classification hyperplane The original optimal separation hyperplane is forcibly translated to the location of the safe sample point, generating a linear translation tangent plane constraint for the actual output of new energy.

[0015] 7) Add the linear translation tangent plane constraint to the constraint set of the objective function, return to step 1), and perform the next round of iterative calculation until the operating short-circuit ratio of all new energy nodes is greater than or equal to the preset short-circuit ratio safety threshold MRSCR. S .

[0016] safe sample points The expression is: ;in, This represents the current unsafe sample point, i.e., the actual injected power value of the new energy node; the safe sample point... The corresponding safe power injection value for new energy nodes.

[0017] Projection value The calculation formula is: .

[0018] The linear translation tangent plane constraint is expressed as: ;in, This represents the actual active power injected by new energy node i during time period t.

[0019] The constraint set of the objective function also includes one or more of the following conventional constraints:

[0020] Water balance constraints: ; ; ;

[0021] Reservoir capacity constraints: ;

[0022] Outbound flow constraints: ;

[0023] Hydropower unit generating capacity constraints: ;

[0024] Hydropower station output constraints: ;

[0025] Hydropower unit output constraints: ;

[0026] Hydropower unit ramping constraints: ;

[0027] Start-up and shutdown constraints for hydropower units: ; ;

[0028] Output constraints for wind and solar power: ; ;

[0029] DC constant operating time constraint: ; ; ;

[0030] DC power ramp-up constraints: ;

[0031] Power balance constraints at sending-end grid nodes: ; ;

[0032] in, This represents the reservoir capacity of hydropower station g during time period t. , Let g represent the inflow and outflow of the hydropower station during time period t, respectively. , These represent the power generation capacity and water discharge capacity of hydropower station g during time period t, respectively. This indicates that the hydropower station g-1, considering the time lag of water flow, is in... Traffic during a specific time period This represents the interval flow between hydropower station g-1 and hydropower station g during time period t. This represents the reservoir capacity of hydropower station g during time period t. , Let g and g represent the minimum and maximum values ​​of the reservoir capacity of the hydropower station, respectively. , These represent the minimum and maximum outflow rates of hydropower station g during time period t, respectively. , Let represent the minimum and maximum generating capacities of the nth hydropower unit in hydropower station g during time period t, respectively. Let represent the generating capacity of the nth hydroelectric generator unit of hydropower station g during time period t. This represents the output of hydropower station i during time period t. , These represent the minimum and maximum output values ​​of hydropower station g during time period t, respectively. , Let represent the minimum and maximum output values ​​of the nth hydropower unit in hydropower station g during time period t, respectively. Let represent the output of the nth hydroelectric generating unit of hydropower station g during time period t. , These represent the maximum downhill and uphill speeds of the hydroelectric generator unit, respectively. This represents the output of hydropower unit h during time period t, where TS and TO represent the minimum shutdown and startup times of the hydropower unit, respectively. This indicates the start-stop status of the hydropower unit h during time period t. , These represent the actual power absorption capacity of the wind and solar turbines during time period t, respectively. , These represent the predicted output values ​​for wind and solar turbines, respectively. This indicates whether the DC power transmission capacity during time period t should be adjusted; 0 indicates no adjustment, and 1 indicates adjustment. Indicates the minimum DC constant operating time. This represents the total power transmitted by the water-wind-solar coupling system during time period t. This represents the maximum power transmitted by the water-wind-solar coupling system during time period t. , These represent the maximum downhill and uphill rates of the DC channel transmission power, respectively. , , These represent the number of hydro, wind, and solar turbine units, respectively. , These represent the power outputs of the wind and solar turbines, w and v, respectively, during time period t. This represents the active power flow from node i to node j during time period t. This represents the active power flow from node j to node i during time period t. This represents the active power load within the base during time period t. This represents the reactive power provided by the hydropower unit h during the time period t. This represents the reactive power flow from node i to node j during time period t. This represents the reactive power flow from node j to node i during time period t. This represents the reactive load within the base during time period t.

[0033] As an inventive concept, the present invention also provides a system for evaluating the transmission capacity of a water-wind-optical coupling system, including a memory, a processor, and a computer program stored in the memory; the processor executes the computer program to implement the steps of the above method.

[0034] As an inventive concept, the present invention also provides a computer-readable storage medium having a computer program / instructions stored thereon; when the computer program / instructions are executed by a processor, they implement the steps of the above-described method.

[0035] Compared with existing technologies, the beneficial effects of this invention are as follows: 1) By accurately quantifying the actual voltage and power flow distribution of nodes through coupled linearized AC power flow constraints, the accuracy and reliability of the power transmission capacity assessment conclusions are improved; 2) In the calculation process, to address the bottleneck of strong nonlinear calculation of the operating short-circuit ratio, an iterative tangent plane method based on improved SVM active learning is used to reconstruct the complex nonlinear operating boundary into a linear constraint that can be efficiently solved by the solver, taking into account both the solution efficiency of the model and the safety of the system; 3) Under the premise of ensuring the system voltage support strength, this invention maximizes the total power transmitted through the DC channel, providing a decision-making scheme for actual grid dispatch that takes into account both operational safety and clean energy consumption. Attached Figure Description

[0036] Figure 1 A flowchart of a method for evaluating the power transmission capacity of a water-wind-solar coupling system considering operational short-circuit ratio constraints, provided in an embodiment of the present invention;

[0037] Figure 2 A flowchart of the active learning iterative tangent plane method based on support vector machine for processing the operating short-circuit ratio constraint in a water-wind-optical coupling system power transmission capacity assessment method considering the operating short-circuit ratio constraint provided in an embodiment of the present invention;

[0038] Figure 3 A simplified schematic diagram of the active learning iterative tangent plane method based on support vector machine to process the translational tangent plane constraint in the power transmission capacity assessment method of a water-wind-optical coupling system considering the short-circuit ratio constraint provided in an embodiment of the present invention; (a) initial classification hyperplane construction, (b) translational tangent plane constraint generation;

[0039] Figure 4 The system wiring diagram used in the example analysis of the power transmission capacity assessment method for a water-wind-optical coupling system considering the short-circuit ratio constraint provided in the embodiments of the present invention;

[0040] Figure 5 The result diagram of the DC transmission scheme of a water-wind-optical coupling system considering the short-circuit ratio constraint for evaluating the transmission capacity of the system is provided in an embodiment of the present invention.

[0041] Figure 6 A diagram showing the unit combination results of a method for evaluating the power transmission capacity of a water-wind-solar coupling system considering operational short-circuit ratio constraints, provided in an embodiment of the present invention.

[0042] Figure 7A node voltage amplitude calculation result diagram for a method for evaluating the transmission capacity of a water-wind-optical coupling system considering operational short-circuit ratio constraints, provided in an embodiment of the present invention;

[0043] Figure 8 The node voltage phase angle calculation results are shown in the figure for a method for evaluating the transmission capacity of a water-wind-optical coupling system considering the short-circuit ratio constraint provided in an embodiment of the present invention.

[0044] Figure 9 The figure shows the verification results of the effectiveness of the short-circuit ratio constraint on the power transmission capacity of a hydro-wind-solar coupled system considering the short-circuit ratio constraint provided in the embodiments of the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] Example 1

[0047] like Figure 1 As shown in the figure, an embodiment of the present invention provides a method for evaluating the power transmission capacity of a water-wind-solar coupling system considering the short-circuit ratio constraint. The method includes the following steps:

[0048] Step S1: Integrate the multi-energy complementary spatiotemporal characteristics of the hydro-wind-solar coupled system, establish a DC transmission capacity evaluation model with the objective function of maximizing the total power of the system's DC transmission channel, and determine core decision variables such as unit output, DC transmission power, and node voltage; impose restrictions and constraints on the objective function, adding constraints on hydropower units, wind and solar power unit output, and DC transmission channel, etc.

[0049] Step S2: Couple linearized AC power flow constraints to accurately quantify the actual node voltage and system power flow distribution, and then construct an improved short-circuit ratio constraint for multiple renewable energy power plants that covers the actual node voltage phasor and the injected power of renewable energy. To address the strong nonlinear calculation bottleneck of this constraint, an iterative tangent plane method based on improved SVM active learning is used to reconstruct the complex nonlinear operating boundary into a linear constraint that can be calculated efficiently. The CPLEX solver is used to calculate the objective function to obtain the optimal DC transmission scheme.

[0050] The specific process is as follows: In step S1, the objective function is to maximize the DC power transmission of the water-wind-solar coupling system, and the decision variable is the DC power transmission during each operating period. The expression for the objective function is: (1);

[0051] Where F represents the total DC power transmitted; N represents the power transmitted by the k-th DC transmission line during time period t; HVDC This indicates the total number of DC transmission channel loops; T indicates the total number of time periods considered in the study.

[0052] In step S2, a state-independent linear power flow model that accurately estimates the voltage magnitude is used. The coupled linearized AC power flow constraint expression is as follows: (2); (3); (4); (5); (6); (7);

[0053] in, This represents the active power flow of AC line l during time period t; This represents the reactive power flow of AC line l during time period t; and Let represent the conductance and susceptance of AC line l (the transmission line connecting node i and node j), respectively. This represents the voltage amplitude at node i during time period t; This represents the voltage phase angle of node i during time interval t; and These represent the upper limits of active and reactive power flow safety for line l, respectively. and These represent the upper and lower safety limits of the voltage amplitude at node i, respectively.

[0054] Taking into account the effects of actual injected power and node voltage at renewable energy power plants, the improved safety constraint expression for the short-circuit ratio of multiple renewable energy power plants is as follows: (8);

[0055] in, This represents the short-circuit ratio of new energy node i during time period t; This represents the per-unit voltage value of node i during time period t; This represents the nominal voltage of node i during time period t; Represents the equivalent impedance matrix of an AC power grid The element in the i-th row and i-th column; N represents the actual apparent power of new energy injected into the grid-connected bus node i during time period t; N represents the number of new energy grid-connected nodes. This indicates the safe limit for the short-circuit ratio during operation.

[0056] To address the challenge of efficiently calculating the aforementioned strongly nonlinear constraints using mature solvers, an improved SVM active learning iterative tangent plane method is used to handle the running short-circuit ratio constraint. Please refer to [link to relevant documentation]. Figure 2 The specific processing steps include:

[0057] 1) Initialization and basic model calculation: Set parameters (including system parameters, operating short-circuit ratio safety threshold, etc.), and set the maximum number of iterations to construct a mathematical model for evaluating the transmission capacity of the water-wind-solar coupled system without adding operating short-circuit ratio constraints; determine whether the maximum number of iterations has been reached. If it has been reached, end the iteration and output a non-convergence prompt; if the maximum number of iterations has not been reached, call the CPLEX solver to calculate the current mathematical model.

[0058] 2) Operational status extraction and validity verification: In the current calculation round, extract the actual apparent power of new energy sources, node voltage amplitude, and voltage phase angle of each node; and substitute the above-extracted operational status variables into the nonlinear operational short-circuit ratio calculation formula described in equation (8) to calculate the actual operational short-circuit ratio of each new energy node in the current system. .

[0059] 3) Convergence criterion: Determine whether the operating short-circuit ratio of all new energy nodes is greater than or equal to the preset short-circuit ratio safety threshold. If all requirements are met, the voltage support strength is deemed to be met, the iteration converges and the final result is output; if the operating short-circuit ratio of any new energy node is lower than the short-circuit ratio safety threshold, the SVM active learning and tangent plane generation mechanism is triggered, and the process proceeds to the next step.

[0060] 4) Active learning and sample construction: For new energy nodes where the short-circuit ratio does not meet the safety limit, extract the current active power output vector of the new energy and define it as an unsafe sample point. Assuming the short-circuit capacity remains approximately constant, the short-circuit ratio is approximately inversely proportional to the active power injected by new energy sources. By introducing a preset safety margin factor α, a safe sample point can be constructed by reverse calculation. Its mathematical formula is: (9);

[0061] in, This indicates the current default operating point, i.e., the actual injected power value of the new energy node; This represents the defined safety factor, to retain a certain safety margin; This indicates the safe power injection value of the new energy node; This indicates the current actual short-circuit ratio; This indicates the safe limit for the short-circuit ratio during operation.

[0062] 5) Linear SVM classifier training: Please refer to Figure 3The unsafe sample points As negative samples, safe sample points As positive class samples, they form an active learning training set; a linear kernel support vector machine with a maximum penalty coefficient is used to classify and train this training set (hard margin is mandatory, misclassification is not allowed, see Zhang Xuegong. On statistical learning theory and support vector machine [J]. Acta Automatica Sinica, 2000, (01): 36-46. U. Shahzad, "Support Vector Machine for Transient Stability Assessment: A Review," 2024 29th International Conference on Automation and Computing (ICAC), Sunderland, United Kingdom, 2024, pp. 1-7), to find the optimal separating hyperplane that strictly divides the two points, and extract the normal vector (i.e., weight vector w) and bias term b of the classification hyperplane.

[0063] 6) Tangent plane translation and model update: Please refer to Figure 3 To more safely and accurately approximate the nonlinear operating boundary of the system and overcome the boundary non-conservatism problem caused by the hard-interval centering of traditional SVM, the safe sample points are calculated. Projection value along the normal direction of the classification hyperplane (i.e., relative offset), its calculation expression is: (10);

[0064] The original classification hyperplane is forcibly translated to the location of the safe sample point, generating a linear translation tangent plane constraint for the actual output of new energy: (11);

[0065] in, This represents the transpose of the weight vector; This represents the actual output of new energy node i during time period t; This represents the projection of the safe sample onto the direction of the normal vector.

[0066] The generated linear translation tangent plane constraint is added to the constraint set of the basic mathematical model to update the model. Then, the process returns to step 1) to perform the next round of iterative calculation until the operating short-circuit ratio of all new energy nodes meets the safety limit requirements, thereby achieving accurate optimization of strong nonlinear constraints.

[0067] To maximize the DC power output of the hydro-wind-solar coupling system while ensuring its safe and stable operation, the objective function should be subject to restrictions and constraints, including: water balance constraints, reservoir capacity constraints, outflow constraints, hydropower unit output and ramp-up constraints, wind and solar power output constraints, DC constant operating time and power ramp-up constraints, and power balance constraints. Assuming a constant water head, the expression for the water balance constraint is: (12);

[0068] (13);

[0069] (14);

[0070] in, This represents the reservoir capacity of hydropower station g during time period t; , These represent the inflow (including natural water inflow and upstream discharge) and outflow of hydropower station g during time period t, respectively. , Let g represent the power generation capacity and water discharge capacity of hydropower station g during time period t, respectively. This indicates the time delay of water flow at hydropower station g, which is upstream of hydropower station g-1. This indicates that the hydropower station g-1, considering the time lag of water flow, is in... Traffic volume during a specific time period; This represents the interval flow between hydropower station g-1 and hydropower station g during time period t.

[0071] The reservoir capacity constraint expression is: (15);

[0072] in, This represents the reservoir capacity of hydropower station g during time period t; , Let g and g represent the minimum and maximum values ​​of the capacity of the reservoir g at the hydropower station, respectively.

[0073] The expression for the outbound flow constraint is: (16);

[0074] in, , These represent the minimum and maximum outflow rates of hydropower station g during time period t, respectively.

[0075] The relationship between the power generation capacity of a hydropower station and the power generation capacity of its hydroelectric generating units is expressed as follows:

[0076] (17);

[0077] in, This represents the total number of hydroelectric generating units included in hydroelectric power station g; Let represent the start-up and shutdown status of the nth hydropower unit of hydropower station g during time period t. It is a 0-1 variable, where 0 indicates that the unit is stopped and 1 indicates that the unit is started. Let represent the generating capacity of the nth hydroelectric generator unit of hydroelectric power station g during time period t.

[0078] The constraint expression for the generating capacity of hydropower units is:

[0079] (18);

[0080] in, , Let represent the minimum and maximum generating capacity of the nth hydropower unit of hydropower station g during time period t, respectively.

[0081] The power output constraint expression for a hydropower station is:

[0082] (19);

[0083] in, This represents the output of hydropower station i during time period t; , These represent the minimum and maximum output values ​​of hydropower station g during time period t, respectively.

[0084] The relationship between the power output of the hydropower station and the power output of its hydro-generating units is expressed as follows:

[0085] (20);

[0086] in, This represents the output of the nth hydroelectric generator unit of hydroelectric power station g during time period t.

[0087] The expression for the coupling relationship between hydraulic and electric power is:

[0088] (twenty one);

[0089] in, This represents the hydropower conversion coefficient.

[0090] The output constraint expression for the hydropower unit is:

[0091] (twenty two);

[0092] in, , Let represent the minimum and maximum output values ​​of the nth hydropower unit in hydropower station g during time period t, respectively.

[0093] The expression for the gradient constraint of the hydroelectric generator is: (twenty three);

[0094] in, , These represent the maximum downhill and uphill speeds of the hydroelectric generator unit, respectively. This represents the output of the hydropower unit h during time period t.

[0095] The start-up and shutdown constraint expressions for hydropower units are as follows: (twenty four); (25);

[0096] Wherein, TS and TO represent the minimum shutdown and startup times of the hydropower unit, respectively; This indicates the start-up and shutdown status of the hydropower unit h during time period t.

[0097] The output of wind and solar power plants can participate in operation scheduling to a certain extent, that is, participate in peak shaving through wind and solar curtailment. The output constraint expressions for wind and solar power are: (26); (27);

[0098] in, , These represent the actual power absorption capacity of the wind and solar turbines during time period t, respectively. , These represent the predicted output values ​​of wind and solar turbines, respectively.

[0099] To ensure the stability of the DC power transmission plan, after a single or continuous adjustment, the transmitted power must maintain stable operation for at least the minimum constant DC operating time. This study adopts a stepped DC transmission approach, and the constraint expression for the constant DC operating time is as follows:

[0100] (28);

[0101] (29);

[0102] (30);

[0103] in, This indicates whether the DC power transmission capacity during time period t is adjusted. It is a 0-1 variable, where 0 means no adjustment and 1 means adjustment. Indicates the minimum DC constant operating time; This represents the total power transmitted by the water-wind-solar coupling system during time period t. This represents the maximum power transmitted by the water-wind-solar coupling system during time period t.

[0104] The expression for the DC power ramp-up constraint is:

[0105] (31);

[0106] in, , These represent the maximum downhill and uphill rates of the DC channel transmission power, respectively.

[0107] The power balance constraint expression for the sending-end grid nodes is:

[0108] (32);

[0109] (33);

[0110] in, , , These represent the number of hydro, wind, and solar turbine units, respectively. , These represent the output of wind turbine w and solar turbine v during time period t, respectively. This represents the active power flow from node i to node j during time period t. This represents the active power flow from node j to node i during time period t. This represents the active power load within the base during time period t. This represents the reactive power provided by the hydropower unit h during time period t; This represents the reactive power flow from node i to node j during time period t. This represents the reactive power flow from node j to node i during time period t. This represents the reactive load within the base during time period t.

[0111] The calculation process for the power transmission capacity assessment model of the water-wind-solar coupling system considering the short-circuit ratio constraint in the embodiments of the present invention is as follows:

[0112] The established evaluation model for the power transmission capacity of the water-wind-solar coupled system includes two types of decision variables: continuous variables and 0-1 variables. The short-circuit ratio constraint is handled based on the improved SVM active learning iterative tangent plane method. Each iteration calculation belongs to the mixed integer programming problem, and CPLEX is selected as the solver for this problem.

[0113] Please see Figure 4This invention employs an improved IEEE RTS-79 test system for simulation experiments. This system includes 32 hydroelectric generators, 3 wind turbine generators, and 2 photovoltaic generators. Known generator parameters include: generator number, generator node number, generator output upper and lower limits, generator ramp rate, minimum start-up and shutdown times, generator inertial time constant, and predicted output values ​​for 5 renewable energy generators. Known network parameters include: branch resistance, branch reactance, 1 / 2 charging susceptance, and branch power flow upper and lower limits. Known load parameters include: power consumed by each load node in 24 time periods, and the total load consumed by each load node in each time period.

[0114] The main parameters of the power system are shown in Table 1:

[0115] Table 1 Main parameters of the power system

[0116]

[0117] Please see Figure 5 The DC transmission scheme based on the calculated evaluation model of the power transmission capacity of the water-wind-solar coupled system considering the short-circuit ratio constraint is as follows: Figure 5 .

[0118] Please see Figure 6 The unit combination scheme of the water-wind-solar coupled system power transmission capacity assessment model considering the short-circuit ratio constraint is as follows: Figure 6 .

[0119] Please see Figure 7 , Figure 8 Using the calculated power transmission capacity assessment model of the water-wind-solar coupled system considering the short-circuit ratio constraint, the voltage amplitude and phase angle of all network nodes are as follows: Figure 7 , Figure 8 .

[0120] Please see Figure 9 The short-circuit ratio of each renewable energy node and its multi-site short-circuit system, as determined by the hydro-wind-solar coupling system transmission capacity assessment model considering the short-circuit ratio constraint proposed in this embodiment of the invention, is not less than 2.5. Moreover, the maximum value of the short-circuit ratio of each node and its multi-site short-circuit system is higher than the calculation result of the conventional DC transmission assessment model. The voltage support capability is significantly enhanced, and the short-circuit ratio constraint of renewable energy multi-site short-circuit system is effective.

[0121] Implementing the embodiments of the present invention has the following beneficial effects:

[0122] This invention provides a method for evaluating the power transmission capacity of a hydro-wind-solar coupled system considering short-circuit ratio constraints. The method includes establishing a DC power transmission capacity evaluation model with the objective function of maximizing the total power of the system's DC power transmission channels, and determining core decision variables such as unit output, DC power transmission, and node voltage. Linearized AC power flow constraints are coupled to accurately quantify the actual node voltage and system power flow distribution. An improved short-circuit ratio constraint for multiple renewable energy power plants is then constructed, encompassing the actual node voltage phasor and the injected power from renewable energy sources. Addressing the strong nonlinear computational bottleneck of this constraint, an iterative tangent plane method based on improved SVM active learning is used to reconstruct the complex nonlinear operating boundary into a computationally efficient linear constraint, resulting in the optimal DC power transmission scheme that guarantees voltage support strength. This invention improves the accuracy and reliability of the power transmission capacity evaluation conclusions.

[0123] Example 2

[0124] Embodiment 2 of the present invention provides an evaluation system corresponding to Embodiment 1 above, including a memory, a processor, and a computer program stored in the memory; the processor executes the computer program in the memory to implement the steps of the method of Embodiment 1 above.

[0125] In some implementations, the memory may be high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device.

[0126] In other implementations, the processor can be any type of general-purpose processor, such as a central processing unit (CPU) or a digital signal processor (DSP), and there is no limitation here.

[0127] Example 3

[0128] Embodiment 3 of the present invention provides a computer-readable storage medium corresponding to Embodiment 1 above, on which a computer program / instructions are stored. When the computer program / instructions are executed by a processor, they implement the steps of the method of Embodiment 1 above.

[0129] A computer-readable storage medium can be a tangible device that holds and stores instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.

[0130] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0131] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0132] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0133] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0134] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for evaluating the power transmission capacity of a water-wind-solar coupling system, characterized in that, Includes the following steps: 1) Set the maximum number of iterations and construct an objective function without adding a short-circuit ratio constraint; determine whether the maximum number of iterations has been reached. If it has, end the process; otherwise, proceed to step 2). 2) Call the CPLEX solver to calculate the current objective function, extract the actual apparent power of new energy sources, node voltage amplitude, and voltage phase angle of each node, and substitute the extracted operating state variables into the operating short-circuit ratio constraint formula to calculate the actual operating short-circuit ratio (MRSCR) of each new energy node in the current system. current ; 3) Determine whether the short-circuit ratio of all new energy nodes is greater than or equal to the preset short-circuit ratio safety limit (MRSCR). S If yes, it is determined that the voltage support strength is met, the final result is output, and the process ends; if the operating short-circuit ratio of any new energy node is lower than the short-circuit ratio safety threshold, then proceed to step 4); the final result refers to the optimal DC transmission scheme that meets the operating short-circuit ratio constraint, including the unit combination result, unit output, DC transmission power curve, and operating short-circuit ratio. 4) Extract the current active power output vector of new energy sources and define it as the unsafe sample point X. unsafe By introducing a preset safety margin coefficient α, safe sample points X are constructed. safe ; 5) The unsafe sample point X unsafe As a negative class sample, the safe sample point X safe As positive class samples, an active learning training set is constructed; a linear kernel support vector machine with a maximum penalty coefficient is used to classify and train the active learning training set to find the optimal separating hyperplane that strictly divides the two points, and the normal vector and bias term b of the optimal separating hyperplane are extracted; the normal vector is the weight vector w. 6) Calculate the safe sample point X using the normal vector and bias term b. safe The projection value val along the normal direction of the classification hyperplane to The original optimal separation hyperplane is forcibly translated to the location of the safe sample point, generating a linear translation tangent plane constraint for the actual output of new energy. 7) Add the linear translation tangent plane constraint to the constraint set of the objective function, return to step 1), and perform the next round of iterative calculation until the operating short-circuit ratio of all new energy nodes is greater than or equal to the preset operating short-circuit ratio safety limit MRSCR. S ; The operating short-circuit ratio safety limit is expressed as follows: ; This represents the per-unit voltage value of node i during time period t. This represents the per-unit voltage value of node j during time period t. This represents the nominal voltage of node i during time period t. Z represents the equivalent impedance matrix of the AC power grid. eq The element in the i-th row and i-th column, Z represents the equivalent impedance matrix of the AC power grid. eq The element in the i-th row and j-th column, This represents the actual apparent power of new energy injected into the grid-connected bus node i during time period t. N represents the actual apparent power of new energy injected into the grid-connected bus node j during time period t, and N represents the number of new energy grid-connected nodes.

2. The method for evaluating the transmission capacity of a water-wind-solar coupling system according to claim 1, characterized in that, The objective function is expressed as: ； s.t. ; ； ； ； ； ； Calculate the objective function to obtain the optimal DC power transmission scheme; Where F represents the total DC power transmitted, P DC,k,t N represents the power transmitted by the k-th DC transmission line during time period t. HVDC This represents the total number of DC transmission channel loops, and T represents the total number of time periods considered in the study. This represents the active power flow of AC line l during time period t. G represents the reactive power flow of AC line l during time period t. ij and b ij U represents the conductance and susceptance of AC line l, respectively. i,t This represents the voltage amplitude at node i during time period t. This represents the voltage phase angle of node i during time period t. and These represent the active power flow safety upper limit and the reactive power flow safety upper limit of AC line l, respectively. and These represent the safe upper and lower limits of the voltage amplitude at node i, respectively. AC line l refers to the transmission line connecting node i and node j. MRSCR i,t This represents the short-circuit ratio of node i during time period t.

3. The method for evaluating the transmission capacity of a water-wind-solar coupling system according to claim 2, characterized in that, Safety Sample Point X safe The expression is: ; where X unsafe This represents the current unsafe sample point, i.e., the actual injected power value of the new energy node, and the safe sample point X. safe The corresponding safe power injection value for new energy nodes.

4. The method for evaluating the transmission capacity of a water-wind-solar coupling system according to claim 2, characterized in that, Projection value val to The calculation formula is: .

5. The method for evaluating the transmission capacity of a water-wind-solar coupling system according to claim 2, characterized in that, The linear translation tangent plane constraint is expressed as: Among them, P WV,i,t This represents the actual active power injected by new energy node i during time period t.

6. The method for evaluating the transmission capacity of a water-wind-solar coupling system according to claim 2, characterized in that, The constraint set of the objective function also includes one or more of the following conventional constraints: Water balance constraints: ; ; ; Reservoir capacity constraints: ; Outbound flow constraints: ; Hydropower unit generating capacity constraints: ; Hydropower station output constraints: ; Hydropower unit output constraints: ; Hydropower unit ramping constraints: ; Start-up and shutdown constraints for hydropower units: ; ; Output constraints for wind and solar power: ; ; DC constant operating time constraint: ; ; ; DC power ramp-up constraints: ; Power balance constraints at sending-end grid nodes: ; ; Among them, V g,t Q represents the reservoir capacity of hydropower station g during time period t. in,g,t Q g,t Let Q represent the inflow and outflow of hydropower station g during time period t. out,g,t Q spill,g,t These represent the power generation capacity and water discharge capacity of hydropower station g during time period t, respectively. This indicates that the hydropower station g-1, considering the time lag of water flow, is in... Traffic during a specific time period R represents the water flow time delay of hydropower station g, which is upstream of hydropower station g-1. g,t V represents the flow rate between hydropower station g-1 and hydropower station g during time interval t. g,t V represents the reservoir capacity of hydropower station g during time period t. g,min V g,max Let Q represent the minimum and maximum values ​​of the reservoir's capacity g, respectively. g,t,min Q g,t,max Let Q represent the minimum and maximum outflow rates of hydropower station g during time period t, respectively. out,g,n,min Q out,g,n,max Let Q represent the minimum and maximum generating capacities of the nth hydropower unit in hydropower station g during time period t, respectively. out,g,t,n Let represent the generating capacity of the nth hydroelectric generator unit of hydropower station g during time period t. This represents the output of hydropower station g during time period t. , P represents the minimum and maximum output of hydropower station g during time period t, respectively. G,g,n,min P G,g,n,max P represents the minimum and maximum output of the nth hydropower unit of hydropower station g during time period t, respectively. G,g,t,n R represents the output of the nth hydroelectric generator unit of hydroelectric power station g during time period t. d R u P represents the maximum downhill and uphill speeds of the hydroelectric generator unit, respectively. G,h,t This represents the output of hydropower unit h during time period t, where TS and TO represent the minimum shutdown and startup times of the hydropower unit, respectively. h,t P represents the start-stop status of the hydropower unit h during time period t. w,t P v,t These represent the actual power absorption capacity of the wind and solar turbines during time period t, respectively. , These represent the predicted output values ​​for wind and solar turbines, respectively. This indicates whether the DC power transmission capacity during time period t should be adjusted; 0 indicates no adjustment, and 1 indicates adjustment. P represents the minimum DC constant operating time. DC,t P represents the total power transmitted by the water-wind-solar coupling system during time period t. DC,max This represents the maximum power transmitted by the water-wind-solar coupling system during time period t. , N represents the maximum downhill and uphill rates of DC channel power transmission, respectively. G N w N v P represents the number of hydro, wind, and solar turbine units, respectively. W,w,t P V,v,t P represents the output of wind and solar turbines w and v respectively during time period t. l,ij,t P represents the active power flow from node i to node j during time period t. l,ji,t P represents the active power flow from node j to node i during time period t. b,t Q represents the active power load within the base during time period t. G,h,t Q represents the reactive power provided by the hydropower unit h during the time period t. l,ij,t Q represents the reactive power flow from node i to node j during time period t. l,ji,t Q represents the reactive power flow from node j to node i during time period t. b,t This represents the reactive load within the base during time period t.

7. A system for evaluating the transmission capacity of a water-wind-solar coupling system, comprising a memory, a processor, and a computer program stored in the memory; characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program / instructions stored thereon; characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 6.

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