Power grid dispatching reserve capacity calculation method considering new energy and load uncertainty and related device

Abstract

The invention provides a power grid dispatching reserve capacity calculation method considering new energy and load uncertainty and a related device, and aims to solve the problems of uncertainty coping, N-1 security constraint satisfaction and calculation efficiency under high-proportion new energy grid connection. The method comprises the following steps: establishing a wind power ellipsoid uncertainty model and a load ellipsoid constraint model containing a spatial smoothing effect; introducing a participation factor to construct a generator adaptive response model, and realizing fluctuation compensation; constructing a main objective function by using conventional operation constraints (power balance, climbing rate and the like), and solving an initial scheduling scheme; the N-1 fault robustness is verified through an auxiliary objective function, and the constraint is iteratively updated; and the optimal reserve capacity is solved by adopting Benders decomposition. The method can accurately quantify the multi-source uncertainty, improve the wind power consumption capability, ensure the safe and economical operation of the system, is high in calculation efficiency, and is suitable for the calculation of the reserve capacity of a novel power system.

Description

Technical Field

[0001] This invention relates to the field of power system technology, specifically a method and related apparatus for calculating grid dispatch reserve capacity considering the uncertainty of new energy sources and loads. Background Technology

[0002] With the increasing proportion of large-scale renewable energy (such as wind power) connected to the grid, the uncertainty of the power system has significantly increased. Traditional economic dispatch models often assume that power output is fixed, making it difficult to cope with the security risks brought about by wind power volatility and load randomness. Existing methods for determining grid reserve capacity can be divided into exogenous and endogenous methods. The traditional exogenous method is based on experience or probabilistic indicators to first determine the reserve capacity demand, and then substitute this demand as a constraint into the dispatch model for solution. The exogenous method is simple, but it is difficult to accurately consider the correlation between multiple uncertain factors and the network constraints of reserve capacity transmission in the process of determining reserve capacity. The reserve capacity determined a priori may be too conservative and cannot guarantee reserve availability. The endogenous method, by reasonably establishing a multi-source uncertainty event model and integrating the uncertainty model with the generation dispatch model, seeks an effective solution method to achieve the solution of the joint dispatch model of generation and reserve, obtains a generation dispatch and reserve allocation scheme that meets security constraints, and gives the reserve size and cost in relation to the penetration rate of new energy output and its uncertainty.

[0003] However, existing grid regulation reserve capacity methods typically incorporate all uncertainty constraints into the optimization model for unified solution, resulting in high model dimensionality, strong nonlinearity, high computational complexity, convergence difficulties, and low solution efficiency. Furthermore, they fail to consider the safety operation constraints under the N-1 criterion in fault scenarios. In addition, most models do not adequately account for the spatial smoothing effect of wind power and the impact of load fluctuation characteristics on system reserve demand. Therefore, there is an urgent need to propose a safety-constrained economic dispatch method that can accurately characterize the uncertainties of wind power and load while efficiently solving for solutions.

[0004] Based on this, this invention addresses the challenges posed by large-scale renewable energy grid integration to power grid dispatch and operation. It proposes a method for calculating grid dispatch reserve capacity that considers both renewable energy uncertainties and grid operation security constraints. This method effectively models renewable energy uncertainties and considers both constraints under normal operation and the N-1 criterion security constraints under fault scenarios. Furthermore, it improves computational efficiency through an effective decomposition calculation method, providing a reference for calculating grid reserve capacity under large-scale renewable energy grid integration. This method is a security-constrained economic dispatch approach that integrates advanced uncertainty modeling and efficient decomposition algorithms. It effectively addresses the challenges brought by high-proportion renewable energy access and possesses significant theoretical value and promising industrial application prospects. Summary of the Invention

[0005] In view of the above-mentioned defects or improvement needs of existing technologies, the uncertainty of power output caused by the high proportion of new energy access, the constraint problem of existing constraints that consider the N-1 condition after a fault, and the problem of low computational efficiency of large-scale systems, this invention provides a method and related device for calculating grid reserve capacity considering the uncertainty of new energy and load.

[0006] To achieve the above objectives, according to one aspect of the present invention, a method for calculating grid reserve capacity considering new energy sources and load uncertainties is provided, comprising the following steps:

[0007] Step 1: Establish a wind power uncertainty model, use ellipsoidal uncertainty set to describe the power output deviation of multiple wind farms, and output wind power output deviation constraint parameters;

[0008] Step 2: Establish a load uncertainty model, use ellipsoidal constraints to limit total load fluctuations, and output load fluctuation constraint parameters;

[0009] Step 3: Introduce participating factors to establish a generator output response model and output generator output response parameters;

[0010] Step 4: Based on the wind power output deviation constraint parameters in Step 1, the load fluctuation constraint parameters in Step 2, and the generator output response parameters in Step 3, construct a conventional operation constraint model and output a set of conventional constraints.

[0011] Step 5: Using the set of regular constraints output in Step 4 as constraints, construct the main objective function (MP) that minimizes the total operating cost and output the initial scheduling scheme;

[0012] Step 6: Based on the initial scheduling scheme output in Step 5, construct an auxiliary objective function (SP) to verify the system robustness under the N-1 fault scenario, and output the fault scenario constraint verification results;

[0013] Step 7: If the verification result of Step 6 does not meet the N-1 criterion, add fault scenario safety constraints to the constraints based on the regular constraint set, and update the comprehensive constraint set.

[0014] Step 8: Using the Benders decomposition method, with the main objective function (MP) from Step 5 and the updated set of comprehensive constraints from Step 7 as input, iteratively solve the master-subproblem until the auxiliary objective function (SP) from Step 6 meets the robustness requirement, and output the optimal reserve capacity value.

[0015] Furthermore, the output results of the generator output response model described in step 3 are directly used as input parameters for the standby capacity constraint and standby availability constraint in step 4, and are used to dynamically adjust the upper and lower limits of the generator standby capacity.

[0016] Furthermore, the set of regular constraints in step 4 includes power balance constraints, output upper and lower limit constraints, line power flow constraints, ramp rate constraints, reserve capacity constraints, and reserve availability constraints.

[0017] Furthermore, in step 8, the iterative process of Benders decomposition uses the solution result of the main objective function in step 5 to update the auxiliary objective function in step 6, and adjusts the safety constraints of the fault scenario in step 7 based on the verification result of the auxiliary objective function.

[0018] A power grid dispatch reserve capacity calculation device considering the uncertainty of new energy sources and loads, comprising:

[0019] The wind power uncertainty model establishment module is used to establish a wind power uncertainty model. It uses an ellipsoidal uncertainty set to describe the power output deviation of multiple wind farms and outputs wind power output deviation constraint parameters.

[0020] The load uncertainty model establishment module is used to establish a load uncertainty model, using ellipsoidal constraints to limit total load fluctuations and outputting load fluctuation constraint parameters;

[0021] The generator output response model establishment module is used to introduce participating factors to establish a generator output response model and output generator output response parameters.

[0022] The module for establishing a conventional operation constraint model is used to construct a conventional operation constraint model based on wind power output deviation constraint parameters, load fluctuation constraint parameters, and generator output response parameters, and output a set of conventional constraints.

[0023] The main objective function construction module is used to construct the main objective function (MP) that minimizes the total running cost using a set of regular constraints as constraints, and outputs the initial scheduling scheme;

[0024] The auxiliary objective function construction module is used to construct an auxiliary objective function (SP) based on the initial scheduling scheme to verify the system robustness under the N-1 fault scenario and output the fault scenario constraint verification results.

[0025] The constraint update module is used to add fault scenario safety constraints to the constraints based on the regular constraint set and update the comprehensive constraint set when the failure scenario constraint verification result does not meet the N-1 criterion.

[0026] The standby capacity calculation module uses the Benders decomposition method to iteratively solve the master-subproblem using the main objective function (MP) and the updated set of comprehensive constraints as input, until the auxiliary objective function (SP) meets the robustness requirement, and outputs the optimal standby capacity value.

[0027] Furthermore, the output results of the generator output response model are directly used as input parameters for the standby capacity constraint and standby availability constraint, and are used to dynamically adjust the upper and lower limits of the generator standby capacity.

[0028] Furthermore, the set of conventional constraints includes power balance constraints, output upper and lower limit constraints, line power flow constraints, ramp rate constraints, reserve capacity constraints, and reserve availability constraints.

[0029] Furthermore, the iterative process of the Benders decomposition updates the auxiliary objective function using the solution result of the main objective function, and adjusts the safety constraints of the fault scenario based on the verification result of the auxiliary objective function.

[0030] A power grid dispatch reserve capacity calculation system that considers the uncertainty of new energy sources and loads includes: a computer-readable storage medium and a processor;

[0031] The computer-readable storage medium is used to store executable instructions;

[0032] The processor is used to read executable instructions stored in the computer-readable storage medium and execute the grid dispatch reserve capacity calculation method that takes into account the uncertainty of new energy sources and load.

[0033] A non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for calculating grid dispatch reserve capacity considering the uncertainty of new energy sources and loads.

[0034] This invention addresses the challenges posed to power grid dispatching by the uncertainty of power output and load demand following the integration of large-scale renewable energy sources and loads. Existing reserve capacity methods lack consideration for flexible generator adjustment and the N-1 criterion safety verification. This invention resolves the impact of uncertainty through precise modeling, making it more closely reflective of wind power fluctuations. Simultaneously, it achieves automatic power redistribution by introducing a generator participation factor. Furthermore, it iteratively identifies key fault scenarios and dynamically adds constraints to ensure the N-1 criterion is met. In addition, it introduces the Benders decomposition method to decompose the large-scale mixed-integer programming problem into a primary objective and an auxiliary objective, significantly improving computational efficiency. In summary, this method has strong engineering applicability and can provide an important reference for power grid dispatching with large-scale renewable energy integration. Attached Figure Description

[0035] Figure 1 This is a flowchart of the power grid dispatch reserve capacity calculation method that takes into account the uncertainty of new energy sources and loads in this invention.

[0036] Figure 2 These are the simulation results of the method proposed in this invention on the IEEE 14-node system. Detailed Implementation

[0037] 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.

[0038] Please see Figure 1 This invention provides a method for calculating grid reserve capacity considering the uncertainty of new energy sources and load, comprising the following steps:

[0039] Step 1: Establish a wind power uncertainty model, use ellipsoidal uncertainty set to describe the power output deviation of multiple wind farms, and output wind power output deviation constraint parameters.

[0040] To reflect the spatial correlation among multiple wind farms and their effect on suppressing overall fluctuations, i.e., the spatial smoothing effect, the actual power output of the i-th wind farm in time period t is:

[0041] (1)

[0042] in, This represents the actual output (MW) of the j-th wind turbine during time period t; This indicates the predicted output (expected value); This represents the output deviation. Furthermore, the total deviation is determined to satisfy the ellipsoidal uncertainty set as follows:

[0043] (2)

[0044] in, This represents the total number of wind farms. Let be the maximum possible deviation of the j-th wind farm; For uncertain budget parameters, control the level of conservatism (0 ≤ ≤1).

[0045] Step 2: Establish a load uncertainty model, use ellipsoidal constraints to limit total load fluctuations, and output load fluctuation constraint parameters.

[0046] The actual load of the m-th load node in time period t is:

[0047] (3)

[0048] in, This represents the actual load. For load forecasting; This represents the load fluctuation.

[0049] Load deviation is subject to the following constraints:

[0050] (4)

[0051] in, This represents the maximum allowable fluctuation of the m-th load. This represents the total load.

[0052] Step 3: Introduce participating factors to establish a generator output response model and output generator output response parameters.

[0053] Specifically, by introducing participation factors to establish a generator output response model, adaptive adjustment of output can be achieved to compensate for fluctuations in new energy sources and load.

[0054] (5)

[0055] For units that only respond to load changes (such as frequency regulating units):

[0056] (6)

[0057] in, Indicates the basic scheduling output; This represents the participation factor of the i-th generating unit in wind power fluctuations; This represents the participation factor for load fluctuations; at the same time, ∑αi=1, ∑βi=1, to ensure actual power balance.

[0058] Step 4: Based on the wind power output deviation constraint parameters in Step 1, the load fluctuation constraint parameters in Step 2, and the generator output response parameters in Step 3, construct a conventional operation constraint model that includes power balance constraints, upper and lower output limits constraints, line power flow constraints, ramp rate constraints, reserve capacity constraints, and reserve availability constraints, and output the conventional constraint set.

[0059] The power balance constraint is as follows:

[0060] (7)

[0061] The upper and lower limits of output constraints are as follows:

[0062] (8)

[0063] The power flow constraints for the line are:

[0064] (9)

[0065] The climbing rate constraint is:

[0066] (10)

[0067] The standby capacity constraint is:

[0068] (11)

[0069] The standby availability is:

[0070] (12)

[0071] Step 5: Construct a scheduling objective model that minimizes the total running cost (Main Problem MP).

[0072] (13)

[0073] Where T is the scheduling period (e.g., 24 hours); This represents the total number of generators. , , Let be the fuel cost coefficient for the i-th generating unit; Reserved for upward and downward rotation; and Reserve cost per unit.

[0074] Step 6: Since the scheduling objective does not include safety constraints after an N-1 failure, an auxiliary objective function (SP) is further added to verify the system robustness. The auxiliary objective function is:

[0075] (14)

[0076] in, , , and This is a slack variable that measures the degree of constraint violation.

[0077] Step 7: If the verification result of Step 6 does not meet the N-1 criterion, add fault scenario safety constraints to the constraints based on the regular constraint set, and update the comprehensive constraint set.

[0078] The following constraints are established after a fault:

[0079] (15)

[0080] (16)

[0081] (17)

[0082] (18)

[0083] Where all slack variables are non-negative:

[0084] (19)

[0085] Step 8: Solve the spare capacity iteratively based on Benders decomposition, adding a cutting plane each time to gradually approximate the true feasible region.

[0086] Step 8.1: Set the iteration count k=0, and the empty cut set Cuts = ∅;

[0087] Step 8.2: Solve for the main scheduling objective MP(k) to obtain the decision variables for the first stage. ;

[0088] Step 8.3: For each time period t and each fault scenario f, Substitute into the auxiliary objective function Solve .if If θ > 0, generate a Benders feasible cut, θ ≥ π^T (X - X*[k]), and add it to the Cuts set; where, the Benders feasible cut is expressed in the form of:

[0089] θ≥π(k)T(X−X[k]) (20)

[0090] Where θ is an auxiliary variable representing the cost of the subproblem; π(k) is the Lagrange multiplier of the subproblem in the k-th iteration; and X is the decision variable vector of the main problem.

[0091] Step 8.4: Determine whether all SPs satisfy ( = 0). If satisfied, If the solution is optimal, proceed to step 8.5; if not, then k = k + 1, add Cuts to MP, and return to step 8.2.

[0092] Step 8.5: Output the final solution X.

[0093] Step 9: End.

[0094] The proposed method for calculating grid dispatch reserve capacity, considering the uncertainties of new energy sources and loads, addresses the uncertainties of new energy sources by introducing spatial smoothing effects and ellipsoidal uncertainty sets through precise modeling, thus more closely reflecting the actual fluctuation characteristics of wind power. Simultaneously, it introduces generator participation factors through a flexible adjustment mechanism to achieve automatic power redistribution. Furthermore, it establishes constraints under fault scenarios and ensures that the N-1 condition also meets the constraint requirements through iterative identification of fault scenarios. Moreover, to address the problem of low efficiency in solving large-scale systems, it decomposes the large-scale mixed integer programming problem into a master-subproblem using the Benders decomposition method, effectively improving computational efficiency.

[0095] To verify the effectiveness and rationality of the model, a 14-node system was used for model validation, and the solution was programmed and solved on the Matlab platform. The 14-node system connects to the wind farm at two nodes, and the predicted load and wind power output at different time periods are shown below:

[0096] The predicted loads for time periods 1, 2, 3, 4, 5, and 6 are 140MW, 130MW, 120MW, 118MW, 125MW, and 124MW, respectively.

[0097] The predicted wind power output for time periods 1, 2, 3, 4, 5, and 6 are 60MW, 62MW, 68MW, 72MW, 74MW, and 76MW, respectively.

[0098] The uncertain range for wind power is taken as 12% of the predicted output, and the uncertain range for load is taken as 2% of the predicted value. Optimized scheduling is performed for 6 time periods. The total minimum output of the units is 25 MW, the minimum output of a single unit is 0, and the minimum net load is 42.73 MW.

[0099] In the generator unit output distribution diagram for each time period, the stacked bar chart shows the output distribution of the five units in each time period, such as... Figure 2 As shown, the red curve represents the net load (total load - wind power output). The simulation demonstrates the dynamic adjustment capability of the proposed method, such as the dynamic adjustment of unit output with changes in net load. During periods 1-2 (high load), multiple units work together to supply power, while during periods 3-6 (low load), some units reduce output or shut down. At the same time, the total output in each period strictly tracks the net load curve to ensure supply and demand balance, and the output changes between adjacent periods are smooth, which meets the ramp rate limit.

[0100] In the reserve capacity configuration diagram, the blue curve represents the upper reserve capacity (to cope with the decrease in wind power or the increase in load), and the red curve represents the lower reserve capacity (to cope with the increase in wind power or the decrease in load). Simulation results show that the reserve capacity configuration is positively correlated with the wind power output scale, and more reserves are configured in time period 6 (the highest wind power of 82.75MW). At the same time, the spatial correlation of multiple wind farms is accurately quantified by the ellipsoidal constraint of formula (2), avoiding excessive conservatism. Furthermore, the Benders decomposition iteration verification ensures that the system can still operate after any line failure, realizing the N-1 safety criterion. Moreover, the reserve capacity meets the safety requirements without over-configuring and causing economic waste.

[0101] In the load and wind power output diagram, the black curve indicates that the predicted load shows a trend of first decreasing and then increasing, the green curve indicates that the wind power output continues to rise, from 59.62MW to 82.75MW, and the red dashed line indicates that the net load (load - wind power) continues to decrease. The simulation results show that the proportion of wind power output to load increased from 42.5% (period 1) to 65.9% (period 6), and the system can effectively absorb a high proportion of wind power. At the same time, although both load and wind power are changing, the dispatch scheme can respond smoothly. In addition, through the participation factors in formulas (5)-(6), automatic power redistribution of generators is realized to compensate for wind power fluctuations. The simulation results verify that the proposed method has strong wind power absorption capacity, a smooth net load curve, and an effective adjustment mechanism of the participation factor.

[0102] In summary, this method is simple to implement and easy to put into engineering. It can effectively address the impact of large-scale new energy fluctuations, provide a method for calculating the reserve capacity of power systems, and play an important guiding role in ensuring the safe operation of new power systems.

[0103] Another embodiment of the present invention provides a power grid dispatch reserve capacity calculation device that considers the uncertainty of new energy sources and loads, comprising:

[0104] The wind power uncertainty model establishment module is used to establish a wind power uncertainty model. It uses an ellipsoidal uncertainty set to describe the power output deviation of multiple wind farms and outputs wind power output deviation constraint parameters.

[0105] The load uncertainty model establishment module is used to establish a load uncertainty model, using ellipsoidal constraints to limit total load fluctuations and outputting load fluctuation constraint parameters;

[0106] The generator output response model establishment module is used to introduce participating factors to establish a generator output response model and output generator output response parameters.

[0107] The module for establishing a conventional operation constraint model is used to construct a conventional operation constraint model based on wind power output deviation constraint parameters, load fluctuation constraint parameters, and generator output response parameters, and output a set of conventional constraints.

[0108] The main objective function construction module is used to construct the main objective function (MP) that minimizes the total running cost using a set of regular constraints as constraints, and outputs the initial scheduling scheme;

[0109] The auxiliary objective function construction module is used to construct an auxiliary objective function (SP) based on the initial scheduling scheme to verify the system robustness under the N-1 fault scenario and output the fault scenario constraint verification results.

[0110] The constraint update module is used to add fault scenario safety constraints to the constraints based on the regular constraint set and update the comprehensive constraint set when the failure scenario constraint verification result does not meet the N-1 criterion.

[0111] The standby capacity calculation module uses the Benders decomposition method to iteratively solve the master-subproblem using the main objective function (MP) and the updated set of comprehensive constraints as input, until the auxiliary objective function (SP) meets the robustness requirement, and outputs the optimal standby capacity value.

[0112] Another embodiment of the present invention provides a power grid dispatch reserve capacity calculation system that considers the uncertainty of new energy sources and loads, comprising: a computer-readable storage medium and a processor;

[0113] The computer-readable storage medium is used to store executable instructions;

[0114] The processor is used to read executable instructions stored in the computer-readable storage medium and execute the grid dispatch reserve capacity calculation method that takes into account the uncertainty of new energy sources and load.

[0115] Another embodiment of the present invention provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the aforementioned method for calculating grid dispatch reserve capacity considering the uncertainty of new energy sources and loads.

[0116] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied 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.

[0117] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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 illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0118] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0119] 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.

[0120] This invention addresses the impact of uncertainties in new energy sources and loads through precise modeling, resulting in a more accurate representation of actual power fluctuation characteristics. Simultaneously, it achieves automatic power redistribution by introducing a generator participation factor. Furthermore, it iteratively identifies key fault scenarios and adds constraints to ensure the N-1 criterion is met. In addition, the Benders decomposition method is introduced to decompose the large-scale mixed-integer programming problem into a primary objective and an auxiliary objective, significantly improving computational efficiency. Therefore, this method fully considers the uncertainties of new energy sources, the N-1 constraint requirements under fault scenarios, and the need for efficient solutions to large-scale computational problems, demonstrating significant potential for practical engineering applications.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for calculating reserve capacity of power grid scheduling considering uncertainty of new energy and load, characterized in that, The method comprises the following steps: Step 1: a wind power uncertainty model is established, an ellipsoid uncertainty set is used to describe output deviation of multiple wind farms, and output wind power output deviation constraint parameters are output; Step 2: a load uncertainty model is established, an ellipsoid constraint is used to limit total load fluctuation, and load fluctuation constraint parameters are output; Step 3: a generator output response model is established by introducing a participation factor, and generator output response parameters are output; Step 4: based on the wind power output deviation constraint parameters in step 1, the load fluctuation constraint parameters in step 2 and the generator output response parameters in step 3, a conventional operation constraint condition model is constructed, and a conventional constraint set is output; Step 5: a main target function (MP) of minimizing total operation cost is constructed by taking the conventional constraint set output in step 4 as a constraint condition, and an initial scheduling scheme is output; Step 6: based on the initial scheduling scheme output in step 5, an auxiliary target function (SP) is constructed to verify system robustness under an N-1 fault scenario, and a fault scenario constraint verification result is output; Step 7: if the verification result in step 6 does not satisfy the N-1 criterion, a fault scenario safety constraint condition is added based on the conventional constraint set as a constraint condition, and an updated comprehensive constraint set is output; Step 8: a Benders decomposition method is used to iteratively solve a main-sub problem by taking the main target function (MP) in step 5 and the updated comprehensive constraint set in step 7 as inputs until the auxiliary target function (SP) in step 6 satisfies the robustness requirement, and an optimal reserve capacity value is output.

2. The method of claim 1, wherein, The output result of the generator output response model in step 3 is directly used as an input parameter of a reserve capacity constraint and a reserve availability constraint in step 4, and is used for dynamically adjusting upper and lower limits of a generator reserve capacity.

3. The method of claim 1, wherein, The conventional constraint set in step 4 comprises a power balance constraint condition, an output upper and lower limit constraint condition, a line power flow constraint, a ramp rate constraint, a reserve capacity constraint and a reserve availability constraint condition.

4. The method of claim 1, wherein, In step 8, the iteration process of the Benders decomposition uses the solving result of the main target function in step 5 to update the auxiliary target function in step 6, and adjusts the fault scenario safety constraint condition in step 7 according to the verification result of the auxiliary target function.

5. A power grid dispatch reserve capacity calculation device considering new energy and load uncertainty, characterized in that, The method comprises: a wind power uncertainty model establishment module, configured to establish a wind power uncertainty model, use an ellipsoid uncertainty set to describe output deviation of multiple wind farms, and output wind power output deviation constraint parameters; a load uncertainty model establishment module, configured to establish a load uncertainty model, use an ellipsoid constraint to limit total load fluctuation, and output load fluctuation constraint parameters; a generator output response model establishment module, configured to introduce a participation factor to establish a generator output response model, and output generator output response parameters; a conventional operation constraint condition model establishment module, configured to construct a conventional operation constraint condition model based on wind power output deviation constraint parameters, load fluctuation constraint parameters and generator output response parameters, and output a conventional constraint set; a main target function construction module, configured to construct a main target function (MP) of minimizing total operation cost by taking the conventional constraint set as a constraint condition, and output an initial scheduling scheme; and a Benders decomposition method is used to iteratively solve a main-sub problem by taking the main target function (MP) in step 5 and the updated comprehensive constraint set in step 7 as inputs until the auxiliary target function (SP) in step 6 satisfies the robustness requirement, and an optimal reserve capacity value is output. The auxiliary objective function construction module is configured to construct an auxiliary objective function (SP) based on the initial scheduling scheme to verify the system robustness under an N-1 failure scenario, and output a failure scenario constraint verification result. The constraint updating module is configured to add a failure scenario safety constraint condition to the constraint condition based on the conventional constraint set when the failure scenario constraint verification result does not satisfy the N-1 criterion, and update the comprehensive constraint set. The backup capacity calculation module is configured to use a Benders decomposition method to take the main objective function (MP) and the updated comprehensive constraint set as inputs, iteratively solve the main-sub problem until the auxiliary objective function (SP) satisfies the robustness requirement, and output an optimal backup capacity value.

6. The apparatus of claim 5, wherein, The output result of the generator output response model is directly used as an input parameter of the backup capacity constraint and the backup availability constraint, and is used for dynamically adjusting the upper and lower limits of the generator backup capacity.

7. The apparatus of claim 5, wherein, The conventional constraint set includes a power balance constraint condition, an output upper and lower limit constraint condition, a line flow constraint, a ramp rate constraint, a backup capacity constraint, and a backup availability constraint condition.

8. The apparatus of claim 5, wherein, The iterative process of the Benders decomposition uses the main objective function solution result to update the auxiliary objective function, and adjusts the failure scenario safety constraint condition according to the verification result of the auxiliary objective function.

9. A power grid dispatch reserve capacity calculation system considering new energy and load uncertainty, comprising: A computer readable storage medium and a processor; The computer readable storage medium is configured to store executable instructions; The processor is configured to read the executable instructions stored in the computer readable storage medium, and execute the backup capacity calculation method for power grid scheduling considering new energy and load uncertainty according to any one of claims 1-4. 10.A non-transitory computer readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the backup capacity calculation method for power grid scheduling considering new energy and load uncertainty according to any one of claims 1-4.

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