Robust optimal load shedding method considering correlation and uncertainty of new energy output

Abstract

The present application relates to a kind of robust optimal load shedding strategies considering new energy output correlation and uncertainty, it is related to power grid planning field.Method specifically includes: based on the output data of new energy station in power grid system, application MVEE algorithm generates the convex hull of new energy scene with space-time correlation;Robust optimal load shedding model considering the uncertainty of new energy output is established, generator unit and energy storage element in the model meet the automatic generation control of power grid system;New energy scene convex hull is embedded into robust optimal load shedding model, and robust optimal load shedding model considering the correlation and uncertainty of new energy output is obtained, and different operating states of power grid system can be solved by optimal load shedding.The present application can be used to accurately calculate the load shedding situation of power grid system containing energy storage element under considering the correlation and uncertainty of new energy output.

Description

Technical Field

[0001] This disclosure relates to the field of power grid planning, and in particular to robust optimal load shedding research for novel power grid systems containing new energy sources and energy storage components, especially to robust optimal load shedding methods that take into account the correlation and uncertainty of new energy output. Background Technology

[0002] More and more new energy power plants and energy storage devices are being connected to the power grid system in order to achieve efficient utilization of various resources, including new energy sources. In practice, the output of new energy power plants not only exhibits strong uncertainty, but also has a certain spatiotemporal correlation due to their concentrated connection to the power grid. Therefore, considering the spatiotemporal correlation and uncertainty of new energy output while studying the optimal load shedding strategy for the power grid system has become an important issue in current power grid system optimization.

[0003] However, current modeling of renewable energy output mostly uses interval methods, neglecting the spatiotemporal correlations between them. Furthermore, there is limited research on robust optimization problems for power grid systems containing energy storage components and renewable energy sources. No research has yet been able to characterize the spatiotemporal correlations of renewable energy sources while considering their uncertainties. Summary of the Invention

[0004] In view of the above-mentioned prior art, the technical problem solved by the present invention is how to simultaneously consider the impact of new energy uncertainty and spatiotemporal correlation on power grid systems containing energy storage.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows.

[0006] In a first aspect, the present invention proposes a robust optimal load shedding strategy that considers the correlation and uncertainty of new energy output, the method comprising the following steps:

[0007] Based on the power output data of new energy power plants in the power grid system, the MVEE algorithm is applied to generate the convex hull of new energy scenarios with spatiotemporal correlation.

[0008] A robust optimal load shedding model is established that takes into account the uncertainty of new energy output. The generator sets and energy storage elements in this model meet the automatic generation control of the power grid system.

[0009] By embedding the convex hull of the new energy scenario into the robust optimal load shedding model, a robust optimal load shedding model that considers the correlation and uncertainty of new energy output is obtained, which can solve the optimal load shedding problem for different operating states of the power grid system.

[0010] In the above-mentioned method and technical solution, based on the power output data of new energy power plants in the power grid system, the MVEE algorithm is applied to generate the convex hull of the new energy scenario, including the following steps:

[0011] Obtain the h-th new energy scenario vector of new energy unit R at time t. The vector consisting of the changes in the h-th new energy scenario at time t

[0012] Using the MVEE algorithm and lifting-projection techniques, we can find a solution that can completely cover... The minimum volume ellipsoid is used to obtain the convex hull for generating new energy scenarios; where:

[0013] Full coverage The minimum volume ellipsoid is solved by the following optimization problem:

[0014]

[0015]

[0016] A T =A, A>0

[0017] Where A represents coverage The matrix represents the direction and shape of the MVEE, and q is the vector representing the center position;

[0018] Full coverage The minimum volume ellipsoid is solved by the following optimization problem:

[0019]

[0020]

[0021] M T =M, M>0

[0022] Where M represents coverage The MVEE is a matrix representing the direction and shape of the MVEE, where m is a vector representing the center position.

[0023] In the above-mentioned method and technical solution, the uncertainty of the output of new energy sources is the worst case of the power grid system. The worst case refers to the situation where the thermal power units, energy storage elements and transmission lines in the power grid system are operating within the safe range, and the output of thermal power units and the charging and discharging power and the rate of change of energy storage capacity of energy storage elements simultaneously meet the constraints. When the output of new energy sources meets the value located in the smallest volume closed ellipsoid, the constraints reach the critical point.

[0024] In the above method and technical solution, the generator sets and energy storage elements in the robust optimal load shedding model satisfy the automatic generation control of the power grid system, by adjusting the output participation factor β. Gi，t ,β Si，t Addressing the uncertainty of new energy output; specifically, the actual output of thermal power units and energy storage components are expressed as follows:

[0025]

[0026]

[0027]

[0028] β represents the set of all new energy generating units; Gi，t Let be the participation factor of the thermal power unit at bus i at time t; P Gi，t This represents the reference output value and actual output value of the thermal power unit at bus i at time t; Represents the collection of all energy storage devices; β Si，t Let be the participation factor of the energy storage element at bus i at time t; This represents the reference charging power and discharging power of the energy storage element at bus i at time t; This represents the actual charging power and discharging power of the energy storage element at bus i at time t.

[0029] In the above-mentioned method and technical solution, the robust optimal load shedding model considering the correlation and uncertainty of new energy output is transformed into a second-order cone form for solution. Specifically, the minimum value of the following objective function under the following constraints is sought:

[0030] Objective function:

[0031] P Li，t P represents the load magnitude at bus i at time t after load shedding; Di This indicates the load magnitude at bus i before load shedding;

[0032] Constraints:

[0033] (1) Thermal power units satisfy the following constraints:

[0034]

[0035]

[0036]

[0037]

[0038] in:

[0039] This is the minimum output limit for the thermal power unit at busbar i;

[0040] This represents the reference output value of the thermal power unit at bus i at time t;

[0041] This indicates the maximum output limit of the thermal power unit at busbar i;

[0042] The minimum ramp rate limit for the thermal power unit at bus i;

[0043] The maximum ramp rate limit for the thermal power unit at bus i;

[0044] Represents the collection of all new energy generating units. For time sets;

[0045] β Gi，t Let be the participation factor of the thermal power unit at bus i at time t;

[0046] e R It is an Nr×1 dimensional column vector;

[0047] M represents coverage. The matrix represents the direction and shape of the MVEE, where m is the vector representing the center position. This represents the vector consisting of the changes in the h-th new energy scenario at time t;

[0048] A represents coverage. The matrix represents the direction and shape of the MVEE, where q is a vector representing the center position. Represents the vector of the h-th new energy scenario at time t.

[0049] d p For all new energy bus lines The sum of;

[0050] d Δp For all new energy bus lines The sum of;

[0051] (2) The energy storage element satisfies the following constraints, and it cannot be charged and discharged simultaneously:

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062] in: Represents the collection of all energy storage devices; β Si，t Let be the participation factor of the energy storage element at bus i at time t; This represents the reference charging power and discharging power of the energy storage element at bus i at time t; This represents the actual charging power and discharging power of the energy storage element at bus i at time t; γ represents the maximum discharge and charge power of the energy storage device at bus i; i，t Indicates the charging and discharging state of the energy storage device at bus i at time t, where 1 represents charging and 0 represents discharging; η C η D The charging and discharging efficiency of the motherboard energy storage; This represents the reference energy of the energy storage element at bus i at time t; Δt represents the maximum energy, minimum energy, and upper and lower limits of the energy storage capacity constraints of the energy storage element at bus i at time t; Δt represents the unit time.

[0063] (3) The sum of the participation factors of thermal power units and energy storage elements is 1, that is:

[0064]

[0065] β Si，t Let be the participation factor of the energy storage element at bus i at time t;

[0066] (4) Power grid constraints:

[0067]

[0068]

[0069]

[0070]

[0071] In the formula: for The vector composed of these components represents the power flow distribution factor vector of thermal power generators, energy storage elements, new energy generators, and loads on the l-th line. for The vector formed represents the reference power vector of thermal power units, energy storage discharge, and energy storage charging at all busbars at time t; for The concatenated vector, β G+S，t For β G，t ,β S，t A concatenated vector; β G，t ,β S，t For β Gi，t ,β Si，t The vector formed represents the participation factor of all thermal power units and energy storage elements at all busbars at time t; P Li，t P represents the load magnitude at bus i at time t after load shedding; Di Indicates the load magnitude at bus i before load shedding; O is an auxiliary matrix that satisfies L is obtained by performing Cholesky decomposition on A.

[0072] Secondly, based on the above method, the present invention implements a corresponding system, namely: the present invention proposes a robust optimal load shedding system considering the correlation and uncertainty of new energy output, the system including a convex hull generation module, a load shedding model intermediate module, and a load shedding model establishment module:

[0073] Convex Hull Generation Module: Based on the power output data of new energy power plants in the power grid system, the MVEE algorithm is applied to generate the convex hull of new energy scenarios with spatiotemporal correlation.

[0074] Intermediate module of load shedding model: Establish a robust optimal load shedding model that takes into account the uncertainty of new energy output. The generator sets and energy storage elements in this model meet the automatic generation control of the power grid system.

[0075] Load shedding model establishment module: The convex hull of the new energy scenario is embedded into the robust optimal load shedding model to obtain a robust optimal load shedding model that considers the correlation and uncertainty of new energy output, which can solve the optimal load shedding problem for different operating states of the power grid system.

[0076] In the above system technical solution, based on the output data of new energy power plants in the power grid system, the MVEE algorithm is applied to generate the convex hull of the new energy scenario, including the following units:

[0077] Acquisition Unit: Acquire the h-th new energy scenario vector of new energy unit R at time t. The vector consisting of the changes in the h-th new energy scenario at time t

[0078] Generating cells: Using the MVEE algorithm and lifting-projection techniques, cells that can completely cover the target area are generated. The minimum volume ellipsoid is used to obtain the convex hull for generating new energy scenarios; where:

[0079] Full coverage The minimum volume ellipsoid is solved by the following optimization problem:

[0080]

[0081]

[0082] A T =A, A>0

[0083] Where A represents coverage The matrix represents the direction and shape of the MVEE, and q is the vector representing the center position;

[0084] Full coverage The minimum volume ellipsoid is solved by the following optimization problem:

[0085]

[0086]

[0087] M T =M, M>0

[0088] Where M represents coverage The MVEE is a matrix representing the direction and shape of the MVEE, where m is a vector representing the center position. Attached Figure Description

[0089] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0090] Figure 1 A robust optimal load shedding framework diagram considering the relevance and uncertainty of new energy sources under one implementation method;

[0091] Figure 2 The diagram shows the robust optimal load shedding model framework for embedding the convex hull of a new energy scenario in one implementation method. Detailed Implementation

[0092] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0093] This invention focuses on the research of robust optimal load shedding strategies for novel power grid systems. Robust optimization requires consideration of the conditions that the power grid system must meet under worst-case conditions. The power grid system mentioned in this invention refers to a power grid system containing energy storage components and new energy sources. The parameters involved in this invention are shown in Table 1.

[0094] Table 1

[0095]

[0096]

[0097]

[0098]

[0099] like Figure 1 As shown, one embodiment of the method of the present invention includes the following steps:

[0100] S1: Collect the output data of new energy power plants in the power grid system, apply the MVEE algorithm to these historical data, and generate the convex hull of the new energy scenario;

[0101] S2: Establish a robust optimal load shedding model that includes new energy uncertainty, energy storage dynamic constraints, thermal power unit constraints, and network constraints as constraints.

[0102] S3: Embed the convex hull of the new energy scenario generated in S1 into the robust optimal shear load model established in S2, and convert this model into a directly solvable second-order cone form.

[0103] The robust optimal load shedding model, which considers the correlation and uncertainty of new energy output, can solve for optimal load shedding under different operating states of the power grid system. When a component fails, it shuts down; for a generator, this means zero output; for a line, it means the line is open and the power flowing through it is zero; for energy storage components, it means the energy storage component shuts down, disconnects from the grid, and has zero output, etc. Therefore, when the operating state of the power grid system changes, it is only necessary to modify the topology of the simulated power grid system according to the current actual operating conditions, and then perform robust optimal load shedding analysis on the modified power grid system to truly reflect the minimum load shedding situation of the power grid system under this operating state.

[0104] The power grid system includes energy storage devices, new energy units, and thermal power units;

[0105] The energy storage devices in the current simulated power grid system do not charge and discharge simultaneously.

[0106] In step S1, historical data on the output of new energy sources are collected. The details are as follows:

[0107]

[0108]

[0109]

[0110] The minimum volume enclosing ellipsoid (MVEE) algorithm can use lifting-projection techniques to find a solution that can completely cover the ellipsoid. The smallest volume ellipsoid. This allows for the generation of the convex hull for new energy scenarios. About The MVEE should satisfy:

[0111]

[0112]

[0113] A T =A, A>0

[0114] Similarly, regarding The MVEE should satisfy:

[0115]

[0116]

[0117] M T =M, M>0

[0118] In step S2, the generator sets and energy storage elements in the robust optimal load shedding model satisfy the automatic generation control of the power grid system by adjusting the output participation factor β. Gi，t ,β Si，t Addressing the uncertainty of new energy output. Specifically, considering the uncertainty of new energy sources, the actual output of thermal power units and energy storage components can be expressed as:

[0119]

[0120]

[0121] The constraints are:

[0122] ①Constraints of thermal power units:

[0123]

[0124]

[0125]

[0126]

[0127] ②Constraints of energy storage elements:

[0128]

[0129]

[0130]

[0131]

[0132]

[0133]

[0134]

[0135]

[0136]

[0137]

[0138] ③ Participation factor constraints:

[0139] According to the principles of automatic control, the sum of the participation factors of thermal power units and energy storage elements should be 1, that is:

[0140] ④ Constraints of the power network:

[0141] The power flow of each branch is calculated using the DC power flow method, with the following specific constraints:

[0142]

[0143]

[0144]

[0145] The optimization objective of the robust optimal load shedding model described in step S2 is to minimize the load shedding amount of the power grid during the scheduling time, that is:

[0146]

[0147] In step S3, the robust optimal load shedding model established in step S2 is embedded into the convex hull of the new energy scenario generated by the MVEE algorithm, and this model is converted into a directly solvable second-order cone form. The objective function remains unchanged, which is still to minimize the amount of load shedding from the power grid over a certain period of time. That is, the optimization objective is to minimize the amount of load shedding. However, the various constraints change, combined with... Figure 2 As shown, the details are as follows:

[0148] ①Constraints of thermal power units:

[0149] Thermal power unit output power limit:

[0150]

[0151]

[0152] Thermal power unit ramp rate limit:

[0153]

[0154]

[0155] As can be seen from the above constraints, they are all linear constraints.

[0156] ②Constraints of energy storage elements:

[0157] Includes linear constraints and 0-1 mixed integer constraints:

[0158] Energy storage element charge / discharge state constraints:

[0159]

[0160]

[0161]

[0162] Energy storage element charge / discharge rate constraints:

[0163]

[0164]

[0165] Energy storage device capacity constraints:

[0166]

[0167]

[0168]

[0169]

[0170]

[0171] ③ Participation factor constraints:

[0172] All participation factors of all components should be greater than 0 and keep the sum to 1. All are linear constraints.

[0173]

[0174] ④ Power network constraints:

[0175] The second-order cone form of the robust optimal load shedding model embedded in the convex hull of the new energy scenario has power network constraints that include both linear constraints and second-order cone constraints:

[0176] Power balance limitations:

[0177]

[0178] Shear load size limit:

[0179]

[0180] Transmission line capacity limitations:

[0181]

[0182]

[0183] In a second-order cone constraint, O satisfies:

[0184] A = L T L

[0185]

[0186] Where A = L T L represents performing Cholesky decomposition on A, where A is the cover. The direction and shape matrix of the MVEE, since A is a positive definite matrix, has a unique L and a unique O.

[0187] In summary, by minimizing the objective function under the relevant constraints, the robust optimal load shedding in step S3 can be obtained. Its value represents the optimal load shedding amount under the worst-case scenario of the power grid system during the scheduling period, reflecting the reliability of the power grid system under this operating state.

[0188] In the above-described implementation of the method of this invention, a robust reliability assessment is performed on a novel power grid system that simultaneously contains energy storage components and new energy sources. The introduction of 0-1 variables ensures that the energy storage devices cannot charge and discharge simultaneously, which aligns with actual conditions. In the robust optimal load shedding calculation, considering the uncertainty of new energy output, the worst-case scenario of the power grid system is studied. The worst-case scenario refers to the situation where, considering the constraints that all components in the power grid system, such as thermal power units, energy storage components, and transmission lines, must operate within safe limits, and that the rate of change of the output of thermal power units and the charging and discharging power and energy storage capacity of energy storage components cannot be too large, some constraints reach a critical point where they are about to exceed limits when the new energy output takes a certain value from the minimum volume closed ellipsoid generated based on its historical scenarios. In this invention, the minimum volume closed ellipsoid of the new energy output scenario is generated using the MVEE algorithm. Compared with the traditional interval method modeling, this considers the spatiotemporal correlation of new energy output, making the calculation results more consistent with reality. Furthermore, when obtaining robust load shedding, the constraints of energy storage components, thermal power units, and power grids all consider the worst-case scenario under the uncertainty and spatiotemporal correlation of new energy sources. Moreover, the constraints only include simple linear constraints, 0-1 mixed integer constraints, and second-order cone constraints, which reduces the computational difficulty while meeting engineering requirements. This can provide guidance for the planning of new power grid systems that include energy storage and new energy sources.

[0189] Through the above description of the embodiments, those skilled in the art can clearly understand that this disclosure can be implemented using software plus necessary general-purpose hardware, or it can be implemented using dedicated hardware including dedicated integrated circuits, dedicated CPUs, dedicated memory, dedicated components, etc. Generally, any function performed by a computer program can be easily implemented using corresponding hardware, and the specific hardware structure used to implement the same function can be diverse, such as analog circuits, digital circuits, or dedicated circuits. However, for this disclosure, software implementation is more often a preferred implementation method.

[0190] Although embodiments of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the specific embodiments and application fields described above. The specific embodiments described above are merely illustrative and instructive, and not restrictive. Those skilled in the art can make many other forms based on the guidance of this specification and without departing from the scope of protection of the claims of the present invention, and all of these are within the scope of protection of the present invention.

Claims

1. A robust optimal load shedding method considering the correlation and uncertainty of new energy output, characterized in that, The method includes the following steps: Based on the power output data of renewable energy power plants in the power grid system, the MVEE algorithm is applied to generate the convex hull of renewable energy scenarios with spatiotemporal correlation, including the following steps: Acquiring new energy units exist t Time of the first h New energy scenario vectors , t Time of the first h A vector composed of changes in various new energy scenarios ; Using the MVEE algorithm and lifting-projection techniques, we can find a solution that can completely cover... , The minimum volume ellipsoid is used to obtain the convex hull for generating new energy scenarios; where: Full coverage The minimum volume ellipsoid is solved by the following optimization problem: in, It represents coverage The matrix of the orientation and shape of the MVEE, It is a vector representing the center position; This represents the total number of new energy busbars; Full coverage The minimum volume ellipsoid is solved by the following optimization problem: in, It represents coverage The matrix of the orientation and shape of the MVEE, It is a vector representing the center position; A robust optimal load shedding model is established that takes into account the uncertainty of new energy output. The generator sets and energy storage elements in this model meet the automatic generation control of the power grid system. By embedding the convex hull of the new energy scenario into the robust optimal load shedding model, a robust optimal load shedding model that considers the correlation and uncertainty of new energy output is obtained, which can solve the optimal load shedding problem for different operating states of the power grid system.

2. The method according to claim 1, characterized in that, The uncertainty of new energy output represents the worst-case scenario for the power grid system. The worst-case scenario refers to the situation where the thermal power units, energy storage elements, and transmission lines in the power grid system operate within safe limits, and the output of the thermal power units and the charging and discharging power and energy storage capacity change rate of the energy storage elements simultaneously meet the constraints. The new energy output satisfies the value located in the smallest volume closed ellipsoid, and the constraints reach the critical point.

3. The method according to claim 1, characterized in that, The generator sets and energy storage components in the robust optimal load shedding model satisfy the automatic generation control of the power grid system by adjusting the output participation factor. Addressing the uncertainty of new energy output; specifically, the actual output of thermal power units and energy storage components are expressed as follows: Represents the collection of all new energy generating units; busbar i Thermal power units at t Participating factors at any given moment; Indicates busbar i Thermal power units at t The reference output value and the actual output value at any given time; Represents the collection of all energy storage devices; busbar i Energy storage elements at t Participating factors at any given moment; Indicates busbar i Energy storage elements at t The reference charging power and discharging power at any given time; Indicates busbar i Energy storage elements at t The actual charging power and discharging power at any given time Represents a set of time periods.

4. The method according to claim 1, characterized in that, The robust optimal load shedding model, which considers the correlation and uncertainty of new energy output, is transformed into a second-order cone form for solution. Specifically, the minimum value of the following objective function under the following constraints is sought: Objective function: This indicates the load magnitude at bus i at time t after load shedding; This indicates the load magnitude at busbar i before load shedding; Represents the set of system load buses; Represents a set of time periods; Constraints: (1) Thermal power units satisfy the following constraints: in: busbar i Minimum output limit of thermal power units at the location; Indicates busbar i Thermal power units at t The baseline output value at any given time; Indicates busbar i The maximum output limit of the thermal power unit at the location; busbar i Minimum ramp rate limit for thermal power units at the location; busbar i The maximum ramp rate limit for thermal power units at the location; Represents the collection of all new energy generating units. For time periods; busbar i Thermal power units at t Participating factors at any given moment; for Nr A 1×1 dimensional column vector; To cover The matrix of the orientation and shape of the MVEE, Let the vector represent the center position. express t Time of the first h A vector composed of changes in various new energy scenarios; A It represents coverage The matrix of the orientation and shape of the MVEE, q It is a vector representing the center position. express t Time of the first h One new energy scenario vector; For all new energy bus lines The sum of; For all new energy bus lines The sum of; (2) The energy storage element satisfies the following constraints, and it cannot be charged and discharged simultaneously: in: Represents the collection of all energy storage devices; busbar i Energy storage elements at t Participating factors at any given moment; Indicates busbar i Energy storage elements at t The reference charging power and discharging power at any given time; Indicates busbar i Energy storage elements at t Actual charging power and discharging power at any given moment; Indicates busbar i The maximum discharge and charging power of the energy storage device at the location; express t Time bus i The charging and discharging status of the energy storage device is indicated by 1 for charging and 0 for discharging. The charging and discharging efficiency of the motherboard energy storage; Indicates busbar i Energy storage elements at t The baseline energy at any given moment; Indicates busbar i Energy storage elements at t The upper and lower limits of the maximum energy, minimum energy, and energy storage capacity at any given time; Indicates a unit of time; (3) The sum of the participation factors of thermal power units and energy storage elements is 1, that is: busbar i Energy storage elements at t Participating factors at any given moment; (4) Power grid constraints: In the formula: for The vector composed of these components represents the power flow distribution factor vector of thermal power generators, energy storage elements, new energy generators, and loads on the l-th line. for The vector formed represents time. The reference power vector for thermal power units, energy storage discharge, and energy storage charging at all busbars; for A vector formed by concatenation. for A vector formed by concatenation; for The vector formed represents time. The participation factors of all thermal power units and energy storage components at all busbars; Indicates after load shedding t Time bus i The load size at the location; Indicates the busbar before load shedding i The load size at the location; Let be an auxiliary matrix that satisfies ,in It is obtained by performing Cholesky decomposition on A.

5. A robust optimal load shedding system considering the correlation and uncertainty of new energy output, characterized in that, The system includes a convex hull generation module, a load shearing model intermediate module, and a load shearing model establishment module. Convex Hull Generation Module: Based on the output data of renewable energy power plants in the power grid system, the MVEE algorithm is applied to generate the convex hull of renewable energy scenarios with spatiotemporal correlation, including the following units: Acquisition Unit: Acquiring New Energy Units exist t Time of the first h New energy scenario vectors , t Time of the first h A vector composed of changes in various new energy scenarios ; Generating cells: Using the MVEE algorithm and lifting-projection techniques, cells that can completely cover the target area are generated. , The minimum volume ellipsoid is used to obtain the convex hull for generating new energy scenarios; where: Full coverage The minimum volume ellipsoid is solved by the following optimization problem: in, It represents coverage The matrix of the orientation and shape of the MVEE, It is a vector representing the center position; This represents the total number of new energy busbars; Full coverage The minimum volume ellipsoid is solved by the following optimization problem: in, It represents coverage The matrix of the orientation and shape of the MVEE, It is a vector representing the center position; Intermediate module of load shedding model: Establish a robust optimal load shedding model that takes into account the uncertainty of new energy output. The generator sets and energy storage elements in this model meet the automatic generation control of the power grid system. Load shedding model establishment module: The convex hull of the new energy scenario is embedded into the robust optimal load shedding model to obtain a robust optimal load shedding model that considers the correlation and uncertainty of new energy output, which can solve the optimal load shedding problem for different operating states of the power grid system.

6. The system according to claim 5, characterized in that, The uncertainty of new energy output represents the worst-case scenario for the power grid system. The worst-case scenario refers to the situation where the thermal power units, energy storage elements, and transmission lines in the power grid system operate within safe limits, and the output of the thermal power units and the charging and discharging power and energy storage capacity change rate of the energy storage elements simultaneously meet the constraints. The new energy output satisfies the value located in the smallest volume closed ellipsoid, and the constraints reach the critical point.

7. The system according to claim 5, characterized in that, The generator sets and energy storage components in the robust optimal load shedding model satisfy the automatic generation control of the power grid system by adjusting the output participation factor. Addressing the uncertainty of new energy output; specifically, the actual output of thermal power units and energy storage components are expressed as follows: Represents the collection of all new energy generating units; busbar i Thermal power units at t Participating factors at any given moment; Indicates busbar i Thermal power units at t The reference output value and the actual output value at any given time; Represents the collection of all energy storage devices; busbar i Energy storage elements at t Participating factors at any given moment; Indicates busbar i Energy storage elements at t The reference charging power and discharging power at any given time; Indicates busbar i Energy storage elements at t The actual charging power and discharging power at any given time Represents a set of time periods.

8. The system according to claim 5, characterized in that, The robust optimal load shedding model, which considers the correlation and uncertainty of new energy output, is transformed into a second-order cone form for solution. Specifically, the minimum value of the following objective function under the following constraints is sought: Objective function: This indicates the load magnitude at bus i at time t after load shedding; This indicates the load magnitude at busbar i before load shedding; Represents the set of system load buses; Represents a set of time periods; Constraints: (1) Thermal power units satisfy the following constraints: in: busbar i Minimum output limit of thermal power units at the location; Indicates busbar i Thermal power units at t The baseline output value at any given time; Indicates busbar i The maximum output limit of the thermal power unit at the location; busbar i Minimum ramp rate limit for thermal power units at the location; busbar i The maximum ramp rate limit for thermal power units at the location; Represents the collection of all new energy generating units. For time periods; busbar i Thermal power units at t Participating factors at any given moment; for Nr A 1×1 dimensional column vector; To cover The matrix of the orientation and shape of the MVEE, Let the vector represent the center position. express t Time of the first h A vector composed of changes in various new energy scenarios; A It represents coverage The matrix of the orientation and shape of the MVEE, q It is a vector representing the center position. express t Time of the first h One new energy scenario vector; For all new energy bus lines The sum of; For all new energy bus lines The sum of; (2) The energy storage element satisfies the following constraints, and it cannot be charged and discharged simultaneously: in: Represents the collection of all energy storage devices; busbar i Energy storage elements at t Participating factors at any given moment; Indicates busbar i Energy storage elements at t The reference charging power and discharging power at any given time; Indicates busbar i Energy storage elements at t Actual charging power and discharging power at any given moment; Indicates busbar i The maximum discharge and charging power of the energy storage device at the location; express t Time bus i The charging and discharging status of the energy storage device is indicated by 1 for charging and 0 for discharging. The charging and discharging efficiency of the motherboard energy storage; Indicates busbar i Energy storage elements at t The baseline energy at any given moment; Indicates busbar i Energy storage elements at t The upper and lower limits of the maximum energy, minimum energy, and energy storage capacity at any given time; Indicates a unit of time; (3) The sum of the participation factors of thermal power units and energy storage elements is 1, that is: busbar i Energy storage elements at t Participating factors at any given moment; (4) Power grid constraints: In the formula: for The vector composed of these components represents the power flow distribution factor vector of thermal power generators, energy storage elements, new energy generators, and loads on the l-th line. for The vector formed represents time. The reference power vector for thermal power units, energy storage discharge, and energy storage charging at all busbars; for A vector formed by concatenation. for A vector formed by concatenation; for The vector formed represents time. The participation factors of all thermal power units and energy storage components at all busbars; Indicates after load shedding t Time bus i The load size at the location; Indicates the busbar before load shedding i The load size at the location; Let be an auxiliary matrix that satisfies ,in It is obtained by performing Cholesky decomposition on A.

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