Robust operation domain construction method and device for flexible resources of new energy power system

Abstract

The invention discloses a robust operation domain construction method and device for flexible resources of a new energy power system, and relates to the field of power system operation control, and the method comprises the steps: obtaining parameters of a generator set, an energy storage system and an HVDC power transmission system, and constructing a flexible resource operation domain model; obtaining an uncertainty set of the generated output of the new energy station and the bus load of the transformer substation; obtaining power grid topology information, and constructing multi-period power balance and line power flow safety constraints; the power value or the energy value is constructed as an uncertain quantity related to an operation domain boundary variable, and a robust operation domain of the flexible resources is obtained through calculation by means of decision-related uncertainty and a robust optimization method; and performing operation control on the generator set, the energy storage system and the HVDC power transmission system according to the robust operation domain. The method can adapt to unexpected requirements of dynamic regulation and control of the new energy power system under the action of a random process, and provides a simple and easy-to-execute strategy for flexible resource regulation and control and new energy consumption.

Description

Technical Field

[0001] The present invention relates to the field of power system operation control, and in particular to a method and device for constructing a robust operation domain of flexibility resources in a new energy power system. Background Art

[0002] Renewable energy generation represented by wind power and photovoltaic power is an effective technology to achieve carbon emission reduction in the power system. At the same time, the uncontrollability and randomness of renewable energy power generation output have brought huge challenges to the balance of power generation and consumption in the power system. New technologies such as flexible transformation of thermal power units, large-scale energy storage, flexible direct current transmission, and demand response provide flexible resources for the temporal and spatial complementarity and balance of power generation and consumption, and are the basic guarantee for the consumption of new energy. However, limited by the inherent technical constraints of flexible resources (power ramp constraints of generator units, capacity constraints of energy storage systems, power transmission constraints of high-voltage direct current transmission systems, etc.), unreasonable operation and control strategies will cause spatial imbalance and temporal short-sightedness of flexible resources, restrict the efficient use of their flexibility, and cause the risk of imbalance of power and electricity in the system.

[0003] Since the output of renewable energy power generation is a random process, the dispatchers of the power system cannot determine in advance the power timing curve of the flexible resources that matches the renewable energy power generation. After research, the inventors found that by constructing an operating domain model for the flexible resources (i.e., the power or energy timing interval, which is in the form of a band), and obtaining a robust operating domain through optimization calculation, the coordinated control of the flexible resource operating point and the adjustment reserve can be achieved with the robust operating domain, which can effectively solve the spatiotemporal coordination problem of the timing operation control of the renewable energy power system under strong uncertainty conditions, realize the economic and reliable absorption of new energy, and ensure the safe operation of the power system. The flexible resource robust operating domain provides dispatchers with a global strategy of "responding to changes with the unchanging", that is, using the operating domain constructed in advance (weeks before, days before, hours before) to cope with the massive scenarios of renewable energy power generation output.

[0004] The existing robust scheduling strategy can only give the unit start and stop plan, the flexibility resource power timing curve under a specific renewable energy power generation output scenario (i.e. a single operating point), and the spare capacity corresponding to the specific operating point. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method and device for constructing a robust operating domain of flexible resources in a new energy power system, which can adapt to the unexpected requirements of dynamic regulation of the new energy power system under the action of random processes, and provide a simple and easy-to-implement strategy for flexible resource regulation and new energy consumption.

[0006] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a method for constructing a robust operation domain of flexibility resources of a new energy power system, comprising the following steps:

[0007] S1: Obtain the parameters of the generator set, energy storage system and HVDC transmission system, and build a flexible resource operation domain model;

[0008] S2: Obtain the uncertainty set of the power generation output of the new energy station and the bus load of the substation;

[0009] S3: Obtain grid topology information and build multi-period power balance and line flow safety constraints;

[0010] S4: Based on the flexible resource operation domain model, uncertainty set, power balance and line flow safety constraints, the power value or energy value is constructed as an uncertainty quantity related to the operation domain boundary variable, and the robust operation domain of the flexible resource is calculated with the help of decision-related uncertainty and robust optimization method;

[0011] S5: Perform operation control of the generator sets, energy storage system and HVDC transmission system according to the robust operation domain.

[0012] Furthermore, the flexibility resource operation domain model in S1 includes:

[0013] For non-energy-constrained generators, construct the upper and lower boundaries of the power generation range in each period ;

[0014] For energy-constrained generators, construct the upper and lower boundaries of the power generation interval in each period ;

[0015] For energy storage systems, construct upper and lower boundaries of energy level intervals in each period ;

[0016] For HVDC transmission systems, the upper and lower boundaries of the power transmission interval are constructed in each period. .

[0017] Furthermore, the uncertainty set of the power generation output of the new energy station and the bus load of the substation is obtained in S2, including any of the following methods:

[0018] Based on the probability prediction system of the power generation output of the new energy station and the bus load of the substation, the power interval of each station and bus control time domain under the given probability confidence level is obtained. As a vector Corresponding elements The upper and lower limits of ,in, For vector The index of the element of is any symbol;

[0019] Based on the historical data of power generation output of new energy stations and bus load of substations, a statistical analysis is performed to construct a polyhedron or ellipsoid uncertainty set, and the obtained The uncertainty set of and vector , positive definite matrix and scalar Parameters obtained for statistical analysis.

[0020] Furthermore, the power balance and line flow safety constraints for multiple time periods constructed in S3 are:

[0021]

[0022] in, and is a matrix, is a vector, are decision variables related to the active power of flexibility resources.

[0023] Furthermore, the S4 includes the following sub-steps:

[0024] S41: For non-energy-constrained generators, during the period The active power related technical constraints are:

[0025]

[0026] in, is a unified index of flexibility resources, is the unified symbol for active power and power boundary decision variables, Resources for flexibility In the period The active power, Resources for flexibility In the period The active power, and is the obtained up and down power climbing ability data, is a binary variable that characterizes the availability of flexible resources. and is the power upper and lower limit data obtained, and Resources for flexibility In the period The lower and upper boundaries of the active power;

[0027] The time period Active power As an uncertainty, it is constructed with the operating domain boundary variables as parameters The uncertainty set is:

[0028]

[0029] in, for The uncertainty set, and Resources for flexibility In the period The lower and upper boundaries of the active power;

[0030] S42: For energy-constrained generators, energy storage systems and HVDC transmission systems, during the period The active power related technical constraints are:

[0031]

[0032] in, is a linear function describing the relationship between energy and power, For the period The energy For the period The energy For the period The energy and Flexibility resources for energy-constrained In the period The lower and upper energy boundaries of and To obtain the upper and lower limit data of energy;

[0033] The time period Energy As an uncertainty, it is constructed with the operating domain boundary variables as parameters The uncertainty set is:

[0034]

[0035] in, for The uncertainty set, and Flexibility resources for energy-constrained In the period The lower and upper energy boundaries of

[0036] S43: A three-layer two-stage robust optimization model is established as:

[0037]

[0038] in, For the robust operation domain decision variables, , and are the decision-related uncertainty parameters that characterize the external uncertainty of new energy and load and flexibility resources, respectively. is the decision variable for the actual regulation of flexible resources, is the system operating cost, is the uncertainty set of power, is the uncertainty set of energy, , and is the vector of power, energy and operating condition indicator variables, is the vector of energy and operating condition indicator variables, is a binary indicator variable for the power generation and consumption conditions of flexible resources, , , , , , , , , and is the coefficient matrix or right-hand side vector of the relevant constraints;

[0039] S44: The three-layer two-stage robust optimization model is iteratively solved by the row and column generation algorithm to obtain the robust operation domain of the flexible resources ,in, and is the optimal value of the lower and upper bounds of the power of the flexibility resource vector, and It is the optimal value of the lower and upper bounds of the energy of the flexibility resource vector.

[0040] Furthermore, the operation control of the generator set, the energy storage system and the HVDC transmission system is performed according to the robust operation domain in S5, including any of the following control strategies:

[0041] Time-series decoupling control strategy: Based on the new energy and load measurement values ​​in the current period, the flexibility resources are regulated under the condition of meeting the robust operation domain;

[0042] Model predictive control strategy: Based on the new energy and load forecast data for current and future time periods, flexible resources are regulated while meeting the robust operation domain.

[0043] The present invention also adopts a technical solution: a robust operation domain construction device for flexibility resources of a new energy power system, the device comprising:

[0044] Memory: used to store computer programs;

[0045] Processor: used to execute the computer program to implement the above-mentioned robust operation domain construction method of the new energy power system flexibility resources.

[0046] The beneficial effects of the present invention are: 1) the model has high accuracy and can simultaneously consider the spatiotemporal correlation characteristics of the random process of renewable energy power generation output and the unpredictability of the timing operation of the renewable energy power system; 2) the decision-making information is highly rich and can provide a set of operating points of various types of flexible resources in all time periods, so as to realize the spatiotemporal coordinated optimization of operating points and adjustment reserves; 3) the strategy is highly intuitive and can explicitly characterize the mapping between the uncertainty set of renewable energy power generation output and the operating domain of flexible resources; 4) it is convenient for engineering deployment and directly provides guidance strategies for the real-time regulation of flexible resources and the consumption of new energy; 5) it has strong robustness and adaptability. The proposed method can construct a robust operating domain for various types of flexible resources in the renewable energy power system, and ensure the dynamic and full consumption of renewable energy power generation within the expected fluctuation range, so as to avoid the risk of power imbalance. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flow chart of a method for constructing a robust operating domain of flexibility resources in a new energy power system according to the present invention.

[0048] Figure 2 A schematic diagram of a model predictive control strategy based on a flexible resource robust operation domain provided in an embodiment of the present invention.

[0049] Figure 3 A schematic diagram of the robust operation domain of a generator set provided in an embodiment of the present invention.

[0050] Figure 4 A schematic diagram of the robust operation domain of an energy storage system provided in an embodiment of the present invention.

[0051] Figure 5 A schematic diagram of a robust operation domain of an HVDC power transmission system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0053] Embodiment 1, as Figure 1 As shown, a method for constructing a robust operation domain of flexibility resources in a new energy power system includes the following steps:

[0054] S1: Obtain the parameters of the generator set, energy storage system and HVDC transmission system, and build a flexible resource operation domain model;

[0055] S2: Obtain the uncertainty set of the power generation output of the new energy station and the bus load of the substation;

[0056] S3: Obtain grid topology information and build multi-period power balance and line flow safety constraints;

[0057] S4: Based on the flexible resource operation domain model, uncertainty set, power balance and line flow safety constraints, the power value or energy value is constructed as an uncertainty quantity related to the operation domain boundary variable, and the robust operation domain of the flexible resource is calculated with the help of decision-related uncertainty and robust optimization method;

[0058] S5: Perform operation control of the generator sets, energy storage system and HVDC transmission system according to the robust operation domain.

[0059] Obtaining the parameters of the generator set, energy storage system and HVDC transmission system includes: for the generator set (or power plant), obtaining the upper and lower limits of the power generation and consumption in each time period, the upper and lower power climbing capabilities, and the upper and lower limits of the cumulative power generation in the last time period. For the energy storage system (including pumped storage units), obtaining the upper and lower limits of the power generation and consumption in each time period, the upper and lower power climbing capabilities, and the upper and lower limits of the energy level (or the water volume of the pumped storage power station) in each time period. For the HVDC transmission system, obtaining the upper and lower limits of the power generation and consumption of the transmission line in each time period, the upper and lower power climbing capabilities, and the upper and lower limits of the cumulative transmission power in the last time period. The above parameters can be obtained through the energy management system and market clearing system of the power dispatching agency.

[0060] Construct a flexible resource operation domain model, which is in the form of decision variables for the upper and lower boundaries of the power or energy time series interval. The operation domain models of various types of flexibility are as follows.

[0061] The flexibility resource operation domain model in S1 includes:

[0062] For non-energy-constrained generators (such as thermal power plants where primary energy is not restricted in the short term or there is no daily power generation constraint), the upper and lower boundaries of the power generation range are constructed for each period. ;

[0063] For energy-constrained generators, construct the upper and lower boundaries of the power generation interval in each period ,in , They are the indexes of the generator set and the control period respectively;

[0064] For energy storage systems, construct upper and lower boundaries of energy level intervals in each period ,in , are the indexes of energy storage system and regulation period respectively;

[0065] For HVDC transmission systems, the upper and lower boundaries of the power transmission interval are constructed in each period. ,in , They are the indexes of HVDC transmission line and regulation period respectively.

[0066] The uncertainty set of the power generation output of the new energy station and the bus load of the substation is obtained in S2, and the uncertainty of the power generation output of the new energy station and the bus load of the substation is constructed as a high-dimensional space-time vector , the set of all possible values ​​of the vector is defined as the uncertainty set, denoted as There are two alternative ways to construct the uncertainty set:

[0067] Solution 1: Based on the probability prediction system of the power generation output of the new energy station and the bus load of the substation, the power interval of each station and bus control time domain under the given probability confidence level is obtained. As a vector Corresponding elements The upper and lower limits of ,in, For vector The index of the element of is any symbol;

[0068] Solution 2: Based on the historical data of power generation output of new energy stations and bus load of substations, a statistical analysis is performed to construct a polyhedron or ellipsoid uncertainty set, and the obtained uncertainty set is The uncertainty set of and vector , positive definite matrix and scalar Parameters obtained for statistical analysis.

[0069] Obtain grid topology information and build multi-time period power balance and line flow safety constraints, including: obtaining transmission network topology information of corresponding voltage levels from the energy management system and market clearing system of the power dispatching agency, including the connection relationship between transmission lines and busbars, voltage level, transmission line capacity, and transmission line impedance.

[0070] The linearized multi-period power flow model is constructed using the topology information of the transmission network to characterize the power generation and consumption balance conditions and the power flow safety constraints of the line. The linear system.

[0071] The power balance and line flow safety constraints for multiple periods constructed in S3 are:

[0072]

[0073] in, and is a matrix, is a vector, are decision variables related to the active power of flexibility resources.

[0074] The S4 comprises the following sub-steps:

[0075] S41: For non-energy-constrained generator sets, the operating domain boundary variable is the generator set active power boundary In order to characterize the power upper and lower limit constraints and power ramp constraints and avoid using the active power realization value across time periods, the time period needs to be Previous period The active power of the generator set is constructed as an uncertain quantity related to the power boundary decision variable , as a period Input information for decision making module. The active power related technical constraints are:

[0076]

[0077] in, is a unified index of flexibility resources, is the unified symbol for active power and power boundary decision variables, Resources for flexibility In the period The active power, Resources for flexibility In the period The active power, and is the obtained up and down power climbing ability data, is a binary variable that characterizes the availability of flexible resources and is related to the start and stop status of the generator set. and is the power upper and lower limit data obtained, and Resources for flexibility In the period The lower and upper boundaries of the active power;

[0078] The time period Active power As an uncertainty, it is constructed with the operating domain boundary variables as parameters The uncertainty set is:

[0079]

[0080] in, for The uncertainty set, and Resources for flexibility In the period The lower and upper boundaries of the active power;

[0081] S42: For energy-constrained generators, energy storage systems and HVDC transmission systems, during the period The active power related technical constraints are:

[0082] For energy-constrained flexible resources (including energy-constrained generators, energy storage systems, HVDC transmission systems, etc.), the operating domain boundary variables are time-period energy boundary decision variables, including the upper and lower boundaries of the cumulative power generation range of the generators. , the upper and lower boundaries of the energy level range stored in the energy storage system , the upper and lower boundaries of the cumulative power transmission range of HVDC transmission lines . The first few periods (represented by ) is constructed as the uncertainty associated with the energy boundary decision variable , as a period The input information of the decision module is used to characterize the flexibility of resources in the time period Active power and related technical constraints. Flexible resources in the time period The active power can be used in the time period Energy and time period Energy value To characterize, that is .function It is a linear function that describes the relationship between energy and power. For energy storage systems, affected by the efficiency of power generation and consumption, this function is a piecewise linear function, and it is necessary to introduce a binary variable indicating the power generation and consumption conditions. To characterize the function . The above-mentioned conversion and characterization technology is used to treat the energy state quantity of flexible resources in massive or infinite scenarios as uncertain quantities, ensuring that the active power regulation of flexible resources is unexpected, thereby satisfying the basic logic of timing operation control under dynamic uncertain conditions. Furthermore, the power upper and lower limit constraints, power ramp constraints, and energy constraints of flexible resources can be uniformly characterized by the following three inequalities:

[0083]

[0084] in, is a linear function describing the relationship between energy and power, For the period The energy For the period The energy For the period The energy and Flexibility resources for energy-constrained In the period The lower and upper energy boundaries of and To obtain the upper and lower limit data of energy;

[0085] The time period Energy As an uncertainty, it is constructed with the operating domain boundary variables as parameters The uncertainty set is:

[0086]

[0087] in, for The uncertainty set, and Flexibility resources for energy-constrained In the period The lower and upper energy boundaries of

[0088] S43: A three-layer two-stage robust optimization model is established as:

[0089]

[0090] in, For the robust operation domain decision variables, , and are the decision-related uncertainty parameters that characterize the external uncertainty of new energy and load and flexibility resources, respectively. is the decision variable for the actual regulation of flexible resources, is the system operating cost, is the uncertainty set of power, is the uncertainty set of energy, , and is the vector of power, energy and operating condition indicator variables, is the vector of energy and operating condition indicator variables, is a binary indicator variable for the power generation and consumption conditions of flexible resources, , , , , , , , , and The coefficient matrix or right-hand side vector of the relevant constraints, specifically, the data of scalar expressions such as the active power-related technical constraints of non-energy-constrained generators, the upper and lower power constraints, power ramp constraints, energy constraints of energy-constrained flexibility resources, and the constraints of uncertainty sets form the coefficient matrix and right-hand side vector;

[0091] In the formula, the first row of constraints represents the constraints of the decision variables of the robust operation domain and their coupling relationship with the flexibility resource regulation decision. The second row of constraints is the system power balance and line flow constraints described by the active power of the flexibility resources, the output of new energy generation and the bus load, and the grid topology. The third row is the coupling relationship between the energy and power of the flexibility resources. The fourth row of constraints describes the flexibility resource time period. Power control decisions and time periods The fifth line constraint describes the flexibility resource period. The robust operation domain decision variables and time periods Energy control decisions and time periods and time period The coupling relationship between the three unknown energy quantities.

[0092] S44: The three-layer two-stage robust optimization model is iteratively solved by the row and column generation algorithm to obtain the robust operation domain of the flexible resources ,in, and is the optimal value of the lower and upper bounds of the power of the flexibility resource vector, and It is the optimal value of the lower and upper bounds of the energy of the flexibility resource vector.

[0093] like Figure 2 As shown, the robust operation domain provides robust upper and lower boundary vectors of the power range of non-energy-constrained generator sets and robust upper and lower boundary vectors of the energy range of energy-constrained flexibility resources.

[0094] The basic strategy of operation control is to maintain the power or energy of flexible resources in the robust operation domain corresponding to each time period. Then, according to the real-time observed new energy power generation output and bus load, the active power of the flexible resources is optimized and adjusted to achieve new energy consumption, power generation and consumption balance, and line flow safety. There are two options available.

[0095] In S5, the operation control of the generator set, the energy storage system and the HVDC transmission system is performed according to the robust operation domain, including any of the following control strategies:

[0096] Solution 1: Time-sequence decoupling control strategy, based on the new energy and load measurement values ​​of the current period, to regulate the flexibility resources under the condition of meeting the robust operation domain;

[0097] The timing decoupling control strategy only considers the current period in real-time operation control, and ignores the subsequent periods. The current period is , obtain the measured values ​​of new energy and load , get the current time period The power and energy values ​​of the flexibility resources in the previous few time periods are known . Then, according to the time period Robust operating domain Adjust flexible resources by time period The goal is to minimize the system operation cost. Under the premise of satisfying the upper and lower power constraints, power ramp constraints, and energy constraints of the flexible resources themselves, the flexible resource time period is The power and energy values ​​are regulated within the robust operation domain and meet system requirements such as power generation and consumption balance, line flow safety, and new energy consumption;

[0098] Solution 2: Model predictive control strategy, based on the new energy and load forecast data of the current and future multiple periods, to regulate the flexibility resources under the condition of meeting the robust operation domain;

[0099] Model predictive control strategy, that is, in real-time operation control, not only the current period but also the subsequent period is considered. The current period is denoted as , the number of forward-looking periods is . Get measurements of new energy and loads , get the current time period The power and energy values ​​of the flexibility resources in the previous few time periods are known , get the follow-up New energy and load forecast data for each period. Robust operating domain Adjust flexible resources by time period System operating costs and future periods The goal is to minimize the sum of the expected values ​​of the system operation costs. Under the premise of satisfying the upper and lower power constraints, power ramp constraints, and energy constraints of the flexibility resources themselves, the flexibility resource time period is The power and energy expectations are controlled within the robust operation domain and are only deployed during the period Flexible resource control strategies can meet system requirements such as power generation and consumption balance, line flow safety, and new energy consumption.

[0100] Both Scheme 1 and Scheme 2 can ensure the robustness of subsequent time periods, while Scheme 2 can release greater adjustment capabilities of flexible resources. Scheme 2 is the preferred scheme recommended by the present invention.

[0101] In one embodiment of the present invention, Figure 3-5 As shown, the present invention determines the robust operation domains of non-energy-constrained generator sets and energy-constrained flexibility resources, specifically the upper and lower power boundaries of non-energy-constrained generator sets in each time period, the upper and lower energy boundaries of energy storage systems in each time period, and the upper and lower energy boundaries of HVDC transmission systems in each time period.

[0102] Embodiment 2, a device for constructing a robust operation domain of flexibility resources in a new energy power system, the device comprising:

[0103] Memory: used to store computer programs;

[0104] Processor: used to execute the computer program to implement the above-mentioned robust operation domain construction method of the new energy power system flexibility resources.

[0105] The robust operating domain method proposed in the present invention can simultaneously consider the spatiotemporal coupling characteristics of the random process of renewable energy power generation output and the unpredictability of the timing operation of the renewable energy power system, and can provide the operating domain of various types of flexible resources for all time periods (i.e., the set of spatiotemporal dimensions of the operating points). It can explicitly characterize the mapping between the uncertainty set of renewable energy power generation output and the operating domain of flexible resources, and provide direct guidance for power and electricity balance, real-time operation control, and renewable energy consumption under dynamic uncertain conditions.

[0106] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the invention.

Claims

1. A method for constructing a robust operation domain of flexibility resources in a new energy power system, characterized in that: The following steps are involved: S1: Obtain the parameters of the generator set, energy storage system and HVDC transmission system, and build a flexible resource operation domain model; S2: Obtain the uncertainty set of the power generation output of the new energy station and the bus load of the substation; S3: Obtain grid topology information and build multi-period power balance and line flow safety constraints; S4: Based on the flexible resource operation domain model, uncertainty set, power balance and line flow safety constraints, the power value or energy value is constructed as an uncertainty quantity related to the operation domain boundary variable, and the robust operation domain of the flexible resource is calculated with the help of decision-related uncertainty and robust optimization method; S5: Perform operation control of the generator sets, energy storage system and HVDC transmission system according to the robust operation domain.

2. The method for constructing a robust operation domain of flexibility resources in a new energy power system according to claim 1 is characterized in that: The flexibility resource operation domain model in S1 includes: For non-energy-constrained generators, construct the upper and lower boundaries of the power generation range in each period ; For energy-constrained generators, construct the upper and lower boundaries of the power generation interval in each period ; For energy storage systems, construct upper and lower boundaries of energy level intervals in each period ; For HVDC transmission systems, the upper and lower boundaries of the power transmission interval are constructed in each period. .

3. The method for constructing a robust operation domain of flexibility resources in a new energy power system according to claim 2 is characterized in that: The uncertainty set of the power generation output of the new energy station and the bus load of the substation is obtained in S2, including any of the following methods: Based on the probability prediction system of the power generation output of the new energy station and the bus load of the substation, the power interval of each station and bus control time domain under the given probability confidence level is obtained. As a vector Corresponding elements The upper and lower limits of ,in, For vector The index of the element of is any symbol; Based on the historical data of power generation output of new energy stations and bus load of substations, a statistical analysis is performed to construct a polyhedron or ellipsoid uncertainty set, and the obtained The uncertainty set of and vector , positive definite matrix and scalar Parameters obtained for statistical analysis.

4. The method for constructing a robust operation domain of flexibility resources in a new energy power system according to claim 3 is characterized in that: The power balance and line flow safety constraints for multiple periods constructed in S3 are: in, and is a matrix, is a vector, are decision variables related to the active power of flexibility resources.

5. The method for constructing a robust operation domain of flexibility resources in a new energy power system according to claim 4 is characterized in that: The S4 includes the following sub-steps: S41: For non-energy-constrained generators, during the period The active power related technical constraints are: in, is a unified index of flexibility resources, is the unified symbol for active power and power boundary decision variables, Resources for flexibility In the period The active power, Resources for flexibility In the period The active power, and is the obtained up and down power climbing ability data, is a binary variable that characterizes the availability of flexible resources. and is the power upper and lower limit data obtained, and Resources for flexibility In the period The lower and upper boundaries of the active power; The time period Active power As an uncertainty, it is constructed with the operating domain boundary variables as parameters The uncertainty set is: in, for The uncertainty set, and Resources for flexibility In the period The lower and upper boundaries of the active power; S42: For energy-constrained generators, energy storage systems and HVDC transmission systems, during the period The active power related technical constraints are: in, is a linear function describing the relationship between energy and power, For the period The energy For the period The energy For the period The energy and Flexibility resources for energy-constrained In the period The lower and upper energy boundaries of and To obtain the upper and lower limit data of energy; The time period Energy As an uncertainty, it is constructed with the operating domain boundary variables as parameters The uncertainty set is: in, for The uncertainty set, and Flexibility resources for energy-constrained In the period The lower and upper energy boundaries of S43: A three-layer two-stage robust optimization model is established as: in, For the robust operation domain decision variables, , and are the decision-related uncertainty parameters that characterize the external uncertainty of new energy and load and flexibility resources, respectively. is the decision variable for the actual regulation of flexible resources, is the system operating cost, is the uncertainty set of power, is the uncertainty set of energy, , and is the vector of power, energy and operating condition indicator variables, is the vector of energy and operating condition indicator variables, is a binary indicator variable for the power generation and consumption conditions of flexible resources, , , , , , , , , and is the coefficient matrix or right-hand side vector of the relevant constraints; S44: The three-layer two-stage robust optimization model is iteratively solved by the row and column generation algorithm to obtain the robust operation domain of the flexible resources ,in, and is the optimal value of the lower and upper bounds of the power of the flexibility resource vector, and It is the optimal value of the lower and upper bounds of the energy of the flexibility resource vector.

6. The method for constructing a robust operation domain of flexibility resources in a new energy power system according to claim 5 is characterized in that: In S5, the operation control of the generator set, the energy storage system and the HVDC transmission system is performed according to the robust operation domain, including any of the following control strategies: Time-series decoupling control strategy: Based on the new energy and load measurement values ​​in the current period, the flexibility resources are regulated under the condition of meeting the robust operation domain; Model predictive control strategy: Based on the new energy and load forecast data for current and future time periods, flexible resources are regulated while meeting the robust operation domain.

7. A robust operation domain construction device for flexibility resources of a new energy power system, characterized in that: The device comprises: Memory: used to store computer programs; Processor: used to execute the computer program to implement the robust operation domain construction method of the new energy power system flexibility resources as described in any one of claims 1-6.

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