Cross-regional support capability-flexibility boundary quantification method and system

Abstract

The invention discloses a cross-regional support capability-flexibility boundary quantification method and system. The method comprises the following steps: representing a flexibility variable of a power system in a target region based on a generalized polynomial chaos approximation principle; constructing an opportunity constraint-containing support model aiming at maximizing the flexibility supply of the unit and minimizing the operation cost; converting the support model containing the opportunity constraint into a deterministic support model by combining a Galerkin projection technology and the represented flexibility variable; traversing external transmission nodes in the target area, and calculating a flexibility insufficient expected value of the power system in the area based on a determinacy support model; and fitting a cross-regional support capability-flexibility boundary curve of the target region by combining the support power of the system and the flexibility insufficient expected value thereof.

Description

Technical Field

[0001] This invention relates to the field of power system operation and control technology, and in particular to a method and system for quantifying the cross-regional support capability-flexibility boundary. Background Technology

[0002] With rapid economic development and continuous population growth, electricity demand is constantly increasing, while the distribution of electricity resources remains uneven. At the same time, frequent extreme weather events and emergencies have also severely impacted power supply. To address these challenges, it is necessary to fully utilize local power generation resources and grid transmission capacity to achieve optimal allocation of electricity resources. Inter-regional peak shaving and supply guarantee, as an effective power allocation method, can strengthen the interconnection and coordination between power grids, improve the reliability and stability of power supply, and thus ensure the electricity demand for economic and social development.

[0003] Meanwhile, due to the uncertainties inherent in renewable energy generation such as wind and solar power, large-scale grid integration significantly reduces the power system's regulation capacity on the power source side, making it difficult to adjust in response to load fluctuations as in the traditional way. This leads to a decrease in the power system's security, stability, and economic efficiency, and weakens its grid balancing capability. Therefore, it is necessary to provide a method that can synergistically quantify cross-regional support capabilities and flexibility margins. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies where the intervention of uncertain resources in the power grid weakens the power system's grid balancing capacity, this invention provides a method and system for quantifying the cross-regional support capability-flexibility boundary. By constructing a support model with opportunity constraints that aims to maximize unit flexibility supply and minimize operating costs, and transforming it into a deterministic support model, the cross-regional support capability and flexibility margin are analyzed in a coordinated and quantitative manner. This improves the security and stability of the power system, facilitates optimal resource allocation, and ensures power supply.

[0005] According to one aspect of the present invention, a method for quantifying cross-regional support capability-flexibility boundaries is provided, comprising:

[0006] The flexibility variables of the power system within the target area are characterized based on the principle of generalized polynomial chaos approximation.

[0007] Construct a support model with opportunity constraints that aims to maximize unit flexibility and minimize operating costs;

[0008] By combining Galerkin projection techniques with the characterized flexibility variables, the opportunity-constrained support model is transformed into a deterministic support model.

[0009] Traverse the external transmission nodes within the target area and calculate the expected value of the inadequacy of the power system flexibility within the area based on the deterministic support model;

[0010] By combining the system's support power and its expected flexibility, a cross-regional support capability-flexibility boundary curve for the target area is fitted.

[0011] Furthermore, the characterization process of the flexibility variables of the power system within the target area includes:

[0012] The flexibility variables of the power system within the target area are described by random variables, and the flexibility variables of the unit system are described by random variables. For each random variable, the generalized polynomial chaotic basis function system is obtained by using the generalized polynomial chaotic method. Then, each random variable is expanded into a chaotic polynomial based on the generalized polynomial chaotic basis function system.

[0013] Furthermore, the construction of a support model with opportunity constraints includes:

[0014] The objective function for support is constructed by maximizing unit flexibility and minimizing operating costs, which can be mathematically expressed as:

[0015]

[0016] In the formula, This indicates the operating cost of the regional power grid system; Number of generating units; , Let be the power generation cost coefficient of unit i; Let i be the output power of unit i; Cost per unit of wind and solar curtailment; Provides a bonus factor for the unit's unit flexible adjustable power; , These are the amounts of abandoned wind power and solar power, respectively. The upward adjustment flexibility for all units is determined by the unit's ramp rate and maximum output power. The downscaling flexibility for all units is determined by the unit's downscaling rate and the lower limit of its output power.

[0017] The constraints of the design model include: unit output constraints, wind and solar curtailment constraints, power balance constraints, line transmission power constraints, and flexibility supply constraints. Among them, wind and solar curtailment constraints and line transmission power constraints are inequality constraints that include opportunity constraints, while power balance constraints and flexibility supply constraints are equality opportunity constraints that include opportunity constraints.

[0018] Furthermore, the mathematical expressions for the constraints of the model are as follows:

[0019] Unit output constraints:

[0020]

[0021] In the formula, Indicates the state of unit i; , These are the upper and lower limits of the output power of unit i, respectively;

[0022] Curtailment of wind and solar power:

[0023]

[0024]

[0025] In the formula, This represents the probability that an event will occur. , These represent the day-ahead predicted power outputs of wind power and solar power at time t, respectively. , These represent the amount of abandoned wind power and solar power at this time segment, respectively. , These are the corrected prediction errors for wind power and solar power output, respectively. , These are the installed capacities of wind power and solar power, respectively. , The confidence levels for the opportunity constraints of wind and solar curtailment are respectively;

[0026] The power balance constraint is expressed as follows:

[0027]

[0028] In the formula, The total number of units within the target area; , These represent the wind power and solar power output values ​​after curtailment and correction for prediction errors, respectively, and are random variables. To superimpose prediction errors The total system load afterwards; To support power;

[0029] The line transmission power constraint is expressed as follows:

[0030]

[0031] In the formula, In the set of random variables The transmission power of line l under the combined effects; This represents the upper limit of power transmission for line l; Indicates the probability of a violation that is not allowed;

[0032] Flexible supply constraints:

[0033] ; ; In the formula, , These refer to the upward and downward flexibility supply of unit i at this time segment; , These represent the uphill and downhill rates of unit i, respectively.

[0034] Furthermore, the method used to transform a support model with opportunity constraints into a deterministic model is to transform the constraint conditions with opportunity constraints into deterministic equations;

[0035] The transformation process of equality constraints containing chance constraints is as follows:

[0036] The equality constraints containing chance constraints are transformed into a general expression of equality constraints. The random variables represented by the generalized polynomial chaotic basis function system are substituted into the expression to obtain the equality constraint equations for the random variables.

[0037] By applying Galerkin projection, the equality constraint equations are projected onto each basis function in the generalized polynomial chaotic basis function system. Based on the orthogonality of the basis functions, the expected calculation equations are obtained. During the solution of the expected calculation equations, the equality constraint equations about random variables are transformed into a system of deterministic equations.

[0038] The transformation process of inequality constraints containing chance constraints is as follows:

[0039] Transform the inequality constraint containing chance constraints into a general expression of inequality chance constraints, and substitute the random variables represented by the generalized polynomial chaotic basis function system into it to obtain the probability constraints on the random variables.

[0040] According to the lemma of probabilistic constraints, the probabilistic constraints on random variables are transformed into deterministic probabilistic constraints on the chaotic coefficients of generalized polynomials.

[0041] Furthermore, the solution process for determining the system flexibility during support is as follows:

[0042] Iterate through all external transmission nodes of the power system within the region, construct a support matrix based on the support power output of each external transmission node, input support matrices with different values, and solve for the system's flexibility deficiency expectation based on a deterministic model.

[0043] Furthermore, the expected system flexibility deficiency within the region is the expectation when the flexibility margin of the external transmission nodes is less than 0. The flexibility margin is defined as a random variable, and the formula for calculating the expected flexibility deficiency is:

[0044] ; Where E represents the expectation of insufficient flexibility; Let be the random variable representing the flexibility margin at time t; y is the value of the random variable. This represents the probability density function of a random variable.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention addresses the uncertainty of source and load by adopting the generalized polynomial chaotic approximation principle to characterize the power flow uncertainty under the combined influence of multidimensional uncertainty factors. By constructing a support model with opportunity constraints that aims to maximize unit flexibility supply and minimize operating costs, and transforming it into a deterministic support model, the present invention provides a coordinated quantitative analysis of cross-regional support capabilities and flexibility margins, thereby improving the safety and stability of the power system, assisting in the optimal allocation of resources, and ensuring power supply. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 A flowchart illustrating a cross-regional support capability-flexibility boundary quantification method provided in an embodiment of the present invention;

[0048] Figure 2 A flowchart illustrating an example of a cross-regional support capability-flexibility boundary quantification method provided in this embodiment of the invention;

[0049] Figure 3 This is a schematic diagram of the cross-regional support capability-flexibility boundary curve for a certain region, provided in an embodiment of the present invention.

[0050] Figure 4 A schematic diagram of a cross-regional support capability-flexibility boundary quantification system provided in an embodiment of the present invention;

[0051] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0052] It should be noted that:

[0053] The terms “comprising” and “having”, and any variations thereof, in the specification, claims, and accompanying drawings of this invention are intended to cover a non-exclusive inclusion, such as a process, method, system, product, or apparatus that includes a series of steps or units, not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0054] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices. The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be decomposed, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0055] 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. In addition, the technical features of the various embodiments or individual embodiments provided by the present invention can be arbitrarily combined to form new technical solutions. Such combinations are not bound by the order of steps and / or structural composition patterns, but must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0056] like Figure 1 As shown, a method for quantifying the cross-regional support capability-flexibility boundary includes:

[0057] The flexibility variables of the power system within the target area are characterized based on the principle of generalized polynomial chaos approximation.

[0058] Construct a support model with opportunity constraints that aims to maximize unit flexibility and minimize operating costs;

[0059] By combining Galerkin projection techniques with the characterized flexibility variables, the opportunity-constrained support model is transformed into a deterministic support model.

[0060] Traverse the external transmission nodes within the target area and calculate the expected value of the inadequacy of the power system flexibility within the area based on the deterministic support model;

[0061] By combining the system's support power and its expected flexibility, a cross-regional support capability-flexibility boundary curve for the target area is fitted.

[0062] Furthermore, the characterization process of the flexibility variables of the power system within the target area includes:

[0063] The flexibility variables of the power system within the target area are described by random variables, and the flexibility variables of the unit system are described by random variables. For each random variable, the gPC basis function system is obtained by using the generalized polynomial chaos method. Then, each random variable is expanded into a chaotic polynomial based on the gPC basis function system.

[0064] Specifically, Generalized Polynomial Chaos (gPC) is a powerful method for quantifying uncertainty. It uses polynomial chaos expansion and orthogonal polynomials to represent random variables, thereby enabling deterministic characterization of uncertain problems.

[0065] The basis functions in the gPC method are constructed based on the probability density functions corresponding to each independent random variable. Let the vector of independent random variables be... ,in, Let each random variable be an independent random variable that makes up the vector of random variables. The table below represents their numbers, and their corresponding probability density functions are as follows: , … Construct the orthogonal polynomial system corresponding to each independent random variable in x, denoted as:

[0066]

[0067] in, It is an orthogonal polynomial basis function system for a single random variable; kd=0,1,…,r, where r is a non-negative integer parameter representing the highest order of the orthogonal polynomial basis functions.

[0068] In this case, the orthogonal polynomial basis function system of the entire independent random variable vector is obtained by constructing the orthogonal polynomial basis function system of each independent random variable through tensor product form:

[0069]

[0070] In the formula, denoted as the orthogonal polynomial basis function of the entire random vector; k represents the multi-index, which is a d-dimensional vector composed of non-negative integers, where each non-negative integer represents the polynomial order corresponding to the random variable, and the subscript is the number of the corresponding independent random variable; , Indicates the index range, in order to satisfy All non-negative integer solutions, The total order of the basis functions is , which contains . A combination of these.

[0071] Let N be a non-negative integer and Z be a one-dimensional random variable, then define the function... The Nth-order generalized polynomial chaotic approximation is:

[0072]

[0073]

[0074] In the formula, Representation function The Nth-order generalized polynomial chaotic approximation; yes In basis functions The approximation coefficients below, Let E be the basis function of an orthogonal polynomial of order j, and let E be the expectation operator. It is a standardized constant.

[0075] In obtaining After obtaining the Nth-order polynomial expansion expression, various statistical properties can be further derived. In particular, The expected value corresponds to the coefficient of its 0th-order term, i.e.:

[0076]

[0077] In the formula, It is a function The expected value; E is the expected value calculation function; It is the probability density function of the random variable Z; yes The constant term is the coefficient of its basis function in the zeroth-order orthogonal polynomial.

[0078] variance The calculation formula is:

[0079]

[0080] By sampling Z using the Monte Carlo method and combining it with the gPC expression, the result can be calculated. The probability density function.

[0081] Specifically, this invention uses the generalized polynomial chaos method to characterize the random variables of wind power, photovoltaic, and load forecasting errors, and obtains the gPC basis functions. Then, each random variable in the system can be expressed using polynomial expansion as follows:

[0082]

[0083] In the formula, let , , Let represent the random variables representing the wind power, photovoltaic, and load forecasting errors at time t, respectively; This represents the wind power output of wind power transmission node m at time t; This represents the photovoltaic power output of wind power transmission node n at time t. Let represent the power flow of line l at time t under the combined influence of random variables, where It is a set of random variables; This represents the load power at time t; Let represent the orthogonal polynomial basis functions of a random vector at time t; , , , Let be the approximation coefficients of wind power, photovoltaic power, line transmission power, and nodal load at time t under the basis functions, where the approximation coefficient of line transmission power is the variable to be determined. Let g be the order of gPC.

[0084] Furthermore, the construction of a support model with opportunity constraints includes:

[0085] The objective function for support is constructed by maximizing unit flexibility and minimizing operating costs, which can be mathematically expressed as:

[0086]

[0087] In the formula, This indicates the operating cost of the regional power grid system; Number of generating units; , Let be the power generation cost coefficient of unit i; Let i be the output power of unit i; Cost per unit of wind and solar curtailment; Provides a bonus factor for the unit's unit flexible adjustable power; , These are the amounts of abandoned wind power and solar power, respectively. The upward adjustment flexibility for all units is determined by the unit's ramp rate and maximum output power. The downscaling flexibility for all units is determined by the unit's downscaling rate and the lower limit of its output power.

[0088] Specifically, the definitions of upward and downward flexibility supply for all generating units are as follows:

[0089]

[0090]

[0091] In the formula, , These are the uphill and downhill rates of unit i, respectively; , These are the upper and lower limits of the output of unit i, respectively.

[0092] The constraints of the design model include: unit output constraints, wind and solar curtailment constraints, power balance constraints, line transmission power constraints, and flexibility supply constraints. Among them, wind and solar curtailment constraints and line transmission power constraints are inequality constraints that include opportunity constraints, while power balance constraints and flexibility supply constraints are equality opportunity constraints that include opportunity constraints.

[0093] Furthermore, the mathematical expressions for the constraints of the model are as follows:

[0094] Unit output constraints:

[0095]

[0096] In the formula, Indicates the state of unit i; , These are the upper and lower limits of the output power of unit i, respectively;

[0097] Curtailment of wind and solar power:

[0098]

[0099]

[0100] In the formula, This represents the probability that an event will occur. , These represent the day-ahead predicted power outputs of wind power and solar power at time t, respectively. , These represent the amount of abandoned wind power and solar power at this time segment, respectively. , These are the corrected prediction errors for wind power and solar power output, respectively. , These are the installed capacities of wind power and solar power, respectively. , The confidence levels for the opportunity constraints of wind and solar curtailment are respectively;

[0101] The power balance constraint is expressed as follows:

[0102]

[0103] In the formula, The total number of units within the target area; , These represent the wind power and solar power output values ​​after curtailment and correction for prediction errors, respectively, and are random variables. To superimpose prediction errors The total system load afterwards; To support power;

[0104] The line transmission power constraint is expressed as follows:

[0105]

[0106] In the formula, In the set of random variables The transmission power of line l under the combined effects; This represents the upper limit of power transmission for line l; Indicates the probability of a violation that is not allowed;

[0107] Specifically, taking wind power and photovoltaic random variables as the research objects, the transmission power of each branch is characterized by the power flow transfer distribution factor, as shown in the following formula:

[0108]

[0109] In the formula, This represents the power transfer distribution factor of node j to line l; , , , These are the power transfer distribution factors corresponding to different node types, namely, unit node, wind power node, photovoltaic node, and load node; , , These are the node numbers for generating units, wind farms, and photovoltaic power stations, respectively. This represents the number of load nodes.

[0110] Flexible supply constraints: ; ; In the formula, , These represent the upward and downward adjustment of flexibility supply for unit i at this time segment.

[0111] Furthermore, the method used to transform the opportunity-constrained support model into a deterministic model is to convert the opportunity-constrained constraints into deterministic equations based on a mixture Gaussian distribution for the uncertainty of the source load.

[0112] The transformation process of equality constraints containing chance constraints is as follows:

[0113] Transform equality constraints containing chance constraints into a general expression for equality constraints:

[0114]

[0115] In the formula, g represents the constraint function; z is the variable to be solved, and p(X) is the random variable;

[0116] Then, substituting the random variable represented by gPC into the general expression of equality constraints, the above equation can be rewritten as an equality constraint equation about the random variable, mathematically expressed as:

[0117]

[0118] In the formula, This represents the gPC expansion coefficients of the variable z to be determined; Denotes the basis functions of gPC orthogonal polynomials; Let g(X) represent the gPC expansion coefficients of the random variable p(X);

[0119] Applying the Galerkin projection, the above equations are projected onto each basis function. Based on the orthogonality of the basis functions, the desired computational equation is obtained, mathematically represented as:

[0120]

[0121] This represents the projection basis function.

[0122] In the process of calculating the expectation, randomness is eliminated during integration, transforming the original equality constraints with chance constraints into a deterministic system of equations.

[0123] The transformation process of inequality constraints containing chance constraints is as follows:

[0124] Transform inequality constraints containing chance constraints into a general expression for inequality chance constraints:

[0125]

[0126] In the formula, Indicates the constraint boundary value; Indicates the probability of a violation that is not allowed;

[0127] Substituting the random variables represented by gPC into the equation yields the probability constraints on the random variables.

[0128] According to the lemma of the bilabial bar probability constraint, the probability constraint on the random variable is transformed into a deterministic probability constraint on the chaotic coefficients of the generalized polynomial, specifically a convex second-order cone constraint on the chaotic coefficients of the generalized polynomial, mathematically expressed as:

[0129]

[0130] In the formula, Let be the inequality constraint coefficient, and ; These are the coefficients of the gPC expansion, representing the probability constraints as deterministic probability constraints with respect to the expansion coefficients.

[0131] Furthermore, the solution process for determining the system flexibility during support is as follows:

[0132] The process iterates through all external transmission nodes of the power system within the region. First, a particle swarm optimization algorithm is used to generate a thermal power unit combination scheme that satisfies start-up and shutdown time constraints, and the unit operating cost is calculated. Under a given unit combination scheme, a support matrix is ​​constructed based on the support power output of each external transmission node. By inputting support matrices with different values, the unit output is optimized and allocated according to a deterministic model to maximize the flexibility supply capacity. The desired value for the system's flexibility deficiency is then solved to meet the flexibility requirements.

[0133] Furthermore, the expected system flexibility deficiency within the region is the expectation when the flexibility margin of the external transmission nodes is less than 0. The flexibility margin is defined as a random variable, and the formula for calculating the expected flexibility deficiency is:

[0134] ; Where E represents the expectation of insufficient flexibility; Let be the random variable representing the flexibility margin at time t; y is the value of the random variable. This represents the probability density function of a random variable.

[0135] Furthermore, by combining the system's support power and its expected flexibility deficit, a cross-regional support capability-flexibility boundary curve for the target area is fitted. The process includes: traversing the feasible range of power transmission nodes between regions, simulating cross-regional support power scenarios from small to large; calculating the expected system flexibility deficit for each scenario, and then fitting and generating a cross-regional support capability-flexibility margin curve.

[0136] An example of the cross-regional support capability-flexibility boundary quantification method of this invention includes the following steps:

[0137] Step S1: Based on the principle of generalized polynomial chaos approximation, characterize the power flow uncertainty under the combined influence of multidimensional uncertainty factors.

[0138] Specifically, in the example process, power flow uncertainty is measured by the system flexibility variables within the target area, including wind power, photovoltaic power, and load forecasting errors. These variables are represented as random variables, and the generalized polynomial chaos method is used to characterize them, obtaining the gPC basis functions. Then, each random variable in the system can be expressed using polynomial expansion as follows:

[0139] (1)

[0140] By sampling Z using the Monte Carlo method and combining it with the gPC expression, the result can be calculated. The probability density function.

[0141] Step S2: Construct an opportunity-constrained model with the objectives of maximizing unit flexibility supply and minimizing operating costs;

[0142] Specifically, the objective function of this model is:

[0143] (2)

[0144] It should be noted that the upward and downward adjustment flexibility for all generating units is defined as follows:

[0145] (3)

[0146] (4)

[0147] The constraints are:

[0148] (1) Unit output constraints:

[0149] (5)

[0150] (2) Curtailment of wind and solar power:

[0151] (6)

[0152] (7)

[0153] (3) Power balance constraints:

[0154] (8)

[0155] (4) Upper and lower limits of transmission channel constraints:

[0156] The power transmission power of each branch is characterized by the power flow transfer distribution factor, as shown in the following formula:

[0157] (9)

[0158] The opportunity constraint on line transmission power is then expressed as follows:

[0159] (10)

[0160] (5) Supply constraints on flexibility:

[0161] (11)

[0162] (12)

[0163] The model transformation is as follows:

[0164] By applying the Galerkin projection technique and combining it with a chance constraint transformation method based on first and second moments, the model with chance constraints is transformed into a deterministic model:

[0165] (1) Equality constraint transformation

[0166] Consider the following equation: General form of equation constraint:

[0167] (13)

[0168] After gPC characterization, the above equation can be rewritten as:

[0169] (14)

[0170] By the properties of generalized chaotic polynomials, the approximation coefficients of each term are also 0, therefore:

[0171] (15)

[0172] Due to the existence of the expectation operator, equation (15) is a set of deterministic equations.

[0173] (2) Inequality constraint transformation

[0174] Consider the following general expression for chance constraints in inequalities:

[0175] (16)

[0176] According to the lemma of the probability constraint of the split bar, the inequality chance constraint can be transformed into a convex second-order cone constraint with respect to the chaotic coefficients of the generalized polynomial. Equation (16) can be transformed into the following form:

[0177]

[0178] Step S3: Calculate the expected value of the system's current supported power flexibility, return to step S1, traverse the feasible range of inter-regional transmission node power, and then simulate cross-regional supported power scenarios from small to large, and calculate the expected value of the system's flexibility flexibility for each scenario.

[0179] Specifically, further, the expected insufficiency of system flexibility within the region is the expectation when the flexibility margin of external transmission nodes is less than 0. Defining the flexibility margin as a random variable, the expected insufficiency is:

[0180] Step S4: Further fit the cross-regional support capability-flexibility boundary curve for each region.

[0181] First, a particle swarm optimization algorithm is used to generate thermal power unit combination schemes that meet start-up and shutdown time constraints, and the unit operating costs are calculated. Under the given unit combination scheme, the unit output is optimized to maximize the flexible supply capacity to meet flexibility requirements. Finally, the feasible range of power transmission nodes between regions is traversed, and the system simulates cross-regional support power scenarios from small to large. For each scenario, the expected value of system flexibility deficiency is calculated, and then a cross-regional support capacity-flexibility margin curve is fitted and generated.

[0182] Specifically, this boundary curve is obtained by fitting different support powers output to external transmission nodes with their corresponding expected system flexibility deficiencies. The cross-regional support capability-flexibility boundary curve for a certain region is shown below. Figure 3 As shown.

[0183] The implementation of the various embodiments of the present invention is based on programmed processing through a system with processor functionality. Therefore, in practical engineering, the technical solutions and functions of the various embodiments of the present invention are encapsulated into various modules. Based on this reality, and building upon the above embodiments, the embodiments of the present invention provide a cross-regional support capability-flexibility boundary quantization system, which is used to execute a cross-regional support capability-flexibility boundary quantization method from the above method embodiments.

[0184] See Figure 4 The system includes:

[0185] The flexibility variable characterization module is used to characterize the flexibility variables of the power system within the target area based on the generalized polynomial chaos approximation principle.

[0186] The Opportunity Constraint Support Model Building Module is used to build an opportunity constraint support model with the goal of maximizing unit flexibility supply and minimizing operating costs.

[0187] The support model transformation module is used to transform a support model with opportunity constraints into a deterministic support model by combining Galerkin projection techniques with the represented flexibility variables.

[0188] The flexibility measurement module is used to traverse the external transmission nodes within the target area and calculate the expected value of the inadequacy of the power system flexibility within the area based on the deterministic support model.

[0189] The results output module is used to fit the cross-regional support capability-flexibility boundary curve of the target area by combining the system's support power and its expected flexibility.

[0190] It should be noted that the system embodiments provided by the present invention are used not only to implement the methods in the above method embodiments, but also to implement the methods in other method embodiments provided by the present invention. The only difference is that corresponding functional modules are set. The principle is basically the same as that of the above system embodiments provided by the present invention. As long as those skilled in the art can improve the system in the above system embodiments by referring to the specific technical solutions in other method embodiments, combining technical features to obtain corresponding technical means and technical solutions composed of these technical means, and ensuring the practicality of the technical solutions, they can obtain corresponding system-class embodiments for implementing the methods in other method-class embodiments.

[0191] The method in this embodiment of the invention is implemented using an electronic device; therefore, it is necessary to introduce the relevant electronic device. For this purpose, embodiments of the present invention provide an electronic device, such as... Figure 5 As shown, the electronic device includes: at least one processor, a communication interface, at least one memory, and a communication bus, wherein the at least one processor, the communication interface, and the at least one memory communicate with each other via the communication bus. The at least one processor invokes logical instructions stored in the at least one memory to execute all or part of the steps of the methods provided in the foregoing method embodiments.

[0192] Furthermore, when the logical instructions in at least one of the aforementioned memories are implemented as software functional units and sold or used as independent products, they are stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, is embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (a personal computer, server, or network device) to execute all or part of the steps of the methods described in the various method embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks—various media for storing program code.

[0193] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, located in one place, or distributed across multiple network units. The purpose of this embodiment is achieved by selecting some or all of the modules according to actual needs. Those skilled in the art will understand and implement this without any inventive effort.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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 them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A method for quantifying the cross-regional support capability-flexibility boundary, characterized in that, include: The flexibility variables of the power system within the target area are characterized based on the principle of generalized polynomial chaos approximation. Construct a support model with opportunity constraints that aims to maximize unit flexibility and minimize operating costs; By combining Galerkin projection techniques with the characterized flexibility variables, the opportunity-constrained support model is transformed into a deterministic support model. Traverse the external transmission nodes within the target area and calculate the expected value of the inadequacy of the power system flexibility within the area based on the deterministic support model; By combining the system's support power and its expected flexibility, a cross-regional support capability-flexibility boundary curve for the target area is fitted.

2. The method for quantifying the cross-regional support capability-flexibility boundary as described in claim 1, characterized in that, The characterization process of the flexibility variables of the power system within the target area includes: The flexibility variables of the power system within the target area are described by random variables. For each random variable, the generalized polynomial chaotic basis function system is obtained by characterizing it using the generalized polynomial chaotic method. Then, each random variable is expanded into a chaotic polynomial based on the generalized polynomial chaotic basis function system.

3. The method for quantifying the cross-regional support capability-flexibility boundary as described in claim 1, characterized in that, The construction of the opportunity-constrained support model includes: The objective function for support is constructed by maximizing unit flexibility and minimizing operating costs, which can be mathematically expressed as: ； In the formula, This indicates the operating cost of the regional power grid system; Number of generating units; , Let be the power generation cost coefficient of unit i; Let i be the output power of unit i; Cost per unit of wind and solar curtailment; Provides a bonus factor for the unit's unit flexible adjustable power; , These are the amounts of abandoned wind power and solar power, respectively. The upward adjustment flexibility for all units is determined by the unit's ramp rate and maximum output power. The downscaling flexibility for all units is determined by the unit's downscaling rate and the lower limit of its output power. The constraints of the design model include: unit output constraints, wind and solar curtailment constraints, power balance constraints, line transmission power constraints, and flexibility supply constraints. Among them, wind and solar curtailment constraints and line transmission power constraints are inequality constraints that include opportunity constraints, while power balance constraints and flexibility supply constraints are equality opportunity constraints that include opportunity constraints.

4. The method for quantifying the cross-regional support capability-flexibility boundary as described in claim 3, characterized in that, The mathematical expressions for the constraints of the model are as follows: Unit output constraints: ； In the formula, Indicates the state of unit i; , These are the upper and lower limits of the output power of unit i, respectively; Curtailment of wind and solar power: ； ； In the formula, This represents the probability that an event will occur. , These represent the day-ahead predicted power outputs of wind power and solar power at time t, respectively. , These represent the amount of abandoned wind power and solar power at this time segment, respectively. , These are the corrected prediction errors for wind power and solar power output, respectively. , These are the installed capacities of wind power and solar power, respectively. , The confidence levels for the opportunity constraints of wind and solar curtailment are respectively; The power balance constraint is expressed as follows: ； In the formula, The total number of units within the target area; , These represent the wind power and solar power output values ​​after curtailment and correction for prediction errors, respectively, and are random variables. To superimpose prediction errors The total system load afterwards; To support power; The line transmission power constraint is expressed as follows: ； In the formula, This represents the transmission power of line l under the combined influence of the set of random variables x; This represents the upper limit of power transmission for line l; Indicates the probability of a violation that is not allowed; Flexible supply constraints: ； ； In the formula, , These refer to the upward and downward flexibility supply of unit i at this time segment; , Let represent the uphill and downhill rates of unit i, respectively.

5. The method for quantifying the cross-regional support capability-flexibility boundary as described in claim 1, characterized in that, The method used to transform the opportunity-constrained support model into a deterministic support model is to transform the opportunity-constrained constraints into deterministic equations. The transformation process of equality constraints containing chance constraints is as follows: The equality constraints containing chance constraints are transformed into a general expression of equality constraints. The random variables represented by the generalized polynomial chaotic basis function system are substituted into the expression to obtain the equality constraint equations for the random variables. By applying Galerkin projection, the equality constraint equations are projected onto each basis function in the generalized polynomial chaotic basis function system. Based on the orthogonality of the basis functions, the expected calculation equations are obtained and solved, thus transforming the equality constraint equations about random variables into a deterministic system of equations. The transformation process of inequality constraints containing chance constraints is as follows: Transform the inequality constraint containing chance constraints into a general expression of inequality chance constraints, and substitute the random variables represented by the generalized polynomial chaotic basis function system into it to obtain the probability constraints on the random variables. According to the lemma of bilabial bar probability constraints, the probability constraints on random variables are transformed into deterministic probability constraints on the chaotic coefficients of generalized polynomials.

6. The method for quantifying the cross-regional support capability-flexibility boundary as described in claim 1, characterized in that, The process for determining the system flexibility during the support phase is as follows: Iterate through all external transmission nodes of the power system within the region, construct a support matrix based on the support power output of each external transmission node, input support matrices with different values, and solve for the system's flexibility deficiency expectation based on a deterministic support model.

7. The method for quantifying the cross-regional support capability-flexibility boundary as described in claim 1, characterized in that, The expected system flexibility deficiency within the region is defined as the expectation when the flexibility margin of the external transmission nodes is less than 0. The flexibility margin is defined as a random variable, and the formula for calculating the expected flexibility deficiency is as follows: ； Where E represents the expectation of insufficient flexibility; Let be the random variable representing the flexibility margin at time t; y is the value of the random variable. This represents the probability density function of a random variable.

8. A cross-regional support capability-flexibility boundary quantification system, characterized in that, include: The flexibility variable characterization module is used to characterize the flexibility variables of the power system within the target area based on the generalized polynomial chaos approximation principle. The Opportunity Constraint Support Model Building Module is used to build an opportunity constraint support model with the goal of maximizing unit flexibility supply and minimizing operating costs. The support model transformation module is used to transform a support model with opportunity constraints into a deterministic support model by combining Galerkin projection techniques with the represented flexibility variables. The flexibility measurement module is used to traverse the external transmission nodes within the target area and calculate the expected value of the inadequacy of the power system flexibility within the area based on the deterministic support model. The results output module is used to fit the cross-regional support capability-flexibility boundary curve of the target area by combining the system's support power and its expected flexibility.

9. An electronic device, characterized in that, The method includes a memory and a processor, the memory storing program instructions that are executed by the processor, the processor invoking the program instructions to perform the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions that cause the computer to perform the method described in any one of claims 1 to 7.

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